Technical Evaluation Report 

on the Content of the U.S. 

Department of Energy’s 

Yucca Mountain Repository 

License Application 

Postclosure Volume: Repository 

Safety After Permanent Closure 

Office of Nuclear Material Safety and Safeguards 

NUREG-2107 
AVAILABILITY OF REFERENCE MATERIALS

IN NRC PUBLICATIONS

NRC Reference Material

As of November 1999, you may electronically access 

NUREG-series publications and other NRC records at 

NRC=s Public Electronic Reading Room at 

http://www.nrc.gov/reading-rm.html.

Publicly released records include, to name a few, 

NUREG-series publications; Federal Register notices; 

applicant, licensee, and vendor documents and 

correspondence; NRC correspondence and internal 

memoranda; bulletins and information notices; 

inspection and investigative reports; licensee event 

reports; and Commission papers and their 

attachments.

NRC publications in the NUREG series, NRC 

regulations, and Title 10, Energy, in the Code of 

Federal Regulations may also be purchased from one 

of these two sources.

1. The Superintendent of Documents

 U.S. Government Printing Office

 Mail Stop SSOP

 Washington, DC 20402B0001

 Internet: bookstore.gpo.gov

 Telephone: 202-512-1800

 Fax: 202-512-2250

2. The National Technical Information Service

 Springfield, VA 22161B0002

 www.ntis.gov 

 1B800B553B6847 or, locally, 703B605B6000

A single copy of each NRC draft report for comment is 

available free, to the extent of supply, upon written 

request as follows:

Address: U.S. Nuclear Regulatory Commission 

 Office of Administration

 Publications Branch

 Washington, DC 20555-0001

E-mail: DISTRIBUTION.SERVICES@NRC.GOV

Facsimile: 301B415B2289 

Some publications in the NUREG series that are 

posted at NRC=s Web site address 

http://www.nrc.gov/reading-rm/doc-collections/nuregs

are updated periodically and may differ from the last 

printed version. Although references to material found 

on a Web site bear the date the material was 

accessed, the material available on the date cited may 

subsequently be removed from the site.

Non-NRC Reference Material

Documents available from public and special technical 

libraries include all open literature items, such as 

books, journal articles, and transactions, Federal 

Register notices, Federal and State legislation, and 

congressional reports. Such documents as theses, 

dissertations, foreign reports and translations, and

non-NRC conference proceedings may be purchased 

from their sponsoring organization.

Copies of industry codes and standards used in a 

substantive manner in the NRC regulatory process are 

maintained atC

The NRC Technical Library 

Two White Flint North

11545 Rockville Pike

Rockville, MD 20852B2738

These standards are available in the library for 

reference use by the public. Codes and standards are 

usually copyrighted and may be purchased from the 

originating organization or, if they are American 

National Standards, fromC

American National Standards Institute

11 West 42nd Street

New York, NY 10036B8002

www.ansi.org 

212B642B4900

Legally binding regulatory requirements are stated only 

in laws; NRC regulations; licenses, including technical 

specifications; or orders, not in 

NUREG-series publications. The views expressed in 

contractor-prepared publications in this series are not 

necessarily those of the NRC.

The NUREG series comprises (1) technical and 

administrative reports and books prepared by the staff 

(NUREGBXXXX) or agency contractors 

(NUREG/CRBXXXX), (2) proceedings of conferences 

(NUREG/CPBXXXX), (3) reports resulting from 

international agreements (NUREG/IABXXXX), (4) 

brochures (NUREG/BRBXXXX), and (5) compilations 

of legal decisions and orders of the Commission and 

Atomic and Safety Licensing Boards and of Directors=

decisions under Section 2.206 of NRC=s regulations 

(NUREGB0750).
Technical Evaluation 

Report on the Content of 

the U.S. Department of 

Energy’s Yucca Mountain 

Repository License 

Application

Postclosure Volume: 

Repository Safety After 

Permanent Closure

Manuscript Completed: July 2011

Date Published: August 2011

Office of Nuclear Material Safety and Safeguards

NUREG-2107

iii 

ABSTRACT 

This “Technical Evaluation Report on the Content of the U.S. Department of Energy’s 

Yucca Mountain License Application; Postclosure Volume: Repository Safety After 

Permanent Closure” (TER Postclosure Volume) presents information on the NRC staff’s 

review of DOE’s Safety Analysis Report (SAR), provided on June 3, 2008, as updated by 

DOE on February 19, 2009. The NRC staff also reviewed information DOE provided in 

response to NRC staff’s requests for additional information and other information that DOE 

provided related to the SAR. In particular, this report provides information on the staff’s 

evaluation of (i) the repository’s barriers and (ii) the Total System Performance Assessments 

(TSPAs) used for the individual protection calculation, the separate groundwater protection 

calculation, and the human intrusion calculation. DOE submitted information consistent with the 

Yucca Mountain Review Plan (YMRP). 

v 

CONTENTS1

Section Page 

ABSTRACT ................................................................................................................................... iii 

FIGURES .................................................................................................................................... xv 

TABLES ...................................................................................................................................... xv 

EXECUTIVE SUMMARY ........................................................................................................... xvii 

ACRONYMS AND ABBREVIATIONS ....................................................................................... xxiii 

INTRODUCTION .......................................................................................................................... 1 

CHAPTER 1 ............................................................................................................................... 1-1 

 2.2.1.1 System Description and Demonstration of Multiple Barriers .............................. 1-1 

 2.2.1.1.1 Introduction .................................................................................... 1-1 

 2.2.1.1.2 Evaluation Criteria ......................................................................... 1-2 

 2.2.1.1.3 Technical Evaluation ...................................................................... 1-2 

 2.2.1.1.3.1 Identification of Barriers ................................. 1-3 

 2.2.1.1.3.2 Description and Technical Basis for 

 Barrier Capability ........................................... 1-4 

 2.2.1.1.3.2.1 Upper Natural Barrier: Topography and 

 Surficial Soils ................................................. 1-7 

 2.2.1.1.3.2.2 Upper Natural Barrier: Unsaturated 

 Zone Above the Repository ........................... 1-8 

 2.2.1.1.3.2.3 Engineered Barrier System: 

 Emplacement Drift ....................................... 1-10 

 2.2.1.1.3.2.4 Engineered Barrier System: Drip Shield ..... 1-12 

 2.2.1.1.3.2.5 Engineered Barrier System: 

 Waste Packages .......................................... 1-14 

 2.2.1.1.3.2.6 Engineered Barrier System: 

 Waste Form and Waste Package 

 Internal Components ................................... 1-16 

 2.2.1.1.3.2.7 Engineered Barrier System: Emplacement 

 Pallet and Invert ........................................... 1-19 

 2.2.1.1.3.2.8 Lower Natural Barrier: Unsaturated Zone 

 Below the Repository ................................... 1-19 

 2.2.1.1.3.2.9 Lower Natural Barrier: Saturated Zone ....... 1-22 

 2.2.1.1.4 Summary of Staff Evaluations ...................................................... 1-24 

 2.2.1.1.4.1 Identification of Barriers ............................... 1-24 

 2.2.1.1.4.2 Description of Barrier Capability to 

 Isolate Waste ............................................... 1-25 

 2.2.1.1.4.3 Technical Basis for Barrier Capability .......... 1-26 

 2.2.1.1.5 NRC Staff Conclusions ................................................................ 1-26 

 2.2.1.1.6 References .................................................................................. 1-26 

 

1

In this Technical Evaluation Report (TER), the section numbering used within the volume is based on the 

Yucca Mountain Review Plan (YMRP). [NRC. 2003. “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

ML032030389. Washington, DC: NRC.] The U.S. Nuclear Regulation Commission (NRC) staff used the YMRP to 

guide its review of information the U.S. Department of Energy (DOE) provided in its Safety Analysis Report (SAR). 
vi 

CONTENTS (continued) 

Section Page 

CHAPTER 2 ............................................................................................................................... 2-1 

 2.2.1.2.1 Scenario Analysis ............................................................................................... 2-1 

 2.2.1.2.1.1 Introduction .................................................................................... 2-1 

 2.2.1.2.1.2 Evaluation Criteria ......................................................................... 2-2 

 2.2.1.2.1.3 Technical Evaluation ...................................................................... 2-3 

 2.2.1.2.1.3.1 Identification of a List of Features, Events, 

 and Processes ............................................... 2-4 

 2.2.1.2.1.3.2 Screening of the List of Features, Events, 

 and Processes ............................................... 2-6 

 2.2.1.2.1.3.3 Technical Evaluation for Formation of 

 Scenario Classes ......................................... 2-71 

 2.2.1.2.1.3.4 Screening of Scenario Classes ................... 2-73 

 2.2.1.2.1.4 NRC Staff Conclusions ................................................................ 2-75 

 2.2.1.2.1.5 References .................................................................................. 2-75 

CHAPTER 3 ............................................................................................................................... 3-1 

 2.2.1.2.2 Identification of Events With Probabilities Greater Than 10-8 Per Year .............. 3-1 

 2.2.1.2.2.1 Introduction .................................................................................... 3-1 

 2.2.1.2.2.2 Event Criteria ................................................................................. 3-1 

 2.2.1.2.2.3 Technical Evaluation ...................................................................... 3-2 

 2.2.1.2.2.3.1 Technical Evaluation for Igneous 

 Event Probabilities ......................................... 3-3 

 2.2.1.2.2.3.2 Technical Evaluation for Seismic 

 Event Probabilities ....................................... 3-10 

 2.2.1.2.2.4 Technical Evaluation of Early Waste Package and Drip Shield 

 Failures Event Probabilities ......................................................... 3-18 

 2.2.1.2.2.5 NRC Staff Conclusions ................................................................ 3-28 

 2.2.1.2.2.6 References .................................................................................. 3-28 

CHAPTER 4 ............................................................................................................................... 4-1 

 2.2.1.3.1 Degradation of Engineered Barriers ................................................................... 4-1 

 2.2.1.3.1.1 Introduction .................................................................................... 4-1 

 2.2.1.3.1.2 Evaluation Criteria ......................................................................... 4-1 

 2.2.1.3.1.3 Technical Evaluation ...................................................................... 4-3 

 2.2.1.3.1.3.1 Drip Shield Degradation ................................ 4-3 

 2.2.1.3.1.3.1.1 Drip Shield General Corrosion ....................... 4-5 

 2.2.1.3.1.3.1.2 Drip Shield Early Failure .............................. 4-14 

 2.2.1.3.1.3.2 Waste Package Degradation ....................... 4-16 

 2.2.1.3.1.3.2.1 General Corrosion of the Waste Package 

 Outer Barrier ................................................ 4-22 

 2.2.1.3.1.3.2.2 Localized Corrosion of the Waste Package 

 Outer Barrier ................................................ 4-33 

 2.2.1.3.1.3.2.3 Stress Corrosion Cracking of the Waste 

 Package Outer Barrier ................................. 4-41 

 2.2.1.3.1.3.2.4 Waste Package Early Failure ...................... 4-54 

 2.2.1.3.1.4 NRC Staff Conclusions ................................................................ 4-55 

 2.2.1.3.1.5 References .................................................................................. 4-56 
vii 

CONTENTS (continued) 

Section Page 

CHAPTER 5 ............................................................................................................................... 5-1 

 2.2.1.3.2 Mechanical Disruption of Engineered Barriers ................................................... 5-1 

 2.2.1.3.2.1 Introduction .................................................................................... 5-1 

 2.2.1.3.2.2 Evaluation Criteria ......................................................................... 5-2 

 2.2.1.3.2.3 Technical Evaluation ...................................................................... 5-4 

 2.2.1.3.2.3.1 Seismic and Fault Displacement Inputs for 

 Mechanical Disruption of 

 Engineered Barriers ....................................... 5-4 

 2.2.1.3.2.3.1.1 Seismic Site-Response Modeling .................. 5-9 

 2.2.1.3.2.3.1.2 Fault Displacement Hazard Analysis ........... 5-10 

 2.2.1.3.2.3.2 Fault Displacement Considerations 

 in TSPA ....................................................... 5-11 

 2.2.1.3.2.3.3 Seismically Induced Drift Degradation ......... 5-14 

 2.2.1.3.2.3.4 Drip Shield Structure/Mechanical 

 Performance in the Context of Its Seepage 

 Barrier Function ........................................... 5-20 

 2.2.1.3.2.3.5 Waste Package Mechanical/Structural 

 Performance ................................................ 5-28 

 2.2.1.3.2.4 NRC Staff Conclusions ................................................................ 5-46 

 2.2.1.3.2.5 References .................................................................................. 5-46 

CHAPTER 6 ............................................................................................................................... 6-1 

 2.2.1.3.3 Quantity and Chemistry of Water Contacting Engineered Barriers and 

 Waste Forms ...................................................................................................... 6-1 

 2.2.1.3.3.1 Introduction .................................................................................... 6-1 

 2.2.1.3.3.2 Evaluation Criteria ......................................................................... 6-1 

 2.2.1.3.3.3 Technical Evaluation ...................................................................... 6-3 

 2.2.1.3.3.3.1 Chemistry of Water Entering Drifts ................ 6-3 

 2.2.1.3.3.3.2 Chemistry of Water in the Drifts ..................... 6-9 

 2.2.1.3.3.3.3 Quantity of Water in Contact With the 

 Engineered Barrier System ......................... 6-18 

 2.2.1.3.3.4 NRC Staff Conclusions ................................................................ 6-27 

 2.2.1.3.3.5 References .................................................................................. 6-27 

CHAPTER 7 ............................................................................................................................... 7-1 

 2.2.1.3.4 Radionuclide Release Rates and Solubility Limits ............................................. 7-1 

 2.2.1.3.4.1 Introduction .................................................................................... 7-1 

 2.2.1.3.4.2 Evaluation Criteria ......................................................................... 7-2 

 2.2.1.3.4.3 Technical Evaluation ...................................................................... 7-4 

 2.2.1.3.4.3.1 In-Package Chemical and 

 Physical Environment .................................... 7-4 

 2.2.1.3.4.3.2 Waste Form Degradation ............................ 7-13 

 2.2.1.3.4.3.3 Concentration Limits .................................... 7-22 

 2.2.1.3.4.3.4 Availability and Effectiveness of Colloids .... 7-29 

 2.2.1.3.4.3.5 Engineered Barrier System 

 Radionuclide Transport ............................... 7-38 
viii 

CONTENTS (continued) 

Section Page 

 2.2.1.3.4.4 NRC Staff Conclusions ................................................................ 7-49 

 2.2.1.3.4.5 References .................................................................................. 7-49 

CHAPTER 8 ............................................................................................................................... 8-1 

 2.2.1.3.5 Climate and Infiltration ........................................................................................ 8-1 

 2.2.1.3.5.1 Introduction .................................................................................... 8-1 

 2.2.1.3.5.2 Evaluation Criteria ......................................................................... 8-2 

 2.2.1.3.5.3 Technical Evaluation ...................................................................... 8-4 

 2.2.1.3.5.3.1 Identification of Features and Processes ...... 8-4 

 2.2.1.3.5.3.2 Climate .......................................................... 8-5 

 2.2.1.3.5.3.2.1 Climate Change for the Next 10,000 Years ... 8-6 

 2.2.1.3.5.3.2.2 Local Spatial and Temporal Variation of 

 Meteorological Conditions ........................... 8-12 

 2.2.1.3.5.3.3 Net Infiltration .............................................. 8-15 

 2.2.1.3.5.4 NRC Staff Conclusions ................................................................ 8-23 

 2.2.1.3.5.5 References .................................................................................. 8-24 

CHAPTER 9 ............................................................................................................................... 9-1 

 2.2.1.3.6 Unsaturated Zone Flow ...................................................................................... 9-1 

 2.2.1.3.6.1 Introduction .................................................................................... 9-1 

 2.2.1.3.6.2 Evaluation Criteria ......................................................................... 9-2 

 2.2.1.3.6.3 Technical Evaluation ...................................................................... 9-3 

 2.2.1.3.6.3.1 Integration Within the Unsaturated Zone ....... 9-4 

 2.2.1.3.6.3.2 Ambient Mountain-Scale Flow Above 

 the Repository ............................................. 9-11 

 2.2.1.3.6.3.3 Thermohydrologic Effects of 

 Water Emplacement .................................... 9-23 

 2.2.1.3.6.3.4 Ambient and Thermal Seepage Models ...... 9-30 

 2.2.1.3.6.3.5 In-Drift Convection and 

 Moisture Redistribution ................................ 9-41 

 2.2.1.3.6.3.5.1 In-Drift Heat Transfer and Convection ......... 9-42 

 2.2.1.3.6.3.5.2 Moisture Redistribution 

 and Condensation ....................................... 9-44 

 2.2.1.3.6.3.6 Ambient Mountain-Scale Flow—Below 

 the Repository ............................................. 9-47 

 2.2.1.3.6.3.6.1 Flow Model Conceptualization ..................... 9-47 

 2.2.1.3.6.3.6.2 Flow Features Below Southern and 

 Northern Portions of Repository .................. 9-48 

 2.2.1.3.6.3.6.3 Adequacy of Flow Fields for Transport ........ 9-54 

 2.2.1.3.6.3.6.4 Summary ..................................................... 9-56 

 2.2.1.3.6.4 NRC Staff Conclusions ................................................................ 9-56 

 2.2.1.3.6.5 References .................................................................................. 9-56 

CHAPTER 10 ........................................................................................................................... 10-1 

 2.2.1.3.7 Radionuclide Transport in the Unsaturated Zone ............................................. 10-1 

 2.2.1.3.7.1 Introduction .................................................................................. 10-1 

 2.2.1.3.7.2 Evaluation Criteria ....................................................................... 10-1 
ix 

CONTENTS (continued) 

Section Page 

 2.2.1.3.7.3 Technical Evaluation .................................................................... 10-3 

 2.2.1.3.7.3.1 System Description and Model 

 Framework ................................................... 10-3 

 2.2.1.3.7.3.1.1 Model Integration for the TSPA Code .......... 10-4 

 2.2.1.3.7.3.1.2 Engineered Barrier System—Unsaturated 

 Zone Boundary Condition ............................ 10-6 

 2.2.1.3.7.3.2 Unsaturated Zone Radionuclide 

 Transport Processes ................................... 10-7 

 2.2.1.3.7.3.2.1 Advection and Dispersion ............................ 10-7 

 2.2.1.3.7.3.2.2 Sorption ....................................................... 10-8 

 2.2.1.3.7.3.2.3 Matrix Diffusion .......................................... 10-11 

 2.2.1.3.7.3.2.4 Colloid-Associated Transport .................... 10-14 

 2.2.1.3.7.3.2.5 Radionuclide Decay and Ingrowth ............. 10-17 

 2.2.1.3.7.4 NRC Staff Conclusions .............................................................. 10-18 

 2.2.1.3.7.5 References ................................................................................ 10-18 

CHAPTER 11 ........................................................................................................................... 11-1 

 2.2.1.3.8 Flow Paths in the Saturated Zone .................................................................... 11-1 

 2.2.1.3.8.1 Introduction .................................................................................. 11-1 

 2.2.1.3.8.2 Evaluation Criteria ....................................................................... 11-2 

 2.2.1.3.8.3 Technical Evaluation .................................................................... 11-3 

 2.2.1.3.8.3.1 System Description and Integration of 

 Models Relevant to Flow Paths in the 

 Saturated Zone ............................................ 11-3 

 2.2.1.3.8.3.2 Sufficiency of Baseline Data to Justify 

 Models of Flow Paths in the 

 Saturated Zone ............................................ 11-7 

 2.2.1.3.8.3.3 Uncertainty in Data Used in Models of Flow 

 Paths in the Saturated Zone ...................... 11-10 

 2.2.1.3.8.3.4 Uncertainty in Flow Paths in the Saturated 

 Zone Models .............................................. 11-14 

 2.2.1.3.8.3.5 Model Support Based on Comparison With 

 Alternative Models or Other Information .... 11-17 

 2.2.1.3.8.4 NRC Staff Conclusions .............................................................. 11-19 

 2.2.1.3.8.5 References ................................................................................ 11-19 

CHAPTER 12 ........................................................................................................................... 12-1 

 2.2.1.3.9 Radionuclide Transport in the Saturated Zone ................................................. 12-1 

 2.2.1.3.9.1 Introduction .................................................................................. 12-1 

 2.2.1.3.9.2 Evaluation Criteria ....................................................................... 12-1 

 2.2.1.3.9.3 Technical Evaluation .................................................................... 12-3 

 2.2.1.3.9.3.1 Conceptual Model and 

 Model Framework ........................................ 12-4 

 2.2.1.3.9.3.1.1 Model Integration for TSPA ......................... 12-8 

 2.2.1.3.9.3.1.2 Unsaturated Zone/Saturated Zone 

 Boundary Condition ..................................... 12-8 

 2.2.1.3.9.3.2 Saturated Zone Transport Processes ........ 12-10 
x 

CONTENTS (continued) 

Section Page 

 2.2.1.3.9.3.2.1 Advection and Dispersion .......................... 12-10 

 2.2.1.3.9.3.2.2 Sorption ..................................................... 12-12 

 2.2.1.3.9.3.2.3 Matrix Diffusion .......................................... 12-18 

 2.2.1.3.9.3.2.4 Colloid-Associated Transport .................... 12-19 

 2.2.1.3.9.3.2.5 Radionuclide Decay and Ingrowth ............. 12-21 

 2.2.1.3.9.4 NRC Staff Conclusions .............................................................. 12-25 

 2.2.1.4.9.5 References ................................................................................ 12-25 

 

CHAPTER 13 ........................................................................................................................... 13-1 

 2.2.1.3.10 Igneous Disruption of Waste Packages............................................................ 13-1 

 2.2.1.3.10.1 Introduction .................................................................................. 13-1 

 2.2.1.3.10.2 Evaluation Criteria ....................................................................... 13-2 

 2.2.1.3.10.3 Technical Evaluation .................................................................... 13-4 

 2.2.1.3.10.3.1 General Approach by DOE .......................... 13-4 

 2.2.1.3.10.3.2 The NRC Staff’s Review of DOE Igneous 

 Intrusion Modeling Case .............................. 13-5 

 2.2.1.3.10.3.3 The NRC Staff’s Review of DOE Volcanic 

 Eruption Modeling Scenario ...................... 13-13 

 2.2.1.3.10.4 NRC Staff Conclusions .............................................................. 13-19 

 2.2.1.3.10.5 References ................................................................................ 13-19 

CHAPTER 14 ........................................................................................................................... 14-1 

 2.2.1.3.12 Concentration of Radionuclides in Groundwater .............................................. 14-1 

 2.2.1.3.12.1 Introduction .................................................................................. 14-1 

 2.2.1.3.12.2 Evaluation Criteria ....................................................................... 14-1 

 2.2.1.3.12.3 Assessment of Well Water Concentration Estimates ................... 14-1 

 2.2.1.3.12.4 NRC Staff Conclusions ................................................................ 14-2 

 2.2.1.3.12.5 References .................................................................................. 14-2 

CHAPTER 15 ........................................................................................................................... 15-1 

 2.2.1.3.13 Airborne Transport and Redistribution of Radionuclides .................................. 15-1 

 2.2.1.3.13.1 Introduction .................................................................................. 15-1 

 2.2.1.3.13.2 Evaluation Criteria ....................................................................... 15-2 

 2.2.1.3.13.3 Technical Evaluation .................................................................... 15-4 

 2.2.1.3.13.3.1 Assessment and Review of the Volcanic 

 Ash Exposure Scenario ............................... 15-6 

 2.2.1.3.13.3.1.1 Airborne Transport Modeling ....................... 15-7 

 2.2.1.3.13.3.1.2 Tephra Redistribution in 

 Fortymile Wash .......................................... 15-16 

 2.2.1.3.13.2.1.3 Downward Migration of Radionuclides 

 in Soil ......................................................... 15-25 

 2.2.1.3.13.3.2 Assessment and Review of Groundwater 

 Exposure Scenarios .................................. 15-30 

 2.2.1.3.13.4 NRC Staff Conclusions .............................................................. 15-35 

 2.2.1.3.13.5 References ................................................................................ 15-35 
xi 

CONTENTS (continued) 

Section Page 

CHAPTER 16 ........................................................................................................................... 16-1 

 2.2.1.3.14 Biosphere Characteristics ................................................................................ 16-1 

 2.2.1.3.14.1 Introduction .................................................................................. 16-1 

 2.2.1.3.14.2 Evaluation Criteria ....................................................................... 16-2 

 2.2.1.3.14.3 Technical Evaluation .................................................................... 16-4 

 2.2.1.3.14.3.1 System Description and 

 Model Integration ......................................... 16-7 

 2.2.1.3.14.3.2 Assessment of Biosphere 

 Transport Pathways ................................... 16-12 

 2.2.1.3.14.3.3 Assessment of Human Exposure .............. 16-23 

 2.2.1.3.14.3.4 Assessment of Dosimetry .......................... 16-27 

 2.2.1.3.14.3.5 Assessment of Integrated Biosphere 

 Modeling Results ....................................... 16-28 

 2.2.1.3.14.4 NRC Staff Conclusions .............................................................. 16-29 

 2.2.1.3.14.5 References ................................................................................ 16-29 

CHAPTER 17 ........................................................................................................................... 17-1 

 2.2.1.4.1 Postclosure Individual Protection Calculation................................................... 17-1 

 2.2.1.4.1.1 Introduction .................................................................................. 17-1 

 2.2.1.4.1.2 Evaluation Criteria ....................................................................... 17-1 

 2.2.1.4.1.3 Technical Evaluation .................................................................... 17-2 

 2.2.1.4.1.3.1 Introduction .................................................. 17-2 

 2.2.1.4.1.3.2 Scenarios Used in Calculation of 

 Annual Dose ................................................ 17-3 

 2.2.1.4.1.3.2.1 Summary of DOE Approach ........................ 17-3 

 2.2.1.4.1.3.2.2 NRC Staff Evaluation of Scenarios Used 

 in Calculation of Annual Dose ..................... 17-6 

 2.2.1.4.1.3.3 Credible Representation of 

 Repository Performance .............................. 17-7 

 2.2.1.4.1.3.3.1 DOE’s TSPA Calculation Related to 

 Groundwater Releases ................................ 17-8 

 2.2.1.4.1.3.3.1.1 Summary of DOE Approach in TSPA .......... 17-8 

 2.2.1.4.1.3.3.1.1.1 Summary of DOE’s TSPA for Seepage of 

 Water Into Drifts ........................................... 17-9 

 2.2.1.4.1.3.3.1.1.2 Summary of DOE’s TSPA for Damage to 

 Engineered Barriers (Drip Shield and 

 Waste Package) ........................................ 17-10 

 2.2.1.4.1.3.3.1.1.3 Summary of DOE’s TSPA for Seepage of 

 Water Into Waste Packages ...................... 17-13 

 2.2.1.4.1.3.3.1.1.4 Summary of DOE’s TSPA for Release 

 of Radionuclides From the 

 Waste Package ......................................... 17-14 

 2.2.1.4.1.3.3.1.1.5 Summary of DOE’s TSPA for Transport of 

 Radionuclides in the Unsaturated and 

 Saturated Zones ........................................ 17-15 
xii 

CONTENTS (continued) 

Section Page 

 2.2.1.4.1.3.3.1.1.6 Summary of DOE’s TSPA for Annual Dose to 

 the Reasonably Maximally Exposed 

 Individual (RMEI) ....................................... 17-18 

 2.2.1.4.1.3.3.1.2 NRC Staff’s Evaluation of DOE’s 

 TSPA Calculation Related to 

 Groundwater Releases .............................. 17-19 

 2.2.1.4.1.3.3.2 DOE’s TSPA Calculation for the Volcanic 

 Eruption Modeling Case ............................ 17-32 

 2.2.1.4.1.3.3.2.1 Summary of DOE Approach ...................... 17-32 

 2.2.1.4.1.3.3.2.2 NRC Staff Evaluation of DOE’s TSPA 

 Calculation for the Volcanic Eruption 

 Modeling Case ........................................... 17-34 

 2.2.1.4.1.3.4 Statistical Stability of Average Annual 

 Dose Estimates ......................................... 17-35 

 2.2.1.4.1.3.4.1 Summary of DOE Approach ...................... 17-35 

 2.2.1.4.1.3.4.2 NRC Staff Evaluation of Statistical Stability 

 of Average Annual Dose ............................ 17-38 

 2.2.1.4.1.4 NRC Staff Conclusions .............................................................. 17-39 

 2.2.1.4.1.5 References ................................................................................ 17-39 

 

CHAPTER 18 ........................................................................................................................... 18-1 

 2.2.1.4.2 Human Intrusion Calculation ............................................................................ 18-1 

 2.2.1.4.2.1 Introduction .................................................................................. 18-1 

 2.2.1.4.2.2. Evaluation Criteria ....................................................................... 18-1 

 2.2.1.4.2.3 Technical Evaluation .................................................................... 18-2 

 2.2.1.4.2.3.1 Timing of Human Intrusion Event ................ 18-3 

 2.2.1.4.2.3.2 Representation of Intrusion Event ............... 18-5 

 2.2.1.4.2.3.3 Annual Dose to RMEI .................................. 18-9 

 2.2.1.4.2.4 NRC Staff Conclusions .............................................................. 18-11 

 2.2.1.4.2.5 References ................................................................................ 18-11 

CHAPTER 19 ........................................................................................................................... 19-1 

 2.2.1.4.3 Separate Groundwater Protection Calculation ................................................. 19-1 

 2.2.1.4.3.1 Introduction .................................................................................. 19-1 

 2.2.1.4.3.2 Evaluation Criteria ....................................................................... 19-1 

 2.2.1.4.3.3 Technical Evaluation .................................................................... 19-2 

 2.2.1.4.3.3.1 Representative Volume Location ................. 19-3 

 2.2.1.4.3.3.2 Representative Volume Dimensions ........... 19-4 

 2.2.1.4.3.3.3 Concentration of Radionuclides in the 

 Representative Volume ............................... 19-6 

 2.2.1.4.3.4 NRC Staff Conclusions ................................................................ 19-7 

 2.2.1.4.3.5 References .................................................................................. 19-7 

CHAPTER 20 ........................................................................................................................... 20-1 

 2.5.4 Expert Elicitation ............................................................................................... 20-1 

 2.5.4.1 Introduction .................................................................................. 20-1 

 2.5.4.2 Evaluation Criteria ....................................................................... 20-1 
xiii 

CONTENTS (continued) 

Section Page 

 2.5.4.3 Technical Evaluation .................................................................... 20-2 

 2.5.4.4 NRC Staff Conclusions ................................................................ 20-7 

 2.5.4.5 References .................................................................................. 20-8 

CHAPTER 21 ........................................................................................................................... 21-1 

 Conclusions .......................................................................................................................... 21-1 

CHAPTER 22 ........................................................................................................................... 22-1 

 Glossary ................................................................................................................................ 22-1 

xv 

FIGURES 

Figure Page 

6-1 Potential Flow Pathways in the Engineered Barrier System .................................. 6-21 

TABLES 

Table Page 

1-1 Summary of Staff’s Barrier Component Review ....................................................... 1-4 

9-1 Quantitative Reduction in Flux From the Ground Surface to Water Entering the 

 Drift Using Flux Averages Over the Repository Footprint ........................................ 9-7 

16-1 Exposure Pathways and Radionuclides Determined the Most Risk Significant 

 in the DOE Performance Assessment for the 10,000-Year Simulation Period ...... 16-5 

16-2 Exposure Pathways and Radionuclides Determined the Most Risk Significant 

 in the DOE Performance Assessment for the 1-Million-Year Simulation Period .... 16-6 

17-1 Scenario Classes and Modeling Cases Included in the DOE’s TSPA ................... 17-9 

17-2 DOE’s Mean Values for the Seepage Rate Into Drifts ......................................... 17-11 

17-3 Cumulative Number of CSNF and CDSP Waste Packages Breached for the 

 Seismic Ground Motion and Igneous Intrusion Modeling Cases in 

 DOE’s TSPA ......................................................................................................... 17-12 

17-4 Radionuclide Transport Times in the Unsaturated Zone for the Northern and 

 Southern Repository Areas From DOE Breakthrough Curves ............................. 17-17 

17-5 Summary of DOE Simulated Transport Times in the Saturated Zone Under 

 Glacial-Transition Climate State ........................................................................... 17-17 

17-6 DOE Groundwater BDCF’s .................................................................................. 17-18 

17-7 NRC Staff Confirmatory Calculation Results for the Volume of Seepage Water 

 Entering Parch Failures in a Single Waste Package for Seismic Ground Motion 

 (Ruptures, Punctures, and General Corrosion) and Igneous Intrusion Modeling 

 Cases for CSNF and CDSP Waste Packages ..................................................... 17-20 

17-8 NRC Staff Confirmatory Calculation Results for the Average Release Rates 

 for Tc-99 (Seismic Ground Motion Modeling Case) for CNSF and CDSP 

 Waste Packages .................................................................................................. 17-22 

17-9 NRC Staff Confirmatory Calculation Results for the Average Release Rates 

 for Np-237 and Pu-242 in the Seismic Ground Motion Modeling Case for CSNF 

 and CDSP Waste Packages ................................................................................ 17-23 

17-10 NRC Staff Confirmatory Calculation Results for the Average Release Rates 

 for Tc-99 in the Igneous Intrusive Modeling Case for CSNF and CDSP 

 Waste Packages .................................................................................................. 17-25 

17-11 NRC Staff Confirmatory Calculation Results for the Average Release Rates 

 for Np-237 and Pu-242 in the Igneous Intrusive Ground Motion Case for CSNF 

 and CDSP Waste Packages ................................................................................ 17-26 
xvi 

TABLES (continued) 

Table Page 

17-12 NRC Staff Confirmatory Calculation Values for the Effectiveness (Expressed as a 

 Percentage Reduction in Release) of the Unsaturated and Saturated Zones for 

 Reducing Release Rates for Specific Radionuclides ........................................... 17-27 

17-13 NRC Staff Confirmatory Calculation Results for the Average Dose Estimates for 

 Tc-99 for the Seismic Ground Motion Modeling Case and Igneous Intrusive 

 Modeling Case ..................................................................................................... 17-28 

17-14 NRC Staff Confirmatory Calculation Results for the Annual Dose for Np-237 

 for the Seismic Ground Motion Modeling Case and Igneous Intrusive 

 Modeling Case ..................................................................................................... 17-29 

17-15 NRC Staff Confirmatory Calculation Results for the Annual Dose for Pu-242 

 for the Seismic Ground Motion Modeling Case and Igneous Intrusive 

 Modeling Case ..................................................................................................... 17-30 

17-16 DOE Volcanic Eruption Modeling Case Short-Term and Long-Term 

 Inhalation BDCFs ................................................................................................. 17-33 

17-17 DOE Volcanic Eruption Modeling Case Combined Ingestion, Radon, 

 and External BDCF .............................................................................................. 17-33 

17-18 NRC Staff Confirmatory Calculation Results for Pu-239 and Am-241 Annual 

 Doses for the Volcanic Eruption Modeling Case (Inhalation Pathway) ................ 17-35 

17-19 NRC Staff Confirmatory Calculation of Sr-90 and Cs-137 Annual Doses 

 for the Volcanic Eruption Modeling Case (External Pathway) .............................. 17-36 

17-20 NRC Staff Confirmatory Calculation of Ra-226 Annual Dose for the Volcanic 

 Eruption Modeling Case (External Pathway) ........................................................ 17-37 
xvii 

EXECUTIVE SUMMARY 

Background 

After docketing the U.S. Department of Energy (DOE) license application seeking a construction 

authorization for the proposed repository at Yucca Mountain, Nevada, the U.S. Nuclear 

Regulatory Commission (NRC) staff began documenting its review in a Safety Evaluation 

Report. On March 3, 2010, DOE filed a motion with the Atomic Safety and Licensing Board 

seeking to withdraw its license application to develop a repository at Yucca Mountain, Nevada. 

In June 2010, the Board denied the DOE motion. To date, petitions asking the Commission to 

reverse or uphold this decision are pending before the Commission. 

On October 1, 2010, the NRC staff began orderly closure of its Yucca Mountain activities. 

As part of orderly closure, the NRC staff prepared this technical evaluation report 

(TER), a knowledge management document. This document captures the NRC staff’s 

technical assessment of information presented in DOE’s Safety Analysis Report (SAR), dated 

June 3, 2008, as amended, and supporting information. The TER describes the NRC staff’s 

technical evaluation of the SAR and, in particular, this TER Postclosure Volume provides 

technical insights on the application of performance assessment in the context of geologic 

disposal. The TER was developed using the regulations at 10 CFR Part 63 and guidance in the 

Yucca Mountain Review Plan (YMRP). The TER does not, however, include conclusions as to 

whether or not DOE satisfies the Commission’s regulations. 

NRC’s regulations at 10 CFR Part 63 provide site-specific criteria for geologic disposal at 

Yucca Mountain. These regulations prescribe requirements governing the licensing 

(including issuance of a construction authorization) of DOE to receive and possess source, 

special nuclear, and byproduct material at a geologic repository operations area sited, 

constructed, or operated at Yucca Mountain, Nevada. Under 10 CFR Part 63, there are several 

stages in the licensing process: the site characterization stage, the construction stage, and a 

period of operations. The period of operations includes the time during which emplacement 

would occur, any subsequent period before permanent closure during which the emplaced 

wastes are retrievable, and permanent closure. In addition, the regulations at 10 CFR Part 63 

represent a risk-informed, performance-based (RIPB) approach to the review of geological 

disposal. The RIPB approach uses risk information to focus the review to areas most significant 

to safety or performance. Therefore, the TER includes discussions regarding how the staff used 

risk information in its review. 

This technical evaluation report presents information on the NRC staff’s assessment of 

the SAR DOE provided on June 3, 2008, as updated on February 19, 2009.1

 The NRC 

staff also reviewed information DOE provided in response to NRC staff’s requests for 

additional information, and other information that DOE provided related to the SAR. 

In conducting its review of DOE’s SAR, the NRC staff was guided by the Yucca Mountain 

Review Plan.2

 

1

DOE. 2009. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 1. ML090700817. 

Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 2

NRC. 2003. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. Washington, DC: NRC 
xviii 

System Description and Demonstration of Multiple Barriers 

A geologic repository is to include multiple barriers, both natural and engineered. Barriers 

prevent or limit the movement of water or radioactive material and thus isolate waste. A multiple 

barrier approach ensures that the overall repository system is robust and not wholly dependent 

on any single barrier to ensure repository safety. DOE is to identify these barriers when it 

calculates how the repository will perform in the future, describe the capability of each barrier, 

and provide the technical basis for its description. In its SAR for the proposed repository at 

Yucca Mountain, DOE identified three barriers: the Upper Natural Barrier, the Engineered 

Barrier System (EBS), and the Lower Natural Barrier. Each of these barriers includes multiple 

features that DOE described as important to waste isolation. 

DOE expects that the Upper Natural Barrier will substantially reduce the amount of water 

that reaches the repository horizon. In semi-arid environments, like that at Yucca Mountain, 

humidity and precipitation are low and surface evaporation rates are high. In addition, plant 

uptake and surface runoff can reduce further the amount of water available to move from the 

surface into the rock layers above the repository. In its SAR, DOE explained that during the first 

10,000 years after the repository is closed, most of the water that does reach the depth of the 

repository is prevented from seeping into the repository and is diverted around waste 

emplacement tunnels, or drifts, because of thermal effects from the waste. After approximately 

10,000 years, DOE concluded that the amount of heat generated from the waste will be low 

enough to allow water to seep into drifts and potentially contact the EBS. 

DOE identified the primary purpose of the EBS as preventing or substantially reducing 

movement of water that actually contacts the waste, and of limiting movement of 

dissolved radionuclides away from the repository. DOE predicts that the walls of the 

waste emplacement drifts will degrade slowly over time. However, DOE expects that 

specific engineered features will mostly stay in place, will remain largely intact, and 

will continue to keep the waste substantially dry for very long time periods. If the repository 

is undisturbed by very large earthquakes or volcanoes, DOE projects that less than 

0.01 percent of the waste will be exposed to water that seeps into the drifts during the first 

10,000 years after the repository is closed. 

In addition to the emplacement drifts, DOE expects other specific features to limit the flow of 

seepage water or dissolved radionuclides. These features include the drip shields, the waste 

packages, the waste forms and waste package internal components, and the emplacement 

pallet and invert. The drip shields divert seepage water away from the waste package. 

Likewise, as long as the waste packages remain intact, seepage water cannot reach or interact 

with the enclosed waste forms. Once waste packages begin to corrode, or form cracks, 

radionuclide releases from the packages are limited by, among other things, the rate at which 

the various waste forms deteriorate and the rate at which continuous liquid pathways can form 

through openings large enough to permit flow. DOE projects that most waste packages will 

remain intact for approximately 200,000 years after the repository is closed, unless large 

earthquakes or volcanoes occur and cause damage. Once the waste packages begin to leak, 

DOE expects that the internal components of the waste packages along with the emplacement 

pallet and invert materials will contribute to a physical and chemical environment around the 

waste that prevents or substantially reduces the movement of water and dissolved radionuclides 

away from the repository. DOE projects that the proposed EBS would limit radionuclide 

releases to the Lower Natural Barrier to less than 0.003 percent of the available inventory at 

10,000 years and to not more than 7 percent of the available inventory at 1 million years after 

the repository is closed. 
xix 

DOE concluded that the Lower Natural Barrier will impede the movement of most, but not 

all, radionuclides from the EBS to the accessible environment. Layers of unsaturated rock 

below the repository 300 m [approximately 1,000 ft] thick are expected to retard the flow of 

dissolved radionuclides on their way to the water table. Once in saturated rock or groundwater 

below the repository, water potentially contaminated with radionuclides from the repository must 

travel through 12–14 km [7–9 mi] of fractured volcanic rock and 4–6 km [2–4 mi] of saturated 

gravels and sands before reaching the human environment. DOE determined that the 

effectiveness of the Lower Natural Barrier in retaining specific individual radionuclides depends 

on the solubility, sorptive properties, and half-life of the specific radionuclide. DOE projects 

that the releases of solubility-limited, strongly sorbed nuclides are reduced by as much as 

99 percent, while those of moderately soluble, low-sorption, long half-life nuclides are reduced 

by lesser amounts. Highly soluble, nonsorbing radionuclides, however, will not be retained and 

will move at roughly the same rate as the groundwater flow. 

The NRC staff has reviewed the SAR and other information DOE has submitted in support of 

a system of multiple barriers and determined DOE identified the design features of the EBS and 

natural barrier features of the geologic setting that are important to isolation of waste at Yucca 

Mountain. DOE showed that multiple barriers important to waste isolation occur both in the 

natural and in the engineered systems at the proposed repository at Yucca Mountain. DOE 

described the capabilities of the barriers it identified and provided a reasonable technical basis 

for these descriptions. DOE reasonably described the time period during which the barriers 

perform their intended functions and DOE reasonably accounted for uncertainty in its 

descriptions of barrier capabilities. DOE’s descriptions of barrier capability are consistent 

with their use in DOE’s Total System Performance Assessment (TSPA). 

Assessment of Repository Performance 

A performance assessment is a systematic analysis that answers three questions: What can 

happen? How likely is it to happen? and What are the resulting consequences? The NRC staff 

reviewed the TSPA DOE provided in support of its SAR for the proposed repository at Yucca 

Mountain. In conducting its review of DOE’s TSPA of the repository, the staff evaluated DOE’s 

system description, demonstration of multiple barriers, and associated supporting scientific and 

analytic methods to focus on those items most important to waste isolation. 

To answer the question “What can happen?” after a repository is closed at Yucca Mountain, 

DOE had to consider a wide range of specific features (e.g., geologic rock types, waste 

package materials), events (e.g., earthquakes, volcanic activity), and processes (e.g., corrosion 

of metal waste packages, sorption of radionuclides on rock surfaces), for possible inclusion 

in (or exclusion from) its evaluation. Once selected, DOE then used these features, events, 

and processes (FEPs) to postulate a range of credible, future scenarios. A scenario is a 

well-defined sequence of events and processes, which can be interpreted as an outline of 

one possible future condition of the repository system. Thus, scenario analysis identifies the 

possible ways in which the repository environment could evolve to develop a defensible 

representation of the system and estimate the range of credible potential consequences. 

After DOE selected appropriate FEPs and used them to postulate scenarios, DOE grouped 

similar scenarios into scenario classes and screened them for use in its performance 

assessment of the facility. The NRC staff reviewed DOE’s scenario analysis to verify that DOE 

did not overlook future conditions at the proposed repository that could significantly enhance or 

degrade its safety. To conduct this review, the staff used its own risk insights from previous 
xx 

performance assessments for the Yucca Mountain site, detailed process-level modeling efforts, 

laboratory and field experiments, and natural analog studies. 

When addressing the question “How likely is it that these events will happen?” DOE 

assessed the likelihood that credible scenario classes could disrupt repository performance. 

A performance assessment used for the individual protection calculation considers events that 

have at least 1 chance in 100 million of occurring. DOE included three disruptive event types 

for inclusion in its postclosure performance assessments: disruption by volcanic (i.e., igneous) 

events, disruptions by earthquake (i.e., seismic) events, and early failure of waste packages or 

drip shields. 

To answer the question “What are the resulting consequences?” DOE made estimated 

projections called “model abstractions” to represent the performance of various parts of the 

repository system. Each model abstraction develops one or more numerical models that 

represent how specific FEPs interact and affect performance of repository systems. DOE also 

included potentially significant variations in site or design characteristics into the models, so that 

a range of potential outcomes would be calculated in the performance assessment. 

To evaluate whether DOE’s model abstractions portrayed the expected consequences when 

implemented in the overall performance assessment, the NRC staff reviewed 13 separate 

categories of model abstractions. The NRC staff selected these abstractions from engineered, 

geosphere, and biosphere subsystems found to be most important to waste isolation on the 

basis of previous performance assessments, knowledge of site characteristics, and careful 

examination of DOE’s proposed repository design. In its review, the staff focused on those 

models of greatest risk significance to repository safety. For the postclosure period, “important 

to repository safety or waste isolation” means important to estimating release of radionuclides to 

the accessible environment and annual dose. 

For each model abstraction, the NRC staff determined whether the data DOE used 

appropriately represented site- and design-specific characteristics, including the variability and 

uncertainty in these characteristics. The NRC staff evaluated how DOE incorporated FEPs in 

the model abstractions and reviewed DOE’s technical bases to support the inclusion or 

exclusion of these FEPs. In addition, the NRC staff reviewed the methods DOE used to 

develop the model abstractions, including how DOE represented model uncertainty. The staff’s 

review also examined how DOE supported the use of its models in the performance 

assessment and how DOE considered the potential effects of alternative models. 

In reviewing DOE’s SAR and other information submitted, the NRC staff notes that DOE’s 

performance assessment evaluations included appropriate FEPs and that a reasonable basis 

was provided for those that were excluded. DOE considered events that have at least 1 chance 

in 100 million per year of occurring (i.e., igneous events, seismic events, and early failure of 

waste packages and drip shield events) in its performance assessment analyses. After 

conducting focused reviews of the 13 model abstractions, the NRC staff notes that those model 

abstractions are reasonable for use in the performance assessments because (i) applicable 

data related to natural systems and the EBS were included, (ii) alternative models were 

appropriately considered, and (iii) appropriate technical bases in support of the model 

abstractions were provided. DOE should confirm its approach for decay chain radionuclide 

behavior by providing, through its performance confirmation program, information to reduce 

uncertainty related to the likelihood of excess Po-210 occurring in the saturated zone, as 

identified in TER Section 2.2.1.3.9.3. 
xxi 

Performance Assessment Results 

DOE used its TSPA to represent the range of behavior of a repository at Yucca Mountain and to 

account for uncertainty in FEPs that could affect the evolution of the repository over the 

postclosure period. DOE developed its analysis of repository performance by grouping scenario 

classes broadly as either nominal or disruptive. The nominal scenario class comprises those 

FEPs that are present under “normal” conditions (i.e., eventual infiltration of water, corrosion of 

waste packages, release of radionuclides, transport of radionuclides in groundwater). During 

the initial 10,000 years after repository closure, DOE’s nominal scenario class does not result in 

any dose to the reasonably maximally exposed individual. Disruptive scenario classes, as 

noted earlier, include additional FEPs that account for the effects of specific events, such as 

earthquakes and volcanoes, which could disturb or alter the performance of the repository in 

ways not included under the nominal scenario class. 

Disruptive events of sufficient magnitude have the potential to result in doses to the reasonably 

maximally exposed individual at any time during the postclosure period. The estimate of 

projected dose resulting from a disruptive event or scenario is to be weighted by the probability 

that the disruptive event will occur. Therefore, a key component of the NRC staff’s review of 

DOE’s performance assessment involves a determination that the probabilities and 

consequences of each of the scenario classes are appropriately included in the average annual 

dose calculations. 

The NRC staff conducted confirmatory calculations to supplement and assist its review of 

DOE’s TSPA. The confirmatory calculations provide both a quantitative understanding of the 

assessment and an understanding of whether there is a general consistency between 

submodels of the analysis and the overall results, including uncertainty. For example, the staff’s 

confirmatory calculations examined whether DOE’s projections of the timing and extent of 

breaching of the waste packages corresponded and were consistent with the projected timing 

and magnitude of the average annual dose. The staff’s confirmatory calculations were 

performed for selected time periods (i.e., 10,000; 100,000; 400,000; and 800,000 years) to 

provide the staff a perspective on the time-dependent nature of waste package failure, 

associated radioactive decay and release of specific radionuclides. In assessing the credibility 

of DOE’s average annual dose curve for the groundwater pathway, the NRC staff conducted 

separate confirmatory calculations for (i) the amount of water entering failed waste packages, 

(ii) the release of radionuclides from the waste packages, (iii) transport of radionuclides through 

the unsaturated and unsaturated zones, (iv) effects of disruptive events, and (v) annual dose to 

the reasonably maximally exposed individual. 

The staff’s confirmatory calculations were based on the NRC staff’s review of DOE’s TSPA 

calculation, including DOE’s models and intermediate outputs. Thus NRC staff’s confirmatory 

calculations address key quantitative attributes of the repository system to help evaluate overall 

performance. This approach provided the staff with a straightforward method for confirming that 

DOE’s TSPA results provided a credible representation of the repository’s performance. In 

other words, NRC staff’s results showed that DOE’s average annual dose curve is consistent 

with the model abstractions, probabilities, and treatment of uncertainties, each of which were 

reviewed separately using the NRC staff’s guidance in the Yucca Mountain Review Plan. 

DOE presented the overall average annual dose curve due to releases from the repository 

over the entire 1-million-year period in its SAR. The peak of the curve of overall average 

annual dose to the RMEI is approximately 0.003 mSv [0.3 mrem] per year over the initial 
xxii 

10,000 years and is approximately 0.02 mSv [2 mrem] per year over the 1-million-year period 

after repository closure. 

DOE estimated the concentrations for the alpha activity (including background) would be 

0.5 pCi/L with the largest contribution coming from natural background radiation already in 

the groundwater. The largest annual release from the repository of relevant alpha-emitting 

radionuclides into the representative volume from the repository was estimated to be more than 

1,000 times less than background levels. 

DOE estimated the dose from beta- and photon-emitting radionuclides would be 0.0006 mSv 

[0.06 mrem] per year for the whole body and the largest dose to any organ would be 

0.0026 mSv [0.26 mrem] per year as a result of drinking 2 L [0.53 gal] of water per day assumed 

to be at peak estimated concentration levels of radionuclides released from the repository in the 

representative volume. 

DOE estimated the concentration from the combined Ra-226 and Ra-228 (including 

background) would be 0.5 pCi/L with the largest contribution coming from natural background 

radiation already in the groundwater. The largest annual release from the repository of Ra-226 

and Ra-228 into the representative volume from the repository was estimated to be almost 

1 million times less than the background levels. 

DOE determined the earliest time after disposal that waste packages would have degraded 

sufficiently such than an intrusion could occur without a driller recognizing it, as part of the 

human intrusion analysis. DOE selected 200,000 years as a conservative assumption of the 

earliest time the waste packages could degrade enough so that an intrusion could occur without 

drillers recognizing it. DOE developed a separate performance assessment to evaluate the 

consequences of a postulated human intrusion event assumed to occur 200,000 years after 

permanent closure of the repository. DOE modified its performance assessment for individual 

protection to represent the human intrusion scenario. DOE’s estimated dose due to releases 

from the repository is approximately 0.0001 mSv [0.01 mrem] per year shortly after the time of 

the intrusion. 

Summary Conclusions 

The NRC staff has reviewed the SAR and other information DOE submitted in support of its 

SAR. DOE submitted information consistent with the guidance in the YMRP. Specifically, 

NRC staff notes that the repository (i) is composed of multiple barriers; (ii) the Total Systems 

Performance Assessments (TSPAs) used for the individual protection, human intrusion, and 

separate groundwater protection calculations are reasonable; and (iii) the technical approach 

and results in DOE’s TSPA, including the average annual dose values and the performance of 

the repository barriers, discussed in this TER, are reasonable. DOE should confirm its 

approach for decay chain radionuclide behavior by providing, through its performance 

confirmation program, information to reduce uncertainty related to the likelihood of excess 

Po-210 occurring in the saturated zone, as identified in TER Section 2.2.1.3.9.3. 
xxiii 

ACRONYMS AND ABBREVIATIONS 

AFM active fracture model 

AMR analysis and model reports 

APE annual probability of exceedance 

ASTM American Society for Testing and Materials 

BDCF biosphere dose conversion factors 

BSC Bechtel SAIC Company, LLC. 

BWR boiling water reactor 

CDSP codisposal packages 

CFR Code of Federal Regulations 

CFu Crater Flat undifferentiated 

CHn Calico Hills nonwelded 

CNWRA® Center for Nuclear Waste Regulatory Analyses 

CRWMS M&O Civilian Radioactive Waste Management Management & Operation 

CSNF commercial spent nuclear fuel 

DHLW defense high-level waste 

DOE U.S. Department of Energy 

DVRGFSM Death Valley Regional Groundwater Flow System Model 

EBS engineered barrier system 

EPA U.S. Environmental Protection Agency 

ERMYN Environmental Radiation Model for Yucca Mountain Nevada 

FAR Fortymile Wash Ash Redistribution 

FEP feature, event, and process 

GROA geologic repository operations area 

ITWI important to waste isolation 

MASSIF Mass Accounting System for Soil Infiltration and Flow 

MCO multicanister overpack 

MDEB mechanical disruption of engineered barrier 

MIC microbially influenced corrosion 

NOAA National Oceanic and Atmospheric Administration 

NRC U.S. Nuclear Regulatory Commission 

NC–EWDP Nye County Early Warning Drilling Program 

OECD Organisation for Economic Co-operation and Development 

PFDHA probabilistic fault displacement hazard analysis 

PGA peak ground acceleration 

PGV peak ground velocity 

PSHA probabilistic seismic hazard assessment 

PTn Paintbrush Tuff nonwelded 

PVHA probabilistic volcanic hazard assessment 

PVHA-U probabilistic volcanic hazard assessment-update 

PWR pressurized water reactor 

RAI request for additional information 

RB repository block 

RMEI reasonably maximally exposed individual 

RMS root-mean-square 

RST residual stress threshold 

SA spectral accelerations 

SAR Safety Analysis Report 
xxiv 

ACRONYMS AND ABBREVIATIONS (continued) 

SCC stress corrosion cracks 

SDFR slip-dissolution aging and film-rupture 

SEF sorption enhancement factor 

SNF spent nuclear fuel 

SNL Sandia National Laboratories 

SSHAC Senior Seismic Hazard Analysis Committee 

SZEE saturated zone flow and transport expert elicitation 

TAD transportation, aging, and disposal 

TEDE total effective dose equivalent 

TER Technical Evaluation Report 

TSPA Total System Performance Assessment 

TSw Topopah Spring welded 

UDEC universal distinct element code 

USGS U.S. Geological Survey 

UZ unsaturated zone 

WRIP water-rock interaction parameter 

YMRP Yucca Mountain Review Plan 
1 

INTRODUCTION 

After docketing the U.S. Department of Energy (DOE) license application seeking a construction 

authorization for the proposed repository at Yucca Mountain, Nevada, the U.S. Nuclear 

Regulatory Commission (NRC) staff began documenting its review in a Safety Evaluation 

Report. On March 3, 2010, DOE filed a motion with the Atomic Safety and Licensing Board 

seeking to withdraw its license application to develop a repository at Yucca Mountain, Nevada. 

In June 2010, the Board denied the DOE motion. To date, petitions asking the Commission to 

reverse or uphold this decision are pending before the Commission. 

On October 1, 2010, the NRC staff began orderly closure of its Yucca Mountain activities. 

As part of orderly closure, the NRC staff prepared this technical evaluation report (TER), 

a knowledge management document. This document captures the NRC staff’s 

technical assessment of information presented in DOE’s Safety Analysis Report (SAR), dated 

June 3, 2008, as amended, and supporting information. The TER describes the NRC staff’s 

technical evaluation of the DOE SAR and, in particular, this TER Postclosure Volume provides 

technical insights on the application of performance assessment in the context of geologic 

disposal. The TER was developed using the regulations at 10 CFR Part 63 and guidance in the 

Yucca Mountain Review Plan (YMRP). The TER does not, however, include conclusions as to 

whether or not DOE satisfies the Commission’s regulations. 

NRC’s regulations at 10 CFR Part 63 provide site-specific criteria for geologic disposal at 

Yucca Mountain. These regulations prescribe requirements governing the licensing (including 

issuance of a construction authorization) of DOE to receive and possess source, special 

nuclear, and byproduct material at a geologic repository operations area sited, constructed, or 

operated at Yucca Mountain, Nevada. Under 10 CFR Part 63, there are several stages in the 

licensing process: the site characterization stage, the construction stage, and a period of 

operations. The period of operations includes the time during which emplacement would occur, 

any subsequent period before permanent closure during which the emplaced wastes are 

retrievable, and permanent closure. In addition, the regulations at 10 CFR Part 63 represent a 

risk-informed, performance-based (RIPB) approach to the review of geological disposal. The 

RIPB approach uses risk information to focus the review to areas most significant to safety or 

performance. Therefore, the TER includes discussions regarding how the staff used risk 

information in its review. In conducting its review, the NRC staff was guided by the YMRP. 

This TER Postclosure Volume documents the results of the NRC staff’s assessment of 

repository performance after a repository is permanently closed. The NRC staff’s technical 

evaluation considered (i) the geologic repository’s multiple barriers, both natural and manmade, 

or engineered, and (ii) the performance assessments, including model abstractions, used for the 

individual protection calculation, the separate groundwater protection calculation, and the 

human intrusion calculation. 

Risk-Informed and Performance-Based Review 

The NRC staff evaluated DOE’s performance assessment using a risk-informed and 

performance-based review. DOE’s performance assessment is a systematic analysis that 

answers three basic questions that often are used to define risk: What can happen? How likely 

is it to happen? and What are the resulting consequences? The Yucca Mountain performance 

assessment is a sophisticated analysis that involves various complex considerations and 

evaluations. Examples include evolution of the natural environment; degradation of engineered 

barriers; and disruptive events, such as seismicity and igneous activity. Because the 
2 

performance assessment encompasses such a broad range of issues, the NRC staff used risk 

information throughout the review process to ensure that the review focused on those items 

most important to waste isolation. YMRP Section 2.2.1 provides guidance to the NRC staff to 

apply risk information throughout the review of the performance assessment. 

To support its risk-informed, performance-based review, the NRC staff initially reviewed DOE’s 

information on the repository’s barriers. Important barriers (engineered and natural) of the 

performance assessment are identified, each barrier’s capability is described, and the technical 

basis for that capability is provided in the SAR. This risk information describes DOE’s 

understanding of each barrier’s capability to prevent or substantially delay the movement of 

water or radioactive materials. The NRC staff’s review of DOE information regarding the 

repository’s barriers provides an understanding of each barrier’s importance to waste isolation, 

which helps focus the NRC staff’s review of DOE’s Total System Performance Assessment 

(TSPA) presented in TER Sections 2.2.1.2, “Scenario Analysis and Event Probability”; 2.2.1.3, 

“Model Abstraction”; and 2.2.1.4, “TSPA Calculations.” Particular parts of the NRC staff’s 

review are emphasized on the basis of the risk insights (i.e., those attributes of the repository 

system most important to repository performance). Additionally, the NRC staff has considered 

independent risk insights from previous performance assessments conducted for the Yucca 

Mountain site, detailed process modeling efforts, laboratory and field experiments, and natural 

analog studies, and has identified this information, as appropriate. 

System Description and Demonstration of Multiple Barriers 

A geologic repository at Yucca Mountain is to include multiple barriers, both natural and 

engineered. Barriers prevent or limit the movement of water or radioactive material. A multiple 

barrier approach ensures that the overall repository system is robust and not wholly dependent 

on any single barrier to ensure repository safety. DOE is to identify these barriers when it 

calculates how the repository will perform in the future, describe the capability of each barrier 

and provide the technical basis for its description. In its SAR for the proposed repository at 

Yucca Mountain, DOE identified three barriers: the Upper Natural Barrier, the Engineered 

Barrier System (EBS), and the Lower Natural Barrier. The Upper Natural Barrier is composed 

of features above the repository (i.e., topography, surficial soils, and the unsaturated zone) 

that reduce the movement of water downward toward the repository, which in turn reduces 

the rate of movement of water from the radioactive waste in the repository to the accessible 

environment. The engineered barrier system includes different engineering features 

(e.g., emplacement drifts, drip shields, waste packages and their internal components, and 

emplacement pallets and inverts) that are designed to (i) enhance the performance of the waste 

package, preventing radionuclide releases while it is intact; (ii) limit radionuclide releases after 

the waste package is breached by limiting the amount of water that can contact the waste 

package; and (iii) limit radionuclide release from the engineered barrier system through 

sorption processes. The Lower Natural Barrier is composed of features below the repository 

(i.e., unsaturated zone) and from the repository location to the boundary of the accessible 

environment and the RMEI location (i.e., saturated zone) that reduce the rate of radionuclide 

movement from the repository to the accessible environment through such processes as the 

slow movement of water and sorption of radionuclides onto mineral surfaces. Each of these 

barriers includes features that DOE described as important to waste isolation. The NRC staff’s 

review is provided in TER Section 2.2.1.1. 
3 

Review of Postclosure Total System Performance Assessment 

A performance assessment is a systematic analysis that answers the three questions 

that define risk: What can happen? How likely is it to happen? and What are the 

resulting consequences? The NRC staff reviewed the Total System Performance 

Assessment (TSPA) analytic models and analyses DOE provided in support of its SAR. 

Scenario Analysis and Event Probability

To answer the question, “What can happen?” after a repository is closed, DOE considered a 

wide range of specific features (e.g., geologic rock types, waste package materials), events 

(e.g., earthquakes, volcanic activity), and processes (e.g., corrosion of metal waste packages, 

sorption of radionuclides on rock surfaces) for possible inclusion in (or exclusion from) its TSPA 

model. Once specific features, events, and processes were selected for inclusion, DOE then 

used these features, events, and processes (FEPs) to postulate a range of credible, future 

scenarios. A scenario is a well-defined sequence of events and processes, which can be 

interpreted as an outline of one possible future condition of the repository system. Thus, 

scenario analysis identifies the possible ways in which the repository environment could evolve 

so that a defensible representation of the system can be developed to estimate the range of 

credible potential consequences. After the features, events, and processes are selected and 

used to postulate scenarios, similar scenarios are grouped into scenario classes, which are 

screened for use in the performance assessment model. The goal of scenario analysis is to 

ensure that no important aspect of the potential high-level waste repository is overlooked in the 

evaluation of its safety. 

Consistent with this general guidance and the review areas in YMRP Section 2.2.1.2.1, 

the NRC staff evaluates DOE’s scenario analysis in four separate TER sections 

(Sections 2.2.1.2.1.3.1 to 2.2.1.2.3.4). Section 2.2.1.2.1.3.1 contains the NRC staff’s 

evaluation of both DOE’s methodology to develop a list of features, events, and processes 

and DOE’s list of the features, events and processes that it considered for inclusion in the 

performance assessment analyses. In Section 2.2.1.2.1.3.2, the NRC staff evaluates 

DOE’s screening of its list of features, events and processes, including DOE’s technical bases 

for the exclusion of features, events, and processes from its performance assessment. 

DOE’s formation of scenario classes and the exclusion of specific scenario classes in 

DOE’s performance assessment analyses are evaluated in Sections 2.2.1.2.1.3.3 and 

2.2.1.2.1.3.4, respectively. 

The NRC staff’s evaluation of DOE’s methodology and conclusions on the probability of events 

included in the performance assessments is addressed in TER Section 2.2.1.2.2. That chapter 

is aimed at the second risk triplet question: How likely is it to happen? 

Model Abstraction

The NRC staff’s evaluation of DOE’s model abstractions focuses on the consequences 

of overall repository performance. In particular, the NRC staff’s evaluation considers the model 

abstractions used in DOE’s TSPA to represent the performance (i.e., expected annual doses) of 

the repository. 

The review of the model abstraction process begins with the review of the repository design and 

the data characterizing the geology and the performance of the design and proceeds through 

the development of models used in the performance assessment. The model abstraction 
4 

review process ends with a review of how the abstracted models are implemented in the 

TSPA model (e.g., parameter ranges and distributions, integration with model abstractions for 

other parts of the repository system, representation of spatial and temporal scales, and 

whether the performance assessment model appropriately implements the abstracted model). 

For example, the review of parameter distributions considers the relevant data, the 

corresponding uncertainty, and effects on repository performance (i.e., the dose to the 

reasonably maximally exposed individual). The potential for risk dilution—the lowering of the 

risk, or dose, from an unsupported parameter range and distribution—is also part of this model 

abstraction review. 

In many applications, a conservative approach can be used to decrease the need to collect 

additional information or to justify a simplified modeling approach. A conservative approach 

may overestimate the dose to the reasonably maximally exposed individual. Approaches 

designed to overestimate a specific aspect of repository performance (e.g., higher 

temperatures within the drifts) may be conservative with respect to temperature but could 

lead to nonconservative results with respect to dose. The TSPA is a complex analysis with 

many parameters, and DOE may use conservative assumptions to simplify its approaches 

and data collection needs. However, a technical basis that supports the selection of models 

and parameter ranges or distributions is provided. The NRC staff’s evaluation of the technical 

bases supporting models and parameter ranges or distributions considers whether the approach 

results in calculated doses that would overestimate, rather than underestimate, the dose to the 

reasonably maximally exposed individual. In particular, the claim of conservatism as a basis for 

simplifying models and parameters is evaluated to verify that any simplifications do not 

unintentionally result in nonconservative results. 

The intentional use of conservatism to manage uncertainty also has implications for the NRC 

staff’s efforts to risk inform its review. The NRC staff evaluates assertions that a given model or 

parameter distribution is conservative from the perspective of overall system performance 

(i.e., the dose to the reasonably maximally exposed individual). The NRC staff used any 

available information to risk inform its review. For example, if DOE used an approach that 

overestimates a specific aspect of repository performance, then the NRC staff would consider 

the effects of this approach on other parts of the TSPA model, overall repository performance, 

and the representation or sensitivity of important phenomena. 

 

The NRC staff has separated the model abstraction review into the following 13 categories that 

are addressed in TER Sections 2.2.1.3.1 through 2.2.1.3.14.1

 

1. Degradation of Engineered Barriers (TER Section 2.2.1.3.1) 

 This TER section provides the NRC staff’s evaluation of the chemical degradation 

of the drip shields and waste packages emplaced in the repository drifts. Chemical 

degradation is primarily associated with the effect of corrosion processes on the metal 

surfaces of the drip shields and waste packages. The drip shields and the waste 

packages are engineered barriers, a subset of the engineered barrier system. The 

general functions of the engineered barrier system are to (i) prevent or significantly 

 

1

It was decided to discuss two of the topics in the YMRP as a single topic in the TER; however, the numbering 

system in the YMRP was retained in the TER to the maximum extent possible (e.g., Biosphere Characteristics is 

Section 2.2.1.3.14 in the YMRP and the TER). The review of Airborne Transport of Radionuclides (YMRP 

Section 2.2.1.3.11) and Redistribution of Radionuclides in Soil (YMRP Section 2.2.1.3.13) is discussed in TER 

Section 2.2.1.3.13 because the NRC staff considers a single discussion of these two topics provides for more clarity 

in the TER. Thus, the TER does not contain a section numbered 2.2.1.3.11. 
5 

reduce the amount of water that contacts the waste, (ii) prevent or significantly 

reduce the rate at which radionuclides are released from the waste, and (iii) prevent 

or significantly reduce the rate at which radionuclides are released from the engineered 

barrier system to the Lower Natural Barrier. The complete engineered barrier system 

consists of the emplacement drifts, the drip shields, the waste packages, the naval spent 

nuclear fuel structure, the waste forms and waste package internal components, and 

emplacement pallets and inverts. 

2. Mechanical Disruption of Engineered Barriers (TER Section 2.2.1.3.2) 

 This TER section provides the NRC staff’s evaluation of the mechanical disruption 

of the drip shields and waste packages emplaced in the repository drifts. Mechanical 

disruption of engineered barrier system components generally results from 

external loads generated by accumulating rock rubble. Rubble accumulation can 

result from processes such as (i) degrading emplacement drifts due to thermal loads, 

(ii) time-dependent natural weakening of rocks, and (iii) effects of seismic events 

(vibratory ground motion or fault displacements). During seismic events, rubble loads 

on engineered barrier system components can increase as the accumulated rock 

rubble is shaken. 

3. Quantity and Chemistry of Water Contacting Engineered Barriers and Waste Forms 

(TER Section 2.2.1.3.3) 

 This TER section provides the NRC staff’s evaluation of (i) the chemistry of water 

entering the drifts, (ii) the chemistry of water in the drifts (tunnels), and (iii) the quantity 

of water in contact with the engineered barrier system. These three abstraction topics 

provide input to model the features and performance of the engineered barrier system 

(e.g., drip shields and waste packages). For example, in its SAR, DOE relied on 

corrosion tests that were conducted on waste package and drip shield materials under a 

range of geochemical environments. The range of testing environments was derived 

from a range of potential starting water compositions and from knowledge of near-field 

and in-drift processes that alter these compositions. 

4. Radionuclide Release Rates and Solubility Limits (TER Section 2.2.1.3.4) 

 This TER section provides the NRC staff’s evaluation of the processes that could result 

in water transport of radionuclides out of the engineered barrier system, including the 

waste packages and the emplacement inverts, and into the unsaturated zone (the rock 

mass directly below the repository horizon and above the water table). The engineered 

barrier system and the transport pathway within the drift (repository tunnel) are the initial 

barriers to radionuclide release. If a waste package is breached and water enters the 

waste package, the radionuclides contained in the package may be released from the 

engineered barrier system. 

5. Climate and Infiltration (TER Section 2.2.1.3.5) 

 This TER section provides the NRC staff’s evaluation of the representation of climate 

and infiltration. This evaluation considers the reduction of water flux from precipitation to 

net infiltration. Because of the generally vertical movement of percolating water through 

the unsaturated zone in DOE’s representation of the natural system, water entering the 
6 

 unsaturated zone at the ground surface (infiltration) is the only source for deep 

percolation water in the unsaturated zone at and below the proposed repository. 

6. Unsaturated Zone Flow (TER Section 2.2.1.3.6) 

 This TER section provides the NRC staff’s evaluation of the abstraction of flow in that 

portion of the repository system above the water table (i.e., the unsaturated zone). 

Water percolating through the unsaturated zone above the repository (i.e., Upper Natural 

Barrier) may enter drifts, providing the means to interact with and potentially corrode the 

waste packages. Water percolating through the unsaturated zone below the repository 

(i.e., Lower Natural Barrier) also provides a flow pathway for transporting radionuclides 

downward to the water table. Once radionuclides pass below the water table, they may 

subsequently move laterally within the saturated zone to the accessible environment. 

7. Radionuclide Transport in the Unsaturated Zone (TER Section 2.2.1.3.7) 

 This TER section provides the NRC staff’s evaluation of the representation of 

radionuclide transport in the unsaturated zone. The NRC staff’s evaluation focuses on 

(i) advection, because most of the radionuclide mass is carried through the unsaturated 

zone by water flowing downwards to the water table; (ii) sorption, because sorption in 

porous media in the southern half of the repository area has the largest overall effect on 

slowing radionuclide transport in the unsaturated zone; (iii) matrix diffusion in fractured 

rock, because matrix diffusion coupled with sorption slows radionuclide transport in the 

northern half of the repository area; (iv) colloid-associated transport, because 

radionuclides attached to colloids may travel relatively unimpeded through the 

unsaturated zone; and (v) radioactive decay and ingrowth, because these processes 

affect the quantities of radionuclides released from the unsaturated zone over time. 

8. Flow Paths in the Saturated Zone (TER Section 2.2.1.3.8) 

 This TER section provides the NRC staff’s evaluation of the representation of flow paths 

in the saturated zone (i.e., the direction and magnitude of water movement in the 

saturated zone). Flow paths in the saturated zone provide the pathway for releases of 

radionuclides to migrate from the saturated zone below the repository to the accessible 

environment {approximately 18 km [11 mi] south of the repository}. The magnitude 

(specific discharge) of water flow is used to determine the speed that water moves 

through the saturated zone. 

9. Radionuclide Transport in the Saturated Zone (TER Section 2.2.1.3.9) 

 This TER section provides the NRC staff’s evaluation of transport of radionuclides 

in the saturated zone. The NRC staff’s technical review focuses on (i) how DOE 

represented the geological, hydrological, and geochemical features of the saturated 

zone in a framework for modeling the transport processes; (ii) how DOE integrated 

the saturated zone transport abstraction with other TSPA abstractions for performance 

assessment calculations; and (iii) how DOE included and supported important transport 

processes in the saturated zone radionuclide transport abstraction. 
7 

10. Igneous Disruption of Waste Packages (TER Section 2.2.1.3.10) 

 This TER section provides the NRC staff’s evaluation of models for the potential 

consequences of disruptive igneous activity at Yucca Mountain if basaltic magma rising 

through the Earth’s crust intersects and enters a repository drift or drifts (DOE’s igneous 

intrusion modeling case) or enters a drift and later erupts to the surface through one or 

more conduits (DOE’s volcanic eruption modeling case). The proposed Yucca Mountain 

repository site lies in a region that has experienced sporadic volcanic events in the past 

few million years, such that DOE previously determined the probability of future igneous 

activity at the site to exceed 1 × 10−8

 per year. The NRC staff’s technical review 

evaluates subsurface igneous processes (i.e., intrusion of magma into repository drifts, 

waste package damage, and formation of conduits to the surface), which involves 

entrainment of waste into the conduit and toward the surface. These processes control 

the amount of radionuclides that can be released during a potential igneous event. 

11. Concentration of Radionuclides in Groundwater (TER Section 2.2.1.3.12) 

 This TER section provides the NRC staff’s evaluation of the concentration of 

radionuclides in groundwater extracted by pumping and used in the annual water 

demand. Radionuclides transported through the saturated zone via groundwater to 

the accessible environment may be available for extraction by a pumping well. 

The reasonably maximally exposed individual is assumed to use well water with 

average concentrations of radionuclides and has an annual water demand of 3.7 × 109

 L 

[3,000 acre-ft]. 

12. Airborne Transport and Redistribution of Radionuclides (TER Section 2.2.1.3.13) 

 This TER section reflects the NRC staff’s evaluation of the volcanic ash exposure 

scenario and the groundwater exposure scenario. First, this TER section provides the 

NRC staff’s evaluation of the airborne transport and deposition of radionuclides expelled 

by a potential future volcanic eruption and the subsequent redistribution of those 

radionuclides in soil. Second, this TER section evaluates redistribution of radionuclides 

in soil that arrive in the accessible environment through groundwater transport. This 

section addresses both airborne transport of radionuclides (YMRP Section 2.2.1.3.11) 

and redistribution of radionuclides in soil (YMRP Section 2.2.1.3.13). 

13. Biosphere Characteristics (TER Section 2.2.1.3.14) 

 This TER section provides the NRC staff’s evaluation of the model used to calculate 

biosphere transport and the annual dose to the reasonably maximally exposed 

individual. The biosphere model calculates the transport of radionuclides within the 

biosphere through a variety of exposure pathways (e.g., soil, food, water, air) and 

applies dosimetry modeling to convert the reasonably, maximally exposed individual 

exposures into annual dose. Exposure pathways in the biosphere model are based on 

assumptions about residential and agricultural uses of the water and indoor and outdoor 

activities. These pathways include ingestion, inhalation, and direct exposure to 

radionuclides deposited to soil from irrigation. Ingestion pathways include drinking 

contaminated water, eating crops irrigated with contaminated water, eating food 

products produced from livestock raised on contaminated feed and water, eating farmed 

fish raised in contaminated water, and inadvertently ingesting soil. Inhalation pathways 
8 

 include breathing resuspended soil, aerosols from evaporative coolers, and radon gas 

and its decay products. 

Total System Performance Assessment (TSPA) Calculations

DOE has conducted an analysis, through its TSPA, that evaluates repository behavior in 

terms of groundwater concentrations and annual dose due to potential releases from the 

repository. The performance assessment provides a method to evaluate the range of features 

(e.g., geologic rock types, waste package materials), events (e.g., earthquakes, igneous 

activity), and processes (e.g., corrosion of metal waste packages, sorption of radionuclides onto 

rock surfaces) that are relevant to the behavior of a Yucca Mountain repository. In reviewing 

DOE’s postclosure performance calculations (i.e., individual protection, human intrusion, and 

separate limits for protection of groundwater), the NRC staff’s review evaluates (i) the 

performance assessment scenario classes (a set or combination of features, events and 

processes that are used in the performance assessment to represent a class or type of 

scenario, such as seismic activity), (ii) the representation of the scenario classes within the 

performance assessment (e.g., the performance assessment results are consistent with the 

models, parameters, and assumptions that make up the performance assessment), and 

(iii) the statistical stability of the annual dose that the performance assessment calculates. 

The NRC staff has separated its review of DOE’s TSPA calculations in TER Sections 2.2.1.4.1 

through 2.2.1.4.3: 

1. Postclosure Individual Protection Calculation (TER Section 2.2.1.4.1) 

 This TER section provides the NRC staff’s review of the individual protection calculation. 

The performance assessment used for the individual protection calculation considers 

both likely and unlikely events and all radiological exposure pathways. 

2. Human Intrusion Calculation (TER Section 2.2.1.4.2) 

 This TER section provides the NRC staff’s review of the human intrusion calculation 

(i.e., exploratory drilling for groundwater results in a borehole penetrating a waste 

package in the repository—the timing of the intrusion is set to the earliest time after 

disposal that the waste packages would degrade sufficiently that a human intrusion 

could occur without the drillers recognizing it). The performance assessment used for 

the human intrusion calculation considers likely events and all radiological 

exposure pathways. 

3. Separate Groundwater Protection Calculation (TER Section 2.2.1.4.3) 

 This TER section provides the NRC staff’s review of the separate groundwater 

protection calculation. The performance assessment used for the separate groundwater 

protection calculation considers likely events and the drinking water exposure pathway. 

Expert Elicitation (TER Section 2.5.4) 

This TER section provides the NRC staff’s review of the three expert elicitations DOE used in 

support of its SAR. Expert elicitations were conducted in the areas of seismic hazard (SAR 

Section 2.2.2.1), igneous activity (SAR Section 1.1.6.2, Section 2.2.2.2, and Section 2.3.11), 

and saturated zone flow and transport (SAR Section 2.3.9.2). 
9 

Expert elicitation is a formal, structured, and well-documented process for obtaining the 

judgments of multiple experts. Expert judgments are routinely used to evaluate and interpret 

the factual bases of SARs. The NRC staff has acknowledged that DOE could elect to use the 

subjective judgments of experts, or groups of experts, to interpret data and address technical 

issues and inherent uncertainties when assessing the long-term performance of a geologic 

repository. In its SAR, DOE used the results of three formal expert elicitations to complement 

and supplement other sources of scientific and technical information such as data collection, 

analyses, and experimentation. In this context, the NRC staff has reviewed DOE’s use of expert 

elicitation, which includes a technical review of the results of these elicitations. 

1-1 

CHAPTER 1 

2.2.1.1 System Description and Demonstration of Multiple Barriers 

2.2.1.1.1 Introduction 

This chapter of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s evaluation of the U.S. Department of Energy’s (DOE’s) description of 

the capabilities of the barriers in the geologic repository. The provisions in 10 CFR Part 63 for 

the repository after permanent closure require that the geologic repository include multiple 

barriers, consisting of both natural barriers and an engineered barrier system. Natural and 

engineered barriers isolate waste by preventing or substantially reducing the rate of movement 

of water or radionuclides from the Yucca Mountain repository to the accessible environment. 

A comprehensive description of the capabilities of the natural and engineered barriers would 

identify the risk-significant attributes for the repository performance. The technical basis for the 

barrier capability is evaluated in TER Section 2.2.1.3. 

A system of multiple barriers is intended to ensure that the repository system is robust and is 

not wholly dependent on a single barrier. Such a system is more robust in handling failures and 

external challenges. Therefore, 10 CFR 63.113 requires that a geologic repository contain both 

natural and engineered barriers. 

The emphasis of the NRC staff’s integrated review of DOE’s performance assessment 

calculation is not solely focused on the isolated performance of individual barriers, but 

rather on ensuring that the repository system is robust. The purpose of this chapter is to 

provide an understanding of how the natural barriers and the engineered barrier system 

work in combination to enhance the resiliency of the geologic repository. Therefore, to 

increase the NRC staff’s understanding of integrated repository performance, 10 CFR 63.115 

requires that DOE 

 Identify the important barriers of the performance assessment evaluation 

 Describe each barrier’s capability 

 Provide a technical basis for that capability which is based on and consistent with the 

performance assessment used to evaluate repository performance 

The description of barrier capability provides risk information that helps the NRC staff interpret 

the performance assessment results by describing how different elements of the performance 

assessment affect the overall performance of the system. This understanding can guide the 

NRC staff in the review of the technical bases for those aspects that are important, thereby 

allowing increased confidence in postclosure performance. The NRC staff can use this risk 

information to implement a risk-informed approach in its review of DOE’s performance 

assessment calculations. 

The NRC staff’s evaluation is based on information provided in the Safety Analysis Report 

(SAR) (DOE, 2008ab), as supplemented by DOE responses to the NRC staff’s requests for 

additional information (DOE, 2009an,bu). DOE provided a description of the barrier capabilities 

in SAR Chapter 2.1. This description, supplemented by DOE’s responses to the NRC staff’s 

requests for additional information, is used by the NRC staff in its review of the technical bases 
1-2 

for the performance assessment, as documented in TER Section 2.2.1.3, which focuses on 

DOE’s description of the barrier capabilities. As discussed in the staff’s Yucca Mountain Review 

Plan (YMRP) Section 2.2.1 (NRC, 2003aa), the multiple barrier review focuses on each barrier’s 

importance to waste isolation. 

2.2.1.1.2 Evaluation Criteria 

NRC staff’s review of multiple barriers is guided by criteria in 10 CFR 63.113(a) and 

10 CFR 63.115(a–c), which require the following: 

 The geologic repository to include multiple barriers, consisting of both natural and 

engineered barriers 

 Identification of those features of the repository that are considered barriers important to 

waste isolation (ITWI) 

 A description of the capabilities of those barriers 

 A technical basis for the description of the capability, which is based on and consistent 

with the technical basis for the performance assessment 

Definitions and discussions of important terms and concepts, such as “barrier” and “important to 

waste isolation,” are located in 10 CFR 63.2 and 10 CFR 63.102(h). For example, 10 CFR 63.2 

states that the term “barrier” means any material, structure, or feature that, for a period to be 

determined by NRC, prevents or substantially reduces the rate of movement of water or 

radionuclides from the Yucca Mountain repository to the accessible environment, or prevents 

the release or substantially reduces the release rate of radionuclides from the waste. 

The NRC staff reviewed information provided in the Safety Analysis Report (SAR) (DOE, 

2008ab), as supplemented by DOE responses to NRC staff’s requests for additional information 

(DOE, 2009an,bu), using the review methods and acceptance criteria provided in YMRP 

Section 2.2.1.1. The three YMRP acceptance criteria that provide guidance for the NRC staff’s 

review of multiple barriers under 10 CFR 63.115 are 

 Identification of barriers is adequate 

 Description of barrier capability to isolate waste is acceptable 

 Technical basis for barrier capability is adequately presented 

The following technical evaluation is largely organized according to these three acceptance 

criteria. Because the description of the barrier capability and the technical basis for the barrier 

capability are interrelated, the review activities associated with these two acceptance criteria are 

discussed together in TER Section 2.2.1.1.3.2. 

2.2.1.1.3 Technical Evaluation 

Summary of DOE SAR 

DOE identified the barriers considered important to waste isolation and summarized their 

capabilities and technical bases in SAR Section 2.1. This summary relies on more extensive 

information documented in SAR Sections 2.2 through 2.4. DOE documented the analyses that 

it used to identify and evaluate barrier capability in SNL (2008ad). As described in that 
1-3 

document, DOE’s identification of the barriers, and the description of the capability of these 

barriers, is based on the scenario analysis summarized in SAR Section 2.2 that identifies and 

evaluates the features, events, and processes to be considered in the Total System 

Performance Assessment (TSPA) model. The TSPA is an abstracted model that quantitatively 

integrates inputs from the various supporting analytic models. DOE used this abstracted 

integrating model to analyze repository performance. 

DOE identified three barriers (upper natural, engineered, and lower natural) and provided the 

features or components that make up these barriers in SAR Section 2.1.1. 

DOE summarized the capability of the barriers in SAR Section 2.1.2. For each barrier, DOE 

identified and provided a brief qualitative description of the key processes and events that 

influence the capability of each barrier. DOE provided some of these descriptions at the level of 

individual barrier features or components (e.g., the waste package component of the 

engineered barrier system). DOE provided other descriptions at an aggregate level (e.g., the 

waste form, waste canisters, and waste package internals taken together). DOE then described 

 The specific function of each barrier component and how the barrier component carries 

out its functions 

 The time period over which the barrier functions and how DOE expects the capability of 

the barrier to evolve over time 

 How uncertainty in the barrier capability has been accounted for in the 

performance assessment 

 The impact of disruptive events on the barrier, if any 

 A quantitative evaluation of the barrier capability to carry out its barrier functions 

DOE summarized the technical basis for the description of barrier capability in SAR 

Section 2.1.3. DOE stated that the technical basis for the barrier capability is the same as 

the technical basis for the model used in the TSPA analyses. DOE also stated that the 

technical basis for the description of the barrier capability is provided in SAR Section 2.3. 

SAR Table 2.1-5 identified which TSPA model abstractions are associated with each barrier; 

SAR Section 2.1.3 identified the location of the technical basis for the description of the barrier 

capability for those abstractions. SAR Section 2.1.3 briefly summarized the technical basis for 

each TSPA model component. Each summary identified the subsection of SAR Section 2.3 

where DOE described the technical basis of the model component in more detail. 

2.2.1.1.3.1 Identification of Barriers 

The NRC staff reviewed DOE’s discussion of how it identified barriers important to waste 

isolation in SAR Section 2.1.1. DOE identified three barriers (upper natural, engineered, and 

lower natural) and then provided the features or components that made up these barriers. 

In SAR Table 2.1-1, DOE identified the safety classification (i.e., whether DOE considers the 

feature or component to be important to waste isolation) of each feature or component. These 

barriers include features that are important to waste isolation from the upper and lower natural 

barrier and components that are important to waste isolation from the engineered barrier 

system. In DOE Enclosure 1 (2009an), DOE expanded SAR Table 2.1-1 to include 
1-4 

 The features, events, and processes considered important to barrier capability 

 A qualitative discussion of how the stated barrier functions are attained 

 For each barrier feature or component considered important to waste isolation, 

a quantitative summary of barrier capability based on information from the performance 

assessment analysis 

Because DOE’s safety classification identified in SAR Table 2.1-1 indicates individually 

whether each feature or component is considered important to waste isolation and is linked to 

its capability and to either the upper natural barrier, engineered barrier system, or the lower 

natural barrier, the NRC staff notes that DOE has identified the barriers that are relied on for 

repository performance. Because this list includes features from both the engineered and 

natural systems, DOE identified barriers that include at least one feature from the engineered 

system and one from the natural system. 

DOE identified three engineered system components as important to waste isolation based 

solely on their capability to reduce the probability of criticality. 

2.2.1.1.3.2 Description and Technical Basis for Barrier Capability 

NRC Staff’s Review Process 

The NRC staff’s review of the description and technical basis for barrier capability is based on a 

list of 22 individual features presented in SAR Table 2.1-1. For purposes of evaluation, the 

NRC staff consolidated these features to yield nine features as shown in Table 1-1. The NRC 

staff consolidated these 22 features by noting that several features appeared to be related in the 

second column of Table 2.1-1. For example, the emplacement drift is referred to twice. The 

NRC staff, therefore, consolidated the two emplacement drift entries into a grouped entry titled 

“Emplacement Drift.” Also, 11 of the features were prefaced with the term “Waste Form and 

Waste Package Internals.” The NRC staff grouped all nine of the features into one component 

titled “Waste Form and Waste Package Internals.” The NRC staff included cladding into this 

Table 1-1. Summary of Staff’s Barrier Component Review 

Barrier Barrier Feature 

SAR Table 2.1-1 

ITWI Components 

SAR Table 2.1-1 

Non-ITWI Components

Upper Natural 

Barrier 

Topography and 

Surficial Soils 

Topography and Surficial 

Soils 

None 

Upper Natural 

Barrier 

Unsaturated Zone 

Above the 

Repository 

Unsaturated Zone Above the 

Repository 

None 

Engineered 

Barrier System 

(EBS) 

Emplacement 

Drift 

Emplacement Drift Emplacement Drift: 

Nonemplacement 

Openings, Closure, 

Ground Support, and 

Ventilation System 
1-5 

Table 1-1. Summary of Staff’s Barrier Component Review (continued) 

Barrier Barrier Feature 

SAR Table 2.1-1 

ITWI Components 

SAR Table 2.1-1 

Non-ITWI Components 

EBS Waste Form and 

Waste Package 

Internals 

 Transport, aging, and 

disposal (TAD) canister 

 Naval canister 

 Commercial spent 

nuclear fuel (SNF) and 

high-level waste glass 

 Naval SNF 

 Naval SNF canister 

system components* 

 TAD canister internals* 

 DOE SNF canister 

internals*

 DOE SNF canister 

 High-level waste 

canister 

 Codisposal package 

internals 

 DOE SNF 

 Cladding 

EBS Emplacement 

Pallet and Invert 

None  Waste Package Pallet 

 Invert 

Lower Natural 

Barrier 

Unsaturated Zone 

Below the 

Repository 

Unsaturated Zone Below 

the Repository 

None 

Lower Natural 

Barrier 

Saturated Zone Saturated Zone None 

*DOE identified these components as important to waste isolation solely in relation to their capability to reduce the 

probability of criticality. 

grouped category by noting that cladding is a component that contains the waste form and is 

internal to the package. The NRC staff also noted that neither the emplacement pallet nor the 

invert were classified as important to waste isolation, and that both components serve to 

support the waste package. The NRC staff, therefore, consolidated these two features into a 

single feature. The resulting consolidated list is consistent with the grouping that DOE used in 

SAR Section 2.1.2.2 in its summary of the features, processes, and characteristics of the 

engineered barrier system that are important to waste isolation. 

The NRC staff reviewed the descriptions of the barrier capability of these nine consolidated 

features. To evaluate the description of the barrier capability, the NRC staff reviewed how DOE 

 Identified the safety classification and primary function of each barrier component 

 Identified the characteristics and processes important to barrier capability, including 

both those that are potentially beneficial and those that are potentially harmful to 

barrier functions 

 Described how the barrier component was represented in the performance assessment 

 Described the qualitative and quantitative capabilities of each barrier component, 

consistent with the performance assessment analyses 
1-6 

 Characterized the time period over which the barrier functions and how DOE expects the 

barrier capability to change over time 

 Accounted for the uncertainty in the description of the barrier capability 

To evaluate the technical basis for the barrier capability, the NRC staff reviewed the consistency 

between the descriptions of the barrier capability documented in SAR Section 2.1.2 and the 

technical bases summarized in SAR Section 2.1.3 and further documented in SAR Section 2.3. 

In addition, the NRC staff reviewed the description of the performance confirmation plan to 

determine whether it was consistent with the descriptions of barrier capability. TER Section 2.4 

contains the results of the NRC staff’s review of the performance confirmation plan. 

The NRC staff also considered the insights gained from NRC Appendix D (2005aa), as 

updated (CNWRA and NRC, 2008aa), to determine whether DOE had omitted any features 

or processes that might contribute significantly to barrier capability in its description of barrier 

capability. In addition, the NRC staff reviewed DOE’s TSPA model described in SNL (2008ag) 

to assess consistency between the descriptions of barrier capability and how the TSPA model 

components actually represented the barrier capability. 

The NRC staff summarizes the results of the review of the individual barrier components, 

as identified in the second column of TER Table 1-1. In each of the following sections, the 

NRC staff’s evaluation 

 Describes whether the barrier capability is explained in terms of a capability to prevent or 

substantially reduce the rate of movement of water or radionuclides 

 Identifies the SAR sections where DOE described the capability of each barrier 

component and briefly summarizes the described capabilities 

 Describes whether the identified capabilities are consistent with the results from the 

Total System Performance Assessment; in reviewing these analyses, the NRC staff 

examined whether the numerical results, used to illustrate barrier capability, were 

consistent with the intermediate results used to compute the dose in the Total System 

Performance Assessment calculation 

 Identifies where DOE has described the time period over which the barrier performs its 

stated function and briefly summarizes whether DOE has described the time period over 

which the barrier performs its stated function 

 Identifies where DOE has described the uncertainty in the barrier capability and 

describes whether DOE accounted for uncertainties in its characterization and modeling 

of the barriers 

 Identifies where DOE summarized the technical basis for barrier capability, describes 

whether this technical basis is consistent with the technical basis for the performance 

assessment models, and describes whether the technical basis is commensurate with 

the importance of each barrier’s capability 
1-7 

2.2.1.1.3.2.1 Upper Natural Barrier: Topography and Surficial Soils 

The NRC staff reviewed DOE’s description of the barrier capability of the topography and 

surface soils. DOE described the barrier capabilities of the topography and the surficial soils 

qualitatively in SAR Section 2.1.2.1.1 and quantitatively in SAR Section 2.1.2.1.6.1. DOE 

supplemented this description in DOE Enclosure 1 (2009an). DOE used net infiltration as a 

percentage of annual precipitation to quantify the barrier capability of topography and surficial 

soils. The NRC staff notes that the information provided by DOE describes a capability that 

substantially reduces infiltration into the unsaturated zone, which in turn reduces the rate of 

movement of water from the nuclear waste in the repository to the accessible environment. 

DOE’s climate and infiltration analyses are summarized in SAR Tables 2.3.1-2, 2.3.1-3, and 

2.3.1-4. DOE stated in SAR Section 2.1.2.1.1 and in DOE Enclosure 1 (2009an) that for 

approximately 10,000 years following closure of the repository, a limited amount of water would 

infiltrate into the unsaturated zone above the repository at Yucca Mountain. DOE attributed the 

low rate of infiltration to low precipitation that is substantially further reduced by high rates of 

evapotranspiration (e.g., uptake by plants, surface evaporation) and surface runoff. In SAR 

Section 2.1.2.1.1, DOE stated that the average net infiltration rate estimates range from about 

3 to 17 percent of the total precipitation, depending upon the climate state and the infiltration 

scenario. For the post-10,000-year period, DOE stated that it used the deep percolation rate in 

the proposed 10 CFR Part 63. In DOE Enclosure 6 (2009cb), DOE stated that use of the 

distribution of deep percolation specified in the final 10 CFR Part 63 led to an insignificant 

increase in dose. 

On the basis of its review of the DOE description of the barrier capabilities of topography and 

surficial soils in SAR Section 2.1.2.1 and in DOE Enclosure 1 (2009an), the NRC staff notes that 

DOE’s description of the barrier capability is consistent with the results from the performance 

assessment calculation because DOE based this description on intermediate results from its 

infiltration model used in the performance assessment, as documented in SAR Section 2.3.1. 

DOE provided information in SAR Section 2.1.2.1.3 on the time period over which this upper 

natural barrier feature performs its intended function. DOE stated that the topography and 

surficial soils are not expected to change significantly in the 10,000 years following closure, but 

changes in climate and vegetation are expected to affect the barrier capability during this period. 

In SAR Tables 2.3.1-17 to 2.3.1-19 and DOE Enclosure 1 (2009an), DOE addressed when it 

expects different climate states to occur and provided infiltration rates under different climate 

scenarios. Because DOE explicitly discussed the time dependence of the infiltration rate, DOE 

has reasonably described the time period over which the topography and surface soils perform 

as a barrier. 

In SAR Section 2.1.2.1.4, DOE described sources of uncertainty that are considered in the 

climate and infiltration model. Sources of uncertainty include (i) the interpretation of the 

geologic record of past climates, (ii) the parameters describing evapotranspiration, (iii) the 

applicability of models, and (iv) the characteristics of the Yucca Mountain site. DOE also 

addressed the uncertainty in the barrier capability by describing results from its infiltration model 

demonstrating the probability of different infiltration scenarios (SAR Section 2.3.2.4.1.2.4.5 and 

Table 2.3.2-27). DOE described in its Enclosure 5 (2009bo) that adjusting the probability 

weighting of these scenarios based on deep subsurface observations of chloride and 

temperature (SAR 2.3.2.4.1.2.4.5 and Table 2.3.2-27) reduced the average infiltration fluxes in 

the initial 10,000 years following permanent closure by approximately 50 percent. DOE 

addressed infiltration uncertainties in the post-10,000-year period in SAR Section 2.3.2.4.1.2.4.2 
1-8 

by using a weighting of net infiltration scenarios to yield a distribution of deep percolation 

fluxes comparable to the distribution specified in the proposed 10 CFR Part 63. DOE 

Enclosure 6 (2009cb) stated that use of the distribution of deep percolation specified in the final 

10 CFR Part 63 led to an insignificant increase in dose. Because DOE described the sources of 

uncertainty and presented the range of uncertainty in its infiltration estimates, DOE accounted 

for uncertainty in its descriptions of barrier capability. 

In SAR Section 2.1.3.1, DOE summarized the technical basis of the barrier capability 

description of the upper natural barrier, which includes the topography and surficial soils 

component. In this discussion, DOE indicated which TSPA models it used to support the 

description of the barrier capability of the upper natural barrier. DOE based its description of 

the barrier capability of the topography and surficial soils on the climate and infiltration model 

described in SAR Section 2.3.1. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.1.1 and 2.1.3.1 

with SAR Section 2.3.1 and notes that the technical basis descriptions are consistent among 

these SAR sections. Further, the NRC staff compared the quantitative representation of the 

barrier capability in SAR Sections 2.1.2.1.1 and 2.1.3.1 with the results of the climate and 

infiltration model described in SAR Section 2.3.1 and notes that the net infiltration calculation 

results are consistently represented among these SAR sections. The NRC staff therefore notes 

that the technical basis for the description of barrier capability in SAR Sections 2.1.2.1.1 and 

2.1.2.1.6.1 is consistent with the technical basis of the climate and infiltration model. 

TER Section 2.2.1.3.5 documents the NRC staff’s evaluation of the infiltration model that 

provides the technical basis for this capability. The NRC staff notes in TER Section 2.2.1.3.5 

that DOE provided reasonable technical bases for the climate and infiltration model and for the 

range of net infiltration values used in the performance assessment calculations. Therefore, the 

NRC staff notes that DOE’s technical basis for the description of the barrier capability of the 

topography and surface soils is commensurate with the barrier capability described in SAR 

Section 2.1.2.1.1, SAR Section 2.1.2.1.6.1, and in DOE Enclosure 1 (2009an). 

In summary, the NRC staff notes that the capability of the topography and surficial soils to 

prevent or substantially reduce the rate of movement of water from the Yucca Mountain 

repository to the accessible environment is reasonably described and that the technical basis 

for the barrier capability is based on and consistent with the technical basis for the 

performance assessment. 

2.2.1.1.3.2.2 Upper Natural Barrier: Unsaturated Zone Above the Repository 

The NRC staff reviewed the DOE description of the barrier capability of the unsaturated zone 

above the repository. DOE described the capability of the unsaturated zone above the 

repository to prevent or substantially reduce seepage qualitatively in SAR Section 2.1.2.1.2 and 

quantitatively in SAR Section 2.1.2.1.6.2. DOE supplemented this description in DOE 

Enclosures 1 and 2 (2009an). The information provided by DOE describes a capability to 

prevent or substantially reduce seepage of water into the emplacement drifts, which in turn 

substantially reduces the rate of water movement from the nuclear waste in the repository to the 

accessible environment. 

In SAR Section 2.1.2.1.6.2, DOE explained that the average percolation flux at the repository 

depth is, at most, a few percent less than the average net infiltration near the surface above the 

repository. In other words, changes in the flow rate of water between the ground surface and 
1-9 

the repository level are relatively small, indicating to the NRC staff that DOE did not credit any 

significant processes resulting in the diversion of water away from the emplacement drift 

location. However, DOE explained in SAR Section 2.1.2.1.2, SAR Section 2.1.2.1.6.2, and in 

DOE Enclosures 1 and 2 (2009an) that capillary diversion of water at the host rock–air interface 

at the drift wall prevents much of the water flowing in the rock at the repository level from 

entering the drift as seepage (i.e., dripping). DOE explained that at some drift locations, all of 

the water is diverted around the drift, resulting in no drips at all; at others, only some of the 

water enters, and the remainder is diverted around the drift. In addition, the short duration, 

relatively higher flow rates resulting from infiltration following brief episodes of precipitation are 

spread out in time and space as they pass through the Paintbrush Tuff. In DOE Enclosure 2 

(2009an), DOE explained that this damping of episodic infiltration pulses by the Paintbrush 

Tuff results in water flow rates below the Paintbrush Tuff that are consistently lower than the 

peak flow rate during the infiltration pulse, but which are more nearly constant over time 

(i.e., steady-state fluxes below the Paintbrush Tuff). DOE explained that because capillary 

diversion processes are more effective at low percolation flow rates, the damping of episodic 

infiltration pulses by the Paintbrush Tuff contributes to the effectiveness of the capillary barrier. 

DOE quantified the barrier capability of the unsaturated zone above the repository for each of 

the five percolation subregions for the climate states projected for the first 10,000 years after 

repository closure (SAR Section 2.1.2.1.2). DOE used an analysis based on the TSPA 

seepage models and inputs to show that average seepage rates range from less than 1 to 

about 17 percent of the percolation fluxes for intact drifts within the first 10,000 years following 

closure, as described in DOE Enclosure 3, Table 11 (2009bo). DOE expects capillary forces 

to divert more than 80 percent of percolation flux away from the intact drifts for the initial 

10,000 years after closure. DOE Enclosure 3, Table 5 (2009bo) identified that for intact drifts, 

the fraction of the repository experiencing dripping conditions (i.e., the seepage fraction) 

ranges from 10 to 70 percent. Results for the collapsed drift case, which is a likely scenario 

in the post-10,000-year period, are shown in DOE Enclosure 3, Table 11 (2009bo). These 

results indicate that mean seepage percentage ranges from about 40 to 56 percent in the 

post-10,000-year period. DOE expects that capillary forces would divert at least 44 percent of 

percolation flux away from collapsed drifts. The post-10,000-year seepage fractions for the 

corresponding flow fields range from about 44 to 89 percent, as described in DOE Enclosure 3, 

Table 8 (2009bo). 

On the basis of its review of the DOE description of the barrier capabilities of the unsaturated 

zone above the repository in SAR Section 2.1.2.1, the NRC staff notes that DOE’s description of 

the barrier capability is consistent with the results from the performance assessment calculation 

because DOE refers to analyses in SAR Section 2.3.3.4.2 and in DOE Enclosure 3 (2009bo) 

that are based on TSPA models and input data. 

In SAR Section 2.1.2.1.3, DOE provided information on the time period over which this upper 

natural barrier feature performs its intended function. DOE stated that the unsaturated zone 

above the repository is not expected to change in the 10,000 years following closure and that 

changes in the barrier capability are due to changes in infiltration. SAR Figure 2.1-5 describes 

how seepage changes as a function of time. DOE has reasonably described the time period 

over which the unsaturated zone above the repository performs as a barrier because DOE 

explicitly considered how the ability of the drift to divert water changes over time. 

In SAR Section 2.1.2.1.4, DOE discussed sources of uncertainty in the barrier capability of the 

unsaturated zone above the repository. These primarily are associated with uncertainties in the 

models and the characteristics of the Yucca Mountain site. SAR Tables 2.1-6 through 2.1-9 

provide the range of uncertainty in seepage fractions. DOE also discussed these uncertainties 
1-10 

in SAR Section 2.3.3.4.2. Because DOE described sources of uncertainty and explained how 

these uncertainties affect the rate and extent of seepage, the NRC staff notes that DOE has 

considered uncertainty in its descriptions of barrier capability. 

SAR Section 2.1.3.1 summarized the technical basis of the barrier capability of the upper 

natural barrier, which includes the unsaturated zone above the repository. In its discussion, 

DOE indicated which TSPA models it used to support the description of the barrier capability of 

the upper natural barrier. DOE based its description of the barrier capability of the unsaturated 

zone above the repository on the unsaturated zone flow model described in SAR Section 2.3.2 

and on the seepage (ambient and thermal) models described in SAR Section 2.3.3. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.1.2 and 

2.1.3.1 with SAR Sections 2.3.2 and 2.3.3 and notes that the technical basis descriptions are 

consistent among these SAR sections. Further, the NRC staff compared the quantitative 

representation of the barrier capability in SAR Sections 2.1.2.1.6.2 and 2.1.3.1 with the results 

of the site-scale unsaturated zone flow and seepage models described in SAR Sections 2.3.2 

and 2.3.3 and notes that the deep percolation, seepage, and seepage fraction estimates are 

consistently represented among these SAR sections. The technical bases for the description of 

barrier capability in SAR Sections 2.1.2.1.2 and 2.1.2.1.6.2, as supplemented in DOE 

Enclosures 1 and 2 (2009an), are consistent with the technical bases of the site-scale 

unsaturated zone flow and seepage models. 

TER Section 2.2.1.3.6 documents the NRC staff’s evaluation of the technical basis for the 

unsaturated zone flow and seepage model abstractions that form the basis for this capability. 

The NRC staff notes in TER Section 2.2.1.3.6 that DOE provided technical bases for the 

site-scale unsaturated zone flow and seepage models and for the ranges of deep percolation, 

seepage, and seepage fraction values used in the performance assessment that address their 

intended use. DOE’s technical bases for the description of the barrier capability of the 

unsaturated zone above the repository are commensurate with the barrier capability described 

qualitatively in SAR Section 2.1.2.1.2, quantitatively in SAR Section 2.1.2.1.6.2, and as 

supplemented by DOE Enclosures 1 and 2 (2009an). 

In summary, the NRC staff notes that (i) the capability of the unsaturated zone above 

the repository to prevent or substantially reduce the rate of movement of water from the 

Yucca Mountain repository to the accessible environment is reasonably described and (ii) the 

technical basis for the barrier capability is based on and is consistent with the technical basis for 

the performance assessment. 

2.2.1.1.3.2.3 Engineered Barrier System: Emplacement Drift 

The NRC staff reviewed the DOE description of the barrier capability of the emplacement drift. 

DOE discussed the barrier capabilities of the emplacement drift in SAR Section 2.1.2.2 under 

the discussion titled “Emplacement Drift” and in DOE Enclosures 1 and 3 (2009an). DOE stated 

that the capability of the emplacement drift to prevent or substantially reduce the movement of 

water is associated with the capillary barrier discussed under the upper natural barrier. DOE 

associated the capability of the drift to prevent or reduce the rate of movement of radionuclides 

with the effect of temperature and water chemistry on various processes affecting the 

degradation of the other EBS components (e.g., drip shield, waste package, and waste form) 

and radionuclide transport. DOE Enclosures 1 and 3 (2009an) specifically identified and 

discussed the roles of individual processes in the capability of the emplacement drifts. 

The NRC staff notes that DOE has described the emplacement drifts capabilities to include 
1-11 

 The intact emplacement drift opening represents a zero-capillarity feature within the rock 

formation that supports diversion of unsaturated zone flow around the opening, which 

reduces the rate of seepage into the drift 

 The collapsed, rubble-filled emplacement drift provides reduced seepage diversion 

capabilities and limits drip shield and waste packages motion under seismic activity 

 The mechanical integrity of the drift provides a stable environment that controls the 

mechanical and chemical degradation of the drip shield and waste package, which divert 

seepage water and prevent or limit the rate of contact of water with the waste form 

 The mechanical integrity of the drift provides a stable environment that controls the rate 

of waste form degradation and the chemical conditions within the waste package, which 

control the rate of movement of radionuclides 

This information shows DOE has reasonably described the capabilities of the emplacement drift 

with respect to drift seepage by describing the effect of an intact and collapsed drift on the 

performance of the capillary barrier associated with the unsaturated zone above the repository. 

DOE has reasonably described the capabilities of the emplacement drift with respect to the 

effect of the in-drift environment because DOE described the effect of the in-drift environment 

on degradation and transport processes within the drift that are described as aspects of the 

other components of the engineered barrier system. 

DOE discussed the time period over which the emplacement drift functions in SAR 

Section 2.1.2.2.3 and in DOE Enclosure 3 (2009an). DOE described the evolution of the 

mechanical stability of the drift and the in-drift environment and discussed how these changes 

affect the major processes associated with emplacement drift performance. These evaluations, 

along with the discussion of the time period over which DOE expects the drift to degrade due to 

seismic events, provide a reasonable understanding of the time period over which the 

emplacement drift performs its function because DOE described both the timing and effect of 

seismically induced drift degradation. In TER Section 2.2.1.3.2.3.2, the NRC staff addresses 

the capabilities of the emplacement drift related to the mechanical integrity of the drift opening. 

DOE discussed the uncertainty in the performance of the emplacement drift in SAR 

Sections 2.1.2.2.4 and 2.3.4.4.8 and in DOE Enclosure 3 (2009an). DOE indicated that the 

uncertainties in the environmental conditions are a primary source of uncertainty in the 

performance of the engineered barrier system. In SAR Section 2.3.4.4.8, DOE discussed the 

sources and treatment of uncertainty in the evaluation of rockfall and presented the effect of 

these uncertainties in SAR Figure 2.1-14, which provides the range of uncertainties in the 

expected fraction of the drift filled with rubble. The NRC staff determines that these 

discussions, supplemented by probabilistic outputs of the rockfall model showing the range of 

times for rubble to accumulate within the drift, show how DOE has accounted for uncertainty in 

its descriptions of barrier capability. 

DOE summarized the technical basis of the engineered barrier system capability, which 

included the emplacement drift, in SAR Section 2.1.3.2. In its discussion, DOE indicated which 

TSPA models it used to support the description of the barrier capability of the engineered 

barrier system. DOE based its description of the barrier capability of the emplacement drift on 

three TSPA submodels: (i) the ambient and thermal seepage models described in SAR 
1-12 

Section 2.3.3, (ii) the engineered barrier system mechanical degradation model described in 

SAR Section 2.3.4, and (iii) the in-drift chemical and physical environment model described in 

SAR Section 2.3.5. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.2 and 2.1.3.2 

with SAR Sections 2.3.3, 2.3.4, and 2.3.5 and notes that the technical basis descriptions 

are consistent among these SAR sections. Further, the NRC staff compared the quantitative 

representation of the barrier capability in SAR Sections 2.1.2.2 and 2.1.3.2 with the results 

of the three emplacement drift TSPA submodels DOE described in SAR Sections 2.3.3, 2.3.4, 

and 2.3.5 and notes that the seepage rate estimates, the expected time of collapse of the 

drifts, and the in-drift physical and chemical environment are consistently represented 

among these SAR sections. Therefore, the technical bases for the description of barrier 

capability in SAR Section 2.1.2.2 are consistent with the technical basis of the emplacement 

drift TSPA submodels. 

In TER Sections 2.2.1.3.2, 2.2.1.3.3, and 2.2.1.3.6, the NRC staff evaluates the technical 

bases used to support the barrier capability of the emplacement drift. The NRC staff notes 

in TER Sections 2.2.1.3.2, 2.2.1.3.3, and 2.2.1.3.6 that DOE provided technical bases for 

the emplacement drift TSPA submodels and for the range of values for the seepage rates and 

the expected time of collapse of the drifts, and the in-drift physical and chemical environment 

used in the performance assessment, that address their intended use. Therefore, DOE’s 

technical bases for the description of the barrier capability of the emplacement drift are 

commensurate with the barrier capability described in SAR Section 2.1.2.2 and in DOE 

Enclosures 1 and 3 (2009an). 

In summary, the NRC staff notes that the capability of the emplacement drift to prevent or 

substantially reduce the rate of movement of water or radionuclides from the Yucca Mountain 

repository to the accessible environment is reasonably described and that the technical bases 

for the barrier capability are based on and consistent with the technical bases for the 

performance assessment. 

2.2.1.1.3.2.4 Engineered Barrier System: Drip Shield 

The NRC staff reviewed the description of the barrier capability of the drip shield. DOE 

discussed the capability of the drip shield to prevent or substantially reduce contact of 

seepage with the waste package in SAR Section 2.1.2.2 under the discussion titled 

“Drip Shield,” in SAR Section 2.1.2.2.1, and quantitatively in SAR Section 2.1.2.2.6. 

DOE supplemented its discussion in DOE Enclosure 1 (2009an). DOE addressed the drip 

shield’s capability to prevent seepage water from contacting the waste package during the 

thermal period in SAR Section 2.1.2.2. During the thermal period, seepage water, if contacting 

the waste package, could lead to water chemistry that may initiate localized corrosion. The 

NRC staff notes that the information provided by DOE describes the capability to prevent or 

substantially reduce the rate of movement of water. 

DOE does not expect extensive drip shield failures before 100,000 years. General corrosion of 

the drip shield enhances the vulnerability of the drip shield to seismic events as the drip shield 

plates become thinner as a result of corrosion. DOE expects drip shields to fail between 

200,000 and 300,000 years, when general corrosion has weakened the drip shield plates 

sufficiently such that a seismic event can rupture them. DOE attributes the capability of the drip 

shield to divert water to corrosion-resistant materials coupled with a low probability of 
1-13 

mechanical damage from seismic events and a relatively benign chemical environment during 

the thermal period. 

On the basis of its review of the DOE description of the barrier capabilities of the drip shield in 

SAR Section 2.1.2.2, SAR Section 2.1.2.2.6, and DOE Enclosure 1 (2009an), the NRC staff 

notes that DOE’s description of the barrier capability is consistent with the results from the 

performance assessment calculation because DOE described the capability using intermediate 

results from the TSPA showing the distribution of drip shield failure times. 

SAR Section 2.1.2.2.3 addressed the period over which the engineered barrier system, 

including the drip shield, performs its barrier function. DOE stated that the barrier capability 

of the drip shield and waste package is not impacted until sufficient corrosion has occurred to 

create breaches in the waste package. SAR Section 2.1.2.2.6 quantified the change in the 

effectiveness of the capability of the drip shield. DOE reasonably described the period over 

which the barrier performs its function because it described how the drip shields degrade over 

time, and DOE supplemented its description with time-dependent outputs from the drip shield 

degradation model. 

DOE described the sources of uncertainty in the drip shield capability in SAR Section 2.1.2.2.4 

and quantitatively described the effect of uncertainties in SAR Section 2.1.2.2.6. These include, 

for example, uncertainties in the environmental conditions affecting the drip shield. DOE 

described specific analyses of uncertainty in the model abstractions for drip shield degradation 

in SAR Sections 2.3.4.5 and 2.3.6.8. The NRC staff notes that these discussions, 

supplemented by probabilistic outputs of the drip shield degradation model showing the range of 

times for failure of the drip shield, show how DOE accounted for uncertainty in its descriptions of 

barrier capability. 

SAR Section 2.1.3.2 summarized the technical basis of the barrier capability of the engineered 

barrier system, which includes the drip shields. In its discussion, DOE identified which TSPA 

models it used to support the description of the barrier capability of the engineered barrier 

system. DOE based its description of the barrier capability of the drip shield on three TSPA 

submodels: (i) the mechanical damage model described in SAR Section 2.3.4.5, (ii) the general 

corrosion model described in SAR Section 2.3.6.8.1, and (iii) the early failure model described in 

SAR Section 2.3.6.8.4. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.2 and 2.1.3.2 

with SAR Sections 2.3.4.5, 2.3.6.8.1, and 2.3.6.8.4 and notes that the technical basis 

descriptions are consistent among these SAR sections. Further, the NRC staff compared the 

quantitative representation of the barrier capability in SAR Sections 2.1.2.2 and 2.1.3.2 with the 

results of the three drip shield TSPA submodels DOE described in SAR Sections 2.3.4.5, 

2.3.6.8.1, and 2.3.6.8.4 and notes that the drip shield failure time estimates are consistently 

represented among these SAR sections. Therefore, the technical bases for the description of 

barrier capability in SAR Section 2.1.2.2 are consistent with the technical basis of the drip shield 

TSPA submodels. 

In TER Sections 2.2.1.3.1 and 2.2.1.3.2, the NRC staff evaluates the technical bases used to 

support the barrier capabilities of the drip shield. The NRC staff notes in TER Sections 2.2.1.3.1 

and 2.2.1.3.2 that DOE provided technical bases for the drip shield TSPA submodels and for 

the range of values for the drip shield failure time used in the performance assessment that 

address their intended use. Therefore, DOE’s technical bases for the description of the barrier 
1-14 

capability of the drip shields are commensurate with the barrier capability described in SAR 

Section 2.1.2.2 and in DOE Enclosure 1 (2009an). 

In summary, the NRC staff notes that the capability of the drip shield to prevent or substantially 

reduce the rate of movement of water or radionuclides from the Yucca Mountain repository to 

the accessible environment is reasonably described and that the technical basis for the barrier 

capability is based on and consistent with the technical basis for the performance assessment. 

2.2.1.1.3.2.5 Engineered Barrier System: Waste Packages 

The NRC staff reviewed the description of the barrier capability of the waste packages. DOE 

discussed the capability of the waste package to prevent or substantially reduce contact of 

seepage with the waste form in SAR Section 2.1.2.2 under the discussion titled “Waste 

Package,” in SAR Sections 2.1.2.2.1 and 2.1.2.2.2, and quantitatively in SAR Section 2.1.2.2.6. 

DOE Enclosures 1 and 4 (2009an) supplemented this discussion. The capabilities DOE 

described identify a capability of the barrier to substantially reduce the movement of water 

or radionuclides. 

DOE also credited the barrier capability of the waste package for radionuclide transport through 

the engineered system. Specifically, the waste package inner vessel contains a large amount of 

stainless steel that corrodes after breach of the outer vessel. The corrosion products contain 

high sorption capabilities for some radionuclides. Although the important to waste isolation 

component that DOE credits for this capability is the waste package inner vessel, DOE 

described the barrier capabilities associated with sorption to corrosion products as an aspect of 

the waste form and waste package internal components. TER Section 2.2.1.1.3.2.6 addresses 

the NRC staff’s evaluation of this barrier capability. 

In SAR Section 2.1.2.2, DOE attributed the capability of the waste package to divert water to 

corrosion-resistant materials coupled with a low probability of mechanical damage from seismic 

events and a relatively benign chemical environment. DOE discussed the incidence of waste 

package failure and concluded that extensive early failures of the waste packages are unlikely. 

DOE does not expect extensive waste package failures to occur until a seismic event capable of 

damaging the waste packages occurs. Although a model for localized corrosion is included in 

the TSPA analysis, DOE expects that the presence of the drip shields over the entire thermal 

period will prevent the occurrence of localized corrosion under the nominal or seismic scenarios. 

DOE indicated that waste package failures before approximately 200,000 years are primarily 

due to seismically induced stress corrosion cracking of codisposal waste packages containing 

DOE standard canisters and high-level waste. DOE attributed the higher resilience of the 

commercial spent nuclear fuel waste packages under seismic conditions, relative to the 

codisposal packages, to the damping provided by the massive transport, aging, and disposal 

canister containing the commercial spent fuel. Upon failure of the drip shield and filling of the 

drift with rubble, damage from further seismic events is unlikely. Subsequent failures are 

largely associated with nominal processes affecting both commercial spent nuclear fuel waste 

packages and codisposal waste packages. Under nominal conditions, DOE expects 

approximately 50 percent of both the commercial spent nuclear fuel and codisposal waste 

packages to fail by stress corrosion cracking by 1 million years. The earliest general corrosion 

waste package failure (at the 95th percentile) is predicted to occur at 560,000 years. At 1 million 

years, about 10 percent of the waste packages are predicted to fail from general corrosion. 

The ability of a breached waste package to prevent or reduce water flow is dependent upon the 

type and extent of the failure. DOE modeled stress corrosion crack breaches as allowing only 
1-15 

diffusive release from the waste package. Larger breaches (primarily due to general corrosion 

and rarely due to rupture or puncture of the waste package during a seismic event) allow water 

flow, but a small breached area may limit the rate at which water may enter the waste 

package. DOE Enclosure 4 (2009an), based on the flux-splitting submodel documented in 

SAR Section 2.3.7.12.3.1, indicated that a waste package breached by general corrosion is still 

capable of significant water diversion provided that the breach area is limited to a small 

percentage of the waste package surface area. 

On the basis of its review of the DOE description of the barrier capabilities of the waste 

packages in SAR Section 2.1.2.2, the NRC staff notes that DOE’s description of the 

barrier capability is consistent with the results from the performance assessment calculation. 

In particular, the barrier capabilities are (i) described by reference to intermediate results from 

the performance assessment showing waste package failure times and breached areas and 

(ii) supported by analyses based on TSPA models and parameters showing water flow rates 

through breached packages. 

SAR Section 2.1.2.2.3 addressed the period over which the engineered barrier system, 

including the waste package, performs its barrier function. DOE stated that the barrier capability 

of the drip shield and waste package is not impacted until sufficient corrosion has occurred to 

create breaches in the waste package. SAR Section 2.1.2.2.6 described the change in the 

effectiveness of the capability by providing time-dependent outputs of the waste package 

degradation model. Because DOE has described how the capability degrades over time, and 

has provided intermediate outputs demonstrating how and when DOE expects the waste 

packages to fail, DOE has reasonably described the time period over which the waste package 

performs its barrier function. 

SAR Section 2.1.2.2.4 described the sources of uncertainty in the modeled performance of the 

engineered barrier system. These include, for example, uncertainties in the environmental 

conditions affecting the waste package, in the temperature dependence of the general corrosion 

rate, in the effect of microbially induced corrosion, and in the treatment of stress corrosion 

cracking and localized corrosion. DOE Enclosure 4 (2009an) also addresses the effects of 

uncertainty in corrosion processes on the ability of the waste package to divert water, noting 

that in most realizations, there is no breach of any waste package. Because DOE has 

described the sources of uncertainty, described how these uncertainties are addressed in the 

TSPA, and provided probabilistic outputs demonstrating the range of uncertainty in waste 

package failure times and breached area, DOE has provided information that shows it has 

accounted for uncertainty in its description of the barrier capability of the waste package. 

DOE summarized the technical basis of the barrier capability description of the engineered 

barrier system, which includes the waste packages, in SAR Section 2.1.3.2. DOE based its 

description of barrier capability of the waste package on six TSPA submodels: (i) the early 

failure model described in SAR Section 2.3.6.6.3, (ii) the general corrosion model described in 

SAR Section 2.3.6.3.3, (iii) the localized corrosion model described in SAR Section 2.3.6.4.3, 

(iv) the stress corrosion cracking model described in SAR Section 2.3.6.5.3, (v) the mechanical 

damage model described in SAR Section 2.3.4.5, and (vi) the flux-splitting model described in 

SAR Section 2.3.7.12.3.1. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.2 and 2.1.3.2 

with SAR Sections 2.3.6.6.3, 2.3.6.3.3, 2.3.6.4.3, 2.3.6.5.3, 2.3.4.5, and 2.3.7.12.3.1 and 

notes that the technical basis descriptions are consistent among these SAR sections. 

Further, the NRC staff compared the quantitative representation of the barrier capability in 
1-16 

SAR Sections 2.1.2.2 and 2.1.3.2 with the results of the six waste package TSPA submodels 

DOE described in SAR Sections 2.3.6.6.3, 2.3.6.3.3, 2.3.6.4.3, 2.3.6.5.3, 2.3.4.5, and 

2.3.7.12.3.1 and notes that the waste package lifetime, waste package failed area, and waste 

package water flow estimates are consistently represented among these SAR sections. 

Therefore, the technical bases for the description of barrier capability in SAR Section 2.1.2.2 are 

consistent with the technical bases of the waste package TSPA submodels. 

TER Sections 2.2.1.3.1, 2.2.1.3.2, and 2.2.1.3.3 address the NRC staff’s evaluation of the 

technical bases used to support the barrier capability of the waste packages. The NRC staff 

notes in TER Sections 2.2.1.3.1, 2.2.1.3.2, and 2.2.1.3.3 that DOE provided technical bases for 

the waste package TSPA submodels and for the ranges of values for the waste package 

lifetime, waste package failed area, and waste package water flow used in the performance 

assessment that address their intended use. Therefore, DOE’s technical bases for the 

description of the barrier capability of the waste packages are commensurate with the barrier 

capability described previously. 

In summary, the NRC staff notes that the capability of the waste package to prevent or 

substantially reduce the rate of movement of water or radionuclides from the Yucca Mountain 

repository to the accessible environment is reasonably described and that the technical basis for 

the barrier capability is based on and consistent with the technical basis used in the 

performance assessment calculation. 

2.2.1.1.3.2.6 Engineered Barrier System: Waste Form and Waste Package 

Internal Components 

The NRC staff reviewed the description of the barrier capability of the waste form and 

waste package internal components. DOE provided a qualitative description of how the 

waste form and waste package internal components limit the release of radionuclides from a 

failed waste package in SAR Section 2.1.2.2 under the discussion titled “Waste Form and 

Waste Package Internals,” in SAR Section 2.1.2.2.2, and in DOE Enclosures 1 and 5 (2009an). 

DOE described the performance of the waste package internal components quantitatively in 

SAR Section 2.1.2.2.6. DOE discussed the impacts of these processes in an aggregated 

fashion, using a metric that indicates the extent to which radionuclides are retained within the 

entire engineered system over time. Specifically, the approach identifies, for selected 

radionuclides, the decayed cumulative release from the engineered system (i.e., the amount of 

radionuclides existing within either the lower natural barrier or the accessible environment 

relative to the total inventory in the entire system). DOE described that the barrier is capable of 

substantially reducing the rate of movement of radionuclides from the waste package. 

DOE attributed the barrier capability of the waste form and waste package internal components 

to a number of significant processes that can affect release rates. These processes include 

waste form degradation, precipitation and dissolution, colloid generation and stability, and 

sorption to and desorption from waste package internal components. These processes are in 

turn affected by the chemistry of the aqueous solution inside the failed waste packages as 

well as the water flow rate within the package. DOE described how these processes limit 

releases from the engineered barrier system and how these processes are associated with the 

different internal components of the waste package in DOE Enclosures 1 and 5 (2009an). NRC 

staff concluded that DOE provided the following information: 

 DOE Enclosure 1 (2009an) provided a discussion of the relationship between specific 

processes and the safety classification of individual waste form and waste package 
1-17 

internal components. For example, DOE explained that it considers the waste package 

inner vessel to be the dominant source of corrosion products for corrosion product 

sorption, and that corrosion of other internal components is not as significant and is 

therefore not considered important to waste isolation. 

 DOE provided specific information on the rate at which waste forms degrade in DOE 

Enclosure 5, Section 1.1 (2009an). DOE provided calculations on the basis of TSPA 

input parameters that evaluate mean waste form lifetimes on the order of up to a few 

thousand years for spent fuel and tens to hundreds of thousands of years for high level 

waste glass waste forms, as identified in DOE Enclosure 5, Tables 1.1-1 and 1.1-2 

(2009an). On the basis of the DOE results provided in DOE Enclosure 5, Tables 1.1-1 

and 1.1-2 (2009an), the high-level waste glass waste form lifetime is significantly more 

uncertain, with waste form lifetimes that can range anywhere from a few hundred years 

to over 100 million years. 

 DOE indicated in DOE Enclosure 5, Section 1.2 (2009an), on the basis of a selection of 

TSPA realizations, the effectiveness of the limited breach area associated with cracks 

for retaining radionuclides under diffusive release conditions and showed that the effect 

of breach area on release is nuclide and breach area dependent. DOE concluded that 

for soluble nuclides, releases are sensitive to the breach area for low breach area 

fractions, but become insensitive to the breach area as the breached area approaches 

just a few hundred square millimeters. For sorbing, solublility-limited nuclides, DOE 

concluded that the sensitivity persists for higher breached areas. 

 DOE Enclosure 5, Sections 1.3 and 1.4 (2009an) indicated the effectiveness of solubility 

limits and sorption to corrosion products for limiting the releases from the waste 

package, on the basis of sensitivity analyses and selected realizations. DOE observed 

that for the relatively insoluble, sorbing nuclides such as Np-237 and Pu-242, both 

precipitation/dissolution processes and sorption onto corrosion products are significant in 

limiting releases from the engineered barrier system. 

 DOE addressed the significance of colloidal processes in DOE Enclosure 5, Section 1.5 

(2009an). DOE does not identify transport facilitated by colloidal suspensions as 

significant to the barrier capability of the waste form and waste package internal 

components. DOE explained that colloids do not facilitate significant releases relative to 

dissolved forms of the same radionuclides. 

On the basis of its review of the information DOE presented in SAR Section 2.1.2.2 and in 

DOE Enclosures 1 and 5 (2009an), the NRC staff notes that the capabilities of the waste form 

and waste package internal components described by DOE and summarized in the preceding 

paragraphs are consistent with the results from the performance assessment calculation 

because DOE described these capabilities using TSPA input parameters and intermediate 

results, and analyses based on the TSPA models and parameters. 

The time period over which the waste form and waste package internals limit the release of 

radionuclides is described in SAR Section 2.1.2.2.3, in which DOE described the degradation 

rates of the different waste forms. DOE supplemented this information in DOE Enclosure 5 

(2009an), which identifies important processes controlling releases at different times in a 

discussion of selected TSPA realizations and described how DOE expects the significance of 

various processes (e.g., solubility, radionuclide sorption) to change with time. SAR 
1-18 

Section 2.1.2.2.6 described the change in the effectiveness of the capability by providing 

intermediate results from the TSPA that provided the time-dependent performance of the 

engineered barrier system to retain selected radionuclides. This information reasonably 

describes the time period over which the waste form and waste package internal components 

perform their barrier functions because the significance of different processes at different times 

is discussed. 

DOE identified the sources of uncertainty in the barrier capabilities in SAR Section 2.1.2.2.4. 

These include, for example, uncertainties in the source term, in the evolution of in-package 

chemistry, in waste form degradation rates, and in radionuclide solubilities and sorption 

behaviors. In SAR Section 2.1.2.2.6, DOE described the effect of uncertainty on predictions of 

the ability of the waste form and waste package internals to limit the release of radionuclides 

from a failed waste package by showing uncertainty bounds on the amount of selected 

radionuclides retained within the engineered barrier system. These discussions show that DOE 

considered uncertainty in its descriptions of barrier capability because DOE identified sources of 

uncertainty and the effect of these uncertainties on radionuclide release. 

DOE summarized the technical basis of the barrier capability description of the engineered 

barrier system, which included the waste form and waste package internal components, in SAR 

Section 2.1.3.2. DOE based its description of barrier capability of the waste form and waste 

package internal components on seven TSPA submodels: (i) the in-package water chemistry 

model described in SAR Section 2.3.7.5; (ii) three waste form degradation models described in 

SAR Sections 2.3.7.7, 2.3.7.8, and 2.3.7.9; (iii) the dissolved concentrations limits model 

described in SAR Section 2.3.7.10; (iv) the colloidal radionuclide availability model described in 

SAR Section 2.3.7.11; and (v) the engineered barrier system flow and transport model 

described in SAR Section 2.3.7.12. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.2 and 2.1.3.2 

with SAR Sections 2.3.7.5 and 2.3.7.7 through 2.3.7.12 and notes that the technical basis 

descriptions are consistent among these SAR sections. Further, the NRC staff compared the 

quantitative representation of the barrier capability in SAR Sections 2.1.2.2 and 2.1.3.2 with the 

results of the seven waste package internals TSPA submodels DOE described in SAR Sections 

2.3.7.5 and 2.3.7.7 through 2.3.7.12, and notes that the radionuclide release rate estimates are 

consistently represented among these SAR sections. Therefore, the technical bases for the 

description of barrier capability in SAR Section 2.1.2.2 are consistent with the technical basis of 

the waste package internals TSPA submodels. 

The NRC staff’s evaluation of the technical bases for these models is documented in TER 

Section 2.2.1.3.4. The NRC staff notes in TER Section 2.2.1.3.4 that DOE provided technical 

bases for the waste package internals in TSPA submodels and for the ranges of values for the 

radionuclide release rates used in the performance assessment that address their intended use. 

The NRC staff therefore determines that DOE’s technical bases for the description of the barrier 

capability of the waste form and waste package internals are commensurate with the barrier 

capability described in SAR Section 2.1.2.2 and in DOE Enclosures 1 and 5 (2009an). 

In summary, the NRC staff notes that the capability of the waste form and waste package 

internal components to prevent or substantially reduce the rate of movement of water or 

radionuclides from the Yucca Mountain repository to the accessible environment is reasonably 

described and that the technical basis for the barrier capability is based on and consistent with 

the technical basis for the performance assessment abstraction of radionuclide release and 

engineered barrier system transport properties. 
1-19 

2.2.1.1.3.2.7 Engineered Barrier System: Emplacement Pallet and Invert 

DOE discussed the capabilities of the emplacement pallet and invert in SAR Section 2.1.2.2 

and in DOE Enclosure 1 (2009an). DOE did not consider the emplacement pallet and invert to 

be important to waste isolation, and DOE, therefore, did not provide a detailed description of 

their capabilities. 

DOE identified a potential barrier capability of the emplacement pallet to reduce diffusive 

releases from the engineered barrier system by preventing contact between the waste package 

and invert, thereby reducing diffusive releases, but explained that this capability was not 

included in the TSPA. The NRC staff notes that the mechanical integrity of the emplacement 

pallet affects the analyses of damage to waste packages during seismic events. Therefore, the 

NRC staff has separately evaluated DOE’s assumptions about the potential mechanical stability 

of the emplacement pallet in TER Section 2.2.1.3.2.3.3. 

DOE explained that the invert contributes to barrier capability because low diffusion rates and 

potential sorption of radionuclides in the crushed tuff ballast slow the release rate of 

radionuclides from the waste package to the unsaturated rock beneath the drift. However, 

DOE determined that the delaying effect in the invert is not significant over long time frames, 

so DOE classified the invert as not important to barrier capability. The NRC staff notes that 

the potential for precipitation of low solubility radionuclides, a process discussed under the 

waste package internal components, is also a potential capability that can be associated with 

the invert. However, this process is likely to be more effective within the failed waste package, 

where water flows are typically much lower, than within the more dilute conditions of the 

invert. The NRC staff’s evaluation of the technical basis for the dissolved concentrations limits 

model, described in SAR Section 2.3.7.10, is documented in TER Section 2.2.1.3.4. 

The NRC staff reviewed DOE’s description of barrier capability for the emplacement pallet and 

invert. Based upon examination of the engineered barrier system radionuclide transport 

abstraction described in SAR Section 2.3.7 and evaluated in TER Section 2.2.1.3.4, the NRC 

staff notes that DOE appropriately considered the emplacement pallet and invert as not 

important to waste isolation, because the capabilities of the invert are not significant in 

comparison with other components of the engineered barrier system identified in 

SAR Table 2.1-1. 

2.2.1.1.3.2.8 Lower Natural Barrier: Unsaturated Zone Below the Repository 

The NRC staff reviewed DOE’s description of the barrier capability of the unsaturated zone 

below the repository. DOE identified the unsaturated zone beneath the repository as important 

to waste isolation because it prevents or substantially reduces the rate of movement of 

radionuclides (SAR Table 2.1-1). DOE provided a qualitative description of the barrier 

capabilities of the unsaturated zone below the repository in SAR Section 2.1.2.3.1. In 

SAR Section 2.1.2.3.6 and in DOE Enclosures 1, 6, and 7 (2009an), DOE quantified the barrier 

capability with calculations of radionuclide travel times and reduction of radionuclide activity 

between the repository and the water table. DOE provided information that describes a 

capability to substantially reduce the rate of movement of radionuclides from the repository to 

the water table. 

DOE explained in SAR Section 2.1.2.3.1 that downward flow from the repository occurs 

primarily in well-connected fracture networks in the Topopah Spring welded tuff. DOE explained 
1-20 

in SAR Section 2.1.2.3.6 that radionuclides leaving the emplacement drift invert will enter either 

the repository host rock matrix (primarily under nondripping conditions, where advective flows 

through the invert are negligible) or the fractures (primarily under dripping conditions, where 

advective flows through the invert are relatively high). In DOE’s unsaturated zone transport 

abstraction (SAR Section 2.3.8), radionuclides may be retarded by sorption in the matrix but 

not in fractures. However, radionuclides can migrate from fractures to the rock matrix by 

matrix diffusion. DOE identified matrix diffusion, coupled with sorption in the matrix, as 

contributing to barrier performance in the fracture-dominated flow paths. DOE explained in SAR 

Section 2.1.2.3.1 and SAR Section 2.1.2.3.6 that radionuclide travel times through the lower 

unsaturated zone are fast in the northern part of the repository area because fracture-dominated 

flow from the repository host rock encounters a low-permeability, sparsely fractured rock unit, 

the zeolitic Calico Hills nonwelded tuff, and the flow is diverted laterally along the interface into 

transmissive faults that connect with the water table. In contrast, in the southern part of the 

repository area, the fracture-dominated flow from the repository host rock passes into the vitric 

Calico Hills nonwelded tuff, a permeable rock unit that is dominated by matrix flow conditions. 

Low flow velocities and the opportunity for sorption in the rock matrix result in long transport 

times through the unsaturated zone in the southern part of the repository area, particularly for 

radionuclides that can undergo sorption in the matrix. 

DOE provided quantitative information on the barrier capability of the unsaturated zone 

below the repository in SAR Sections 2.1.2.3.6 and 2.3.8.5.4 and in DOE Enclosures 1, 6, and 7 

(2009an). Using results from the TSPA model with median parameter values, SAR 

Figures 2.3.8-43 through 2.3.8-49 indicate that the barrier performance of the lower unsaturated 

zone varies according to the location of the radionuclide release (i.e., northern or southern part 

of the repository area) and the mode of release from the repository drift into the unsaturated 

zone (i.e., into fractures or matrix). DOE explained in SAR Sections 2.1.2.3.6 and 2.3.8.5.4 and 

in DOE Enclosures 1, 6, and 7 (2009an) that fracture flow dominates in the welded tuffs beneath 

the northern part and matrix flow dominates in the vitric Calico Hills tuff beneath the southern 

part of the repository. Radionuclides released from a northern location will therefore tend to 

reach the water table much faster than those released from a southern location. However, initial 

releases into the rock matrix will result in slow travel times regardless of release location. For 

example, DOE calculated that the median travel time of an unretarded tracer (Tc-99) through 

the lower unsaturated zone in the northern area is about 20 years for releases into fractures and 

about 5,000 years for releases into the matrix. For a southern release location, the calculated 

median travel times to the water table are slow regardless of whether releases are into fractures 

or the matrix, with either release mode resulting in a median arrival time of about 2,000 years 

(SAR Figure 2.3.8-49). Analyses documented in DOE Enclosures 1, 6, and 7 (2009an) showed 

that radioactive decay in the unsaturated zone coupled with a combination of matrix diffusion 

and sorption in the northern repository area and sorption in the vitric Calico Hills tuff layer in the 

southern repository area would substantially reduce releases of sorbing, short-lived 

radionuclides such as Cs-137. For longer lived radionuclides, DOE’s analyses showed that 

sorption slows but does not prevent their transport through the unsaturated zone. For example, 

DOE Enclosure 7, Tables 1-5 through 1-8 (2009an) indicated that the unsaturated zone beneath 

the repository (northern and southern areas combined) reduced the release of long-lived 

radionuclides such as Np-237 (weakly sorbing) and Pu-242 (moderately to strongly sorbing) 

from the engineered barrier system to the saturated zone by about 30–50 percent during the 

10,000-year period and by about 5 to 30 percent over a million-year time frame. 

On the basis of its review of information DOE provided in SAR Section 2.1.2.3, the NRC staff 

notes that DOE’s description of the barrier capability of the unsaturated zone below the 

repository is consistent with the results from the performance assessment calculation because 
1-21 

DOE described these capabilities using analyses based on TSPA models and input data as well 

as by reference to intermediate results from the performance assessment model. 

 

In SAR Section 2.1.2.3.3 and DOE Enclosure 7, Section 1.3 (2009an), DOE discussed the 

time period over which the unsaturated zone functions as a barrier. DOE stated that 

the hydrogeology and physical characteristics of the lower natural barrier, which includes 

the unsaturated zone below the repository, are not expected to significantly change 

within 10,000 years after closure. DOE assumed that the intrinsic hydrologic, geologic, and 

geochemical characteristics of the lower natural barrier will not change significantly after 

10,000 years following closure. DOE expects changes in the unsaturated zone capability to be 

associated with projected increases in percolation and in the water table elevation due to 

changes in climate. DOE Enclosure 7 (2009an) indicated that the relative barrier capability of 

the unsaturated zone decreases compared to the saturated zone for the post-10,000-year 

period because of faster travel times in the unsaturated zone. Information in DOE Enclosures 6 

and 7 (2009an) stated that the barrier capability of the unsaturated zone is more pronounced for 

the initial 10,000-year time frame than for a million-year time frame because sorption slows but 

does not prevent the release of long-lived sorbing radionuclides to the saturated zone. DOE 

stated that delay times on the order of 1,000 years significantly affect short-lived radionuclides 

and that even the transported mass of long-lived radionuclides may be diminished by long travel 

times in the unsaturated zone. This information reasonably describes the time period over 

which the unsaturated zone performs its stated barrier functions because it identifies which 

aspects of the capability will change and which will remain constant. 

DOE discussed and evaluated the uncertainties in the unsaturated zone in SAR 

Sections 2.1.2.3.4 and 2.3.8.5.5. DOE attributed the main uncertainties for barrier capability 

to (i) the variability of site characteristics and future climate and (ii) applicability of the models 

and assumptions used to estimate the performance of the repository system (SAR 

Section 2.1.2.3.4). Some examples of the uncertain characteristics included percolation flux, 

the extent of fracture–matrix interaction, matrix diffusion coefficients, and radionuclide 

distribution coefficients. DOE incorporated uncertainty in the TSPA unsaturated zone transport 

model by using sampled probabilistic distributions for parameter uncertainty and by using 

assumptions in models that would not overestimate performance. DOE presented the impact of 

various uncertainties in SAR Figures 2.3.8-50 to 2.3.8-62. It supplemented this information with 

discussions of sensitivity analyses for various parameters in DOE Enclosure 7, Section 1.2 

(2009an). DOE considered uncertainty in its descriptions of barrier capability for the 

unsaturated zone below the repository because DOE described specific sources of uncertainty 

and indicated the performance impact of different sources of uncertainty. 

In SAR Section 2.1.3.3, DOE summarized the technical basis of the barrier capability 

description of the lower natural barrier, which includes the unsaturated zone below the 

repository. DOE based its description of barrier capability of the unsaturated zone below 

the repository on the unsaturated zone flow model described in SAR Section 2.2.2.4 and on 

the unsaturated zone transport model described in SAR Section 2.3.8. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.3.1 and 2.1.3.3 

with SAR Section 2.3.8 and notes that the technical basis descriptions are consistent among 

these SAR sections. Further, the NRC staff compared the quantitative representation of the 

barrier capability in SAR Sections 2.1.2.3.6 and 2.1.3.3 with the results of the unsaturated zone 

transport model described in SAR Section 2.3.8 and notes that the estimates of radionuclide 

travel time and reduction in radionuclide activity within the unsaturated zone are consistently 

represented among these SAR sections. Therefore, the technical bases for the description of 
1-22 

barrier capability in SAR Sections 2.1.2.3.1 and 2.1.2.3.6 are consistent with the technical bases 

of the unsaturated zone transport model. 

The NRC staff documents its evaluation of the technical basis for the unsaturated zone flow 

model abstraction in TER Section 2.2.1.3.6 and for the unsaturated zone transport model 

abstraction in TER Section 2.2.1.3.7. The NRC staff notes in TER Section 2.2.1.3.7 that DOE 

provided reasonable technical basis for the unsaturated zone transport. Therefore, DOE’s 

technical bases for the description of the barrier capability of the unsaturated zone below the 

repository are commensurate with the barrier capability described in SAR Section 2.1.2.3.1, 

SAR Section 2.1.2.3.6, and in DOE Enclosures 1, 6, and 7 (2009an). 

In summary, the NRC staff notes that the capability of the unsaturated zone below the 

repository to prevent or substantially reduce the rate of movement of radionuclides from the 

Yucca Mountain repository to the accessible environment is reasonably described and that the 

technical basis for the barrier capability is based on and consistent with the technical basis for 

the performance assessment. 

2.2.1.1.3.2.9 Lower Natural Barrier: Saturated Zone 

The NRC staff reviewed the description of the barrier capability of the saturated zone. DOE 

described the barrier capabilities of the saturated zone qualitatively in SAR Section 2.1.2.3.2 

and used median transport times and reduction of radionuclide activity between the water table 

below the repository footprint and the accessible environment to quantify the barrier capability in 

SAR Section 2.1.2.3.6 and in DOE Enclosures 1, 6, and 7 (2009an). The NRC staff determined 

that DOE identified a capability of the barrier to substantially reduce the rate of movement of 

radionuclides from the water table below the repository footprint to the accessible environment. 

In SAR Section 2.1.2.3.2, DOE explained that the saturated zone component of the lower 

natural barrier flows initially through approximately 12–14 km [7.4–8.7 mi] of fractured volcanic 

rocks. Beyond this distance, flow is predominantly within a saturated layer of alluvium. DOE 

explained that the flow in the fractured volcanic aquifers occurs primarily in the fractures. DOE 

explained that hydraulic conductivities are much lower in the matrix of the volcanic tuffs than in 

the fractures, because the rock matrix is more porous than the fractures. These relative 

properties support exchange of radionuclides between the fractures and matrix through matrix 

diffusion. Hence, diffusion into the matrix followed by matrix sorption function to delay 

radionuclide transport to the accessible environment. DOE explained that flow and transport 

occur in the intergranular pores of the alluvial sediments after leaving the fractured volcanic 

aquifer. Because of the low water velocity, the rate of radionuclide movement is slow, allowing 

more time for sorption to occur onto the mineral surfaces to further delay radionuclide transport 

to the accessible environment. DOE also explained that the presence of colloids also affects 

the rate of movement of radionuclides in the saturated zone. Radionuclides embedded in or 

irreversibly sorbed onto colloids are retarded when the associated colloids are temporarily 

filtered from transport. Radionuclides that are sorbed reversibly to colloids are delayed by 

matrix diffusion in the volcanic aquifers and by sorption in the alluvial sediments. 

DOE provided quantitative information on the barrier capability of the saturated zone in SAR 

Sections 2.1.2.3.6 and 2.3.9.3.4.1. SAR Figures 2.3.9-16 and 2.3.9-45 through 2.3.9-47 

illustrated the combined effects of matrix diffusion and sorption in delaying radionuclide 

transport to the accessible environment. Median transport times ranged from about 10 to 

10,000 years for nonsorbing radionuclides (e.g., Tc-99) and from 100 to 100,000 years for 

moderately sorbing radionuclides (e.g., Np-237). Median transport times generally exceeded 
1-23 

10,000 years for highly sorbing radionuclides (e.g., Pu-239). The median transport times for 

radionuclides irreversibly attached onto colloids ranged from 100 to 600,000 years. In DOE 

Enclosures 6 and 7 (2009an), DOE used TSPA results to provide quantitative information on the 

barrier capability of the saturated zone in terms of reduction of radionuclide activity between the 

release from the unsaturated zone into the water table and the release into the accessible 

environment. DOE presented information on the performance of the saturated zone in 

Tables 1-5 through 1-8. This information indicates that DOE expects activities of soluble, short 

half-life radionuclides (e.g., Cs-137 and Sr-90) to drop by 100 percent during transport to the 

accessible environment. For radionuclides with moderate to strong sorption and long half-life 

(e.g., Np-237 and Pu-242), DOE calculated the activities to drop by 70 to 98 percent during the 

10,000-year period and by 20 to 50 percent during the post-10,000-year period over the 

transport time in the saturated zone to the accessible environment. 

On the basis of its review of the DOE description of the barrier capabilities of the saturated zone 

in SAR Section 2.1.2.3, the NRC staff notes that DOE’s description of the barrier capability for 

the saturated zone is consistent with the results from the performance assessment calculation 

because DOE describes the capabilities using TSPA intermediate results, supported by 

analyses based on TSPA models and parameters. 

DOE provided information on the time period over which the saturated zone performs its 

intended function in SAR Section 2.1.2.3.3 and DOE Enclosure 7, Section 1.3 (2009an). 

Additional information on the time period over which the saturated zone functions as a barrier is 

contained in SAR Section 2.3.9.3.4.1. DOE stated that the hydrogeology and physical 

characteristics of the lower natural barrier, which includes the saturated zone, are not expected 

to change in any significant way within 10,000 years after closure. DOE assumed that the 

intrinsic hydrologic, geologic, and geochemical characteristics of the lower natural barrier will 

not change significantly after 10,000 years following closure. DOE addressed changes in the 

barrier function of the saturated zone by reference to an expected increase in groundwater 

recharge under projected wetter future climate conditions, resulting in a rise in the water table 

and increased groundwater flow. DOE did not expect these changes in groundwater flow to 

change the processes of sorption and matrix diffusion that control radionuclide transport to the 

accessible environment. DOE explained that sorption increases the barrier capability because it 

delays the release and allows for radioactive decay within the natural system to reduce the 

radionuclide mass in the system. Information in DOE Enclosures 6 and 7 (2009an) showed that 

the barrier capability of the saturated zone is more pronounced for the initial 10,000-year time 

frame than for a million-year time frame. DOE provided information that shows sorption slows 

but does not prevent the release of long-lived sorbing radionuclides to the saturated zone and 

that the effects of a delay are more pronounced in the initial 10,000 years after closure. DOE 

showed that delay times on the order of 1,000 years significantly affect short-lived radionuclides 

and that even the transported mass of long-lived radionuclides may be diminished by long travel 

times in the saturated zone. The NRC staff notes that this information reasonably describes the 

time period over which the saturated zone performs its stated barrier functions because it 

identifies which aspects of the capability will change and which will remain constant. 

In SAR Section 2.1.2.3.4, DOE described the uncertainty in the barrier capability in terms of the 

conceptual and numerical models, observational data, and parameters used to represent water 

flow and radionuclide transport processes in the saturated zone. Some examples of the 

uncertain characteristics include groundwater-specific discharge, porosity, the spatial variation 

of aquifer properties, matrix diffusion coefficients, and radionuclide distribution coefficients. 

DOE incorporated parameter uncertainty in the TSPA saturated zone transport model through 

various probabilistic distributions. Effects of transport parameter uncertainty on radionuclide 
1-24 

breakthrough at the accessible environment are presented in SAR Section 2.3.9.3.4.1 and 

SAR Figures 2.3.9-16 and 2.3.9-45 through 2.3.9-47. DOE also provided quantitative 

evaluations of the impacts of barrier uncertainties on saturated zone flow processes in 

SAR Section 2.3.9.2.3.4. DOE supplemented this information with discussions of sensitivity 

analyses for various parameters in DOE Enclosure 7, Section 1.2 (2009an). The NRC staff 

notes DOE has considered uncertainty associated with the modeling of water flow and 

radionuclide transport processes in its descriptions of barrier capability because DOE 

described specific sources of uncertainty and indicated the performance impact of different 

sources of uncertainty. 

In SAR Section 2.1.3.3, DOE summarized the technical basis of the barrier capability 

description of the lower natural barrier, which includes the saturated zone. DOE based its 

description of barrier capability of the saturated zone on the saturated zone flow model 

described in SAR Section 2.3.9.2 and the saturated zone transport model described in SAR 

Section 2.3.9.3. 

The NRC staff compared the technical basis descriptions in SAR Sections 2.1.2.3.2 and 2.1.3.3 

with SAR Section 2.3.9 and notes that the technical basis descriptions are consistent among 

these SAR sections. Further, the NRC staff compared the quantitative barrier capability 

description for the saturated zone in SAR Sections 2.1.2.3.6 and 2.1.3.3 with the results of the 

saturated zone transport model described in SAR Section 2.3.9 and notes that the estimates of 

radionuclide travel time and reduction in radionuclide activity within the saturated zone are 

consistently represented among these SAR sections. Therefore, the technical bases for the 

description of barrier capability in SAR Sections 2.1.2.3.2 and 2.1.2.3.6 are consistent with the 

technical basis of the saturated zone flow and transport models. 

The NRC staff’s evaluation of the technical bases for these models is documented in 

TER Sections 2.2.1.3.8 and 2.2.1.3.9, respectively. The NRC staff notes in TER 

Sections 2.2.1.3.8 and 2.2.1.3.9 that DOE provided technical bases for the saturated 

zone flow and transport models that address their intended use. Therefore, DOE’s technical 

bases for the description of the barrier capability of the saturated zone are commensurate with 

the barrier capability described in SAR Section 2.1.2.3.2, SAR Section 2.1.2.3.6, and in DOE 

Enclosures 1, 6, and 7 (2009an). 

In summary, the NRC staff notes that the capability of the saturated zone to prevent or 

substantially reduce the rate of movement of radionuclides from the Yucca Mountain 

repository to the accessible environment is reasonably described and that the technical 

basis for the barrier capability is based on and consistent with the technical basis for the 

performance assessment. 

2.2.1.1.4 Summary of Staff Evaluations 

2.2.1.1.4.1 Identification of Barriers 

As discussed in TER Section 2.2.1.1.3.1, DOE has identified specific features and components 

that are relied upon for repository performance. DOE has linked these features and 

components to a description of their capability. These features and components include at least 

one from the engineered system and one from the natural system. Therefore, the NRC staff 

notes that DOE has identified the barriers, consistent with applicable guidance in the YMRP. 
1-25 

2.2.1.1.4.2 Description of Barrier Capability to Isolate Waste 

Upper Natural Barrier 

The NRC staff reviewed the description of the barrier capabilities of the upper natural barrier. 

This barrier comprises two features: (i) the topography and surface soils at the repository 

location and (ii) the unsaturated zone above the repository. On the basis of the evaluations 

documented in TER Sections 2.2.1.1.3.2.1 and 2.2.1.1.3.2.2, NRC staff notes that the capability 

of the upper natural barrier has been reasonably described. The descriptions address a 

capability to prevent or substantially reduce the rate of movement of water from the Yucca 

Mountain repository to the accessible environment. The descriptions of the capabilities are 

consistent with the results from the Total System Performance Assessment in that they refer by 

reference to component-specific intermediate results from the performance assessment or by 

analyses based on models and data used in the performance assessment. Information on the 

time period over which this upper natural barrier feature performs its intended function has been 

provided. DOE has reasonably described the uncertainty associated with the barrier capability 

by identifying sources of uncertainty and describing how these uncertainties affect repository 

performance. Therefore, DOE has reasonably described the capability of the upper natural 

barrier to prevent or substantially reduce the rate of movement of water or radionuclides from 

the Yucca Mountain repository to the accessible environment. 

Engineered Barrier 

The NRC staff reviewed the description of the barrier capabilities of the engineered barrier 

system. This barrier comprises five components: (i) the emplacement drift, (ii) the drip 

shield, (iii) the waste package, (iv) the waste form and waste package internals, and (v) the 

emplacement pallet and invert. On the basis of the evaluation documented in TER 

Sections 2.2.1.1.3.2.3 through 2.2.1.1.3.2.7, the NRC staff notes that the capability of the 

engineered barrier system has been reasonably described. The descriptions identify a 

capability to prevent or substantially reduce the rate of movement of water or radionuclides 

from the Yucca Mountain repository to the accessible environment. The described capabilities 

are consistent with the results from the Total System Performance Assessment in that they 

are described by reference to component-specific intermediate results from the performance 

assessment or by analyses based on models and data used by the performance assessment. 

Information on the time period over which the features of the engineered barrier system 

perform their intended functions has been provided. DOE has reasonably addressed 

uncertainty in the description of the barrier capability by identifying sources of uncertainty and 

describing how these uncertainties affect repository performance. Therefore, DOE has 

reasonably described the capability of the engineered barrier system to prevent or substantially 

reduce the rate of movement of water or radionuclides from the Yucca Mountain repository to 

the accessible environment. 

Lower Natural Barrier 

The NRC staff reviewed the description of the barrier capabilities of the lower natural barrier. 

This barrier comprises two features: the unsaturated zone below the repository and the 

saturated zone. On the basis of the evaluation documented in TER Sections 2.2.1.1.3.2.8 and 

2.2.1.1.3.2.9, the NRC staff notes that the capability of the lower natural barrier has been 

reasonably described. The described capabilities address a capability to prevent or 

substantially reduce the rate of movement of radionuclides from the Yucca Mountain repository 

to the accessible environment. The described capabilities are consistent with the results from 
1-26 

the Total System Performance Assessment in that they are described by reference to input data 

or component-specific intermediate results from the performance assessment or by analyses 

based on models and data used in the performance assessment. Information on the time 

period over which the features of the lower natural barrier system perform their intended 

functions has been reasonably addressed. DOE has reasonably addressed uncertainty in the 

description of the barrier capability by identifying sources of uncertainty and describing how 

these uncertainties affect repository performance. Therefore, DOE has reasonably described 

the capability of the lower natural barrier to prevent or substantially reduce the rate of movement 

of water or radionuclides from the Yucca Mountain repository to the accessible environment. 

2.2.1.1.4.3 Technical Basis for Barrier Capability 

The SAR presents an overview of the technical bases for the models used to represent the 

performance of the barriers in the TSPA. This overview, summarized in SAR Section 2.1 and 

more fully documented in SAR Section 2.3, identifies the types of field investigations, laboratory 

studies, analog studies, literature surveys, and other technical approaches used to develop the 

conceptual TSPA model components. The NRC staff notes that the technical bases for the 

descriptions of barrier capability summarized in TER Sections 2.2.1.1.3.2.1 through 

2.2.1.1.3.2.9 are consistent with the technical bases of the abstraction models described in SAR 

Section 2.3 and evaluated in TER Sections 2.2.1.3.1 through 2.2.1.3.9. The technical basis is 

commensurate with the barrier capability described in SAR Sections 2.1 and 2.3 and in DOE 

responses to NRC requests for additional information documented in DOE (2009an). 

2.2.1.1.5 NRC Staff Conclusions 

The NRC staff notes that the multiple barriers of the repository, as described in DOE’s SAR and 

other information DOE submitted, are consistent with the guidance in the YMRP. NRC staff 

also notes that the repository includes both natural and engineered barriers and that the 

technical basis for each barrier’s capability, as discussed in this chapter, is reasonable. 

2.2.1.1.6 References 

CNWRA and NRC. 2008aa. “Risk Insights Derived From Analyses of Model Updates in the 

Total-system Performance Assessment Version 5.1 Code.” ML082240343. San Antonio, 

Texas: CNWRA. 

DOE. 2009an. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.1, Set 1.” Letter (February 6) J.R. Williams to J.H. Sulima (NRC). ML090400455. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bo. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.2 and 2.3.3), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (June 1) J.R. Williams to J.H. Sulima (NRC). 

ML091530403. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bu. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.1), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.1, Set 1.” Letter (April 16) J.R. Williams to J.H. Sulima 

(NRC). ML091070088. Washington, DC: DOE, Office of Technical Management. 
1-27 

DOE. 2009cb. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 5.” Letter (June 5) J.R. Williams to J.H. Sulima (NRC). 

ML091590581. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

ML051360241. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

SNL. 2008ad. “Postclosure Nuclear Safety Design Bases.” ANL–WIS–MD–000024. Rev. 01. 

ACN 01, ERD 01, ERD 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

2-1 

CHAPTER 2 

2.2.1.2.1 Scenario Analysis 

2.2.1.2.1.1 Introduction

This chapter provides the U.S. Nuclear Regulatory Commission (NRC) staff’s evaluation of the 

U.S. Department of Energy’s (DOE) scenario analysis used in its performance assessment. 

The NRC staff evaluated information in the Safety Analysis Report (SAR) (DOE, 2009av) as 

supplemented by the DOE responses to the staff’s requests for additional information (RAIs) 

(DOE, 2009ab,ae,af,ah–aj,al,bo,bv–bz,ca–cj,co,cq,gp,gq, 2010ad,ah). 

A performance assessment is a systematic analysis that answers the following triplet risk 

questions: What can happen? How likely is it to happen? What are the resulting 

consequences? Scenario analysis answers the first question: What can happen? A scenario is 

a well-defined, connected sequence of features, events, and processes (FEPs) that can be 

interpreted as an outline of a possible future condition of the repository system. Thus, a 

scenario analysis identifies possible ways in which a geologic repository environment can 

evolve so that a defensible representation of the system can be developed to estimate 

consequences. The goal of scenario analysis is to ensure that no important aspect of the 

potential high-level waste repository is overlooked in the evaluation of its safety. 

A scenario analysis is generally composed of four parts (Nuclear Energy Agency, 2001aa). 

First, a scenario analysis identifies FEPs relevant to the geologic repository system. Second, in 

a process known as screening, the scenario analysis evaluates and identifies FEPs for 

exclusion from or inclusion into the performance assessment calculations. Third, included FEPs 

are considered to form scenarios and scenario classes (i.e., related scenarios) from a reduced 

set of events. Fourth, the scenario classes are screened for implementation into the 

performance assessment. 

Consistent with this general approach and the review areas in the Yucca Mountain Review Plan 

(YMRP) Section 2.2.1.2.1 (NRC, 2003aa), the NRC staff evaluates DOE’s scenario analysis in 

four separate sections [Technical Evaluation Report (TER) Sections 2.2.1.2.1.3.1 to 

2.2.1.2.1.3.4]. TER Section 2.2.1.2.1.3.1 evaluates both DOE’s methodology to develop a list of 

FEPs and its list of FEPs. In TER Section 2.2.1.2.1.3.2, the NRC staff evaluates DOE’s 

screening of its list of FEPs, including DOE’s technical bases for the exclusion of FEPs. DOE’s 

formation of scenario classes and the exclusion of classes in DOE’s performance assessments 

are evaluated in TER Sections 2.2.1.2.1.3.3 and 2.2.1.2.1.3.4, respectively. 

The NRC staff’s evaluation of DOE’s methodology and conclusions on the probability of events 

included in the performance assessments is presented in TER Section 2.2.1.2.2. That section 

is aimed at the second risk triplet question: How likely is it to happen? The NRC staff’s 

evaluation of DOE’s model abstraction is documented in TER Sections 2.2.1.3.1–2.2.1.3.14 and 

Sections 2.2.1.4.1–2.2.1.4.3. These sections focus on the included FEPs and the third risk 

triplet question (What are the resulting consequences?) and present the NRC staff’s evaluation 

of the adequacy of assessment of consequences of included FEPs and scenario classes used 

in DOE’s performance assessments. 
2-2 

2.2.1.2.1.2 Evaluation Criteria

A performance assessment is an analysis that identifies the features, events, 

processes (except human intrusion), and sequences of events and processes (except human 

intrusion) that might affect the Yucca Mountain disposal system and their probabilities of 

occurring. A functional overview of the performance assessment used to demonstrate 

compliance with the postclosure performance objectives is presented in 10 CFR 63.102(j). 

10 CFR 63.102(j) also contains criteria for including FEPs [those expected to materially affect 

compliance with 10 CFR 63.113(b) or be potentially adverse to performance] in the performance 

assessment. 10 CFR 63.102(j) provides that events (event classes or scenario classes) that 

are very unlikely (less than 1 chance in 10,000 over 10,000 years) can be excluded from 

the analysis. 

The postclosure performance objectives of 10 CFR 63.113 stipulate that a performance 

assessment must be used to demonstrate compliance with (i) the individual protection standard 

after permanent closure (10 CFR 63.311); (ii) the human intrusion standard (10 CFR 63.321 

and 63.322); and (iii) the separate standards for protection of groundwater (10 CFR 63.331). 

Criteria for a performance assessment used to calculate annual doses for 10,000 years are 

presented in 10 CFR 63.114(a). 10 CFR 63.114(a)(4) states that the performance assessment 

consider only features, events, and processes consistent with the limits on performance 

assessment specified at 10 CFR 63.342. 10 CFR 63.114(a)(5)–(6) defines criteria for inclusion 

of the features, events, and processes into the performance assessment (specific features, 

events, and processes need detailed evaluation if the magnitude and time of the resulting 

radiological exposures to the reasonably maximally exposed individual, or radionuclide releases 

to the accessible environment, for 10,000 years after disposal, would be significantly changed 

by their omission). 10 CFR 63.114(b) calls for performance assessments consistent with 

paragraph (a) of that section to be considered sufficient for the performance assessment for the 

period of time after 10,000 years and through the period of geologic stability. 

According to 10 CFR 63.342(a), the performance assessment for 10,000 years after disposal 

need not include features, events, and processes with less than 1 chance in 100 million per year 

of occurring. Also, 10 CFR 63.342(a) provides that the performance assessments need not 

evaluate the impacts resulting from any feature, event, and process or sequence of events 

and processes with a higher chance of occurring if the results of the performance assessments 

would not be changed significantly in the initial 10,000-year period after disposal. Thus, 

10 CFR 63.342 defines the conditions for exclusion of FEPs on the basis of probability 

or consequence. 

For performance assessments for human intrusion and groundwater protection, 

10 CFR 63.342(b) provides that unlikely features, events, and processes or sequences 

of events and processes (i.e., those that are estimated to have less than 1 chance in 

100,000 per year of occurring and at least 1 chance in 100 million per year of occurring) can 

be excluded from the performance assessment. 

10 CFR 63.342(c) specifies how to project the continued effects of FEPs beyond 10,000 years 

in the performance assessment models. 10 CFR 63.342(c) requires a performance 

assessment to project the continued effects of the features, events, and processes included in 

10 CFR 63.342(a) beyond the 10,000-year postdisposal period through the period of 

geologic stability. 10 CFR 63.342(c)(1) requires that the effects of seismic and igneous 

activity scenarios are evaluated subject to the probability limits in 10 CFR 63.342(a) 

for very unlikely features, events, and processes, or sequences of events and processes. 
2-3 

10 CFR 63.342(c)(1)(i) states that the seismic analysis may be limited to the effects caused by 

damage to the drifts in the repository, failure of the waste packages, and changes in the 

elevation of the water table under Yucca Mountain (i.e., the magnitude of the water table 

rise under Yucca Mountain). 

10 CFR 63.342(c)(1)(ii) specifies limitations for the igneous activity analysis and igneous event. 

10 CFR 63.342(c)(2) requires an assessment of the effects of climate change and specifies 

that the climate change analysis may be limited to the effects of increased water flow through 

the repository as a result of climate change, and the resulting transport and release of 

radionuclides to the accessible environment. In addition, 10 CFR 63.324(c)(2) specifies that 

the nature and degree of climate change may be represented by constant-in-time climate 

conditions. 10 CFR 63.342(c)(3) specifies an assessment of the effects of general corrosion on 

engineered barriers and allows for either the use of a constant representative corrosion rate 

throughout the period of geologic stability or a distribution of corrosion rates correlated to other 

repository parameters. 

Guidance in YMRP Section 2.2.1.2.1.3, p. 2.2-9 provides that specific FEPs and scenario 

classes can be excluded on the basis that they are specifically ruled out by regulation or 

are contrary to stated regulatory assumptions. For example, 10 CFR 63.305 defines 

characteristics of the reference biosphere. Thus FEPs that are contrary to definitions in 

10 CFR 63.305 can be excluded. 

2.2.1.2.1.3 Technical Evaluation

DOE summarized in SAR Section 2.2.1 its five-step scenario analysis method used to 

develop a performance assessment model: (i) identification and classification of a list of FEPs, 

(ii) evaluation of the FEPs for inclusion or exclusion from the performance assessment model, 

(iii) formation of scenario classes, (iv) screening of scenario classes, and (v) definition of the 

implementation of scenario classes in the performance assessment model and documentation 

of the treatment of included FEPs. The first four steps are evaluated in this section. Step five is 

evaluated in TER Sections 2.2.1.4.1, 2.2.1.4.2, and 2.2.1.4.3. 

The staff’s evaluation of the scenario analysis follows the methodologies and acceptance 

criteria identified in YMRP Section 2.2.1.2.1, as supplemented by additional guidance for the 

period beyond 10,000 years after permanent closure (NRC, 2009ab). The guidance in YMRP 

Section 2.2.1.2.1 provides four criteria for a scenario analysis. 

 The identification of a list of features, events, and processes is adequate. 

 Screening of the list of features, events, and processes is appropriate. 

 Formation of scenario classes using the reduced set of events is adequate. 

 Screening of scenario classes is appropriate. 

Additionally, YMRP Section 2.2.1 provides guidance to the NRC staff on how to apply risk 

information in its review of the DOE analyses. Following the YMRP guidance, the NRC staff 

considered DOE’s risk information (derived from DOE’s treatment of multiple barriers) and risk 

insights in SAR Section 2.4.2.2.1.2. The level of detail of the NRC staff’s review of particular 

parts of the scenario analysis is based on (i) the risk insights DOE developed; (ii) consideration 

of the risk insights identified in NRC Appendix D (2005aa), as updated (CNWRA and NRC, 

2008aa); and (iii) detailed process modeling efforts, laboratory and field experiments, and 

natural analog studies. 
2-4 

Because the purpose of the first acceptance criterion in YMRP Section 2.2.1.2.1 is to ensure the 

completeness and comprehensiveness of the FEPs list, the NRC staff did not consider risk 

information in its review of DOE’s identification of a list of FEPs (TER Section 2.2.1.2.1.3.1). In 

TER Section 2.2.1.2.1.3.2, the NRC staff reviewed DOE’s screening of the list of FEPs following 

the second acceptance criterion in YMRP Section 2.2.1.2.1 and as supplemented by additional 

guidance for the period beyond 10,000 years after permanent closure (NRC, 2009ab). The 

acceptance criterion includes the following three separate aspects: (i) all features, events, and 

processes that are excluded are identified; (ii) justification for each excluded feature, event, and 

process is provided [an appropriate justification for excluding FEPs is that either the feature, 

event, and process is specifically excluded by regulation; probability of the feature, event, and 

process (generally an event) falls below 10−4

 in 10,000 years; or omission of the FEP does not 

significantly change the magnitude and time of the resulting radiological exposures to the 

reasonably maximally exposed individual, or radionuclide releases to the accessible 

environment]; and (iii) an adequate technical basis for each excluded feature, event, and 

process is provided. The NRC staff evaluated the technical bases of the 222 excluded features, 

events, and processes. In reviewing the technical basis for exclusion of FEPs, the NRC staff 

focused in greater detail on items that were deemed to have the largest impact on risk and used 

progressively less detail on items that were considered to have lower to negligible impact on 

risk. For example, drift collapse is a process that could affect multiple aspects of the repository 

(e.g., temperature, moisture distribution, rock loads acting on the drip shield, response of the 

drip shield subjected to seismic excitations) (Ofoegbu, et al., 2007aa) and that could affect the 

performance of multiple engineered barrier components that impact risk (NRC, 2005aa). 

Accordingly, the NRC staff devoted greater effort to evaluate the technical basis for exclusion 

of the Drift Collapse FEP. On the other hand, a number of FEPs were deemed to be not risk 

significant (e.g., Meteorite Impact, Copper Corrosion in the Engineered Barrier System), 

and these FEPs were evaluated in less detail. In this chapter, in general, the FEPs 

discussed are those for which the NRC staff issued RAIs to DOE to supplement the technical 

bases for exclusion. 

2.2.1.2.1.3.1 Identification of a List of Feature, Events, and Processes

Identification of a list of FEPs is the initial step in the scenario analysis and is aimed at 

assembling a list that includes all FEPs with the potential to influence repository performance. 

This technical evaluation of the identification of the list of FEPs follows the methodology 

established in YMRP Section 2.2.1.2.1.2, p. 2.2-7. 

DOE summarized the process to identify the list of FEPs in SAR Section 2.2.1.1.1 and in SNL 

(2008ac). DOE has published two major versions of the list of FEPs for the Yucca Mountain 

project: the FEPs list for site recommendation and the SAR FEPs list. DOE stated that the site 

recommendation FEPs list was developed based on a Nuclear Energy Agency compilation of 

FEPs, supplemented with Yucca Mountain project literature, information in analysis reports, 

technical workshops, and reviews and resulted in a collection of 328 FEPs considered in the site 

recommendation Total System Performance Assessment, as outlined in SNL p. 6-1 (2008ac). 

DOE stated that the site recommendation FEPs list was further refined to enhance classification 

strategies and to achieve a consistent level of detail among FEPs, and that additional FEPs 

were identified on the basis of audits and technical information updates subsequent to the site 

recommendation, such as changes in design parameters. DOE stated that to verify 

comprehensiveness in the list of FEPs, an alternative list was developed using a top-down 

functional analysis of the repository (SNL, 2008ac). Each function was divided into smaller, 
2-5 

more specialized functions until a level of detail was attained comparable to the existing list of 

FEPs. This alternative list was then compared to the SAR FEPs list to build confidence that the 

SAR FEPs list was indeed complete or to identify missing FEPs. DOE further compared the 

SAR FEPs list to a version of an international list of FEPs by the Nuclear Energy Agency, 

Organisation for Economic Co-operation and Development (OECD), Appendix D (Nuclear 

Energy Agency, 2000aa) to inquire about the completeness of the list of FEPs. DOE noted that 

the International FEPs Database was updated in 2006 (Nuclear Energy Agency, 2006aa); 

however, DOE concluded that the update did not present additional scope beyond the FEPs 

already addressed in the SAR FEPs list (SAR p. 2.2-8) and SNL Appendix F (2008ab). 

DOE stated that further analyses were applied to address changes in the regulations and in the 

design of the repository and disposal packages. The final count of FEPs is 374. DOE stated 

that the iterative approach, including expanding on the existing FEPs list, brainstorming, multiple 

reviews by subject matter experts, top-down elicitation from an independent classification 

scheme, and use of the Yucca Mountain project analyses support the conclusion that the SAR 

FEPs list was complete, as described in SNL p. 6-4 (2008ac). 

The NRC staff evaluated the adequacy of the list of FEPs. The FEPs were classified by 

technical area (Leslie, 2010aa), following a similar approach as in NRC Table 5.1.2.1-2 

(2005aa). In numerous instances, the same FEP was classified as pertaining to several 

technical areas, to cover broad aspects, consequences, and couplings associated with that 

FEP (Leslie, 2010aa). The objective of assigning an FEP to multiple technical areas was to 

attain a thorough and integral review of the list of FEPs covering multiple technical perspectives 

and to facilitate indentifying aspects potentially overlooked by the existing FEPs. The NRC 

staff evaluated the description of the scope for the individual FEPs, the screening decision of 

the individual FEPs, the technical basis for excluding FEPs, and the disposition for the included 

FEPs. The NRC staff’s review of the identification of the list of FEPs was based on knowledge 

gained reviewing the Yucca Mountain site and regional characterization data, including previous 

independent Yucca Mountain-related studies, and DOE’s description of the modes of 

degradation, deterioration, and alteration of the engineered barriers. A previous NRC staff’s 

review of DOE’s identification of FEPs (Pickett and Leslie, 1999aa) was also considered. 

The NRC staff also used available, internationally developed generic lists of FEPs 

(Nuclear Energy Agency, 1997ab) to evaluate the completeness of the DOE list of features, 

events, and processes. 

The NRC staff noted in NRC Section 5.1.2.1.4.1 (2005aa) that the features, 

events, and processes list for site recommendation was based on a Nuclear Energy Agency 

international database of FEPs (Nuclear Energy Agency, 1997ab). Using SNL Appendix G 

(2008ab), a cross comparison of the FEPs lists for site recommendation, and the SAR, the NRC 

staff noted that the SAR FEPs list encompasses the FEPs list for site recommendation. Using 

SNL Appendix F (2008ab), tables that map the SAR FEPs into the Organisation for Economic 

Co-operation and Development FEPs, and vice versa, the NRC staff confirmed that the SAR list 

of FEPs encompasses the Organisation for Economic Co-operation and Development features, 

events, and processes. 

Summary of NRC Staff’s Review for Identification of a List of Feature, Events, 

and Processes 

Because the SAR FEPs list encompasses generic comprehensive lists of internationally 

approved FEPs (Nuclear Energy Agency, 2006aa, 2000aa, 1997ab) and is consistent with the 

site characterization data and the SAR design features, DOE’s list of FEPs is reasonable. 
2-6 

DOE’s complete listing of FEPs considered (SAR Table 2.2-5) includes FEPs that address 

potentially disruptive events related to igneous activity (e.g., FEP 1.2.04.03.0A and 

FEP 1.2.04.07.0A); seismic shaking (e.g., FEP 1.2.03.02.0A and FEP 1.2.03.02.0B); 

tectonic evolution (e.g., FEP 1.2.01.01.0A); climatic change (e.g., FEP 1.3.01.00.0A and 

FEP 1.3.07.02.0B); and criticality (e.g., FEP 2.1.14.16.0A and FEP 2.1.14.17.0A). 

On the basis of the information in SAR Section 2.2.1.1.1 and references cited therein, the NRC 

staff notes that 

 SAR Table 2.2-5 contains a complete list of features, events, and processes related to 

the geologic setting or the degradation, deterioration, or alteration of engineered barriers 

(including those processes that would affect the performance of natural barriers) that 

have the potential to influence repository performance 

 The list of features, events, and processes in SAR Table 2.2-5 is consistent with the site 

characterization data 

 The feature, event, and process list includes, but is not limited to, potentially disruptive 

events related to igneous activity, seismic shaking, tectonic evolution, climatic change, 

and criticality 

Therefore, DOE’s identification of a list of features, events, and processes is reasonable. 

2.2.1.2.1.3.2 Screening of the List of Features, Events, and Processes

Screening of the list of FEPs is aimed at identifying FEPs that should be evaluated in detail in 

the performance assessment due to their clear potential to influence repository performance. 

The technical evaluation of the screening of the list of FEPs follows the methodology 

established in YMRP Section 2.2.1.2.1.2, p. 2.2-7, as supplemented by additional guidance for 

the period beyond 10,000 years after permanent closure (NRC, 2009ab). 

DOE summarized the screening of FEPs in SAR Section 2.2.1.2. SAR Table 2.2-5 

summarized the screening decision (to include or exclude) for each FEP and the justification 

for exclusion. SAR Table 2.2-5 cited other SAR tables summarizing the technical basis for 

including the FEPs and also cited SNL (2008ab) as the document that detailed the technical 

basis for excluding FEPs. 

In SAR Section 2.1.2.2, DOE described that the regulations call for inclusion of certain 

FEPs in the performance assessment evaluations that are conducted to compute doses 

for the period after 10,000 years after disposal, but within the period of geologic stability. 

DOE described that FEPs associated with these criteria were evaluated for inclusion in 

the appropriate performance assessments. DOE stated that no changes to screening 

decisions were necessary to address the inclusion of FEPs specified by the proposed 

10 CFR 63.342(c)(1), (2), and (3). The DOE restated this issue in two parts. First, DOE 

stated that the FEPs specified by regulation to be included in the performance assessments for 

the period after the first 10,000 years following disposal, but within the period of geologic 

stability, are also included in the performance assessments for the 10,000 years after disposal. 

Second, DOE stated that FEPs that are excluded from the performance assessments for the 

10,000 years after disposal remain excluded in the performance assessments for the period 

after the first 10,000 years after disposal, but within the period of geologic stability. In SAR 
2-7 

Section 2.1.2.2 DOE identified the specific included FEPs that are consistent with the 

proposed 10 CFR 63.342(c)(1)(i) regulatory criteria and described that excluded FEP 

1.2.03.02.0B, Seismic Induced Rockfall Damages Engineered Barrier System (EBS) 

Components, was also evaluated with respect to proposed 10 CFR 63.342(c)(1)(i). 

DOE Enclosure 6 (2009cb) identified that the excluded FEP 1.2.10.01.0A, Hydrologic 

Response to Seismic Activity, is consistent with proposed 10 CFR 63.342(c)(1)(i). In SAR 

Section 2.1.2.2 DOE also identified the included FEPs that are consistent with proposed 

10 CFR 63.342(c)(1)(ii), 10 CFR 63.342(c)(2), and 10 CFR 63.342(c)(3). 

DOE Enclosure 6 (2009cb) performed a detailed comparison between the proposed 

10 CFR Part 63 (NRC, 2005af) rule and the final 10 CFR Part 63 rule that became effective on 

April 13, 2009, and identified material changes in the final rule and how those changes may 

materially impact information in the SAR and supporting documents. DOE Enclosure 6 

(2009cb) specifically discussed (i) arithmetic mean of projected doses; (ii) water table rise due 

to seismic activity; (iii) changes to the range of deep percolation rates; and (v) dosimetry. In 

addition, DOE Enclosure 6, Section 1.6 (2009cb) evaluated the potential impacts of all changes 

identified in DOE Enclosure 6, Appendix, Table A-1 (2009cb) and concluded that none of the 

conclusions in the SAR require modification as a result of the final rule. 

With respect to procedural safety controls and design configuration controls, DOE stated that 

SAR Table 2.2-3 identified FEPs that relate to parameters requiring procedural safety controls 

or design configuration control to ensure that the performance assessment analysis basis is 

met. SAR Table 1.9-9 summarized the parameters requiring such controls. DOE noted that the 

repository design (as defined in the included FEP 1.1.07.00.0A, Repository Design, and the 

controlled design parameters in SAR Table 2.2-3) was used to define the initial state 

or boundary conditions in the models and the analyses that are abstracted in the postclosure 

performance assessment. DOE also stated in SAR Section 2.2.1.2 that controlled parameters 

and the repository design were used as a basis for describing other FEPs and as a basis for 

screening decisions of included and excluded FEPs. According to DOE, SAR Table 1.9-9 

presented design control parameters that describe the bases for the repository design. 

The NRC staff initially noted that a number of FEPs lacked adequate technical basis to support 

the DOE’s exclusion conclusion. DOE supplemented (DOE, 2009ab,ae,af,ah–aj,al,bv–bz, 

ca–ci,co,cq,gp,gq, 2010ad,ah) the information in SNL (2008ab) to respond to the NRC staff’s 

requests for additional information. The NRC staff reviewed the information in SNL (2008ab), 

the supporting analyses referenced therein, and the DOE responses to the requests for 

additional information (DOE, 2009ab,ae,af,ah–aj,al,bo,bv–bz,ca–cj,co,cq,gp,gq, 2010ad,ah). 

NRC Staff’s Evaluation 

The NRC staff used the YMRP, as supplemented by additional guidance for the period beyond 

10,000 years after permanent closure (NRC, 2009ab), to evaluate whether the screening of the 

list of FEPs is appropriate. The YMRP provides guidance on (i) whether DOE identified all 

FEPs that have been excluded, (ii) whether DOE provided justification for exclusion of those 

FEPs, and (iii) whether DOE provided adequate technical basis for exclusion of those FEPs. 

The NRC staff noted that DOE has identified all FEPs related to either the geologic setting 

or to the degradation, deterioration, or alteration of engineered barriers (including those 

processes that would affect the performance of natural barriers) that have been excluded. 

SAR Table 2.2-5 listed all of the FEPs DOE considered, and it identified the excluded FEPs. 

With regard to point (ii) , the NRC staff determines that DOE’s criteria for exclusion on the basis 
2-8 

of low probability, low consequence, or by regulation is reasonable because these criteria are 

consistent with criteria for scenario analysis discussed in TER Section 2.2.1.2.1.2. DOE clearly 

stated in SAR Table 2.2-5 the criterion it applied for exclusion of each FEP; thus, DOE has 

provided reasonable justification for the excluded FEPs (on the basis of low probability, low 

consequence, or by regulation). 

With regard to point (iii), the NRC staff’s evaluation of the technical basis for the exclusion of 

FEPs is provided in the remainder of this section. First, the NRC staff evaluates DOE’s 

information on screening of FEPs for the period after 10,000 years after disposal, but within the 

period of geologic stability. Then, the NRC staff evaluates DOE’s information on screening of 

FEPs for the 10,000 years after disposal. 

The NRC staff reviewed DOE’s information in SAR Section 2.1.2.2 on screening of FEPs for 

the period after 10,000 years after disposal, but within the period of geologic stability using 

the guidance for the period beyond 10,000 years after permanent closure (NRC, 2009ab). 

The NRC staff notes the following DOE statements are reasonable: (i) FEPs included in the 

performance assessments for the period after the first 10,000 years following disposal, but 

within the period of geologic stability, are also included in the performance assessments for 

the 10,000 years after disposal and (ii) FEPs that are excluded from the performance 

assessments for the 10,000 years after disposal remain excluded in the performance 

assessments for the period after the first 10,000 years after disposal, but within the period of 

geologic stability. The NRC staff evaluates the adequacy of DOE’s analyses of included FEPs 

in TER Sections 2.2.1.3.1–2.2.1.3.14 and Sections 2.2.1.4.1–2.2.1.4.3. The NRC staff 

evaluates DOE’s technical basis for the exclusion of FEPs in this section. 

The NRC staff evaluated all of the descriptions, screening decisions, and screening 

justifications (i.e., DOE’s technical basis) of the FEPs DOE classified as excluded (Leslie, 

2010aa; DOE excluded a total of 222 FEPs). Only those FEPs that the NRC staff identified as 

requiring additional information or clarification are specifically discussed in this section, and as 

described next, DOE’s clarifications were sufficient for the NRC staff to complete its evaluation. 

The discussed FEPs correspond to approximately 10 percent of the total number of excluded 

FEPs and are summarized later in this section under individual FEP headings (with the 

exception of the criticality FEPs that are all reviewed under the criticality FEPs heading). For 

each FEP discussed under an individual FEP heading, and for the group of criticality FEPs, the 

NRC staff’s evaluation includes a summary of DOE’s information followed by the NRC staff’s 

review of the technical basis for the exclusion of the individual FEP (or FEPs, in the case of the 

criticality FEPs). Additional subheadings, where needed to enhance the readability, are used to 

identify the NRC staff’s review or to identify the review of specific technical aspects associated 

with the individual FEP (or FEPs, in the case of the criticality FEPs). 

On the basis of the NRC staff’s review (Leslie, 2010aa), the NRC staff notes that the excluded 

FEPs that are not discussed in this section (i.e., the remaining 90 percent of the excluded 

FEPs) were adequately defined and that adequate technical bases were provided 

to support DOE’s exclusion decision. The following are examples of FEPs that are not 

discussed in this section and that were excluded by regulation, probability, and consequence. 

Exclusion by Regulation 

For each FEP that DOE excluded by regulation and that is not explicitly addressed in TER 

Section 2.2.1.2.1.3.2, the NRC staff checked to see whether DOE provided an appropriate 

regulatory citation to exclude the FEP. The NRC staff reviewed the adequacy of the technical 
2-9 

basis (SNL, 2008ab) for each FEP DOE excluded by regulation to ensure the technical basis 

was consistent with the cited regulation. The NRC staff notes that DOE provided adequate 

technical bases for these FEPs. 

Exclusion by Probability 

For each FEP that was excluded by probability and that is not explicitly addressed in TER 

Section 2.2.1.2.1.3.2, the NRC staff reviewed the adequacy of technical basis (SNL, 2008ab) 

provided for each FEP. DOE’s technical basis for excluding low probability FEPs by showing 

that the annual probability is less than 10−8

 in the first 10,000 years is reasonable. For example, 

for FEP 1.5.01.01.0A, Meteorite Impact, DOE provided a quantitative analysis of meteorite 

impact probability and used crater information, repository site information, and a design 

parameter to demonstrate that the low probability criterion would be met. DOE’s basis for 

FEP 1.5.01.01.0A is reasonable because DOE used impact rates that are consistent with data 

from available literature. DOE overestimated the impact footprint of the repository, and DOE’s 

analysis was consistent with repository site characteristics and the repository’s design. 

Similarly, for the other FEPs excluded on the basis of low probability, the NRC staff notes that 

DOE provided reasonable technical bases. 

Exclusion by Low Consequence 

For each FEP that was excluded by low consequence and that is not explicitly addressed 

in TER Section 2.2.1.2.1.3.2, the NRC staff reviewed the adequacy of technical basis 

(SNL, 2008ab) provided for each FEP. For those FEPs DOE excluded on the basis of low 

consequence, DOE provided suitable technical basis by showing that omission of the FEP does 

not significantly change the magnitude and time of the resulting radiological exposures to 

reasonably maximally exposed individual, or radionuclide releases to the accessible 

environment. For example, DOE excluded FEP 1.1.02.00.0A, Chemical Effects of Excavation 

and Construction in the Engineered Barrier System, on the basis that (i) relevant construction 

materials are design-controlled parameters or subject to controls and (ii) analyses show 

negligible impact from engineered materials on the groundwater chemistry (SNL, 2008ab). 

DOE’s technical basis for excluding FEP 1.1.02.00.0A is reasonable because DOE described 

the analyses which evaluated the effects and identified the controls that would be imposed 

(e.g., constraints will be imposed on the administrative control of tracers, fluids, and materials; 

construction materials; and committed materials). Similarly, for the other FEPs excluded on the 

basis of low consequence, the NRC staff notes that DOE provided reasonable technical bases. 

The NRC staff notes that DOE’s use of repository design and controlled parameters to define 

FEPs is reasonable; this use is also reasonable as a technical basis for screening. The NRC 

staff notes DOE’s proposed approach to control parameters identified in SAR Table 1.9-9, 

through use of management systems is an adequate basis for the repository design considered 

in the development of screening justifications for FEPs. The NRC staff notes that it is 

appropriate to use the repository design to define the scope of FEPs as well as to define initial 

states or boundary conditions of systems analyzed in the performance assessment. In addition, 

SAR Table 2.2-3 is an adequate mechanism to track interdependencies and identify FEPs with 

screening technical bases that would need reevaluation if some parameters were to depart from 

initial design considerations. The NRC staff notes that the design information and the design 

assumptions are appropriate. 
2-10 

FEP 1.1.01.01.0B, Influx Through Holes Drilled in Drift Wall or Crown 

DOE excluded Influx Through Holes Drilled in Drift Wall or Crown on the basis of low 

consequence SNL (2008ab) and supplemented its technical basis for exclusion in DOE 

Enclosures 2 and 7 (2009cb). As defined by DOE, FEP 1.1.01.01.0B addresses the potential of 

openings (or holes) that may be drilled through the drift walls or crown to promote flow or 

seepage into the drifts and onto the waste packages. These holes may be drilled for a variety of 

reasons including, but not limited to, rock bolt and ground support, monitoring and testing, or 

construction-related activities. For boreholes, FEPs 1.1.01.01.0B and 2.1.06.04.0A, Flow 

Through Rock Reinforcement Materials in the Engineered Barrier System, according to DOE’s 

definitions, cover similar processes and features because open space will be present in 

boreholes regardless of whether rock bolts degrade. 

DOE stated in SNL (2008ab) that boreholes into the walls of emplacement drifts will be drilled 

for ungrouted rock bolts and ground support and identified in SAR Table 2.2-3 that Control 

Parameters 01-15 and 01-16 apply to FEPs 1.1.01.01.0B and 2.1.06.04.0A. Using a modified 

version of the seepage model used for the performance assessment in BSC Sections 6.5 and 

6.6.4 (2004be), DOE examined the potential for liquid water flowing into open rock bolt 

boreholes that extend vertically upwards from the drift crown. DOE concluded, supported by 

numerical simulations, that boreholes have only a minor effect on seepage, increasing the 

predicted seepage rates by less than 2 percent compared to seepage simulations without rock 

bolts. DOE based this result on the following considerations and assumptions: (i) an open 

borehole without grout acts as a capillary barrier to unsaturated flow; (ii) the cross-sectional 

area of the rock bolt borehole, onto which flow may be incident, is small; and (iii) water that may 

have seeped into the borehole can imbibe back into the rock matrix elsewhere along the 

borehole length. On the basis of this analysis, DOE concluded that the presence of boreholes 

drilled in the drift wall or crown would not have a significant effect on seepage into drifts, and 

excluded the FEP Influx Through Holes Drilled in Drift Wall or Crown from the performance 

assessment model on the basis of low consequence. 

NRC Staff’s Review 

The NRC staff assessed the seepage modeling evaluation for boreholes and considered 

observations from ambient and thermally perturbed field tests. Given the widespread presence 

of boreholes in the drifts, the NRC staff performed a more detailed evaluation of the exclusion 

basis for FEP 1.1.01.01.0B. The NRC staff estimated that there will be approximately 26 rock 

bolts per waste package in the circumferential extent of the drift wall used to estimate seepage. 

This number was derived from the repository design whereby rock bolts will be installed with 

circumferential and axial spacing of 1.25 m [4.1 ft] in a 240° arc around the drift periphery and 

above the invert structure (SAR Section 1.3.4.4.1). 

DOE’s previously listed considerations and assumptions (i), (ii), and (iii) [(i) an open borehole 

without grout acts as a capillary barrier to unsaturated flow; (ii) the cross-sectional area of the 

rock bolt borehole, onto which flow may be incident, is small; and (iii) water that may have 

seeped into the borehole can imbibe back into the rock matrix elsewhere along the borehole 

length] are reasonable bases for supporting the exclusion of the feature, event, and process. 

The NRC staff evaluated the adequacy of the technical bases supporting the considerations and 

assumptions that DOE provided in a discussion of the results from the seepage modeling 

exercise. The NRC staff analyzed the consistency of observations from field tests and site 

characterization with results from the DOE seepage modeling exercise for boreholes. First, 

observations of temperature fluctuations from the heater tests may be indicative of water flowing 
2-11 

in boreholes at host-rock temperatures above boiling (Green, et al., 2008aa). Second, posttest 

visual observations indicate water entered the drifts and thus did not absorb back into the wall of 

the borehole, though timing and temperature at which this occurred is not known (Green, et al., 

2008aa). Third, secondary mineralization in large aperture (open) fractures that DOE attributed 

to percolating water under ambient conditions suggests capillary diversion may not keep water 

from entering boreholes. Fourth, observations of liquid water in the drift during the passive test 

may be explained and modeled as vapor flux through fractures from within the host rock and 

condensation in cooler rock spots (Salve and Kneafsey, 2005aa) rather than by a 

capillarity-based seepage model of liquid water dripping into drifts. 

In response to an NRC staff’s request for additional information, DOE Enclosures 2 and 7 

(2009cb) supplemented the technical basis and provided additional information on the 

relationship of field observations to flow in boreholes and seepage into drifts. DOE framed 

the supplemental information in terms of effects during thermal and ambient periods and 

relied on a total-system performance perspective; in particular, on the drip shield seepage 

barrier function. For the thermal period, DOE Enclosure 7 (2009cb) pointed out the drip 

shield function of diverting water that has entered the drift. According to DOE, the drip 

shields are expected to divert water during the thermal period and are expected to fail by 

general corrosion and cease to be a barrier against seepage well after the thermal pulse has 

dissipated and the system has returned to ambient conditions. DOE Enclosure 5 (2009bo) 

referred to supplemental analyses showing that radionuclide releases are relatively insensitive 

to the occurrence of seepage in the event of seismic damage to waste packages under intact 

drip shields. 

DOE also analyzed other cases where the drip shield may fail during the thermal period 

(e.g., early failure, seismic fault displacement, and seismic ground motion modeling cases) and 

concluded that in none of those cases would borehole effects on seepage significantly alter the 

dose estimates. For the early failure case, DOE referred to the low contribution of this case to 

the total mean dose and stated that changes in reflux would marginally affect the dose. For the 

fault displacement modeling case, DOE stated that full collapse of the drift is generally 

associated with fault displacement, and thus, thermal reflux in open boreholes has a negligible 

effect on the mean annual dose from seismic fault displacement, as described in DOE 

Enclosure 7 (2009cb). In the seismic modeling case, DOE described that the drip shield would 

fail only for large magnitude seismic events, which would be accompanied by large rockfall and 

borehole collapse. Therefore, thermal reflux in such boreholes would have a negligible effect on 

dose estimates, as detailed in DOE Enclosure 7 (2009cb). The exclusion of FEP 1.1.01.01.0B 

during the thermal period is reasonable because the drip shield protects the waste package 

against seepage and because of the relatively weak dependence of the mean dose on seepage 

in DOE’s performance assessment. 

The NRC staff’s evaluation for the ambient period focused on the potential increase in water 

entering the drift, either by seepage from boreholes or by vapor flux through boreholes. For 

seepage from boreholes, FEP 1.1.01.01.0B (SNL, 2008ab) cited sensitivity analyses suggesting 

a 2 percent increase in seepage compared to domains without boreholes. This difference would 

readily fall in the range of uncertainty incorporated in the seepage results for the performance 

assessment. Furthermore, boreholes are not a factor in the seismic ground motion and igneous 

intrusion modeling cases, which are the two largest contributors to dose. According to the DOE 

model, seismic events would cause significant collapse of the host rock above drifts (e.g., SAR 

Figure 2.1-14; DOE, 2008ab) by the time drip shields are expected to fail by general corrosion 

(e.g., SAR Figure 2.1-11; DOE, 2008ab), thus eliminating any potential effect of boreholes on 

seepage. In addition, the DOE abstraction for the igneous intrusion modeling case eliminates 
2-12 

the seepage barrier capability of drifts. For the vapor flux through boreholes, DOE Enclosure 2 

(2009cb) described that the magnitude of the vapor flux asymptotically decreases from the latter 

stages of the thermal period to the ambient period. Consistent with DOE’s technical basis is the 

suggestion that the water observed in the drift of the passive test would coincide with early 

entrance of vapor into the drifts. This flux will decrease with time as the entire system (drift and 

rock) moves closer to hydrological equilibrium. Using its condensation model, DOE stated that 

the magnitude of the condensation flux estimated for later times (after the thermal period) is 

much less than the estimated seepage flux derived from the seepage model. To provide 

confidence in the condensation flux estimate for early times, DOE stated that a conservative 

assumption of relative humidity at the drift wall of 100 percent was used in the condensation 

model. It is reasonable to use this assumption to estimate condensation on the basis of the 

condensation model review in TER Section 2.2.1.3.6.3.5, where the NRC staff noted that the 

condensation model was reasonable for its intended purpose within the context of the 

performance assessment model. DOE’s technical basis for excluding FEP 1.1.01.01.0B from 

the performance assessment is reasonable on the basis of low consequence. 

FEP 1.1.03.01.0A, Error in Waste Emplacement 

DOE excluded Error in Waste Emplacement on the basis of low consequence (DOE, 2009av; 

SNL, 2008ab) and supplemented its technical basis for exclusion in DOE Enclosure 1 (2009af) 

and DOE Enclosure 2(2009cq). FEP 1.1.03.01.0A, according to DOE, refers to deviations from 

the design or errors in waste emplacement that could affect long-term performance of the 

repository. DOE identified two types of waste emplacement errors: the first concerns spacing 

of waste packages and the second concerns emplacement of a waste package on a fault. DOE 

described controls that will be carried out to restrict by detection, evaluation, and mitigation the 

probability of both types of waste emplacement errors. These controls include controlled 

parameters and management controls. DOE also assessed the potential consequences of 

undetected and unmitigated waste emplacement errors in DOE Enclosure 1 (2009af) and DOE 

Enclosure 2 (2009cq). DOE assessed the probability for waste package misplacement and 

violation of the thermal limits for the repository. DOE estimated the mean number of misplaced 

waste packages to be less than one. DOE compared the consequences of waste emplacement 

errors to the consequences of the waste package early failure modeling case and the seismic 

fault displacement modeling case; DOE included both of these in the performance assessment. 

NRC Staff’s Review 

The NRC staff reviewed the screening justification and the technical basis for excluding 

FEP 1.1.03.01.0A. DOE provided an exclusion justification of low consequence in 

DOE Enclosure 1 (2009af) and DOE Enclosure 2 (2009cq). The NRC staff used its knowledge 

of the proposed repository operations and repository performance assessments to assess 

potential consequences. On the basis of DOE’s description of the feature, event, and process 

and the NRC staff’s knowledge, both types of waste emplacement errors that DOE identified are 

sufficient to evaluate potential consequences on repository performance from waste 

emplacement errors. The NRC staff assessed whether the controls DOE identified in DOE 

Enclosure 1, Tables 1 and 2 (2009af) and DOE Enclosure 2 (2009cq) were adequate to limit 

errors in emplacing the waste. The NRC staff notes DOE’s proposed controls are appropriate. 

DOE’s assessment of the probabilities of undetected and unmitigated waste emplacement 

errors is reasonable because the rates are consistent with error rates for comparable controlled 

activities reviewed in TER Section 2.2.1.2.2.3. DOE identified that both the probability and 

consequence of waste emplacement error is less than that assessed in the waste package early 

failure model case. Both the low probability and the comparison to the early failure case to 
2-13 

assess waste emplacement spacing errors are reasonable because in the early failure case, 

DOE assumed damaged waste packages, while waste emplacement errors do not necessarily 

imply the presence of a damaged waste package leading to radionuclide release. On the basis 

of the low consequences associated with the seismic fault displacement modeling case (SAR 

Section 2.4.2.2.1.2.2.2) and the NRC staff’s independent assessment of the risk from seismic 

fault displacement (Waiting, et al., 2003aa), DOE’s conclusion that waste emplacement on a 

fault is of low consequence is reasonable. Therefore, the justification for exclusion is 

reasonable. The NRC staff notes it is reasonable to exclude FEP 1.1.03.01.0A, Error in Waste 

Emplacement, on the basis of low consequence. 

FEP 1.2.04.07.0B, Ash Redistribution in Groundwater 

DOE excluded Ash Redistribution in Groundwater on the basis of low consequence. According 

to DOE’s definition of FEP 1.2.04.07.0B, during a volcanic eruption, magma may interact with 

waste packages, resulting in erupted deposits of volcanic ash contaminated with radionuclides. 

DOE limited FEP 1.2.04.07.0B to the leaching of radionuclides from the ash and their 

subsequent transport in groundwater through the subsurface to the reference boundary or 

location of the RMEI. DOE considered other processes, such as ash remobilization by wind, 

in separate FEPs. 

The DOE volcanic eruption model considers the mass and types of waste impacted by erupted 

magma (a maximum of seven damaged waste packages), the fraction of waste-containing 

magma that is incorporated into a tephra plume, and the fraction of the tephra plume that is 

deposited near the eruptive vent (i.e., in or near the repository footprint) (SNL, 2008ag). In 

contrast, the DOE igneous intrusion model assumes that (i) all waste packages in the repository 

are compromised by an igneous intrusion and (ii) the subsequent release of waste is not 

reduced by the amount that could be transported to the surface in an accompanying eruption 

(SNL, 2007ab). DOE reasoned that because eruptive events are always associated with 

intrusive events, the potential dose consequences from radionuclides leached into groundwater 

from volcanic ash are small compared with the consequences of exposing the same inventory of 

radionuclides to seepage due to igneous intrusion (SNL, 2008ab). However, for the modeled 

fraction of volcanic ash that is deposited near the accessible environment boundary, the 

groundwater transport path is short compared to the transport path for radionuclides released 

from the repository. Short transport pathways to the accessible environment boundary have 

potential dose consequences for short-lived, high-activity radionuclides such as Cs-137, Sr-90, 

Am-241, and Pu-238, which have half-lives on the order of decades or hundreds of years. The 

short-lived radionuclides are important contributors to dose at early times in the volcanic 

eruption modeling case (e.g., SAR Figure 2.4-32). 

In DOE Enclosure 1 (2009ab), DOE supplemented its technical basis for exclusion 

and assessed the relative importance of leaching and groundwater transport of contaminated 

ash deposited near the accessible environment. DOE’s supporting calculation addressed 

differences in travel times depending on where the ash was deposited within the drainage basin 

of Fortymile Wash (i.e., very short flow paths for leaching of ash deposited near the accessible 

environment boundary and longer flow paths for ash deposited upstream). The transport 

calculation included the effects of radioactive decay and retardation of radionuclides and 

indicated that leaching of contaminated ash would not contribute significantly to mean annual 

dose compared to the volcanic eruption modeling case, as detailed in DOE Enclosure 1, 

Figure 1 (2009ab). 
2-14 

NRC Staff’s Review 

On the basis of the NRC staff’s comparison of the conditions and assumptions in DOE’s 

volcanic eruption modeling case and igneous intrusion modeling case, the NRC staff notes, for 

volcanic ash deposited near the eruptive vent, that DOE’s technical basis for exclusion of FEP 

1.2.04.07.0B in SNL (2008ab) is reasonable. Potential dose consequences near the eruptive 

vent are bounded by the dose consequences of igneous intrusion because the groundwater 

transport pathways are similar for both examples. For leaching and transport from ash 

deposited in Fortymile Wash, as described in DOE Enclosure 1 (2009ab), the NRC staff’s 

evaluation focused on short-lived radionuclides because of their high radioactivity levels that 

could dominate dose estimates for this fast pathway scenario. The NRC staff contrasted DOE’s 

computations for this fast pathway scenario with the contribution to dose from the same 

short-lived radionuclides in DOE’s volcanic eruption modeling case (e.g., SAR Figure 2.4-32). 

The NRC staff noted that DOE provided the technical basis for excluding FEP 1.2.04.07.0B with 

respect to leaching and transport in short groundwater flow pathways because the supporting 

calculations showed that the transport of the short-lived radionuclides was delayed sufficiently, 

even in the relatively short groundwater transport pathways in Fortymile Wash, to allow 

radioactive decay to significantly diminish their potential contribution to dose. The models DOE 

used for those supporting calculations are consistent with those models that the NRC 

staff reviewed for water flow paths and radionuclide transport in TER Sections 2.2.1.3.8 and 

2.2.1.3.9, which the NRC staff notes are adequate in the context of DOE’s performance 

assessments. Therefore, the model used in the supporting calculations to evaluate potential 

fast pathways is reasonable. DOE’s technical basis for excluding FEP 1.2.04.07.0B on the 

basis of low consequence is reasonable. 

FEP 1.2.07.01.0A, Erosion/Denudation 

DOE excluded FEP 1.2.07.01.0A, Erosion/Denudation, from the performance assessment on 

the basis of low consequence. Erosion involves the transport of surficial material away from the 

site by mechanisms including glacial, fluvial, eolian, and chemical processes. As part of FEP 

1.2.07.01.0A, DOE also considered processes such as weathering, mass wastage processes 

(e.g., landslides), and local uplift (SNL, 2008ab). 

DOE cited site characterization studies concluding erosion ranging from 0.4 to 2.7 cm [0.16 to 

1.06 in] in 10,000 years for bedrock outcrops and 0.2 to 6 cm [0.08 to 2.4 in] in 10,000 years for 

unconsolidated material in hillslopes. DOE concluded that the maximum expected erosion of 

6 cm [2.4 in] in 10,000 years is consistent with existing surface irregularities and that erosion 

would be negligible compared with the minimum distance of 200 m [656.2 ft] from the ground 

surface to the repository emplacement areas (SNL, 2008ab). DOE considered the effect of 

erosion on the extent of net infiltration and concluded it was negligible. Further, DOE described 

that the homogenizing action of the Paintbrush nonwelded hydrogeologic unit would buffer any 

localized change in net infiltration (SNL, 2008ab). DOE stated that bedrock weathering could 

increase the soil thickness and decrease the net infiltration. Thus, disregarding weathering is a 

conservative approach. DOE referred to site characterization studies to conclude that 

processes such as landslides and debris flows do not play a significant role in the erosional 

regime at Yucca Mountain. 

DOE stated that climatic conditions strongly influence erosional patterns, with 

deposition occurring during wetter periods and erosion occurring during drier periods. 

Because the 10,000-year period after closure is dominated by the glacial-transition climate 

(8,000 years of wetter climate), deposition is expected to be the dominant geomorphic process 
2-15 

for the 10,000-year period after closure. DOE stated that deposition leads to soil buildup, and 

thus, disregarding deposition is conservative. Another process affecting erosion is uplift, and 

DOE stated that local rates of uplift are low, on the order of 0.01 mm/yr [3.94 × 10−4

 in/yr]. 

NRC Staff’s Review 

On the basis of the analysis DOE developed, the technical basis for excluding FEP 

1.2.07.01.0A, Erosion/Denudation, is reasonable. The erosion rates DOE quoted are consistent 

with site description data at BSC (2004bi) and are expected to cause negligible amounts of 

erosion in 10,000 years. DOE’s conclusion that neglecting the effects of erosion in the 

performance assessment would not significantly affect the timing or magnitude of radionuclide 

releases into the accessible environment is reasonable. Therefore, the exclusion of FEP 

1.2.07.01.0A, Erosion/Denudation, on the basis of low consequence is reasonable. 

FEP 1.2.10.01.0A, Hydrologic Response to Seismic Activity 

DOE excluded Hydrologic Response to Seismic Activity on the basis of low consequence 

(SNL, 2008ab), and the technical basis was supplemented as described in DOE Enclosure 19, 

(2009ab), DOE Enclosures 1–6 (2009by), DOE Enclosure 1 (DOE, 2009bz), DOE 

Enclosures 1–2 (2009ca), and DOE Enclosure 6 (2009cb). In supplementing the technical 

basis for this feature, event, and process, DOE also evaluated water table rise due to seismic 

activity beyond the 10,000-year postdisposal period through the period of geologic stability and 

included information on potential permanent changes in hydrologic properties in DOE 

Enclosure 6 (2009cb). According to DOE’s definition of FEP 1.2.10.01.0A, seismic activity 

associated with fault movement may enhance existing or create new flow pathways or 

connections and barriers between stratigraphic units, or it may change the stress (and therefore 

fluid pressure) within the rock. These responses have the potential to change groundwater flow 

directions, water level, water chemistry, and temperature. Seismically induced changes to the 

local stress fields can cause a transient change in the water table elevations and lead to seismic 

pumping—a phenomenon DOE defined as the temporary change in water table elevation 

resulting from fault movement and the opening and closing of fractures during an earthquake. 

The low consequence screening decision is based on DOE’s conclusion that seismic events will 

result in relatively minor changes to the Yucca Mountain hydrologic system—changes that have 

no impact on repository performance. DOE’s rationale is based on implicit assumptions of how 

the repository will respond to seismic loads typical for relatively large-magnitude western U.S. 

earthquakes, observational evidence from recent earthquakes, and modeling results used to 

support the National Research Council study (National Research Council, 1992aa) on the 

effects of earthquakes on the water table at Yucca Mountain. 

In SNL (2008ab), DOE cited modeling investigations that have been conducted to estimate 

the hydrologic response (i.e., change in water table elevations), given predicted fault 

displacements in National Research Council Chapter 5 (1992aa). As described in SNL 

(2008ab) the National Research Council study estimated, using two fault displacement 

modeling approaches (i.e., a dislocation approach and a “changes in the regional stress” 

approach), that the maximum seismically induced water table rise over a 10,000-year period 

would be 17 m [56 ft] for the dislocation approach and 50 m [160 ft] for the regional stress 

approach. In addition, SNL (2008ab) described that the hydrologic effects of three seismic 

events in 1992 that were observed in groundwater monitoring wells at Yucca Mountain provide 

estimates of water-level fluctuations occurring in response to earthquakes. DOE examined the 

effects of the Landers–Big Bear–Little Skull Mountain earthquake sequence that occurred 
2-16 

June 28–29, 1992, and indicated that the water table rise observed at several Yucca 

Mountain vicinity monitoring wells ranged from 0.2 to 0.9 m [0.7 to 3 ft]. On the basis of the 

earthquake-caused water table change data and analyses in the National Research Council 

Study (1992aa), DOE concluded the maximum change will be no more than a 50-m [160-ft] 

water table rise beneath the repository. 

SNL (2008ab) also cited Gauthier, et al., pp. 163–164 (1996aa), who analyzed the potential 

effects of seismic activity resulting from three fault displacement types (normal, listric, and 

strike-slip) with 1-m [0.3-ft] displacement and 30-km [19-mi] rupture length. Gauthier, et al. 

(1996aa) concluded that a strike-slip seismic event would cause a water table rise of 50 m 

[160 ft] within 1 hour and would return to steady-state conditions within 6 months. Other types 

and magnitudes of displacement were shown to cause smaller water table rises with similar 

transient durations. 

DOE revised the rationale in SNL (2008ab) for excluding FEP 1.2.10.01.0A, Hydrologic 

Response to Seismic Activity, in supplemental documents DOE Enclosure 19 (2009ab), DOE 

Enclosures 1–6 (2009by), DOE Enclosure 1 (DOE, 2009bz), DOE Enclosures 1–2 (2009ca), 

and DOE Enclosure 6 (2009cb). First, DOE drew a distinction between two modeling types it 

used to evaluate water table rise from seismic activity in the Yucca Mountain area: the regional 

stress change model and the dislocation model. In making this distinction, DOE emphasized 

the bounding nature of the regional stress change model; this model gave high values of 

predicted water table rise (higher than the dislocation model) that should be regarded as 

representative of the upper limits (bounds) of potential water table rise. DOE attributed these 

high estimates of seismically induced water table rise to a series of simplifying assumptions in 

the model. Using data from several studies since 1992, DOE cited evidence to suggest that the 

dislocation model more realistically represents the actual magnitude of seismically induced 

water table rise. DOE concluded that the water table rise values of the regional stress change 

model are overestimates of the seismically induced water table rise at Yucca Mountain. 

Using the (bounding) regional stress change model and results from the Probabilistic Seismic 

Hazard Assessment (PSHA), DOE performed calculations to evaluate the potential of local 

(to Yucca Mountain) faults as sources for future water table rise at Yucca Mountain. Using the 

likely seismic characteristics of faults as given in the PSHA, DOE generated scenarios to 

calculate the values of maximum water table rise for each fault. Of 3,150 calculated scenarios, 

13 generated water table rise exceeding 175 m [574 ft]. DOE then calculated the probabilities 

that such events will occur using the PSHA hazard probabilities. Although some of the 

probabilities are greater than the 10−8

 per year threshold, DOE contended that because the 

regional stress change model overestimates water table rise, these results support excluding 

this feature, event, and process. Through the use of Probabilistic Fault Displacement Hazard 

Assessment results, DOE estimated that slip events with a 10−8

 per year probability of 

exceedance would produce water table rise values between 30 and 122 m [100 and 400 ft]. 

DOE described that water table rises of these magnitudes are not sufficient to reach the 

proposed repository, even in the case of future wetter climate conditions. DOE estimated that 

the highest water table elevation beneath the repository footprint due to future wetter climate 

conditions would be limited to 850 m [2,790 ft] above sea level. This assumed water table 

elevation is generally consistent with results of a separate analysis by DOE that used the 

saturated zone site-scale flow model. This separate analysis evaluated the potential effects of a 

future wetter climate on saturated zone flow and estimated future climate-induced water table 

elevations as high as 875 m [2,870 ft] above sea level (SNL, 2007ax) beneath northwestern 

portions of the repository. Given that the range of repository drift elevations falls between 1,040 
2-17 

and 1,100 m [3,400 and 3,610 ft] above sea level, DOE concluded that water table depths under 

a future wetter climate would range between 187 and 250 m [620 and 820 ft] below the 

repository floor. Therefore, the additional transient water table rise due to a seismic event 

would remain below the repository drifts. 

SNL (2008ab) addressed, as part of the FEP 1.2.10.01.0A, Hydrologic Response to Seismic 

Activity, long-term changes in water table elevations that could be associated with 

seismic-induced permanent changes in regional permeability. SNL (2008ab) described that 

longer term changes are not expected to result from such permanent changes in stress, 

because the existing data do not show any relationship between the long-term state of stress 

and water table elevation. DOE Enclosure 6 (2009cb) described that the effects of seismic 

activity that could lead to permanent changes in hydrologic properties were evaluated (SNL, 

2008ab) in the screening justifications for excluded FEPs 2.2.06.01.0A (Seismic Activity 

Changes Porosity and Permeability of Rock), 2.2.06.02.0A (Seismic Activity Changes Porosity 

and Permeability of Faults), and 2.2.06.02.0B (Seismic Activity Changes Porosity and 

Permeability of Fractures). These three FEPs were defined to address localized changes in 

porosity and permeability in intact rock, faults, and fractures and were excluded based on 

results from the drift-scale test, the PSHA, modeling and sensitivity studies, and information 

from the National Research Council (1992aa). 

NRC Staff’s Review 

The NRC staff reviewed the information DOE provided in DOE Enclosure 19, (2009ab), 

DOE Enclosures 1–6 (2009by), DOE Enclosure 1 (DOE, 2009bz), DOE Enclosures 1–2 

(2009ca), DOE Enclosure 6 (2009cb), and SNL (2008ab, 2007ax) and notes that exclusion of 

FEP 1.2.10.01.0A is supported by information and analyses in the SAR and supplemental 

documents, and that the technical basis for exclusion is appropriate for the following reasons. 

First, DOE supported the poroelastic model and transient nature of any water-level changes due 

to an earthquake with observations from historical earthquakes, including earthquakes in the 

western United States and earthquakes in the vicinity of Yucca Mountain. DOE’s conclusion 

that there are no permanent changes in water table elevations that could be associated with 

seismic-induced permanent changes in regional permeability is reasonable because existing 

data do not show any relationship between the long-term state of stress and water table 

elevation and because DOE Enclosure 6 (2009cb) identified that based on National Research 

Council (1992aa), earthquake-induced water table rise is expected to be transient. Thus, DOE’s 

conclusion that any potential changes to the water table from earthquakes in the vicinity of 

Yucca Mountain are transient is reasonable. DOE’s conclusion is reasonable that even in the 

least likely case of an earthquake that causes water levels to rise sufficiently to wet the waste 

packages, water levels would return to ambient elevations quickly, within a few years after the 

earthquake. In addition, the NRC staff also notes that the risk (probability-weighted 

consequences) would be negligible, because the likelihood of earthquakes with magnitudes 

large enough to induce changes in the water table is small (less than about 10−6

/yr). Thus, the 

potential impacts on repository performance would be negligible. 

Second, the statement in DOE’s supplemental assessments DOE Enclosure 19, (2009ab), DOE 

Enclosures 1 and 5 (2009by), DOE Enclosure 1 (DOE, 2009bz), and DOE Enclosures 1–2 

(2009ca) that the analyses used to estimate seismically induced water table rise overestimate 

the extent of seismically induced water table rise is reasonable. The modeling and analyses the 

National Research Council Study (1992aa) and Kemeny and Cook (1992aa) relied on are based 

on assumed confined aquifer conditions. The water table below Yucca Mountain in the tuff 
2-18 

aquifer is indicative of unconfined aquifer conditions. As DOE documented in DOE Enclosure 1 

(2009ca), recent observations of changes to water table elevations in unconfined aquifers from 

large earthquakes in Taiwan and Japan were substantially smaller than the changes in the 

hydraulic head of nearby confined aquifers. DOE attributed differences in the reaction between 

confined and unconfined aquifers to the substantially smaller storability of confined aquifers. 

Third, both the National Research Council (1992aa) and the Kemeny and Cook (1992aa) 

analyses relied on a regional stress change model. DOE’s view is reasonable that of the 

two modeling approaches (i.e., the dislocation model and the regional stress change model), 

the regional stress change model overestimates the seismically induced water table rise. 

The NRC staff also notes that DOE’s view that two simplifying assumptions in the regional 

stress change model—uniform stress changes throughout the rock body and uniform changes 

in pore pressures—cause this model to overestimate the seismically induced water table rise 

is reasonable. Because the regional stress change model’s overestimates of seismically 

induced water table rise indicate the water table will remain below the level of waste 

emplacement drifts after an earthquake, even during future wetter climates, the NRC staff notes 

that DOE’s technical basis supports the exclusion of FEP 1.2.10.01.0A from the performance 

assessment model. 

Further, DOE’s screening rationale is also applicable to the period of geologic stability, because 

DOE considered, in general, seismic events with recurrence rates of at least 10−8

/yr, as 

described in DOE Enclosure 6 (2009cb). The NRC staff notes that although DOE did not 

consider the information in National Research Council p. 94 (1995aa) (i.e., “Results indicate 

a probable maximum transient rise on the order of 20 m of less.”) in determining the magnitude 

of water table rise from seismic activity for the beyond the 10,000-year postdisposal period 

through the period of geologic stability, DOE Enclosure 6 (2009cb) did consider results in the 

National Research Council (1992aa) investigations in determining the magnitude of the water 

table rise from seismic activity. Because the analyses used to estimate seismically induced 

water table rise overestimate the extent of seismically induced water table rise, including those 

identified in National Research Council (1992aa), and because DOE’s technical basis supports 

the exclusion of FEP 1.2.10.01.0A from the performance assessment model for the initial 

10,000-year period after repository closure, DOE’s technical basis to exclude FEP 1.2.10.01.0A 

from the performance assessment analysis beyond the 10,000-year postdisposal period through 

the period of geologic stability is reasonable. 

 

FEP 1.4.01.00.0A, Human Influences on Climate 

DOE excluded Human Influences on Climate, due to future human activities, to avoid 

speculative prediction of changes to human behavior. DOE stated that present and past 

human influences on climate (which are within the scope of the included FEP 1.3.01.00.0A, 

Climate Change) are implicitly included in estimates of modern climate used in the performance 

assessment (SNL, 2008ab) and, as such, are evaluated in TER Section 2.2.1.3.5. 

The NRC staff notes that the DOE approach to constrain the scope of the FEP to future 

changes in human activities is reasonable, as well as exclusion of the FEP from the 

performance assessment because speculative prediction of human behavior is avoided. 

FEP 1.4.01.02.0A, Greenhouse Gas Effects 

DOE excluded Greenhouse Gas Effects on climate, due to future human activities, to avoid 

speculative prediction of changes to human behavior. DOE constrained the scope of the 
2-19 

feature, event, and process to future changes in human activities that may influence the 

concentrations of atmospheric gases. Present and past increases in greenhouse gases 

attributed to human activity are within the scope of the included FEP 1.3.01.00.0A, Climate 

Change. Those present and past changes are implicitly included in estimates of modern climate 

used in the performance assessment (SNL, 2008ab) and, as such, are evaluated in TER 

Section 2.2.1.3.5. . 

The NRC staff notes that the DOE approach to constrain the scope of the FEP to future 

changes in human activities is reasonable, as well as exclusion of the FEP from the 

performance assessment because speculative prediction of human behavior is avoided. 

FEP 1.4.07.03.0A, Recycling of Accumulated Radionuclides From Soils to Groundwater 

DOE excluded Recycling of Accumulated Radionuclides from Soil to Groundwater on the 

basis of low consequence using a recycling model that estimated effects on the total 

system performance results (SNL, 2008ab). DOE supplemented its technical basis in DOE 

Enclosures 2–4 (2009af). DOE used this feature, event, and process to refer to the downward 

migration of contaminated irrigation water to the water table and the subsequent recapture and 

reuse (i.e., recycling) by irrigation wells within the contaminant plume that can potentially 

increase the concentration of radionuclides in the groundwater and dose to the reasonably 

maximally exposed individual. According to DOE, this contaminant concentration through 

recycling can occur only when the infiltrating irrigation water is applied within the capture zone 

of a pumping well that is also capturing all or part of the contaminant plume. 

The DOE screening analysis for radionuclide recycling in groundwater is based on a model 

that assumes a single hypothetical water supply well with an uninterrupted withdrawal rate of 

3.715 × 109

 L [3,000 acre-ft] per year from the center of a contaminant plume. Capture zone 

dimensions for this hypothetical well are computed based on the local-groundwater-specific 

discharge and saturated aquifer thicknesses upgradient and downgradient of the well. DOE 

considered three mechanisms by which radionuclides can be lost from the recycling process: 

(i) irrigation water usage on fields located outside of the capture zone, (ii) residential 

water usage at locations outside of the capture zone, and (iii) erosion of soil from irrigated 

fields to locations outside of the recycling system. On the basis of current water usage in 

Amargosa Valley, about 90 percent of withdrawn water is used for irrigation. DOE’s screening 

analysis concludes that recycling could increase the total mean annual dose by approximately 

7 to 11 percent for the seismic ground motion and igneous intrusion scenarios for the 

1-million-year simulation period and by an average of 11 percent for the 10,000-year simulation 

period (SNL, 2008ab), which is not significant compared with the range of uncertainty simulated 

by the Total System Performance Assessment model. On the basis of this result, DOE 

excluded this feature, event, and process from the performance assessment. 

NRC Staff’s Review 

The NRC staff reviewed DOE’s screening analysis [SNL, (2008ab); DOE 

Enclosures 2–4 (2009af)] and consulted available literature relevant to irrigation 

practices, infiltration of irrigation water, and methods for determining capture zone 

geometry. The NRC staff evaluated the reasonableness of DOE’s supplemental information 

in DOE Enclosures 2–4 (2009af) that addressed the technical basis of the three aspects of 

DOE’s screening analysis: (i) assumed capture zone geometry, (ii) assumed distances 

between the hypothetical pumping well and irrigated fields, and (iii) the assumption that 

radionuclides which reach the water table and are within the well capture zone are returned to 
2-20 

the well volume without accounting for transport within the saturated zone does not 

underestimate doses at later times. 

The assumed geometry of the capture zone for the hypothetical pumping well in DOE’s analysis 

(SNL, 2008ab) was based on an idealized system of a pumping well applied to a background of 

uniform, parallel groundwater flow lines, whereas the observed pattern of water levels in the 

Amargosa region indicates a converging flow field. A converging flow field can lead to a wider 

capture zone compared to the one used in DOE’s analysis in SNL (2008ab), which in turn could 

result in increased recycling and concentrations of radionuclides in groundwater. DOE showed, 

as identified in DOE Enclosure 2 (2009af), that the results of its screening analysis are not 

affected significantly when a converging flow field is considered. DOE’s conclusion is 

reasonable on the basis that DOE showed that converging flow fields did not significantly 

change the capture fraction (i.e., the fraction of irrigation recharge that is captured by the 

reasonably maximally exposed individual’s well). 

The result of the screening model is strongly dependent on the capture fraction, which DOE 

calculated to be approximately 10 percent. This value is a reflection of the spatial distribution of 

irrigated fields relative to the steady-state capture zone (which DOE assumed to be located 

anywhere within the community). DOE used a probabilistic distribution based on evidence of 

field locations in the Amargosa Valley community and considered a single hypothetical water 

supply well. This approach tended to spread the distances between the fields and the well, 

potentially resulting in a relatively small capture fraction. Farmers might minimize the distance 

between the fields and the well as a cost-cutting approach. For example, in a study by 

Stonestrom, et al. (2003aa) on estimates of deep percolation, each of the three fields 

investigated had its own well for irrigation. A reduction in the distances of fields to the 

hypothetical pumping well could result in a greater well recapture fraction and increased 

radionuclide recycling. DOE explained in DOE Enclosure 3 (2009af) that the distances between 

irrigated fields and the well were not intended to represent actual distances. Rather, the 

screening analysis was a stylized approach constrained by requiring the pumping well to be at 

the location of highest concentration in the plume. DOE’s supplementary analysis in DOE 

Enclosure 3 (2009af) was based on a model in which the pumping wells within and adjacent to 

the plume are coincident with irrigated fields that vary in location and pumping duration during a 

10,000-year simulation period. This supplemental analysis, as identified in DOE Enclosure 4 

(2009af), explicitly accounted for transport time of recycled irrigation water through the saturated 

zone before the water is potentially recaptured by other randomly located irrigation wells. The 

analysis indicated that the average increase in radionuclide concentrations due to recycling of 

pumped water was 4.9 percent for nonsorbing radionuclides and negligible for sorbing 

radionuclides. This updated model does not use the steady-state approach involving a single 

well intersecting the highest concentration of the plume as in the original model in SNL 

(2008ab). DOE concluded that the updated model is more reasonable and realistic, mimicking 

current practices. 

To evaluate the case where the well intersects the highest concentration in the plume and 

irrigated fields are in proximity to the well, the NRC staff considered a hypothetical case where a 

well was used to irrigate a number of fields. The NRC staff considered the well located within 

the accessible environment and above the maximum plume concentration. If the irrigated fields 

were distributed in space at random, half of the fields would be located upstream from the well 

and half downstream. As an approximation, the NRC staff considered that upstream fields 

would be within the well capture zone and downstream fields outside the well capture zone. 

Therefore, in this simplified assessment, if pumping were to continue indefinitely with no soil 

erosion losses, a maximum of 50 percent of the radionuclides in the irrigated water could be 
2-21 

recycled, causing concentrations of radionuclides to double at most. The NRC staff considers 

that a factor-of-two increase in the concentrations and dose consequences is a relatively 

moderate effect, given that this simplified analysis represents a hypothetical case and DOE’s 

largest mean dose estimate is on the order of 0.01 msv [1 mrem] per year for the groundwater 

pathway. On the basis of DOE’s analysis and the NRC staff’s independent risk insights, DOE’s 

technical basis to exclude the feature, event, and process from the performance assessment on 

the basis of low consequence is reasonable. 

FEP 2.1.03.02.0B, Stress Corrosion Cracking of Drip Shields 

DOE excluded the Stress Corrosion Cracking of Drip Shields feature, event, and process from 

the performance assessment model on the basis of low consequence (SNL, 2008ab) and 

supplemented its technical basis for exclusion in DOE Enclosure 2 (2009ab). DOE used this 

feature, event, and process to consider consequences of stress corrosion cracking on drip 

shield materials. DOE stated that the stress corrosion cracking of Titanium Grades 7 and 29 

could occur when tensile stresses exceed a threshold tensile stress value of 80 percent and of 

50 percent of the yield strength at a given temperature, respectively (SNL, 2007bb). DOE 

stated that there are four possible sources of residual tensile stresses: (i) weld induced, 

(ii) caused by thermal expansion (i.e., thermal loading), (iii) plasticity caused by seismic events, 

and (iv) produced by rockfall and drift collapse. DOE stated that an annealing process will be 

used to reduce weld-induced residual stresses below the threshold tensile stress {annealing by 

furnace heating at 593 °C ± 10 °C [1,100 °F ± 50 °F] for a minimum of 2 hours}. 

DOE considered that stress corrosion cracking may occur due to residual stresses caused by 

seismic events or due to stresses caused by rockfall and drift collapse. Under such conditions, 

through-wall cracks may form on the drip shield and seepage water may flow through those 

cracks. DOE supplemented the screening justification in DOE Enclosure 2 (2009ab), explaining 

that even if stress-corrosion cracks are assumed to penetrate the drip shield plates and remain 

open to water flow and if drift seepage flows through the cracks and contacts the waste package 

during the thermal period, the potential consequences to waste isolation are insignificant. DOE 

provided an additional probabilistic analysis to compute the expected number of failed waste 

packages within 10,000 years on the basis of the assumption that (i) waste packages could be 

breached by localized corrosion as a result of seismic-induced residual-stress damage of the 

drip shield and (ii) stress corrosion cracks on the drip shield remain open for 10,000 years and 

seepage water flows through unplugged cracks. The probabilistic analysis in DOE Enclosure 2 

(2009ab) estimated that the mean of the expected number of waste package failures due to 

advection through open stress corrosion cracks on drip shields is two to three orders of 

magnitude lower than the mean of the expected number of waste packages failed due to early 

failure of the drip shields or due to seismic fault displacement involving advective flow through 

the waste packages (the latter cases are included in the performance assessment model). DOE 

concluded that because the early failure drip shields and seismic fault displacement cases are 

not the major contributors to the mean annual dose in the performance assessment, as shown 

in SAR Figure 2.4-18 and DOE Enclosure 2, Section 1.2 (2009ab), the inclusion of stress 

corrosion cracks on the drip shields would not significantly change the results of the 

performance assessment. 

NRC Staff’s Review 

The NRC staff notes that the proposed stress-relieving process conditions are consistent 

with recommended industry practice (ASM International, 2003aa) to reduce residual stresses. 

Therefore, stress corrosion cracking of the drip shield is unlikely to occur because of 
2-22 

weld-induced residual stresses. The thermal expansion of drip shield joints may cause residual 

stresses; however, DOE stated that drip shield connectors are designed to allow for thermal 

expansion with no effect on drip shield performance up to 300 °C [572 °F]. The thermal 

expansion coefficient of Titanium Grades 7 and 29 is 9.2 × 106 K1 and 9.5 × 106 K1

, 

respectively (ASM International, 1994aa). DOE’s conclusion that thermal expansion will 

not cause any significant tensile stresses because the drip shield temperature remains below 

300 °C [572 °F] is reasonable. Below this temperature, the values of the thermal expansion 

coefficients of Titanium Grades 7 and 29 are not significantly different and will not lead to stress 

corrosion cracking. 

This determination is reasonable for the following reasons: (i) DOE quantified the additional 

number of waste packages that could fail by localized corrosion, as a consequence of seepage 

infiltrating the failed drip shields, following an approach consistent with the waste package 

localized corrosion model evaluated in TER Section 2.2.1.3.1 and the seismic consequence 

abstraction model evaluated in TER Section 2.2.1.3.2; (ii) DOE concluded that the additional 

number of failed waste packages would be less than the number of failed waste packages for 

the early failure and seismic fault displacement modeling cases; and (iii) given that the 

contribution to the total dose of these latter cases is minimal, DOE adequately concluded that 

the dose contribution from the additional failed waste packages would be negligible. In addition, 

DOE pointed out in DOE Enclosure 2, Sections 1.2 and 1.6 (2009ab) that volumetric flow 

through open (unplugged) cracks is expected to be smaller than the seepage flow approaching 

drip shields. DOE’s conclusion on the flow reduction is reasonable because (i) openings can act 

as capillary barriers to seep water under unsaturated conditions and (ii) DOE provided 

experimental evidence for the flow reduction through cracks in DOE Enclosure 2, Figure 1 

(2009ab). DOE provided the same justifications to also exclude FEP 2.1.03.10.0B, Advection of 

Liquids and Solids Through Cracks in the Drip Shield, and the NRC staff also notes the 

technical basis is adequate for this other feature, event, and process. In summary, DOE’s 

technical basis to exclude both FEPs 2.1.03.02.0B, Stress Corrosion Cracking of Drip Shields, 

and 2.1.03.10.0B, Advection of Liquids and Solids Through Cracks in the Drip Shield, on the 

basis of low consequence is reasonable. 

FEP 2.1.03.03.0B, Localized Corrosion of Drip Shields 

As identified in the conclusion of the screening justification (technical basis) for this feature, 

event, and process (SNL, 2008ab), DOE excluded the Localized Corrosion of Drip Shields 

feature, event, and process from the performance assessment model on the basis of low 

consequence. DOE used this FEP to consider consequences of localized corrosion on drip 

shields. DOE stated that it evaluated Titanium Grade 7 over all the anticipated ranges of pH, 

chloride concentration, and temperature relevant to the proposed repository. On the basis of 

available information, DOE concluded that localized corrosion of Titanium Grade 7 is not 

expected to occur. Literature results suggest that the presence of fluoride ions can enhance the 

general corrosion rate of titanium alloys and possibly lead to localized corrosion. DOE stated it 

examined localized corrosion of titanium alloys in fluoride-containing solutions and concluded 

that these types of solutions would rarely occur and that low fluoride concentration in 

combination with expected inhibiting species (such as nitrate, carbonate, and sulfate) is unlikely 

to lead to localized corrosion (SNL, 2008ab). DOE noted that long-term corrosion tests of 

titanium alloys in repository-relevant environments up to 5 years did not indicate any evidence 

of localized corrosion. DOE acknowledged that data on Titanium Grade 29 are sparse and that 

it is less resistant to localized corrosion. DOE, therefore, postulated that localized corrosion 

may initiate on Titanium Grade 29. In other words, DOE stated that existing information on 

localized corrosion on Titanium Grade 29 is not sufficient to rule out this process or support a 
2-23 

notion that localized corrosion is of low probability. DOE noted that the majority of the Titanium 

Grade 29 components except the side framework, however, would be located underneath the 

Titanium Grade 7 plates and would be exposed to benign environments. Therefore, DOE 

concluded that the drip shield could experience localized corrosion only on the side framework. 

However, if these side frameworks collapsed, DOE concluded that the drip shield would 

continue to function and protect the waste package against seepage through the Titanium 

Grade 7 plates (SNL, 2008ab). Therefore, DOE excluded the feature, event, and process on 

the basis of low consequence. 

NRC Staff’s Review 

The NRC staff reviewed the technical basis (SNL, 2008ab) and supporting information provided 

by DOE [BSC (2004as); DOE Enclosure 6 (2009ab)]. The NRC staff examined DOE’s model 

assumptions and model support in the area related to localized corrosion of the drip shield. The 

NRC staff notes that the 2.5- and 5-year testing DOE conducted indicates that the possibility for 

localized corrosion of the drip shield is small in the potential repository environment. The NRC 

staff performed independent analyses that indicated the concentration of fluoride, which at 

higher levels increases the localized corrosion susceptibility, would not likely achieve high levels 

in the proposed repository (Lin, et al., 2003aa; Pabalan, 2010aa). The independent analyses 

indicate that fluoride precipitates with common chemicals in the groundwater limiting the 

concentration of free fluoride ions in the water. Independent localized corrosion analyses of 

Titanium Grade 7 support the notion that localized corrosion of Titanium Grade 7 is not likely 

under repository conditions (Brossia and Cragnolino, 2000aa). Further NRC staff’s evaluation 

of fluoride effects and long-term immersion tests are provided in TER Section 2.2.1.3.1.3.1.1. 

Measurements of hydrogen absorption described in DOE’s information in DOE Enclosure 6 

(2009ab) and literature information (e.g., Okada, 1983aa) imply a state of passivity. NRC staff 

notes that the passivity of titanium and titanium alloyed with platinum or nickel is likely to be 

preserved, even in acid solutions with pH as low as 3.5 at 25 °C [77 °F] under cathodic 

polarization. Corrosion studies by Smailos, et al. (1992aa) on titanium alloyed with 0.17 percent 

palladium did not show localized corrosion in German rock salt repository environments under 

gamma radiation and temperatures ranging from 90 to 200 °C [194 to 392 °F]. In other studies 

by the same group, the metallic samples were subjected to adhering salts and corrosion 

products without significant corrosion affecting the titanium alloys (Smailos and Köster, 1987aa). 

NRC staff conducted corrosion tests in concentrated chloride solutions at 95 °C [203 °F] of 

Titanium Grade 7 galvanically coupled with Alloy 22 to form a crevice and found no indication of 

localized or galvanic corrosion of Titanium Grade 7 (He, et al., 2007ab). Therefore, DOE’s 

technical basis to exclude localized corrosion of Titanium Grade 7 from the performance 

assessment is reasonable. 

The NRC staff, in TER Section 2.2.1.3.2, evaluated the ability of the drip shield to maintain its 

seepage barrier function if the side framework, made of Titanium Grade 29, buckled. On the 

basis of that evaluation, the following DOE conclusions are reasonable: (i) the drip shield would 

continue to function and protect the waste package against seepage through the Titanium 

Grade 7 plates and (ii) localized corrosion of the drip shield would not have a significant effect 

on dose calculations. Thus, DOE’s technical basis for exclusion of the Localized Corrosion of 

Drip Shields feature, event, and process is reasonable. 
2-24 

FEP 2.1.03.04.0B, Hydride Cracking of Drip Shields 

DOE excluded Hydride Cracking of Drip Shields from the performance assessment model on 

the basis of low probability (SNL, 2008ab) and supplemented its technical basis for exclusion in 

DOE Enclosure 1 (2009cb). According to DOE’s definition, this feature, event, and process 

refers to the absorption of hydrogen into the titanium drip shield materials to form mechanically 

weak hydrides, which could lead to the formation of cracks. DOE noted that hydrogen 

absorption in titanium alloys could occur under repository conditions. DOE evaluated hydride 

cracking by developing a model where hydrogen-induced cracking is assumed to occur if the 

absorbed hydrogen resulting from general corrosion of the drip shield into Titanium Grades 7 

and 29 exceeds a critical hydrogen concentration (SNL, 2008ab). DOE estimated that the 

amount of hydrogen uptake in 10,000 years would be below this critical hydrogen concentration. 

DOE tracked the drip shield materials and thickness in SNL Design Control Parameter 07-04, 

Table 7-5 (2008ad). 

DOE also evaluated uphill diffusion along Titanium Grade 29 to Grade 7 welds, which could 

lead to locally elevated hydrogen concentrations near the welds. DOE concluded in DOE 

Enclosure 8 (2009ab) that the use of a filler metal (Titanium Grade 28) with a composition 

comparable to both welded components would eliminate this particular issue. By using Titanium 

Grade 28, DOE intended to provide a gradual aluminum concentration gradient to restrict 

hydride formation due to hydrogen redistribution. DOE tracked the drip shield design including 

welds in SNL Table 7-5, Design Control Parameter 07-01 (2008ad) and the use of Titanium 

Grade 28 in SNL Table 7-4, Design Control Parameter 07-12 (2008ad) as weld filler material for 

Titanium Grade 7 to Grade 29 welds. 

DOE concluded that, given the limited extent of hydrogen formation and the use of Titanium 

Grade 28 filler material on weld lines, Hydride Cracking of Drip Shields can be excluded from 

the performance assessment model (SNL, 2008ab). 

NRC Staff’s Review 

The NRC staff reviewed the feature, event, and process screening technical basis in SNL 

(2008ab) and DOE Enclosures 3–8 (2009ab). The NRC staff analyzed DOE’s 

model assumptions and model support in areas related to hydride cracking induced by 

hydrogen absorption resulting from general corrosion of the drip shield and hydrogen 

diffusion along dissimilar titanium welds. From this review, the NRC staff noted that the critical 

hydrogen concentrations DOE assumed to lead to fast fracture were reasonable. Although 

delayed hydride cracking is possible at hydrogen concentrations as low as 30 ppm in Titanium 

Grade 5 steel, the applied stress intensification for the delayed hydride cracking is near to the 

fracture toughness limit, as described in DOE Enclosure 3 (2009ab). DOE described that 

palladium and ruthenium played a beneficial role by increasing the critical hydrogen 

concentration value and decreasing the hydrogen absorption. This is reasonable because 

independent literature data indicate that palladium and ruthenium can increase the critical 

hydrogen concentration and because the repository is predicted to be an oxic environment, as 

outlined in DOE Enclosure 4 (2009ab). DOE’s assessment of hydrogen absorption efficiency, 

as indentified in DOE Enclosure 6 (2009ab), is reasonable because the experimental condition 

used to test hydrogen absorption bounds the range of conditions important to this mode of 

degradation. DOE provided distributions of hydrogen in titanium due to uphill diffusion by a 

stress gradient in DOE Enclosure 7 (2009ab) and due to uphill diffusion by aluminum 

concentration in SNL (2008ab). The NRC staff noted those hydrogen distributions are 

reasonable on the basis of the analysis of DOE’s assumptions. Furthermore, the NRC staff 
2-25 

developed an uphill diffusion model (Mintz and He, 2009aa), applied the model to potential 

repository conditions to examine the hydrogen concentration around the weld zones, and noted 

that hydrogen concentrations would be minimal. 

DOE excluded Hydride Cracking of Drip Shields from the performance assessment model on 

the basis of low probability (SNL, 2008ab). DOE updated the technical basis to show that the 

probability of hydride cracking of drip shields is less than 10−4

 in 10,000 years, as detailed in 

DOE Enclosure 1 (2009cb). DOE described that even with a high corrosion rate at a 

probability level of 2.5 × 10−5

 (applied for 10,000 years), the hydrogen concentration would be 

below the critical hydrogen concentration for hydride cracking. The exclusion of the feature, 

event, and process on the basis of low probability is reasonable because DOE (i) considered 

high corrosion rates to overestimate the amount of hydrogen produced from the general 

corrosion process and (ii) concluded that the hydrogen concentration would not suffice to induce 

hydride cracking on the drip shield plate and frame. DOE’s low probability conclusion on the 

basis of the high corrosion rate analysis and the selection and control of titanium alloy material 

and weld filler metal are reasonable. Therefore, DOE’s technical basis to exclude hydride 

cracking of the drip shields from the performance assessment model on the basis of low 

probability is reasonable. 

FEP 2.1.03.10.0B, Advection of Liquids and Solids Through Cracks in the Drip Shield 

DOE excluded the Advection of Liquids and Solids Through Cracks in the Drip Shield from the 

performance assessment model on the basis of low consequence (SNL, 2008ab) and 

supplemented its technical basis for exclusion in DOE Enclosure 2 (2009ab). According to 

DOE’s definition of the feature, event, and process, if cracks develop on the drip shield, water 

could flow through those cracks and contact the waste package. DOE presented technical 

reasons for excluding the potential of advective flow of water through cracks in a drip shield. 

These involved 

(i) Creep/stress relaxation in a drip shield (of Titanium Grade 7) could limit the development 

and penetration of stress corrosion cracks 

(ii) A small damaged area (less than 0.5 percent) on the drip shield surface from 

seismic-induced rockfall could limit the surface area available for advective flow of 

seepage water 

(iii) A low chance of large rockfall from lithophysal rock zone above the drip shield could 

cause sufficient stress corrosion cracks and denting of a drip shield 

(iv) A low chance of large rock-block falls from the nonlithophysal rock zone above the drip 

shield could occur due to low probability of seismic events of sufficient magnitude 

(v) Potential filling and plugging of stress corrosion cracks by mineral precipitates and 

corrosion products could potentially limit the advective flow of water through a drip shield 

(vi) A low chance of perfect alignment of tight and tortuous cracks on a drip shield surface 

could occur with impinging seepage drips from the drift wall 

(vii) In the absence of drip shields, in less than 10 percent of the waste packages localized 

corrosion would be initiated [SNL Appendix O (2008ag)] 
2-26 

(viii) If leakage through a crack-damaged drip shield caused a localized corrosion of the 

waste package, only a small flux {4 mL/yr [0.244 in3

/yr]} would directly flow into the 

waste package, which would be insignificant from the repository performance standpoint 

(SNL, 2008ab) 

Therefore, DOE excluded the feature, event, and process from the performance assessment 

model on the basis of low consequence. 

DOE supplemented the screening justification with DOE Enclosure 2 (2009ab) and described 

that potential consequences to waste isolation are insignificant even if stress corrosion cracks 

are assumed to penetrate the drip shield plates and remain open to water flow and if drift 

seepage flows through the cracks and contacts the waste package during the thermal period. 

DOE provided an additional probabilistic analysis in DOE Enclosure 2 (2009ab) to compute the 

expected number of failed waste packages within 10,000 years on the basis of the assumption 

that (i) waste packages could be breached by localized corrosion as a result of seismic-induced, 

residual-stress damage to the drip shields and (ii) stress corrosion cracks on the drip shield 

remain open for 10,000 years and seepage water flows through unplugged stress corrosion 

cracks. DOE concluded that the additional number of failed waste packages would be too small 

to change dose estimates. In addition, DOE stated in DOE Enclosure 2, Sections 1.2 and 1.6 

(2009ab) that volumetric flow through open (unplugged) cracks is expected to be smaller than 

volumetric seepage approaching drip shields and provided experimental evidence in DOE 

Enclosure 2, Figure 1 (2009ab) to support this statement. The technical basis for exclusion of 

the feature, event, and process is reasonable, for reasons provided under FEP 2.1.03.02.0B, 

Stress Corrosion Cracking of Drip Shields, in this TER. In summary, DOE’s technical basis to 

exclude the feature, event, and process on the basis of low consequence is reasonable. 

FEP 2.1.06.04.0A, Flow Through Rock Reinforcement Materials in the Engineered 

Barrier System 

DOE excluded Flow Through Rock Reinforcement Materials in the Engineered Barrier System 

on the basis of low consequence (SNL, 2008ab) and supplemented its technical basis for 

exclusion in DOE Enclosures 2 and 7 (2009cb). As defined by DOE, this feature, event, and 

process addresses the potential of groundwater flow to occur through the ground support 

materials such as wire mesh, rock bolts, grout, and liner. This feature, event, and process also 

evaluates the potential for ground support or its degradation products to enhance or decrease 

seepage (groundwater flow) into emplacement drifts, or to divert water flow within the drifts. In 

the performance assessment model, DOE assumes that seepage is not affected by any rock 

reinforcement materials. For boreholes, FEPs 1.1.01.01.0B, Influx Through Holes Drilled in Drift 

Wall or Crown, and 2.1.06.04.0A, as defined by DOE, cover similar processes and features 

because open space will be present in boreholes regardless of whether rock bolts degrade. 

DOE stated plans to employ friction-type carbon steel rock bolts with plates for use as 

temporary ground support during construction of the emplacement drifts, to be left in place 

between the rock and the permanent (Bernold-type sheets) ground support shown in SNL 

Design Parameter Number 01-15 (2008ad). DOE stated in the screening justification that the 

seepage model indicates the presence of rock bolts does not lead to significant seepage 

enhancement. DOE supported this conclusion by assuming that (i) an open borehole without 

grout acts as a capillary barrier to unsaturated flow; (ii) a cross-sectional area of the rock bolt 

borehole, onto which flow may be incident, is small; and (iii) water that may have seeped into 

the borehole can imbibe back into the rock along its length (assumptions also related to 

FEP 1.1.01.01.0B, Influx Through Holes Drilled in Drift Wall or Crown). In addition, DOE 
2-27 

indicated that the Bernold-type sheets, which are bolted to the drift walls and roof, may divert 

seepage. However, DOE stated that this diversion may be limited as these sheets will be 

perforated and the supporting rock bolts will degrade as outlined in SNL Design Parameter 

01-16 (2008ad). Therefore, DOE chose not to take credit for seepage diversion by the 

Bernold-type liner sheets for the period before the liner would fully corrode. 

 

DOE stated that neither the rock bolts used as temporary ground support nor those holding the 

Bernold-type sheets will have a significant effect on the seepage flow rate. DOE also noted that 

the ground support system is expected to degrade as a result of drift degradation (BSC, 

2004al). Therefore, DOE described that excluding the temporary ground support in the 

representation of seepage in the performance assessment model is a realistic representation of 

the system with respect to groundwater flow into the drift. DOE thus excluded Flow Through 

Rock Reinforcement Materials in the Engineered Barrier System from the performance 

assessment model. 

The NRC staff‘s evaluation for FEP 1.1.01.01.0B, Influx Through Holes Drilled In Drift 

Wall or Crown, discussed previously in this TER, also applies to the rock bolt aspect of 

FEP 2.1.06.04.0A. The basis for excluding the feature, event, and process in DOE Enclosures 

2 and 7 (2009cb) included the function of the drip shield, the effect on seepage rates caused by 

vapor flux, and the uncertainty of capillary diversion in boreholes. Thus, for boreholes used for 

rock bolts, DOE provided a reasonable technical basis for excluding the feature, event, and 

process on the basis of low consequence (also see discussion under FEP 1.1.01.01.0B, Influx 

Through Holes Drilled in Drift Wall or Crown). For the Bernold-type sheets, DOE’s view that the 

water diversion capability of these engineered components should be neglected is reasonable 

because they may only partially divert seeping water from contacting the drip shield or waste 

package until a time when the liners would fully corrode. Therefore, DOE’s technical basis to 

exclude the FEP Flow Through Rock Reinforcement Materials in the Engineered Barrier System 

from the performance assessment on the basis of low consequence is reasonable. 

FEP 2.1.06.06.0B, Oxygen Embrittlement of Drip Shields 

DOE excluded Oxygen Embrittlement of Drip Shields from the performance assessment model 

on the basis of low probability (SNL, 2008ab) and supplemented its technical basis for exclusion 

in DOE Enclosure 9 (2009ab). DOE used this feature, event, and process to refer to oxygen 

embrittlement as a potential failure mechanism for the drip shields, resulting from diffusion of 

oxygen in titanium alloys. According to DOE, oxygen embrittlement may affect mechanical 

properties of the drip shield materials. DOE’s screening justification considered oxygen 

diffusion data at 300 °C [572 °F] by Rogers, et al. (1988aa), who used single crystal, pure 

titanium to estimate the oxygen lattice diffusion coefficient in alpha-phase titanium. DOE 

considered oxygen lattice diffusion data to estimate oxygen penetration depth for Titanium 

Grade 7 and concluded that any penetration depth would be minimal in 10,000 years. DOE 

used 300 °C [572 °F] as the bounding drip shield temperature for analysis of oxygen 

embrittlement. DOE stated that the 300°C [572 °F] temperature selected for the analysis could 

only be exceeded in the case of a drift collapse and that the probability of conditions leading to 

drip shields exceeding 300 °C [572 °F] is about 1 in 10,000 within the first 10,000 years of 

disposal. Therefore, because of this low probability and minimal oxygen penetration that may 

occur in 10,000 years, oxygen embrittlement of the drip shields was deemed unlikely and this 

process was excluded from the performance assessment model on the basis of low probability. 

The NRC staff reviewed the screening rationale and DOE’s conclusion in DOE Enclosure 9 

(2009ab) and SNL (2008ab) that the penetration depth would be minimal in 10,000 years. DOE 
2-28 

cited the work of Liu and Welsch (1988aa) to support the statement that for alpha-phase 

titanium (e.g., Titanium Grade 7), oxygen diffusivity is independent of the form of the material 

(single crystal of polycrystalline) and that mass transport of oxygen is controlled by bulk 

diffusion through the alpha matrix (which is a slow process). For an alpha-beta alloy such as 

Titanium Grade 29, DOE cited additional work by Liu and Welsch (1988ab) to support the 

statement that the properties of the alpha phase solely control the overall oxygen embrittlement. 

Therefore, on the basis of work DOE cited, the NRC staff notes that the use of the bulk 

diffusivity of oxygen through the alpha matrix for the embrittlement calculation for both alpha 

(Titanium Grade 7) and alpha-beta (Titanium Grade 29) alloys is reasonable. Also, the NRC 

staff notes that DOE overestimated the oxygen penetration depth due to the assumption of a 

constant temperature of 300 °C [572 °F] in analyses in DOE Enclosure 9 (2009ab) and SNL 

(2008ab). DOE’s results of temperature computations for the drift-collapsed case indicate 

average waste package temperatures below 300 °C [572 °F] (SAR Figure 2.3.4-98), implying 

drip shield temperatures also below 300 °C [572 °F]. The NRC staff evaluates system 

temperature computations in TER Section 2.2.1.3.6. DOE’s conclusion that oxygen penetration 

would be minimal is reasonable on the basis of DOE’s computations at 300 °C [572 °F], which 

indicate that oxygen embrittlement of the drip shield is unlikely. Therefore, DOE’s technical 

basis to exclude the feature, event, and process on the basis of low probability is reasonable. 

FEP 2.1.07.02.0A, Drift Collapse 

As defined by DOE, this feature, event, and process considered nonseismic drift collapse; 

specifically, the degradation of emplacement drifts that may result from the combination of 

excavation-induced rock stress and thermal loading in the absence of significant seismic events. 

DOE considered seismically induced drift collapse as a separate feature, event, and process 

that was included in its performance assessment evaluation. Seismically induced drift collapse 

is reviewed as a model abstraction in TER Section 2.2.1.3.2.5 and, hence, is not addressed in 

this subsection. 

DOE stated that degradation of waste emplacement drifts can occur from stresses that exceed 

the strength of the rock mass surrounding the drift. These stresses are attributed to several 

causes. One cause is excavation-induced stresses that are superimposed on the in-situ

(geostatic) stresses soon after the drifts are constructed. Another cause is thermally generated 

stresses. After waste emplacement, thermal stresses develop in the rocks from heat generated 

through radioactive decay of the emplaced waste. In addition, rocks under the influence of 

combined mechanical and thermal stresses may experience a gradual weakening with time. 

Rocks can be expected to fail when any of the stresses, individually or in combination, exceed 

the rock strength. Such failures can cause a gradual accumulation of rubble on and around the 

engineered barriers as a result of a continuing but slow process of rockfall. Alternatively, rocks 

above the emplacement drift could collapse due to a combination of all the stresses that exceed 

the strength of the rock mass. (In this context, “rock mass strength” refers to the strength of the 

larger volume of rock around the waste emplacement drift whose behavior under stress is 

controlled by the presence of fractures, discontinuities, and cavities, as opposed to the strength 

of a small-sized intact rock core sample used in laboratory testing.) Both the gradual 

accumulation of rubble and instantaneous collapses of massive rocks may have undesirable 

consequences on the performance of the engineered barriers, depending on their magnitude 

(e.g., small, medium, or large amounts of rockfall). 

DOE characterized the rock properties and applied several analytical tools and numerical 

models to assess the long-term behavior of rocks under coupled natural and repository-induced 
2-29 

processes as a function of time. Uncertainties in the long-term behavior of the rocks were 

incorporated in the analyses. 

DOE stated that drift degradation could occur rapidly if the stress change is large enough to 

cause instantaneous rock failure or gradually if the stress change is too small to cause rapid 

failure but large enough to weaken the rock with time. DOE summarized its basis for excluding 

drift collapse in SNL (2008ab). DOE provided detailed supporting analyses in BSC (2004al). 

DOE also supplemented its technical basis for exclusion of the feature, event, and process in 

DOE Enclosures 1–8 (2009ae), DOE Enclosure 1 (2009cd), DOE Enclosure 1–2 (2009ce), DOE 

Enclosure 1 (2009cf), DOE Enclosure 1 (2009cg), and DOE Enclosure 1 (2009ch). DOE 

concluded that nonseismic drift degradation would cause only minor, localized rockfall that 

results in insignificant impact on the thermal and hydrologic conditions of the drift and minimal 

consequences to the engineered barrier system components. 

DOE addressed the analytic models it developed for lithophysal and nonlithophysal rock types 

in BSC Sections 6.3 and 6.4 (2004al). The predominant surroundings of the emplacement drifts 

consist of lithophysal rocks. 

DOE evaluated effects of postexcavation and thermal stresses in lithophysal rocks using a 

two-dimensional, drift-scale discontinuum Voronoi block model when applying the UDEC code 

(Itasca International, Inc., 2004ac, as described in BSC, 2004al) to analyze the mechanical 

behavior of drifts for five rock-strength categories of lithophysal rock, as detailed in BSC 

Section 6.4.2.1 (2004al). UDEC is a computer code used internationally by the rock 

mechanics and mining industries both as a research tool and a design tool. There are 

numerous, extensively peer-reviewed scientific papers and refereed journal articles on the use 

of UDEC code. In its implementation of the UDEC code, DOE chose the discontinuum Voronoi 

approach because the model allows computation of both the time-dependent stress-strain 

response of rock to thermal loading and the dynamic response of the rock mass under seismic 

events that can lead to rockfall. The processes considered within the Voronoi domain are 

gravitational stresses, excavation-induced stresses, thermally induced stresses, and 

time-dependent strength degradation. Under the defined model domain and boundary 

conditions, the UDEC–Voronoi model is used to calculate mechanical response of the 

Voronoi domain to a set of imported temperature distributions that are updated at 45 discreet 

timesteps to cover a 10,000-year period following repository closure. 

NRC Staff’s Review 

The NRC staff used YMRP Section 2.2.1.2, Review Method 2, to evaluate DOE’s analyses. 

Specifically, the NRC staff evaluated DOE’s analyses and calculations supporting its screening 

basis and its use of bounding or representative estimates for the consequences. The NRC staff 

performed independent calculations using analytical tools and numerical models to scope 

potential issues and to verify or confirm DOE’s conclusions. In evaluating DOE’s technical basis 

for excluding the feature, event, and process, the NRC staff also considered its independent 

analysis (Ofoegbu, et al., 2007aa). 

DOE summarized its basis for excluding drift collapse in SNL (2008ab). DOE provided detailed 

supporting analyses in BSC (2004al). DOE also supplemented its technical basis in DOE 

Enclosures 1–8 (2009ae), DOE Enclosure 1 (2009cd), DOE Enclosure 1–2 (2009ce), DOE 

Enclosure 1 (2009cf), DOE Enclosure 1 (2009cg), and DOE Enclosure 1 (2009ch). On the 

basis of the NRC staff’s review of that information, the NRC staff focused its detailed review on 
2-30 

the following aspects of the DOE model that are important in estimating rock response to 

postexcavation and thermal stresses: 

 Characterization of rock mechanical and thermal properties 

 Model domain and boundary conditions 

 Initial stress state and rock temperature inputs 

 Block size and shape in the Voronoi domain 

 Model calibration 

 Model results (extent and timing of rockfall) 

 Model support 

 Treatment of time-dependent failure 

 Alternative conceptual models 

The following subsections summarize DOE’s approach and the NRC staff’s evaluations for each 

of these aspects of the DOE technical basis and conclusions. 

Characterization of Rock Mechanical and Thermal Properties 

The Yucca Mountain site-specific geologic characterization of the rock units was accomplished 

by geologic mapping of the Topopah Spring Tuff, which was identified as the host rock. The 

Topopah Spring Tuff includes both lithophysal and nonlithophysal rock units. Approximately 

15 percent of the emplacement block consists of nonlithophysal rocks that are hard, strong, 

fractured masses. The remaining 85 percent of the repository block consists of lithophysal 

rocks that are more deformable with lower compressive strength than the nonlithophysal units. 

Different rockfall analysis methods were used for these two rock types (SNL, 2008ab). Because 

the predominant surroundings of the emplacement drifts consist of lithophysal rocks, the NRC 

staff focused its review of DOE’s technical basis for excluding drift collapse on DOE’s modeling 

of lithophysal rocks. 

DOE performed laboratory and in-situ testing to derive the mechanical and thermal properties 

of the lithophysal rocks used in its analysis. The mechanical and physical properties included 

elastic moduli, unconfined and triaxial compressive strength, tensile strength, density, porosity, 

normal and shear stiffness, and shear strength. The geometric rock fracture properties included 

dip and dip direction, spacing, length, surface roughness, and microstructure. These properties 

were obtained from laboratory tests of small- and large-diameter cores. The rock mass 

strength properties were established by in-situ measurements. Thermal properties measured 

in the laboratory and in situ included thermal conductivity, thermal expansion coefficient, and 

heat capacity. 

DOE studied the time dependence of intact rock strength. These parameters 

(e.g., static-fatigue data at given environmental conditions of moisture and temperature) were 

used in the time-dependent drift degradation calculations to define the rate of strength decay as 

a function of stress state. The effects of sample size, anisotropy, and sample saturation were 

studied. DOE showed that the unconfined compressive strength decreases with increases in 

sample size. DOE reported a maximum anisotropy of 10 percent in the average matrix moduli, 

which, according to DOE, is a second-order effect compared to the effect of lithophysae and 

fracturing on moduli and strength, as described in BSC Appendix E (2004al). DOE also found 

that the variability in elastic and strength properties is not a function of lateral or vertical position 

within the repository host horizon, but primarily is a function of porosity of the samples (BSC, 

2004al). DOE accounted for uncertainty in modeling the time dependence of intact rock 
2-31 

strength by bounding the range of rock mechanical properties as a function of porosity, 

temperature, and saturation. 

DOE stated that the major difference in fracture characteristics between the nonlithophysal and 

the lithophysal rocks is the trace, or fracture length. For the nonlithophysal rocks, the average 

fracture length is greater than or equal to 1 m [3.28 ft]; for the lithophysal rocks, fracture 

lengths average less than 1 m [3.28 ft]. The abundant small-scale fractures in the lithophysal 

rocks result in the weaker nature of this rock, and the potential failure will be in a raveling mode 

that results in generally small block sizes. The major fracture differences between lithophysal 

and nonlithophysal rocks influenced DOE’s choice for the numerical codes used for the drift 

stability analyses. 

The thermal properties of the lithophysal rocks were derived from laboratory and field 

measurements (BSC, 2004al). To account for the uncertainty in the thermal properties, 

DOE used a coefficient for intact rock in the thermal-mechanical rockfall analysis, which 

DOE concludes leads to larger and, thus, conservative, thermally induced stresses as shown in 

BSC Appendix E (2004al). 

A commercial discontinuum numerical modeling particle flow code PFC2D [Itasca International, 

Inc. (2004ab), as described in BSC (2004al)] was used to evaluate the effect of lithophysal size, 

shape, and distribution on the variability of the mechanical properties. This numerical analysis 

simulates the basic deformation and failure response mechanism of lithophysal tuff (BSC, 

2004al). Bounding ranges for mechanical properties were established using this method. 

To determine rock-strength characteristics, DOE combined the PFC modeling results with 

the laboratory test data. The unconfined compressive strength was plotted as a function of 

the Young’s modulus in BSC Appendix E (2004al). The analysis identified a lower bound 

strength cutoff at 10 MPa for lithophysal rocks. The sensitivity studies using stress analysis 

models found that instability would be expected to occur if the in-situ rock strength was 

below about 10 MPa [BSC Appendix E (2004al)]. DOE supported this conclusion with field 

observations from the existing Exploratory Studies Facility and the Enhanced Characterization 

of the Repository Block cross-drift tunnels. Thus, this strength–Young’s modulus plot is used as 

the basis for dividing the lithophysal rocks into five strength categories for rockfall modeling. 

DOE stated in BSC Section 6.4.1.2 (2004al) that the lowest ranges of strength categories with 

porosity greater than 20 percent likely underestimate the true rock-mass strength. 

NRC Staff’s Review of Characterization of Rock Mechanical and Thermal Properties 

The NRC staff reviewed the methods described in BSC (2004al) and notes that DOE followed 

standard industry practices and methods for host rock characterization. The mechanical and 

thermal properties of the rocks were acquired through laboratory and field tests with samples 

and/or sites to adequately characterize uncertainty in relevant parameters. DOE’s use of 

numerical analyses to supplement laboratory data and field measurements is reasonable due to 

the practical limitations of obtaining large samples in weakly coherent lithophysal rocks. The 

NRC staff reviewed DOE’s use of the PFC2D modeling code [Itasca International, Inc. (2004ab) 

as described in BSC (2004al)] to simulate deformation and failure response mechanisms of 

lithophysal rocks. This modeling approach is reasonable, because DOE applied standard 

industry practices and qualified methods, as detailed in BSC Section 3 (2004al), for 

characterizing the rock properties. The data uncertainty and their natural variability were 

captured and used in the numerical modeling to analyze drift stability. 
2-32 

In a request for additional information, the NRC staff asked DOE how uncertainties in 

stress-strain relationships for lithophysal rocks were characterized by the number of 

laboratory tests conducted, as outlined in DOE Enclosure 1 (2009ce). DOE Enclosure 1 

(2009ce) provided additional details on the stress-strain relationships for lithophysal rocks, 

which showed that the tested rocks have a more ductile response (i.e., less prone to failure at 

peak stress) than the simulated rock mass in the UDEC [Itasca International, Inc. (2004ac), as 

described in BSC (2004al)] models. The NRC staff reviewed this information and noted that 

uncertainties in the stress-strain relationships for lithophysal rocks would not affect the model 

results significantly, because DOE represents the modeled rock mass as more prone to brittle 

failure than the actual rock mass. 

Model Domain and Boundary Conditions 

DOE used the NUFT thermo-hydrology continuum model (Lawrence Livermore National 

Laboratory, 1998aa) as described in BSC (2004al) to simulate the two-dimensional, drift-scale, 

thermal-hydrologic behavior and the FLAC 2-D continuum code [Itasca International, Inc. 

(2004aa), as described in BSC (2004al)] to calculate thermally induced stresses. DOE used the 

UDEC 2-D discontinuum computer code [Itasca International, Inc. (2004ac), as described in 

BSC (2004al)] for the drift stability analysis in lithophysal rock because the discontinuum 

approach best represented the highly fractured character of the lithophysal rock. In the UDEC 

lithophysal rockfall model, the region around the drift, where inelastic deformation is expected to 

occur, is discretized into discrete blocks using a mathematical relationship called a Voronoi 

tessellation. The Voronoi model was used to represent the random orientations of the rock 

blocks. DOE obtained the specified average dimension from the characterization of the rocks. 

In the UDEC model, the Voronoi block domain around the drift is bounded by large, continuous 

blocks with elastic properties. The temperature-time history from NUFT was mapped onto the 

UDEC grid blocks. To assess the repository edge effects and topographic influences on the 

temperature and thermal stress distributions, DOE performed coupled, three-dimensional 

(multiple drifts), regional- and drift-scale calculations using FLAC3D [three-dimensional 

continuum code; Itasca International, Inc. (2004aa), as described in BSC (2004al)]. 

A coupled three-dimensional regional- and drift-scale thermal-mechanical calculation was 

conducted to support the two-dimensional drift-scale calculation. The three-dimensional 

analysis was performed in two steps. First the regional scale thermal-mechanical calculation 

was used to calculate the temperature and stress changes on the entire mountain. Then the 

detailed local scale [also called large scale in BSC Appendix C (2004al)] thermal-mechanical 

analysis was performed such that the boundary conditions for temperature and stresses were 

obtained from the regional-scale calculation, as outlined in BSC Section 6.2 (2004al). 

The temperatures and stresses calculated by the drift-scale model (NUFT-FLAC results), in 

which simplified rigid boundary conditions (zero displacement) are assumed for the vertical and 

bottom boundary planes, were compared with the coupled, three-dimensional, regional- and 

drift-scale model (FLAC3D results). The comparison showed that the simplified rigid boundary 

condition used in the two-dimensional drift-scale model resulted in higher horizontal stresses 

compared to the three-dimensional regional model, especially at the repository edge where the 

confinement and temperatures are less than in the middle of the repository. Thus, DOE 

concluded in BSC Section 6.2 (2004al) that the two-dimensional model provides conservative 

conditions for use in the drift degradation analyses. 

In the drift-scale calculation, a symmetric boundary condition is applied on a vertical plane 

halfway between the emplacement drifts. This modeling technique results in zero 
2-33 

displacements (i.e., full confinement) perpendicular to the boundary and zero heat flux across 

the boundary, as described in BSC Section 6.2 (2004al). These boundaries account for the 

symmetry of mechanical behavior on either side of the vertical plane between parallel drifts, 

assuming that parallel drifts undergo similar thermal loads. DOE compared the stresses 

calculated using these boundary conditions to stresses from the coupled regional and drift-scale 

calculations. On the basis of this comparison, DOE concluded in BSC Section 6.2 (2004al) the 

vertical boundary conditions in the UDEC–Voronoi model overestimate the thermal stress for 

drifts near the margins of the repository area. 

The bottom boundary of the UDEC–Voronoi model is also fixed, which treats the underlying 

Earth’s crust as a rigid body. The top of the model is assigned a constant-stress boundary 

condition, fixed at the estimated vertical in-situ stress at a 300-m [984-ft] depth. In BSC 

Appendix W (2004al), DOE provided sensitivity analyses that show the calculated stresses at 

the drift walls are insensitive to extension of the model boundaries beyond the distances 

considered in the current models. 

NRC Staff’s Review of Model Domain and Boundary Conditions 

The NRC staff reviewed the details of DOE’s numerical models and related calculations used to 

determine boundary conditions. The computer codes DOE used in the thermal-mechanical 

boundary calculations are well tested and appropriate for their intended use. 

To evaluate the adequacy of DOE’s selection of boundary conditions, the NRC staff conducted 

confirmatory thermal-mechanical calculations (Cao, 2010aa) using analytical and finite element 

methods (Abaqus computer code; Dassault Systèmes Simulia Corp, 2009aa) for a single 

heated drift. The NRC staff used the analytical solution of Kirsch (Jaeger, et al., 2007aa) for a 

circular tunnel as the sum of in-situ stress and excavation-induced stress and then added the 

thermal stress, which was calculated by solving the Laplace equation assuming symmetrical 

temperature distribution in the radial direction. Using this approach, the NRC staff calculated 

similar stress values at the crown and sidewall areas, as DOE analyzed in BSC Figures 6-31 to 

6-33 (2004al), when boundary conditions similar to the DOE UDEC–Voronoi model were used. 

The NRC staff’s confirmatory calculation with the rigid boundary condition also supports DOE’s 

conclusion that horizontal stresses are overestimated for drifts near the edge of the repository. 

By using a fixed boundary condition for the UDEC–Voronoi model, DOE does not allow for 

potential horizontal expansion to reduce the accumulation of horizontal stress from thermal 

expansion of the rock. The use of fixed vertical boundaries in the DOE model is reasonable, 

because this assumption will not underestimate the potential effects of thermal stress on rocks 

surrounding the heated drifts. 

The NRC staff reviewed the technical details of DOE’s analyses, presented in BSC Appendix W 

(2004al), to determine whether the boundary conditions in DOE’s model were appropriately 

selected. The NRC staff notes that the model boundary below the drift is located within the 

outer limits of the thermally disturbed zone around a drift. This implies that some component of 

thermal expansion may not have been fully considered in the model. However, any thermal 

expansion in this zone does not influence the rockfall estimates significantly, because only 

a small increase in rock stress would be expected for this small increment in temperature. 

On the basis of the sensitivity analyses DOE provided, the NRC staff notes that the magnitude 

of that potential component is negligible and would not significantly affect the calculated 

stresses near the drift. The dimensions of the DOE model domain are reasonable, because 
2-34 

consideration of an extended region does not affect significantly the potential effects of thermal 

stress on rocks surrounding heated drifts. 

Initial Stress State and Temperature Inputs 

The DOE model assesses the preexcavation in-situ stresses of 7 MPa vertical and 3.5 MPa 

horizontal for all simulations. The vertical component represents the stress at an overburden 

depth of 300 m [984 ft], and the horizontal component is simplified to be 3.5 MPa on the basis of 

an average horizontal-to-vertical stress ratio of 0.5, as identified in BSC Section 6.3.1.1 

(2004al). To obtain the postexcavation equilibrium state as the initial condition for the thermal 

simulations, DOE performed a quasi-static simulation in which the preexcavation stresses are 

applied and the model is allowed to equilibrate, as detailed in BSC Section 6.4.2.2 (2004al). 

Once the initial postexcavation stress state is established, spatial temperature distributions are 

mapped onto the model grid blocks and updated for 45 discreet timesteps as a function of time 

over the 10,000-year simulation period following closure. The temperature inputs as a function 

of time are derived from the drift-scale model using the NUFT code (Lawrence Livermore 

National Laboratory, 1998aa, as outlined in BSC Appendix U (2004al), and interpolated onto the 

UDEC model grid. The UDEC–Voronoi model then computes changes in stress state with each 

update in temperature input for each of the timesteps. 

NRC Staff’s Review of Initial Stress State and Temperature Inputs 

The NRC staff reviewed DOE’s evaluation of the initial stress state at the repository horizon. 

By a confirmatory hand calculation, using rock density and distance from the surface to the drift, 

the NRC staff reproduced and noted that DOE’s analysis of the average vertical load of 7 MPa 

is reasonable for the lithostatic stress at a depth of 300 m [984 ft] beneath Yucca Mountain. 

The NRC staff reviewed the references DOE cited in BSC Section 6.3.1.1 (2004al) regarding 

measurements of in-situ horizontal stress at Yucca Mountain. The referenced literature 

indicated the horizontal component of in-situ stress from hydraulic fracturing measurements is 

likely to be 1–2 MPa lower than DOE assumed. DOE’s use of 3.5 MPa is reasonable, because 

a 1–2 MPa overestimate in the in-situ horizontal stress would increase the magnitude of 

horizontal stress from thermal effects and, thus, overestimates the potential for rockfall. 

DOE calculated the temperature inputs for the UDEC model using a detailed flow and 

transport code. The NRC staff performed confirmatory temperature calculations using an 

alternative flow and transport code (Manepally, et al., 2004aa). By comparison to NRC staff’s 

independent temperature calculations, the DOE temperature inputs to the UDEC model are 

reasonable and would not underestimate the thermal response of the heated drifts. 

Block Size and Shape in the Voronoi Domain 

In DOE Enclosure 2 (2009ae), DOE’s evaluation of the rock types concluded that a relatively 

ductile and highly jointed rock mass will fail and separate from the main body preferentially 

along existing discontinuities, such as fractures, joints, and intersect lithophysal cavities, and 

crumble. In a brittle, nonlithophysal rock mass, new fractures are expected to penetrate intact 

rock blocks. Thus, DOE concluded in DOE Enclosure 2 (2009ae) that thermal expansion of the 

Topopah Spring lower lithophysal tuff could result in movement along existing joints and 

deformation of lithophysal voids, whereas thermal expansion of the Topopah Spring 

nonlithophysal tuff could cause spalling of platy rock fragments from drift walls along newly 

created fractures. 
2-35 

To represent the lithophysal tuff, DOE used a Voronoi tessellation approach in the UDEC model 

(Itasca International, Inc., 2004ac) to generate a series of model elements that represent 

random blocks of rock surrounding the drift opening, as described in BSC Section 6.4.2.1 

(2004al). The interfaces between the blocks are intended to represent the approximate spacing 

and random nature of existing fractures and voids in the lithophysal rock. The blocks average 

30 cm [11.8 in] in diameter and are relatively uniform in size, with the largest blocks being twice 

the size of the smallest blocks, as outlined in DOE Enclosure 2 (2009ae). DOE concluded that 

an average 30-cm [11.8-in] block diameter is representative of the internal discontinuities 

(i.e., fractures and voids) within the lithophysal tuff. DOE conducted sensitivity analyses 

using average block sizes of 20 cm [7.9 in], as detailed in BSC Sections 6.4.2.3.1 and 7.6.7.1 

(2004al); 10 cm [3.9 in], as outlined in DOE Enclosure 2 (2009ae); and 4 cm [1.6 in], as 

identified in DOE Enclosure 1, (2009ch). Although some realizations showed a small increase 

in the amount of fracturing and rockfall with decreasing average block size, DOE concluded 

these small increases are not significant with respect to the engineered barrier system 

performance. DOE concluded that the results of the UDEC analyses are insensitive to 

variations in average block size from 4 to 30 cm [1.6 to 11.8 in]. 

NRC Staff’s Review of Block Size and Shape in the Voronoi Domain 

The NRC staff reviewed DOE’s technical basis used to represent lithophysal rock in the UDEC 

model. The NRC staff evaluated DOE’s conclusion in DOE Enclosure 2 (2009ae) that yielding 

in heated lithophysal tuff should occur preferentially on existing structural discontinuities 

because the strength of the intact blocks is at least twice the strength of the discontinuous rock 

mass. DOE’s conclusion, outlined in BSC Section 7.6.5.1 (2004al), is reasonable because 

fractures are distributed in a manner that rock movement associated with thermal expansion 

can be accommodated by slippage along a fracture path composed of coalescing “potential 

fractures” to form a distinct separation plane. Thus, the NRC staff reviewed how DOE’s model 

represents yielding of the rock mass along an organized fracture network that is oriented 

appropriately to the applied stress. 

Although DOE represents block surfaces in the Voronoi model as randomly oriented with 

effective blocks on the order of 30 cm [11.8 in], DOE characterized, in BSC Section 7.3.2 

(2004al), the Topopah Spring lower lithophysal tuff as having primarily vertical fractures with 

spacing between the fractures on the order of several centimeters. DOE provided additional 

basis for its conclusions in DOE Enclosure 2 (2009ae), describing that the presence of 

lithophysal voids creates a generally isotropic rock mass. How such voids randomized the 

potential effects of a strongly vertical anisotropy in the rock mass was addressed in DOE 

Enclosure 1 (2009ch). DOE stated that visually apparent anisotropy does not affect damage 

and fracturing mechanics of the drift crown where the major principal stress and stress-induced 

fractures are normal to the subvertical fractures. This response is reasonable because at the 

crown area, the horizontal stress causes fracturing, and thus the rock deformation is not 

affected by the vertical fractures. Therefore, the model will not underestimate the magnitude of 

rockfall. DOE also provided observations to show the random locations and shapes of the 

lithophysae and the close spacing and short trace lengths of fractures indicating that a 

homogeneous, isotropic model provides a model of the lithophysal unit. The size of the internal 

structure and spacing of fractures is much smaller than the size of a drift, and therefore DOE’s 

conclusions with respect to the drift-scale behavior of rock degradation are reasonable. 

DOE Enclosure 4 (2009ae) analyses showed that the crown of the heated drifts has an 

overstressed zone that is approximately tens of centimeters thick. This overstressed zone is 

spanned by only one to two Voronoi blocks in DOE’s model. The NRC staff noted that, 
2-36 

according to DOE’s analyses, a larger number of blocks might be needed to form a coherent 

network of surfaces to represent yielding within the rock mass BSC Section 7.6.5.1 (2004al). 

Because of the random distribution of block surfaces in the UDEC model, it was not clear that a 

coherent fracture network could form within the thin, overstressed zone. Although some block 

surfaces are oriented to allow yielding, these surfaces usually terminate against adjacent block 

boundaries that cannot yield. Thus, movement along the yielding surfaces is effectively 

transferred to elastic strain along nonyielding blocks within the overstressed zone. The elastic 

strain within the nonyielding blocks inhibits the formation of a coherent fracture network within 

the overstressed zone, which is necessary to represent potential yielding within the rock mass, 

as identified in BSC Section 7.6.5.1 (2004al). DOE addressed this issue in DOE Enclosure 1, 

(2009ch). 

In DOE Enclosure 1 (2009ch), DOE reduced the average size of the discretized blocks to 4 cm 

[1.6 in]. This sensitivity analysis simulated a larger number of small-scale fractures, resulting 

in minor rockfall, but leading to the same depth of fracturing as the models with larger block 

sizes. This result is consistent with the NRC staff’s confirmatory calculation (Cao, 2010aa), 

in which the balance between the confined rock strength and the total applied stress, thermal 

and in-situ, determines the rockfall depth. When the rockfall reaches a certain depth, where 

the balance is achieved, the self-arresting of rockfall is also reached. DOE’s response clarified 

that a coherent fracture pattern forms when the block size is much smaller than the 

dimension of the overstressed zone. The fracturing may not be coherent if the block size is 

comparable to the dimension of the overstressed zone, but the failure will still be evident in the 

UDEC–Voronoi block model. This is true even if there are only two blocks in the zone width. 

DOE emphasized that the 20 to 30-cm [7.9 to 11.8-in] block sizes are appropriate because of 

the existing average spacing of the “preexisting” discontinuities in the rock. Thus, the rocks 

would result in incoherent fracture pattern with minor rockfall. On the basis of these 

discussions, the quantity of rockfall is not underestimated when implementing the average block 

size adopted in DOE analyses. 

Model Calibration 

 

In BSC Sections 7.6.3 and 7.6.4 (2004al), DOE described the approach used to calibrate the 

Young’s modulus and unconfined compressive strength of the rock mass modeled to the 

expected characteristics of the lithophysal rock. Five rock-strength categories were considered 

in the calibration to represent the range of values for estimated Young’s modulus in the 

lithophysal rock. For each of the five rock-strength categories considered, DOE used a mean 

value for unconfined compressive strength as indicated in BSC Appendix E, Figure E-13 

(2004al). DOE then adjusted four Voronoi block interface properties to achieve the calibration: 

(i) cohesion, (ii) friction angle, (iii) normal stiffness, and (iv) shear stiffness. The calibration was 

repeated iteratively until the UDEC model reasonably reproduced the mean unconfined 

compressive strength and mean Young’s modulus for each rock-strength category. Separate 

calibrations were performed using different values of mean block size. A 30-cm [11.8-in] 

average block size was used for the screening analysis. Models with average block sizes of 

20 cm [7.9 in] in BSC Sections 6.4.2.3.1 and 7.6.7.1 (BSC, 2004al) and 10 cm [3.9 in] in DOE 

Enclosure 2 (2009ae) were developed for sensitivity analyses to ensure convergence of results. 

A potentially important uncertainty in the DOE model is the representation of spatial variability in 

rock properties. DOE addressed this uncertainty by developing calibrated models for five 

different rock-strength categories, which are distinguished by different values of rock mass 

modulus. In conducting its calibration, DOE used the mean value of unconfined compressive 

strength as the calibration target for each selected value of rock mass modulus. DOE data, on 
2-37 

the other hand, showed a range of potential values of unconfined compressive strength for a 

given value of rock mass modulus [BSC Appendix E, Figure E–13 (2004al)]. DOE stated 

(SAR p. 2.3.4-73) that a number of parametric studies were conducted in which the Young’s 

modulus and strength parameters were varied to account for the bounding ranges of lithophysal 

and nonlithophysal rocks. 

NRC Staff’s Review of Model Calibration 

The NRC staff reviewed the results of DOE’s analyses using the lower bound strength and 

Young’s modulus for rock mass Categories 2, 3, 4, and 5, as outlined in DOE Enclosure 1 

(2009cd). The NRC staff compared the results of these lower bound analyses with analyses 

using mean values and notes that there is no significant difference in the amount of 

rockfall calculated using either parameter set. Thus, DOE has appropriately accounted for 

uncertainty in rock properties that are important in the UDEC–Voronoi model. 

The NRC staff observed from DOE’s analyses in DOE Enclosure 1, Table 1 (2009cd) that 

the amount of rockfall at the crown area is the same for lithophysal rock Categories 2 to 5 

for a range of lower bound unconfined compressive strength. DOE explained that rockfall is 

insensitive to the change of lower bound strength because the associated change of Young’s 

modulus, which causes the ratios between lower bound strength and Young’s modulus to 

vary by only a few percent as shown in DOE Enclosure 1, Table 1 (2009cd). The NRC staff 

notes that this relationship is reasonable and that uncertainty in important rock-strength 

properties is appropriately considered in the UDEC–Voronoi model. 

Model Results 

DOE conducted extensive modeling studies to estimate the timing and extent of thermal drift 

degradation. The following summarizes DOE’s modeling results: 

 The combined in-situ and thermal mechanical stress reached in the drift crown is about 

7 MPa for Category 1 and about 37 MPa for Category 5 lithophysal rocks, respectively, 

as shown in BSC Figures 6-142 and 6-144 (2004al). 

 These stress values can, in some conditions, slightly exceed the unconfined 

compressive strength of lithophysal rock. 

 The elastic stress paths cover a time range of 10,000 years’ variation of temperature. 

 The amount of thermally induced rockfall is small for all five categories of 

lithophysal rocks. 

 Basic rock mechanics principles show that the potential for the thermally induced rockfall 

process should cease at a short distance from the drift, where the confined strength of 

the rock is greater than the sum of mechanical and thermal stresses. 

NRC Staff’s Review of Model Results 

To evaluate the amount of stress likely produced during thermal heating of the 

rocks surrounding the drifts, the NRC staff conducted confirmatory calculations using the 

Abaqus continuum model (Cao, 2010aa). The results of these calculations reasonably 
2-38 

represented the stress levels DOE calculated for different categories of lithophysal rocks. The 

stress level for Category 1 rocks remains below the strength of the rock. Consequently, rockfall 

is not expected to occur for Category 1 rocks. The NRC staff’s analyses confirmed that for 

lithophysal rock Categories 2–5, compressive stresses in some parts of the drift wall can exceed 

the unconfined compressive strength of the rock mass. 

The NRC staff reviewed the results of UDEC–Voronoi simulations that showed limited amounts 

of rockfall occur when an overstressed zone (i.e., where horizontal compressive stress may 

exceed the unconfined compressive strength of the rock) develops in the drift wall. The NRC 

staff’s confirmatory calculations (Cao, 2010aa) showed that an overstressed zone is expected to 

occur within the first tens of centimeters of the drift wall, which is comparable to the depths 

calculated in DOE Enclosure 1 (2009cd). 

The NRC staff focused on determining the reasonableness of DOE model results that showed 

only limited amounts of rockfall can occur in the overstressed zone. The UDEC–Voronoi model 

relies on accommodating some degree of rock stress by movement along the interfaces 

between adjacent blocks. When the applied stress exceeds the ability of the blocks to move, 

the interfaces can fail and blocks can separate from the modeled rock mass. The NRC staff 

questioned whether the block size in the UDEC–Voronoi model was small enough to capture a 

through-going failure of adjacent blocks within the narrow overstressed zone. In response, DOE 

provided supplemental analyses in DOE Enclosure 1 (2009ch) that demonstrated failure 

patterns in models with 4-cm [1.6-in] average block sizes are comparable to failure patterns in 

models with larger block sizes. The NRC staff reviewed these results and notes that the 

UDEC–Voronoi model appears capable of representing block failure in the overstressed zone 

for average block sizes that range from 4 to 30 cm [1.6 to 11.8 in]. 

The NRC staff evaluated information provided in DOE Enclosure 5 (2009ae) that further 

explained why failure of the rock mass is expected only in a thin zone around the drift walls. 

The NRC staff’s confirmatory calculations (Cao, 2010aa) verified that local block failure at 

the drift wall should cause stresses to accumulate farther outward from the drift surface, 

where the rock mass is confined and more resistant to failure. Thus, as blocks fail along the 

overstressed zone on the drift wall, the stress concentration is expected to shift outward from 

the drift wall into the more confined area where the strength is higher. This process reasonably 

explains why only a limited amount of rockfall is expected from thermal-mechanical effects on 

lithophysal rocks. 

In reviewing the UDEC–Voronoi model results, the NRC staff observed that some blocks appear 

to maintain cohesion with adjoining blocks when the interface between the blocks is in an 

apparent state of failure. DOE provided additional information in DOE Enclosure 2 (2009ce) to 

show that although some part of the interface failed, some other parts of the interface retained 

sufficient strength to support the hanging block. Blocks also can remain intact if the geometry of 

adjacent blocks continues to support the block after an interface has failed, as shown in DOE 

Enclosure 2 (2009ce). The NRC staff reviewed the results of DOE’s calculation, which showed 

that the UDEC code appropriately analyzes cohesion within adjoining blocks (i.e., beam 

support), and notes that the UDEC–Voronoi model appropriately calculates limited amounts of 

rockfall as occurring from the overstressed zone in the drift walls. 

The NRC staff conducted confirmatory calculations to support the review of DOE’s calculation of 

limited amounts of rockfall from the thermal-mechanical effects of waste emplacement (Cao, 

2010aa). For example, Kaiser, et al. (2000aa) showed that rocks that are subject to spallation 

(i.e., an assumed mode of failure from thermal-mechanical effects on the drift wall) typically form 
2-39 

inverted v-shaped notches in the drift crown that limit the extent of rockfall. The NRC staff 

used the Abaqus computer program to evaluate the differences in stress conditions between a 

circular drift and a circular drift with an upside-down v-shaped notch. The NRC staff’s analysis 

confirmed that the presence of the v-shaped notch would lead to self-arrest of spallation, 

because the tangential stresses on both sides of the notch were released because of 

confinement loss or because there was room for physical expansion for stress release. 

The v-shaped notch had a dimension approximately equal to the depth of the overstressed 

zone above the crown. Tangential stresses in the plane of the tunnel were compared to the 

confined or unconfined rock strength, as appropriate. The calculation shows that even if the 

thermo-elastic stresses exceed the unconfined compressive strength of the rock, as the failure 

zone narrows, the intact zone above the failed zone provides higher strength due to 

confinement. This condition either limits or entirely prevents further failure or considerably 

delays the process and eventually self-arrests the degradation process (i.e., no rockfall). Both 

DOE’s results and the NRC staff’s independent calculations show that the thermal degradation 

should stop within one radius of depth into the drift’s roof. On the basis of the NRC staff’s 

confirmatory calculation, DOE’s assertion is reasonable that under the repository mechanical 

and thermal stress conditions, the confined rock strength at one radius depth is more than twice 

the unconfined compressive strength. Therefore, for rock Categories 2 to 5, thermal 

degradation should be limited to depths shallower than one radius above the drift’s crown. 

Model Support and Consistency With Available Observations 

DOE supported the use of the UDEC–Voronoi model in the thermal-mechanical analyses 

through four investigations, as identified in BSC Section 7.6.5 (2004al). DOE compared 

modeled failure mechanisms to large-core lithophysal sample failure mechanisms observed in 

the laboratory. DOE concluded that the UDEC model could simulate the observed patterns of 

fracturing due to (i) the axial splitting failure mode of lithophysal samples in unconfined 

compression tests and (ii) the measured strength and Young’s modulus of the samples. 

Modeled drift-scale fracturing of the lower lithophysal tuff in the Enhanced Characterization of 

the Repository Block Cross-Drift also compared favorably to observations of stress-induced 

tunnel sidewall fracturing in the Enhanced Characterization of the Repository Block Cross-Drift. 

DOE conducted detailed modeling of the Drift-Scale Heater Test to determine whether the 

UDEC model could reasonably represent the spallation of nonlithophysal tuff observed during 

the test. Small amounts of spallation from the drift crown were observed during the heater tests. 

Once the UDEC model was calibrated to appropriate Topopah Spring nonlithophysal tuff 

characteristics, the model was able to calculate small amounts of rockfall from the overstressed 

crown of the heated drift. DOE provided additional details of this analysis in DOE Enclosure 7 

(2009ae), including quantification and favorable comparison of the calculated and observed 

amounts of rockfall for this test. 

DOE used a continuum-based approach to model elastic and inelastic rock stress for a range of 

conditions representative of heated drifts, as described in BSC Section 7.6.5.4 (2004al). 

Although DOE does not consider continuum-based models as appropriate for calculating 

rockfall due to thermal-mechanical processes, as identified in BSC Section 7.4.1 (2004al), DOE 

concluded that both the continuum and the discontinuum (UDEC) models appropriately 

represent stress distributions prior to reaching the yielding point of the rock. 

DOE supported the use of the calibrated rock-mass characteristics by comparing laboratory 

experiments of lithophysal rocks, as detailed in BSC Section 7.6.4 (2004al). DOE stated in 

BSC p. 7-61 (2004al) that the number and types of laboratory and in-situ experiments were 
2-40 

insufficient to describe the complete constitutive behavior of the lithophysal tuff with a high 

level of confidence, particularly in the postpeak strain range and for confined conditions. 

Consistent with common engineering practice, DOE analyzed the continuum constitutive 

Mohr-Coulomb models ranging from perfectly plastic to perfectly brittle to bound the possible 

behavior of the lithophysal rock mass on damage and deformation. To accommodate the 

uncertainty represented by the limited characterization of the lithophysal tuff, DOE calibrated 

the UDEC–Voronoi model to give a more brittle stress-strain response than observed in 

tested samples, as described in BSC p. 7-38 (2004al). According to DOE, this approach 

enhanced the ability of rockfall to occur in the UDEC–Voronoi model, as identified in DOE 

Enclosure 6 (2009ae). 

NRC Staff’s Review of Model Support and Consistency With Available Observations 

The NRC staff reviewed the information DOE provided to support its use of the UDEC model in 

the thermal-mechanical analyses for drift stability. A key element of the UDEC–Voronoi model 

is the representation of postpeak strain. DOE presented several analyses showing calculated 

postpeak strains for simulated rock masses in BSC Section 7.6.4 (2004al). DOE presented 

limited information on postpeak strain characteristics for the Topopah Spring lithophysal tuff. 

Although the single comparison between the lithophysal tuff and UDEC calculation for 

stress-strain characteristics showed a calculated response that is more brittle than exhibited by 

the laboratory experiment in DOE Enclosure 1 (2009cd), this information did not address the 

range of characteristics represented by the five lithophysal rock-mass categories used in the 

UDEC analyses. Additionally, strength characteristics for only six samples from the Topopah 

Spring lower lithophysal tuff are reported in BSC Table 6-69 (2007be). DOE Enclosure 1 

(2009ce) provided additional information on the adequacy of the six samples to represent the 

range of strength characteristics needed to support the UDEC analyses in BSC Figure 7-16 

(2004al). 

In the response to a request for additional information related to the rock-mass categories [DOE 

Enclosure 1 (2009ce)], DOE stated that in modeling the rock mass responses, it applied a 

bounding approach to those five rock mass categories (lower bound relations between stiffness 

and strength cover and bound the loading response). This approach is meant to encompass 

the variability and uncertainties of the laboratory and field data. For postpeak response, the 

UDEC–Voronoi block model was calibrated to bound the brittleness of the lithophysal rock mass 

observed from the experimental data. This was achieved by bounding all test data in the axial 

stress versus axial strain curve, as outlined in DOE Enclosure 1 (2009ce). This approach is 

reasonable, because biasing the model calibration to a more brittle response than observed in 

real rock will not underestimate the potential for rockfall to occur. 

Treatment of Time-Dependent Failure 

Time-dependent failure refers to the potential for rock to fail by gradual weakening under 

stresses less than the rock strength, if the rock is subjected to that stress for long periods of 

time. DOE considers the potential for time-dependent failure as a function of the ratio of applied 

stress to the rock strength. DOE evaluated the relationship of time to failure on the basis of two 

sets of test data for stress ratios ranging from about 0.8 to 1.0. A best linear fit between the 

stress ratio and the logarithm of time was calculated and used to extrapolate times to failure for 

stress ratios less than 0.8. For the extrapolated portion of this curve, predicted times to failure 

ranged from approximately 12 days (106

 s) at a ratio of 0.8 to about 32,000 years (1012 s) at a 

ratio of 0.6. Below values of 0.55, no time-dependent failure is predicted. In BSC Appendix S 

(2004al), DOE supported the use of a linear fit approximation by comparison to a previous study 
2-41 

of data from Lac du Bonnet granite and concluded that the linear fit is appropriate. DOE 

evaluated the uncertainty in the time-to-failure estimates by running the UDEC model for rock 

Categories 1, 2, and 5 using times to failure based on the Lac du Bonnet data. 

In the response to the NRC staff’s request for additional information related to the linear 

relationship fit to represent the time-to-failure versus stress ratio data for tuff [DOE Enclosure 3 

Number 2 (2009ae)], DOE acknowledged uncertainty in the data used for the linear fit and cited 

observations from the Enhanced Characterization of the Repository Block and Exploratory 

Studies Facility as additional evidence that time to failure is not overestimated. DOE stated that 

stress ratios in the range of 0.58 to 1.0 are represented at unsupported drift spring-lines for a 

longer time (greater than 10 years) than is available from any experiment, and no significant 

degradation has occurred. 

NRC Staff’s Review of Treatment of Time-Dependent Failure 

The NRC staff evaluated the extent to which time-to-failure estimates could affect predicted 

drift degradation, especially in the range of stress ratios between 0.6 and 0.7, for which 

time-to-failure data for tuff are not available but relatively long times to failure are predicted 

(i.e., 32 years for a ratio of 0.7 and to 32,000 years for a ratio of 0.6). There is uncertainty in 

these estimates because the data points are few and the correlation coefficient for the linear fit 

to the data is relatively low as shown in BSC Figure S-27 (2004al). Numerical analyses by 

DOE, shown in BSC Figures S-14 through S-21 (2004al), also suggested times to failure for this 

range of stress ratio could be on the order of a few days to a few years. In DOE Enclosure 4 

(2009ae), DOE cited observations from the Enhanced Characterization of the Repository Block 

and Exploratory Studies Facility tunnels, stating that these tunnels represent stress ratios 

between 0.58 and 1.0; however, significant rock failure has yet to occur. 

In DOE Enclosure 1 (2009cg), DOE indicated that the uncertainty in time-dependent strength 

degradation of the lithophysal tuff was not represented in the thermo-mechanical calculations 

of drift stability, because the static-fatigue curve, based on the 1997 tuff data, bounds the 

potential for thermally induced drift degradation. Bounding was achieved by applying the 

Lac du Bonnet static-fatigue relationships for granite to the lithophysal tuff data. The NRC 

staff notes that this approach underestimates the time to failure for tuff and, thus, the analytic 

model maximizes the potential for thermally induced drift degradation by calculating degradation 

earlier than expected, as shown in BSC Figure S-30 (2004al). DOE also indicated that 

temperatures in the range between ambient and 200 °C [392 °F] have a small effect on the tuff 

mechanical properties, including short-term strength and time to failure. The NRC staff 

reviewed DOE’s information and notes this relationship is reasonable. The static-fatigue curve 

for tuff, based on the 1997 and 2004 DOE data sets, predicts more rapid drift degradation than 

the observed conditions in the Exploratory Studies Facility and Enhanced Characterization of 

the Repository Block Cross-Drift. The DOE approach for modeling time-dependent failure is 

reasonable, because this bounding approach will not underestimate the amount of rockfall for 

lithophysal rocks. 

Alternative Conceptual Models 

DOE considered alterative conceptual models that were based on assumptions and 

simplifications that differed from those of the base-case models discussed previously and 

described in BSC Section 6.7 (2004al). The conceptual models DOE considered included 

continuum models. In a continuum model, the lithophysae and fractures are smeared into the 

elements of a continuous rock mass, where there is no slip between model elements. In the 
2-42 

discontinuum model, lithophysae and fractures are represented by joints between the Voronoi 

blocks and slip can occur between these model elements. Although a continuum model can 

simulate the accumulation and distribution of stress prior to yielding, the model cannot 

accurately represent stress-strain relationships once the unconfined compressive strength of the 

rock is reached. Thus, DOE concluded a continuum-based approach is inappropriate for 

representing rockfall in lithophysal rock, because the relatively ductile characteristics of this rock 

type require an understanding of post-peak stress response. Nevertheless, DOE did use a 

continuum model to evaluate the thermal-mechanical conditions for the discontinuum model, 

prior to initiation of rockfall. 

NRC Staff’s Review of Alternative Conceptual Models 

The NRC staff reviewed the technical basis DOE provided in its evaluation of alternative 

conceptual models to the UDEC–Voronoi approach. As discussed in previous sections, DOE 

characterized the stress-strain relationships expected for lithophysal tuff. This characterization 

showed that the lithophysal tuff is not expected to fail once the unconfined compressive strength 

is reached and that postpeak strength is available through ductile deformation to accommodate 

additional stress. In contrast, a continuum-based approach assumes that there is no post-peak 

strength to the strained rock mass and that rock failure occurs once the unconfined compressive 

strength is reached. 

The Center for Nuclear Waste Regulatory Analyses (CNWRA®) performed an independent 

analysis that considered a continuum-based model to assess the technical issues for thermally 

induced drift degradation. The drift degradation study (Ofoegbu, et al., 2007aa ) used the 

Abaqus two-dimensional continuum method. That method, as applied by CNWRA, did not 

incorporate the physics of time-dependent stress-strain releases that occurs during the rock 

spalling process. The analysis did not account for the development of fracture structures 

leading to failure regions with variable extent of rock cohesion. Consequently, the results are 

unrealistically conservative. Nonetheless, CNWRA analyses led to the development of technical 

questions transmitted to DOE, as referenced in this section. 

The NRC staff notes that a discontinuum-based approach, such as that used by the 

UDEC–Voronoi model, provides a more accurate representation of rock response to 

thermal-mechanical effects than a continuum-based approach. Although both NRC and DOE 

have used continuum-based models to evaluate stress distributions around heated drifts and to 

provide insights on rock mechanical processes, the NRC staff notes that continuum-based 

models are not appropriate for representing the stress-strain relationships that control the 

occurrence of rockfall in lithophysal tuff. DOE has considered continuum-based models and 

has provided a reasonable basis to exclude the use of these models, per BSC Section 6.4.2 

(BSC, 2004al), in the performance assessment. 

Summary of DOE’s Analysis and NRC Staff’s Review 

DOE adequately analyzed the thermally induced stresses causing instability of the waste 

emplacement drifts, compared the calculated stresses to the estimated strength of the rock 

mass, and estimated the timing and extent of potential drift degradation under anticipated 

loads. The methodology is reasonable because such analyses allow a systematic study of 

potential rock mass behavior under a range of anticipated loading scenarios. The NRC staff 

has reviewed SNL (2008ab), associated references (BSC, 2004al), and responses to the 

NRC staff’s requests for additional information in its evaluation of DOE’s exclusion of drift 

collapse due to thermal stresses and time-dependent rock weakening. The NRC staff 
2-43 

performed independent confirmatory analyses in its evaluation of DOE’s application. The NRC 

staff’s independent analyses (Cao, 2010aa), using standard analytic techniques, confirmed 

DOE’s conclusions. 

DOE accounted for variability in rock types and a range of mechanical properties and 

strength characteristics, on the basis of laboratory tests and field investigations. DOE has 

presented technical bases for its conclusions that rockfall in lithophysal rocks, with natural 

fractures and weak planes along which preferential failures occur, can be evaluated by 

the discontinuum models. DOE has used reasonable technical approaches for quantifying the 

amount of rockfall that potentially results from nonseismic-induced drift collapse. DOE’s 

methods to quantify the amount of thermally induced rockfall are reasonable. Thus, DOE’s 

conclusion that combined effects of mechanical, thermal, and time-dependent weakening of 

rocks can be excluded from its performance assessment is reasonably supported. 

Summary of NRC Staff’s Review for FEP 2.1.07.02.0A, Drift Collapse 

The NRC staff reviewed the models and results DOE used for screening out thermally induced 

drift degradation at the proposed Yucca Mountain repository using risk-informed, 

performance-based review methods described in the YMRP. Significant aspects of this 

review included determining whether DOE used reasonable model domains and boundary 

conditions, appropriate initial stress states and temperature inputs, appropriate rock block 

characteristics in the UDEC model, and suitable methods to calibrate and support the 

UDEC model. On the basis of the results of this review, the DOE technical basis for excluding 

FEP 2.1.07.02.0A, Drift Collapse, is reasonable. 

FEP 2.1.07.05.0B, Creep of Metallic Materials in the Drip Shield

DOE excluded Creep of Metallic Materials in the Drip Shield from the performance assessment 

model on the basis of low consequence (SNL, 2008ab) and supplemented its technical basis for 

exclusion in DOE Enclosure 5 (2009af). Creep refers to time and temperature-dependent 

plastic (i.e., permanent) deformation of material caused by static loading. DOE used the 

feature, event, and process Creep of Metallic Materials in the Drip Shield to consider creep as a 

potential degradation process affecting the drip shield. Due to the long 10,000-year period of 

consideration and the possibility of early drift collapse after the waste emplacement, DOE noted 

the importance of the analysis of time-dependent deformation and the stability of the drip shield 

when nonuniformly loaded by the rock rubble mass. 

DOE developed constitutive equations to express the amount of creep strain for Titanium 

Grades 7 and 29 as a function of temperature, applied stress, and time. DOE Enclosure 5, 

Section 1.2.1 (2009af) assumed a drip shield temperature of 150 °C [302 °F] for the screening 

analysis. DOE stated that higher drip shield temperatures would only be reached in the event 

of near-complete drift collapse within the first few hundred years after repository closure and 

that, even for early drift collapse, the temperature will drop below 150 °C [302 °F] within 600 to 

1,000 years after waste disposal. DOE concluded that it was reasonable to assume a constant 

temperature of 150 °C [302 °F] for the screening analysis because the creep susceptibility of 

titanium alloys generally decreases with decreasing temperature and 150 °C [302 °F] is an 

overestimate of the drip shield temperature for most of the postclosure period. 

In BSC Attachment I (2005an), DOE used titanium creep data from the literature to derive creep 

equations for Titanium Grades 7 and 29 at 150 °C [302 °F]. Because there are limited creep 

data in the literature for titanium alloys for temperatures around 150 °C [302 °F], DOE first 
2-44 

derived equations to represent the creep behavior at room temperature, then rescaled those 

equations to a temperature of 150 °C [302 °F] using information about effects of temperature on 

creep kinetics. To derive the room temperature creep equation for Titanium Grade 7, DOE fitted 

a power-law-type equation to the 27-year creep data for Titanium Grade 2 from Drefahl, et al. 

(1985aa). DOE used BSC Eq. (I-8) (2005an) to represent the room-temperature creep of 

Titanium Grade 7. To derive the room-temperature creep equation for Titanium Grade 29, DOE 

fitted a power-law-type equation to the 1,000-hour creep data for Titanium Grade 5 from 

Odegard and Thompson (1974aa). DOE used BSC Eq. (I-19) (2005an) to represent the 

room-temperature creep of Titanium Grade 29. To rescale the room-temperature creep 

equations to represent the creep behavior at 150 °C [302 °F], DOE first accounted for the 

difference in yield stress at the respective temperatures, using BSC Eq. (I-7) (2005an). DOE 

then rescaled the creep equations using BSC Eq. (I-12) (2005an) assuming an activation 

energy of 30 kJ/mol. In this manner, DOE derived BSC Eqs. (I-15) and (I-22) (2005an) to 

represent the creep behavior of Titanium Grades 7 and 29, respectively, at 150 °C [302 °F]. 

DOE compared the creep strains the equations calculated to literature data for creep of titanium 

alloys at 150 °C [302 °F] (Kiessel and Sinnott, 1953aa; Odegard and Thompson, 1974aa). DOE 

stated that the equations used to represent the creep behavior of Titanium Grades 7 and 29 at 

150 °C [302 °F] are reasonable because they predict greater creep strain than reported in the 

technical literature. 

In the second part of the DOE creep analysis, DOE performed a finite element structural 

analysis of the drip shield, considering six potential loading scenarios derived from BSC 

(2004al) and using the constitutive creep equations to analyze the extent of drip shield creep. 

DOE assumed that creep will cause the drip shield to collapse when tertiary creep begins at 

any point on the drip shield. Tertiary creep refers to a rapid increase in creep strain rate 

associated with material instability, leading to rupture. DOE assumed a tertiary creep threshold 

of 10 percent strain and concluded that this threshold is conservative because experimental 

observations (Drefahl, et al., 1985aa) indicated that the onset of tertiary creep in titanium alloys 

occurs at about 15 percent strain. On the basis of creep analyses cited in the feature, event, 

and process screening justification (SNL, 2008ab), DOE concluded that the maximum strain is 

below the onset strain for tertiary creep. Therefore, DOE concluded that creep would not impact 

the drip shield’s ability to divert seepage and protect the waste package from anticipated loads. 

DOE concluded that it is appropriate to exclude the feature, event, and process Creep of 

Metallic Materials in the Drip Shield from the performance assessment model on the basis of 

low consequence (SNL, 2008ab). 

NRC Staff’s Review 

The NRC staff reviewed DOE’s justification for assuming a constant temperature of 150 °C 

[302 °F] for the creep analysis. In BSC (2005an), DOE represented titanium creep as a 

thermally activated process, where the susceptibility to creep increases with increasing 

temperature. The treatment of creep as a thermally activated process is consistent with the 

technical literature (Orava, 1967aa; Stetina, 1969aa; Zeyfang, et al., 1971aa; Miller, et al., 

1987aa). In BSC Assumption 3.2.4 (BSC, 2005an), however, DOE stated that the drip shield 

temperature may exceed 150 °C [302 °F] for several hundred years in the event of early drift 

collapse. This suggests that, in the event of early drift collapse, the susceptibility of the drip 

shield to creep could be greater than represented by the DOE analysis for 150 °C [302 °F]. 

As such, the NRC staff submitted an request for additional information to DOE requesting that it 

provide the rationale for using 150 °C [302 °F] as the analysis temperature. DOE stated in DOE 

Enclosure 5, Section 1.2.1 (2009af) that 300 °C [572 °F] is a reasonably bounding temperature 

because there is less than 10−4

 probability that a drip shield will exceed this temperature for 
2-45 

early drift collapse. Further, in DOE Enclosure 5, Section 1.2.3 (2009af), DOE stated that 

the creep equations for 150 °C [302 °F], will not underestimate the extent of creep at 300 °C 

[572 °F] because above 150 °C [302 °F] creep becomes an athermal process (i.e., the 

susceptibility to creep does not increase with temperature). DOE attributed this behavior to the 

phenomenon of dynamic strain aging: a process whereby solute impurity atoms diffuse to areas 

of dislocations and impede dislocation motion. The NRC staff reviewed the technical literature 

and confirmed that investigators (Moskalenko and Puptsova, 1972aa; Stetina, 1980aa) have 

reported a transition in creep control from thermal to athermal processes. There is some 

uncertainty in the transition temperature, as values were reported in the range of 150 to 400 °C 

[302 to 752 °F]. The NRC staff recognizes on the basis of the cited references, however, that 

the transition temperature tends to decrease with decreasing strain rate and approaches 150 °C 

[302 °F] for the low strain rates generally associated with creep. Therefore, DOE’s 

representation of creep as an athermal process at temperatures above 150 °C [302 °F] is 

reasonable. DOE’s assumption that the drip shield temperature is 150 °C [302 °F] for the creep 

analysis is reasonable because (i) creep is likely independent of the temperature at 

temperatures above 150 °C [302 °F] and (ii) the drip shield could experience temperatures 

above 150 °C [302 °F] only during a relatively short period compared to the 10,000-year period 

considered in the creep analysis. Thus, because of (i) and (ii), DOE did not underestimate the 

amount of creep strain in its analysis for the postclosure period. The evaluation of DOE’s 

temperature computation is addressed in TER Section 2.2.1.3.6, where the NRC staff noted that 

temperature computations were appropriate for their intended use within the performance 

assessment model. 

The NRC staff reviewed DOE’s methodology to develop equations to represent the creep of 

Titanium Grades 7 and 29 at room temperature. With respect to Titanium Grade 7, DOE’s 

approach to consider published empirical creep data (Drefahl, et al., 1985aa) as input to the 

analysis is reasonable. The NRC staff notes that the difference in chemical composition 

between Titanium Grades 2 and 7 is the addition of a small amount of palladium in the latter, 

which has a minimal effect on creep behavior because it does not significantly change the alloy 

microstructure. Moreover, the Titanium Grade 2 material Drefahl, et al. (1985aa) studied had 

large grain sizes, which, according to the technical literature (e.g., Ankem, et al., 1994aa; 

Aiyanger, et al, 2005aa) makes it susceptible to creep at temperatures from room temperature 

to 150 °C [302 °F]. On the basis of this information, DOE’s use of the creep data from Drefahl, 

et al. Figure 3 (1985aa) to model the creep behavior of Titanium Grade 7 in the drip shield is 

reasonable. Because BSC Eq. (I-8) (2005an) calculates greater creep strain than Drefahl, et 

al., Figure 3 (1985aa), the use of this equation to represent the room-temperature creep of 

Titanium Grade 7 is reasonable. 

DOE’s approach to consider published empirical creep data for Titanium Grade 5 (Odegard 

and Thompson, 1974aa) as input to the creep analysis of Titanium Grade 29 is reasonable. 

The NRC staff notes that the difference in chemical composition between Titanium Grades 5 

and 29 is the addition of a small amount of ruthenium in the latter, which is expected to have a 

minimal effect on the creep behavior because it does not significantly change the alloy 

microstructure. Odegard and Thompson (1974aa) studied thermally aged Titanium Grade 5; 

DOE described that the microstructure of Titanium Grade 5 is similar to Titanium Grade 29 

given the small differences in composition between Grades 5 and 29. On the basis of this 

information, DOE’s use of the creep data from Odegard and Thompson Figure 3 (1974aa) to 

model the creep behavior of Titanium Grade 29 in the drip shield is reasonable. Because BSC 

Eq. (I-19) (2005an) calculates greater creep strain than Odegard and Thompson Figure 3 

(1974aa), the use of this equation to represent the room-temperature creep of Titanium 

Grade 29 is reasonable. 
2-46 

The NRC staff reviewed DOE’s methodology to rescale the room-temperature creep equations 

for Titanium Grades 7 and 29 to 150 °C [302 °F]. In rescaling the room-temperature creep 

equations, DOE accounted for the temperature effect twice: once using the difference in yield 

stress for the respective temperatures and again using the activation energy. DOE asserted 

that this redundancy is conservative because the activation energy alone should quantify the 

effects of temperature on creep kinetics. The NRC staff noted, however, that there is 

uncertainty in the value of the activation energy for creep of titanium alloys. DOE’s selected 

activation energy of 30 kJ/mol is lower than the activation energy of approximately 150 kJ/mol 

Kiessel and Sinnott (1953aa) and Stetina (1969aa) reported. In BSC (2005an), DOE 

represented the creep strain temperature-dependence as an exponential function of the 

activation energy, such that a small change in the activation energy would yield a large change 

in the calculated creep strain. Therefore, the NRC staff sent a request for additional information 

that DOE address how its methodology for rescaling the room-temperature creep equations to 

150 °C [302 °F] accounts for the uncertainty in the creep temperature dependence. In DOE 

Enclosure 6 (2009af), DOE stated that the activation energy for titanium creep depends on the 

rate-limiting deformation mechanism which, in turn, depends on a number of parameters 

including the alloy microstructure, phase composition, and strain rate. DOE further stated that 

literature reports that give higher activation energy than used in its creep analysis do not provide 

sufficient information about the material and test conditions to support a direct comparison of 

the activation energies. DOE asserted, however, that conservative aspects of its approach to 

quantify creep temperature dependence yield creep equations that calculate greater creep 

strains than have been experimentally measured for Titanium Grades 7 and 29 in the 

temperature range of room temperature to 150 °C [302 °F]. 

The NRC staff reviewed the information DOE provided in DOE Enclosure 6 (2009af). The NRC 

staff compared the creep strains DOE’s temperature-scaled creep equations calculated to 

literature values of creep strain at temperatures comparable to 150 °C [302 °F]. The NRC staff 

confirmed that for Titanium Grade 7, the DOE calculated greater creep strain at 125 °C [257 °F] 

than Teper (1991aa) measured for Titanium Grade 2 at that temperature. Further, DOE 

calculated greater creep strain at 99 and 204 °C [210 and 399.2 °F] than Kiessel and Sinnott 

(1953aa) measured for commercially pure titanium at these temperatures. For Titanium 

Grade 29, the NRC staff confirmed that DOE calculated greater creep strain at 66 and 149 °C 

[150 and 300.2 °F] than Thompson and Odegard (1973aa) measured for Ti-5Al-2.5Sn at these 

temperatures, even though Ti-5Al-2.5Sn has greater susceptibility to creep than Titanium 

Grade 29. In spite of uncertainty in the creep activation energy, DOE overestimated the creep 

strain, in part, because it used creep data from alloys that had microstructures particularly 

susceptible to creep for deriving the room-temperature creep equation. Moreover, DOE 

accounted for the temperature dependence of creep using the difference in yield stress at room 

temperature and 150 °C [302 °F], in addition to the activation energy, whereas the effects of 

temperature on creep kinetics should be physically quantified only in the latter. On the basis of 

this information, DOE’s use of BSC Eqs. (I-15) and (I-22) (2005an) to represent the creep 

behavior of Titanium Grade 7 and 29, respectively, at 150 °C [302 °F] is reasonable, because 

these equations do not underestimate the creep strain of the drip shield. 

The NRC staff reviewed DOE’s assumption that a creep strain of 10 percent anywhere on the 

drip shield will cause its collapse and that any strain smaller than that will not significantly affect 

the drip shield. Long-term creep data for Titanium Grades 2 and 5 from Drefahl, et al. (1985aa) 

show a transition from steady-state secondary creep to unstable tertiary creep at creep strain of 

approximately 15 percent. The NRC staff expects that the creep behavior of Titanium Grades 7 

and 29 will be analogous to those of Titanium Grades 2 and 5, respectively, because the 
2-47 

addition of a small amount of palladium or ruthenium will not significantly affect the alloy 

microstructure. Therefore, 10 percent strain is a reasonable threshold for the onset of tertiary 

creep because it does not underestimate the threshold strain. 

The NRC staff reviewed DOE’s finite difference structural analyses on creep deformation of the 

drip shield exposed to six loading scenarios (BSC, 2004al) presented in BSC (2005an). In 

these analyses, DOE considered the highest vertical pressure applied to the drip shield crown of 

154.81 kPa [22.45 psi]. The NRC staff notes the range of loads DOE considered is reasonable. 

This is further addressed in TER Section 2.2.1.3.2. 

In summary, (i) DOE did not underestimate the amount of creep strain in its analysis for the 

postclosure period, (ii) DOE developed reasonable equations to represent the creep of Titanium 

Grades 7 and 29 at room temperature, (ii) DOE’s methodology to rescale the room-temperature 

creep equations for Titanium Grades 7 and 29 to 150 °C [302 °F] is reasonable, (iv) DOE’s use 

of 10 percent strain is a reasonable threshold for the onset of tertiary creep because it does not 

underestimate the threshold strain, and (v) DOE considered a reasonable range of loads. On 

the basis of the results of this review, the DOE technical basis for excluding FEP 2.1.07.05.0B, 

Creep of Metallic Materials in the Drip Shield, is reasonable. 

FEP 2.1.09.03.0B, Volume Increase of Corrosion Products Impacts Waste Package

DOE excluded Volume Increase of Corrosion Products Impacts Waste Package from the 

performance assessment model on the basis of low consequence (SNL, 2008ab) and 

supplemented its technical basis for exclusion in DOE Enclosures 10–11 (2009ab). In the 

feature, event, and process, DOE considered volume increase of corrosion products 

(increase due to the higher molar volume of corrosion products than intact, uncorroded material) 

from the waste form, cladding, and waste package as a mechanism that could damage the 

waste package. 

DOE excluded the effect of volume increase of corrosion products on the basis of the following: 

(i) if the outer container is not breached, there will be negligible corrosion products; (ii) there are 

unlikely events leading to early waste package outer container failure; (iii) extended time 

(thousands of years) is needed for corrosion products to fill the space between the outer and 

inner containers before any significant stress buildup occurs; (iv) due to the higher Alloy 22 

mechanical strength compared to the stainless steel strength, there is a higher likelihood for the 

inner stainless steel container to deform or crack if additional stresses develop due to the 

corrosion product buildup; and (v) extensive time is needed for the development of stresses 

needed to promote stress corrosion cracking on the waste package outer container. 

The NRC staff reviewed the summary technical basis in the FEP document (SNL, 2008ab) 

and DOE Enclosures 10–11 (2009ab). DOE stated that prior to breach of the Alloy 22 waste 

package outer container, only dry oxidation by residual moisture is possible on the Alloy 22 

inner surface or on the surface of the stainless steel inner container. DOE concluded that the 

residual moisture in the waste package will not result in a large volume of corrosion products to 

cause mechanical damage to the Alloy 22 or stainless steel container. DOE’s conclusion is 

adequate because waste packages are expected to include minimal residual moisture that is not 

sufficient to significantly oxidize metallic containers. 

DOE assessed that for significant corrosion of Alloy 22 inner surface and the stainless steel 

container, the Alloy 22 outer container must first be breached. The Alloy 22 general corrosion 

rates are low. Stress corrosion cracking in the absence of weld flaws or seismic activity would 
2-48 

not breach the outer container in 10,000 years after waste emplacement, according to DOE. 

Combinations of large flaws and stresses are uncommon, and large magnitude seismic events 

capable to fail the waste packages within the first 10,000 years after waste emplacement are 

rare, according to DOE. Nonetheless, to address the case of container failure due, for 

example, to seismic events, DOE assumed failure of the outer container and conducted two 

analyses to estimate the magnitude and timing of stresses on the waste package outer 

container from the corrosion products from the inner vessel corrosion. DOE performed 

analyses, as detailed in DOE Enclosure 10 (2009ab), to show that stresses sufficient to 

enhance degradation of the outer container would not develop within 10,000 years after breach 

of the waste package outer container. 

In DOE’s assessment of the dependence of volume increase of corrosion products on outer 

container corrosion, DOE considered information on Alloy 22 general corrosion rates. The NRC 

staff noted that these corrosion rates were consistent with DOE’s general corrosion model 

evaluated in TER Section 2.2.1.3.1.3.2. On the basis of the NRC staff’s determinations in TER 

Section 2.2.1.3.1.3.2 that the laboratory test results and models for long-term prediction were 

reasonable, the Alloy 22 corrosion rates were adequate for their intended use within 

the performance assessment model. Thus, the assessment of the effect of outer container 

corrosion on the volume increase of corrosion products was reasonable. With regard to early 

failure, localized corrosion, or igneous intrusion model cases, DOE stated in DOE Enclosure 10 

(2009ab) that the performance assessment for these model cases does not take credit for the 

further presence of the waste package; thus, volume increase of corrosion products would 

not change the estimated consequences. 

On the basis of DOE Enclosures 10–11 (2009ab), the NRC staff notes that DOE assessed all 

possible corrosion modes for the inner and failed outer containers. The corrosion modes that 

DOE considered include crevice corrosion, stress corrosion cracking, and galvanic corrosion. 

DOE estimated that the gap between the inner and outer containers would be filled with 

corrosion products after thousands of years (between 1,400 and 37,000 years) after breaching 

of the outer container (SNL, 2008ab). The time for stress buildup sufficient to cause stress 

corrosion cracking on the waste package outer container would exceed 10,000 years after the 

initial waste package breach, according to DOE Enclosure 10 (2009ab). The NRC staff notes 

that uncertainties remain with respect to the magnitude of stainless steel corrosion rates, their 

environmental dependence, and longer term values (He, et al., 2007ab). Higher stainless steel 

corrosion rates could fill the gap with corrosion products and cause stress buildup earlier than 

estimated in DOE’s analyses, increasing the waste package cracked area. However, DOE 

described that the extent of the area compromised by cracks is overestimated by the 

consideration of a crack distribution that fills a two-dimensional space, and consideration of a 

stress level equal to the yield strength of the material (as opposed to allowing the stress to relax 

when cracks form or grow), as detailed in DOE Enclosures 10 and 5, respectively, (2009ab,cj) 

and SNL Section 6.7.3 (2007bb). On the basis of DOE’s information, and supported by the 

NRC staff’s evaluation of stress corrosion crack size and density in TER Section 2.2.1.3.1.3.2.3 

(wherein the NRC staff noted that DOE appropriately abstracted the dimension of waste 

package area damaged by stress corrosion cracking for the intent of the performance 

assessment), the NRC staff notes that DOE provided adequate support for its conclusion that 

additional stresses from the stainless steel corrosion products are unlikely to significantly 

increase the extent of the waste package area covered by cracks. In the case of large weld 

flaws leading to stress corrosion cracking initiation, DOE’s conclusion that the results of the 

performance assessment would not significantly change is reasonable; DOE reached this 

conclusion by considering stress buildup from stainless steel corrosion products leading to a 

larger waste package area covered by cracks (larger than the weld cracks alone). This is 
2-49 

because DOE stated (i) large welds flaws leading to stress corrosion cracking are rare and (ii) it 

could take thousands of years for enough corrosion product buildup to fill inner and outer 

container gaps and even longer to develop sufficient stress buildup. Therefore, on the basis of 

the DOE analyses considering a complete range of waste package failure modes, computation 

of the time to fill gaps and produce significant stresses on the waste package outer container, 

and computations of the area compromised by cracks that are likely to overestimate the waste 

package damage area, the exclusion of the feature, event, and process Volume Increase of 

Corrosion Products Impacts Waste Package by low consequence is reasonable. 

FEP 2.1.09.28.0A, Localized Corrosion on Waste Package Outer Surface Due 

to Deliquescence 

DOE excluded Localized Corrosion on Waste Package Outer Surface Due to Deliquescence on 

the basis of low consequence (SNL, 2008ab) and supplemented its technical basis for exclusion 

in DOE Enclosures 12–15 (DOE, 2009ab). In the feature, event, and process, DOE considered 

that moisture from air could be absorbed by salts in dust deposited on the waste package, even 

at low relative humidity; this moisture could dissolve the salts and create concentrated aqueous 

solutions or brine. According to DOE, these brines could promote localized corrosion of the 

waste package outer surface. 

DOE’s analysis of the penetration of the Alloy 22 waste package outer barrier by localized 

corrosion induced by dust deliquescence brines was based on the following five questions from 

SNL, pp. 6-705 to 6-710 (2008ab): 

1. Can multiple-salt deliquescent brines form at elevated temperature? 

2. If deliquescent brines form at elevated temperature, will they persist? 

3. If deliquescent brines persist, will they be corrosive? 

4. If deliquescent brines are potentially corrosive, will they initiate localized corrosion? 

5. Once initiated, will localized corrosion penetrate the waste package? 

In SNL (2008ab), DOE stated that the answers to those questions are (1) yes, (2) sometimes, 

(3) not expected, (4) no, and (5) no, respectively. Because all of the questions must be 

answered affirmatively for outer container penetration to be possible, DOE concluded that 

localized corrosion was unlikely. In summary, DOE concluded that brines formed by dust 

deliquescence are not expected to be aggressive; the amount of brine volume that will be 

distributed on the waste package will be extremely small and will not support the initiation of 

localized corrosion; and several processes will stifle localized corrosion limiting, penetration of 

the waste package outer container (SNL, 2008ab). Accordingly, DOE excluded Localized 

Corrosion on Waste Package Outer Surface Due to Deliquescence from the performance 

assessment model. 

NRC Staff’s Review 

The NRC staff reviewed the technical basis in the feature, event, and process 

document (SNL, 2008ab), additional information in BSC (2005aa) and SNL (2007al), and 

the analysis to supplement the screening justification in DOE Enclosures 12–15 (2009ab). 

DOE provided a key technical basis: the brine volume will be extremely small {2 μL/cm2

[7.87 × 10−4

 in3/in2]} and it will be mixed with a large amount of insoluble dust on the waste 

package surfaces in the repository setting. Under this condition, the NRC staff notes that DOE’s 

conclusion in DOE Enclosures 12–15 (2009ab) that localized corrosion will not initiate nor 

propagate even if initiated is reasonable. DOE provided preliminary experimental results to 
2-50 

support the analysis, obtained with specimens made of Alloy 22 and a series of less corrosion 

resistant analog materials (Inconel® 825, Hastelloy® C-276, and 80:20 Ni:Cr alloy). Some of the 

specimens were creviced specimens formed with a polytetrafluoroethylene-lined ceramic former 

and coated with a layer of salt mixtures expected to deliquesce under the repository conditions. 

DOE considered that the salt loading in the tests was greater than expected on the waste 

packages, as identified in DOE Enclosure 13, p. 6 (2009ab). The specimens were placed in 

a humidity chamber at 180 °C [356 °F] and at a relative humidity that enabled the coated salts 

to deliquesce. After an exposure of 25 or 50 days, the specimens were examined and no signs 

of localized corrosion were observed for Alloy 22, or in the less corrosion resistant Inconel 825, 

as described in BSC Section 6.4.2.2(a) (2005aa) and DOE Enclosures 12–15 (2009ab). On 

the basis of these short-term experiments showing that localized corrosion did not initiate 

under specific conditions enabling deliquescence of salts, the NRC staff notes that DOE 

provided a technical basis to exclude the feature, event, and process from the performance 

assessment model. DOE’s conclusion that there is no evidence that localized corrosion could 

initiate and be sustained for extended periods in deliquescent solutions is reasonable. Thus, 

DOE’s technical basis to exclude the feature, event, and process from the performance 

assessment is reasonable. 

FEP 2.1.11.06.0A, Thermal Sensitization of Waste Packages 

DOE excluded Thermal Sensitization of Waste Packages from the performance assessment 

model on the basis of low consequence (SNL, 2008ab) and supplemented its technical basis for 

exclusion in DOE Enclosures 16–17 (2009ab). According to DOE’s definition of the feature, 

event, and process, phase changes in waste package materials could result from long-term 

storage under repository thermal conditions; phase changes could affect the corrosion 

resistance and mechanical properties of waste package materials. DOE described a model for 

long-term thermal aging and phase stability of Alloy 22 based on experimental measurements 

and theoretical calculations (BSC, 2004ab). The phase stability studies included tetrahedrally 

close-packed phase precipitation in the base metal and in the welded regions, and long-range 

ordering reactions. DOE conducted thermodynamic and kinetic modeling to predict the rate of 

precipitation of tetrahedrally close-packed phases and long-range ordering in Alloy 22 using the 

Thermo-Calc and DICTRA software and databases. DOE assessed validity of the aging and 

phase stability model and the databases in BSC (2004ab) and DOE Enclosures 16–17 

(2009ab). According to the calculated time-temperature-transformation diagrams for the 

formation of P, σ, and ordered phases in Alloy 22 base metal, DOE stated that even if the 

temperature were to remain at the peak temperature for all time (which is an extremely 

conservative consideration), the transformation would still not have progressed 5 percent to 

completion after well over 1 million years. According to DOE, the planned solution annealing 

and quenching conditions for the waste package outer container are sufficient to prevent 

phase instability in Alloy 22. DOE compared the model results to the extent of tetrahedrally 

close-packed phase precipitation obtained from short-term aging experiments at temperature 

ranges exceeding those expected in the repository and the extent of long-range ordering from 

microhardness measurements. On the basis of these results, DOE concluded that insignificant 

aging and phase instability would occur in Alloy 22 under conditions that bound repository 

temperatures DOE estimated. 

DOE also evaluated the effects of welding and thermal aging on the corrosion rate and localized 

corrosion resistance of Alloy 22 in the mill-annealed, as-welded, and as-welded plus thermally 

aged conditions (SNL, 2008ab, 2007al). On the basis of the results of short-term 

electrochemical tests, DOE stated that thermal aging and phase instability do not adversely 

affect the corrosion resistance of Alloy 22. In summary, DOE concluded that, on the basis of 
2-51 

the model predictions and experimental evidence, long-term thermal aging is insignificant and 

phase instability is not expected to adversely affect the corrosion resistance of the waste 

package outer container. Therefore, DOE excluded the feature, event, and process from the 

performance assessment model on the basis of low consequence. As described in DOE 

Enclosure 16 (2009ab), DOE has imposed a restricted Alloy 22 composition range (SAR 

Table 1.9-9, Design Control Parameter 03-19), which has a narrower range of chemical 

compositions for Cr, Mo, Fe, and W compared to the composition limits specified in the 

standard ASTM B 575-04 (ASTM International, 2004aa). According to DOE, the design 

properties for Alloy 22 are in compliance with the ASME SB–575 specification (American 

Society of Mechanical Engineers, 2001aa). 

NRC Staff’s Review 

The NRC staff reviewed DOE’s technical basis for excluding the feature, event, and process 

and its model assumptions and model support in areas related to long-term thermal aging and 

phase stability of Alloy 22, as detailed in DOE Enclosures 16–17 (2009ab) and BSC (2004ab). 

The NRC staff has performed independent analyses of potential effects of thermal exposures to 

elevated temperatures on the phase stability of Alloy 22 and noted that thermal aging and 

fabrication processes could enhance precipitation of tetrahedrally close-packed phases (Pan, et 

al., 2005aa) and decrease the localized corrosion resistance of Alloy 22 (Dunn, et al., 2006aa). 

However, the potential effect of this decreased corrosion resistance is bounded by DOE’s 

general and localized corrosion model abstractions for the waste package outer container, 

which as described by DOE, tend to overestimate the extent of general corrosion damage 

(due, for example, to a bias toward higher corrosion rates to account for differences in sizes of 

experimental metal coupons and size of “patches” on the waste package surface for the 

performance assessment computations, consideration of an enhancement factor to account for 

microbially enhanced corrosion, and other factors discussed in TER Section 2.2.1.3.1.3.2.1) and 

the frequency of localized corrosion (e.g., as discussed in TER Section 2.2.1.3.1.3.2.2, DOE’s 

localized corrosion model predicts initiation of localized corrosion for some environmental 

conditions under which localized corrosion is not experimentally observed). The NRC staff 

reviewed DOE’s description of the extent of general corrosion damage and the frequency of 

localized corrosion in TER Section 2.2.1.3.1.3.2 and, as described therein, notes that DOE’s 

approach tends to overestimate the extent of general corrosion damage and the frequency of 

localized corrosion and is reasonable. The mechanical properties of the Alloy 22 fabrication 

welds will meet or exceed ASME SB–575 (American Society of Mechanical Engineers, 

2001aa)—specified minimum mechanical property requirements, as DOE indicated in DOE 

Enclosures 16–17 (2009ab). On the basis of DOE’s thermal aging and phase stability 

analyses and DOE’s description that abstractions for general and localized corrosion of the 

waste package outer container are bounding, DOE’s conclusion that long-term phase stability of 

Alloy 22 is of low consequence is reasonable. Therefore, it is reasonable to exclude the feature, 

event, and process Thermal Sensitization of Waste Packages by low consequence (with respect 

to corrosion resistance and mechanical properties of Alloy 22). 

FEP 2.2.07.05.0A, Flow in the Unsaturated Zone (UZ) From Episodic Infiltration 

DOE excluded Flow in the Unsaturated Zone from Episodic Infiltration from the performance 

assessment model on the basis of low consequence (SNL,2008ab), supplemented its technical 

basis for exclusion in DOE Enclosure 8 (2009cb) and DOE Enclosure 1 (2009cc), and provided 

supplemental material relevant to the technical basis for exclusion in DOE Enclosure 5 

(2009bo). This feature, event, and process refers to the influence of episodic flow on 

radionuclide transport in the unsaturated zone; specifically, transient flow arising from episodic 
2-52 

infiltration events. DOE stated that episodic flow through and below the repository horizon is 

expected to be strongly attenuated by the overlying Paintbrush Tuff nonwelded (PTn) 

hydrogeologic unit. 

DOE stated that periods of high precipitation and water percolation are expected to occur during 

future rain storms, and also that the porous rock matrix in the PTn unit is expected to strongly 

attenuate episodic percolation fluxes. DOE described the PTn unit as ranging from 

approximately 21 m [70 ft] to over 120 m [400 ft] within the repository area. DOE stated that 

flow attenuation by the PTn is predicted to yield steady flow below the PTn in the unsaturated 

zone, except for volumetrically insignificant rapid flow through preferential pathways in the PTn. 

DOE asserted that transient flow below the PTn may occur in the southern part of Solitario 

Canyon because the PTn is completely offset by the Solitario Canyon Fault. However, episodic 

flow is not expected to significantly affect performance, because the emplacement drifts would 

be located away from Solitario Canyon in the affected area. 

DOE based the assessment of episodic flow attenuation on two transient, one-dimensional 

simulations reported in SNL Section 6.9(a) (2007bf). DOE observed that the maximum flux 

below the PTn for these two simulations was around 17 mm/yr [0.67 in/yr], compared to the 

overall percolation flux uncertainty for the post-10,000-year period of 51 mm/yr [2 in/yr]. DOE 

supported its analysis by citing other studies considering one-, two-, and three-dimensional 

simulations, all using earlier estimates for PTn parameters. DOE stated that the other studies, 

in general, show similar damping of percolation flux by the PTn matrix. 

DOE further supported the assessment of episodic flow attenuation in the PTn using results 

from (i) a water-release test within the PTn (in Alcove 4), (ii) line surveys of fracture minerals 

in tunnels below the PTn, (iii) inferred stagnation of a wetting pulse below the channel of 

Pagany Wash, and (iv) inferred long residence times in the PTn on the basis of C-14 

observations from boreholes. 

DOE considered Cl-36 observations from tunnels below the PTn, some of which have 

a radioisotope signature indicating that a portion of the in-situ waters infiltrated during 

or subsequent to the period of nuclear device testing from 1954 through 1970. DOE concluded 

that high observed concentrations of Cl-36 in some samples taken from the Exploratory Studies 

Facility tunnel possibly indicate relatively small amounts of fracture flow penetrating as fast 

pathways, either steady or transient, through fault zones between the ground surface and the 

repository elevation. DOE used flow and transport models to examine the Cl-36 observations, 

concluding that the quantity of water penetrating the PTn as a result of fast pathways is 

approximately 1 percent of total infiltration, and characterized this quantity as negligible with 

respect to repository performance. 

DOE also considered tritium data from boreholes and tunnels below the PTn. DOE concluded 

that some observations of tritium below the PTn within the Exploratory Studies Facility and 

Enhanced Characterization of the Repository Block tunnels, and from five boreholes, also have 

a bomb-pulse or post-bomb-pulse radioisotope signature. DOE’s analysis of the data led to the 

following conclusions: (i) all of the elevated tritium observations from the Exploratory Studies 

Facility are associated with faults, (ii) the elevated observations in the boreholes may be 

associated with lateral flow from faults, and (iii) most elevated tritium observations from the 

Enhanced Characterization of the Repository Block are not associated with faults but may be 

associated with fast and focused (but not necessarily episodic) flow pathways. 
2-53 

DOE concluded that the PTn will attenuate most episodic flow, resulting in approximately 

steady-state flow in the repository host rock and below, and the volume of flow that could lead to 

episodic flow in the repository host rock is small. Therefore, DOE excluded the feature, event, 

and process Flow in the Unsaturated Zone from Episodic Infiltration from the performance 

assessment model. 

NRC Staff’s Review 

Because both the modeling results and field observations are consistent with DOE’s description 

of the Flow in the Unsaturated Zone from Episodic Infiltration feature, event, and process, the 

information DOE provided supports the concept that the PTn matrix has a strong potential for 

dampening large pulses with matrix imbibition and episodic flow is most likely to be associated 

with prominent structural features (e.g., faults and intensely fractured zones). The information 

provided supports DOE’s conclusion that bomb-pulse tritium observations in boreholes below 

the PTn are likely associated with lateral flow from faults and localized fast flow pathways that 

are not necessarily episodic flow. However, DOE did not show that episodic flow below the PTn 

is entirely precluded based on a relationship of the episodic flow to prominent structural 

features, because (i) 11 of the 22 tritium observations in the Enhanced Characterization of the 

Repository Block exhibit a modern signature despite being located more than 100 m [330 ft] 

from a mapped fault or intensely fractured zone and (ii) the travel times through the PTn that 

DOE concluded were necessary to explain these observations without invoking episodic flow 

are more than an order of magnitude faster than those obtained from the calibrated parameters. 

The NRC staff notes that the information DOE provided supports the DOE conclusion that 

episodic flow has a low consequence for the performance assessment. DOE considered 

increased seepage into emplacement drifts to be the largest performance consequence that 

would arise from episodic flow, but expects that any additional seepage would be small relative 

to the difference in percolation flux considered during calibration of infiltration uncertainty. In 

TER Section 2.2.1.3.6.3.2, the NRC staff considers episodic flow in the larger context of DOE’s 

representation of the spatial and temporal variability of ambient percolation flux above and 

through the proposed repository horizon during performance assessment. In its review of 

information related to flow above the repository horizon (TER Section 2.2.1.3.6.3.2), the NRC 

staff noted that (i) systematic increases in seepage arising from episodic flow are small relative 

to the difference in percolation flux considered during calibration of infiltration uncertainty using 

DOE’s assessment of fast pathways, (ii) increases in seepage are comparable using a 

conservative assessment of episodic pathways, and (iii) DOE showed that calculated maximum 

mean annual dose in the first 10,000 years is not substantially sensitive to systematic changes 

in seepage considered during calibration of infiltration uncertainty. Thus, DOE’s technical basis 

to exclude the feature, event, and process Flow in the Unsaturated Zone from Episodic 

Infiltration from the performance assessments on the basis of low consequence is reasonable. 

FEP 2.2.08.03.0A, Geochemical Interactions and Evolution in the Saturated Zone 

DOE excluded Geochemical Interactions and Evolution in the Saturated Zone on the basis of 

low consequence, as outlined in SNL (2008ab) and supplemented its technical basis for 

exclusion in DOE Enclosure 1 (2009ai). According to DOE’s feature, event, and process 

definition, groundwater chemistry and other characteristics may change over time as a result of 

disposal system evolution or from mixing with other waters. Geochemical interactions may lead 

to dissolution and precipitation of minerals along the groundwater flow path, affecting 

groundwater flow, rock properties, and sorption of radionuclides (SNL, 2008ab). 
2-54 

In DOE Enclosure 1 (2009ai), DOE further examined natural groundwater geochemical 

variations in the immediate vicinity and downgradient from Yucca Mountain as a function of 

space and time. DOE stated in DOE Enclosure 1 (2009ai) that chemical compositions exhibit 

spatial variability that may be related to mixing of waters. (The NRC staff evaluates DOE’s 

model abstractions of flow paths in the saturated zone in TER Section 2.2.1.3.8.) DOE stated 

that temporal changes in properties that may affect radionuclide sorption, such as pH, 

temperature, and major ion chemistry, are gradual and fall within the range of groundwater 

chemistries it considered in developing the transport parameter (sorption coefficients or Kd) 

values used in the saturated zone transport model of the performance assessment (SAR 

Section 2.3.9.3). 

In its model abstraction for radionuclide transport through the saturated zone, DOE assumed 

oxidizing conditions along the flow paths through the tuff and alluvium. DOE stated in DOE 

Enclosure 1 (2009ai) that redox potential has a strong effect on the transport of redox-sensitive 

radionuclides. DOE also stated that other groundwater conditions such as reducing zones that 

may affect radionuclide sorption are localized in extent and unlikely to be changed at a larger 

scale for at least 10,000 years after repository closure. To support this statement, DOE 

reasoned that (i) there is sufficient pyrite in reducing hydrogeological units of the saturated zone 

to sustain those reducing conditions, (ii) the long residence time of water in the saturated zone 

causes its oxidation state to be largely determined by water–rock interactions, and (iii) no 

current mechanism is known to support the concept that reducing zones will become more 

extensive along the saturated zone path (SNL, 2008ab). For these reasons, DOE excluded 

Geochemical Interactions and Evolution in the Saturated Zone from the performance 

assessment model on the basis of low consequence (SNL, 2008ab). 

DOE also presented performance assessment calculations that indicated the radionuclides that 

contribute the most to the calculated mean annual dose during the first 10,000 years after 

repository closure are nonsorbing, and that radionuclides whose sorption is most affected by 

changes in these geochemical parameters (Pu-239 and -240, Np-237, and Se-79) only 

constitute about 20 percent of the total mean annual dose during the first 10,000 years after 

repository closure. DOE also indicated in DOE Enclosure 1 (2009ai) that for the igneous 

intrusion modeling case, the release rates of plutonium and neptunium are only slightly sensitive 

to Kd values in volcanic rocks, but not sensitive to Kd values in the alluvium. 

NRC Staff’s Review 

The NRC staff reviewed the model assumptions and field and laboratory data DOE used to 

support its screening justification, as identified in SNL (2008ab) and DOE Enclosure 1 (2009ai). 

To address uncertainty associated with natural variations in pH, temperature, and major ion 

chemistry, DOE considered a range in aqueous water chemistries in developing parameter 

distributions for the model abstraction of radionuclide transport through the saturated zone for 

the performance assessment (SAR Section 2.3.9.3). The NRC staff is aware of information that 

suggests temporal variations in key geochemical parameters that may influence potential 

sorption in the regional aquifers around Yucca Mountain (Perfect, et al., 1995aa; Turner and 

Pabalan, 1999aa). The NRC staff notes, however, that the likely changes in radionuclide 

sorption from evolving groundwater geochemistry are adequately captured by the range of Kd

distributions considered in the performance assessment. Although DOE did not consider 

variability in redox conditions in developing the transport parameter distributions, the available 

information indicates that reducing conditions are limited to localized areas that do not appear to 

be widespread on a regional scale, as described in SNL Appendix F (2007ba). On the basis of 
2-55 

the foregoing considerations and discussion of information DOE provided, the exclusion of this 

feature, event, and process on the basis of low consequence is reasonable. 

FEP 2.2.08.03.0B, Geochemical Interactions and Evolution in the Unsaturated Zone 

DOE excluded the feature, event, and process of Geochemical Interactions and Evolution in the 

Unsaturated Zone on the basis of low consequence following SNL (2008ab) and supplemented 

its technical basis for exclusion in DOE Enclosure 18 (2009ab). According to DOE’s description 

of the feature, event, and process, the geochemical environment of the unsaturated zone may 

evolve over time in response to thermal and chemical perturbations introduced by the repository 

system. Precipitation or dissolution of minerals or changes in groundwater chemistry may affect 

the flow and composition of seepage into drifts or the transport of radionuclides in the near-field 

environment (SNL, 2008ab). In the screening justification, DOE considered (i) how elevated 

temperatures would affect geochemical interactions between water and rock in the vicinity of the 

emplacement drifts and (ii) how changes in water chemistry due to reactions with repository 

construction materials would subsequently affect flow and transport properties in the 

unsaturated zone (SNL, 2008ab). DOE, in DOE Enclosure 18 (2009ab), also considered how 

geochemical interactions between waste package effluent and the solids and ambient waters 

might affect radionuclide transport in the crushed tuff invert and in the unsaturated rock beneath 

the repository drift. 

DOE cited model analyses of geochemical interactions that estimated how drift seepage 

chemistry and near-field flow properties would be affected by changes in temperature, pH, 

redox potential, ionic strength and other compositional variables, time dependency, precipitation 

or dissolution, and resaturation times, as described in SNL (2007ai) and SNL Section 7.1.2.2 

(2007ak). DOE determined that the expected changes would be limited to small changes near 

the drift wall or, at a larger scale, within the range of variation that is already considered in the 

performance assessment. DOE reasoned that there would be little potential for cementitious 

materials in the repository to affect radionuclide transport by forming an alkaline cement 

leachate plume because (i) of the minor amount of cementitious material to be used in 

construction of the repository, none will be used in the waste emplacement drifts themselves 

and (ii) high pH conditions in an alkaline cement leachate plume would be rapidly neutralized in 

the unsaturated zone by reaction with ambient carbon dioxide. As a result, DOE concluded 

there would be little opportunity for the cement leachate to interact chemically with radionuclides 

or to affect radionuclide transport pathways by precipitation of calcite. 

DOE also concluded that there would be little potential for evolved waste package fluids to 

cause more than minor changes in unsaturated zone fluid compositions. In DOE Enclosure 18 

(2009ab) DOE cited the description of waste package chemistry (SAR Section 2.3.7.5.3.1) in 

stating that the main chemical factors in the effluent that affect radionuclide solubility will 

generally overlap the expected ranges of composition of the ambient unsaturated zone waters. 

Any change in effluent composition by reaction with the main chemical components of the 

engineered materials (iron, chromium, nickel) will be limited by the formation of low-solubility 

corrosion products inside the waste package. DOE reasoned that the waste package effluent 

may become concentrated by evaporation or consumption of water by degradation reactions, 

but upon exiting the waste package, the mixing of effluent with ambient waters in the invert and 

unsaturated zone would quickly dilute the effluent, resulting in no significant changes in bulk 

water chemistry in the unsaturated zone. 
2-56 

NRC Staff’s Review 

The NRC staff analyzed DOE’s model assumptions, empirical data, and model support related 

to water–rock interactions at elevated temperatures and related to other geochemical 

interactions in the unsaturated zone influenced by reaction with cementitious engineering 

materials or waste package effluent. The geochemical modeling analyses DOE cited support 

DOE’s explanation that changes in the unsaturated zone resulting from geochemical 

interactions at elevated temperatures, and those involving waste package effluent, are within 

the expected range of ambient conditions. Additionally, DOE’s basis for excluding geochemical 

interactions with alkaline cement leachate is reasonable because (i) the repository design limits 

the use of cementitious materials near the waste emplacement drifts and (ii) geochemical 

interactions with carbon dioxide would neutralize the effects of an alkaline plume in the 

unsaturated zone. Therefore, DOE’s consideration of potential geochemical processes, as 

summarized in the preceding paragraphs, provides a reasonable basis to exclude the feature, 

event, and process Geochemical Interactions and Evolution in the Unsaturated Zone on the 

basis of low consequence. 

FEP 2.2.08.04.0A, Re-Dissolution of Precipitates Directs More Corrosive Fluids to 

Waste Packages 

DOE excluded Re-Dissolution of Precipitates Directs More Corrosive Fluids to Waste Packages 

on the basis of low consequence (SNL, 2008ab). According to DOE’s description of the feature, 

event, and process, the heat generated by radioactive decay inside the waste packages is 

expected to dry out the rock surrounding the emplacement drifts. Evaporation of the pore 

waters will leave behind precipitates that may plug pores. Re-dissolution of precipitates may 

produce a pulse of fluid reaching the waste packages when gravity-driven flow resumes, which 

is more corrosive than the original fluid in the rock (SNL, 2008ab). 

DOE expects rewetting of the host rock around the drifts to occur as the temperature drops 

below the boiling point of water. Initially, DOE explained, precipitates could dissolve and form 

brines. During the initial stages of rewetting, re-dissolution of precipitated minerals may 

temporarily concentrate chloride and other soluble components relative to ambient solutions. 

As rewetting continues, DOE expects the brines to become diluted and pore waters to return to 

ambient compositions. DOE stated that the drip shield is expected to perform its diversion 

function during the time when the transient changes in pore water composition could occur, 

preventing potentially corrosive waters from contacting waste packages (SNL, 2008ab). 

In addition to the undisturbed repository performance, DOE also evaluated this feature, event, 

and process in the event of early drip shield failure and seismic events. In the event of early 

drip shield failure, DOE assumed that a waste package under a compromised drip shield and at 

a seepage location would fail by localized corrosion; thus no additional failures would occur as a 

result of compositional changes due to re-dissolution of precipitates. In the event of a seismic 

event prior to rewetting and re-dissolution of precipitates, DOE described that the frequency 

and extent of drip shield failure would be generally insignificant. DOE also excluded 

FEP 2.1.03.10.0B, Advection of Liquids and Solids Through Cracks in the Drip Shield, on the 

basis of low consequence, as described in SNL (2008ab) and DOE Enclosure 2 (2009ab). In 

that feature, event, and process, DOE analyzed in the screening justification scenarios allowing 

for water infiltrating failed drip shields and contacting the waste packages and concluded that 

those scenarios would not change the magnitude of the dose estimates, as identified in SNL 

(2008ab) and DOE Enclosure 2 (2009ab). 
2-57 

NRC Staff’s Review 

DOE’s technical basis for exclusion is based on the drip shield performance. On the basis of 

the review documented in TER Section 2.2.1.3.2, the DOE conclusion that the drip shield would 

protect the waste package during a potential re-dissolution period is reasonable. The NRC staff 

also considered the DOE screening justifications for FEP 2.1.03.10.0B, Advection of Liquids and 

Solids Through Cracks in the Drip Shield, and FEP 2.1.03.02.0B, Stress Corrosion Cracking of 

Drip Shields. DOE provided a probabilistic analysis in DOE Enclosure 2 (2009ab) to estimate 

the additional number of waste packages failed and additional radionuclide releases if drip 

shields failed in the first 10,000 years due to seismic events. DOE concluded that additional 

consequences would be too small to change the dose estimates. As explained in the NRC 

staff’s reviews of FEP 2.1.03.10.0B, Advection of Liquids and Solids Through Cracks in the Drip 

Shield, and FEP 2.1.03.02.0B, Stress Corrosion Cracking of Drip Shields, the DOE conclusion 

is reasonable and consistent with the review documented in TER Section 2.2.1.3.2. Therefore, 

exclusion of the feature, event, and process is reasonable on the basis of the drip shield 

function, which will prevent contact of potentially corrosive fluids with the waste packages during 

the thermal period when the potential for such conditions would exist. With respect to drip 

shield failure by seismic events in the first 10,000 years, DOE’s conclusion that additional 

consequences would not affect dose estimates is reasonable. Past the thermal pulse period, 

the DOE abstraction predicts that there is a low probability for the repository environment 

(i.e., temperature, pH, and chemical composition of in-drift waters) to support localized corrosion 

of the waste package even if the drip shield fails and allows seepage water to contact the waste 

package. The staff determined that DOE abstractions for the chemistry of water in the drifts and 

localized corrosion, evaluated in TER Sections 2.2.1.3.3.3.2 and 2.2.1.3.1.3.2.2 are reasonable. 

Thus, as related to this feature, event, and process and its technical basis for exclusion, there 

are no potential consequences from seismic events beyond the 10,000-year postdisposal period 

through the period of geologic stability. Therefore, the exclusion of the feature, event, and 

process Re-Dissolution of Precipitates Directs More Corrosive Fluids to Waste Packages from 

the performance assessment is reasonable. 

FEP 2.2.09.01.0B, Microbial Activity in the Unsaturated Zone 

DOE excluded Microbial Activity in the Unsaturated Zone on the basis of low consequence 

(SNL, 2008ab) and supplemented its technical basis for exclusion in DOE Enclosure 1 (2009ci). 

According to DOE’s definition of the feature, event, and process, microbial activity may affect 

radionuclide sorption processes in the unsaturated zone by changing groundwater pH and 

redox conditions, by adding complexing agents to the water, or by changing the valence state of 

certain radionuclides by biotransformation. In addition, a microbe suspended in water may act 

as a biocolloid, facilitating the transport of radionuclides in the unsaturated zone by sorbing to 

the microbe itself. DOE also considered in DOE Enclosure 1 (2009ci) that increased microbial 

activity associated with condensation in the unsaturated zone during the early thermal period 

could affect the chemistry of water entering the drifts as seepage. 

In the screening justification, DOE cited information provided in another excluded 

FEP 2.1.10.01.0A, Microbial Activity in the Engineered Barrier System (SNL, 2008ab), and in 

a supporting report, BSC Section 6 (2004aq), that evaluated the potential impact of microbial 

activity on drift chemistry. DOE stated in BSC Section 6.3 (2004aq) that although laboratory 

analyses have identified a diverse microbial population in Yucca Mountain tuff samples, the 

microbes are largely dormant under ambient conditions due primarily to constraints on the 

availability of nutrients and water in the unsaturated tuffs, as identified in BSC Section 6.4 

(2004aq). DOE stated that any variation in radionuclide sorption coefficients that might be 
2-58 

caused by changes in water chemistry due to microbial activity are within the existing range of 

the sorption coefficient distributions used in the performance assessment calculations. 

Similarly, DOE reasoned that the uncertain, small concentration of biocolloids in Yucca 

Mountain groundwaters is encompassed by the wide range of concentration values for naturally 

occurring colloids that is already sampled for radionuclide transport calculations. DOE also 

noted that the uncertainty distributions specified for the sorption coefficients in the performance 

assessment calculations implicitly included the effects of naturally occurring microbial activity 

because DOE developed the radionuclide Kd distributions from sorption experiments on the 

basis of the chemistry of Yucca Mountain water samples, as described in FEP 2.2.09.01.0B 

(SNL, 2008ab). 

NRC Staff’s Review 

The NRC staff has reviewed the adequacy of DOE’s sorption-related and colloid-associated 

unsaturated zone transport parameters in TER Section 2.2.1.3.7 and notes that the ranges of 

parameter estimates in the performance assessment are reasonable. On the basis of the NRC 

staff’s evaluation of the site-specific data DOE used to support the unsaturated zone 

radionuclide transport abstraction, DOE’s screening analysis for excluding microbial activity in 

the unsaturated zone adequately assessed the potential effects of microbial activity under 

ambient conditions. 

For perturbed conditions in the unsaturated zone during the repository’s early thermal period, 

DOE stated in DOE Enclosure 1 (2009ci) that microbial activity associated with water vapor 

condensation in the near-field rock beyond the dryout zone would not significantly affect 

seepage chemistry for three main reasons. First, a change from ambient to warmer 

temperatures in the near-field rocks would not increase the availability of already scarce 

nutrients in the rock, so the nutrient limitation on microbial activity would persist. Second, DOE 

estimated from modeling calculations that even for ambient conditions, the relative humidity of 

the densely welded Topopah Spring tuff at the repository horizon is already at the upper end of 

conditions that support optimal microbial activity in the matrix pore spaces. As a result, DOE 

reasoned that an increase in microbial activity in the condensation zone would be limited 

primarily to water that condensed in fractures. Third, DOE stated that for the near-field 

environment as a whole, any increased microbial activity in the condensation zone would be 

offset by reduced microbial activity in the dryout zone during the same period. 

The NRC staff reviewed DOE’s screening justification for microbial activity during the thermal 

period in the context of DOE’s thermohydrologic models for Yucca Mountain, as described in 

SNL Section 6.1 (2008aj); site characterization data for the densely welded Topopah Spring tuff, 

described in BSC Section 7.2 (2004bi); Yucca Mountain field tests and interpretations of fracture 

flow and transport processes in BSC Section 7.6.3.1 (2004bi) and BSC Section 6.2.4 (2006aa); 

and observations of increased microbial activity in fractures during an unsaturated zone 

infiltration and transport experiment at Yucca Mountain, explained in BSC Section 6.1.2 

(2006aa). 

 

The NRC staff has reviewed DOE’s thermohydrologic models in TER Section 2.2.1.3.6 and 

notes they provide a basis for the performance assessment abstraction. DOE’s information 

on increased microbial activity in the unsaturated zone during the thermal period in DOE 

Enclosure 1 (2009ci) identified that scarcity of nutrients in the rocks is one of the main factors 

limiting microbial activity. DOE has provided site characterization data to support the statement 

that low concentrations of specific nutrients, such as phosphate and organic carbon, are 

expected to persist in the subsurface environment throughout the postclosure period for ambient 
2-59 

as well as thermal conditions. DOE appropriately concluded that microbial activity would 

continue to be restricted by nutrient availability during the thermal period because there are no 

significant processes that would enhance the concentration of nutrients in the rocks at elevated 

temperatures compared to the ambient concentrations. 

DOE stated that despite the limited availability of nutrients, the increased availability of water in 

fractures may contribute to an increase in microbial activity in the condensation zone. The 

technical basis for DOE’s statement is supported by observations from a Yucca Mountain 

large-scale unsaturated zone field experiment, the Large Plot Test, which reported in BSC 

Section 6.1.2 (2006aa) the growth of biofilms (slime layers excreted by certain microbes at 

solid–water interfaces) in fractures on a wetted floor in the subsurface at Yucca Mountain. DOE 

provided a reasonable technical basis for the statement that increased microbial activity in the 

condensation zone would be limited to water in the fractures because water evaporated from 

the dryout zone would migrate preferentially through fractures and condense in cooler locations 

outside the dryout zone. DOE’s technical basis stated that the water in fractures represented 

only a small fraction of the total water content of the rock, but DOE’s technical basis did not 

address the potential effect of increased microbial activity in the condensation zone fractures in 

the context of (i) the importance of water in fracture-dominated flow pathways as potential 

seepage into drifts (SAR Section 2.3.3.1) or (ii) the potentially large fraction of water transferred 

by evaporation from the dryout zone to the fractures of the condensation zone during the 

thermal period, as detailed in SNL Section 6.1.2 (2008aj). However, DOE’s conceptual 

thermohydrologic model, which was described in SNL Section 6.1.3 (2008aj), shows that during 

the thermal period, the water condensed in the fractures does not remain there but continually 

drains downwards and away from the dryout zone along a network of connected fractures 

between the emplacement drifts. The NRC staff notes that DOE’s analysis supports the DOE 

conclusion that little of the microbially affected water would remain in fractures above the drifts 

by the time ambient flow returns to the repository near-field rocks after the thermal period. 

DOE also stated an increase in microbial activity in the condensation zone would be offset by 

restricted microbial activity in the dryout zone, so there would be no net increase in microbial 

activity during the thermal period. This explanation is supported by information from microbial 

studies that suggest elevated temperatures in the dryout zone would restrict microbial activity at 

that location during the thermal period (DOE, 2009ci). The NRC staff notes that the 

DOE-provided information supports the DOE conclusion that the increased availability of water 

for microbial activity in the condensation zone would be balanced by the loss of the same 

amount of water from the dryout zone. 

In summary, DOE provided a technical basis that showed that increased microbial activity in the 

unsaturated zone would not significantly affect the chemistry of water seeping into repository 

drifts, because (i) the limited availability of nutrients would restrict microbial activity in both 

ambient and thermal conditions and (ii) only a fraction of the water potentially affected by 

increased microbial activity in the condensation zone during the thermal period would be 

available as seepage to the repository drifts when ambient flow paths were restored. DOE 

provided a reasonable technical basis to exclude the feature, event, and process Microbial 

Activity in the Unsaturated Zone on the basis of low consequence. 

FEP 2.2.10.09.0A, Thermo-Chemical Alteration of the Topopah Spring Basal Vitrophyre 

DOE excluded Thermo-Chemical Alteration of the Topopah Spring Basal Vitrophyre on the 

basis of low consequence (SNL, 2008ab). According to DOE’s information, the Topopah Spring 

basal vitrophyre is a glassy, densely welded tuff that forms the lowermost part of the Topopah 
2-60 

Spring tuff hydrogeologic unit. DOE used the feature, event, and process to examine the 

possibility that temperatures elevated by repository heating might cause the volcanic glass in 

the vitrophyre to alter to zeolites and clay minerals, potentially changing permeability and flow 

paths in the basal vitrophyre, and increasing the sorptive properties of the unit (SNL, 2008ab). 

In the screening justification, DOE stated that although heat from the repository will locally 

increase temperatures in the unsaturated zone for hundreds to several thousand years, the 

potential for any thermo-chemical alteration of the vitrophyre would be of limited spatial extent 

and of short duration compared to the previous alteration history of the Topopah Spring tuff. 

DOE cited fluid inclusion and isotope studies of fracture minerals in the Topopah Spring tuff 

units to support the screening argument. The studies identified that regionally elevated 

temperatures {above 80 °C [176 °F]} occurred in the tuffs at least 10 million years ago, 

followed by gradual cooling over several million years to near-ambient conditions. Despite 

its long exposure to the elevated temperatures, the basal vitrophyre remained largely unaltered. 

DOE reasoned that, by comparison, the relatively brief and spatially limited postclosure thermal 

pulse from the repository will only minimally alter the vitrophyre to secondary minerals, so that 

any effects on the sorptive or hydrologic properties of the unit will not result in a significant 

adverse change in the magnitude or timing of either radiological dose to the reasonably 

maximally exposed individual or radionuclide release to the accessible environment. 

NRC Staff’s Review 

The NRC staff examined DOE’s cited modeling studies, empirical data, and glass alteration 

rates (SNL, 2008ab) that supported DOE’s representation of the past thermal history of the 

Topopah Spring tuff units and that supported DOE’s thermal model predictions for repository 

heating. On the basis of the information provided, DOE supported the screening argument that 

repository heating will not cause significant thermo-chemical alteration of the Topopah Spring 

basal vitrophyre. Because thermo-chemical alteration of the Topopah Spring basal vitrophyre 

will only minimally alter the vitrophyre and the effects on flow and sorption will be small, any 

changes in the sorptive or hydrologic properties of the unit would be of low consequence for 

repository performance. DOE’s technical basis to exclude the feature, event, and process from 

the performance assessment on the basis of low consequence is reasonable. 

FEPs 2.1.14.15.0A Through 2.1.14.26.0A and 2.2.14.09.0A Through 2.2.14.12.0A, Criticality 

Features, Events, and Processes

The criticality features, events, and processes encompass FEPs 2.1.14.15.0A through 

2.1.14.26.0A and 2.2.14.09.0A through 2.2.14.12.0A (SAR Table 2.2-5) and are classified as 

events. DOE excluded all of the criticality features, events, and processes from the 

performance assessment on the basis of low probability (SAR Table 2.2-5; SNL, 2008ab). 

As described in SAR Section 2.2.1.1.2 the potential for criticality events is determined by a 

number of precursor conditions that must occur for the inventory to achieve a potentially critical 

configuration. An initiating event must occur that causes a breach of the waste package before 

any other sequence of events on that waste package could lead to criticality. Additional 

precursor conditions include (i) presence of a moderator (i.e., water), (ii) separation of 

fissionable material from the neutron absorber material or an absorber material selection error 

during the canister fabrication process, and (iii) the accumulation (external) or presence of a 

critical mass of fissionable material in a critical geometric configuration. As described in SNL 

(2008ab), the probability of developing a configuration with criticality potential is insignificant 

unless the initiating event and all three of the precursor conditions are realized. 
2-61 

DOE’s technical basis for exclusion of the criticality features, events, and processes consisted 

of a probability analysis based on location, initiating events, and state of degradation (e.g., the 

waste package internal structures and the waste form may degrade). DOE divided the 

criticality features, events, and processes into three locations for four initiating event scenarios, 

as described in SAR Section 2.2.1.4.1: internal to the waste package in an intact or 

degraded condition, near field, and far field. DOE used four initiating event scenarios: 

nominal, seismic, rockfall, and igneous. DOE described that these scenarios are different from 

the scenario classes described in SAR Section 2.2.1.3 because the scenario classes 

described in SAR Section 2.2.1.3 were formulated for analyses of included events in the 

performance assessments. DOE’s analysis of the probability of criticality classified the waste 

forms by type—commercial spent nuclear fuel (CSNF), high-level radioactive waste glass, and 

DOE spent nuclear fuel (SNF)—which were further subdivided, in some cases. As described in 

SAR Section 2.2.1.4.1, analyses of the in-package probability of criticality for naval spent 

nuclear fuel are described in the Naval Nuclear Propulsion Program Technical Support 

Document (classified) Section 2.2.1.4.1 and subsequent discussions of near- and far-field 

criticality in SAR Section 2.2.1.4.1 are applicable to naval spent nuclear fuel as well as all other 

waste form types. 

In TER Section 2.2.1.2.1.3.3, the NRC staff reviews formation of scenario classes. 

The formation of scenario classes refers to the aggregation of features, events, and processes 

into event classes or scenario classes for the purpose of further screening or analyses. As 

described in SAR Section 2.2.1.3, DOE aggregated criticality events into the criticality event 

class. SAR Section 2.2.1.4.1 described DOE’s nuclear criticality considerations for the 

repository during the postclosure period and reviewed the technical basis by which the nuclear 

criticality event class is screened from the postclosure performance assessment on the basis of 

low probability. 

DOE’s technical basis for screening out the individual criticality features, events, 

and processes was provided in SNL (2008ab). DOE supplemented its technical basis for 

exclusion of individual features, events, and processes and, in some cases, the technical 

basis for exclusion of the criticality event class in DOE Enclosures 1–18 (2009aj), DOE 

Enclosures 1–11 (2009al), DOE Enclosures 1–5 (2009gp), DOE Enclosures 1–2 (2009gq), DOE 

Enclosures 1–10 (2009bv), DOE Enclosures 1–11 (2009bw), DOE Enclosures 1–2 (2009bx), 

DOE Enclosures 1–4 (2009co), DOE Enclosures 1–2 (2010ah), DOE Enclosures 1–2 (2010ad), 

and DOE Enclosures 3–5 (2009cb). 

In this section the NRC staff reviews DOE’s technical basis for excluding the individual criticality 

features, events, and processes and the technical basis for excluding the criticality event class. 

The NRC staff reviewed the information provided in the application, including the cited 

references, and noted that the in-package intact configuration and in-package degraded 

configurations are the most risk significant configurations. As a result, these configurations 

were evaluated in greater detail. 

Criticality features, events, and processes that consider igneous events (FEPs 2.1.14.24.0A 

through FEP 2.1.14.26.0A and FEP 2.2.14.12.0A) were not evaluated in detail, because the 

igneous events have a low probability of occurrence (TER Sections 2.2.1.2.2 and 2.2.1.3.10). 

When the probability of the igneous events are combined with other low probability physical 

requirements for a critical configuration to occur, the total combined probability is negligible. 

 

For the near- and far-field criticality features, events, and processes (FEP 2.1.14.17.0A, 

FEP 2.1.14.20.0A, FEP 2.1.14.23.0A, FEP 2.1.14.26.0A, and FEPs 2.2.14.09.0A through 
2-62 

FEP 2.2.14.12.0A), DOE adopted a number of assumptions to conservatively overestimate the 

probability of criticality. The two most significant assumptions were (i) fissile material would 

accumulate in the optimum geometry for criticality and (ii) there would be no nearby neutron 

absorbers or fission products. Despite these assumptions, near- and far-field criticality is a 

negligible contributor to the overall probability of criticality [see SAR Table 2.2-8; the cutoff for 

including probabilities in this table is two orders of magnitude lower than 10–8 per year]. 

Therefore, the NRC staff did not perform a detailed review of the near- and far-field criticality 

features, events, and processes. 

DOE’s design basis configuration model incorporates some rearrangement and degradation 

of fuel and neutron absorbers to overestimate the probability of a criticality event. DOE 

concluded that under such configurations, a criticality event could occur only if the waste 

package was misloaded or if a manufacturing error resulted in missing neutron absorbers, as 

identified in FEP 2.1.14.15.0A (SNL, 2008ab). On the basis of the NRC staff’s review of the 

information provided in the application, including the cited references, on the in-package intact 

configuration and in-package degraded configurations, the NRC staff focused its review on the 

following aspects of the DOE criticality analysis that are important in estimating the probability of 

criticality and the conditions that are inputs into and that might limit the applicability of the 

probability analysis: 

 Moderator intrusion 

 Misloaded fuel 

 Neutron absorbers 

 Burnup credit 

 Criticality code validation 

The following subsections summarize DOE’s approach and the NRC staff’s evaluations for each 

of these aspects of the DOE technical basis and conclusions. 

Moderator Intrusion 

The presence of a moderator is necessary for the spent fuel to go critical. Because water is a 

neutron moderator, its presence results in increased reactivity. Unbreached waste packages do 

not allow ingress of water (neutron moderator) and thus do not pose a criticality concern. DOE 

conservatively assumed that enough water is available to act as a neutron moderator in the 

criticality calculations whenever any breach of the package is calculated—even for cracks on 

the waste package that are too small to permit liquid infiltration (SNL, 2008ab). No credit is 

taken for only partial filling of the waste package, nor for the drip shield’s diversion of liquid from 

the package (DOE, 2009bx); thus the waste packages, once breached, are assumed to be 

flooded. Because DOE assumed that failed waste packages would be flooded (SNL, 2008al), 

the probability of criticality is overestimated. In the nominal modeling case, DOE estimates 

that the waste packages would not be breached in 10,000 years (e.g., SAR Figure 2.1-10). 

However, other modeling cases (early failure, seismic ground motion, seismic fault 

displacement, and igneous intrusion) account for failure of the waste package within 

10,000 years (e.g., SAR Figure 2.4-18). Because of the low probability for igneous intrusion 

and fault displacement, DOE concluded that criticality events could only occur within 

10,000 years for the early failure and seismic scenarios. In its criticality analysis, 

DOE considered various reactivity control mechanisms within a waste package (e.g., neutron 

absorbers) to ensure that all probable configurations remain subcritical. 
2-63 

NRC Staff’s Review of Moderator Intrusion 

The NRC staff evaluated (i) whether the probability calculation was performed correctly, 

(ii) the appropriateness of the inputs used in the calculation, (iii) consideration of uncertainties, 

and (iv) DOE’s determination that the probability of the criticality features, events, and 

processes is less than 1 chance in 10,000 of occurrence within 10,000 years of disposal. The 

NRC staff reviewed documents with analyses supporting screening arguments for excluding 

criticality events from the performance assessment analysis (SNL, 2008aa,ab,ad,ae,al) and 

supplemental analyses in DOE Enclosures 1–18 (2009aj), DOE Enclosures 1–11 (2009al), DOE 

Enclosures 1–5 (2009gp), DOE Enclosures 1–2 (2009gq), DOE Enclosures 1–10 (2009bv), 

DOE Enclosures 1–11 (2009bw), DOE Enclosures 1–2 (2009bx), DOE Enclosures 1–4 

(2009co), DOE Enclosures 1–2 (2010ah), DOE Enclosures 1–2 (2010ad), and DOE Enclosures 

3–5 (2009cb). DOE assumed that given a postulated breach of the spent fuel package, no 

matter how small a breach, the waste package would fill with enough water to support criticality 

(i.e., the availability of water was not assumed to be a limiting factor for criticality). This 

assumption is conservative because it results in an overprediction of the potential for criticality. 

It allows for enhanced neutron moderation compared to the much more probable situation of 

limited water ingress into the waste package, especially for those waste packages breached by 

very small cracks. 

The NRC staff notes that DOE adequately identified and quantified conditions that could lead to 

in-package criticality. The NRC staff reviewed the conditions DOE identified as necessary for 

in-package criticality, as well as the methodology used to identify these conditions. The total 

probability of criticality is dependent on the probability to attain those necessary conditions 

(e.g., waste package failure, moderator intrusion, configuration changes, package/absorber 

degradation, and fuel characterization). DOE stated that a criticality event was possible only 

after a waste package breach and under the following conditions: (i) accidentally loading a fuel 

assembly with higher reactivity than permissible into the waste package (a mistake referred to 

as a misload) or (ii) manufacturing errors resulting in missing neutron absorber material. The 

misloading criteria and manufacturing performance criteria used in the analysis are reasonable, 

because DOE applied human reliability data developed by the industry and the international 

community. The data appropriately considered dependencies and the human factors in 

manufacturing and loading procedures. Waste packages are safety-related equipment 

(preclosure) and important to waste isolation equipment (postclosure). Therefore, their 

manufacturing process requires stringent quality assurance programs. 

The performance numbers are consistent with industry practices and performance when 

applying those quality assurance processes. DOE’s quality assurance program is intended to 

complement the performance criteria credited for the waste package, the manufacturing and 

loading of neutron absorbers, and the loading of fuel assemblies, so that these factors can be 

relied on as part of the technical basis for exclusion of the criticality features, events, and 

processes and the criticality event class. 

DOE identified in SAR Sections 1.5.1.4.1.2.6.3 and 2.2.1.4.1 that analyses of the in-package 

probability of criticality for naval spent nuclear fuel (SNF) are described in the Naval Nuclear 

Propulsion Program Technical Support Document (classified) Section 2.2.1.4.1. However, 

DOE summarized (nonclassified) aspects of the postclosure criticality analysis for naval spent 

nuclear fuel in SAR Sections 1.5.1.4.1.2.2.2, 1.5.1.4.1.2.5.2, 1.5.1.4.1.2.6.1, 1.5.1.4.1.2.6.3, 

and 2.2.1.4.1. As described in SAR Section 1.5.1.4.1.2.2.2, criticality control of naval spent 

nuclear fuel (i.e., assurance of a low probability that criticality involving naval spent nuclear 

fuel could occur) is provided by controlling one or more of the following characteristics of the 
2-64 

loaded naval spent nuclear fuel canister: the amount of fissile material; the materials used 

for naval spent nuclear fuel canisters, baskets, spacers, naval corrosion-resistant cans, 

control rods, and installed neutron poison assemblies and their retention hardware; and 

geometric separation of naval spent nuclear fuel assemblies. As identified in SNL Table B-1 

(2008al), naval waste packages are subject to the same breach scenarios as other packages. 

The NRC staff reviewed the DOE information and determined that the assumptions used in 

the models were conservative and appropriately applied. The NRC staff notes that DOE 

considered the parameters important to criticality for naval fuel and conservatively or 

realistically represented them in the screening calculation, which resulted in a 10,000-year 

probability of criticality (7.1 × 10-6; SAR Table 2.2-8)—well below the screening criteria. 

Therefore, DOE has reasonably screened out in-package criticality for naval spent fuel on the 

basis of low probability. 

Misloaded Fuel 

DOE presented loading curves for commercial spent nuclear fuel in SNL Figures 6-32 and 6-33 

(2008aa) and SAR Figures 2.2-7 and 2.2-8 and defined “acceptable” and “underburnt” 

assemblies that could be loaded into waste packages based on minimum burnup, as a function 

of initial fuel assembly enrichment. DOE considered assemblies above the loading curve 

acceptable, while those below the loading curve were considered underburnt. A canister filled 

with underburnt assemblies could exceed the critical limits listed in SAR Table 2.2-11(or 

become critical), if flooded, without additional criticality control mechanisms. Typographical 

errors in rows one and three in SAR Table 2.2-11 were corrected in DOE Enclosure 10 

(2009bv), and DOE stated it would correct SAR Table 2.2-11. These underburnt assemblies 

comprise the potential misload inventory. Although DOE did not specify what additional 

reactivity control mechanisms or analysis will be used to meet the critical limit, DOE stated that 

the underburnt assemblies would have to be loaded into canisters with additional reactivity 

control mechanisms (e.g., disposal control rod assemblies) and individually analyzed to ensure 

subcriticality (SAR Section 2.2.1.4.1.1.3). 

The waste package configuration used to compute the loading curves was selected to bound 

degraded configurations that were not explicitly evaluated in the screening argument [e.g., the 

conversion of UO2 into schoepite, as described in SNL Sections 6.1.3 and 6.2.5 (2008aa)]. 

DOE considered it safe (i.e., criticality events are not expected) to load canisters with spent fuel 

whose burnup exceeds the minimum burnup defined by the loading curve for the initial 

enrichment of the fuel. DOE implemented these bounding analyses to provide confidence that 

waste package configurations not explicitly analyzed are less reactive than those that were 

analyzed and used to generate the loading curve. 

DOE defined a misload as the process of loading, by mistake, a fuel assembly into a canister 

without enough criticality prevention controls for that fuel assembly (specifically, commercial 

spent nuclear fuel, when an assembly from the misload inventory is loaded into a canister). 

DOE assumed that a misload may cause a criticality event if the misloaded assembly is 

significantly more reactive than accounted for in the waste package design. DOE assumed 

misloads result from operator error and used representative human reliability data and prototype 

loading procedures to estimate the probability of misloads, because actual data and procedures 

are not available (DOE, 2003aa). 

DOE assumed that misloads will not occur for DOE spent nuclear fuel, because the physical 

differences in fuel types allow operators to easily distinguish spent fuel types and in some cases 

physically prevent misloads [SNL, 2008ab; FEP 2.1.14.15.0A, In-package Criticality (Intact 
2-65 

Configuration)]. A commercial spent nuclear fuel misload would occur if an underburned 

assembly is loaded; however, a criticality event would only be possible if the other assemblies 

were not burned enough to compensate for the underburned assembly. 

NRC Staff’s Review of Misloaded Fuel 

The NRC staff reviewed DOE’s determination of the misload probability, which is one of the 

inputs into the overall probability of criticality (4.4 × 10−5

; SAR Table 2.2-8). DOE modeled all 

the misloads as random human error in the selection of fuel assemblies to be loaded, because 

human error is the dominant cause of misloads. This calculation resulted in a combined 

misload probability of 1.18 × 10−5

 per canister. This is the probability that the operator will 

misload a single assembly into a canister. 

DOE also calculated an additional probability related to misloads: the probability that a criticality 

event would occur given one assembly has been misloaded into a canister. As described in 

DOE Enclosure 13 (2009aj) the probability of criticality due to an assembly misload in a 

breached Pressurized Water Reactor (PWR) waste package is the product of the probability of 

a misload (1.18 × 10−5

) and the conditional probability of criticality given a misload (0.014). 

To calculate the conditional probability of criticality given a misload, DOE assumed that 

misloaded assemblies would be positioned in the center (i.e., along the axis) of the waste 

package. This assumption is reasonable because this position is the most reactive due to 

neutron interaction with the surrounding assemblies, maximizing the likelihood of criticality. In 

other words, this assumption is conservative with regard to estimates of the probability of 

criticality. However, DOE’s analysis did not account for the probability of criticality if there are 

two or more misloads. Multiple misloads are likely to result from a common mode failure and 

cannot be accurately modeled as random misloads. In DOE Enclosure 13 (2009aj), DOE 

supplemented its previous analysis by performing a sensitivity study that assumed a criticality 

event resulted whenever a misload occurred (which could be a single assembly misloaded or 

the first of several assemblies being misloaded). Thus this analysis changed the value of the 

conditional probability of criticality from a misload from 0.014 to 1. DOE identified that this 

change in the conditional probability only causes the overall probability of criticality to increase 

to a value of 5.76 × 10−5

 in 10,000 years, which is less than the exclusion criterion. Because in 

this sensitivity study every misload is assumed to cause a criticality event, the probability of 

criticality given multiple misloads is bounded. On the basis of this sensitivity study, DOE’s 

conclusion that multiple misload assemblies alone would not cause criticality to exceed a 

probability of 10−4

 in 10,000 years is reasonable. 

Neutron Absorbers 

To model the effects of missing neutron absorbers, DOE reduced the amount of absorber in the 

models. It assumed that loss of absorber results from manufacturing error or corrosion of 

neutron absorber materials. For the corrosion of the absorber plates in the transportation, 

aging, and disposal canister, DOE assumed that after 10,000 years, 6 mm [0.24 in] out of the 

initial 11 mm [0.43 in] of borated stainless steel would remain in place. In response to the NRC 

staff’s RAIs on use of average corrosion values in DOE Enclosures 1 and 2 (2009bv) and in 

SAR Section 1.5.1.1.1.2.2.2, DOE indicated that the 5 mm [0.2 in] material thinning was based 

on a borated stainless steel corrosion rate of 250 nm/yr [0.01 mil/yr]. This corrosion rate is 

about nine times the average corrosion rate on 304B4 borated stainless steel Lister, et al. 

(2007aa) measured. Lister, et al. (2007aa) measured the corrosion rate at 60 °C [140 °F] 

in an aerated simulated in-package solution and determined an average value of 27 nm/yr 

[0.001 mil/yr] with a standard deviation of 10.1 nm/yr [4 × 10−4

 mil/yr]. Although some boron 
2-66 

would remain in the steel as separate chromium boride particles left behind as insoluble 

products during corrosion, this remaining boron was not credited in DOE’s criticality models. In 

its criticality models with the SCALE and MCNP computer codes, DOE modeled 75 percent of 

the boron that exists in the stainless steel as per the guidance in NUREG–1567 (NRC, 2000ab). 

DOE assumed that if a manufacturing error resulted in neutron absorbers not being installed or 

too little absorber material being installed, a criticality event could occur. DOE used a surrogate 

analysis to model the probability of this error occurring as the probability of a material selection 

error multiplied by the probability that an independent inspection does not detect it to derive a 

mean value of 1.25 × 10-7 per canister. DOE considered representative reliability data and 

prototype manufacturing procedures to develop an overestimate of the probability of misloading 

the neutron absorber plates. 

The naval waste packages use hafnium (SAR Section 1.5.1.4.1.2.2.2)—a strong thermal 

neutron absorber that is extremely corrosion resistant (Rishel, et al., 2000aa)—as a neutron 

absorber. For the absorbers considered in the DOE spent nuclear fuel canisters, DOE 

evaluated the solubility, retention, and distribution of the neutron absorbers in DOE Enclosure 5 

(2009cb). DOE has not yet completed the design of the neutron-absorbing shot that will be 

added to some waste forms (waste forms DOE referred to as DOE1, DOE5, and DOE8). DOE 

stated in DOE Enclosure 5 (2009cb) that due to its high corrosion resistance, GdPO4 is the most 

likely form of neutron absorber. 

NRC Staff’s Review of Neutron Absorbers 

The NRC staff’s review of DOE’s SAR and supporting documents related to the neutron 

absorber design and performance notes that potential degradation of neutron absorbers was 

reasonably addressed in the criticality analyses. The NRC staff reviewed the corrosion rates of 

borated and nonborated stainless steel reported in the literature, such as Beavers and Durr 

(1991aa); BSC (2004ae); Beavers, et al. (1992aa); McCright, et al. (1987aa); Glass, et al. 

(1984aa); Fix, et al. (2004aa); and Lister, et al. (2007aa,ab). The NRC staff noted that in fresh 

water, J-13, simulated J-13, J-13 with crushed tuff water, and simulated concentrated waters in 

a temperature range of 28−100 °C [82.4−212 °F], 304 and 316 stainless steels have similar 

general corrosion properties in these solutions, but if borated, the corrosion rate of stainless 

steel increases. The information in Fix, et al. (2004aa), BSC Tables 6-4 and 6-7 (2004ae), and 

SNL (2007am) indicated that the corrosion rates of borated stainless steels were higher than for 

unborated 304 and 316 stainless steels. BSC (2004ae) also indicated that the corrosion rate of 

borated 304 stainless steel with 1.5 percent boron was about 14 times higher than that with 

0.3 percent boron in boiling freshwater. The corrosion rates of borated stainless steel ranged 

from tens of nanometers per year (Lister, et al., 2007aa) to tens of micrometers per year in 

BSC Table 6-7 (2004ae), depending on simulated environmental conditions. 

The NRC staff reviewed the data and the applicable environmental conditions that DOE 

considered credible to reside within the waste package and surroundings. High corrosion rates 

in the range of micrometers per year were obtained from immersion tests. These high corrosion 

rates may lead to thinning of the borated stainless steel to below the 6-mm [0.24-in] thickness 

DOE considered. However, the NRC staff noted that those high corrosion rates are unlikely and 

need no further consideration in DOE’s criticality analysis. These high corrosion rates are 

unlikely because they (i) require full water immersion conditions and (ii) require the presence of 

ionic species that only seepage water can supply. Given that the extent of damage of the waste 

packages that could fail in 10,000 years (waste packages could fail due to early failure, due to 

localized corrosion if under early failed drip shields, or due to stress corrosion cracking) is 
2-67 

limited and that drip shield failure is required to allow seepage water ingress into the waste 

package, the NRC staff noted that sufficient ingress of the ionic species present in seepage 

water to maintain a water chemistry needed to support high corrosion rates is unlikely. 

Because liquid water seepage into a waste package is unlikely, water ingress into the waste 

packages in 10,000 years will most likely be in the form of water vapor. Accordingly, the NRC 

staff reviewed literature data on borated stainless steel corrosion under humid air conditions. 

Beavers and Durr (1991aa) studied corrosion of 304 stainless steel under vapor and aqueous 

conditions at 90 °C [194 °F] and concluded that corrosion rates were under their detection limit 

of 200 nm/yr [7.9 × 10-3 mil/yr]. The corrosion rate value DOE used to support the use of a 

6-mm [0.24-in]-thick plate of borated stainless steel in the criticality computations, 27 nm/yr 

[0.001 mil/yr], is below the upper bound of 200 nm/yr [7.9 × 10-3 mil/yr] estimated by Beavers 

and Durr (1991aa). No precise estimates of corrosion of borated stainless steel under vapor are 

available. The NRC staff noted, however, that even if the corrosion rate under humid air 

exceeded 27 nm/yr [0.001 mil/yr], stainless steel is not expected to continuously corrode from 

the time of closure to 10,000 years after closure {as assumed in DOE’s derivation of the 6-mm 

[0.24-in]-thick value}, because the waste packages could fail at different times in the 

10,000-year period and the spent fuel is a heat source that could mobilize water away. 

Also, boron present in corrosion products could still be an effective neutron absorber. 

Accordingly, use of a 6-mm [0.24-in]-thick plate of borated stainless steel in the criticality 

analysis is adequate. 

DOE’s incorporation of a degraded neutron absorber in its criticality analyses is reasonable. 

The NRC staff notes that the neutron absorbers in the transportation, aging, and disposal 

canister, with the assumption that neutron absorber plates are at least 6 mm [0.24 in] thick for 

10,000 years, provide adequate reactivity control. Given the stricter quality assurance 

requirements DOE proposed for components that are classified as important to safety and 

important to waste isolation, DOE’s quality assurance program for design and construction of 

the neutron absorber plates will provide reasonable basis that the probability of a manufacturing 

error was adequately evaluated. 

With respect to the naval waste packages, DOE’s analysis of the system reactivity 

considering the presence of hafnium as a neutron absorber material is reasonable because of 

its well-known corrosion resistance properties and the Navy’s long experience with its use in 

reactors to control criticality. 

The NRC staff reviewed DOE’s proposed use of a gadolinium absorber shot, which prevents 

criticality for some of the most reactive DOE spent nuclear fuel types. The NRC staff notes that 

filling the DOE spent nuclear fuel canisters with enough absorber shot to keep the most reactive 

intact or degraded configuration subcritical, with an adequate margin, is a reasonable use of 

neutron absorbers. This is because DOE models both degraded and undegraded absorber 

shots, which provided the NRC staff’s confidence that the absorber shot will perform its 

intended function. 

Burnup Credit 

DOE uses the burnup of the commercial spent nuclear fuel to control criticality in much the 

same way that neutron absorber plates are used. However, unlike absorber plates, the 

amounts of absorbers (and fissile material) in each assembly vary and must be computed 

analytically. DOE accounted for the change in reactivity caused by changes in fuel composition 

that resulted from irradiation in a reactor and radioactive decay. This is mostly due to the 
2-68 

buildup of fission products that are neutron absorbers and to the depletion of fissile material, 

although some fissile material is also created. To compute the change in reactivity, DOE 

modeled commercial spent nuclear fuel as being composed of 29 principal isotopes (SAR 

Table 2.2-9) considered to be the most relevant and concluded in DOE Section 6 (2004ab) that 

increasing the burnup of commercial spent nuclear fuel decreases its reactivity. As described in 

SAR Section 2.2.1.4.1.1.2.4.1, DOE validated the isotopic model by comparing the results of the 

model to the results of radiochemical assays that measured the amount of some or all of the 

principal isotopes in small samples of commercial spent nuclear fuel (DOE, 2004ab). 

DOE used reactor records to determine the burnup of the fuel (SAR Section 2.2.1.4.1.1.4.1) and 

a computer model to generate the isotopic composition for a given burnup and enrichment. 

DOE accounted for the uncertainties in the reactor records by using a burnup that is 5 percent 

less than reported. This adjustment bounded the highest reactor record uncertainty identified in 

AREVA (2004), which was 4.2 percent. The reactor record uncertainty was determined from the 

difference between the calculated and measured values of burnup. Measured burnup was 

determined with calibrated in-core instrumentation. Calculated burnup was determined using 

analytic methods of the reactor core power distribution. Uncertainties in the isotopic 

composition were accounted for by using modeling parameters that would conservatively 

overestimate the reactivity of the fuel and thus overestimate the probability of criticality (SAR 

Section 2.2.1.4.1.1.2.4.1). In DOE Enclosure 2 (2009bx), DOE committed to using a maximum 

burnup of 50 GWd/MTU with respect to the burnup credit loading curves in criticality analyses 

for commercial pressurized water reactor fuel unless additional validation data are provided to 

extend the burnup beyond 50 GWd/MTU. DOE used a decay time for the isotopic composition 

of 5 years to bound the increase in reactivity caused by Am-241 and Pu-240 decay. 

For boiling water reactor (BWR) spent nuclear fuel, DOE performed two different criticality 

analyses. One analysis applies to BWR spent nuclear fuel with initial enrichments up to 4.5wt% 

U-235; this analysis does not incorporate burnup credit in the analysis. For the second 

analysis, some BWR burnup credit is necessary to accommodate fuel assemblies with higher 

initial enrichments (i.e., > 4.5wt% U-235) than the projected waste stream inventory in SNL 

Figure 6-33 (2008aa). As discussed in DOE Enclosure 2 (2010ah), only a small amount of 

burnup credit is required for disposal; in other words, only a fraction of the burnup needs to be 

credited. While this analysis was performed with a “simple” BWR assembly design as 

compared to the “modern” and more advanced fuel designs, the more advanced designs 

ultimately result in a net decrease in the total fissile mass available in the fuel assembly at 

discharge. DOE stated that assembly designs that have not been explicitly analyzed 

(like the advanced BWR designs) are required to be evaluated to ensure appropriateness 

from a postclosure criticality perspective in accordance with SAR Section 5.10.2 and SAR 

Table 5.10-3. As stated by DOE in DOE Enclosure 2 (2010ah), SAR Table 5.10-3 described a 

number of administrative controls to be included in license specifications, and these 

administrative controls would be compiled in the Technical Requirements Manual. 

NRC Staff’s Review of Burnup Credit 

The NRC staff reviewed DOE’s use and justification of BWR fuel burnup credit. For BWR fuel 

assemblies with enrichment below 4.5 wt% U-235, DOE’s analytic methodology that does not 

incorporate burnup credit is reasonable. However, a very small percentage of BWR fuel 

assemblies may contain enrichment greater than 4.5wt% U-235. For these assemblies, DOE 

requested burnup credit. While DOE did not have the requisite data to support the isotopic 

composition and associated reactivity of these fuel assemblies (DOE, 2004ab), the NRC staff 

does not consider DOE’s inability to accurately predict the isotopic composition and reactivity of 
2-69 

modern BWR spent nuclear fuel to be risk significant. This is because DOE uses conservative 

input parameters to the irradiation and depletion calculations when generating the BWR isotopic 

database spent fuel isotopic compositions with a small amount of burnup being credited. As a 

result, DOE only takes credit for a portion of the burnup credit from the principal isotopes that 

are expected to be available. Additionally, staff does not consider granting BWR burnup credit 

to be risk significant, because the administrative controls described in SAR Section 5.10.2 and 

SAR Table 5.10-3 require that similar analyses (similar to the analysis performed for the simple 

BWR design) be completed prior to receiving individual waste forms or canisters/waste package 

design configurations that are not explicitly analyzed in the SAR. Therefore, the NRC staff 

considers it reasonable for DOE to take burnup credit for BWR spent nuclear fuel. The NRC 

staff reviewed DOE’s use and justification of pressurized water reactor burnup credit. DOE 

used conservative input parameters (such as fuel types and operating histories) in the 

irradiation and depletion calculations when generating the isotopic composition of the 

pressurized water reactor fuel. Taking full credit for the neutron absorptive properties of Mo-95, 

Tc-99, Ru-101, Rh-103, and Ag-109 was technically unjustified due to insufficient and 

inadequate radiochemical assay data, which were primarily based on the 11 TMI-1 

radiochemical assay samples. However, in DOE Enclosure 1 (2010ah), DOE showed that the 

isotopic bias and uncertainty incorporated into the critical limit should make up for the errors and 

uncertainties in the predictions of these five isotopes. Furthermore, the presence of some 

neutron-absorbing fission products that DOE did not credit in the analysis, the radiochemical 

assay data from a variety of reactors, and conservative input parameters provide staff’s 

confidence that the overall uncertainties in the isotopic predictions of the 29 principal isotopes 

for pressurized water reactor burnup credit are not sufficient to cause a significant increase in 

the probability of criticality. 

Criticality Code Validation 

In SAR Section 2.2.1.4.1.1.2.4, DOE described that its validation process used for the criticality 

model is summarized in four steps: (i) selection of benchmark experiments, (ii) establishment of 

the range of applicability of the benchmark experiments, (iii) extension of the range of 

applicability (as necessary), and (iv) development of critical limits. DOE described that its 

validation methodology was performed in accordance with ANSI/ANS 8.1-1983, Section 4.3 

and Appendix C (American Nuclear Society, 1983aa). DOE also described that Regulatory 

Guide 3.71 (NRC, 1998aa) endorses the use of ANSI/ANS-8 nuclear criticality safety standard 

documents and states that the procedures and recommendations in the ANSI/ANS-8 standards 

should be followed to prevent and mitigate nuclear criticality event sequences. 

An important aspect in assessing criticality code validation is the applicability of the selected 

benchmark experiments, which must be similar in form and composition to the systems to be 

evaluated. DOE described the benchmarks used for the criticality code validation, and the 

determination of the lower bound tolerance limits for commercial spent nuclear fuel and DOE 

spent nuclear fuel, in DOE (2004aa) and Radulescu, et al. (2007aa). DOE included in the 

criticality model document (DOE, 2004aa) analyses for the various types of commercial and 

DOE spent nuclear fuel. The DOE updated its criticality validation methodology and 

benchmarks for commercial spent nuclear fuel in Radulescu, et al. (2007aa) as explained in 

DOE Enclosure 7 (2009al). DOE has not yet updated the benchmark selection and validation 

for DOE spent nuclear fuel with the new methodology used in Radulescu, et al. (2007aa). In the 

note to SAR Table 2.2-11, DOE stated DOE spent nuclear fuel interim critical limits have not 

been rigorously established for all fuel groups, but will be confirmed prior to waste acceptance 

as identified in Table 5.10-3. However, in DOE Enclosure 1 (2010ad), DOE stated it would 

update SAR Section 2.2.1.4.1.1.2.4.2 to show that the bias used in establishing loading limits for 
2-70 

DOE spent nuclear fuel canisters conservatively envelopes any uncertainty associated with the 

limited availability of applicable benchmarks. 

An important consideration in the development of critical limits is the determination and 

implementation of an administrative margin. As described in SAR Section 2.2.1.4.1.1.2.4.2, the 

administrative margin is an arbitrary margin ensuring subcriticality and turning the criticality limit 

function into an upper subcritical limit function. DOE described that this term is not applicable 

for use in postclosure analyses because there is no risk associated with a subcritical event. 

DOE described that in contrast to “traditional” nuclear criticality safety analyses and associated 

governing regulations, in which the purpose is to ensure prevention of criticality and 

corresponding protection of personnel and facilities, the purpose of the postclosure criticality 

evaluation is to determine the probability of a criticality event in the postclosure time period. 

The probability of criticality is then compared to a probability of 10−4

 in 10,000 years to reach a 

decision relative to the inclusion or exclusion of a criticality event in the evaluation of the total 

system performance for the facility. Thus, DOE used a zero administrative margin and provided 

additional justification for its use of a zero administrative margin in DOE Enclosure (2009bv). 

NRC Staff’s Review of Criticality Code Validation 

The NRC staff reviewed DOE’s documentation on criticality models, calculations, and results. 

The NRC staff also performed calculations (Sippel, 2010aa) for a sample of the configurations 

DOE considered subcritical and confirmed that the resulting keff is below the upper subcritical 

limit. This NRC staff’s review and the confirmatory analysis provides the NRC staff’s confidence 

that DOE appropriately modeled the potentially critical configurations when determining 

reactivity. DOE’s calculation of keff is reasonable because DOE modeled neutron physics using 

standard industry computer codes that the NRC staff previously approved for criticality 

computations, as described in NRC Section 8.4.4.1 (2000ab). 

The upper subcritical limit was calculated properly and the calculation was performed in accord 

with the NRC staff-endorsed methods (NRC, 2005ac). The verification and validation 

methodology in Radulescu, et al. (2007aa) is reasonable to the NRC staff because the predictor 

variables used are considered sufficient to fully characterize the bias across the range of 

applicability. The NRC staff also notes that DOE’s validation methodology and selection of 

benchmarks for DOE spent nuclear fuel were inadequate and inconsistent with DOE’s updated 

methodology in Radulescu, et al. (2007aa). However, the statement in DOE Enclosure 1 

(2010ad) that DOE would show that the bias used in establishing loading limits for DOE spent 

nuclear fuel canisters conservatively envelopes any uncertainty associated with the limited 

availability of applicable benchmarks and the reasonable validation of the criticality model for 

commercial spent nuclear fuel provides NRC staff confidence that the DOE conclusion to 

exclude criticality from the performance assessment is reasonable. 

The NRC staff reviewed DOE’s justification for an administrative margin of subcriticality of zero. 

In a traditional criticality analysis, the administrative margin is used to protect against the 

possibility that the critical limit has been incorrectly defined. DOE considered code validation 

and conservatism in modeling parameters when assessing the proposed administrative margin 

of zero. In criticality calculations, some minimum level of assurance is sought to determine that 

the evaluated conditions are subcritical. In DOE Enclosure 15 (2009aj), DOE discussed the 

estimated margin in DOE spent nuclear fuel, and NRC staff notes that there is significant margin 

associated with not taking BWR burnup credit for most assemblies. DOE discussed in DOE 

Enclosure 2 (2010ad) how conservative parameters, rather than mean as recommended in 

NUREG–1804, were used in calculating the probability of breach. Had mean parameters 
2-71 

been used, it would have significantly decreased the probability of criticality reported in SAR 

Table 2.2-8. Therefore, the analysis of the probability of criticality is reasonable without the use 

of an administrative margin. 

FEPs 2.1.14.15.0A Through 2.1.14.26.0A and 2.2.14.09.0A Through 2.2.14.12.0A, Summary 

of NRC Staff’s Review for Criticality Features, Events, and Processes 

The NRC staff reviewed the models, calculations, and results DOE used for screening out 

criticality at the proposed Yucca Mountain repository using risk-informed, performance-based 

review methods described in the YMRP. Important areas of the review included reviewing the 

inputs to and validation of the isotopic irradiation and depletion model, the criticality models, the 

probability models, and the methodologies used in the models. On the basis of the results of 

this review and on DOE’s analyses in DOE Enclosure 10 (2009bv), DOE Enclosure 2 (2009bx), 

and DOE Enclosure 1 (2010ad), DOE’s technical basis for excluding the criticality features, 

events, and processes (FEPs 2.1.14.15.0A through 2.1.14.26.0A and 2.2.14.09.0A through 

2.2.14.12.0A) from the performance assessment on the basis of low probability is reasonable. 

The NRC staff notes that DOE developed an adequate technical basis for screening out the 

criticality event class on the basis of low probability because the overall probability of criticality 

(4.4 × 10-5; SAR Table 2.2-8) is less than 10–4 in 10,000 years. 

Summary of Technical Evaluation of Screening of the List of Features, Events, 

and Processes 

As described previously, the NRC staff makes the following evaluation. First, DOE has 

identified all features, events, and processes related to either the geologic setting or to the 

degradation, deterioration, or alteration of engineered barriers (including those processes 

that would affect the performance of natural barriers) that have been excluded. Second, DOE 

has provided a justification for exclusion of each feature, event, and process and DOE’s 

criteria for exclusion (i.e., justification) on the basis of low probability, low consequence, or 

exclusion by regulation are reasonable. Third, DOE has provided an adequate technical 

basis for each feature, event, and process, excluded from the performance assessment, to 

support the conclusion that either the feature, event, or process is specifically excluded by 

regulation; the probability of the feature, event, and process falls below a probability of 10−4

 in 

10,000 years; or omission of the feature, event, and process does not significantly change the 

magnitude and time of the resulting radiological exposures to the reasonably maximally 

exposed individual, or radionuclide releases to the accessible environment. Fourth, the 

technical basis for FEP 1.2.10.01.0A, Hydrologic Response to Seismic Activity, and DOE’s 

supplemental analyses, comprise an analysis of seismic effects on the elevation of the water 

table beyond the 10,000-year postdisposal period throughout the period of geologic stability. 

DOE reasonably excluded features, events, and processes from the performance assessments 

beyond the 10,000-year postdisposal period throughout the period of geologic stability and 

properly identified particular features, events, and processes to be included in these 

performance assessments. 

2.2.1.2.1.3.3 Technical Evaluation for Formation of Scenario Classes

Summary of the DOE Approach to Formation of Scenario Classes 

DOE described the approach to the definition of event class and scenario class formation in 

SAR Section 2.2.1.3. DOE defined an event class as consisting of all possible initiating events 

that are caused by a common natural process. DOE extended the definition to allow event 
2-72 

classes to represent the aggregation of initiating events with a common characteristic, either 

natural or man-made. DOE stated that the objective of scenario class development is to define 

a limited set of scenario classes that could be analyzed quantitatively while still covering the 

range of possible future states of the repository system (SAR Section 2.2.1.3, p. 2.2-22). 

SAR Section 2.2.1.3.1 presented the scenario class formation for (i) individual protection and 

(ii) groundwater protection. On the basis of the probabilities in SAR Section 2.2.2 (which were 

evaluated in the TER Section 2.2.1.2.2), DOE retained the following events—seismic, igneous, 

and early waste package and drip shield failure—and developed general scenario classes from 

these retained events. The DOE defined the nominal scenario as the scenario that does not 

include any of these events, but that accounts for all other included features, events, and 

processes. DOE stated that the broad event classes (seismic, igneous, and early waste 

package and drip shield failure) are independent but not mutually exclusive. To define mutually 

exclusive classes that encompass a complete span of possible future states of the repository 

system, DOE considered a total of eight independent combinations from the three events and 

the nominal scenario, and summarized those combinations in the diagram in SAR Figure 2.2-3. 

DOE concluded that these eight scenario class combinations cover all of the possibilities of 

damaging events or processes that could affect a waste package during the timeframe of the 

analysis (e.g., a waste package could be damaged by both an early failure event and a seismic 

event). DOE did not explicitly implement these eight mutually exclusive scenario classes and 

their probabilities for the computation of aggregated consequence estimates. DOE introduced 

simplifications (SAR Section 2.4.2.1) and derived consequence estimates on the basis of the 

three broad scenario classes (seismic, igneous, and early waste package and drip shield 

failure), the nominal scenario class, and their probabilities. 

DOE discussed scenario class formation for the human protection standard in SAR 

Section 2.2.1.3.2 and defined assumptions to support the development of the scenario. 

NRC Staff’s Evaluation of Formation of Scenario Classes 

The NRC staff evaluated the formation of scenario classes for performance assessments for 

individual protection and groundwater protection by evaluating whether they cover the full range 

of potential future states of the repository system. The NRC staff notes the classifications are 

adequate and comprehensive as they encompass all possibilities (aside from human intrusion) 

leading to radionuclide release consistent with the set of included features, events, and 

processes. These four scenarios are not mutually exclusive (e.g., an initially failed waste 

package could also be affected by a seismic event). However, to assess the effect of combined 

scenarios, DOE considered the total of eight possible combinations from the three event and the 

nominal scenarios. These eight scenario class combinations cover all of the possibilities of 

damaging events or processes that could affect a waste package during the timeframe of the 

analysis. Therefore, DOE left no situation that could lead to radionuclide release without 

consideration and evaluation. DOE considered the effect of these eight combinations in 

determining aggregated dose estimates. 

DOE’s formation of scenario classes is reasonable on the following basis. DOE developed an 

initial set of scenario classes that covers all possible included degradation processes and 

events that could lead to release of radionuclides. From these broad scenario classes, DOE 

considered mutually exclusive and complete class combinations, and explained how these 

mutually exclusive class combinations were accounted for in the aggregated consequence 

estimates in the performance assessment computations. DOE clearly documented the set of 

scenario classes. The NRC staff notes that the DOE-defined scenario classes to support its 
2-73 

performance assessments are complete (i.e., the classes account for all of the included 

features, events, and processes potentially leading to release of radionuclides) and that 

no relevant event was overlooked. Thus, DOE provided a technically sound basis for the 

formation of scenario classes. The scenario classes—nominal, seismic, igneous, and early 

waste package and drip shield failure—as defined by DOE, are reasonable classes to support 

a scenario screening process. The NRC staff notes that DOE considered class combinations 

that cover all possibilities leading to radionuclide release consistent with the set of included 

features, events, and processes and that DOE assessed the effect of these combinations in the 

dose estimates (SAR Section 2.4.2.1). Thus, the scenario classes for performance 

assessments for the individual protection and groundwater protection are complete, clearly 

documented, and technically supported. 

The NRC staff notes the DOE analysis for human intrusion is reasonable, on the basis of 

the NRC staff’s evaluation of the human intrusion scenario analysis performed in TER 

Section 2.2.1.4.2.3. 

2.2.1.2.1.3.4 Screening of Scenario Classes

Summary of the DOE Approach to Screening of Scenario Classes 

In SAR Section 2.2.1.4, DOE described step 4 (screening for scenario classes) of the scenario 

analysis (SAR Section 2.2.1). DOE included the following four scenario classes for the 

performance assessment computations to analyze individual protection: nominal, early failure, 

seismic, and igneous scenario classes. DOE asserted that all of these scenario classes have a 

probability greater than 10−4

 in 10,000 years. 

For the performance assessment to analyze groundwater protection, DOE excluded the igneous 

scenario class on the basis that its probability is less than 0.1 in 10,000 years. 

The human intrusion scenario class is excluded from DOE’s performance assessments to 

analyze individual protection and groundwater protection. 

NRC Staff’s Evaluation of Screening of Scenario Classes

DOE screened scenario classes on the basis of probability, consequences, and consistency 

with regulations. Accordingly, the NRC staff’s review of DOE’s exclusion of scenarios classes 

on the basis of low probability used the results of the NRC staff’s evaluation documented in TER 

Section 2.2.1.2.2. In addition, the NRC staff reviewed whether the scenario classes that DOE 

ruled out by regulation were identified and justified. 

In SAR Section 2.2.1.3, DOE defined scenario classes referred to as nominal, early failure, 

seismic, igneous scenario, and human intrusion, which were used as the starting point for 

the screening of scenario classes. As described in TER Section 2.2.1.2.1.3.3, these 

scenario classes are reasonable because the scenario classes were clearly documented and 

technically justified. 

In SAR Section 2.2.1.3.1, DOE stated that, on the basis of probabilities described in SAR 

Section 2.2.2, seismic, igneous, and early waste package and drip shield failure were retained 

in the formation of scenario classes used in the performance assessments. The NRC staff 

reviewed the DOE basis for estimating these event probabilities in TER Section 2.2.1.2.2.3 and 

noted that the event probability for each of these events exceeds 1 chance in 10,000 of 
2-74 

occurring within 10,000 years of disposal. The NRC staff also noted that DOE appropriately 

considered information from site and regional characterization, natural analog studies, and 

repository design in its evaluation of probability for each of the events. 

DOE included the nominal, early failure, seismic, and igneous scenarios in the performance 

assessment computations to analyze individual protection. The human intrusion scenario was 

analyzed separately. The NRC staff notes that the inclusion of the four scenario classes is well 

supported. As stated in the previous section on formation of scenario classes, the four scenario 

classes incorporate all events (human intrusion aside) that could lead to significant radionuclide 

release. DOE’s exclusion of human intrusion from the performance assessment is reasonable. 

 

DOE included the nominal, early failure, and seismic scenarios in the performance assessment 

to analyze the groundwater protection scenario and excluded the igneous and human intrusion 

scenarios. The exclusion of the igneous scenario on the basis that igneous events are unlikely 

(i.e., a chance less than 1 in 100,000 per year of occurring) is reasonable on the basis of the 

NRC staff’s evaluation in TER Section 2.2.1.2.2. In that section, staff noted that it was 

reasonable to consider the probability of igneous events to be below 1 in 100,000 per year of 

occurring; thus, DOE provided sufficient technical basis to exclude the igneous scenario. The 

igneous scenario class is sufficiently broad to encompass a range of events. Thus the NRC 

staff noted that this scenario class was not prematurely excluded by a narrow definition. Finally, 

exclusion of human intrusion from the groundwater protection scenario is reasonable, because 

such a scenario is to include only undisturbed performance. 

DOE’s screening of scenario classes is reasonable on the basis of the previously described 

evaluations. DOE’s screening of scenario class was comprehensive, clearly documented, and 

technically sound. Scenario classes that DOE excluded from the performance assessment on 

the basis that they are specifically ruled out by the regulation were identified, and sufficient 

justifications were provided. Those scenario classes that DOE screened from the performance 

assessment on the basis that their probabilities fall below 10−4

 in 10,000 years were identified 

and justified. 

DOE included nominal, early failure, seismic, and igneous scenario classes in the performance 

assessment for individual protection, and this is reasonable on the basis that the probabilities of 

these scenario classes exceed 10−8

 per year (probabilities evaluated in TER Section 2.2.1.2.2) 

and that these scenario classes incorporate all included events that could lead to significant 

radionuclide release. For the groundwater protection scenario, DOE excluded the igneous 

scenario class from the performance assessment model, and this exclusion is reasonable 

because DOE provided sufficient justification to show that igneous events are unlikely. In the 

individual protection and groundwater protection computations, DOE excluded the human 

intrusion scenario from the performance assessments, and this exclusion is reasonable 

because DOE provided sufficient justification. DOE’s screening of scenario classes is 

reasonable for performance assessments to analyze individual protection and groundwater 

protection. Evaluation of event classes in regard to computations to analyze human intrusion is 

included in TER Section 2.2.1.4.2. 

The NRC staff has reviewed the information in the SAR and other DOE information and notes 

that the scenario classes that have been screened out from the performance assessment 

calculations were justified. 
2-75 

2.2.1.2.1.4 NRC Staff Conclusions 

NRC staff notes that the DOE approach for Scenario Analysis is consistent with the guidance in 

the YMRP. The NRC staff also notes that the DOE technical approach for scenario analysis 

discussed in this chapter is reasonable to support performance assessments. 

2.2.1.2.1.5 References

Aiyanger, A.K., B.W. Neuberger, P.G. Oberson, and S. Ankem. 2005aa. “The Effect of Stress 

Level and Grain Size on the Ambient Temperature Creep Deformation Behavior of an Alpha 

Ti-1.6 Wt Pct V Alloy.” Metallurgical and Materials Transactions A. Vol. 36A. pp. 637–644. 

American Nuclear Society. 1983aa. "Nuclear Criticality Safety in Operations with Fissionable 

Materials Outside Reactors." ANSI/ANS–8.1–1983. La Grange Park, Illinois: American 

Nuclear Society. 

American Society of Mechanical Engineers. 2001aa. ASME Boiler and Pressure Vessel Code. 

New York City, New York: American Society of Mechanical Engineers. 

Ankem, S., C.A. Greene, and S. Singh. 1994aa. “Time Dependent Twinning During Ambient 

Temperature Creep of Alpha-Ti-Mn Alloys.” Scripta Metallurgica. Vol. 30. pp. 803–808. 

AREVA 2004. "Reactor Record Uncertainty Determination." 32-5041666-02, Rev 02. 

ACC: DOC. 20040623.0002.ML092310715. ML092310716. 5/26/04. 

ASM International. 2003aa. ASM Handbook: Heat Treating. Vol. 4. Metal Park, Ohio: 

ASM International. 

ASM International. 1994aa. Materials Properties Handbook: Titanium Alloys. Metal Park, 

Ohio: ASM International. 

ASTM International. 2004aa. “Standard Specification for Low-Carbon Nickel-MolybdenumChromium, Low-Carbon Nickel-Chromium-Molybdenum, Low-Carbon Nickel-ChromiumMolybdenum-Copper, Low-Carbon Nickel-Chromium-Molybdenum-Tantalum, and Low-Carbon 

Nickel-Chromium-Molybdenum-Tungsten Alloy Plate, Sheet, and Strip.” B 475-04: Annual 

Book of ASTM Standards. Vol. 02.04. West Conshohocken, Pennsylvania: 

ASTM International. 

Beavers, J.A. and C.L. Durr. 1991aa. NUREG/CR–5598, “Immersion Studies on Candidate 

Container Alloys for the Tuff Repository.” Washington, DC: NRC. 

Beavers, J.A., N.G. Thompson, and C.L. Durr. 1992aa. NUREG/CR–5709, “Pitting, Galvanic, 

and Long-Term Corrosion Studies on Candidate Container Alloys for the Tuff Repository.” 

Washington, DC: NRC. 

Brossia, C.S. and G.A. Cragnolino. 2000aa. “Effects of Environmental, Electrochemical, and 

Metallurgical Variables on the Passive and Localized Dissolution of Ti Grate 7.” CORROSION 

2000. Paper No. 00211. Houston, Texas: NACE International. 

BSC. 2007be. “Subsurface Geotechnical Parameters Report.” ANL–SSD–GE–000001. 

Rev. 00. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 
2-76 

BSC. 2006aa. “Analysis of Alcove 8/Niche 3 Flow and Transport Tests.” 

ANL–NBS–HS–000056. Rev. 00. ACN 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2005aa. “Analysis of Dust Deliquescence for FEP Screening.” ANL–EBS–MD–000074. 

Rev. 01. AD 01, ACN 001, ACN 002. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2005an. “Creep Deformation of the Drip Shield.” CAL–WIS–AC–000004. Rev. 00A. 

Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004ab. “Aging and Phase Stability of Waste Package Outer Barrier.” 

ANL–EBS–MD–000002. Rev. 02. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004ae. “Aqueous Corrosion Rates for Waste Package Materials.” 

ANL–DSD–MD–000001. Rev. 01. ACN 001, ERD 01, ERD 02. Las Vegas, Nevada: Bechtel 

SAIC Company, LLC. 

BSC. 2004al. “Drift Degradation Analysis.” ANL–EBS–MD–000027. Rev. 03. ACN 001, 

ACN 002, ACN 003, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004aq. “Evaluation of Potential Impacts of Microbial Activity on Drift Chemistry.” 

ANL–EBS–MD–000038. Rev. 01. ACN 01, ACN 02, ERD 01, ERD 02. Las Vegas, Nevada: 

Bechtel SAIC Company, LLC. 

BSC. 2004as. “General Corrosion and Localized Corrosion of the Drip Shield.” 

ANL–EBS–MD–000004. Rev. 02. AD 01, ACN 01, ACN 02, ERD 01. Las Vegas, Nevada: 

Bechtel SAIC Company, LLC. 

BSC. 2004be. “Seepage Model for PA Including Drift Collapse.” MDL–NBS–HS–000002. 

Rev. 3. ACN 001. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bi. “Yucca Mountain Site Description.” TDR–CRW–GS–000001. Rev. 02 ICN 01. 

ERD 01, ERD 02. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

Cao, T. 2010aa. “Yucca Mountain Confirmatory Drift Degradation Calculations.” Memo 

(September 21) T. Cao to J. Guttmann (NRC). ML102640197. Washington, DC: NRC 

CNWRA and NRC. 2008aa. “Risk Insights Derived From Analyses of Model Updates in the 

Total-system Performance Assessment Version 5.1 Code.” ML082240343. San Antonio, 

Texas: CNWRA. 

Dassault Systèmes Simulia Corp. 2009aa. “Abaqus V6.9-1.” Providence, Rhode Island: 

Dassault Systèmes Simulia Corp. 

DOE. 2010ad. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2.1.2), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 4.” Letter (January 20) J.R. Williams to 

J.H. Sulima (NRC). ML100210164. Washington, DC: DOE, Office of Technical Management. 
2-77 

DOE. 2010ah. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2.1.2), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 4.” Letter (January 7) J.R. Williams to 

J.H. Sulima (NRC). ML100110028. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ab. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (February 23) J.R. Williams to J.H. Sulima (NRC). 

ML090550101. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ae. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.1, Set 1.” Letter (January 23) J.R. Williams to J.H. Sulima (NRC). 

ML090260710. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009af. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 3.” Letter (March 4) J.R. Williams to J.H. Sulima (NRC). 

ML091830594. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ah. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (March 3) J.R. Williams to J.H. Sulima (NRC). 

ML090860902. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ai. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report, Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (March 23) J.R. Williams to J.H. Sulima (NRC). 

ML090830357. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009aj. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report, Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (March 24) J.R. Williams to J.H. Sulima (NRC). 

ML090840280. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009al. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (March 26) J.R. Williams to J.H. Sulima (NRC). 

ML090860424. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009av. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 1. 

ML090700817. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2009bo. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.2 and 2.3.3), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (June 1) J.R. Williams to J.H. Sulima (NRC). 

ML091530403. Washington, DC: DOE, Office of Technical Management. 
2-78 

DOE. 2009bv. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (March 25) J.R. Williams to J.H. Sulima (NRC). 

ML090900069. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bw. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (October 26) J.R. Williams to J.H. Sulima (NRC). 

ML093070154. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bx. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2.1.2), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 4.” Letter (December 15) J.R. Williams to 

J.H. Sulima (NRC). ML093500245. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009by. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2, Table 2.2-5), 

Safety Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (June 4) J.R. Williams to 

J.H. Sulima (NRC). ML091560538. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bz. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2, Table 2.2-5), 

Safety Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (June 25) J.R. Williams to 

J.H. Sulima (NRC). ML091760913. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ca. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2, Table 2.2-5), 

Safety Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (July 31) J.R. Williams to 

J.H. Sulima (NRC). ML092150623. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cb. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 5.” Letter (June 5) J.R. Williams to J.H. Sulima (NRC). 

ML091590581. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cc. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2, Table 2.2-5), 

Safety Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 5.” Letter (August 12) J.R. Williams to 

J.H. Sulima (NRC). ML092250006. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cd. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 6.” Letter (November 5) J.R. Williams to J.H. Sulima (NRC). 

ML093090335. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ce. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 6.” Letter (November 17) J.R. Williams to J.H. Sulima (NRC). 

ML093220119. Washington, DC: DOE, Office of Technical Management. 
2-79 

DOE. 2009cf. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 6.” Letter (November 24) J.R. Williams to J.H. Sulima (NRC). 

ML093360134. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cg. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 6.” Letter (December 3) J.R. Williams to J.H. Sulima (NRC). 

ML093380138. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ch. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 6.” Letter (December 4) J.R. Williams to J.H. Sulima (NRC). 

ML093410044. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ci. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 7.” Letter (October 30) J.R. Williams to J.H. Sulima (NRC). 

ML093060134. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cj. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.6.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.1, Set 3.” Letter (May 7) J.R. Williams to J.H. Sulima (NRC). ML091280184. 

ML091280185. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009co. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (August 11) J.R. Williams to J.H. Sulima (NRC). 

ML092310639. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cq. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2, Table 2.2-5), 

Safety Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 3.” Letter (December 4) J.R. Williams to 

J.H. Sulima (NRC.) ML093410044. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009gp. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (June 19) J.H. Sulima to J.R. Williams (NRC). ML091730195. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009gq. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.1.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.1, Set 4.” Letter (July 1) J.H. Sulima to J.R. Williams (NRC). ML091880036. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2004aa. “Criticality Model.” CAL–DS0–NU–000003. Rev. 00A. ECN 1. Las Vegas, 

Nevada: DOE, Office of Civilian Radioactive Waste Management. 
2-80 

DOE. 2004ab. “Isotopic Model for Commercial SNF Burnup Credit.” CAL–DSU–NU–000007. 

Rev. 00B. ECN 1. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste 

Management. 

DOE. 2003aa. “Commercial Spent Nuclear Fuel Waste Package Misload Analysis.” 

CAL–WHS–MD–000003. Rev. 00A. ECN 1. Las Vegas, Nevada: DOE, Office of Civilian 

Radioactive Waste Management. 

Drefahl, K., P. Wincierz, U. Zwicker, and P. Delarbre. 1985aa. “The 230,000 h Creep 

Properties of Titanium Produced From Electrolytic and Sponge Material and TiAl6V4 Alloy 

at 20 °C.” Proceedings from the 5th International Conference on Titanium, Congress-Center, 

Munich, Germany. pp. 2,387–2,394. 

Dunn, D.S., Y.-M. Pan, L. Yang, and G.A. Cragnolino. 2006aa. “Localized Corrosion 

Susceptibility of Alloy 22 in Chloride Solutions: Part 2—Effect of Fabrication Processes.” 

Corrosion. Vol. 62, No. 1. pp. 3–12. 

Fix, D.V., J.C. Estill, L.L. Wong, and R.B. Rebak. 2004aa. “General and Localized Corrosion 

of Austenitic and Borated Stainless Steels in Simulated Concentrated Ground Waters.” 

Proceedings of the 2004 ASME/JSME Pressure Vessel and Piping Conference: 

Transportation, Storage, and Disposal of Radioactive Materials. Report No. PVP–Vol. 483. 

San Diego, California. 

Gauthier, J.H., M.L. Wilson, D.J. Borns, and B.W. Arnold. 1996aa. “Impacts of Seismic Activity 

on Long-Term Repository Yucca Mountain.” Proceedings of the American Nuclear Society 

Topical Meeting on Methods of Seismic Hazards Evaluation: Focus '95, September 18–20, 

1995, Las Vegas, Nevada. pp. 159–168. 

Glass, R.S., G.E. Overturf, R.E. Garrison, and R.D. McCright. 1984aa. “Electrochemical 

Determination of the Corrosion Behavior of Candidate Alloys Proposed for Containment of High 

Level Nuclear Waste in Tuff.” Report UCID–20174. Livermore, California: Lawrence Livermore 

National Laboratory. 

Green, R.T., C. Manepally, R.W. Fedors, and M.M. Roberts. 2008aa. “Examination of Thermal 

Refluxing in In-Situ Heater Tests.” San Antonio, Texas: CNWRA 

He, X, D.S. Dunn, and A.A. Csontos. 2007ab. “Corrosion of Similar and Dissimilar Metal 

Crevices in the Engineered Barrier System of a Potential Nuclear Waste Repository.” 

Electrochimica Acta. Vol. 52. pp. 7,556–7,569. 

Itasca International, Inc. 2004aa. “Fast Lagrangian Analysis of Continua (FLAC®).” 

Minneapolis, Minnesota: Itasca International, Inc. 

Itasca International, Inc. 2004ab. “Particle Flow Code in Two Dimensions (PFC2DTM).” 

Minneapolis, Minnesota: Itasca International, Inc. 

Itasca International, Inc. 2004ac. “Universal Distinct Element Code (UDECTM).” Minneapolis, 

Minnesota: Itasca International, Inc. 

Jaeger, J.C., N.G.W. Cook, and R.W. Zimmerman. 2007aa. Fundamentals of Rock Mechanics. 

4th Edition. Malden, Massachusetts: Blackwell Publishing. 
2-81 

Kaiser, P.K., M.S. Diederichs, C.D. Martin, J. Sharp, and W. Steiner. 2000aa. “Underground 

Works in Hard Rock Tunnelling and Mining.” Proceeding of GeoEng 2000, Melbourne, 

Australia. Lancaster, Pennsylvania: Technomic Publishing Company. pp. 841–926. 

Kemeny J.M. and N.G.W. Cook. 1992aa. “Water Table Change Due to a Normal Faulting 

Earthquake.” Section 6: Demonstration of a Risk-Based Approach to High-Level Waste 

Repository Evaluation—Phase 2. EPRI–TR–100384. Palo Alto, California: Electric Power 

Research Institute. 

Kiessel, W.R. and M.J. Sinnott. 1953aa. “Creep Properties of Commercially Pure Titanium.” 

Journal of Metals. Vol. 5. pp. 331–337. 

Lawrence Livermore National Laboratory. 1998aa. “Nonisothermal Unsaturated Flow and 

Transport (NUFT).” Livermore, California: Lawrence Livermore National Laboratory. 

Leslie, B. 2010aa. "Table Summarizing Review of all Features, Events, and Processes." 

Memo (December 2) to J. Guttmann (NRC). ML102720603. Washington, DC: NRC. 

Lin C., B. Leslie, R. Codell, H. Arlt, and T. Ahn. 2003aa. “Potential Importance of Fluoride 

to Performance of the Drip Shield.” Proceedings of the American Nuclear Society 

10th International High-Level Radioactive Waste Management Conference, March 30–April 2, 

2003, Las Vegas, Nevada. 

Lister, T., R. Mizia, A. Erickson, and S. Birk. 2007aa. “Electrochemical Corrosion Testing of 

Borated Stainless Steel Alloys.” INL/EXT–07–12633. Rev. 1. Idaho Falls, Idaho: Idaho 

National Laboratory. 

Lister T., R. Mizia, A. Erickson, and T. Trowbridge. 2007ab. “Electrochemical Corrosion 

Testing of Neutron Absorber Materials.” INL/EXT–06–11772. Rev. 1. Idaho Falls, Idaho: 

Idaho National Laboratory. 

Liu, Z. and G. Welsch. 1988aa. “Literature Survey on Diffusivities of Oxygen, Aluminum, 

and Vanadium in Alpha Titanium, Beta Titanium, and Rutile.” Metallurgical Transactions A. 

Vol. 19A, No. 4. pp.121–125. 

Liu, Z. and G. Welsch. 1988ab. “Effects of Oxygen and Heat Treatment on the Mechanical 

Properties of Alpha and Beta Titanium Alloys.” Metallurgical Transactions A. Vol. 19a, No. 3. 

pp. 527–542. 

Manepally, C., A. Sun, R. Fedors, and D. Farrell. 2004aa. “Drift-Scale Thermohydrological 

Process Modeling—In-Drift Heat Transfer and Drift Degradation.” CNWRA 2004-05. 

ML042160447. San Antonio, Texas: CNWRA 

McCright, R.D., W.G. Halsey, and R.A. Van Konynenburg. 1987aa. “Progress Report on 

the Results of Testing Advanced Conceptual Design Metal Barrier Materials Under Relevant 

Environmental Conditions for a Tuff Repository.” Report No. UCID–21044. Livermore, 

California: Lawrence Livermore National Laboratory. 

Miller, W.H., R.T. Chem, and E.A. Starke. 1987aa. “Microstructure, Creep and Tensile 

Deformation in Ti-6Al-2Nb-1Ta-0.8Mo.” Metallurgical Transactions A. Vol. 18a. 

pp. 1,451–1,468. 
2-82 

Mintz, T. and X. He. 2009aa. “Modeling of Hydrogen Uphill Diffusion in Dissimilar Titanium 

Welds.” Proceedings of the CORROSION 2009 Conference. Paper No. 09430. Houston, 

Texas: NACE International. 

Moskalenko, V.A. and V.A. Puptsova. 1972aa. “Thermal Activation Parameters of the Plastic 

Deformation of Single Crystals of "-Titanium.” Fizika Metallov I Metallovedenie. Vol. 34. 

pp. 1,264–1,269. 

National Research Council. 1995aa. Technical Bases for Yucca Mountain Standards. 

Washington, DC: National Academies Press. 

National Research Council. 1992aa. Ground Water at Yucca Mountain: How High Can It 

Rise? Washington, DC: National Academies Press. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

ML051360241. Washington, DC: NRC. 

NRC. 2005ac. Regulatory Guide 3.71, “Nuclear Criticality Safety Standards for Fuel and 

Material Facilities.” Rev. 1. Washington, DC: NRC. 

NRC. 2005af. “Implementation of a Dose Standard After 10,000 Years: Proposed Rule.” 

Federal Register. Vol. 70, No. 173. pp. 53313–53320. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

NRC. 2000ab. NUREG–1567, “Standard Review Plan for Spent Fuel Dry Storage Facilities.” 

Washington, DC: NRC, Spent Fuel Project Office. 

NRC. 1998aa. Regulatory Guide 3.71, "Nuclear Criticality Safety Standards for Fuels and 

Material Facilities. Rev. 0. Washington, DC: NRC. 

Nuclear Energy Agency. 2006aa. “The NEA International FEP Database, Version 2.1.” 

Issy-les-Moulineaux, France: Organisation for Economic Cooperation and Development, 

Nuclear Energy Agency. 

Nuclear Energy Agency. 2001aa. “Scenario Development Methods and Practice: An 

Evaluation Based on the NEA Workshop on Scenario Development, Madrid, May 1999.” 

Paris, France: Organisation for Economic cooperation and Development, Nuclear 

Energy Agency. 

Nuclear Energy Agency. 2000aa. “Features, Events, and Processes (FEPs) for Geologic 

Disposal of Radioactive Waste: An International Database.” Paris France: Organisation for 

Economic Co-operation and Development, Nuclear Energy Agency. 
2-83 

Nuclear Energy Agency. 1997ab. “Safety Assessment of Radioactive Waste Repositories: 

An International Database of Features, Events, and Processes.” Ver. 3. SAM–J012–R1. Paris, 

France: Organisation for Economic Co-operation and Development, Nuclear Energy Agency. 

Odegard, B.C. and A.W. Thompson. 1974aa. “Low Temperature Creep of Ti-6Al-4V.” 

Metallurgical Transactions A. Vol. 5A. pp. 1,207–1,213. 

Ofoegbu, G., R. Fedors, C. Grossman, S. Hsiung, L. Ibarra, C. Manepally, J. Myers, 

M. Nataraja, O. Pensado, K. Smart, and D. Wyrick. 2007aa. “Summary of Current 

Understanding of Drift Degradation and Its Effects on Performance at a Potential Yucca 

Mountain Repository.” Rev. 1. CNWRA 2006-02. ML071030115. San Antonio, Texas: 

CNWRA. 

Okada, T. 1983aa. “Factors Influencing the Cathodic Charging Efficiency of Hydrogen by 

Modified Titanium Electrodes.” Electrochimica Acta. Vol. 28. pp. 1,113–1,120. 

Orava, R.N. 1967aa. “On the Deformation Mechanisms in Alpha-Titanium Below 0.4 TM.” 

Scripta MetallurgicaI. Vol. 1. pp. 153–156. 

Pabalan, R.T. 2010aa. “Quantity and Chemistry of Water Contacting Engineered Barriers 

Integrated Subissue.” Electronic Scientific Notebook 930E. San Antonio, Texas: CNWRA. 

Pan, Y.-M., D.S. Dunn, and G.A. Cragnolino. 2005aa. “Topologically Close-Packed Phase 

Precipitation and Thermal Stability in Alloy 22.” Metallurgical and Materials Transactions. 

Vol. 36A. pp. 1,143–1,151. 

Perfect, D.L., C.C. Faunt, W.C. Steinkampf, and A.K. Turner. 1995aa. “Hydrochemical Data 

Base for the Death Valley Region, California and Nevada.” USGS Open-File Report 94-305. 

Denver, Colorado: U.S. Geological Survey. 

Pickett, D.A. and B.W. Leslie. 1999aa. “An Audit of the DOE Treatment of Features, Events, 

and Processes at Yucca Mountain, Nevada, With Emphasis on the Evolution of the Near-Field 

Environment.” San Antonio, Texas: CNWRA. 

Radulescu, G., D.E. Mueller, S. Goluoglu, D.F. Hollenbach, and P.B. Fox. 2007aa. “Range of 

Applicability and Bias Determination for Postclosure Criticality of Commercial Spent Nuclear 

Fuel.” ACC: LLR.20071120.0179. ML092310677. Oak Ridge, Tennessee: Oak Ridge 

National Laboratory. 

Rishel, D.M., J.D. Smee, and B.F. Kammenzind. 2000aa. “The Corrosion Behavior of Hafnium 

in High-Temperature Water Environments.” B–T–3232. West Mifflin, Pennsylvania: Bettis 

Atomic Power Laboratory. 

Rogers, J.W., Jr., K.L. Erickson, D.N. Belton, R.W. Springer, T.N. Taylor, and J.G. Berry. 

1988aa. “Low Temperature Diffusion of Oxygen in Titanium and Titanium Oxide Films.” 

Applied Surface Science. Vol. 35, No. 1. pp. 137–152. 

Salve, R. and T.J. Kneafsey. 2005aa. “Vapor-Phase Transport in the Near-Drift Environmental 

at Yucca Mountain.” Water Resources Research. Vol. 41. W01012. Washington, DC: 

American Geophysical Union. 
2-84 

Sippel, T. 2010aa. “Yucca Mountain Confirmatory Criticality Calculations.” Memo (August 26) 

to J. Guttmann (NRC). ML102380107. Washington, DC: NRC. 

Smailos, E. and R. Köster. 1987aa. “Corrosion Studies on Selected Packaging Materials for 

Disposal of High Level Wastes.” Materials Reliability in the Back End of the Nuclear Fuel Cycle. 

Proceedings of a Technical Committee Meeting, Vienna, Austria, September 2–5, 1986. 

TECHDOC–421. Vienna, Austria: International Atomic Energy Agency. pp. 7–24. 

Smailos, E., W. Schwarzkopf, and R. Storch. 1992aa. “Corrosion Studies on Packaging 

Materials for High-Level Waste Dispsoal in a Rock-Salt Repository.” 12th Scandinavian 

Corrosion Congress & Eurocorr '92. Espoo, SF. May 31–June 4, 1992. Vol. II. pp. 327–338. 

Helsinki, Finland: Corrosion Society of Finland. 

SNL. 2008aa. “CSNF Loading Curve Sensitivity Analysis.” ANL–EBS–NU–000010. Rev. 00. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ac. “Features, Events, and Processes for the Total System Performance 

Assessment: Methods.” ANL–WIS–MD–000026. Rev. 00. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2008ad. “Postclosure Nuclear Safety Design Bases.” ANL–WIS–MD–000024. Rev. 01. 

ACN 01, ERD 01, ERD 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ae. “Screening Analysis of Criticality Features, Events, and Processes for License 

Application.” ANL–DS0–NU–00001. Rev. 00. ACN 01, ERD 01, ERD 02. Las Vegas, Nevada: 

Sandia National Laboratories. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008aj. “Multiscale Thermohydrologic Model.” ANL–EBS–MD–000049. Rev. 03. 

ADD 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008al. “Waste Package Flooding Probability Evaluation.” CAL–DN0–NU–000002. 

Rev. 00C. AD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ab. “Atmospheric Dispersal and Deposition of Tephra From a Potential Volcanic 

Eruption at Yucca Mountain, Nevada.” MDL–MGR–GS–000002. Rev. 03. ERD 01. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ai. “Drift-Scale THC Seepage Model.” MDL–NBS–HS–000001. Rev. 05. ERD 01. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ak. “Engineered Barrier System: Physical and Chemical Environment.” 

ANL–EBS–MD–000033. Rev. 06. ERD 01. Las Vegas, Nevada: Sandia 

National Laboratories.
2-85 

SNL. 2007al. “General Corrosion and Localized Corrosion of Waste Package Outer Barrier.” 

ANL–EBS–MD–000003. Rev. 03. ACN 01, ERD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007am. “Geochemistry Model Validation Report: Material Degradation and Release 

Model.” ANL–EBS–GS–000001. Rev. 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ax. “Saturated Zone Site-Scale Flow Model.” MDL–NBS–HS–000011. Rev. 03. 

ACN 01, ERD 01, ERD 02. ERD 03. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ba. “Site-Scale Saturated Zone Transport.” MDL–NBS–HS–000010. Rev. 03. ACN 

01, AD 001. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007bb. “Stress Corrosion Cracking of Waste Package Outer Barrier and Drip Shield 

Materials.” ANL–EBS–MD–000005. Rev. 04. ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007bf. “UZ Flow Models and Submodels.” MDL–NBS–HS–000006. Rev. 03. ACN 01, 

ERD 01, ERD 02, ERD 03. Las Vegas, Nevada: Sandia National Laboratories. 

Stetina, K. 1980aa. “Creep of Titanium Between 30 °C and 300 °C.” Proceedings of the 

International Conference on Engineering Aspects of Creep, September 1980, University of 

Sheffield, England. Paper No. C194/80. London, England: Institute of Mechanical Engineers. 

Stetina, K. 1969aa. “Comments on ‘On the Deformation Mechanisms in Alpha-Titanium 

Below 0.4 TM.’” Scripta Metallurgica. Vol. 3. pp. 57–58. 

Stonestrom, D.A., D.E. Prudic, R.J. Laczniak, K.C. Akstin, R.A. Boyd, and K.K. Henkelman. 

2003aa. “Estimates of Deep Percolation Beneath Native Vegetation, Irrigated Fields, and the 

Amargosa-River Channel, Amargosa Desert, Nye County, Nevada.” USGS Open-Field 

Report 03-104. Denver, Colorado: U.S. Geological Survey. 

Teper, B. 1991aa. “Activities Aimed at Qualification of a HLW Disposal Canister.” Proceedings 

of the Second Annual International Conference on High-Level Radioactive Waste Management, 

Las Vegas, Nevada, April 1991. La Grange Park, Illinois: American Nuclear Society. 

Thompson, A.W. and B.C. Odegard. 1973aa. “The Influence of Microstructure on Low 

Temperature Creep of Ti-5Al-2.5Sn.” Metallurgical Transactions. Vol. 4. pp. 899–908. 

Turner, D.R. and R.T. Pabalan. 1999aa. “Abstraction of Mechanistic Sorption Model Results 

for Performance Assessment Calculations at Yucca Mountain, Nevada.” Waste Management. 

Vol. 19. pp. 375–388. 

Waiting, D.J., J.A. Stamatakos, D.A. Ferrill, D.W. Sims, A.P. Morris, P.S. Justus, and 

K.I. Abou-Bakr. 2003aa. “Methodologies for the Evaluation of Faulting at Yucca Mountain, 

Nevada.” Proceedings of the10th International High-Level Radioactive Waste Management 

Conference, Las Vegas, Nevada, March 30–April 2, 2003. La Grange Park, Illinois: American 

Nuclear Society. pp. 377–387. 

Zeyfang, R., R. Martin, and H. Conrad. 1971aa. “Low Temperature Creep of Titanium.” 

Materials Science and Engineering. Vol. 8. pp. 134–140. 

3-1 

CHAPTER 3 

2.2.1.2.2 Identification of Events With Probabilities Greater 

Than 10−8 Per Year 

2.2.1.2.2.1 Introduction 

This chapter evaluates the U.S. Department of Energy’s (DOE) information on event probability 

used in its performance assessments evaluations. The U.S. Nuclear Regulatory Commission 

(NRC) staff evaluated information provided in the Safety Analysis Report (SAR) (DOE, 2008ab, 

2009av), as supplemented by the DOE responses to the NRC staff’s requests for additional 

information (RAIs) (DOE, 2009aa–ad,aq,bd), and the references cited therein. 

A performance assessment is a systematic analysis that answers the following triplet risk 

questions: What can happen? How likely is it to happen? What are the consequences? 

Scenario analysis answers the first question: What can happen? The NRC staff’s 

evaluation of the DOE scenario analysis is documented in its Technical Evaluation Report 

(TER) Section 2.2.1.2.1. One result from the scenario analysis is the identification of the events 

to be included in the performance assessment calculations used to evaluate potential doses. 

This chapter addresses the second question: How likely is it that these events will happen? 

A performance assessment evaluation considers events that have at least 1 chance in 

100 million per year of occurring. DOE identified and described those events that exceeded 

this probability threshold. Performance assessments are also used to evaluate the human 

intrusion and groundwater protection calculations. These performance assessments have 

different considerations for event probabilities than those used for the individual protection 

calculation. The DOE approach for quantifying the event probabilities and the technical 

basis for determining the probability estimates assigned to each event type are addressed in 

this chapter. 

2.2.1.2.2.2 Evaluation Criteria 

This chapter of the TER documents the NRC staff’s review of event probabilities used in 

its performance assessments evaluations, which is guided by the requirements of 

10 CFR 63.114(a)(1), 10 CFR 63.114(a)(2), 10 CFR 63.114(a)(4), 10 CFR 63.114(a)(7), 

and 10 CFR 63.114(b). 

10 CFR 63.114(a)(1) requires that any performance assessment used to evaluate repository 

performance objectives after permanent closure must include data related to the geology, 

hydrology, and geochemistry (including disruptive processes and events) of the Yucca Mountain 

site, and the surrounding region to the extent necessary, and information on the design of the 

engineered barrier system used to define, for 10,000 years after disposal, parameters and 

conceptual models used in the assessment. 

10 CFR 63.114(a)(2) requires that any performance assessment must account for uncertainties 

and variabilities in parameter values, for 10,000 years after disposal, and provide for the 

technical basis for parameter ranges, probability distributions, or bounding values used in the 

performance assessment. 
3-2 

10 CFR 63.114(a)(4) requires that any performance assessment must consider only features, 

events, and processes consistent with the limits on performance assessment provided in 

10 CFR 63.342. 10 CFR 63.342(a) states that a performance assessment need not 

consider very unlikely events (i.e., those that are estimated to have a less than 1 chance in 

100 million per year of occurring). Further, 10 CFR 63.342(b) provides that the performance 

assessment used for the human intrusion and groundwater protection calculations excludes 

unlikely events (i.e., those events that are estimated to have an annual probability of occurring 

between 1 in 100,000 and 1 in 100 million). 

10 CFR 63.114(a)(7) requires that the technical basis for models used to represent the 

10,000 years after disposal in the performance assessment, such as comparisons made with 

outputs of detailed process-level models and/or empirical observations (e.g., laboratory testing, 

field investigations, and natural analogs), be provided. 

10 CFR 63.114(b) states that the performance assessment methods used to address 

10 CFR 63.114(a) are considered sufficient for the performance assessment for the period 

of time after 10,000 years and through the period of geologic stability. As defined in 

10 CFR 63.302, the period of geologic stability means the time during which the variability of 

geologic characteristics and their future behavior in and around the Yucca Mountain site can 

be bounded (i.e., they can be projected within a reasonable range of possibilities). This period 

is defined to end at 1 million years after disposal. 10 CFR 63.342(c) requires that DOE’s 

performance assessment project the continued effects of the features, events, and processes 

included in 10 CFR 63.342(a) beyond the 10,000-year postdisposal period through the period of 

geologic stability. 10 CFR 63.342(c)(1) specifies that DOE is to assess the effects of seismic 

and igneous activity scenarios, subject to the probability limits in 10 CFR 63.342(a) for very 

unlikely features, events, and processes, or sequences of events and processes. 

The NRC staff used the review guidance provided in the Yucca Mountain Review Plan (YMRP) 

(NRC, 2003aa), as supplemented by additional guidance for the period beyond 10,000 years 

after permanent closure (NRC, 2009ab). YMRP Section 2.2.1.2.2 contains the following five 

acceptance criteria: 

1. Events are adequately defined 

2. Probability estimates for future events are supported by appropriate technical bases 

3. Probability model support is adequate 

4. Probability model parameters have been adequately established 

5. Uncertainty in event probability is adequately evaluated 

Additionally, YMRP Section 2.2.1 provides guidance to the NRC staff on a process to apply risk 

information in its review of the DOE SAR. Following the YMRP guidance, the NRC staff 

considered DOE’s risk information (derived from DOE’s treatment of multiple barriers) and risk 

insights that are identified in SAR Section 2.4.2.2.1.2. The level of detail of the NRC staff’s 

review on particular parts of the SAR is based on the risk insights DOE developed and from 

consideration of the risk insights identified in NRC Appendix D (2005aa), as updated (CNWRA 

and NRC, 2008aa). Accordingly, the NRC staff used a graded approach in its review per the 

guidance identified in YMRP Section 2.2.1. 

2.2.1.2.2.3 Technical Evaluation 

In SAR Section 2.2.2, DOE considered events for inclusion into the postclosure performance 

assessments. Initial considerations included five event types: igneous events, seismic events, 
3-3 

early failure events, criticality events, and human intrusion events. As described in TER 

Section 2.2.1.2.1, DOE screened out the individual criticality features, events, and processes 

and DOE determined that the probability of the nuclear criticality event class has less than 

1 chance in 100 million per year of occurring. 

DOE retained igneous activity, seismic activity, and early failure events in its performance 

assessment evaluation of the individual protection scenario. The NRC staff’s evaluations of 

DOE information for igneous events, seismic events, and early failure events are documented in 

TER Sections 2.2.1.2.2.3.1, 2.2.1.2.2.3.2, and 2.2.1.2.2.3.3, respectively. 

2.2.1.2.2.3.1 Technical Evaluation for Igneous Event Probabilities 

This section presents the NRC staff’s evaluation of information DOE presented to estimate 

the probability of future igneous events. The NRC staff reviewed SAR Sections 2.1 and 2.4; 

SAR Sections 1.1.6, 2.2.2.2, and 2.3.11; and material provided in response to the NRC 

staff’s request for additional information (RAIs) (DOE, 2009aa,bd) and the references cited 

therein. DOE’s description of past igneous activity in the Yucca Mountain region (SAR 

Sections 1.1.6 and 2.3.11) and the overall approach for treatment of igneous events in the SAR 

are summarized next. 

DOE indicated that periods of igneous activity have resulted in the eruption of basalt magmas 

in the Yucca Mountain region during the last 11 million years and identified that the risk to 

the repository from future igneous activity could come from rising basalt magmas. The age 

and location of basaltic rocks that formed during at least six volcanic events in the last 

5 million years, within approximately 50 km [31.1 mi] of the repository, are provided in SAR 

Figure 1.1-152. In presenting information on the location of volcanism in the Yucca Mountain 

region, DOE described the geologic and geophysical techniques it used to characterize past 

activities. DOE indicated that basalts in the Yucca Mountain region appear to be products of 

partial melting of lower lithospheric mantle material, but acknowledged that there is a poor 

understanding of the exact mechanism of mantle melting. DOE characterized the basaltic 

volcanism in the Crater Flat volcanic field, which is in close proximity to Yucca Mountain, as 

having a relatively long lifetime with a small volume of erupted material (SAR Section 1.1.6.1.1). 

DOE included igneous events within the igneous event scenario class (SAR Section 2.2.1.3.1). 

DOE’s Total System Performance Assessment (TSPA) evaluation divides the igneous event 

scenario class into separate modeling cases for intrusive events and extrusive events (SAR 

Section 2.4.1.2.3). Intrusive events involve the rise of molten rock (i.e., magma) from deep in 

the Earth with the magma intersecting the repository drifts (tunnels). DOE made the 

assumption that if magma flows into drifts, it damages the barrier capabilities of the drip shields 

and waste packages and allows subsequent radionuclide release through the hydrologic (water) 

transport pathway (SAR Section 2.3.11.3). DOE viewed extrusive events as an extension of 

intrusive events: after the magma has entered a repository drift and reached the surface, 

a conduit may develop from which most of the magma erupts, producing a volcano. Of the 

intrusive igneous events that intersect the repository footprint, DOE only considered a subset of 

these events to develop a conduit within the repository and form a volcano at the surface. DOE 

further assumed that only in some cases will waste packages be intersected by this conduit and 

release their contents into the rising magma. The magma and incorporated waste is then 

explosively erupted (expelled) from the surface volcano and transported by airborne dispersion 

for some distance downwind of the vent (SAR Section 2.3.11.4). Thus, the probabilities of 

intrusive and extrusive (volcanic eruptive) igneous events that may disperse waste and 

radionuclides into the environment, either in the subsurface or via atmospheric transport, are 
3-4 

different (SAR Section 2.4.1.2.3). To assess the probability of a future igneous event 

intersecting the repository, DOE conducted a probabilistic volcanic hazard assessment (PVHA) 

using an elicitation process consisting of recognized experts (SAR Section 5.4). 

Risk Perspective 

If an igneous event occurred at the proposed repository, the igneous intrusion modeling case 

would constitute most of the calculated dose for the first 1,000 years following closure, as 

shown in SAR Figure 2.4-18(a), and is approximately half the calculated dose for the seismic 

ground motion modeling case in the ensuing 9,000 years. Moreover, DOE indicated that for the 

post-10,000-year period, the igneous intrusion modeling case and seismic ground motion 

modeling case provide approximately equal contributions to the total mean annual dose to the 

reasonably maximally exposed individual for the last 300,000 years of the time period. This 

statement (in SAR Section 2.1) is supported by the results presented in SAR Figure 2.4-18(b). 

In SAR Section 2.4.2.2.1.2.3, DOE provided the probability-weighted consequences of 

igneous activity (intrusive and extrusive) using the probability distribution from the probabilistic 

volcanic hazard assessment review. For igneous intrusive events, DOE assumed that all 

waste packages will fail (approximately 11,000), whereas for an igneous extrusive event, 

DOE estimated only a few waste packages (<10) will result in radionuclide release (SAR 

Section 2.3.11.4.2.1). DOE identified that the probability-weighted igneous intrusive mean dose 

is estimated to be less than 0.001 mSv/yr [0.1 mrem/yr] for the 10,000-year period and the 

median dose is less than 0.005 mSv/yr [0.5 mrem/yr] for the post-10,000-year time period (SAR 

Section 2.4.2.2.1.2.3.1). The DOE estimates for probability-weighted igneous extrusive 

(volcanic eruptive) mean dose are about 10−6

 mSv/yr [0.0001 mrem/yr] for the 10,000-year 

period and for the median dose are less than 6 × 10−7

 mSv/yr [6 × 105 mrem/yr] for the 

post-10,000-year time period (SAR Section 2.4.2.2.1.2.3.2). The difference in magnitude for 

the dose consequences between the two igneous scenarios (intrusive and extrusive) 

predominately results from the differing number of waste package failures estimated to occur 

for each scenario. 

Summary of the DOE SAR on Igneous Event Probability 

DOE evaluated the risk of future igneous activity, in part, by considering the probability that 

a future igneous event could intersect the repository. To quantify the probability of future 

igneous activity at the proposed Yucca Mountain repository, DOE conducted an expert 

elicitation review (probabilistic volcanic hazard assessment) (CRWMS M&O, 1996aa). 

This expert elicitation review resulted in a quantification of the mean annual probability 

of intersection of the repository by a future basaltic dike (an igneous intrusion) and its 

associated uncertainty distribution. 

In SAR Sections 2.2.2.2.1–2.2.2.2.5, DOE described the probability of a future igneous event 

intersecting the repository, the technical basis for the probability estimate, the probability model 

support including alternative estimates of the intersection probability, the probability model 

parameters, and the uncertainties associated with the probability estimate, respectively. 

Subsequent to the probabilistic volcanic hazard assessment evaluation, DOE conducted an 

aeromagnetic survey and drilling program to increase confidence in site characterization 

results related to igneous activity. As described in TER Section 2.5.4, DOE updated the 

probabilistic volcanic hazard assessment evaluation (CRWMS M&O, 1996aa) in a study 

known as probabilistic volcanic hazard assessment-update (PVHA-U) (SNL, 2008ah). In SAR 

Section 5.4.1 (DOE, 2009av), DOE stated that the PVHA-U was conducted in a manner that is 
3-5 

consistent with NUREG–1563 (NRC, 1996aa) and past practices. The average annual 

probability for an intrusive igneous event calculated in the PVHA-U is approximately twice as 

high as calculated in the original probabilistic volcanic hazard assessment evaluation (Boyle, 

2008aa). DOE stated that the difference between these two event probability distributions 

would not significantly affect the estimates of repository performance over either 10,000 years 

or 1 million years, and in SAR Section 5.4.1, DOE further stated that the PVHA-U results are 

confirmatory of the original probabilistic volcanic hazard assessment technical basis 

(DOE, 2009av; also see Boyle, 2008aa). 

NRC Staff’s Evaluation of Igneous Event Probability 

The NRC staff reviewed DOE’s igneous event probability in SAR Sections 2.2.2.2.2, 2.2.2.2.4, 

and 2.2.2.2.5. These three SAR sections address the technical bases of probability 

estimates, probability model parameters, and uncertainty in event probability, respectively. 

The NRC staff’s review focused on DOE’s information that addressed the event definition 

(SAR Section 2.2.2.2.1) and probability model support(SAR Section 2.2.2.2.3). The NRC staff’s 

evaluation of DOE’s igneous intrusive event probability is provided next, following the NRC 

staff’s evaluation of event definition and probability model support. 

Event Definition 

In SAR Section 2.2.2.2.1.1, DOE stated that the output of the probabilistic volcanic hazard 

assessment evaluation is the annual frequency of intersection of the proposed repository by an 

intrusive basaltic dike [CRWMS M&O, Section 4.2, Figure 4-32 (1996aa)]. The probabilistic 

volcanic hazard assessment expert elicitation program computed the mean annual probability of 

intersection of the proposed repository by an igneous basaltic dike as 1.5 × 10−8

 per year 

(CRWMS M&O, 1996aa). The probabilistic volcanic hazard assessment results increased only 

slightly when these probability estimates were recalculated to reflect postelicitation changes to 

the size, shape, and location of the proposed repository footprint (BSC, 2004af); the 

recalculated mean annual probability from probabilistic volcanic hazard assessment thus 

became 1.7 × 10−8

 per year (SAR Section 2.2.2.2.1.2). In the TSPA, DOE sampled a 

distribution of probability values for the likelihood of intrusive intersection with a mean value of 

1.7 ×10−8

 per year and computed the 5th and 95th percentiles of the uncertainty distribution at 

7.4 × 10−10 and 5.5 × 10−8, respectively. 

DOE also calculated the proportion of the intersections that include development of a conduit 

(i.e., an eruption through the repository). It incorporated information from the probabilistic 

volcanic hazard assessment and model calculations that are supported by information obtained 

from studies of analog volcanoes with exposed intrusive rocks from depths of 200–300 m 

[656-984 ft] (SNL, 2007ae). DOE subsequently calculated that 28 percent of intrusive events 

would develop a volcanic conduit within the repository footprint (SAR Sections 2.2.2.2.1.3 and 

2.3.11.4.2.1). As described in SAR Section 2.3.11.4.2.1.3, this fraction was determined by 

considering that a conduit can form at any location on a dike that intersects the repository and 

thus may not necessarily form within the footprint and by considering several other factors, 

including the distribution for the number of dikes in a swarm, consistent with analog basaltic 

volcanic events that included multiple dikes and in which conduit(s) formed on the widest dike. 

This conditional probability is for a conduit that develops within the repository. DOE then 

applied a second conditional probability of 0.297 (SAR Section 2.3.11.4.2.1) to represent the 

fraction of conduits that may intersect a drift containing waste packages and eject the waste 

contents through a volcanic vent (SAR Section 2.3.11.4.2.1). 
3-6 

Some PVHA panel members (CRWMS M&O, 1996aa) used event definitions for igneous events 

that included characteristics of both intrusive and extrusive events. For example, recurrence 

rates for intrusive events often were determined by interpreting the number of volcanic vents 

associated with a single event. However, the dike lengths used to represent these recurrence 

rates in probability models were independent of the relevant vent counts. See, for example, the 

discussion by McBirney in CRWMS M&O Appendix E (1996aa) that gave 90 percent weight to 

the 12-km [7.5-mi]-long chain of Quaternary Crater Flat volcanoes as representing a single 

event, but gave 90 percent weight that the dike supporting this event is less than 5 km [3.1 mi] 

{the dike must be at least 12 km [7.5 mi] long to feed the chain of volcanoes}. These 

inconsistencies in event definitions were resolved in the probabilistic volcanic hazard 

assessment-update evaluation (SNL, 2008ah), which used consistent definitions for intrusive 

and extrusive (volcanic) igneous event probabilities. 

The probability of an intrusive disruption of the repository differed between probabilistic volcanic 

hazard assessment and its update by a factor of 1.8 (1.7 × 10−8

 versus 3.1 × 10−8 per year, 

respectively). The event definition for the extrusive (volcanic) case in probabilistic volcanic 

hazard assessment-update was the formation of a conduit within the repository that would 

support an (explosive) eruption column; thus it was different and more specific than the 

probabilistic volcanic hazard assessment conditional probability of a conduit forming. The 

probability of an eruptive conduit event developing within the repository differed between 

probabilistic volcanic hazard assessment and its update by a factor of 2.5 (4.8 × 10−9

 versus 

1.2 × 10−8 per year, respectively). However, the two values are not directly comparable for the 

reason stated previously (Boyle, 2008aa). Therefore, any potential effects of inconsistencies 

related to event definitions within the probabilistic volcanic hazard assessment and the 

probabilistic volcanic hazard assessment-update evolutions have less than a factor of two effect 

on the uncertainty associated with the mean annual probability. Given the breadth of new 

information considered in the PHVA-U, the potential effect of uncertainty in event definitions 

would have less than a factor of two effect on the estimated mean annual igneous event 

probability. Because the mean doses DOE calculated for the intrusive and extrusive (volcanic) 

igneous cases (SAR Section 2.4) are on the order of 0.01 mSv/yr [1 mrem/yr] or less, the NRC 

staff notes that an approximate doubling of the igneous probabilities would have limited, if any, 

significance to risk (SNL, 2008ah; Boyle, 2008aa). Further information and review of the 

intrusive and extrusive (volcanic) igneous scenarios are provided by DOE in SAR Section 2.3.11 

and by the NRC staff’s evaluation in TER Section 2.2.1.3.10. 

On the basis of the information provided in SAR Section 2.2.2.2 and DOE’s statement that the 

updated probabilistic volcanic hazard assessment results confirmed the conclusions of the 

original probabilistic volcanic hazard assessment technical basis [SAR Section 5.4.1 (DOE, 

2009av)], DOE has adequately defined an igneous intrusive event and an extrusive (volcanic 

eruptive) event that may affect the proposed repository. These definitions are consistent with 

their use in the performance assessment for the igneous abstraction. DOE has reasonably 

evaluated separate probabilities, including uncertainties, for the igneous intrusion and volcanic 

eruptive events. 

Probability Model Support 

In describing the geologic basis for the probabilistic volcanic hazard assessment evaluation 

(SAR Section 2.2.2.2.3.1), DOE indicated that the PVHA combined multiple alternative 

conceptual models into a single distribution that captured the uncertainty in the expert panel’s 

conceptual models for the physical behavior of volcanism in the Yucca Mountain region. 

DOE also stated that for regional volcanism, no single conceptual model is appropriate 
3-7 

because the underlying physical processes that control the precise timing and location of 

volcanic events within a particular region remain uncertain (BSC, 2004af). To support the 

probabilistic volcanic hazard assessment evaluation, DOE provided its elicitation panel with a 

variety of published information on igneous features, tectonics, and geophysical characteristics 

of the Yucca Mountain region (see CRWMS M&O, 1996aa). The probabilistic volcanic hazard 

assessment panel concluded that basaltic volcanism in the Yucca Mountain region resulted 

from complex interactions in the lithospheric mantle that produced episodes of small-volume 

basaltic magma. Because these mantle processes were viewed as uncertain, the probabilistic 

volcanic hazard assessment panel members did not explicitly include mantle processes in their 

probability models. 

DOE has also indicated that past volcanic activity has occurred in the tectonically active 

Yucca Mountain region and could continue into the future with a very small probability of 

occurrence. During the period from 14 to 10 million years ago, major explosive eruptions 

involving rhyolite magma from volcanic centers lying roughly 20–40 km [12–25 mi] north of the 

repository site formed large caldera volcanoes and deposited the volcanic ash-flow tuffs of the 

region, some of which are the host rocks for the proposed repository. DOE concluded that the 

chance of a recurrence within the repository lifetime is less than 1 in 10,000 during 10,000 years 

[SAR Sections 2.2.2.2.2.1 and 2.3.11.2.1.1; see also Detournay, et al. (2003aa)] because there 

has been a sufficient time gap since caldera-forming volcanic activity ended about 8 million 

years ago (BSC, 2004bi). On the basis of available literature on the age and duration of this 

major phase of caldera-forming volcanism in the Yucca Mountain region (Sawyer, et al., 

1994aa; Fleck, et al., 1996aa) and the NRC staff’s consideration of the lifespans of caldera 

volcanoes in other parts of the world (e.g., Costa, 2008aa), DOE’s conclusion has a reasonable 

technical basis. 

Many probabilistic volcanic hazard assessment models for representing spatial variability in 

igneous events rely on interpretations of how tectonic processes (such as location of faults or 

distributions of crustal-scale stresses) affect the rise of magma to the Earth’s surface. For 

example, many of the probabilistic volcanic hazard assessment source-zone models, described 

in CRWMS M&O Appendix E (1996aa), were defined by interpretations of tectonic influence on 

the spatial distribution of past events. 

The NRC staff considers, in this regard, that a key concept discussed in BSC (2004af) is that 

although it is known that tectonic processes affect the rise of magma to the Earth’s surface, 

a process-level understanding of tectonic controls on magma rise is beyond current scientific 

understanding. Thus, to address this conceptual uncertainty, probability models should 

consider a variety of alternative models for possible tectonic influences on the location of future 

igneous events at Yucca Mountain. The NRC staff notes that the probability models DOE 

provided considered an adequate variety of models over a range of scales to account for 

possible tectonic influences on spatial patterns of igneous events. These models include, at the 

broadest scale, the structural setting of the Yucca Mountain region within the southern Great 

Basin area of the Basin and Range Province, the more local influence of the Crater Flat 

structural domain adjacent to Yucca Mountain, and the presence of buried (igneous) anomalies 

(SAR Sections 2.2.2.2 and 2.3.11.2.1.1). 

To support the probabilistic volcanic hazard assessment probability estimates developed prior to 

2002, DOE characterized known basaltic igneous features within approximately 80.5 km [50 mi] 

of the Yucca Mountain site (BSC, 2004af). These investigations provided the location, age, and 

basic characteristics necessary to support probability estimates in the probabilistic volcanic 

hazard assessment evaluation. DOE also conducted geophysical investigations and borehole 
3-8 

drilling to further characterize buried igneous features in the Yucca Mountain region (O’Leary, 

et al., 2002aa; Perry, et al., 2005aa). This new information was considered in the probabilistic 

volcanic hazard assessment-update evaluation (SNL, 2008ah). The NRC staff reviewed the 

information in these documents and determined that DOE provided support for the probability 

model development, by providing an appropriate level of detail on the location, age, and basic 

characteristics of igneous features in the Yucca Mountain region. 

The NRC staff also conducted independent investigations in the Yucca Mountain region to 

support the evaluation of uncertainties in the location, age, and characteristics of buried igneous 

features (e.g., Magsino, et al., 1998aa; Stamatakos, et al., 1997ab; Hill and Connor, 2000aa; 

Hill and Stamatakos, 2002aa; Stamatakos, et al., 2007aa). Results from these investigations 

confirm that the location, age, and characteristics of buried igneous features were uncertain 

prior to 2002, but that these uncertainties were relatively small when considered in probability 

estimates after 2002. As shown by comparing the results of the probabilistic volcanic hazard 

assessment with its update, new information on igneous features has no more than a factor of 

two effect on the DOE probability estimate. Given the breadth of new information considered in 

the PHVA-U, the NRC staff notes that the potential effect of uncertainty in event definitions 

would have less than a factor of two effect on the estimated mean annual probability. DOE has 

sufficiently characterized igneous features in the Yucca Mountain region to support probability 

models for future igneous events that may affect the proposed repository at Yucca Mountain. 

DOE discussed alternative estimates of the annual probability of an intrusive event 

intersecting the repository footprint (SAR Section 2.2.2.2.3.2). Both the NRC staff and 

the State of Nevada independently sponsored the development of these published 

models (SAR Table 2.2-18). Some of these models use methods and data developed after 

the 1996 probabilistic volcanic hazard assessment elicitation. Annual probability estimates 

for these published models range from 3 × 10−10 to 3 × 10−7

. DOE stated that these values 

cluster at slightly greater than 10−8

 per year (SAR Section 2.2.2.2.3.2) and concluded that the 

apparent clustering near 10−8

 per year provides confidence that the probabilistic volcanic 

hazard assessment probability estimate is robust. The NRC staff notes that the published 

probability model results do not strongly cluster at the value of the central tendency in the 

probabilistic volcanic hazard assessment probability distribution. However, the published values 

encompass a similar range to that of the PVHA and the published probability estimates for 

igneous events generally fall within the range of 10−8

 to 10−7 per year. This range coincides with 

the probabilistic volcanic hazard assessment mean annual probability estimate. 

In the discussion of probability model support (SAR Section 2.2.2.2.3.2), DOE did not address 

published models by Ho and Smith (1997aa) and Ho, et al. (2006aa). The NRC staff requested 

additional information from DOE to address these published probability estimates. In its 

response to the NRC staff, DOE (2009bd) stated that the calculations in Ho and Smith (1997aa) 

were performed as sensitivity analyses, which included parameter ranges selected from either 

expert knowledge or for “mathematical convenience,” as stated in Ho and Smith, p. 621 

(1997aa). DOE (2009bd) stated that the probability model approach developed in Ho and Smith 

(1997aa) was captured in the range of probability estimates Ho and Smith (1998aa) presented 

subsequently. The NRC staff reviewed the information in the DOE response and notes that the 

probability estimates in Ho and Smith (1998aa) reasonably represent the probability models 

developed in Ho and Smith (1997aa) using expert knowledge. Thus, DOE has appropriately 

considered the results from Ho and Smith (1997aa) in establishing confidence that the DOE 

probability estimate is robust. Although DOE stated that Ho, et al. (2006aa) did not present 

disruption probability results (DOE, 2009bd), a probability estimate of 10−7

 is presented in Ho, et 

al., p. 121 (2006aa) on the basis of the author’s interpretation of recurrence rates in Smith, et al. 
3-9 

(2002aa). The NRC staff notes that the probability estimate in Ho, et al. (2006aa) is consistent 

with the information DOE presented in SAR Section 2.2.2.2.3.2 and would not significantly affect 

the rationale DOE presented to support confidence in the DOE probability estimate. 

As an independent confirmatory estimate, the NRC staff examined whether DOE’s probability 

model results are consistent with past patterns of basaltic igneous events in the Yucca Mountain 

region that are younger than approximately 11 million years old in the Yucca Mountain region. 

Characteristics of this region provide insight into the range of mean annual probabilities that 

can reasonably represent past patterns of igneous activity. As shown in BSC (2004af), during 

the past approximately 11 million years, about 20 basaltic igneous events have occurred in 

the Crater Flat–Amargosa Valley area. These events are the basic event data used in most 

probability models for Yucca Mountain (CRWMS M&O, 1996aa; SAR Section 2.2.2.2.1.3). 

Within this area, an igneous event occurs, on average, once every million years or less 

(e.g., an annual recurrence of approximately 10−6

). However, only 1 out of these 20 past events 

occurred adjacent to the proposed repository site (i.e., the dike along Solitario Canyon fault) 

(SAR Section 2.3.11.2.1.1). This event occurrence pattern shows that there has been a roughly 

1 in 1 million chance each year that a volcano occurred anywhere in the area. If a volcano 

did erupt in the future, this pattern indicates that there would be between a 1 in 10 (10−1

) to a 

1 in 100 (10−2

) chance that this volcano would form near the proposed repository site. 

Therefore, a first-order estimate for mean annual probability of a future igneous event that might 

intersect the proposed repository is from 10−7

 (i.e., 10−6 × 10−1) to 10−8 (i.e., 10−6 × 10−2) per 

year. The NRC staff’s first-order confirmatory analysis, therefore, shows that the probabilistic 

volcanic hazard assessment and the PVHA-U mean annual probability values for intrusion into 

the repository by a basaltic dike of 1.7 × 10−8

 and 3.1 × 10−8, respectively, are consistent with 

patterns for known basaltic igneous events in the Yucca Mountain region that are less than 

approximately 11 million years old. The NRC staff notes that alternative probability models, 

including those in SAR Section 2.2.2.2.1.3 and the results of the PVHA-U, are consistent with 

these past patterns of basaltic igneous activity in the Yucca Mountain region. 

NRC Staff’s Conclusions 

Uncertainties in the DOE probability estimates (i.e., probabilistic volcanic hazard assessment as 

provided by the PVHA and supported by the PVHA-update results) are not risk significant for the 

following reasons: 

 The preponderance of information indicates that the mean annual probability for igneous 

disruption of the proposed repository by a basaltic dike (intrusive case) is on the order of 

10−8

 to 10−7, and the probabilistic volcanic hazard assessment and associated update 

mean probabilities are within this range. 

 Mean annual probability values significantly higher (i.e., 10−6

) or lower (i.e., 10−9) than 

this range are not consistent with past patterns of activity in the Yucca Mountain region 

and therefore are not considered credible. 

 The DOE (2009aa) analyses indicate that changes in dose consequences from igneous 

events are directly proportional to changes in igneous event probabilities, such that a 

change in mean annual probability from 1.7 × 10−8

 to 1 × 10−7 would result in a factor of 

six increase in expected annual dose, as identified in SNL Appendix P (2008ag). 
3-10 

 Model or data uncertainties represented by an increase in mean annual probability 

to 10−7

 would increase the expected annual dose from igneous events from less than 

tenths of millirems to no more than several millirems per year. 

DOE’s expert elicitation mean annual probability of 1.7 × 10−8

 for the intersection of the 

proposed repository by a basaltic dike, and its associated uncertainty distribution for igneous 

event probability, has an adequate technical basis. DOE reasonably defined igneous events 

(igneous intrusion and volcanic eruptive) for use in the performance assessment evaluation. 

The events were defined adequately for DOE to calculate the probability of intrusive and 

extrusive (volcanic) events separately. DOE reasonably supported its probability models. 

DOE confirmed the results of its probability models through appropriate comparisons with 

the volcanic and tectonic history of the area and comparison to other published estimates of 

the intersection probability. The NRC staff notes that the original and explicit intent of the 

probabilistic volcanic hazard assessment was to develop an igneous hazard assessment for 

the 10,000-year time domain. However, because of the breadth of data evaluated in the 

PVHA expert elicitation, the probabilistic volcanic hazard assessment results are a reasonable 

estimate of igneous hazard at Yucca Mountain throughout the period of geologic stability 

(i.e., 1 million years). 

The DOE estimate for the mean annual igneous intrusive event probability, 1.7 × 10-8 per year, 

with the uncertainty distribution described in SAR Section 2.2.2.2.1.2, is reasonable for use in its 

performance assessment. DOE has provided sufficient information to demonstrate that 

uncertainties represented by a potential increase in mean annual probability to 107

 would not 

affect the results of the performance assessment significantly. 

The NRC staff has reviewed the information in the SAR and other information DOE submitted 

and notes that specification of the igneous event probabilities is consistent with the guidance 

in the YMRP. The SAR (i) included geologic data and used that data to adequately define 

the igneous event and adequately establish probability model parameters; (ii) accounted for, 

and adequately evaluated, uncertainties in the igneous event probability model; (iii) provided 

appropriate technical bases supporting the models used and the estimated probability; 

and (iv) included an igneous activity analysis that was consistent with the limits on 

performance assessment. 

2.2.1.2.2.3.2 Technical Evaluation for Seismic Event Probabilities 

This section reviews and evaluates information DOE presented to estimate the probability 

of seismic ground motion and fault displacement at the proposed repository site. This technical 

evaluation of seismic event probabilities follows the review guidance provided in the YMRP 

Sections 2.2.1 and 2.2.1.2.2, as supplemented by additional guidance for the period beyond 

10,000 years after permanent closure (NRC, 2009ab). As part of its technical evaluation, the 

NRC staff reviewed SAR Sections 2.2.2.1 and 2.3.4 and additional information provided in 

response to the NRC staff’s request for additional information in DOE Enclosure 19 (2009ab) 

and DOE Enclosures 6, 7, and 8 (2009aq) and the references cited therein.

Risk Perspective 

As described in TER Section 2.2.1.2.2.3.1, DOE indicated that the seismic ground motion 

modeling case dominates the mean annual dose for the first 10,000 years after permanent 

closure and that the mean annual dose from seismic ground motion is on the order of 

0.001 mSv/yr [0.1 mrem/yr]. As shown in SAR Figure 2.4-18(a), the seismic ground motion 
3-11 

modeling case constitutes most of the calculated dose after the first 2,000 years following 

closure. Moreover, DOE indicated that for the post-10,000-year period, the igneous intrusion 

modeling case and seismic ground motion modeling case provide approximately equal 

contributions to the total mean annual dose to the reasonably maximally exposed individual for 

the last 300,000 years of the time period, and that the calculated dose in each modeling case is 

on the order of 0.01 mSv/yr [1 mrem/yr]. 

Summary of DOE SAR on Seismic Event Probability 

SAR Section 2.2.2.1 described DOE’s overall approach to developing a seismic hazard 

assessment for Yucca Mountain, including fault displacement hazards. This overall approach 

involves the following three general steps. 

1. DOE conducted an expert elicitation program in the late 1990s to develop a probabilistic 

seismic hazard assessment for Yucca Mountain. This assessment included 

a probabilistic fault displacement hazard assessment (CRWMS M&O, 1998aa; BSC, 

2004bp). The probabilistic seismic hazard assessment was developed for a reference 

bedrock outcrop, specified as a free-field site condition with a mean shear wave velocity 

of 1,900 m/sec [6,233 ft/sec] and located adjacent to Yucca Mountain. This value was 

derived from a shear wave velocity profile of Yucca Mountain with the top 300 m [984 ft] 

of tuff and alluvium removed, as provided in Schneider, et al., Section 5 (1996aa). 

2. DOE conditioned the probabilistic seismic hazard assessment ground motion results to 

constrain the large low-probability ground motions to ground motion levels that, 

according to DOE, are more consistent with observed geologic and seismic conditions at 

Yucca Mountain, as provided in BSC, ACN02 (2005aj). 

3. DOE modified the conditioned probabilistic seismic hazard assessment results, using 

site-response modeling, to account for site-specific rock material properties of the tuff in 

and beneath the emplacement drifts and the site-specific rock and soil material 

properties of the strata beneath the geologic repository operations area (GROA). DOE 

used the results of the site response to develop inputs for preclosure seismic design and 

the preclosure seismic safety analysis as well as inputs to its postclosure TSPA 

calculation, as provided in BSC (2005aj) and BSC, ACN 02 (2008bl). 

DOE applied these three steps equally to the preclosure seismic design and safety analyses 

as well as to its postclosure performance assessment. The NRC staff documented its 

evaluation of Step 1 in TER Section 2.5.4. The NRC staff’s evaluation of those aspects of 

DOE’s seismic hazard assessment (Steps 2 and 3) that are pertinent to postclosure 

performance assessment is documented in this TER chapter. 

DOE Probabilistic Seismic Hazard Assessment Expert Elicitation 

DOE conducted an expert elicitation on probabilistic seismic hazard assessment in the late 

1990s (CRWMS M&O, 1998aa; BSC, 2004bp) on the basis of the methodology described in the 

Yucca Mountain Site Characterization Project (DOE, 1997aa). DOE stated that its probabilistic 

seismic hazard assessment methodology followed the guidance of the DOE-NRC-Electric 

Power Research Institute-sponsored Senior Seismic Hazard Analysis Committee (Budnitz, 

et al., 1997aa). On SAR p. 2.2-67, DOE concluded that the methodology used for the 

probabilistic seismic hazard assessment expert elicitation is consistent with the NRC expert 

elicitation guidance, which is described in NUREG–1563 (NRC, 1996aa). 
3-12 

To conduct the probabilistic seismic hazard assessment evaluation, DOE convened two panels 

of experts. The first expert panel consisted of six, 3-member teams of geologists and 

geophysicists (seismic source teams) who developed probabilistic distributions to characterize 

relevant potential seismic sources in the Yucca Mountain region. These distributions included 

location and activity rates for fault sources, spatial distributions and activity rates for background 

sources, distributions of moment magnitude and maximum magnitude, and site-to-source 

distances. The second panel consisted of seven seismology experts (ground motion experts) 

who developed probabilistic point estimates of ground motion for a suite of earthquake 

magnitudes, distances, fault geometries, and faulting styles. These point estimates 

incorporated random and unknown uncertainties that were specific to the regional crustal 

conditions of the western Basin and Range. The ground motion attenuation point estimates 

were then fitted to yield the ground motion attenuation equations used in the probabilistic 

seismic hazard assessment. The two expert panels were supported by technical teams from 

DOE, the U.S. Geological Survey, and Risk Engineering Inc., who provided the experts with 

relevant data and information; facilitated the formal elicitation, including a series of workshops 

designed to accomplish the elicitation process; and integrated the hazard results. 

The resulting ground motion hazard curves express increasing levels of ground motion 

as a function of the annual probability that the ground motion will be exceeded. These 

curves include estimates of uncertainty (see SAR Figure 2.2-9; for example, probabilistic 

seismic hazard assessment curves). The SAR provided probabilistic seismic hazard 

assessment findings on horizontal and vertical components of peak acceleration (defined at 

100 Hz); spectral accelerations at frequencies of 0.3, 0.5, 1, 2, 5, 10, and 20 Hz; and peak 

ground velocity. 

The NRC staff’s review of the probabilistic seismic hazard assessment evaluation notes that 

DOE’s expert elicitation process followed the NRC guidance provided in NUREG–1563 to 

quantify probabilistic seismic hazards (e.g., Cornell, 1968aa; McGuire, 1976aa). The NRC 

staff’s review of the probabilistic seismic hazard assessment expert elicitation process is 

documented in TER Section 2.5.4. The basic elements of this process are (i) identification of 

seismic sources such as active faults or seismic zones; (ii) characterization of each of the 

seismic sources in terms of their activity, recurrence rates for various earthquake magnitudes, 

and maximum magnitude; (iii) ground motion attenuation relationships to model the distribution 

of ground motions that will be experienced at the site when a given magnitude earthquake 

occurs at a particular source; and (iv) incorporation of the inputs into a logic tree to integrate the 

seismic source characterization and ground motion attenuation relationships, including 

associated uncertainties. Each logic tree pathway represents one expert’s weighted 

interpretations of the seismic hazard at the site. The computation of the hazard for all possible 

pathways results in a distribution of hazard curves that is representative of the seismic hazard at 

a site, including variability and uncertainty. 

DOE provided information that shows that the probabilistic seismic hazard assessment was 

supported by a broad range of data, process models, empirical models, and seismological 

theory. Both the seismic source and ground motion characterization panels built their 

respective inputs to the probabilistic seismic hazard assessment on the basis of this information, 

which included (i) cause and effect analysis of recent instrumented events such as the 1992 

MW 5.6 Little Skull Mountain earthquake (where MW is the moment magnitude), (ii) historic 

seismicity included in the probabilistic seismic hazard assessment historic catalog [as provided 

in CRWMS M&O Appendix G (1998aa)], (iii) ground motion parameters derived from empirical 

studies of worldwide ground motion data (e.g., Spudich, et al., 1999aa), and (iv) 52 exploratory 
3-13 

trenches and excavations across fault traces with known or suspected Quaternary Period 

(last ~1.8 million years) fault movements (Keefer, et al., 2004aa). 

Probabilistic Fault Displacement Hazard Assessment 

The seismic source teams also developed a probabilistic fault displacement hazard assessment 

as part of the probabilistic seismic hazard assessment. To assess the postclosure 

performance, DOE relied on the probabilistic fault displacement hazard assessment results to 

support the TSPA analyses of mechanical degradation of engineered barrier systems. In 

SAR Section 2.3.4, DOE described how the information from the probabilistic fault displacement 

hazard assessment was used to develop the fault displacement abstraction and to generate 

inputs to the TSPA. The NRC staff’s evaluation of DOE’s analysis of postclosure fault 

displacement effects on engineered barriers is described in TER Section 2.2.1.3.2.3.2. 

In the probabilistic fault displacement hazard assessment, the experts derived probabilistic fault 

displacement hazard curves for nine demonstration points at or near Yucca Mountain (SAR 

Table 2.2-15 and SAR Figure 2.2-12). These demonstration points represent a range of faulting 

and related fault deformation conditions in the subsurface and near the proposed surface facility 

sites in the geologic repository operations area, including large block bounding faults such as 

the Solitario Canyon Fault, smaller mapped faults within the repository footprint such as the 

Ghost Dance Fault, unmapped minor faults near the larger faults, fractured tuff, and intact tuff. 

The fault displacement hazard curves (e.g., SAR Figure 2.2-13) are analogous to seismic 

hazard curves, in which increasing levels of fault displacements are computed as a function of 

the annual probability that those displacements will be exceeded. 

For the largest mapped faults at Yucca Mountain (i.e., those that form the boundary of the 

major fault block that comprises the Yucca Mountain geologic features), the probabilistic 

fault displacement hazard curves were largely based on the same detailed paleoseismic 

and earthquake data used to characterize these faults as potential seismic sources 

(CRWMS M&O, 1998aa). However, for smaller faults and fractures that were not part of the 

seismic source characterization, there were no established techniques available to the experts. 

Because of the complexity of Yucca Mountain fault analyses, the experts relied on both 

available information and expert judgment to develop conceptual models of distributed faulting 

and estimated the probabilities of secondary faulting in the repository (Youngs, et al., 2003aa; 

CRWMS M&O, 1998aa). 

The probabilistic fault displacement hazard assessment experts derived these curves using two 

different methods, which DOE referred to as the displacement approach and earthquake 

approach. The displacement approach uses fault-specific data, such as cumulative 

displacement, fault length, paleoseismic data from trenches, and historic seismicity. The 

earthquake approach relates the frequency of the fault slip events to the frequency of 

earthquakes on the fault as defined in the seismic source models developed for the 

corresponding seismic hazard analysis. 

For the displacement approach, the experts relied on direct observations of faulting, deriving the 

two required parameters directly from paleoseismic displacement and recurrence rate data, 

geologically derived slip rate data, or scaling relationships that relate displacement to fault 

length and cumulative fault displacement. For the earthquake approach, the experts used 

earthquake recurrence models from the seismic hazard analysis. For this approach, the experts 

assessed three probabilities: 
3-14 

1. The probability that an earthquake will occur. The experts derived the probability that an 

earthquake will occur from the frequency distribution of earthquakes for each source 

(fault or area) used on the seismic hazard assessment and based on geologic, historical 

seismic, or paleoseismic data. 

2. The probability that this earthquake will produce surface rupture on the fault generating 

the earthquake (the primary fault where the earthquake occurs). The expert teams 

determined the probability of surface rupture by a statistical regression of historical 

earthquake and surface rupture data from the Basin and Range and focal depth 

calculations. In the focal depth calculations, the size and shape of the fault rupture for 

each earthquake (generally considered circular or elliptical) was estimated from 

empirical scaling relationships (e.g., Wells and Coppersmith, 1994aa). Depending on 

focal depth, the experts determined the surface displacement (if any) along the fault. 

Because the maximum surface displacement of a fault may not coincide with the 

demonstration point, an additional variable that randomized the rupture along the fault 

length was also introduced. 

3. The probability that the earthquake will produce distributed surface displacement on 

other faults, primary or secondary. The experts determined the probability of distributed 

faulting by using a statistical best fit to data from Basin and Range historical ruptures in 

which distributed faulting was mapped after the earthquake (e.g., Pezzopane and 

Dawson, 1996aa) or by using slip tendency analysis (Morris, et al., 1996aa). 

DOE provided information that shows the probabilistic fault displacement hazard assessment 

methodology used to evaluate fault displacement hazard at Yucca Mountain is reasonable. The 

probabilistic fault displacement hazard assessment is supported by the same broad range of 

data, process models, empirical models, and seismological theory used in the probabilistic 

seismic hazard assessment. The two methods the experts used, the displacement approach 

and the earthquake approach, were originally defined as the faulting-occurrence method and 

magnitude-occurrence method by Cornell and Toro (1992aa). The methods have been 

published in the scientific literature (Youngs, et al., 2003aa) and have been accepted by the 

NRC staff for sites other than Yucca Mountain, including the license application for the Private 

Fuel Storage facility in Skull Valley, Utah (Private Fuel Storage Limited Liability Company, 

2001aa; NRC, 2002aa). 

On the basis of the expert elicitation process performed to support the fault displacement 

hazard estimate in the SAR, results of the probabilistic fault displacement hazard assessment 

are reasonable. 

Conditioning of Low Probability Ground Motions 

Since completion of the probabilistic seismic hazard assessment in 1998, several studies and 

reports, including ones from the NRC staff (NRC, 1999aa), the Nuclear Waste Technical Review 

Board Panel on Natural Systems and Panel on Engineered Systems (Corradini, 2003aa), and 

DOE itself (e.g., BSC, 2004bj), questioned whether the very large ground motions the 

probabilistic seismic hazard assessment predicted at low annual exceedance probabilities 

(below ~10−6

/yr) were physically realistic. For example, strong motion recordings of 

acceleration and velocity that DOE scaled to the unbounded probabilistic seismic hazard 

assessment curve at 10−7

 annual exceedance probability yield peak ground acceleration as high 

as 20 g [~640 ft/s2

] and peak ground velocities up to 1,800 cm/sec [~60 ft/s] (BSC, 2004bj). 

These values were based on extrapolating the expert elicitation results and are well beyond the 
3-15 

limits of any recorded earthquake accelerations and velocities. That includes the largest 

recorded earthquakes worldwide. These large ground motions also are deemed physically 

unrealizable (e.g., Kana, et al., 1991aa) because they require a combination of earthquake 

stress drop, rock strain, and fault rupture propagation that cannot be sustained without 

wholesale fracturing of the bedrock. 

In the past, probabilistic seismic hazard curves were used to estimate ground motions with 

annual exceedance probability to 10−4

 or 10−5 (typical annual exceedance probability values for 

nuclear power plant design and safe shutdown earthquakes). For Yucca Mountain, however, 

the seismic hazard curves are extrapolated to estimate ground motions with annual exceedance 

probabilities as low as 10−8

. At these low probabilities, the seismic hazard estimates are driven 

by the tails of the untruncated lognormal distributions of the input ground motion attenuation 

models (e.g., Bommer, et al., 2004aa). 

To account for these large ground motions, DOE conditioned, or reduced the hazard using two 

approaches. The first approach used geological observations at the repository level to develop 

a limiting distribution on shear strains experienced at Yucca Mountain (BSC, 2005aj). The 

shear-strain threshold distribution was then related to the distribution of horizontal peak ground 

velocity through ground motion site-response modeling. DOE used laboratory rock mechanics 

data, corroborated by numerical modeling, to develop the shear-strain threshold distribution. 

DOE derived the shear-strain levels to initiate unobserved stress-induced failure of 

lithophysal deposition of the Topopah Spring Tuff. DOE’s site-response calculation used 

the random-vibration-theory-based equivalent-linear model to compute the mean motions: 

strains for the deaggregation earthquakes that dominate the contribution of ground 

motion hazard of the specified annual probability of exceedance. Later, this approach was 

(i) generalized to other than horizontal peak ground velocity; (ii) modified to use the inferred 

shear-strain threshold at the repository waste emplacement level to determine the level of 

ground motion not experienced at the reference rock outcrop, rather than at the waste 

emplacement level; (iii) refined to include variability in shear-strain levels and integration over 

the entire hazard curve; and (iv) updated to incorporate additional geotechnical data onsite tuff 

and alluvium properties in the site-response part of the approach (BSC, 2008bl). 

The second approach used expert judgment (BSC, 2008bl) to develop a distribution of 

extreme stress drop in the Yucca Mountain vicinity, which results in strong motion far 

exceeding the recorded data. The distribution is based on available data (stress drop 

measurements and apparent stress from laboratory experiments) and interpretations. It is 

used in the random-vibration-theory method for point sources to develop distributions of peak 

ground velocity and peak ground acceleration at the reference rock outcrop. The extreme 

stress drop is characterized by a lognormal distribution with a median value of 400 bars and  ln

of 0.6 (mean of 480 bars). This distribution is discretized to three values of 150, 400, and 

1,100 bars with the weighting factors of 0.2, 0.6, and 0.2, respectively. This distribution is 

mapped into a distribution of extreme ground motion for the reference rock outcrop through the 

random-vibration-theory site-response modeling. 

The unconditioned hazard curve, which is the annual probability of exceedance as a function of 

ground motion, is convolved with the distribution of extreme ground motion for the reference 

rock outcrop to produce the conditioned ground motion hazard of the same rock outcrop. SAR 

Section 1.1.5.2.5.1 stated that the conditioning is done using combined shear-strain-threshold 

and extreme-stress-drop approaches. However, the shear-strain-threshold conditioning has a 

marginal impact as compared to the extreme-stress-drop approach. For example, for an annual 
3-16 

probability of exceedance of 10−8

, the shear-strain-threshold-conditioned peak ground velocity 

hazard is reduced from 1,200 to about 1,100 cm/sec [39.4 to about 36.1 ft/sec] or about 

10 percent; the stress-drop-conditioned peak ground velocity hazard is reduced from 1,200 to 

about 480 cm/sec [38.4 to about 15.7 ft/sec] or about 60 percent, as identified in BSC Section 

A4.5.1 (2008bl). The combined conditioning has almost no impact on design basis ground 

motions. However, for annual probabilities of exceedance of 10−5

, 10−6, 10−7, and 10−8, the 

impact is tremendous (SAR Section 1.1.5.2.5.1). SAR Figures 1.1-79 and 1.1-80 compare 

the unconditioned and conditioned peak ground acceleration and peak ground velocity mean 

hazard curves for the reference rock outcrop. 

NRC Staff’s Evaluation of the DOE SAR 

The NRC staff reviewed the information in the SAR with regard to DOE’s probabilistic seismic 

hazard assessment and probabilistic fault displacement hazard assessment and notes that 

these assessments are reasonable. Additionally, the staff notes the following: 

 The NRC staff notes in TER Section 2.5.4, that DOE developed an expert elicitation 

program that is consistent with NRC guidance provided in NUREG–1563 (NRC, 

1996aa). DOE’s expert elicitation program was also consistent with the methodology for 

conducting a seismic probabilistic seismic hazard assessment elicitation as described in 

NUREG/CR–6372 (Budnitz, et al., 1997aa). 

 The geological, geophysical, and seismological information DOE provided to the 

probabilistic seismic hazard assessment experts and described in the SAR and 

supporting documents, adequately described the site and regional seismological 

conditions. The information provided sufficient technical basis to support the 

development of expert judgment within DOE’s expert elicitation program. 

Thus, because DOE relied on the collective judgment of established experts, followed a 

procedure to elicit and document the experts’ conclusions that is consistent with NRC guidance, 

and supported the experts’ elicitation with sufficient technical and scientific information, the 

results of the elicitations are reasonable for use in the other portions of the SAR. 

DOE reasonably defined faulting and seismicity as events without ambiguity, and used these 

definitions consistently in developing probability models from the probabilistic seismic hazard 

assessment and probabilistic fault displacement hazard assessment expert elicitations. The 

probabilistic estimates of faulting and seismicity were derived by DOE from appropriate 

geological, geophysical, and seismological data and analyses.

DOE’s probabilistic seismic hazard assessment, the conditioning of the probabilistic seismic 

hazard assessment ground motions, and probabilistic fault displacement hazard 

assessment are supported by appropriate technical bases that include the expert elicitation 

program in which the experts considered the full range of information available when the 

probabilistic seismic hazard assessment was developed. During the expert elicitation 

process, the seismic source teams considered a range of information provided by DOE, the 

U.S. Geological Survey, other project-specific Yucca Mountain studies, and information 

published in the scientific literature. This information included data and models for the geologic 

and seismotectonic setting, seismic sources, historical and instrumented seismicity, earthquake 

recurrence, maximum magnitude, and ground motion attenuation. Detailed evaluations of this 

information are provided in NUREG–1762, (NRC, 2005aa). The NRC staff notes that DOE’s 

elicitation process (see the NRC staff’s evaluation in TER Section 2.5.4) for the probabilistic 
3-17 

seismic hazard assessment was implemented in accordance with NRC guidance in 

NUREG–1563 (NRC, 1996aa). Because the probabilistic seismic hazard assessment expert 

elicitation process is consistent with NRC guidance and DOE provided supporting information, 

DOE’s probabilistic seismic hazard assessment and probabilistic fault displacement hazard 

assessment programs adequately characterized the seismic and fault displacement hazards at 

Yucca Mountain. Additional geological, geophysical, and seismological information discovered 

since the elicitation was performed is consistent with the probabilistic seismic hazard 

assessment results (except for the overly conservative information on large ground motions at 

low annual exceedance, as described previously regarding the conditioning of low probability 

ground motions). 

DOE’s probabilistic fault displacement hazard assessment, the probabilistic seismic hazard 

assessment, the conditioning of the probabilistic seismic hazard assessment ground motions, 

and underlying models are adequately supported by detailed process models and empirical 

observations. Both the seismic source and ground motion characterization panels provided 

inputs to the probabilistic seismic hazard assessment. The panels considered a wide variety of 

geological, geophysical, and seismological information. DOE’s probabilistic seismic hazard 

assessment report (CRWMS M&O, 1998aa) documents how the experts considered this 

information. Additionally, in DOE Enclosure 19 (2009ab), DOE showed that its treatment of low 

probability seismic ground motions in features, events, and processes screening justifications is 

consistent with their use in postclosure dynamic analyses and the TSPA analyses. The NRC 

staff thus notes (see the NRC staff’s evaluation in TER Section 2.2.1.2.1) that the DOE models 

are consistent with other relevant features, events, and processes. 

The NRC staff notes that the probabilistic seismic hazard assessment experts adequately 

established the probability model parameters that form the input nodes to the probabilistic fault 

displacement hazard assessment and probabilistic seismic hazard assessment logic tree. 

DOE provided an example of a partial logic tree for one of the seismic source teams in SAR 

Figure 2.2-21. The experts developed these probabilistic inputs to the probabilistic seismic 

hazard assessment by assessing the information the technical support teams provided. These 

inputs were based on the experts’ first-hand knowledge of the information, detailed vetting of the 

information at the probabilistic seismic hazard assessment public workshops, and sensitivity 

analyses the technical support team provided as feedback to the experts. Each expert or team 

of experts documented its rationale for the input parameters in DOE’s probabilistic seismic 

hazard assessment report (CRWMS M&O, 1998aa). 

The NRC staff notes that the probabilistic fault displacement hazard assessment and the 

probabilistic seismic hazard assessment experts evaluated the uncertainties of the probability 

model parameters, which also form the input nodes to the probabilistic seismic hazard 

assessment logic tree. The experts assessed information from the technical support teams on 

the basis of the experts’ first-hand knowledge of the information, detailed vetting of the 

information at the probabilistic seismic hazard assessment workshops, and sensitivity analyses 

the technical support team provided as feedback to the experts. Each expert or team of experts 

documented its rationale for the uncertainty in parameters in DOE’s probabilistic seismic hazard 

assessment report (CRWMS M&O, 1998aa). 

In addition, DOE considered new information acquired since the development of the 

probabilistic seismic hazard assessment in 1998. In particular, DOE’s conditioned hazard 

curves, which reflect geological and seismological information that suggests limits on the low 

probability ground motions in the probabilistic seismic hazard assessment, are reasonable. 

DOE’s conditioning approach follows basic mechanical and material behaviors consistent with 
3-18 

the current understanding of seismological phenomena. DOE’s assumption that the tectonic 

setting and therefore the stress drops of earthquakes from the existing faults at Yucca Mountain 

are not going to change significantly in the next million years is also reasonable on the basis of 

the NRC staff’s understanding of the seismotectonic history of the Yucca Mountain region. 

Evaluation Conclusions for Seismic Event Probabilities 

The NRC staff has reviewed the information in the SAR and other information submitted 

and concludes that the specification of the seismic event probabilities is consistent with the 

guidance in the YMRP. The SAR (i) included geologic data and used those data to adequately 

define the faulting and seismic events and adequately establish probability model parameters; 

(ii) accounted for, and adequately evaluated, uncertainties in the faulting and seismic event 

probability models; (iii) provided appropriate technical bases supporting the models used and 

the estimated probabilities; and (iv) included a seismic activity analysis that was consistent with 

the limits on performance assessment. 

2.2.1.2.2.4 Technical Evaluation of Early Waste Package and Drip Shield 

 Failures Event Probabilities 

This section evaluates information DOE presented to estimate the probability of early failure of 

waste packages and the drip shield at the proposed repository site. This technical evaluation of 

early failure event probabilities follows the review guidance provided in YMRP Sections 2.2.1 

and 2.2.1.2.2. The NRC staff reviewed SAR Sections 1.3.4, 1.5.2, 2.2.2.3, 2.3.6.6, and 

2.3.6.8.4, and additional information provided in response to the NRC staff’s request for 

additional information (DOE, 2009ac,ad) and the references cited therein. 

Summary of the DOE Information 

In SAR Section 2.2.2.3.1, DOE defined early failure of a waste package or drip shield as 

through-wall penetration of the barrier caused by the presence of a manufacturing- or 

handling-induced defects at a time earlier than would be predicted by mechanistic degradation 

models for a defect-free waste package or drip shield. 

In SAR Section 2.2.2.3.2, DOE summarized the early waste package failure probability 

and stated that the probability values are based on the waste package fabrication and 

handling processes described in SAR Section 1.5.2. DOE further stated that details of 

technical bases for the probability estimates, including the parameters and data used and 

their associated uncertainties, are described in SAR Section 2.3.6.6. In SAR Section 2.2.2.3.3, 

DOE summarized the early drip shield failure probability and stated that the probability 

values are based on the drip shield fabrication and handling processes described in SAR 

Section 1.3.4. DOE further stated that details of technical bases for the probability estimates, 

including the parameters and data used and their associated uncertainties, are described in 

SAR Section 2.3.6.8.4. 

DOE’s approach for early failure probability calculations is to quantify errors in manufacturing or 

handling of waste packages or drip shields, respectively, and to quantify the potential that the 

error goes undetected prior to emplacement. In such instances, the defective waste package or 

drip shield is assumed to experience early failure. 

DOE first systematically identified the types of errors or defects that could lead to early 

failure of the waste package and drip shield, respectively. It reviewed the technical literature 
3-19 

for empirical data of similar systems and components (i.e., industrial analogues). DOE 

identified five industrial analogues, which can generally be described as welded metallic 

containers: (i) boilers and pressure vessels, (ii) nuclear fuel rods, (iii) underground storage 

tanks, (iv) radioactive cesium capsules, and (v) dry storage casks for spent nuclear fuel (SNF). 

For these analogues, DOE obtained qualitative and quantitative information on the types of 

manufacturing and handling errors that may occur, and their associated frequency for the 

occurrence, as identified in SNL Section 6.1 (2007aa). 

SAR Table 2.3.6-21 identifies the specific types of defects and their occurrence rates for these 

analogues. From these industrial analogues, DOE developed a list of 13 generic errors or 

defects that could lead to early failure of welded metallic containers (SAR Section 2.3.6.6.2.1). 

Given that the industrial analogues are only partly analogous to the waste package and drip 

shield in terms of manufacturing techniques, intended safety function, and operating 

environment, DOE determined that only some of the generic defects applicable to welded 

metallic containers are applicable to the waste package and drip shield, as identified in SAR 

Sections 2.3.6.6.3.1 and 2.3.6.8.4.3.1 and described in SNL Section 6.1.6 (2007aa). DOE 

evaluated the defect types and eliminated from further consideration those defects not 

applicable to the waste package and drip shield (SAR Sections 2.3.6.6.3.1 and 2.3.6.8.4.3.1). 

DOE considered that weld flaws, particularly in the waste package closure weld, could affect the 

performance of the waste package, but would not necessarily lead to early failure. It considered 

weld flaws as potential initiation sites for stress corrosion cracking. SAR Section 2.3.6.5 

addressed weld flaws and is evaluated in TER Section 2.2.1.3.1.3.2.3. 

For the waste package, DOE identified six types of defects or errors that could lead to 

early failure (SAR Section 2.3.6.6.3.2). These waste package errors are (i) improper base metal 

selection; (ii) improper weld filler material selection; (iii) improper heat treatment of the outer 

corrosion barrier; (iv) improper heat treatment of outer lid; (v) improper low-plasticity burnishing; 

and (vi) improper handling. For the drip shield, DOE identified four types of defects or errors 

that could lead to early failure (SAR Section 2.3.6.8.4.3.2). These drip shield errors are (i) base 

metal flaws; (ii) improper weld filler material; (iii) improper heat treatment; and (iv) improper 

handling and installation. Those defects that could lead to early failure of the waste package or 

failure of the drip shield were further analyzed. DOE developed event trees to identify event 

sequences that could lead to undetected defects or errors in the waste package and drip shield, 

respectively, as identified in SNL Sections 6.3 and 6.4 (2007aa). The event sequences 

generally consist of an equipment or process failure event, followed by human errors event(s), 

where the equipment or process failure is undetected or uncorrected. To quantify the 

probabilities for the respective event sequences, each basic event in the sequences was 

assigned a probability distribution. For the equipment or process failure events, the probability 

distribution was based on data that were generated from similar components or processes at 

nuclear power plants (e.g., Babcock and Wilcox, 1979aa; Blanton and Eide, 1993aa). For 

human reliability data, the probability distributions were taken from data for nuclear power plant 

activities (Swain and Guttmann, 1983aa; Benhardt, et al., 1994aa). 

DOE used Monte Carlo simulations to analyze the event trees and calculate the probability 

distributions for event sequences that could lead to undetected errors or defects in the waste 

package or drip shield, respectively. DOE described end state probabilities for event sequences 

as lognormal distributions. DOE grouped probability distributions for all of the event sequences 

that could lead to the presence of an undetected defect in the waste package to calculate the 

overall probability that the waste package contains at least one undetected defect (i.e., the 

waste package early failure probability). DOE followed the same process for the drip shield 
3-20 

to calculate the overall probability that the drip shield contains at least one undetected defect 

(i.e., the drip shield early failure probability). DOE clarified (2009ac) that the mean 

probabilities for the event sequences leading to early failure of the waste package, as 

reported in SAR Sections 2.3.6.6.3.2.1 to 2.3.6.6.3.2.6, were incorrect and that the mean 

probabilities for the event sequences leading to early failure of the drip shield, as reported in 

SAR Sections 2.3.6.8.4.3.2.1 to 2.3.6.8.4.3.2.4, were also incorrect. DOE’s clarification 

(2009ac) provided corrected values for SAR Sections 2.3.6.6.3.2.1 to 2.3.6.6.3.2.6 and SAR 

Sections 2.3.6.8.4.3.2.1 to 2.3.6.8.4.3.2.4 and stated that the overall probability values for waste 

package and drip shield early failures listed in the SAR are correct. DOE described the early 

failure probability for the waste package as a lognormal distribution with a mean of 1.13 × 10−4

per waste package and an error factor of 8.17 (SAR Sections 2.2.2.3.2 and 2.3.6.6.3.2.7). 

DOE described the early failure probability for the drip shield as a lognormal distribution 

with a mean of 2.21 × 10−6

 per drip shield and an error factor of 14 (SAR Sections 2.2.2.3.3 

and 2.3.6.8.4.3.2.5). 

DOE compared its probability estimates for early failure of the waste package and drip shield, 

respectively, with the defect-related failure rates for the industrial analogues. The failure 

rates for the industrial analogues for pressure vessels, nuclear fuel rods, underground storage 

tanks, and radioactive cesium capsules DOE cited ranged from 10−6

 to 10−4 per component 

(SAR Table 2.3.6-21). DOE did not identify any cases of SNF casks that failed due to 

undetected defects after entering service. 

The probability estimates for early failure of the waste package and drip shield are implemented 

in the TSPA model in the Early Failure Scenario Class, as described in SAR Section 2.4.1.2.3. 

This implementation is reviewed by the NRC staff in TER Section 2.2.1.4.1. The NRC staff’s 

review of the implementation of the model abstraction for early failure is documented in TER 

Sections 2.2.1.3.1.3.1.2 and 2.2.1.3.1.3.2.4. 

NRC Staff’s Evaluation of DOE Information 

DOE defined the early failure probability event without ambiguity. Early failure refers to 

through-wall penetration of the waste package or drip shield at a time earlier than the design 

life because of undetected manufacturing- or handling-induced defects. Early failure is 

distinguished from other events and processes that could lead to through-wall penetration 

(e.g., corrosion, tensile rupture). Further, the event definition is consistent with the barrier 

functions of the waste package and drip shield, respectively, as stated in SAR Table 1.9-8. 

Thus, the events are adequately defined. 

The NRC staff reviewed DOE’s assumption that the early failure probabilities for the waste 

package and drip shield, respectively, are equivalent to the probabilities that there are 

undetected manufacturing- or handling-induced defects in the respective barriers. This 

assumption is reasonable. Empirical observation of industrial analogues indicates that a waste 

package or drip shield with a manufacturing- or handling-induced defect will likely maintain 

some barrier capability [as identified in SNL Section 6.1 (2007aa)], and the presence of a 

defect, in itself, is unlikely to cause through-wall penetration of an engineered barrier. A 

secondary process (e.g., seismically induced loading, stress corrosion cracking) would likely 

need to act upon the defective barrier to cause through-wall rupture. 

The NRC staff reviewed DOE’s use of industrial analogues to identify the generic types of 

defects or errors that, if undetected, could lead to early failure. This use of industrial analogue 

information is reasonable. While there are no direct analogues to the waste package or drip 
3-21 

shield, respectively, consideration of a number of generally similar analogues with different 

specific manufacturing techniques, intended functions, and operating environments provides an 

appropriate technical basis to identify the most risk-significant defects or errors that could lead 

to early failure. In particular, the analogues are similar to the waste package and drip shield in 

that they are all metallic, generally cylindrical or spherical in shape, welded, heat treated, and 

closed (i.e., designed to act as a barrier or container). 

The NRC staff reviewed DOE’s decision to eliminate from further consideration some of 

the generic errors and defects from the early failure probability models and notes the following: 

 DOE’s decision to eliminate improper weld-flux material from the early failure analyses 

for the waste package and drip shield is reasonable because the welding method to be 

employed for waste packages (SAR Section 1.9.2) and the welding method to be 

employed for drip shield [SNL Section 6.2.3 (2007aa)] do not use weld-flux material. 

 DOE’s decision to eliminate weld flaws from the drip shield and waste package early 

failure analyses is reasonable because (i) SAR Section 1.3.4.7 stated that the drip shield 

will be fully stress relieved, thus preventing stress corrosion crack initiation from weld 

flaws; (ii) the waste package is solution annealed to remove welding stresses, meaning 

that only waste package closure weld flaws will act as possible stress corrosion cracking 

locations; and (iii) the waste package closure weld flaws are modeled in SAR Section 

2.3.6.5 as part of the stress corrosion cracking model (not part of the juvenile failure). 

The NRC staff confirmed that Postclosure Design Control Parameter 07-13 in SAR 

Table 1.9-9 stated that the drip shield will be stress relieved. The NRC staff determined 

that without external stress, weld flaw propagation is unlikely in a stress-relieved drip 

shield. Therefore, conformance with the Postclosure Design Control Parameters 

provides a reasonable basis for DOE to eliminate weld flaws from the drip shield early 

failure analysis. 

 DOE’s decision to eliminate poor weld-joint design from further early failure analyses for 

the waste package and drip shield is reasonable because controls specified in SAR 

Section 1.9.2 required extensive development and testing of waste package and drip 

shield joints. The NRC staff confirmed that Postclosure Design Control Parameters in 

SAR Table 1.9-9 (03-12 and 03-14 for the waste package and 07-09 and 7-10 for the 

drip shield) require that fabrication welds are conducted in accordance with standard 

nuclear industry practice, including inspection and nondestructive examination. 

Therefore, conformance with the Postclosure Design Control Parameters provides a 

reasonable basis for DOE to eliminate poor weld-joint design from the drip shield and 

waste package early failure analyses. 

 DOE’s decision to eliminate missing welds from further early failure analyses for the 

waste package and drip shield is reasonable because controls specified in SAR 

Section 1.9.2 require extensive inspection and nondestructive examination of the 

welds. The NRC staff confirmed that Postclosure Design Control Parameters in SAR 

Table 1.9-9 (03-13 and 03-14 for the waste package and 07-09 and 7-10 for the drip 

shield) require that fabrication welds be conducted in accordance with standard nuclear 

industry practice, including inspection and nondestructive examination. Literature 

reviews identified a low probability for missing welds and identified that the 

consequences of a missing weld (e.g., the waste package lid falling off) would be 

apparent prior to emplacement. Thus, because the Postclosure Design Control 
3-22 

Parameters 03-13, 03-14, 07-09 and 07-10 in SAR Table 1.9-9 require extensive 

inspection and nondestructive evaluation of waste packages and drip shields, 

conformance with the Postclosure Design Control Parameters provides a reasonable 

basis for DOE to eliminate missing welds from the drip shield and waste package early 

failure analyses. 

 DOE’s decision to eliminate mislocated welds from further early failure analyses for 

the waste package and drip shield is reasonable because controls specified in SAR 

Section 1.9.2 require extensive inspection and nondestructive examination of the 

welds. The NRC staff confirmed that Postclosure Design Control Parameters in SAR 

Table 1.9-9 (03-13 and 03-14 for the waste package and 07-09 and 07-10 for the drip 

shield) require that fabrication welds be conducted in accordance with standard nuclear 

industry practice, including inspection and on nondestructive examination. Thus, 

because the Postclosure Design Control Parameters 03-13, 03-14, 07-09 and 07-10 in 

SAR Table 1.9-9 require extensive inspection and nondestructive evaluation of waste 

packages and drip shields, conformance with the Postclosure Design Control 

Parameters provides a reasonable basis for DOE to eliminate mislocated welds from the 

drip shield and waste package early failure analyses. 

 DOE’s decision to eliminate surface contaminants (e.g., material that could enhance 

the corrosion rate) from further early failure analyses for the waste package and drip 

shield is reasonable because controls specified in SAR Section 1.9.2 require that 

fabrication and handling processes will limit the type and amount of surface 

contamination. The NRC staff confirmed that Postclosure Design Control Parameters in 

SAR Table 1.9-9 (03-21 for the waste package; 07-14 for the drip shield) require 

stringent controls on waste package and drip shield fabrication and handling. Therefore, 

conformance with the Postclosure Design Control Parameters provides a reasonable 

basis for DOE to eliminate surface contaminants from the drip shield and waste package 

early failure analyses. 

 DOE’s decision to eliminate improper low-plasticity burnishing from the drip shield early 

failure analysis is reasonable because the drip shield is not low-plasticity burnished. The 

NRC staff reviewed the description of the drip shield design in SAR Section 1.3.4.7 and 

confirmed that drip shield welds will not be low-plasticity burnished. 

 DOE’s decision to eliminate handling damage from early failure analysis for the drip 

shield is reasonable because the high strength-to-weight ratio of titanium makes it 

resilient to scratches and denting from handling-induced impacts, and because DOE will 

control drip shield handling. The NRC staff confirmed that Postclosure Design Control 

Parameter 07-14 in SAR Table 1.9-9 requires controls on drip shield handling that will 

minimize damage and impacts to the drip shield, and drip shield emplacement will be 

monitored by equipment that can detect damage. Therefore, conformance with the 

Postclosure Design Control Parameters provides a reasonable basis for DOE to 

eliminate handling damage from early failure analysis for the drip shield. 

 DOE’s decision to eliminate administrative or operational errors as distinct errors in the 

waste package and drip shield early failure analyses is reasonable because DOE 

implicitly incorporated such errors (e.g., failure to follow a written procedure) into the 

analyses of the defects that were not screened out. 
3-23 

The NRC staff reviewed the event trees and event sequences DOE used to calculate the 

probabilities for the errors that could cause early failure. The NRC staff reviewed the extent 

to which DOE identified key processes involved with waste package and drip shield handling 

and manufacturing, and whether the event sequences could estimate the undetected defect 

(i.e., early failure) probabilities. The NRC staff notes the following regarding DOE’s event trees 

and event sequences used to calculate probabilities. 

 The NRC staff reviewed the DOE event tree for waste package fabrication with improper 

base metal selection, which is shown in SNL Figure 6-9 (2007aa). In response to the 

NRC staff’s request for additional information, DOE (2009ac) stated that the composition 

of the base metal will be certified by the supplier and independently checked upon 

receipt at the fabrication facility. The NRC staff notes DOE identified the key processes 

for this event sequence and that the event sequence is realistic because DOE 

reasonably described the processes and stated that fabrication will be accomplished 

under stringent controls and in accordance with standard nuclear industry practices. 

In this regard, the NRC staff further notes that DOE’s reliance on Postclosure Design 

Control Parameter 03-19 in SAR Table 1.9-9, which specifies the waste package outer 

corrosion barrier material specifications, and DOE’s use of Postclosure Design Control 

Parameter 03-02, which requires that the waste package material be controlled by the 

configuration management system, is reasonable. Therefore, the event sequence for 

this defect is reasonable, because DOE identified the key processes for this event 

sequence and adequately described the processes and their controls. 

 The NRC staff reviewed the DOE event sequence for waste package fabrication with 

improper weld filler material selection, which is shown in SNL Figure 6-14 (2007aa). 

In response to the NRC staff’s request for additional information, DOE (2009ac) stated 

that the composition of the weld filler metal will be certified by the supplier and 

independently checked upon receipt at the fabrication facility. The NRC staff notes that 

DOE identified the key processes for this event sequence. The event sequence is 

realistic, because DOE reasonably described the processes and stated that fabrication 

will be accomplished under stringent controls and in accordance with standard nuclear 

industry practices. In this regard, DOE’s reliance on Postclosure Design Control 

Parameter 03-14 in SAR Table 1.9-9, which specifies that the waste package fabrication 

welds shall be conducted in accordance with standard nuclear industry requirements, is 

reasonable; and DOE’s use of Postclosure Design Control Parameter 03-02, which 

requires that the waste package material be controlled by the configuration management 

system, is reasonable. Therefore, the event sequence for this defect is reasonable, 

because DOE identified the key processes for this event sequence and adequately 

described the processes and their controls. 

 The NRC staff reviewed the DOE event sequence for the waste package fabrication 

with improper heat treatment for the waste package outer shell, which is shown in 

SNL Figure 6-10 (2007aa). The NRC staff notes that DOE identified the key processes 

for this event sequence. For example, in SNL Section 6.3.3 (2007aa), DOE stated that 

the critical steps during heat treatment are moving the heated shell from the furnace to 

the quench tank and the subsequent quench. The event sequence is realistic because 

DOE reasonably described the processes and stated that fabrication will be 

accomplished under stringent controls. DOE’s reliance on Postclosure Design Control 

Parameter 03-16 in SAR Table 1.9-9, which specifies the waste package heat treatment 

conditions, is reasonable. Therefore, the event sequence for this defect is reasonable, 
3-24 

because DOE identified the key processes for this event sequence and adequately 

described the processes and their controls. 

 The NRC staff reviewed the DOE event sequence for waste package fabrication with 

improperly heat-treated outer lid, which is shown in SNL Figure 6-11 (2007aa). The 

NRC staff notes that DOE identified the key processes for this event sequence. For 

example, in SNL Section 6.3.4 (2007aa), DOE stated that the critical steps during heat 

treatment are moving the heated lid from the furnace to the quench tank and the 

subsequent quench. The event sequence is realistic because DOE reasonably 

described the processes and stated that fabrication will be accomplished under 

stringent controls. DOE’s reliance on Postclosure Design Control Parameter 03-16 in 

SAR Table 1.9-9, which specifies the waste package heat treatment conditions, is 

reasonable. Therefore, the event sequence for this defect is reasonable, because DOE 

identified the key processes for this event sequence and adequately described the 

processes and their controls. 

 The NRC staff reviewed DOE’s event sequence for waste package fabrication 

with improper low-plasticity burnishing of the closure weld, which is shown in SNL 

Figure 6-12 (2007aa). The NRC staff notes that DOE identified the key processes for 

this event sequence. For example, in SNL Section 6.3.5 (2007aa), DOE stated that 

burnishing will be performed by a dedicated, automated system, with subsequent 

inspection to assure that the appropriate procedures were followed. The event 

sequence is realistic because DOE reasonably described the processes and stated that 

fabrication will be accomplished under stringent controls. DOE’s reliance on Postclosure 

Design Control Parameter 03-17 in SAR Table 1.9-9, which requires process controls to 

ensure adequate stress relief, along with subsequent nondestructive examination, is 

reasonable. Therefore, the event sequence for this defect is reasonable, because DOE 

identified the key processes for this event sequence and adequately described the 

processes and their controls. 

 The NRC staff reviewed the DOE event sequence for improper handling of the 

waste package, which is shown in SNL Figure 6-13 (2007aa). The NRC staff notes 

that DOE identified the key processes for this event sequence. For example, in SAR 

Section 2.3.6.6.3.2.5 and DOE (2009ad), DOE defined damage as any visible gouging 

or denting of the waste package surface that occurs between receipt and drip shield 

installation. Damage would be any such gouging or denting that could jeopardize the 

performance of the outer barrier. Because handling procedures have not been fully 

developed, DOE assumed that the waste package could be damaged by any one of 

eight generic events, each of which is analogous to fuel assembly handling events at 

nuclear power plants. In response to the NRC staff’s request for additional information, 

DOE (2009ac) stated that this comparison is appropriate because fuel assemblies are 

handled in tightly controlled conditions similar to those expected at the repository. The 

event sequence is realistic because DOE reasonably described the processes and 

stated that handling will be accomplished under stringent controls. In this regard, DOE 

reliance on Postclosure Design Control Parameters 03-18, 03-21, and 03-22 in SAR 

Table 1.9-9 is reasonable. The Postclosure Design Control Parameters require the 

waste package to be handled in a controlled manner to minimize damage, including 

inspection for surface damage prior to emplacement and monitoring during 

emplacement activities. Therefore, the event sequence for this defect is reasonable, 

because DOE identified the key processes for this event sequence and adequately 

described the processes and their controls. 
3-25 

 The NRC staff reviewed the DOE event sequence for drip shield fabrication with 

out-of-specification base metal, which is shown in SNL Figure 6-16 (2007aa). In 

response to the NRC staff’s request for additional information, DOE (2009ac) stated 

that the composition of the base metal will be certified by the supplier and independently 

checked upon receipt at the fabrication facility. The NRC staff notes that DOE identified 

the key processes for this event sequence and that the event sequence is realistic 

because DOE reasonably described the processes and stated that fabrication will be 

accomplished under stringent controls and in accordance with standard nuclear industry 

practices. DOE’s reliance on Postclosure Design Control Parameter 07-09 in SAR 

Table 1.9-9, which specifies that the drip shield shall be fabricated in accordance with 

standard nuclear industry practices, including material control, is reasonable; DOE’s use 

of Postclosure Design Control Parameter 07-01 in SAR Table 1.9-9, which requires that 

the drip shield materials be controlled by the configuration management system, is 

reasonable. Therefore, the event sequence for this defect is reasonable, because DOE 

identified the key processes for this event sequence and adequately described the 

processes and their controls. 

 The NRC staff reviewed the DOE event sequence for drip shield fabrication with 

out-of-specification weld filler metal, which is shown in SNL Figure 6-18 (2007aa). 

In response to the NRC staff’s request for additional information, DOE (2009ac) stated 

that the composition of the base metal will be certified by the supplier and independently 

checked upon receipt at the fabrication facility. DOE identified the key processes for this 

event sequence and that the event sequence is realistic because DOE reasonably 

described the processes and stated that fabrication will be accomplished under stringent 

controls and in accordance with standard nuclear industry practices. In this regard, 

DOE’s reliance on Postclosure Design Control Parameter 07-09 in SAR Table 1.9-9, 

which specifies that the drip shield shall be fabricated in accordance with standard 

nuclear industry practices, including material control and welding, is reasonable; DOE’s 

use of Postclosure Design Control Parameter 07-01 in SAR Table 1.9-9, which requires 

that the drip shield materials be controlled by the configuration management system, is 

reasonable. Therefore, the event sequence for this defect is reasonable, because DOE 

identified the key processes for this event sequence and adequately described the 

processes and their controls. 

 The NRC staff reviewed the DOE event sequence for drip shield fabrication with 

improper heat treatment, which is shown in SNL Figure 6-17 (2007aa). The NRC staff 

notes that DOE identified the key processes for this event sequence. For example, in 

SNL Section 6.4.2 (2007aa), DOE stated that the drip shield temperature during heat 

treatment will be monitored by calibrated thermocouples in contact with the material, and 

that the drip shield will be subject to a post-heat-treatment inspection to ensure that the 

heat-treatment procedure was properly followed. The event sequence is realistic 

because DOE reasonably described the processes and stated that fabrication will be 

accomplished under stringent controls. In this regard, DOE’s reliance on Postclosure 

Design Control Parameters 07-09 and 07-13 in SAR Table 1.9-9 is reasonable. The 

Postclosure Design Control Parameters (i) require that the drip shield heat treatment be 

performed in a manner consistent with standard nuclear industry practice and (ii) specify 

the drip shield heat treatment conditions. Therefore, the event sequence for this defect 

is reasonable, because DOE identified the key processes for this event sequence and 

adequately described the processes and their controls. 
3-26 

 The NRC staff reviewed the DOE event sequence for improper drip shield installation, 

which is shown in SNL Figure 6-19 (2007aa). The NRC staff notes that DOE identified 

the key processes for this event sequence. For example, in SNL Section 6.4.4 

(2007aa), DOE stated that drip shields will be visually inspected at the surface facilities, 

that emplacement activities will be monitored by camera, and that the inspections will be 

independently checked and documented. In response to the NRC staff’s request for 

additional information (RAI), DOE (2009ad) provided additional justification for the 

probability value of a camera not detecting improper interlocking between adjacent drip 

shields, and a demonstration that mechanical or equipment reliability is not a significant 

component of the drip shield emplacement failure analysis. The event sequence is 

realistic because DOE reasonably described the processes and stated that installation 

will be accomplished under stringent controls. DOE’s reliance on Postclosure Design 

Control Parameters 07-02 and 07-14 in SAR Table 1.9-9 is reasonable. The 

Postclosure Design Control Parameters require that drip shield handling and 

emplacement be monitored by appropriate equipment, including an alarm, with an 

operator and independent inspector verifying proper installation. Therefore, the event 

sequence for this defect is reasonable, because DOE identified the key processes for 

this event sequence and adequately described the processes and their controls. 

In summary, the NRC staff has reviewed in detail the event trees DOE used to evaluate the 

probabilities that could lead to damage of the waste package and the drip shield. The NRC staff 

notes that DOE has identified the key processes involved and has implemented event 

sequences consistent with the guidance in the YMRP. 

In SAR Sections 2.3.6.6.4.2 and 2.3.6.8.4.4.2, DOE compared its probability estimates for early 

failure of the waste package and drip shield, respectively, with the defect-related failure rates for 

the industrial analogues. For pressure vessels, nuclear fuel rods, underground storage tanks, 

and radioactive cesium capsules, the failure rates DOE cited are in the range of 10−6

 to 10−4 per 

component (SAR Table 2.3.6-21), which is consistent with the calculated early failure rates for 

the waste package and drip shield. DOE did not identify any cases of SNF casks that failed 

after entering service. 

The NRC staff notes that the waste package and drip shield are sufficiently similar to 

the industrial analogues to support a general comparison of the manufacturing- and 

handling-induced failure rates. In particular, the waste package, drip shield, and 

industrial analogues are (i) metallic, (ii) cut from sheet and formed into a cylindrical-type 

shape, (iii) welded, (iv) heat treated, and (v) closed/sealed (i.e., intended to act as a 

container or barrier). 

DOE has identified key processes involved with manufacturing and handling of the respective 

components, and that it has developed realistic event sequences to calculate the early failure 

probabilities. DOE’s model support for estimating waste package and drip shield early failure 

probabilities is reasonable. Predicted early failure rates for the waste package and drip shield 

are close to those of the industrial analogues, as discussed previously. 

DOE developed event sequences to calculate the probabilities for undetected errors or defects 

(i.e., the early failure probabilities) in the waste package and drip shield, respectively. The 

event sequences generally consist of an equipment or process failure (e.g., probability that a 

motorized valve fails to open on demand), followed by human error(s), where the equipment or 

process failure is undetected (e.g., probability that the responsible technician does not detect 

the failure of the valve to open) or uncorrected. As described in SAR Sections 2.3.6.8.4.2 and 
3-27 

2.3.6.6.2, each event in the event sequences was assigned a probability distribution that was 

obtained from external data sources, as identified in SNL Section 4.1 (2007aa). 

The external data used to establish the probability distributions for key processes and events in 

waste package and drip shield manufacturing and handling come from nuclear power plant 

activities. For the equipment or process failure events, DOE used reliability data that were 

generated from similar components or activities at nuclear power plants (e.g., Babcock and 

Wilcox, 1979aa; Blanton and Eide, 1993aa). For human error events, the probability 

distributions for these events were taken from nuclear power plant human reliability analyses 

(Swain and Guttmann, 1983aa; Benhardt, et al., 1994aa). 

The NRC staff reviewed the external data to determine whether such data could be used to 

quantify the reliability of events and processes associated with manufacturing and handling 

of the waste package and drip shield. The external data come from reliable, reputable 

sources that are widely accepted in the nuclear industry, as identified in SNL Section 4.1 

(2007aa). Further, NRC staff noted that SAR Section 1.9.2 specified rigorous controls on the 

manufacturing and handling of the waste package and drip shield, including conformance 

with nuclear industry standards and codes (e.g., American Society of Mechanical Engineers 

Boiler and Pressure Vessel Code). The NRC staff also recognized that the Design Control 

Parameters in the SAR are subject to management systems. On the basis of the nature of 

the data DOE used, and DOE’s adherence to industry codes and standards, DOE has 

appropriately used data from nuclear power plants to establish the probability distributions for 

key processes and events in waste package and drip shield manufacturing and handling. 

Therefore, probability model parameters have been adequately established, consistent with the 

guidance in the YMRP. 

DOE represented each basic event in an event sequence that can lead to an undetected defect 

(i.e., early failure) by a lognormal distribution. For the human error events, the external human 

reliability data DOE cited specify lognormal distributions with particular mean values and error 

factors (presented in SAR Table 2.3.6-22). For equipment or process failure events, the 

reliability data DOE cited typically specify only point (mean) values. As a result, DOE assigned 

an error factor to the probability data given in the literature as point (mean) values, as identified 

in SNL Section 5.3 (2007aa). DOE assumed that this point (mean) value is the mean of an 

unspecified probability distribution and that it is therefore appropriate to characterize the 

reliability with any reasonable, probability distribution. DOE used the lognormal distribution to 

be consistent with the human reliability data. 

DOE used Monte Carlo simulations to calculate the probability distributions for the end states of 

the event sequences that could lead to early failure. Because the probability distributions for the 

basic events in the sequences may have different error factors, DOE stated that the mean value 

of the probability distribution for the end state of the sequence is not just a simple product of the 

mean of each basic event in the sequence, as identified in SNL Section 6.5.1 (2007aa). As 

described in DOE (2009ac), the probability distributions for all of the event sequences that could 

lead to an undetected defect in the waste package were combined to give the overall probability 

that the waste package has at least one undetected defect, which is assumed to be equivalent 

to the waste package early failure probability. The same was done for the drip shield. DOE ran 

90,000 realizations to obtain the probability distributions for early failure of the waste package 

and drip shield, respectively, as identified in SNL Section 6.5.1 (2007aa). 

The NRC staff reviewed the treatment of uncertainty in the early failure probability calculations. 

DOE has established reasonable uncertainty distributions for the events in the event sequences 
3-28 

that can lead to undetected defects (i.e., early failure). The lognormal distributions used for 

human reliability events are consistent with common practice (Swain and Guttmann, 1983aa). 

For those events given in the literature as a mean failure rate, DOE has assumed an uncertainty 

range that is consistent with human reliability events and does not overestimate the reliability of 

components and processes. Further, DOE has reasonably propagated uncertainty through the 

early failure probability calculations for the waste package and drip shield, respectively. Use of 

Monte Carlo simulation is appropriate to ensure that the output is unbiased. DOE ran a 

sufficient number of realizations with Monte Carlo sampling to support its probability estimates. 

In summary, the NRC staff notes that uncertainty in event probability has been addressed 

because DOE used reasonable uncertainty distributions, the assumptions that DOE used 

do not overestimate the reliability of components and processes, and DOE adequately 

propagated uncertainty. 

The NRC staff notes that the probability distributions and values DOE provided for the 

probabilities of waste package and drip shield early failure are lognormal distributions. 

There is a mean of 1.13 × 10-4 failures per waste package and an error factor of 8.17 (SAR 

Section 2.3.6.6.3.2.7). There is a mean of 2.21 × 10!6

 failures per drip shield and an error 

factor of 14 (SAR Section 2.3.6.8.4.3.2.5). Use of these distributions and values in DOE’s 

Yucca Mountain TSPA is reasonable. 

Evaluation Conclusions for Early Waste Package and Drip Shield Event Probabilities 

The NRC staff has reviewed the information in the SAR and other information submitted and 

notes that the specification of the early waste package and drip shield event probabilities are 

consistent with the guidance in the YMRP. The SAR (i) included information on the design of 

the engineered barrier system to adequately define the waste package and drip shield early 

failure events and adequately establish probability model parameters; (ii) accounted for, 

and adequately evaluated, uncertainties in waste package and drip shield early failure 

analyses; and (iii) provided appropriate technical bases supporting the analyses used and 

the estimated probabilities. 

2.2.1.2.2.5 NRC Staff Conclusions 

NRC staff notes that the DOE description of the identification of events with probabilities 

greater than 10-8 per year is consistent with the guidance in the YMRP. NRC staff also notes 

that the DOE technical approach discussed in this chapter is reasonable for use in the 

performance assessment. 

2.2.1.2.2.6 References 

Babcock and Wilcox. 1979aa. “Records Investigation Report Related to Off-Chemistry 

Welds in Material Surveillance Specimens and Response to IE Bulletin 78-12 and 

78-12A—Supplement.” Mt. Vernon, Indiana: Babcock and Wilcox. 

Benhardt, H.C., S.A. Eide, J.E. Held, L.M. Olsen, and R.E. Vail. 1994aa. “Savannah River Site 

Human Error Data Base Development for Nonreactor Nuclear Facilities.” WSRC–TR–93–581. 

Aiken, South Carolina: Savannah River Site, Westinghouse Savannah River Company. 
3-29 

Blanton, C.H. and S.A. Eide. 1993aa. “Savannah River Site Generic Data Base 

Development (U).” WSRC–TR–93–262. Aiken, South Carolina: Westinghouse Savannah 

River Company. 

Bommer, J.J., N.A. Abrahamson, F.O. Strasser, A. Pecker, P.-Y. Bard, H. Bungum, F. Cotton, 

D. Fäh, F. Sabetta, F. Scherbaum, and J. Struder. 2004aa. “The Challenge of Defining Upper 

Bounds on Earthquake Ground Motions.” Seismological Research Letters. Vol. 75. pp. 82–95. 

Boyle, W.J. 2008aa. “Transmittal of Report: Probabilistic Volcanic Hazard Analysis Update 

(PVHA-U) for Yucca Mountain, Nevada.” Letter (October 17) to Director, DHLWRS, NRC. 

Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

BSC. 2008bl. “Supplemental Earthquake Ground Motion Input for a Geologic Repository at 

Yucca Mountain, NV.” MDL–MGR–GS–000007. Rev. 00. ACN 01, ACN 02. Las Vegas, 

Nevada: Bechtel SAIC Company, LLC. 

BSC. 2005aj. “Peak Ground Velocities for Seismic Events at Yucca Mountain, Nevada.” 

ANL–MGR–GS–000004. Rev. 00. ACN 01, ACN 02. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2004af. “Characterize Framework for Igneous Activity at Yucca Mountain, Nevada.” 

ANL–MGR–GS–000001. Rev. 02. ACN 01, ERD 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2004bi. “Yucca Mountain Site Description.” TDR–CRW–GS–000001. Rev. 02 ICN 01. 

ERD 01, ERD 02. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bj. “Technical Basis Document No. 14: Low Probability Seismic Events.” Rev. 1. 

MOL 20000510.0175. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bp. “Characterize Framework for Seismicity and Structural Deformation at 

Yucca Mountain, Nevada.” ANL–CRW–GS–000003. Rev. 00. MOL20000510.0175. 

DOC20040223.0007. Las Vegas Nevada: Bechtel SAIC Company, LLC. 

Budnitz, R.J., G. Apostolakis, D.M. Boore, L.S. Cluff, K.J. Coppersmith, C.A. Cornell, and 

P.A. Morris. 1997aa. NUREG/CR–6372, “Recommendations for Probabilistic Seismic 

Hazard Analysis: Guidance on Uncertainty and Use of Experts—Main Report.” Vol. 1. 

Washington, DC: NRC. 

CNWRA and NRC. 2008aa. “Risk Insights Derived From Analyses of Model Updates in the 

Total-system Performance Assessment Version 5.1 Code.” ML082240343. San Antonio, 

Texas: CNWRA. 

Corradini, M.L. 2003aa. “Board Comments on February 24, 2003 Panel Meeting on Seismic 

Issues.” Letter (June 27) to Dr. Margaret S.Y. Chu, DOE, Office of Civilian Radioactive Waste 

Management. Washington, DC: United States Nuclear Waste Technical Review Board. 

Cornell, C.A. 1968aa. “Engineering Seismic Risk Analysis.” Bulletin of the Seismological 

Society of America. Vol. 58. pp. 1,583–1,606. 
3-30 

Cornell, C.A. and G. Toro. 1992aa. Techniques for Determining Probabilities for Geologic 

Events and Processes. R.L. Hunter and C.J. Mann, eds. New York City, New York: Oxford 

University Press. 

Costa, F. 2008aa. “Residence Times of Silicic Magmas Associated With Calderas.” 

Developments in Volcanology, Vol. 10. Caldera Volcanism: Analysis, Modeling, and Response. 

J. Gottsmann and J. Marti, eds. Amsterdam, The Netherlands: Elsevier. pp. 1–55. 

CRWMS M&O. 1998aa. “Probabilistic Seismic Hazard Analyses for Fault Displacement and 

Vibratory Ground Motion at Yucca Mountain, Nevada.” WBS 1.2.3.2.8.3.6. Las Vegas, 

Nevada: CRWMS M&O. 

CRWMS M&O. 1996aa. “Probabilistic Volcanic Hazard Analysis for Yucca Mountain, Nevada.” 

BA0000000–01717–2200–00082. Rev. 0. Las Vegas, Nevada: CRWMS M&O. 

Detournay, E., L.G. Mastin, J.R.A. Pearson, A.M. Rubin, and F.J. Spera. 2003aa. “Final Report 

of the Igneous Consequences Peer Review Panel.” DN2000219072. MOL20031014:0097. 

Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

DOE. 2009aa. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.2.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.2, Set 2.” Letter (January 27) J.R. Williams to J.H. Sulima (NRC). 

ML090280281. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2009ab. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (February 23) J.R. Williams to J.H. Sulima (NRC). 

ML090550099. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2009ac. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.6.6, 2.3.6.8.4, and 2.2.2.3), Safety 

Evaluation Report Volume 3, Chapter 2.2.1.2.2, Set 2.” Letter (January 9) J.R. Williams to 

J.H. Sulima (NRC). ML090120301. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

DOE. 2009ad. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.6.6, 2.3.6.8.4, and 2.2.2.3), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.2.2, Set 2.” Letter (January 16) J.R. Williams to 

J.H. Sulima (NRC). ML090210465. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

DOE. 2009aq. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 1.1.10, 1.2.2, 1.1.5.2, and 1.1.5.3), Safety 

Evaluation Report Vol. 2, Chapter 2.1.1.1, Set 1.” Letter (January 12) J.R. Williams to 

C. Jacobs (NRC). Enclosures (10). ML090270750. Las Vegas, Nevada: DOE, Office of 

Civilian Radioactive Waste Management. 

DOE. 2009av. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository License 

Application.” Rev. 01. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste 

Management. 
3-31 

DOE. 2009bd. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.2.2.), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.2, Set 3.” Letter (July 20) J.R. Williams to J.H. Sulima (NRC). ML092010472. 

Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2008ab. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository 

License Application.” Rev. 0. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

DOE. 1997aa. “Topical Report YMP/TR–002–NP: Methodology To Assess Fault 

Displacement and Vibratory Ground Motion Hazards at Yucca Mountain.” Rev. 1. Las Vegas, 

Nevada: DOE, Office of Civilian Radioactive Waste Management. 

Fleck, R.J., B.D. Turrin, D.A. Sawyer, R.G. Warren, D.E. Champion, M.R. Hudson, and 

S.A. Minor. 1996aa. “Age and Character of Basaltic Rocks of the Yucca Mountain Region, 

Southern Nevada.” Journal of Geophysical Research. Vol. 101, No. B4. pp. 8,205–8,227. 

Hill, B.E. and C.B. Connor. 2000aa. “Technical Basis for Resolution of the Igneous Activity Key 

Technical Issue.” San Antonio, Texas: CNWRA. 

Hill, B.E. and J.A. Stamatakos. 2002aa. “Evaluation of Geophysical Information Used To 

Detect and Characterize Buried Volcanic Features in the Yucca Mountain Region.” 

San Antonio, Texas: CNWRA. 

Ho, C.H. and E.I. Smith. 1998aa. “A Spatial-Temporal/3-D Model for Volcanic Hazard 

Assessment: Application to the Yucca Mountain Region, Nevada.” Mathematical Geology. 

Vol. 30, No. 5. pp. 497–510. 

Ho, C.H. and E.I. Smith. 1997aa. “Volcanic Hazard Assessment Incorporating Expert 

Knowledge: Application to the Yucca Mountain Region, Nevada, U.S.A.” Mathematical 

Geology. Vol. 29. pp. 615–627. 

Ho, C.H., E.I. Smith, and D.L. Keenan. 2006aa. “Hazard Area and Probability of Volcanic 

Disruption of the Proposed High-Level Radioactive Waste Repository at Yucca Mountain, 

Nevada, USA.” Bulletin of Volcanology. Vol. 69. pp. 117–123. 

Kana, D.D., B.H.G. Brady, B.W. Vanznat, and P.K. Nair. 1991aa. NUREG/CR–5440, “Critical 

Assessment of Seismic and Geotechnical Literature Related to a High-Level Nuclear Waste 

Underground Repository.” Washington DC: NRC. 

Keefer, W.R., J.W. Whitney, and E.M. Taylor. 2004aa. “Quaternary Paleoseismology and 

Stratigraphy of the Yucca Mountain Area, Nevada.” U.S. Geological Survey Professional 

Paper 1689. Denver, Colorado: U.S. Geological Survey. 

Magsino, S.L., C.B. Connor, B.E. Hill, J.A. Stamatakos, P.C. LaFemina, D.A. Sims, and 

R.H. Martin. 1998aa. “CNWRA Ground Magnetic Surveys in the Yucca Mountain Region, 

Nevada (1996–1997).” CNWRA 98-001. San Antonio, Texas: CNWRA. 

McGuire, R.K. 1976aa. “FORTRAN Computer Program for Seismic Risk Analysis.” 

U.S. Geological Survey Open-File Report 76-67. Reston, Virginia: U.S. Geological Survey. 
3-32 

Morris, A.P., D.A. Ferrill, and D.B. Henderson. 1996aa. “Slip-Tendency Analysis and Fault 

Reactivation.” Geology. Vol. 24. pp. 275–278. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

ML051360241. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

NRC. 2002aa. “Consolidated Safety Evaluation Report Concerning the Private Fuel Storage 

Facility.” ML020850233. Docket No. 72-22. Washington, DC: NRC. 

NRC. 1999aa. “Issue Resolution Status Report, Key Technical Issue: Structural Deformation 

and Seismicity.” Rev. 2. Washington, DC: NRC. 

NRC. 1996aa. NUREG–1563, “Branch Technical Position on the Use of Expert Elicitation in 

the High-Level Radioactive Waste Program.” Washington, DC: NRC. 

O’Leary, D.W., E.A. Mankinen, R.J. Blakely, V.E. Langenheim, and D.A. Ponce. 2002aa. 

“Aeromagnetic Expression of Buried Basaltic Volcanoes Near Yucca Mountain, Nevada.” 

U.S. Geological Survey Open-File Report 02-020. Denver, Colorado: U.S. Geological Survey. 

Perry, F.V., A.H. Cogbill, and R.E. Kelley. 2005aa. “Uncovering Buried Volcanoes at Yucca 

Mountain.” Eos, Transactions, American Geophysical Union. Vol. 86, No. 47. pp. 485–488. 

Pezzopane, S.K. and T.E. Dawson. 1996aa. “Fault Displacement Hazard: A Summary of 

Issues and Information in Seismotectonic Framework and Characterization of Faulting at 

Yucca Mountain, Nevada.” J.W. Whitney, report coord. U.S. Geological Survey 

Milestone Report 3GSH100M, Chapter 9. MOL.19970129.0041. Denver, Colorado: 

U.S. Geological Survey. 

Private Fuel Storage Limited Liability Company. 2001aa. “Safety Analysis Report for the 

Private Fuel Storage Facility.” Rev. 22. Docket No. 72-22. LaCrosse, Wisconsin: Private Fuel 

Storage Limited Liability Company. 

Sawyer, D.A., R.J. Fleck, M.A. Lanphere, R.G. Warren, D.E. Broxton, and M.R. Hudson. 

1994aa. “Episodic Caldera Volcanism in the Miocene Southwestern Nevada Volcanic Field: 

Revised Stratigraphic Framework, 40Ar/39Ar Geochronology, and Implications for Magmatism 

and Extension.” Geological Society of America Bulletin. Vol. 106, No. 10. pp. 1,304–1,318. 

Schneider, J.F., N.A. Abrahamson, and T.C. Hanks. 1996aa. “Ground Motion Modeling of 

Scenario Earthquakes at Yucca Mountain: Final Report for Activity 8.3.1.17.3.3.” 

MOL.19980617.0477. Las Vegas, Nevada: Yucca Mountain Project. 

Smith, E.I., D.L. Keenan, and T. Plank. 2002aa. “Episodic Volcanism and Hot Mantle: 

Implications for Volcanic Hazard Studies at the Proposed Nuclear Waste Repository at 

Yucca Mountain, Nevada.” GSA Today. Vol. 12, No. 4. pp. 4–11. 
3-33 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ah. “Probabilistic Volcanic Hazard Analysis Update (PVHA-U) for Yucca Mountain, 

Nevada.” Rev. 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007aa. “Analysis of Mechanisms for Early Waste Package/Drip Shield Failure.” 

ANL–EBS–MD–000076. Rev. 00. ACN 01, ERD 01, ERD 02. Las Vegas, Nevada: 

Sandia National Laboratories. 

SNL. 2007ae. “Characterize Eruptive Processes at Yucca Mountain, Nevada.” 

ANL–MGR–GS–000002. Rev. 03. ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

Spudich, P., W.B. Joyner, A.G. Lindh, D.M. Boore, B.M. Margaris, and J.B. Fletcher. 1999aa. 

“SEA99: A Revised Ground Motion Prediction Relation for Use in Extensional Tectonic 

Regimes.” Bulletin of the Seismological Society of America. Vol. 89, No. 5. pp. 1,156–1,170. 

Stamatakos, J.A., S. Biswas, and M. Silver. 2007aa. “Supplemental Evaluation of Geophysical 

Information Used To Detect and Characterize Buried Volcanic Features in the Yucca Mountain 

Region.” San Antonio, Texas: CNWRA. 

Stamatakos, J.A., C.B. Connor, and R.H. Martin. 1997ab. “Quaternary Basin Evolution and 

Basaltic Volcanism of Crater Flat, Nevada, From Detailed Ground Magnetic Surveys of the Little 

Cones.” Journal of Geology. Vol. 105. pp. 318–330. 

Swain, A.D. and H.E. Guttmann. 1983aa. NUREG/CR–1278, “Handbook of Human Reliability 

Analysis With Emphasis on Nuclear Power Plant Applications Final Report.” Washington, DC: 

NRC. 

Wells, D.L. and K.J. Coppersmith. 1994aa. “New Empirical Relationships Among Magnitude, 

Rupture Length, Rupture Width, Rupture Area, and Surface Displacement.” Bulletin of the 

Seismological Society of America. Vol. 84. pp. 974–1,002. 

Youngs, R.R., W.J. Arabasz, R.E. Anderson, A.R. Ramelli, J.P. Ake, D.B. Slemmons, 

J.P. McCalpin, D.I. Doser, C.J. Fridrich, F.H. Swan, III, A.M. Rogers, J.C. Yount, 

L.W. Anderson, K.D. Smith, R.L. Bruhn, P.L. Knuepfer, R.B. Smith, C.M. dePolo, 

D.W. O’Leary, K.J. Coppersmith, S.K. Pezzopane, D.P. Schwartz, J.W. Whitney, S.S. Olig, 

and G.R. Toro. 2003aa. “A Methodology for Probabilistic Fault Displacement Hazard Analyses 

(PFDHA).” Earthquake Spectra. Vol. 19, No. 1. pp. 191–219. 

4-1 

CHAPTER 4 

2.2.1.3.1 Degradation of Engineered Barriers 

2.2.1.3.1.1 Introduction 

This chapter addresses the chemical degradation of the drip shield and waste packages 

stored in the repository drifts. The drip shield and the waste packages are engineered 

barriers, a subset of the Engineered Barrier System. The general functions of the Engineered 

Barrier System are to (i) prevent or significantly reduce the amount of water that contacts the 

waste, (ii) prevent or significantly reduce the rate at which radionuclides are released from the 

waste, and (iii) prevent or significantly reduce the rate at which radionuclides are released from 

the engineered barrier system to the Lower Natural Barrier [Safety Analysis Report (SAR) 

Section 2.1.1.2 (DOE, 2008ab)]. The engineered barrier system consists of the emplacement 

drift, the drip shield, the waste package, the naval spent nuclear fuel structure, the waste form 

and waste package internals (e.g., transportation, aging, and disposal canisters), the waste 

package pallet, and invert features (SAR Figure 2.1-7). 

In the postclosure performance assessment, the U.S. Department of Energy (DOE) evaluated 

whether the ability of the engineered barrier system components to perform their barrier 

functions could be compromised by features, events, and processes (FEPs) that degrade their 

physical structure. In particular, DOE considered that the engineered barrier system 

components were subject to mechanical degradation caused by seismic ground motion (SAR 

Section 2.3.4). The U.S. Nuclear Regulatory Commission (NRC) staff’s review of DOE’s Total 

System Performance Assessment (TSPA) models for mechanical degradation of the engineered 

barrier system is found in TER Section 2.2.1.3.2. The other class of engineered barrier system 

degradation that DOE considered in the postclosure performance assessment was chemical 

degradation, or corrosion, caused by reactions between the engineered barrier system materials 

and the environment. In SAR Section 2.3.6, DOE described the TSPA model abstractions for 

chemical degradation of the drip shield and the waste package outer barrier. This chapter 

reviews DOE’s TSPA model abstractions for chemical degradation of the drip shield and the 

waste package outer barrier. 

2.2.1.3.1.2 Evaluation Criteria 

The NRC staff’s review of model abstractions used in DOE’s postclosure performance 

assessment, including those considered in this chapter for degradation of engineered barriers, 

is guided by 10 CFR 63.114 (Requirements for Performance Assessment) and 63.342 (Limits 

on Performance Assessments). The resulting DOE Total System Performance Assessment 

(TSPA) is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 
4-2 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment in 10 CFR 63.342, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is given in TER Chapter 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for 

the performance assessment methods for the time from 10,000 years through the period 

of geologic stability, defined in 10 CFR 63.302 as 1 million years following disposal. 

These sections require that through the period of geologic stability, with specific limitations, the 

dose calculation should 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

 Include in the performance assessment those features events and processes used in the 

performance assessment for the initial 10,000-year period 

For this model abstraction of degradation of engineered barriers, 10 CFR 63.342(c)(3) further 

calls for the assessment of the effects of general corrosion on engineered barriers and specifies 

either the use of a constant representative rate throughout the period of geologic stability or a 

distribution of rates correlated to other parameters. DOE elected to use a distribution of 

corrosion rates in its SAR; thus this method is reviewed for the post-10,000-year period. 

The NRC staff’s review of the SAR and supporting information follows the guidance in Yucca 

Mountain Review Plan (YMRP) Sections 2.2.1.3.1, Degradation of Engineered Barriers, (NRC, 

2003aa) as supplemented by additional guidance for the period beyond 10,000 years after 

permanent closure (NRC, 2009ab). The YMRP acceptance criteria for model abstractions 

provide guidance on information the NRC staff could use to evaluate the performance 

assessment. Following the guidance, the NRC staff’s review of DOE’s abstraction of 

degradation of engineered barriers considered five criteria: 

1. System description and model integration are adequate. 

2. Data are sufficient for model justification. 

3. Data uncertainty is characterized and propagated through the abstraction. 

4. Model uncertainty is characterized and propagated through the abstraction. 

5. Model abstraction output is supported by objective comparisons. 

The NRC staff review used a risk-informed approach and the guidance provided by the YMRP, 

as supplemented by NRC (2009ab), to the extent reasonable for aspects of degradation of 

engineered barriers important to repository performance. The NRC staff considered all five 

criteria provided in the YMRP in its review of information provided by DOE. In the context of 

these criteria, only those aspects of the model abstraction that substantively affect the 

performance assessment results, as determined by the NRC staff, are discussed in detail in this 
4-3 

chapter. The NRC staff’s determination is based both on risk information provided by DOE, and 

on NRC staff’s knowledge gained through experience and independent analyses. 

2.2.1.3.1.3 Technical Evaluation 

DOE’s models for chemical degradation of the engineered barrier systems focus on the drip 

shield and the waste package outer barrier, respectively. Consistent with the YMRP guidance, 

the NRC staff performed a risk-informed, performance-based review, focusing on those aspects 

of the DOE models for chemical degradation of the drip shield and the waste packages that are 

most important to the calculations of barrier capability. DOE concluded that seepage flux is the 

primary source of water that may react with the engineered barrier system components (SAR 

Section 2.3.7.12.1). In the DOE model for flow of seepage water through the engineered barrier 

system, the water must first pass through the drip shield and then through the waste package 

before contacting and mobilizing the waste form. As such, this chapter first concentrates on 

DOE’s models for chemical degradation of the drip shield and then addresses DOE’s models for 

chemical degradation of the waste package. 

2.2.1.3.1.3.1 Drip Shield Degradation 

The drip shield, which DOE described in SAR Section 1.3.4.7, is an engineered metal 

barrier designed to divert water that enters the drift and prevent it from contacting the 

waste package. DOE stated that the drip shield will be fabricated from Titanium Grade 7 

(UNS R52400). Titanium Grade 7 is a commercially pure titanium alloy with the addition 

of a small amount of palladium (approximately 0.2 wt%) to enhance its corrosion resistance. 

The drip shield structural supports will be fabricated from Titanium Grade 29 (UNS R56404), 

which is a titanium alloy composed of approximately 6 wt% aluminum and 4 wt% vanadium for 

strength, plus approximately 0.1 wt% ruthenium for corrosion resistance. 

In developing the postclosure performance assessment analysis, DOE evaluated a number of 

features, events, and processes (in SAR Table 2.2-5) related to chemical degradation of the drip 

shield, including 

 General corrosion of the drip shields [features, events, and processes 

(FEP) 2.1.03.01.0B] 

 Stress corrosion cracking of the drip shields (FEP 2.1.03.02.0B) 

 Localized corrosion of the drip shields (FEP 2.1.03.03.0B) 

 Hydride cracking of the drip shields (FEP 2.1.03.04.0B) 

 Microbially influenced corrosion (MIC) of the drip shields (FEP 2.1.03.05.0B) 

 Early failure of the drip shields (FEP 2.1.03.08.0B) 

 Oxygen embrittlement of the drip shields (FEP 2.1.06.06.0B) 

 Creep of metallic materials in the drip shield (FEP 2.1.07.05.0B) 
4-4 

 Localized corrosion on drip shield surfaces due to deliquescence (FEP 2.1.09.28.0B) 

 Thermal sensitization of the drip shields (FEP 2.1.11.06.0B) 

With the exception of general corrosion and early failure of drip shields, these features, events, 

and processes were screened out from the performance assessment on the basis of low 

consequence or low probability (SAR Table 2.2-5). The NRC staff’s evaluation of DOE’s bases 

for excluding these features, events, and processes from the performance assessment is 

addressed in TER Section 2.2.1.2.1. 

With respect to the features, events, and processes that are included in the performance 

assessment, DOE described general corrosion of the drip shield as the uniform thinning of 

both the Titanium Grade 7 drip shield plates and the Titanium Grade 29 structural supports 

(SAR Section 2.3.6.8.1.1). In SAR Section 2.2.2.3, DOE defined drip shield early failure as 

through-wall penetration caused by manufacturing- and handling-induced defects, at a time 

earlier than would be expected for a nondefective drip shield. 

In the TSPA analysis, DOE calculated that conditions in the drift (e.g., temperature, pH, 

seepage water chemistry) may support localized corrosion of the waste package if the drip 

shield fails and allows seepage water to contact the waste package within approximately 

12,000 years after repository closure, as detailed in DOE Enclosure 1 (2009dg). The Total 

System Performance Assessment (TSPA) analysis calculates that few drip shields will fail within 

12,000 years after repository closure. Therefore, the probability of waste package breach by 

localized corrosion is low in the DOE model. Following 12,000 years after repository closure, 

DOE calculated that there is a low probability for conditions in the drift to support localized 

corrosion of the waste package even if the drip shield fails and allows seepage water to contact 

the waste package. 

Other than for localized corrosion, the integrity of the drip shield does not have a significant 

effect on the DOE model abstractions for chemical degradation of the waste package. In the 

TSPA Nominal Modeling Case, DOE’s models for general corrosion and stress corrosion 

cracking of the waste package conservatively assume aqueous degradation conditions, even for 

the intact drip shield. In the Seismic Ground Motion Modeling Case in the TSPA analysis, the 

presence of the drip shield does have some effect on stress corrosion cracking of the waste 

package because DOE calculated that the waste package under an intact drip shield will have a 

greater likelihood of being damaged under low-probability seismic ground motion events than it 

would under the assumption of a failed drip shield condition. This is because an intact drip 

shield permits unobstructed free movement of the waste package, thereby potentially causing 

damage as waste packages strike one another (SAR Section 2.3.4.5). Under a collapsed drip 

shield event, waste packages are constrained from significant movement and unable to strike or 

bump into each other. Consequently, a waste package under an intact drip shield is more 

susceptible to stress corrosion cracking if a low probability seismic event occurs that imparts the 

required energy for the waste packages to strike each other. Nevertheless, DOE calculated that 

the probability of a seismic ground motion with sufficient magnitude to damage the waste 

package is so low, even in the Seismic Ground Motion Modeling Case, that the presence of the 

drip shield has an insignificant effect on the postclosure performance assessment beyond 

12,000 years after repository closure, as described in DOE Enclosure 5 (2009cn). 

The NRC staff’s reviews of the DOE model abstractions for general corrosion and early failure 

of the drip shield are presented in the following sections. Because the presence of the drip 

shield is important for the DOE calculations that localized corrosion of the waste package is 
4-5 

unlikely within 12,000 years after repository closure, the NRC staff focused on those aspects of 

the models that were most important to the DOE calculations of the drip shield lifetime. 

2.2.1.3.1.3.1.1 Drip Shield General Corrosion 

In SAR Section 2.3.6.8.1, DOE described the model for general corrosion of the drip shield that 

was implemented in the TSPA. The drip shield is constructed of titanium alloys that are 

assumed to be highly corrosion resistant because of their passivity. Passivity refers to a state in 

which metals and alloys lose their chemical reactivity under certain environmental conditions. 

The passive state is generally attributed to the presence of a thin, protective oxide film on the 

metal surface. Because the maintenance of the passive state is important to the corrosion 

performance of the drip shield, the NRC staff first reviewed the drip shield’s long-term passive 

film stability in the repository conditions. The NRC staff then reviewed the model abstraction 

used to calculate the drip shield general corrosion rate in the TSPA model. 

Drip Shield’s Long-Term Passive Film Stability 

In BSC Section 1.1 (2004as), DOE presented literature references (Pourbaix, 1974aa; Schutz 

and Thomas, 1987aa) which indicated that the passive films on titanium alloys are stable over 

wide ranges of chemical potential and pH, and that, should the passive film rupture, titanium 

has a strong tendency for repassivation in the type of oxidizing conditions that are expected in 

the repository. DOE, however, also cited literature references (e.g., Lorenzo de Mele and 

Cortizo, 2000aa; Brossia, et al., 2001aa; Brossia and Cragnolino, 2000aa, 2001ab, 2004aa; 

Pulvirenti, et al., 2002aa, 2003aa) which indicated that dissolved fluoride in brine solutions can 

increase the general corrosion rates for titanium alloys and possibly compromise the stability of 

the passive film . Therefore, DOE evaluated the uncertainty in long-term drip shield passive film 

persistence associated with possible passive film degradation by fluoride-bearing seepage 

water brines, as described in BSC Section 6.5.7 (2004as). 

In BSC Section 6.5.7.2 (2004as), DOE reviewed and analyzed passive film instability. DOE 

cited literature references that described the onset of localized corrosion on titanium specimens 

that were exposed to fluoride shortly after the passive film was manually removed by polishing 

(e.g., Brossia and Cragnolino, 2000aa, 2001ab; Brossia, et al., 2001aa). When the specimens 

were in an oxidizing environment, for as little as 4 days prior to fluoride exposure the specimens 

exhibited resistance to fluoride attack (Lorenzo de Mele and Cortizo, 2000aa). DOE stated 

that it expects the drip shield to have an extended period of dry thermal oxidation between 

the time the repository is closed and the time at which seepage water may fall onto the drip 

shield, as described in BSC Section 6.5.7 (2004as). Even for thermally oxidized Titanium 

Grade 7 specimens, however, passive film instability in fluoride-rich solution with low pH (~4) 

has been observed (Lian, et al., 2005aa). However, DOE concluded that such conditions are 

not representative of the environment expected in the repository, as outlined on SNL p. 6-408 

(2008ac). DOE stated that even if seepage water brines in the repository contain fluoride, 

high concentrations of other species will also be present that will suppress or neutralize any 

fluoride attack. DOE identified studies of alloys with similar composition to Titanium Grade 7 

in environments with temperatures up to 177 °C [351 °F], pH as low as 1, and fluoride, along 

with other species such as calcium, magnesium chloride, and silicate (Thomas and Bomberger, 

1983aa; Schutz and Grauman, 1986aa). The studies showed that the titanium had a high 

passive film persistence, which was attributed to calcium reducing the fluoride ion solubility 

by precipitation of calcium fluoride, as well as the displacement of fluoride from absorption on 

the passive film by other species. Moreover, DOE presented its own test results in BSC 

Section 6.5 (2004as) in which Titanium Grade 7 specimens showed no evidence of passive 
4-6 

film instability after 5 years’ exposure to simulated concentrated water, which contained 

fluoride, as well as chloride, silica, sulfate, nitrate, and bicarbonate (composition given in SAR 

Table 2.3.6-1). Therefore, DOE concluded that the drip shield passive film will be stable during 

the postclosure period given the expected composition of seepage water brines, as described 

on SNL p. 6-410 (2008ac). 

Summary of NRC Staff’s Review for Drip Shield's Long-Term Passive Film Stability 

The NRC staff reviewed DOE’s assessment of drip shield passivity. On the basis of the 

information DOE provided in BSC Section 6.5.7 (2004as), the NRC staff notes that uncertainty 

in the long-term persistence of the titanium passive film is primarily related to potential passive 

film degradation by fluoride-bearing brines. There was no evidence of localized corrosion of 

Titanium Grade 7 exposed to fluoride-bearing simulated concentrated water for 5 years and 

thus that the passive film was stable when in contact with a brine having this composition. The 

NRC staff notes that the extent of titanium passive film degradation will generally decrease with 

decreasing fluoride concentration in the brines (Brossia, et al., 2001aa). Analyses by the NRC 

staff (Pabalan, 2010aa) indicate that the fluoride concentration in simulated concentrated water 

is greater than would be expected in water that would contact the drip shield in repository 

conditions because other species in the waters, such as calcium, can precipitate fluoride ions 

out of solution, thus limiting the free-chloride concentration. Therefore, the NRC staff notes that 

the 5-year testing of the Titanium Grade 7 specimens in fluoride-bearing simulated concentrated 

water supports the DOE assumption of passive film stability in repository conditions. The 

literature references DOE cited (Thomas and Bomberger, 1983aa; Schutz and Grauman, 

1986aa) further show that other species in the waters, such as calcium, can protect the passive 

film by causing fluoride ions to precipitate out of solution. Finally, independent analyses the 

NRC staff conducted (Lin, et al., 2003aa; Codell and Leslie, 2006aa) also suggest that seepage 

water brines that may contact the drip shield will have insufficient fluoride concentration to 

significantly affect passive film stability on the drip shield titanium alloys. On the basis of this 

information, the NRC staff notes DOE’s assumption that the drip shield is protected by a passive 

oxide film during the postclosure period is reasonable. 

Drip Shield General Corrosion Conceptual Model 

In SAR Section 2.3.6.8.1, DOE described the conceptual model for general corrosion of the drip 

shield that was implemented in the TSPA. In the DOE model, corrosion begins at the time of 

repository closure and progresses at a constant rate over time. DOE assumed aqueous 

conditions in the drift and also that the general corrosion rate is independent of in-drift 

environmental conditions (e.g., temperature, relative humidity). 

The NRC staff requested DOE’s technical basis for assuming that the general corrosion rate of 

the drip shield is independent of temperature. In DOE Enclosure 3 (2009cn), DOE stated that at 

the start of the general corrosion process the corrosion rates of titanium alloys are temperature 

dependent. However, over time, the corrosion rates at different temperatures tend to converge. 

DOE observed a noticeable trend of increasing corrosion rate with increasing temperature for 

Titanium Grade 7 specimens tested in the range of 50 to 110 °C [122 to 230 °F] after 4 weeks’ 

exposure, but DOE also observed that the corrosion rate was less temperature dependent after 

8 weeks (Hua and Gordon, 2004aa). Further, DOE referenced 3-year corrosion tests of titanium 

plus 0.2 weight percent palladium, which has nearly the same composition as Titanium Grade 7 

in the temperature range of 90 to 200 °C [194 to 392 °F] in a pH 4.9 chloride-sulfate brine 

(Smailos and Köster, 1987aa). DOE concluded that the corrosion rates initially showed some 
4-7 

temperature dependence, but were effectively identical within 3 years as shown in Smailos and 

Köster Figure 1 (1987aa). 

Summary of NRC Staff’s Review for Drip Shield General Corrosion Conceptual Model 

The NRC staff reviewed DOE’s conceptual model for general corrosion of the drip shield. 

DOE’s assumed aqueous conditions are reasonable because titanium general corrosion 

proceeds more rapidly in aqueous conditions than in dry conditions. Further, the staff notes that 

data from DOE testing (SAR Figure 2.3.6-49) and independent corrosion data DOE referenced 

for material similar to the drip shield titanium alloys (Smailos and Köster, 1987aa) indicate that 

the general corrosion rates decrease for titanium alloys over time. The technical literature 

indicates that the decreasing corrosion rate may correspond to thickening of the passive oxide 

film (Jones, 1996aa). Thus, the NRC staff notes DOE’s use of a constant corrosion rate over 

time is reasonable because this assumption will not underestimate the corrosion rate. 

In addition, the NRC staff reviewed the DOE assumption that the general corrosion rate is 

independent of temperature. The studies DOE cited (e.g., Smailos and Köster, 1987aa; Hua 

and Gordon, 2004aa) considered materials analogous to the drip shield titanium alloys and 

environmental conditions that are similar to or more aggressive than those expected in the 

repository. On the basis of information provided in these studies, the NRC staff notes that, 

although the corrosion rates of titanium alloys may have some temperature dependence during 

an initial period of exposure to corrosive brines, there is little temperature dependence after this 

period. The information DOE provided is also consistent with independent analyses by NRC 

(Mintz and He, 2009aa), which confirmed that corrosion rates for titanium alloys do not show 

significant temperature dependence for temperatures that are representative of the repository 

conditions. Therefore, the NRC staff notes DOE’s assumption that the corrosion rates of the 

drip shield titanium alloys are independent of temperature is reasonable because this 

assumption will not underestimate the corrosion rate. 

Long-Term Corrosion Test Data 

The corrosion rates for Titanium Grades 7 and 29 that were sampled in the Total System 

Performance Assessment (TSPA) were based on data from weight-loss corrosion tests at the 

Long-Term Corrosion Test Facility (SAR Section 2.3.6.8.1.2.1). The following summarizes the 

NRC staff’s review of DOE’s data implemented in the TSPA analysis. 

Titanium Grade 7 

The corrosion rate for Titanium Grade 7 that was sampled in the TSPA was based on 2.5-year 

tests of Titanium Grade 7 crevice and weight-loss specimens with wrought (base metal-type) 

and as-welded metallurgical conditions (SAR Section 2.3.6.8.1.2.1). Some specimens were 

fully immersed in solution (i.e., aqueous phase), whereas others were in the saturated vapor 

above the aqueous phase. DOE exposed the test specimens to different solutions, including 

simulated acidified water, simulated dilute water, and simulated concentrated water, the 

compositions of which are given in SAR Table 2.3.6-1. The tests were performed at 

temperatures of 60 and 90 °C [140 and 194 °F]. DOE measured the material weight loss during 

the test period and used these data to calculate the general corrosion rates, following American 

Society of Testing and Materials (ASTM) G1-90 (ASTM International, 1999aa). DOE observed 

that the corrosion rates of crevice specimens were lower than those of weight-loss specimens 

(SAR Figure 2.3.6-44). Therefore, DOE chose to use only the data from the weight-loss 

specimens in the model abstraction because it will calculate a higher corrosion rate in the TSPA 
4-8 

analysis. For the weight-loss specimens, DOE did not observe a significant difference in 

corrosion rates between wrought and as-welded materials, but did observe that the corrosion 

rates depended upon the chemistry of the test solution. In particular, the corrosion rates for 

specimens tested in the simulated concentrated water aqueous phase were as high as 50 nm/yr 

[1.97 × 10-6 in/yr], whereas the corrosion rates for the specimens tested in the aqueous and 

vapor phases of simulated acidified water and simulated dilute water , as well as for specimens 

tested in simulated concentrated water vapor phase, were below 20 nm/yr [7.87 × 10-7 in/yr] as 

shown in BSC Figures 6.6[a] and 6.7[a] (2004as). 

In the TSPA analysis, DOE assumed that corrosion occurs simultaneously on the inner surface 

and the outer surface of the Titanium Grade 7 drip shield plates, with different corrosion rates 

for the respective surfaces. DOE assumed the outer surface of the plate corroded faster than 

the inner surface because the outer surface is expected to be exposed to a more aggressive 

environment, including dust and dripping seepage water, as detailed in BSC Section 6.1.6[a] 

(2004as). DOE used the data from the most aggressive test condition, obtained from the 

simulated concentrated water aqueous phase, to derive the distribution from which the outer 

surface corrosion rate was sampled in the TSPA model. In aqueous simulated concentrated 

water, DOE measured higher corrosion rates for Titanium Grade 7 at 90 °C [194 °F] than at 

60 °C [140 °F] as shown in BSC Figure 6.6[a] (2004as). DOE did not, however, consider 

temperature dependence for the titanium general corrosion rate. Instead, DOE elected to use 

only the data from the 90 °C [194 °F] tests because these gave a higher corrosion rate. These 

data (“Aggressive Condition” in SAR Figure 2.3.6-46) have a mean corrosion rate of 46.1 nm/yr 

[1.81 × 10-6 in/yr]. For the general corrosion rate on the underside of the drip shield plates, DOE 

used the data from specimens tested at 60 and 90 °C [140 and 194 °F] in the aqueous and 

vapor phases of the simulated acidified water and the simulated dilute water , respectively, as 

well as specimens tested at 60 and 90 °C [140 and 194 °F] in the simulated concentrated water 

vapor phase, as detailed in BSC Section 6.1.7(a) (2004as). These data (“Benign Condition” in 

SAR Figure 2.3.6-46) have a mean corrosion rate of 5.1 nm/yr [2.01 × 10-7 in/yr]. 

DOE considered uncertainty in the measured corrosion rates, which it attributed to difficulties 

in cleaning and weighing corrosion specimens, particularly given the very small weight 

losses associated with low corrosion rates, as well as randomness in the general corrosion 

processes, as described in BSC Section 6.1.6.1[a] (2004as). DOE determined that the 

corrosion rate for the outside of the drip shield plates is best represented by a normal 

distribution, the mean of which is sampled from a t-distribution, described in SNL Table 6.3.5-3 

(2008ag). The t-distribution is a broader distribution DOE used given that this set of corrosion 

rate data only has six data points. The mean of the t-distribution is approximately 46.1 nm/yr 

[1.81 × 10-6 in/yr], with 2.5th and 97.5th percentile values of approximately 43.0 and 49.1 nm/yr 

[1.69 × 10-6 in/yr and 1.93 x 10-6 in/yr], respectively, as detailed in BSC Section 6.1.6.2[a] 

(2004as). The variability of distributions for the general corrosion rate on the outside of the drip 

shield plates was shown in BSC Figure 6-11[a] (2004as). For the inside of the drip shield 

plates, DOE determined that the general corrosion rate is best represented by a gamma 

distribution, the mean of which is sampled from a normal distribution, described in 

SNL Table 6.3.5-3 (2008ag). The gamma distribution is a continuous skewed distribution 

function used to describe the distribution of variables that are positive and unbound. The 

mean of the normal distribution is approximately 5.1 nm/yr [2.01 × 10-7 in/yr], with 2.5th and 

97.5th percentile values of approximately 3.5 nm/yr and 6.8 nm/yr [1.38 × 10-7 in/yr and 

2.68 × 10-7 in/yr], respectively, as outlined in BSC Section 6.1.7.2[a] (2004as). The variability 

distributions for the general corrosion rate on the inside of the drip shield plates were shown in 

BSC Figure 6-19[a] (2004as). 
4-9 

DOE compared the corrosion rate sampled in the TSPA code to independently reported 

corrosion rates for analogous alloys in environments similar to or more aggressive than 

those expected in Yucca Mountain, as detailed in SAR Section 2.3.6.8.1.5 and BSC 

Section 7.2.1[a] (2004as). DOE concluded that the TSPA-calculated corrosion rates are 

consistent with corrosion rates measured by Smailos and Köster (1987aa) for titanium plus 

0.2 wt% palladium in the temperature range of 90 to 200 °C [194 to 392 °F] in a pH 4.9 

chloride-sulfate brine. 

In response to the NRC staff’s request for additional information (RAI) on how the experimental 

uncertainties associated with sample cleaning, weighing, and measuring were incorporated into 

the sampled corrosion rate distributions, DOE (2009cn) stated that subsequent examination of 

corrosion test specimens revealed that posttest specimen cleaning did not adequately remove a 

residual oxide film. This resulted in under-measurements of specimen weight loss and, in turn, 

an underestimation of the general corrosion rates for the inside and outside of the drip shield 

plates. To assess the effect of the incomplete specimen cleaning procedure on corrosion rate 

uncertainties, DOE conducted cross section analyses of the chemically cleaned posttest 

specimens. DOE estimated that the general corrosion rates for Titanium Grade 7, presented in 

SAR Section 2.3.6.8.1, were underestimated by, at most, a factor of two. Consequently, DOE 

conducted a sensitivity analysis in which it considered corrosion rates up to four times those 

given in SAR Section 2.3.6.8.1. This shortened the drip shield framework and plate lifetime 

compared to those calculated in the TSPA model. DOE stated that this sensitivity analysis 

showed that corrosion rates of up to four times higher than those given in SAR Section 2.3.6.8.1 

resulted in negligible differences in the expected dose curves, as shown in DOE Enclosure 5, 

Figure 2 (2009cn). Therefore, DOE concluded that the data presented in SAR Section 2.3.6.8.1 

were reasonable to use in the TSPA model because unquantified experimental uncertainties 

had negligible impact on the postclosure performance assessment. Nevertheless, DOE 

stated it would update the SAR to incorporate the analysis provided in the response to DOE 

Enclosure 1 (2009cn). 

In DOE Enclosure 4 (2009cn), DOE responded to the NRC staff’s request for additional 

information seeking justification that the immersion test conditions in simulated brines to 

determine general corrosion rates are adequate to model the corrosion behavior of the drip 

shield, considering that some passive alloys may be more susceptible to corrosion in dripping 

conditions than in immersion conditions (e.g., Lee and Solomon, 2006aa). DOE stated that the 

temperatures at which dripping effects on corrosion behavior have been observed in other 

passive alloys are greater than the temperatures expected for the drip shield in dripping 

conditions. Moreover, DOE stated that data in the technical literature indicate that titanium 

alloys are highly resistant to dripping effects because of their tenacious passive film (Schutz, 

2005aa). Therefore, DOE concluded that the immersion tests in the simulated brines were 

adequate to model the corrosion behavior of the drip shield in the repository because they 

accounted for potential dripping conditions. 

Summary of NRC Staff’s Review for Corrosion of Titanium Grade 7 

The NRC staff reviewed the information DOE provided in SAR Section 2.3.6.8.1 and DOE 

(2009cn) and noted the following: 

 With regard to the material conditions, DOE’s testing of Titanium Grade 7 specimens 

with wrought and as-welded microstructures is reasonable. DOE’s tests on the material 

microstructures are reasonable because they are representative of the microstructures 
4-10 

expected for Titanium Grade 7 drip shield plates in the base metal and in the region of 

the weld, respectively, on the basis of the fabrication procedures set forth in BSC 

(2007bu). 

 With respect to the corrosion test solutions, DOE tested the Titanium Grade 7 

specimens in a range of corrosion test solutions, including simulated acidified water, 

simulated dilute water, and simulated concentrated water. The corrosion rate for the drip 

shield in the repository may depend on such factors as the pH and concentration of ionic 

species in water that contacts the drip shield. Therefore, the NRC staff reviewed the 

corrosion test solutions to determine whether they are adequate to model the drip shield 

corrosion rate for the conditions expected in the repository. The NRC staff notes that 

DOE’s corrosion test solutions are more chemically aggressive than waters expected to 

occur within the repository drifts, including starting water compositions in the DOE 

near-field chemistry model described in SAR Section 2.3.5.5, and waters considered 

in NRC staff’s independent analysis of in-drift water evolution, described in TER 

Section 2.2.1.3.3.3.2. Therefore, DOE’s model of the drip shield corrosion rate based on 

tests in simulated acidified water, simulated concentrated water, and simulated dilute 

water is reasonable. 

 With regard to the testing conditions, DOE used immersion corrosion tests to represent 

corrosion behavior, including potential dripping conditions in the drift. Literature DOE 

cited (Schutz, 2005aa) and other independent literature (He, et al., 2007aa) show that 

titanium alloys are highly resistant to dripping-induced corrosion effects at temperatures 

expected in the repository because of their strong tendency to passivity. Therefore, 

DOE reasonably performed corrosion tests in immersion because this will not 

underestimate the corrosion rate in dripping conditions. 

In addition, the NRC staff evaluated DOE’s experimental procedures for cleaning, weighing, 

and measuring the corrosion rates of the test specimens. DOE identified deficiencies with its 

specimen preparation and cleaning that initially led to unquantified experimental uncertainties in 

the general corrosion rate for Titanium Grade 7 reported in SAR Section 2.3.6.8.1. DOE 

addressed the deficiencies by demonstrating that the actual corrosion rates for the Titanium 

Grade 7 drip shield plates would not exceed a factor of two to four times the corrosion rates 

sampled in the Total System Performance Assessment (TSPA) analysis and that the higher 

corrosion rates resulted in negligible changes in calculated results. In the TSPA, the sum of 

the mean general corrosion rates for the inside and outside of the Titanium Grade 7 drip 

shield plates is approximately 51.2 nm/yr [2.06 × 10-6 in/yr]. Therefore, increasing the 

corrosion rate by a factor of two to four would increase the mean corrosion rate to approximately 

100 to 200 nm/yr [3.93 × 10-6 to 7.87 × 10-6 in/yr]. At this range of corrosion rates, breach of 

the 15-mm [0.59-in]-thick Titanium Grade 7 plate by general corrosion would generally 

occur between 75,000 and 150,000 years after repository closure. This is well beyond 

12,000 years after repository closure. Prior to 12,000 after repository closure, DOE calculated 

that the waste package outer barrier may undergo localized corrosion if contacted by seepage 

water (SAR Section 2.3.6.4). Therefore, DOE’s demonstration is reasonable that unquantified 

experimental uncertainties in the general corrosion rate for Titanium Grade 7, reported in 

SAR Section 2.3.6.8.1, will have a negligible effect on the postclosure performance 

assessment results. 

Finally, the NRC staff compared the Titanium Grade 7 corrosion rates sampled in the TSPA 

code to independently reported corrosion rates for the same or similar material. The corrosion 

rates sampled in the TSPA code are similar to those measured in independent testing that DOE 
4-11 

cited (Smailos and Köster, 1987aa; Mattsson and Olefjord, 1990aa). Further, the NRC staff 

considered the report by Schutz (2005aa), which stated that the corrosion rates of titanium 

alloys are negligible in a variety of solutions, including seawater up to 260 °C [500 °F], 

62 percent CaCl2 at 150 °C [302 °F], boiling solutions of 10 percent and 30 percent FeCl3, and 

boiling saturated MgCl2 solution. Additionally, the NRC staff performed independent corrosion 

tests of Titanium Grade 7 in 1 M NaCl solution at 95 °C [203 °F] and measured a corrosion rate 

of approximately 87 nm/yr [3.43 × 10-6 in/yr] (Brossia, et al., 2001aa; Brossia and Cragnolino, 

2004aa). The NRC staff determined that the titanium alloys tested in these studies are 

analogous to the drip shield titanium alloys and that the environmental conditions 

(e.g., temperature and brine chemistry) considered in these studies are chemically and 

thermally more aggressive than those expected for the postclosure period in the repository. 

The corrosion rates measured in these studies are similar to the Titanium Grade 7 drip shield 

plate corrosion rates the TSPA code calculated. Therefore, the NRC staff notes that 

independent reports in the technical literature provide support for the corrosion rate DOE 

calculated in the TSPA code. 

Titanium Grade 29 

The corrosion rate for Titanium Grade 29 that was applied in the TSPA analysis was 

based on 42-day weight-loss measurements of Titanium Grades 7 and 29 specimens in 

solutions that DOE stated were representative of seepage water and deliquescent brines 

expected in the repository [SAR Section 2.3.6.8.1.3 and BSC Section 6.2.1[a] (2004as)]. 

The compositions of the brines are given in BSC Table 6-7[a] (2004as). For tests at 120 and 

150 °C [248 and 302 °F], DOE calculated the ratios of the corrosion rates of Titanium Grade 29 

to those of Titanium Grade 7. For a given test environment, DOE calculated that the corrosion 

rate of Titanium Grade 29 could be a factor of one to seven times higher than that of Titanium 

Grade 7 (SAR Figure 2.3.6-48). From these data, DOE developed a discrete probability 

distribution function summarized in BSC Table 6-8[a] (2004as), which gave the ratio for the 

corrosion rate of Titanium Grade 29 to that of Titanium Grade 7. To calculate the corrosion rate 

for the Titanium Grade 29 structural supports in the TSPA model, DOE sampled the ratio from 

this probability distribution function and multiplied the sampled ratio by the corrosion rate on the 

outside of the Titanium Grade 7 plate (i.e., under aggressive conditions). 

In BSC Section 6.2[a] (2004as), DOE acknowledged that it did not have long-term general 

corrosion data for Titanium Grade 29. DOE stated, however, that the passive films for both 

Titanium Grade 7 and Grade 29 are likely to be predominantly titanium oxide. DOE also stated 

that data show that the passive behavior for the respective alloys is the same for the range of 

brines expected in the repository (Andresen and Kim, 2006aa). Therefore, DOE concluded that 

the corrosion processes for Titanium Grades 7 and 29 are similar and that comparing the 

corrosion rates of the respective alloys in short-term tests is an adequate basis for calculating 

the long-term corrosion rate for Titanium Grade 29. 

DOE reanalyzed the comparative corrosion data (DOE, 2009cn). DOE determined that the 

weight loss for the respective alloys was measured by a weighing balance that had uncertainty 

larger than most of the measured weight change values. DOE concluded that it was unable to 

make a meaningful distinction between actual material weight loss and measurement 

uncertainty. Further, DOE stated that for the same tests, corrosion rates were also measured 

by electrochemical impedance spectroscopy and linear polarization resistance (Andresen and 

Kim, 2006aa), with negligible difference for the respective alloys. On the basis of this 

reanalysis, DOE determined that, because there was no measurable difference between the 

corrosion rates for the respective alloys in the 42-day tests, the corrosion rate ratio described in 
4-12 

SAR Section 2.3.6.8.1.3 was not needed. DOE decided to follow an alternative approach in 

which the corrosion rates for the Titanium Grade 29 structural supports are the same as the 

corrosion rate for the outer surface of the Titanium Grade 7 plate. DOE stated that this 

approach is justified because the corrosion rates measured by electrochemical impedance 

spectroscopy and linear polarization resistance for the respective alloys in the 42-day tests were 

nominally identical (Andresen and Kim, 2006aa). DOE also referenced Schutz (2005aa), which 

showed that the corrosion rates of Titanium Grades 7 and 29 are similar when exposed in a 

chloride solution with pH greater than 1, as shown in BSC Figure 6-22[a] (2004as). 

In addition, DOE (2009cn) performed a sensitivity analysis using the TSPA model that 

compared the approach described in SAR Section 2.3.6.8.1.3 (in which the ratio for the 

corrosion rate of Titanium Grade 29 to that of Titanium Grade 7 was sampled from a probability 

distribution function with a value in the range of approximately one to seven) to the new 

approach, in which the corrosion rate of Titanium Grade 29 is assumed to be equivalent to that 

of Titanium Grade 7. The analysis revealed that the drip shield structural framework failure time 

occurred later for the new approach, as shown in DOE Enclosure 2, Figure 1 (2009cn). The 

analysis also showed that, in the event of a seismic ground motion, the new approach gives a 

median dose that is about 25 percent higher between 80,000 and 300,000 years after repository 

closure, due to increased probability of waste package damage, as shown in DOE Enclosure 2, 

Figure 3 (2009cn). DOE stated that the mean expected dose was nearly the same for the 

respective approaches because the contribution of the seismic ground motion modeling case to 

the total mean annual dose is small during this time period. Therefore, DOE concluded that the 

data presented in SAR Section 2.3.6.8.1 were usable in the TSPA calculation, because 

unquantified experimental uncertainties had a negligible effect on the results from the 

postclosure performance assessment calculation. Nevertheless, DOE stated it would update 

the SAR to incorporate the analysis provided in DOE (2009cn). 

Summary of NRC Staff’s Review for Corrosion of Titanium Grade 29 

The NRC staff reviewed the DOE approach to calculate the general corrosion rate of Titanium 

Grade 29. The DOE assumption of equivalent corrosion rates for Titanium Grades 7 and 29, 

respectively, is primarily based on corrosion testing of the alloys in simulated seepage water 

and deliquescent brines, including fluoride-bearing solutions. The corrosion rate for Titanium 

Grade 29 in the repository may depend on such factors as the pH and concentration of ionic 

species in water that contacts the drip shield. Therefore, the NRC staff reviewed the corrosion 

test solutions to determine whether they are adequate to model the Titanium Grade 29 

corrosion rate in repository conditions. The NRC staff notes that the DOE corrosion test 

solutions are more chemically aggressive than waters expected to occur within repository drifts, 

including starting water compositions in the DOE near-field chemistry model described in 

SAR Section 2.3.5.5, and waters considered in NRC staff’s independent analysis of in-drift 

water evolution, described in TER Section 2.2.1.3.3.3.2. As such, the solutions given in BSC 

Table 6-7[a] (2004as) may be used to calculate the general corrosion rate of Titanium Grade 29, 

because they are more chemically aggressive than waters expected to occur within the 

repository drifts. Additional support for the assumption of equivalent corrosion rates was 

provided by Schutz (2005aa), who showed the equivalent corrosion rate is observed even in 

acidic solutions. Finally, the NRC staff also observed nearly equivalent corrosion rates during 

testing of Titanium Grades 7 and 29 in 1 M NaCl and 4 M MgCl2 solutions at elevated 

temperatures (Mintz and He, 2009aa). The NRC staff recognizes that the assumption of 

equivalent corrosion rates would extend the drip shield framework lifetime compared to that 

calculated in the TSPA model, where the corrosion rate of Titanium Grade 29 was up to a factor 

of seven times faster than that for Titanium Grade 7. The NRC staff notes that the DOE 
4-13 

sensitivity analysis indicated that the TSPA calculations would not significantly differ when 

implementing the assumption of equivalent corrosion rates. 

Abstraction and Integration 

For the Nominal Modeling Case in the TSPA analysis, DOE implemented the model abstraction 

for general corrosion of the drip shield in the waste package and Drip Shield Degradation 

Submodel, as described in SNL Section 6.3.5.1 (2008ag), in which the drip shields were 

distributed in the five percolation subregions. The Waste Package and Drip Shield Degradation 

Submodel considers only general corrosion breach of the Titanium Grade 7 plates. DOE 

concluded that the drip shield would protect the waste package against seepage if the drip 

shield plates are intact, even if the drip shield supports collapsed and the sidewall buckled 

(SAR Section 2.3.4.5.3.1). For each realization, DOE sampled one general corrosion rate for 

the outside of the drip shield plates (under the aggressive condition) and one for the inside of 

the drip shield plates (under the benign condition) from the respective distributions given in 

SNL Table 6.3.5-3 (2008ag). The corrosion rates were applied to all drip shields, regardless 

of the percolation subregion, such that all drip shields in a given realization failed at the same 

time. The output of the Waste Package and Drip Shield Degradation Submodel was a fraction 

of that for drip shields in each percolation subregion breached by general corrosion as a 

function of time. This output was provided to the Waste Form Degradation and Mobilization 

Model Component and the Engineered Barrier System Flow and Engineered Barrier System 

Transport Submodels. 

SAR Figures 2.1-8 and 2.4-24 showed the distribution of calculated failure times for the 

Titanium Grade 7 drip shield plates in the Nominal Modeling Case, on the basis of the model 

described in SAR Section 2.3.6.8.1. DOE’s analyses calculated that most drip shield failures 

occur between 260,000 and 340,000 years after repository closure. DOE Enclosure 5, Figure 1 

(2009cn) showed a modified distribution of failure times considering both a higher corrosion rate 

(based on additional uncertainties associated with specimen cleaning) and lower corrosion rate 

(based on potential decrease in corrosion rate over time). The modified distribution shows that 

most drip shield plate failures occur between 80,000 and 500,000 years after repository closure. 

In either case, there is negligible probability of drip shield plate breach by nominal processes 

within 12,000 years after repository closure, the time period during which DOE calculates that 

the waste package is susceptible to localized corrosion if contacted by seepage water. 

For the Seismic Ground Motion Modeling Case in the TSPA analysis, DOE also implemented 

the Waste Package and Drip Shield Degradation Submodel to calculate the timing and 

magnitude of drip shield plate breach by general corrosion, as outlined in SNL Section 6.6.1 

(2008ag). Both the Titanium Grades 7 and 29 corrosion rates are sampled in this modeling 

case. The Titanium Grade 7 corrosion rate was sampled in the same manner as in the Nominal 

Modeling Case. For Titanium Grade 29 structural supports, DOE sampled the ratio of the 

corrosion rate of Titanium Grade 29 to that of Titanium Grade 7 once per realization from the 

discrete probability distribution function summarized in BSC Table 6-8[a] (2004as). The ratio 

was applied to all drip shields in a realization. SAR Figures 2.1-11 and 2.4-24 showed the 

distribution of failure times for the Titanium Grade 7 drip shield plates in the Seismic Ground 

Motion Modeling Case. Most plate failures occur between 100,000 and 300,000 years after 

repository closure. There is negligible probability of drip shield breach within 12,000 years after 

repository closure because the general corrosion rate of the Titanium Grade 7 drip shield plates 

is low and the likelihood of plate failure by a seismic event is negligible before that time period. 

For the Titanium Grade 29 structural supports, DOE calculated that most drip shield frameworks 

failed between 20,000 and 170,000 years after repository closure, using the model described in 
4-14 

SAR Section 2.3.6.8.1 and DOE Enclosure 2, Figure 1 (2009cn). For the alternative approach, 

in which DOE assumed equivalent corrosion rates for the structural supports and the plate, DOE 

calculated that most frameworks failed between about 80,000 and 170,000 years after 

repository closure, as shown in DOE Enclosure 2, Figure 1 (2009cn). 

Summary of NRC Staff’s Review for Abstraction and Integration of General Corrosion of 

the Drip Shield 

The NRC staff reviewed the implementation and integration of the model abstraction for general 

corrosion of the drip shield used in the postclosure performance assessment calculation. The 

NRC staff notes that DOE has provided information for the NRC staff to understand how the 

conceptual model is implemented in the TSPA code and how the model inputs and outputs are 

integrated with other model components. The NRC staff determined that DOE’s use of the 

model abstraction was consistent with the design features of the drip shield, including materials 

of construction and dimensions given in SAR Section 1.3.4.7. Further, with respect to the 

general corrosion rates, DOE justified the ranges of these parameters and accounted for 

uncertainty in the model abstraction. Therefore, DOE’s implementation of the drip shield 

general corrosion model abstraction in the TSPA code is reasonable because it would not 

underestimate the timing or magnitude of radionuclide release to the accessible environment. 

NRC Staff’s Summary of Conclusions for General Corrosion of the Drip Shield 

The NRC staff reviewed the DOE model abstraction for general corrosion of the drip shield that 

was implemented in the TSPA code. DOE used appropriate experimental tests and other 

independent technical literature to provide support for the model abstraction. Therefore, DOE 

reasonably accounted for general corrosion of the drip shield in the TSPA code. 

2.2.1.3.1.3.1.2 Drip Shield Early Failure 

In SAR Section 2.3.6.8.4, DOE described how it developed the probability distribution for 

early failure of the drip shield that was sampled in the TSPA code. DOE assumed that a 

drip shield underwent early failure if it was emplaced in the repository with an undetected 

manufacturing- or handling-induced defect. On the basis of the processes associated with 

drip shield manufacturing and handling, DOE concluded that the probability of a drip shield 

early failure is best represented in the TSPA by a lognormal distribution with a mean of 

2.21 × 10!6

 per drip shield and an error factor of 14, as shown in SNL Table 6-7 (2007aa). 

The NRC staff reviewed the adequacy of this probability distribution in TER Section 2.2.1.2.2.4. 

The implementation of this probability distribution is addressed in this section. 

Drip Shield Early Failure Conceptual Model 

In DOE’s conceptual model for early drip shield failure, a drip shield with an undetected 

manufacturing- or handling-induced defect completely fails (i.e., is removed as a barrier to the 

flow of water) at the time of repository closure (SAR Section 2.3.6.8.4.4.1). DOE selected this 

representation because there are uncertainties associated with the timing and extent of breach 

for defective drip shields and a completely degraded drip shield at the time of repository closure 

will not underestimate the timing and magnitude of radionuclide releases, as described in SNL 

Section 6.5.2 (2007aa). DOE concluded that this is a conservative representation of the early 

failed drip shield because the most likely consequence of improper drip shield manufacturing or 

handling would be stress corrosion cracking. DOE excluded drip shield stress corrosion 

cracking from the performance assessment because even if cracking occurred, the cracks 
4-15 

would not affect the drip shield performance, because advective flow through cracks in the drip 

shield is also excluded from the performance assessment (SAR Section 2.3.6.8.3). 

Summary of NRC Staff’s Review of Drip Shield Early Failure Conceptual Model 

The NRC staff reviewed DOE’s conceptual model for drip shield early failure, as described in 

SAR Section 2.3.6.8.4. The NRC staff notes that DOE attributed no barrier capability to the 

early failed drip shield. However, consequences of manufacturing- or handling-induced defects 

(e.g., increased probability of stress corrosion cracking) would likely allow the drip shield to 

maintain some barrier capability, which limits radionuclide releases. Because early failed drip 

shields in the DOE model are assumed to have no barrier capability, the NRC staff notes that 

the model will not cause DOE to underestimate the timing or magnitude of radionuclide 

releases. Therefore, the DOE conceptual model drip shield early failure is reasonable. 

Abstraction and Integration 

The model abstraction for early failure of the drip shield was implemented in the 

TSPA calculation in the Drip Shield Early Failure Modeling Case, as described in SAR 

Section 2.4.2.1.5.2 and SNL Section 6.4.1 (2008ag). This modeling case uses most of the 

same modeling components and submodels as were implemented in the Nominal Modeling 

Case. In the Nominal Modeling Case, however, the Waste Package and Drip Shield 

Degradation Submodel calculates the waste package and drip shield breached areas as a 

function of time and passes this to the Engineered Barrier System Flow and Transport 

Submodels and the Waste Form Degradation and Mobilization Model Components. In the Drip 

Shield Early Failure Modeling Case, the Waste Package and Drip Shield Degradation Submodel 

was replaced with the drip shield early failure mode, which simulated early failure by removing a 

selected drip shield as a barrier to seepage at the time of repository closure. 

In the Drip Shield Early Failure Modeling Case, the underlying waste package immediately 

experienced initiation of localized corrosion if the early failed drip shield was in seepage 

conditions. If the early failed drip shield was not in seepage conditions, the underlying waste 

package did not experience initiation of localized corrosion. In the TSPA model, DOE 

calculated the dose consequence of a drip shield early failure in each of the five percolation 

subregions for both commercial spent nuclear fuel (CSNF)-type and codisposal (CDSP)-type 

waste packages. DOE then calculated the expected dose using the early failure probability 

[sampled from the distribution given in SNL Table 7-1 (2007aa)], the distribution for the waste 

package type, and the seepage fraction for each percolation bin. 

DOE calculated that there is approximately 98.3 percent probability of no drip shield early 

failures, approximately 1.6 percent probability of one drip shield early failure, and 

approximately 0.1 percent probability of two or more drip shield early failures, as shown in 

SNL Table 6.4-1 (2008ag). Using the TSPA model, DOE calculated that drip shield early 

failure has a negligibly small contribution to the calculated mean annual dose during the first 

10,000 years following closure {less than 10-5 mSv [0.001 mrem]}, with a declining contribution 

thereafter (SAR Figure 2.4-18). 

Summary of NRC Staff’s Review for Abstraction and Integration of the Drip Shield Early 

Failure Model 

The NRC staff reviewed the implementation of the drip shield early failure model in the TSPA 

calculation, as described in SNL Section 6.4.1 (2008ag). DOE has provided information for the 
4-16 

NRC staff to understand how the conceptual model is implemented in the TSPA code and how 

the model inputs and outputs are integrated with other model components. The NRC staff 

determined that the model abstraction was consistent with the design features of the drip shield, 

including materials of construction and dimensions given in SAR Section 1.3.4.7. Further, DOE 

justified the ranges of parameters and accounted for uncertainty in the model abstraction. 

Therefore, DOE’s implementation of the drip shield early failure model abstraction in the TSPA 

analysis is reasonable because it will not underestimate the timing or magnitude of radionuclide 

release to the accessible environment. 

NRC Summary of Conclusions for Drip Shield Early Failure 

The NRC staff reviewed the DOE model abstraction for early failure of the drip shield that 

was implemented in the TSPA code. The NRC staff notes that DOE reasonably accounted for 

drip shield early failure in the Total System Performance Assessment (TSPA). 

2.2.1.3.1.3.2 Waste Package Degradation 

In SAR Section 1.5.2, DOE stated that waste packages are relied upon to limit water contacting 

the waste form and to prevent the mobilization of radionuclides. The waste package will have 

an outer barrier that is fabricated from a material that is expected to be corrosion resistant in the 

range of environmental conditions expected in the repository (SAR Section 2.3.6.1). In 

particular, the waste package outer barrier will be fabricated from Alloy 22 (UNS N06022), which 

is a nickel-chromium-molybdenum alloy. 

In developing the postclosure performance assessment, DOE evaluated a number of features, 

events, and processes in SAR Table 2.2-5 related to chemical degradation of the waste 

package. These features, events, and processes include 

 General corrosion of waste packages [features, events, and processes 

(FEP) 2.1.03.01.0A] 

 Stress corrosion cracking of waste packages (FEP 2.1.03.02.0A) 

 Localized corrosion of waste packages (FEP 2.1.03.03.0A) 

 Hydride cracking of waste packages (FEP 2.1.03.04.0A) 

 Microbially influenced corrosion (MIC) of waste packages (FEP 2.1.03.05.0A) 

 Internal corrosion of waste packages prior to breach (FEP 2.1.03.06.0A) 

 Early failure of waste packages (FEP 2.1.03.08.0A) 

 Creep of metallic materials in waste packages (FEP 2.1.07.05.0A) 

 Localized corrosion on waste packages outer surface due to deliquescence 

(FEP 2.1.09.28.0A) 

 Thermal sensitization of waste packages (FEP 2.1.11.06.0A) 
4-17 

DOE included general corrosion, stress corrosion cracking, localized corrosion, MIC, and 

early failure in the postclosure performance assessment. The other features, events, and 

processes were screened from the performance assessment on the basis of low consequence 

or low probability (SAR Table 2.2-5). The NRC staff’s evaluation of DOE’s bases for excluding 

these features, events, and processes from the performance assessment is found in TER 

Section 2.2.1.2.1.3. 

In the TSPA analysis, DOE calculated that, due to its corrosion resistance, the waste package 

will significantly reduce the amount of water contacting the waste form for hundreds of 

thousands of years after repository closure (SAR Section 2.1.2.2.6). Because of the importance 

of the waste package in the postclosure performance assessment, the NRC staff reviewed the 

DOE model abstractions for waste package chemical degradation. In the context of these 

reviews, the NRC staff recognized that DOE attributed the high corrosion resistance of Alloy 22, 

in part, to the presence of its passive film. In the event of deterioration or loss of waste package 

passivity, the time to waste package breach may be sooner and the size of the breached area 

may be larger than DOE calculated in the TSPA code. As such, DOE stated that long-term 

persistence of the passive film on Alloy 22 is one of the key issues that it considered to 

determine the long-term performance of the waste package in the repository, as described in 

SNL Section 6.4.1.1 (2007al). In NRC Appendix D, Section 4.3.1 (2005aa), NRC also identified 

the long-term persistence of the passive film on the waste package outer barrier as being of 

high significance to risk for waste isolation. 

Therefore, the following presents the NRC staff’s first reviews DOE’s approach to support its 

assessment of Alloy 22 passive film stability under the repository conditions. The NRC staff 

then conducts a detailed review of DOE’s model abstractions for chemical degradation of the 

waste package, including general corrosion, microbially influenced corrosion (MIC), localized 

corrosion, stress corrosion cracking, and early failure. 

Passivity of Alloy 22 

In SAR Section 2.3.6.3.1 and SNL (2007al), DOE indicated that the stability of the Alloy 22 

passive film depends primarily upon its physical and chemical properties, including 

microstructure, composition, and thickness. On Alloy 22 corrosion specimens, DOE 

investigated these passive film properties with various surface analytic techniques, including 

Auger electron spectroscopy, transmission electron microscopy, x-ray photoelectron 

spectroscopy, and electron energy loss spectroscopy (Orme, 2005aa). DOE performed 

short-term polarization tests, exposing Alloy 22 samples at 90 °C [194 °F] to solutions with a 

range of chemical compositions that DOE assumed were similar to, or more aggressive than, 

those expected in the repository (Orme, 2005aa). The solutions used in short-term polarization 

tests were either buffered 1 M NaCl solutions or multi-ionic solutions, including simulated 

acidified water, simulated concentrated water, and basic saturated water (compositions given in 

SAR Table 2.3.6-1). To assess the long-term passive film behavior, DOE examined 5-year 

U-bend samples of Alloy 22 exposed to simulated acidified water, simulated concentrated water, 

and simulated dilute water at 90 °C [194 °F] (Orme, 2005aa). 

For both short- and long-term tests, DOE observed a thin, adherent passive oxide film on 

the surface of Alloy 22 corrosion specimens. The film typically had thickness in the range of 

2 to 7 nm [7.87 × 10-8 to 2.76 × 10-7 in] and tended to be rich in chromium (III) oxides (Cr2O3

and/or NiCr2O4). In the solutions of acidic and near-neutral pH, a thick outer layer was also 

observed on the top of the inner chromium-rich oxide layer (Orme, 2005aa). The outer 

layer was porous and consisted mostly of nickel oxide and the oxides of some other alloying 
4-18 

elements, including iron, tungsten, and molybdenum. In basic saturated water (pH ~12–13), 

DOE observed a thick silica deposit on Alloy 22 specimens (Orme, 2005aa), which DOE 

concluded arose from dissolution of test cell glassware or precipitation of silica from the 

test solution. In the case of 5-year U-bend samples exposed to simulated acidified water, 

simulated concentrated water, and simulated dilute water, all of the immersed samples had 

100 to 5,000-nm [3.94 × 10-6 to 1.97 × 10-4-in]-thick carbon and iron deposits on their 

surfaces. DOE determined the deposits are formed as leachates from either the walls of the 

test tanks or other metals in the tanks (Orme, 2005aa). Oil from the mill processing is also 

considered to be included in the deposits. DOE stated that, underneath these deposits, the 

passive film was still close to 5 nm [1.97 × 10-7 in] thick after 5 years’ exposure. The presence 

of chromium-rich oxide passive film on the Alloy 22 surface was also observed at high 

temperatures {in the range of 120 to 220 °C [248 to 428 °F} in NaCl–NaNO3–KNO3 solutions 

(Orme, 2005aa; Dixit, et al., 2006aa). 

To support the assessment of long-term passive film stability, DOE performed thermodynamic 

modeling with the EQ3/6 program (Orme, 2005aa). DOE concluded that this showed 

that chromium-rich oxides are stable on Alloy 22, which is consistent with empirical observation 

of passive film chemistry. Although the tests that DOE used to characterize the passive film 

of Alloy 22 were for a period of, at most, 5 years, DOE referenced the point defect film growth 

model, which states that the passive film on Alloy 22 will maintain steady-state thickness 

as the porous outer layer dissolves and the compact chromium-rich oxide inner layer is 

continuously regenerated. 

After the SAR was submitted, DOE examined some 5- and 9.5-year Alloy 22 specimens from 

the Long-Term Corrosion Test Facility (SNL, 2009aa,ab). DOE identified thick organic deposits 

on some specimens. DOE responded to the NRC staff’s request for additional information on 

evaluation of the effects of the carbon deposits on DOE’s assessment of long-term passivity and 

corrosion behavior (DOE, 2009cl, 2010ae). In its response, DOE stated that the organic 

deposits observed on the Alloy 22 specimens most likely originated from lubricant or grease 

from mechanical equipment in the corrosion test facility (DOE, 2009cl, 2010ae). DOE did not 

identify evidence of either increased general corrosion rate or localized corrosion attack on the 

specimens. For these specimens, DOE measured a corrosion rate of 3 to 5 nm/yr [1.18 × 10-7

to 1.97 × 10-7 in] in simulated concentrated water at 60 °C [140 °F] after 9.5 years. DOE 

determined that this corrosion rate was consistent with that of uncontaminated specimens, as 

well as the waste package corrosion rate used in the Total System Performance Assessment 

(TSPA) model. 

Therefore, DOE concluded that the organic deposits did not affect the assessment of long-term 

passivity or corrosion behavior. 

Summary of NRC Staff’s Review for Passivity of Alloy 22 

The NRC staff reviewed the DOE approach to establish the stability of the waste package 

passive film in repository conditions. The NRC staff determined that DOE’s assumption that 

the passive film will remain stable during the postclosure period is based, in part, on tests of 

Alloy 22 specimens in a range of corrosion test solutions, including simulated acidified water, 

simulated concentrated water, simulated dilute water, and basic simulated water. Stability of the 

waste package passive film may depend on such factors as the pH and concentration of ionic 

species in water that contacts the waste package. Therefore, the NRC staff reviewed the 

corrosion test solutions to determine whether they are adequate in assessing waste package 

passive film stability in repository conditions. The NRC staff notes that the corrosion test 
4-19 

solutions are more chemically aggressive than waters expected to occur within repository drifts, 

including starting water compositions in the DOE near-field chemistry model described in SAR 

Section 2.3.5.5 and waters considered in NRC staff’s independent analysis of in-drift water 

evolution, described in TER Section 2.2.1.3.3.3.2. Therefore, the DOE observations of Alloy 22 

passive film stability in simulated acidified water, simulated concentrated water, simulated dilute 

water, and basic simulated water support the DOE assumption of waste package passive film 

stability in repository conditions. Further, the NRC staff (Dunn, et al., 2005aa) and others 

(Lloyd, et al., 2003aa, 2004aa; Gray, et al., 2006aa; Montemor, et al., 2003aa; Hur and Park, 

2006aa; Mintz and Devine, 2004aa) reported similar descriptions of the passive film for Alloy 22 

and analogous nickel-based alloys (e.g., Alloy C-4, C276, 600, 625, and 690). 

In regard to the organic deposits on the corrosion specimens, the NRC staff reviewed the DOE 

reports (SNL, 2009aa,ab) and determined that the presence of the deposits did not significantly 

affect the measured corrosion rates, given the range of uncertainty for such measurements, nor 

was there evidence of localized corrosion (e.g., pitting) on the specimens. The NRC staff notes 

that DOE conducted a reasonable analysis to show that organic deposits are not likely to affect 

anodic and cathodic reactions on the Alloy 22 surface. Therefore, the carbon deposits on some 

Alloy 22 specimens did not affect the assessment of the passive film stability. 

The NRC staff also reviewed DOE’s use of thermodynamic modeling and the point defect film 

growth model to support the assessment of the waste package passive film stability. The NRC 

staff conducted an independent analyses and obtained similar results as DOE for temperatures 

up to 180 °C [356 °F] (Pensado, et al., 2002aa; Jung, et al., 2008aa). With respect to 

application of the point defect model in DOE’s assessment of passive film stability, DOE’s test 

results (Orme, 2005aa) indicate that a passive film with steady-state thickness would provide a 

nearly constant corrosion rate over time at a particular temperature, potential, and pH. The 

NRC staff notes that the results from the Miserque, et al. (2006aa) study confirm the suitability 

of the DOE approach for using a point defect film growth model to support passive film stability. 

Although the SAR contained information regarding waste package passive film stability in 

repository conditions, the NRC staff identified three primary technical issues and requested 

additional information from DOE. These issues involved passive film degradation by (i) anodic 

sulfur segregation, (ii) dripping seepage water, and (iii) silica deposits on the waste package. 

The NRC staff’s reviews of these technical issues are presented next. 

Effect of Anodic Sulfur Segregation on Passive Film Stability 

By independent analyses and review of the technical literature, the NRC staff identified anodic 

sulfur segregation as a potential mechanism that could compromise the long-term stability of the 

passive film on the waste package outer barrier (NRC, 2005aa; U.S. Nuclear Waste Technical 

Review Board, 2001aa). Anodic sulfur segregation is a process that reduces the corrosion 

resistance of nickel and nickel-iron alloys by inhibiting the formation of the passive film 

(Marcus and Talah, 1989aa; Marcus, et al., 1988aa, 1984aa,ab, 1980aa). During anodic sulfur 

segregation, sulfur, which may be an impurity in Alloy 22, segregates to the metal-passive film 

interface because of selective dissolution of bulk metal elements such as nickel and iron. When 

the amount of sulfur at the metal-passive film interface reaches a critical concentration of about 

one atomic layer thickness, passive film breakdown has been observed (Marcus and Grimal, 

1990aa). Assuming that 100 percent of the sulfur atoms in the alloy are retained at the 

metal-film interface, Marcus (2001aa) estimated that it would take about 900 years for the 

passive film of Alloy 22 to break down if the sulfur content in Alloy 22 is 5 weight parts per 

million (ppm) and the passive current density is 1 nA/cm2 [6.45 nA/in2

]. 
4-20 

In DOE Enclosure 5 (2009cl), DOE stated that the potential for anodic sulfur segregation would 

be mitigated by the presence of the alloying elements chromium and molybdenum in Alloy 22. 

In particular, molybdenum would bond with sulfur to form a molybdenum sulfide that dissolves 

under aqueous conditions, thus preventing a stable sulfur monolayer from forming at the 

alloy-passive film interface. Citing the work of NRC (Jung, et al., 2007aa), DOE also stated that 

chromium oxides are thermodynamically stable compared with sulfides. Thus the presence of 

chromium will promote passivation in spite of adsorbed sulfur. 

Summary of NRC Staff’s Review for the Effect of Anodic Sulfur Segregation on Passive 

Film Stability 

The NRC staff reviewed the DOE assessment of anodic sulfur segregation of the waste 

package outer barrier. The effects of chromium and molybdenum on sulfur segregation were 

not considered in the time to passive film breakdown estimated in Marcus (2001aa). Further, 

the NRC staff determined that reports of passive film breakdown by anodic sulfur 

segregation are primarily associated with iron and iron-nickel alloys that lack the alloying 

elements molybdenum and chromium. The presence of the alloying elements molybdenum and 

chromium has been shown to prevent passive film breakdown by anodic sulfur segregation in 

materials similar to Alloy 22. Literature reports (Costa and Marcus, 1993aa; Marcus and Grimal, 

1990aa) indicate that chromium in Ni-xCr-10Fe alloys (x = 8, 19, and 34 at%) counteracted the 

detrimental effects of sulfur and promoted alloy passivation. Further, it was reported that 

preadsorbed sulfur monolayers on the surface of Ni-2~6Mo and Fe-17Cr-14.5Ni-2.3Mo 

stainless steel, respectively, were removed in the form of soluble molybdenum sulfides during 

alloy dissolution (Marcus and Moscatelli, 1989aa; Elibiache and Marcus, 1992aa). Moreover, 

Alloy 625 (Ni-21.5Cr-9Mo-4Fe) was resistant to localized creviced corrosion in 1 M NaCl 

solution containing 0.01 M Na2S2O3 up to 80 °C [176 °F], whereas Alloy 600 (Ni-15.5Cr-8Fe) 

with no molybdenum was attacked by crevice corrosion in the same solution at 20 °C [68 °F] 

(Mulford and Tromans, 1988aa). 

The NRC staff also determined that the information DOE provided is consistent with 

independent investigations NRC conducted (Ahn, et al., 2008aa; Jung, et al., 2007aa, 2008aa). 

In particular, NRC performed electrochemically accelerated dissolution tests in aggressive 

solutions to assess whether sulfur would segregate to the surface of Alloy 22 during anodic 

dissolution. Surface analysis showed that almost all sulfur on the surface dissolved during the 

tests, such that a critical sulfur concentration for passive film breakdown was not reached. 

Scratch repassivation tests of Alloy 22 in sulfide-containing solutions showed that even if 

passive-film breakdown occurred, the material repassivated within a few seconds, which would 

limit the potential for corrosion degradation (Jung, et al., 2008aa). These experimental results 

also are supported by thermodynamic calculations that calculate a formation of stable chromium 

oxide (Cr2O3) and possible soluble molybdenum sulfide (MoS2) in the range of temperatures, 

potentials, and pH expected for repository conditions (Jung, et al., 2007aa, 2008aa). 

The NRC staff therefore notes that in the aerated oxic environment expected in the repository, 

sulfur that accumulates on the waste package surface will most likely be removed by dissolution 

in either the reduced form or as soluble molybdenum sulfides. These processes are expected 

to mitigate the potential for anodic sulfur segregation to affect the long-term stability of passive 

films on Alloy 22. The NRC staff notes that DOE has provided information to show that the 

potential effects of sulfur segregation do not need to be considered in the DOE model for 

passive film stability on the waste package outer barrier. 
4-21 

Dripping Effects on the Passive Film Stability 

DOE used corrosion data from immersion experiments to develop the model abstraction for 

general corrosion of the waste package outer barrier (SAR Section 2.3.6.3.2). DOE identified 

the possibility, however, that conditions in the repository may lead to dripping seepage water 

contacting the waste package surface (SAR Section 2.1.2.2.6). The NRC staff determined that 

literature information in Ashida, et al. (2007aa, 2008aa) and Oka, et al. (2007aa) suggests that 

general corrosion processes may be affected if environmental conditions changed from 

immersion to dripping. Ashida, et al. (2008aa) observed salt deposit formation and localized 

corrosion (i.e., pitting and intergranular corrosion) on Alloy 22 specimens exposed to dripping of 

simulated concentrated water for 40 days at 90 °C [194 °F]. The micropits observed were, 

however, not stable, and there was no evidence for propagation of these micropits. Ashida, 

et al. (2007aa) also reported an increase of the passive current density of Alloy 22 due to 

dripping induced temperature fluctuations at 90 °C [194 °F]. 

In DOE Enclosure 1 (2009cm), DOE assessed the corrosion behavior of Alloy 22 under dripping 

conditions in the repository environment. DOE stated that the Alloy 22 sample tested in Ashida, 

et al. (2008aa) was thermally aged, resulting in a significant second phase precipitation. 

This precipitate can decrease a resistance to localized corrosion. DOE stated that the Alloy 22 

for the waste package outer barrier will be solution annealed, eliminating the second phase 

precipitates. Therefore, DOE concluded the material Ashida, et al. (2008aa) evaluated was not 

relevant for the waste package. In the case of the change in passive current density due to 

temperature fluctuation as shown in the polarization test in Ashida, et al. (2007aa), DOE stated 

that the current TSPA model of general corrosion of Alloy 22 considers the change of general 

corrosion rate depending on the temperature (see TER Section 2.2.1.3.1.3.2.1); therefore, the 

increase of corrosion rate observed in Ashida, et al. (2007aa) is consistent with the TSPA 

general corrosion model. 

Summary of NRC Staff’s Review for Dripping Effects on the Passive Film Stability 

The NRC staff reviewed the information provided in DOE Enclosure 1 (2009cm). The NRC staff 

notes that solution annealing of the waste package will eliminate the second phase particles that 

decreased localized corrosion resistance in the specimens tested by Ashida, et al. (2008aa). 

Nevertheless, the NRC staff determined that the second phase may not be eliminated in the 

region of the waste package closure weld, because this weld will not be solution annealed 

(SAR Section 1.5.2.7). Although Ashida, et al. (2008aa) observed micropits in such 

material conditions, the same report shows no evidence for stable pit propagation (Ashida, et 

al., 2008aa). Moreover, NRC’s independent analyses (Dunn, et al., 2006ab) identified no 

localized corrosion (e.g., pitting or intergranular corrosion) of the mill-annealed Alloy 22 after 

dripping simulated pore waters onto the Alloy 22 specimens at 110 °C [230 °F] for 10 days. 

Therefore, micropitting of the waste package closure weld is unlikely to have a significant effect 

on the waste package barrier capability. 

Further, the NRC staff noted that the temperature of the water used in the dripping tests in 

Ashida, et al. (2007aa) was close to room temperature. This resulted in a relatively large 

difference in the temperature between the room temperature of the dropped water and the hot 

surface of the Alloy 22 test specimen {i.e., 90 °C [194 °F]}, thereby contributing to the observed 

increase in passive current due to temperature fluctuation. Such temperature fluctuations will 

be much smaller for the waste package in the repository because of a relatively smaller 

difference in temperature between the waste package and the drift wall (Jung, 2010aa). The 
4-22 

NRC staff notes that this small temperature fluctuation is not likely to have a significant effect on 

the waste package general corrosion rate. 

The NRC staff notes that DOE has provided information to show that dripping conditions in the 

repository will not affect the assessment of the waste package passive film stability. 

Effect of Silica Deposits on Alloy 22 Passivity 

DOE data and information in the technical literature indicate that silica deposits on Alloy 22 may 

affect the passive film. In basic simulated water (pH ~12 to 13), DOE observed a thick silica 

deposit on Alloy 22 specimens (Orme, 2005aa), which DOE concluded arose from dissolution of 

test cell glassware or precipitation of silica from the test solution. DOE also noted the presence 

of silica in salt deposits on Alloy 22 specimens exposed to simulated dilute water, simulated 

acidified water, and simulated concentrated water in the Long-Term Corrosion Test Facility as 

shown in Wong, et al., Table 4, Figures 2 and 3 (2004aa). In another experiment (Dixit, et al., 

2006aa) DOE observed silica deposits on Alloy 22 specimens that experienced localized 

corrosion in a deaerated concentrated solution at 220 °C [428 °F]. Finally, information in 

Sala, et al. (1993aa, 1996aa, 1998aa, 1999aa) indicated that the presence of silica deposits 

can be associated with intergranular attack and stress corrosion cracking in nickel-based alloys 

in steam generator environments. 

In DOE Enclosure 4 (2009cl) and DOE Enclosure 2 (2009cm), DOE stated that the presence 

of silicate did not significantly impact the corrosion potential and corrosion rate of Alloy 22 

for tests conducted in simulated acidified water and NaCl solutions. DOE also presented 

experimental data (Andresen and Kim, 2007aa) for Alloy 22 tests in solutions of nitrate, 

chloride, and bicarbonate with 0.27 m silicate. The tests showed that the general corrosion 

rate of Alloy 22 was 3 to 4 nm/yr [1.18 × 10-7 to 1.57 × 10-7 in/yr] after 62 months’ immersion at 

95 °C [203 °F], which is close to the measured corrosion rate in solutions without silicate. 

Finally, DOE stated that Sala, et al. (1993aa, 1996aa, 1998aa, 1999aa) and Dixit, et al. 

(2006aa) considered more aggressive environmental conditions than those expected in the 

repository. Therefore, DOE concluded that the observations are not relevant to the waste 

package in the repository. 

Summary of NRC Staff’s Review for Effect of Silica Deposits on Alloy 22 Passivity 

The NRC staff reviewed DOE’s approach to assess the effects of silica deposits on the waste 

package passive film stability. On the basis of the information DOE provided (e.g., Andresen 

and Kim, 2007aa), the NRC staff notes that silica deposits do not adversely impact Alloy 22 

passivity, because there is not a significant difference in measured corrosion rates for 

specimens with and without silica deposits. Moreover, Sala, et al. (1993aa, 1996aa, 1998aa, 

1999aa) and Dixit, et al. (2006aa) considered more aggressive environmental conditions than 

those expected in the repository. 

DOE has provided information in SAR Section 2.3.6.3.1 and in responses to the NRC staff’s 

request for additional information to support the concept that the waste package passive film will 

be stable during the postclosure period. 

2.2.1.3.1.3.2.1 General Corrosion of the Waste Package Outer Barrier 

In SAR Section 2.3.6.3, DOE defined general corrosion of the waste package outer barrier as 

uniform thinning by electrochemical processes at its corrosion potential. General corrosion 
4-23 

could lead to the release of radionuclides from the waste package if the waste package wall is 

breached. General corrosion thinning may also make the waste package more susceptible to 

degradation processes such as stress corrosion cracking (SAR Section 2.3.6.5) or impacts 

caused by seismic ground motion (SAR Section 2.3.4.5). This section of the TER includes the 

NRC staff’s review of the DOE model abstraction for general corrosion of the waste package 

outer barrier. 

Waste Package General Corrosion Conceptual Model 

In DOE’s conceptual model for general corrosion of the waste package outer barrier, general 

corrosion starts at the time of repository closure (SAR Section 2.3.6.2.2). DOE assumed 

aqueous conditions because wet conditions give higher corrosion rates than dry conditions 

(SAR Section 2.3.6.3). In DOE’s model, the general corrosion rate is a function of the waste 

package temperature and, at a given temperature, it is assumed to be constant over time (SAR 

Section 2.3.6.3.1). DOE used an Arrhenius-type equation (SAR Eq. 2.3.6-3) to calculate the 

temperature-dependent general corrosion rate of the waste package outer barrier in the 

temperature range of 25 to 200 °C [77 to 392 °F]. DOE also considered using a decreasing 

general corrosion rate over time as an alternative conceptual model (SNL, 2007al) but 

concluded that this would calculate a longer time to waste package failure. 

DOE also considered that microbial activity in the repository could affect the waste 

package corrosion behavior—a phenomenon called microbially influenced corrosion (MIC) 

(SAR Section 2.3.6.3.3.2). DOE stated that microorganisms can change the electrochemical 

reactions on the material surface and change the type or degree of corrosion compared to that 

which would be measured in the absence of microorganisms. For example, microbially 

influenced corrosion can enhance the general corrosion rate of Alloy 22. In the DOE conceptual 

model, the waste package outer barrier is subject to microbially influenced corrosion when the 

relative humidity is sufficiently high for microbial activities. The effect of microbially influenced 

corrosion on the general corrosion rate is quantified by a unitless scalar called the microbially 

influenced corrosion enhancement factor. If the relative humidity is sufficiently high, the general 

corrosion rate in the absence of the microorganisms (SAR Eq. 2.3.6-3) is multiplied by the 

microbially influenced corrosion enhancement factor to give the enhanced general corrosion 

rate (SAR Eq. 2.3.6-4). 

Summary of NRC Staff’s Review for Waste Package General Corrosion Conceptual Model 

The NRC staff reviewed the DOE conceptual model for general corrosion of the waste 

package outer barrier. The NRC staff reviewed the DOE model assumption that the 

temperature dependence of the general corrosion rate can be quantified with the Arrhenius-type 

equation. Corrosion involves chemical and/or electrochemical reactions and the transport 

of reacting species and ions on the metal surface—a process known to be thermally activated 

(Fontana and Greene, 1978aa). Further, the Arrhenius relationship is commonly used to 

characterize the temperature dependence of thermally activated processes (ASM 

International, 1987aa) and has frequently been used to describe the temperature dependence 

of the general corrosion rate (e.g., Pensado, et al., 2002aa; Dunn, et al., 2005aa; Lloyd, et al., 

2003aa; Hua and Gordon, 2004aa). As such, DOE’s use of SAR Eq. 2.3.6-3 to derive at the 

temperature-dependent general corrosion rate for the waste package outer barrier 

is reasonable. 

The NRC staff also reviewed the DOE model assumption that the corrosion rate is constant over 

time at a given temperature. DOE provided experimental data showing that the measured 
4-24 

general corrosion rate of Alloy 22 decreases over time at a given temperature for experiments 

up to 5 years in duration (SAR Figure 2.3.6-13). This decrease of the corrosion rate with time is 

also observed in other Alloy 22 corrosion tests (Hua and Gordon, 2004aa; Evans, et al., 

2005aa,ab), including independent tests NRC performed (Dunn, et al., 2005aa). The NRC staff 

also reviewed DOE’s alternative conceptual model where the temperature was realistically 

decreased as a function of time. The NRC staff notes that this alternative model would result in 

a longer time to waste package failure compared to DOE’s primary conceptual model. 

Therefore, the use of a constant corrosion rate in the TSPA calculation is reasonable because it 

would not underestimate the time to waste package breach by general corrosion. 

Finally, the NRC staff reviewed the DOE model assumption that microbially influenced corrosion 

will occur above a relative humidity threshold value. The NRC staff determined that, although 

adequate water supply may be a critical requirement for microbial growth in the repository, other 

factors may limit microbial growth even if there is sufficient water. As detailed in SNL Section 

6.4 (2004ab) and Amy, et al. (2002aa), DOE’s conceptual model did not take credit for a 

number of these factors, including 

 Waste package temperatures may be too high to support microbial growth 

 The seepage water brine’s ionic strength may be too high to support microbial growth 

 Nutrient supplies may be inadequate to support microbial growth 

 The oxic environment in the repository may inhibit microbially influenced corrosion 

caused by sulfate- or nitrate- reducing microbes 

DOE’s use of a threshold relative humidity value for the onset of microbially influenced 

corrosion is reasonable because this will not underestimate the probability of microbially 

influenced corrosion. 

General Corrosion Rate by Long-Term Weight-Loss Measurements 

In SAR Eq. 2.3.6-3, DOE established the general corrosion rate at the baseline temperature of 

60 °C [140 °F] from 5-year weight-loss experiments (SAR Section 2.3.6.3.2.1). DOE performed 

corrosion tests in the Long-Term Corrosion Test Facility at 60 and 90 °C [140 and 194 °F], using 

Alloy 22 specimens with two different geometries: weight-loss specimens and crevice 

specimens. For both specimen types, tests were performed on specimens with different 

metallurgical conditions (i.e., mill annealed and as-welded) and in different corrosion test 

solutions, including simulated acidified water, simulated concentrated water, and simulated 

dilute water. After 5 years of exposure to the test solutions, every specimen was covered with 

surface deposits. Therefore, the posttest specimens were cleaned and descaled in accordance 

with ASTM G 1-90 (ASTM International, 1999aa). DOE stated that the cleaning methods used 

to remove the scale from the tested samples did not significantly affect untested control 

samples. Therefore, without correction of any possible mass loss from the replicate untested 

control foil sample, DOE determined the general corrosion rates of Alloy 22 based upon the 

formula defined in ASTM G 1-90. 

DOE summarized the results of its corrosion tests in SNL Section 6.4.3.2 (2007al). 

DOE stated that there was no appreciable difference between the general corrosion 

rates for mill-annealed and as-welded specimens, as shown in SNL Figures 6-14 and 
4-25 

6-19 (2007al). However, the measured corrosion rates for crevice specimens were 

higher than those for weight-loss specimens, as shown in SNL Figure 6-22 (2007al). 

For the weight-loss specimens, DOE determined that the mean general corrosion rate 

was 3.15 nm/yr [1.24 × 10-7 in/yr], with the ±1 standard deviation of 2.71 nm/yr [1.07 × 10-7 in/yr]. 

For the crevice specimens, DOE calculated a mean general corrosion rate of 7.36 nm/yr 

[2.90 × 10-7 in/yr] with ±1 standard deviation of 4.93 nm/yr [1.94 × 10-7 in/yr]. Because the 

crevice specimens tend to give higher corrosion rates, DOE only used the crevice data to 

develop the distribution from which the 60 °C [140 °F] general corrosion rate parameter was 

sampled in the TSPA code. 

DOE determined that uncertainty and variability in the measured corrosion rate could be 

attributed both to measurement uncertainty, given the very small weight loss associated with 

low corrosion rates, and to actual variation in the corrosion processes on the material surface, 

as outlined in SNL Section 6.4.3.3 (2007al). In the model abstraction, DOE accounted for this 

uncertainty by fitting the 5-year corrosion data to the Weibull cumulative distribution functions, 

which are sampled in the Total System Performance Assessment (TSPA) code, as detailed in 

SNL Section 6.4.3.3.2 (2007al). DOE stated that the Weibull distribution was determined to be 

the best fit to the experimental data as compared to other fits such as uniform distribution, 

normal distribution, lognormal distribution, and gamma distribution. DOE characterized the 

Weibull distribution with two parameters: the scale factor and the shape factor. DOE used 

three different scale factor/shape factor pairs, corresponding to low, medium, and high 

uncertainty levels, to define three different Weibull distributions for the 60 °C [140 °F] general 

corrosion rate parameter, as shown in SNL Table 6-7 (2007al). In the TSPA code, the low, 

medium, and high general corrosion rate distributions were sampled such that the low and high 

distributions were each used for 5 percent of the realizations and the medium distribution was 

used for 90 percent of the realizations. DOE used the 5–90–5 percent partitioning to ensure 

that there were sufficient differences in the general corrosion rates for the respective 

distributions, yet that each distribution was amply sampled to produce a meaningful contribution 

(SAR Section 2.3.6.3.3.1). The Weibull distributions from which the 60 °C [140 °F] general 

corrosion rate of the waste package outer barrier is sampled in the TSPA code are shown in 

SAR Figure 2.3.6-9. 

In response to the NRC staff’s request for additional information that requested justification for 

the representation of uncertainties associated with cleaning the long-term corrosion specimens, 

DOE responded in DOE Enclosure 3 (2009cl) that the specimens were not adequately cleaned 

prior to performing weight-loss measurements. In particular, DOE determined that the initial 

weight of the specimens was artificially high because of the failure to remove mill-annealed 

oxide and surface contamination. This oxide and surface contamination, however, were 

removed during posttest cleaning. Nevertheless, DOE assumed that the associated weight loss 

was attributable to the general corrosion of Alloy 22. Thus, DOE concluded that it 

overestimated the actual weight loss of Alloy 22 and, in turn, overestimated the general 

corrosion rate. DOE stated that it is in the process of recleaning and reanalyzing the 

specimens following the procedures in ASTM G 1-03 (ASTM International, 2003ab). DOE 

provided preliminary data from the reanalysis for the weight-loss specimens in DOE Enclosure 3 

(2009cl) and stated that those data gave the most accurate estimate for the general 

corrosion rate of Alloy 22. As shown in DOE Enclosure 3, Figure 8 (2009cl), the corrosion 

rate from the recleaned weight-loss specimens is close to or lower than that calculated by 

the three Weibull distributions DOE used in the TSPA code for the Alloy 22 general corrosion 

rate—particularly for corrosion rates with high cumulative probabilities. DOE stated that only 

corrosion rates with cumulative probabilities of 0.96 and above {corrosion rate greater than 

~15 nm/yr [5.91 × 10-7 in/yr]} are important for waste package performance. Because the 
4-26 

data from the recleaned, reanalyzed specimens provide lower corrosion rates than those 

calculated by the Weibull distributions at the high cumulative probabilities, DOE concluded 

that use of the Weibull distributions shown in SAR Figure 2.3.6-9 are reasonable because 

the waste package failure time was not overestimated. Nevertheless, DOE stated that the final 

recleaning and reanalysis of the corrosion specimens will be completed and documented in an 

update to the SAR. 

Summary of NRC Staff’s Review for General Corrosion Rate by Long-Term 

Weight-Loss Measurements 

The NRC staff reviewed the DOE approach to establish the waste package general corrosion 

rate by long-term tests. With regard to the material conditions for the corrosion tests, DOE 

tested Alloy 22 specimens with mill-annealed and as-welded microstructures. The NRC staff 

notes that DOE performed tests on materials with these microstructures because they are 

representative of those expected for the waste package base metal and weld region, 

respectively, on the basis of the fabrication procedures set forth in SAR Section 1.5.2.7. With 

regard to the test solutions for the corrosion tests, DOE tested Alloy 22 specimens in a range of 

solutions, including simulated acidified water, simulated concentrated water, and simulated 

dilute water. The corrosion rate for the waste package in the repository may depend on such 

factors as the pH and concentration of ionic species in water that contacts the waste package. 

Therefore, the NRC staff reviewed the corrosion test solutions to determine whether they are 

adequate to model the waste package corrosion rate in repository conditions. The NRC staff 

notes that the corrosion test solutions are more chemically aggressive than waters expected to 

occur within repository drifts, including starting water compositions in the DOE near-field 

chemistry model described in SAR Section 2.3.5.5, and waters considered in the NRC staff’s 

independent analysis of in-drift water evolution, described in TER Section 2.2.1.3.3.3.2. 

Therefore, it was reasonable for DOE to model the waste package corrosion rate on the basis of 

tests in these simulated brines. 

The NRC staff also notes that DOE adequately identified deficiencies with specimen preparation 

and cleaning that led to unquantified experimental uncertainties in the general corrosion rate for 

the waste package outer barrier reported in SAR Section 2.3.6.3. Further, the methodology 

DOE used for recleaning and reanalyzing weight-loss specimens is reasonable for such 

measurements and follows the standards specified in ASTM G–1-03 (ASTM International, 

2003ab). Therefore, the recleaned, reanalyzed weight-loss data presented in DOE Enclosure 3 

(2009cl) are reasonable to represent the general corrosion rate of Alloy 22 at 60 °C [140 °F]. 

The NRC staff compared the general corrosion rates measured from the recleaned, reanalyzed 

weight-loss specimens to the general corrosion rates the Weibull distributions calculated in the 

TSPA code. The NRC staff notes that, at lower cumulative probabilities, the corrosion rate 

calculated from the recleaned, reanalyzed weight-loss specimens may be slightly higher 

{by less than approximately 3 nm/yr [1.18 × 10-7 in/yr]} than the rate calculated from the 

Weibull distributions. The NRC staff determined, however, that because the corrosion rates are 

very low at this cumulative probability {less than approximately 7 nm/yr [2.76 × 10-7 in/yr]}, 

a difference of less than 3 nm/yr [1.18 × 10-7 in/yr] will have little effect on the waste package 

performance during the postclosure period. The NRC staff recognizes that, at higher cumulative 

probabilities (greater than approximately 0.8), the corrosion rate calculated from the recleaned, 

reanalyzed weight-loss specimens is less than the corrosion rates calculated from the 

Weibull distributions, even at the low uncertainty level. The NRC staff notes that the corrosion 

rates at the high end of the cumulative distribution, where the corrosion rate may be 10 nm/yr 

[3.94 × 10-7in/yr] or higher, are most important for waste package performance. For 
4-27 

corrosion rates at this level, the waste package may fail by general corrosion breach during 

the 1-million-year postclosure period or may be susceptible to damage in the event of seismic 

ground motion. Therefore, the NRC staff notes that, for corrosion rates most significant 

to waste package performance, the Weibull distributions DOE used to sample the 60 °C [140 °F] 

general corrosion rate in the TSPA calculate a higher corrosion rate than the most accurate 

experimental measures of the general corrosion rate. Further, sampling from the low, 

medium, and high Weibull distributions in 5, 90, and 5 percent of the realizations, respectively, 

is a reasonable approach to represent uncertainty in the general corrosion rate. The NRC 

staff notes that greater partitioning between the distributions (e.g., 1–98–1 percent) would give 

too few samples from the low and high distributions to make a statistically meaningful 

contribution to the cumulative distribution, whereas smaller partitioning between the distributions 

(e.g., 30–40–30 percent) would not give a statistically meaningful distinction between the 

respective distributions. On the basis of this information, the Weibull distributions from which 

DOE sampled the 60 °C [140 °F] general corrosion rate in the TSPA analysis are reasonable 

because they will not underestimate the general corrosion rates at high cumulative probabilities, 

which are most important for waste package performance. 

Temperature Dependence of the General Corrosion Rate 

DOE conducted experiments to determine the temperature dependence of the general corrosion 

rate of the waste package outer barrier by measuring the activation energy for general corrosion 

of Alloy 22 (SAR Section 2.3.6.3.2.2). DOE used the short-term electrochemical polarization 

resistance technique following the ASTM G 59-97 (ASTM International, 1998aa). Mill-annealed 

and welded specimens were tested in a range of solutions containing NaCl and KNO3 at 

temperatures ranging from 60 to 100 °C [140 to 212 °F] (SAR Table 2.3.6-4). DOE used these 

solutions because they simulate the conditions of moderate relative humidity where calcium is 

expected to be a minor component in the aqueous environment in the repository, as outlined in 

SNL Section 6.4.3.4 (2007al). 

From these data (SAR Figure 2.3.6-7), DOE used a linear mixed-effects statistical analysis 

to calculate a mean activation energy of 40.78 kJ/mol [9.74 kcal/mol], with a standard 

deviation 11.75 kJ/mol [2.81 kcal/mol]. DOE selected a normal distribution to represent the 

temperature-dependence term on the basis of statistical fitting techniques. The activation 

energies for the individual solutions used to determine the distribution of the activation 

energy are shown in SAR Table 2.3.6-5. DOE confirmed the activation energy calculated from 

these short-term polarization tests by comparisons to the activation energy from the long-term 

5-year weight-loss data of Alloy 22 specimens immersed in simulated concentrated water at 

60 and 90 °C [140 and 194 °F], respectively, as described in SNL Section 6.4.3.4 (2007al). 

DOE calculated a mean activation energy of 40.51 kJ/mol [9.68 kcal/mol] for the 5-year 

corrosion data, which is close to the mean calculated from the short-term polarization technique. 

From the 5-year corrosion data, the activation energy distribution was also obtained. This 

distribution was best represented by truncating the normal distribution of the short-term 

polarization tests at −3 and +2 standard deviations. 

The deficiencies with cleaning and weighing Alloy 22 corrosion specimens discussed in DOE 

Enclosure 3 (2009cl), however, led DOE to reevaluate the calculation of the temperature 

dependence of the general corrosion rate. The deficiencies were not associated with the 

short-term polarization data, but rather the comparison of 5-year general corrosion rates for 

specimens immersed in simulated concentrated water. For the latter, DOE recalculated the 

activation energy using the corrosion rates measured for the recleaned, reanalyzed weight-loss 

specimens. From these data, DOE calculated a mean activation energy of approximately 
4-28 

32.26 kJ/mol [7.71 kcal/mol], with minimum and maximum values of 3.37 and 60.05 kJ/mol 

[0.81 and 14.3 kcal/mol], respectively, as shown in DOE Enclosure 3, Figure 9 (2009cl). 

These values are approximately 20 percent lower than the activation energies sampled from 

the truncated normal distribution described in SAR Section 2.3.6.3, which was sampled in the 

TSPA code. Using both the updated distribution for the activation energy, as shown in DOE 

Enclosure 3, Figure 9 (2009cl), and the updated distribution for the 60 °C [140 °F] general 

corrosion rate, as shown in DOE Enclosure 3, Figure 8 (2009cl), DOE calculated the 

temperature-dependent general corrosion rate of Alloy 22, using SAR Eq. 2.3.6-3. DOE 

compared the corrosion rates calculated using the updated distributions to the corrosion rates 

calculated using the model described in SAR Section 2.3.6.3. As shown in DOE Enclosure 3, 

Figures 10–12 (2009cl), for the temperature range of 25 to 200 °C [77 to 392 °F], the updated 

distributions derived from recleaned, reanalyzed weight-loss specimens give lower corrosion 

rates than obtained by the model described in SAR Section 2.3.6.3, which was implemented in 

the TSPA code. As such, DOE concluded that the TSPA code did not underestimate the waste 

package general corrosion rate. 

Summary of NRC Staff’s Review for Temperature Dependence of the General 

Corrosion Rate 

The NRC staff reviewed the DOE approach to calculate the temperature dependence of 

the waste package general corrosion rate. The NRC staff reviewed the material and 

environmental conditions at which DOE measured the activation energy for general corrosion 

of the waste package outer barrier that was sampled in the TSPA code. With regard to the 

material conditions for the tests, DOE tested Alloy 22 specimens with mill-annealed and 

as-welded microstructures. The NRC staff notes that it was reasonable for DOE to perform 

tests on materials with these microstructures because they are representative of those expected 

for the waste package base metal and weld region, respectively, on the basis of the fabrication 

procedures set forth in SAR Section 1.5.2.7. With regard to the test solutions for the corrosion 

tests, the corrosion behavior of the waste package in the repository may depend upon such 

seepage water characteristics as the pH and concentration of ionic species. The corrosion rate 

for the waste package in the repository may depend on such factors as the pH and 

concentration of ionic species in water that contacts the waste package. Therefore, the NRC 

staff reviewed the corrosion test solutions described in SAR Section 2.3.6.2.2 to determine 

whether they are adequate to calculate the waste package general corrosion rate activation 

energy in repository conditions. The NRC staff notes that the corrosion test solutions are more 

chemically aggressive than waters expected to occur within repository drifts, including starting 

water compositions in the DOE near-field chemistry model described in SAR Section 2.3.5.5 

and waters considered in NRC staff’s independent analysis of in-drift water evolution, described 

in TER Section 2.2.1.3.3.3.2. As such, the NRC staff notes that it was reasonable for DOE 

to calculate the activation energy for general corrosion of the waste package on the basis of 

tests in the brines described in SAR Section 2.3.6.2.2. The NRC staff also reviewed the 

experimental methodology used to measure the activation energy that was sampled in the 

TSPA code. The short-term polarization test used to measure the activation energy was 

reasonable for such measurements and conformed with ASTM G–59-97 (ASTM International, 

1998aa). 

Additionally, the NRC staff compared the activation energy for general corrosion of the 

waste package outer barrier calculated from the recleaned, reanalyzed weight-loss 

specimens to the activation energy calculated by the short-term polarization test. Both 

calculations, with means of 40.78 kJ/mol [9.74 kcal/mol] and 32.26 kJ/mol [7.71 kcal/mol], 

respectively, give an activation energy in the range that is independently reported in the 
4-29 

technical literature, which is between approximately 25 and 55 kJ/mol [5.97 and 11.9 kcal/mol] 

(Lloyd, et al., 2003aa; Scully, et al., 2001aa; Hua and Gordon, 2004aa; Smailos and Köster, 

1987aa). In particular, the independent testing NRC previously conducted gives an activation 

energy of approximately 45 kJ/mol [10.7 kcal/mol] (Dunn, et al., 2005aa; Pensado, et al., 

2002aa). This activation energy is close to the mean value of 40.78 kJ/mol [9.74 kcal/mol] used 

in the TSPA code. Therefore, DOE reasonably identified and analyzed the uncertainties in 

activation energy introduced by deficiencies in DOE’s weighing and measuring the 5-year 

weight-loss specimens. The staff also notes that the application of the normal distribution with 

truncation in the TSPA code is reasonable to represent the range of the activation energy. 

Furthermore, in regard to DOE’s calculated temperature-dependent general corrosion rates 

used in the TSPA model, the NRC staff assessed DOE’s calculated corrosion rates by 

comparing them to the rates reported in independent studies in the technical literature. At 25 °C 

[77 °F], DOE's model calculates that the corrosion rate is less than 7 nm/yr [2.76 × 10-7 in/yr] 

(SAR Figure 2.3.6-11). This corrosion rate is lower than the rates measured at room 

temperature in other studies (McMillion, et al., 2005aa; Dunn, et al., 2005aa), which range from 

about 7 to 137 nm/yr [2.76 × 10-7 to 5.39 × 10-6 in/yr]. However, the NRC staff notes that the 

data of McMillion, et al. (2005aa) and Dunn, et al. (2005aa) are from short-term tests, whereas 

long-term experimental results show that the general corrosion rate of Alloy 22 decreases by up 

to two orders of magnitude as experiments progress beyond a few years. Therefore, the 

corrosion rates reported by McMillion, et al. (2005aa) and Dunn, et al. (2005aa) were 

overestimated in terms of long-term general corrosion rate by as much as two orders of 

magnitude at room temperature. The corrosion rate DOE calculated for 25 °C [77 °F] is not 

underestimated, and except as noted, is consistent with the corrosion rate in the literature 

(McMillion, et al., 2005aa; Dunn, et al., 2005aa). The NRC staff notes that DOE’s corrosion 

rates at elevated temperatures of 150 and 200 °C [302 and 392 °F] (SAR Figure 2.3.6-11) are 

similar to, or greater than, those NRC measured (Yang, et al., 2007aa) for Alloy 22 at the same 

temperature range. The NRC staff also notes that these corrosion rates are greater than the 

corrosion rate of 120 nm/year [4.72 × 10-6 in/yr] that Smailos (1993aa) reported for Alloy C-4 (an 

analogue for Alloy 22) after 6 months’ exposure to NaCl-rich brines at 150 °C [302 °F]. On the 

basis of this information, the distribution from which DOE samples the temperature dependence 

of the waste package outer barrier general corrosion rate in the TSPA model is reasonable 

because it is unlikely that DOE underestimated the corrosion rate for the range of temperatures 

that may be reasonably expected in the repository. 

Microbially Influenced Corrosion Effects 

The NRC staff reviewed DOE’s approach to establish the conditions for the onset of microbially 

influenced corrosion and to quantify the extent to which microbially influenced corrosion may 

affect general corrosion behavior. 

Threshold Relative Humidity for the Onset of Microbially Influenced Corrosion 

As discussed in SAR Section 2.3.6.3.3.2 and DOE Enclosure 10 (2009cl), DOE concluded that 

the relative humidity in the repository must be greater than a threshold value for microbially 

influenced corrosion to increase the general corrosion rate of the waste package outer barrier. 

DOE used experimental data and information from the independent studies to determine this 

threshold relative humidity. DOE performed experiments in which Alloy 22 specimens were 

embedded in crushed Yucca Mountain tuff and placed in chambers with Yucca Mountain native 

microorganisms at different temperature and humidity levels (Else, et al., 2003aa). DOE 

reported that the optimum condition for microbial growth is at a temperature of 30 °C [86 °F] and 
4-30 

100 percent relative humidity. Microbial growth was extremely limited at higher temperatures or 

lower humidity. In SNL Section 6.4 (2004ab), DOE also cited several studies which indicate that 

robust growth of most microorganisms requires a relative humidity of 90 percent or higher, 

although limited growth is seen at relative humidity as low as 75 percent (e.g., Brown, 1976aa; 

Pedersen and Karlsson, 1995aa). In the TSPA code, DOE accounts for uncertainty in the 

threshold relative humidity by sampling this threshold relative humidity from a uniform 

distribution between 75 and 90 percent (SAR Section 2.3.6.3.3.2). 

Summary of NRC Staff’s Review for Threshold Relative Humidity for the Onset of 

Microbially Influenced Corrosion 

The NRC staff reviewed the DOE approach to establish the threshold relative humidity for the 

onset of microbially influenced corrosion. DOE used experimental data and information from 

independent studies to establish the threshold relative humidity for microbial growth in 

conditions similar to Yucca Mountain. Moreover, the NRC staff notes that DOE did not credit 

additional factors that may preclude microbial growth in the repository even if the relative 

humidity exceeded the threshold value. Therefore, DOE’s distribution for the threshold relative 

humidity is reasonable because it will not underestimate the probability of the onset of 

microbially influenced corrosion. 

Microbially Influenced Corrosion (MIC) Factor 

If the relative humidity at the waste package surface is greater than the threshold value, the 

microbially influenced corrosion-enhanced general corrosion rate for the waste package outer 

barrier is calculated by multiplying the general corrosion rate in the absence of the 

microorganisms (given by SAR Eq. 2.3.6-3) by the MIC enhancement factor. DOE performed 

laboratory tests to determine the extent to which microbially influenced corrosion may affect the 

general corrosion rate of Alloy 22 (SAR Section 2.3.6.3.2.3). DOE used the electrochemical 

polarization technique to measure the corrosion rate of Alloy 22 specimens in nutrient-enriched, 

simulated Yucca Mountain well water, with and without the presence of microbes (Lian, et al., 

1999aa). Test results were shown in SNL Table 6-16 and Figure 6-54 (2007al). DOE 

concluded that the general corrosion rate for Alloy 22 in the microbial-rich solution was up to a 

factor of approximately two higher than the general corrosion rate in sterile solution. DOE 

represented epistemic uncertainty in the microbially influenced corrosion enhancement factor to 

account for natural variation in the expected extent of microbial activity in repository conditions. 

Thus, in TSPA code, DOE samples the microbially influenced corrosion enhancement factor 

from a uniform distribution between one (i.e., microbially influenced corrosion has no effect on 

the general corrosion rate) and two (i.e., general corrosion rate would be double the nominal 

general corrosion rate). 

Summary of NRC Staff’s Review for Microbially Influenced Corrosion (MIC) Factor 

The NRC staff reviewed the DOE approach to calculate the microbially influenced corrosion 

enhancement factor. The NRC staff notes that the corrosion tests DOE performed to measure 

the microbially influenced corrosion enhancement factor were performed in conditions that 

would support the growth of microbes. Further, the NRC staff determined that the polarization 

tests used to measure the microbially influenced corrosion effect conformed to ASTM G–59-97 

(ASTM International, 1998aa), which is appropriate for such measurements. The NRC staff 

also notes that DOE did not use sterile conditions in the long-term (5 years) corrosion tests used 

to determine the nominal general corrosion rate for the waste package outer barrier. DOE 

indicated that some samples from these tests contain a significant amount of microbial bacteria, 
4-31 

even though no bacteria were deliberately introduced (Horn, et al., 2005aa). Therefore, DOE 

has selected a reasonable range to represent the microbially influenced corrosion enhancement 

factor in the TSPA analysis because it is unlikely to underestimate the extent to which Yucca 

Mountain microorganisms may increase the general corrosion rate of the waste package 

outer barrier.

Abstraction and Integration 

The model abstraction for general corrosion of the waste package outer barrier is implemented 

in the Waste Package and Drip Shield Degradation Submodel in the Total System Performance 

Assessment (TSPA) code in SNL Section 6.5.3 (2008ag). The inputs that are needed for the 

model abstraction are temperature of the waste package and the relative humidity in the drift. 

These inputs come from the Engineered Barrier System Thermohydrologic Environment 

Submodel. The Waste Package and Drip Shield Degradation Submodel includes both the 

commercial spent nuclear fuel configuration that uses the transportation, aging, and disposal 

canister configuration parameters and the codisposal waste package configuration that uses the 

five high-level waste/one DOE spent nuclear fuel long configuration parameters. DOE assumed 

a total of 11,629 waste packages divided into 5 percolation subregions, each of which is subject 

to different environmental conditions, as detailed in SNL Section 6.3.5.1.3 (2008ag). The Waste 

Package and Drip Shield Degradation Submodel general corrosion calculations are performed 

for both waste package configurations in each of the percolation subregions. 

In the Waste Package and Drip Shield Degradation Submodel, the waste package surface 

is divided into subareas, referred to as patches, to account for the spatial variability of general 

corrosion on the waste package surface, as outlined in SNL Section 6.3.5.1.2 (2008ag). 

Each patch may have a different general corrosion rate. The submodel uses a patch area of 

231.5 cm2 [35.88 in2

], so the commercial spent nuclear fuel and the codisposal waste packages 

have 1,430 and 1,408 patches, respectively. For each realization, each patch is assigned a 

different value for the 60 °C [140 °F] general corrosion rate, which is sampled from the Weibull 

distributions derived from 5-year weight-loss corrosion data from creviced Alloy 22 specimens 

(SAR Section 2.3.6.3.3.1). Because the size of the crevice specimens that DOE used to 

measure the 5-year general corrosion was about one-fourth the patch size, DOE sampled the 

60 °C [140 °F] general corrosion rate four times for each patch and applied the highest of the 

four sampled rates to the patch, as described in SNL Section 6.2.5.1.2 (2008ag). The effect of 

rescaling the 60 °C [140 °F] general corrosion rate distribution, shown in SNL Figure 6.3.5-6 

(2008ag), resulted in rates that are approximately twice those of the nominal distribution. 

To account for the temperature dependence of the general corrosion rate, a single value of the 

temperature-dependence parameter is sampled in each realization from the distribution derived 

from short-term polarization tests and applied to all waste package patches. To account for 

potential microbially influenced corrosion, the value of the threshold relative humidity for 

microbially influenced corrosion is sampled once per realization from a uniform distribution in 

the range of 75 to 90 percent and applied to all waste packages. If the relative humidity in the 

drift exceeds the threshold, the microbially influenced corrosion enhancement factor is sampled 

from a uniform distribution in the range of one to two and applied to the patches. 

DOE considered a waste package outer barrier to be breached by general corrosion when one 

or more patches are penetrated. When the waste package was breached, the general corrosion 

model was also applied to the inner surface of the waste package outer barrier. The output for 

the general corrosion model gave the percentage of breached waste packages as a function of 

time and the average number of patch penetrations per breached waste package as a function 
4-32 

of time. This output was transferred to and used in the waste form degradation, and the 

mobilization model component and the engineered barrier system flow and engineered barrier 

system transport submodels. SAR Figures 2.1-10(b) and 2.1-16(b) showed the fraction of 

commercial spent nuclear fuel waste packages breached by general corrosion and the fraction 

of the waste package surface area breached per breached waste package, respectively, for the 

commercial spent nuclear fuel waste package in the Nominal Modeling Case. 

A mean of less than 10 percent of commercial spent nuclear fuel waste packages are breached 

over 1 million years, and of the breached waste packages, the mean breached area is less than 

0.3 percent of the total waste package surface area. The results for the codisposal waste 

package in the Nominal Modeling Case are similar [SAR Figure 2.1-17(b)]. DOE Enclosure 1, 

Figures 9 and 10 (2009bj) showed the fraction of commercial spent nuclear fuel and codisposal 

waste packages, respectively, breached by general corrosion in the Seismic Ground Motion 

Modeling Case. For both waste packages, the mean is approximately 10 percent breached in 

1 million years. DOE Enclosure 1, Figures 11 and 12 of its response to the NRC staff’s request 

for additional information (DOE, 2009bj) showed the fraction of the surface area breached for 

the commercial spent nuclear fuel and the codisposal waste packages breached by general 

corrosion in the Seismic Ground Motion Modeling Case. For both waste packages, the fraction 

is approximately 1 percent of the surface area. 

In regard to the activation energy for general corrosion in TSPA model, the NRC staff notes 

that DOE performed sensitivity analyses which show that the expected dose has a strong 

correlation to the activation energy for general corrosion of the waste package outer barrier and 

tends to increase with decreasing activation energy for general corrosion (SAR Figures 2.4-151 

and 2.4-155). 

Summary of NRC Staff’s Review for Abstraction and Integration for General Corrosion of 

the Waste Package Outer Barrier 

The NRC staff reviewed the implementation and integration of the model abstraction for general 

corrosion of the waste package outer barrier in the postclosure performance assessment. DOE 

has provided information for the NRC staff to understand how the conceptual model is 

implemented in the TSPA code and how the model inputs and outputs are integrated with other 

model components. The NRC staff determines that the model abstraction is consistent with the 

design features of the waste package, including materials of construction and dimensions given 

in SAR Section 1.5.2.7. Further, the NRC staff notes that, with respect to the general corrosion 

rate, DOE adequately justified the ranges of these parameters and accounted for uncertainty in 

the model abstraction. Moreover, DOE reasonably accounted for spatial variability in the 

general corrosion rate on the waste package surface by applying the corrosion rate on a patch 

scale and by rescaling the corrosion rate to account for the difference between the patch size 

and size of the corrosion specimens. The NRC staff performed independent calculations to 

confirm the Waste Package and Drip Shield Degradation Submodel in the TSPA (Jung, 

2010aa). The NRC staff’s calculations, with respect to the timing and magnitude of waste 

package breach by general corrosion, were consistent with DOE’s calculations. Therefore, 

DOE’s implementation of the waste package general corrosion model abstraction in the TSPA 

code is reasonable because it would not underestimate the timing or magnitude of radionuclide 

release to the accessible environment. 
4-33 

NRC Summary of Conclusions for the General Corrosion of the Waste Package 

Outer Barrier 

The NRC staff reviewed the DOE model abstraction for general corrosion of the waste 

package outer barrier that was implemented in the TSPA code. DOE used reasonable 

experimental tests and other independent technical literature to provide adequate support for 

the model abstraction. Therefore, DOE reasonably accounted for general corrosion of the 

waste package outer barrier in the TSPA code. 

2.2.1.3.1.3.2.2 Localized Corrosion of the Waste Package Outer Barrier 

Localized corrosion is a process where corrosion occurs at discrete sites, in contrast to 

general corrosion, which uniformly thins the entire surface of a material. Localized corrosion 

usually occurs in metals and alloys, such as Alloy 22, whose corrosion resistance is attributed to 

the presence of a passive oxide film. Localized corrosion can initiate if the passive film is 

removed or damaged. When localized corrosion does occur, it tends to cause degradation 

much faster than general corrosion. In SAR Section 2.3.6.4, DOE considered that localized 

corrosion could lead to the release of radionuclides from the waste package if the waste 

package wall is breached. 

DOE determined that localized corrosion requires the presence of a liquid water film on the 

waste package surface, which may come from dripping seepage water or salt deliquescence in 

dust particles, as outlined in SNL Section 6.3.5.2 (2008ag). DOE’s evaluation of salt 

deliquescence indicated that brines produced from dust deposits will not lead to localized 

corrosion [features, events, and processes (FEP) 2.1.09.28.0A; SNL (2008ac)]. Consequently, 

DOE excluded localized corrosion caused by deliquescence from the performance assessment 

(SAR Table 2.2-5) and concluded that seepage water must contact the waste package for 

localized corrosion to occur. 

In the TSPA code, DOE calculated that in-drift conditions (i.e., temperature, pH, and 

concentration of ionic species in seepage water) may support localized corrosion of the waste 

package for approximately 12,000 years after repository closure, as described in DOE 

Enclosure 1 (2009dg). The TSPA code, however, also calculates that few drip shields fail within 

12,000 years after repository closure. Therefore, the probability of waste package breach by 

localized corrosion is low in the DOE model. Following 12,000 years after repository closure, 

DOE calculated that there is a low probability for conditions in the drift to support localized 

corrosion of the waste package even if the drip shield fails and allows seepage water to contact 

the waste package. 

In TER Sections 2.2.1.3.1.3.1 and 2.2.1.3.2.6, respectively, the NRC staff notes that chemical 

and mechanical degradation of the drip shield are very unlikely to cause failure of the drip 

shield plates and allow seepage water to contact the waste package within 12,000 years of 

repository closure. Therefore, there is a low probability of waste package breaches by localized 

corrosion within 12,000 years of repository closure. Nevertheless, there are uncertainties 

related to the barrier capability of the drip shield beyond 12,000 years after repository closure, 

and seepage water may contact the waste package during this period. As such, this section 

provides the NRC staff’s review of the DOE model abstractions for initiation and propagation of 

localized corrosion, focusing on localized corrosion behavior beyond 12,000 years after 

repository closure. 
4-34 

Waste Package Localized Corrosion Conceptual Models 

DOE implemented models for both initiation and propagation of localized corrosion of the waste 

package outer barrier in the TSPA code. The following addresses the NRC staff’s review of 

these models. 

Corrosion Initiation Models 

DOE considered two potential mechanisms by which localized corrosion could be initiated on 

the waste package outer barrier under seepage conditions. In SAR Section 2.3.6.4, DOE 

described the first mechanism, which is related to the waste package open-circuit corrosion 

potential, or corrosion potential. The DOE model initiates localized corrosion if the corrosion 

potential for the waste package is greater than or equal to a critical potential. DOE defined 

critical potential as the potential above which a passive film will not spontaneously reform if 

damaged (SAR Section 2.3.6.4.1). While localized corrosion typically encompasses both pitting 

and crevice corrosion, DOE treated all waste package localized corrosion as crevice corrosion 

because crevice corrosion initiates in less aggressive thermal and chemical conditions than 

pitting corrosion, as described in SNL Section 6.4.4 (2007al). As such, DOE assumed that the 

critical potential for localized corrosion is equivalent to the crevice repassivation potential (SAR 

Section 2.3.6 4.1). 

The second initiation mechanism, referred to as salt separation, was described in SAR 

Section 2.3.5.5.4.2.1. During salt separation, the relative humidity at the waste package surface 

drops below a salt precipitation threshold while seepage is occurring, causing chloride salts to 

precipitate out of solution. Nitrate that is still in solution moves away by advection. A chemically 

aggressive chloride-rich, nitrate-depleted brine forms when the relative humidity increases 

above this threshold value. DOE did not, however, model localized corrosion by salt separation 

in the TSPA code, as summarized in SAR Section 2.4. Consequently, the NRC staff submitted 

a request for additional information that DOE provide technical details evaluating the 

significance of salt separation effects on the performance assessment of waste packages in the 

proposed repository environment. DOE Enclosure 1 (2009dg) provided additional information 

indicating that the salt separation aspects of localized corrosion initiation were not implemented. 

DOE further stated that the information and analysis provided in DOE Enclosure 1 (2009dg) 

(i.e., that the salt separation aspects of localized corrosion initiation were not implemented) will 

be included in a future SAR update. 

Summary of NRC Staff’s Review for Corrosion Initiation Models 

The NRC staff reviewed the DOE conceptual models for localized corrosion initiation on the 

waste package outer barrier, as described in SAR Sections 2.3.6.4 and 2.3.5.5. The NRC staff 

notes that DOE’s model assumption that localized corrosion will initiate when the corrosion 

potential for the waste package is greater than or equal to a critical potential (i.e., the crevice 

repassivation potential) is consistent with the understanding of corrosion processes in the 

technical literature (Evans, et al., 2005aa,ab; Dunn, et al., 2000aa). Moreover, this initiation 

model will not underestimate the probability of localized corrosion initiation, because the 

initiation of Alloy 22 crevice corrosion generally requires less aggressive conditions than the 

initiation of pitting corrosion (Rebak, 2005aa; Cragnolino, et al., 1999aa; Dunn, et al., 2000aa). 

For initiation by salt separation, the NRC staff notes that DOE’s model of chloride salt 

precipitation from brines in low humidity conditions is consistent with the thermodynamic physics 

of salt solutions (Yang, et al., 2006aa). 
4-35 

The NRC staff also notes that crevice corrosion is typically associated with small volumes of 

stagnant solution in holes, under surface deposits, or underneath fasteners (Fontana and 

Greene, 1978aa). Moreover, experimental data indicate that some crevice couples that could 

form in the repository do not support localized corrosion even if they form a tight crevice 

(He, et al., 2007ab; Shan and Payer, 2007aa). Alloy 22-to-Alloy 22, Alloy 22-to-titanium, and 

Alloy 22-to-ceramic couples have low susceptibility to crevice corrosion in concentrated sodium 

chloride solutions, which may be representative of the nitrate-depleted brine that forms after salt 

separation. Therefore, the DOE model that localized corrosion could occur on any part of the 

waste package surface exposed to seepage water will not underestimate that fraction of the 

waste package surface that undergoes localized corrosion. 

Corrosion Propagation Model 

In DOE’s model for localized corrosion propagation, corrosion propagates at a constant rate 

over time (SAR Section 2.3.6.4.3.2). DOE also considered an alternative conceptual model 

in which the corrosion rate decreased over time (SAR Section 2.3.6.4.3.2.2). The alternative 

model, based on pit growth, gives a corrosion propagation rate lower than that calculated 

using the primary model assumption of constant corrosion rate. Therefore, DOE implemented 

the model with constant corrosion rate in TSPA code because it calculated an earlier waste 

package breach time. 

Summary of NRC Staff’s Review for Corrosion Propagation Model 

The NRC staff reviewed the DOE model for propagation of localized corrosion of the waste 

package outer barrier, as described in SAR Section 2.3.6.4.3.2. DOE presented experimental 

evidence for a decreasing localized corrosion rate over time in a range of metals and alloys 

(Hunkeler and Boehni, 1983aa; Marsh, et al., 1991aa; Mughabghab and Sullivan, 1989aa; 

Sharland, et al., 1994aa; Ishikawa, et al., 1994aa). Also, the NRC staff notes that localized 

corrosion kinetics in Alloy 22 is likely to be slower than these materials because of its persistent 

passive film. DOE’s use of the assumption of a constant localized corrosion rate over time for 

the waste package outer barrier is reasonable because it will not underestimate the corrosion 

propagation rate. 

Localized Corrosion Initiation Conditions 

For localized corrosion initiation by corrosion potential and salt separation, respectively, 

DOE used empirical data to establish the conditions in which localized corrosion of the 

waste package outer barrier could initiate. The NRC staff reviewed DOE’s localized corrosion 

initiation conditions. 

Initiation by Critical Potential 

In the TSPA code, DOE compared the waste package outer barrier corrosion potential to the 

repassivation potential to determine whether electrochemical conditions on the waste package 

would lead to passive film breakdown. DOE assumed that the corrosion potential and 

repassivation potential for the waste package outer barrier, respectively, depended on the 

environmental conditions in the drift, including temperature, pH, chloride concentration, and 

nitrate concentration. DOE derived equations to represent the potentials as functions of these 

parameters by performing tests in which it measured the potentials for Alloy 22 specimens while 

varying the environmental parameters (SAR Section 2.3.6.4.2). 
4-36 

DOE established the dependence between the corrosion potential of Alloy 22 and the 

environmental parameters using data from 5-year tests (SAR Section 2.3.6.4.2.1). For a 

range of temperatures, DOE measured the corrosion potentials for Alloy 22 creviced 

samples with various metallurgical conditions (i.e., as welded, mill annealed, stress relieved) 

exposed to a range of simulated brine solutions, including simulated dilute water , simulated 

acidified water, and simulated concentrated water, the compositions of which were given in SAR 

Table 2.3.6-1. The corrosion potential data used in DOE’s model development were shown in 

SAR Table 2.3.6-6. Using regression analyses, DOE applied SAR Eq. 2.3.6-7 to the data 

shown in SAR Table 2.3.6-6, representing the corrosion potential as a function of temperature, 

pH, and nitrate and chloride ion concentrations. 

DOE established the dependence between the repassivation potential of Alloy 22 and the 

environmental parameters using data from cyclic potentiodynamic polarization tests (SAR 

Sections 2.3.6.4.2.2). The tests were performed using the methodology of ASTM G–61-86 

(ASTM International, 2003aa). In DOE Enclosure 9 (2009cl), DOE stated that while 

repassivation potentials can be measured by methods other than cyclic potentiodynamic 

polarization, the differences in measured potentials tended to be small and the cyclic 

potentiodynamic polarization method generally predicted greater corrosion susceptibility in 

aggressive brines. Similar to DOE’s tests for corrosion potential, DOE used Alloy 22 specimens 

with different metallurgical conditions (mill annealed and as welded) in a range of simulated 

brine solutions. DOE used ceramic wrapped with polytetrafluoroethylene tape to form a crevice 

with Alloy 22 in the cyclic potentiodynamic polarization tests. The crevice repassivation 

potential data used in DOE’s model development were shown in SAR Table 2.3.6-7. Sample 

specimens that did not show evidence of localized corrosion attack were not used to develop 

the model. For experiments showing localized corrosion, DOE used regression analyses to fit 

SAR Eq. 2.3.6-6 to the data shown in SAR Table 2.3.6-7, representing the crevice repassivation 

potential as a function of temperature and nitrate and chloride ion concentrations. In the TSPA 

code, DOE accounted for fitting uncertainty in SAR Eq.s 2.3.6-6 and 2.3.6-7 by varying the 

values of the fitting parameters of the respective equations according to a Monte Carlo 

algorithm and by using data in the regression analysis from multiple samples for a given 

environmental condition, as detailed in SNL Section 6.3.5.2.2 (2009ag). 

DOE compared the model calculations of corrosion potential and repassivation potential, 

respectively, to establish the environmental conditions that would support initiating localized 

corrosion on the waste package outer barrier (SAR Section 2.3.6.4.3.1.2). In the DOE model, 

the probability for initiation of localized corrosion at temperatures less than 90 °C [194 °F] 

generally increases with decreasing pH and decreasing nitrate-to-chloride ion ratio. DOE 

compared the localized corrosion initiation conditions to experimental observations of localized 

corrosion initiation on Alloy 22 specimens. DOE’s model calculated that localized corrosion may 

initiate on Alloy 22 exposed to simulated acidified water at 90 °C [194 °F], whereas in 

experimental tests, localized corrosion was not observed on Alloy 22 specimens exposed to this 

solution for 5 years (SAR Table 2.3.6-12). DOE presented a number of additional corrosion test 

solutions for which its model calculated that localized corrosion may initiate, yet for which 

localized corrosion was not observed on Alloy 22 specimens (SAR Table 2.3.6-13). DOE 

concluded that its model overestimates the probability of localized corrosion initiation. 

Summary of NRC Staff’s Review for Initiation by Critical Potential

The NRC staff reviewed DOE’s experimental methodology to measure the corrosion potential 

and the crevice repassivation potential of Alloy 22. The NRC staff notes that DOE’s tests 

were consistent with the general practice for measuring the corrosion potential and 
4-37 

repassivation potential of metallic materials. In particular, the cyclic potentiodynamic 

polarization method is a reasonable way to measure the repassivation potential because it 

predicts higher corrosion susceptibility than other measures of this parameter, as confirmed 

by independent analyses by the NRC staff (He, et al., 2009aa). Also, the NRC independent 

analyses (He, et al., 2007aa) and Shan and Payer (2007aa) suggest that ceramic wrapped 

with polytetrafluoroethylene tape, which DOE used to form a crevice with Alloy 22 in the cyclic 

potentiodynamic polarization tests, is a material combination that is more favorable for 

localized corrosion than many combinations that would be expected in the repository, including 

Alloy 22-to-Alloy 22, Alloy 22-to-titanium, and Alloy 22-to-ceramic couples. 

The NRC staff reviewed DOE’s calculated values for the corrosion potential and repassivation 

potential, respectively, to confirm the environmental conditions (i.e., temperature, pH, 

concentration of ionic species in seepage water) under which the DOE model calculates that 

localized corrosion of the waste package will initiate. The NRC staff examined the model 

functional dependencies and confirmed that in the DOE model, the probability for localized 

corrosion initiation at a given temperature generally increases with decreasing pH and 

decreasing nitrate-to-chloride ion ratio. Therefore, the NRC staff notes that the DOE model may 

calculate that localized corrosion initiates in acidic solutions such as simulated acidified water or 

in solutions with very low nitrate-to-chloride ratios (SAR Tables 2.3.6-12 and 2.3.6-13). 

Nevertheless, Alloy 22 specimens did not show evidence of localized corrosion initiation during 

5 years’ immersion in a range of corrosion test solutions, including simulated acidified water, at 

temperatures up to 90 °C [194 °F] (SAR Table 2.3.6-12) and that for shorter tests localized 

corrosion was only observed in concentrated chloride brines (SAR Table 2.3.6-13). On the 

basis of these data, Alloy 22 is resistant to localized corrosion in brine solutions that are more 

chemically aggressive than the waters expected to occur within the repository drifts, including 

starting water compositions in the DOE near-field chemistry model described in SAR 

Section 2.3.5.5 and waters considered in the NRC staff’s independent analysis of in-drift water 

evolution, described in TER Section 2.2.1.3.3.3.2. As such, the DOE model is reasonable 

because it calculates a higher probability of localized corrosion initiation than is expected based 

on experimental data. 

Initiation by Salt Separation 

DOE used thermodynamic analyses to calculate the threshold relative humidity below which 

chloride-bearing salts could precipitate out of seepage water (SNL, 2007ak). In these analyses, 

DOE considered that the threshold relative humidity depends upon the group water type 

(i.e., 1–4 as defined in SAR Section 2.3.5), quantity of alkali feldspar to be tritiated into the 

seepage waters [i.e., the water–rock interaction parameter ( WRIP)], the partial pressure of CO2

in the drift, and the waste package temperature. In SAR Figure 2.3.5-55, the thermodynamic 

analyses showed that the chloride-to-nitrate ratio in a range of conditions is nearly constant until 

the activity of water (i.e., relative humidity) drops below a value in the range of approximately 

77 to 65 percent. In the TSPA model, DOE concluded that this range represents the threshold 

relative humidity below which localized corrosion initiation by salt separation can occur (SAR 

Section 2.3.5.5). 

Summary of NRC Staff’s Review for Initiation by Salt Separation 

The NRC staff reviewed the DOE approach for establishing the relative humidity threshold for 

the initiation of localized corrosion by salt separation, as DOE described in SAR Section 2.3.5.5. 

The methodology DOE used to determine the threshold relative humidity is reasonable because 

it is based on well-established concepts regarding the thermodynamic stability of aqueous 
4-38 

solutions. In particular, the threshold values DOE calculated are consistent with calculations for 

pure sodium chloride by Greenspan (1977aa), which are incorporated into ASTM E104-02 

(ASTM International, 2007aa). Also, the NRC staff performed independent tests and 

observed that even in pure 5 M sodium chloride solution, localized corrosion was not initiated on 

Alloy 22 specimens in open circuit conditions without the addition of copper chloride as an 

oxidant (He and Dunn, 2005aa). The DOE model assumes that salt separation can cause the 

corrosion potential to exceed the repassivation potential. In experimental tests, however, DOE 

did not observe localized corrosion in many cases where the corrosion potential was greater 

than the repassivation potential, including 5-year tests in simulated acidified water (SAR 

Tables 2.3.6-12 and 2.3.6-13). On the basis of this information, the threshold relative humidity 

that DOE calculated is reasonable because it will not underestimate the probability of localized 

corrosion initiation. 

Localized Corrosion Propagation Rate 

In the Total System Performance Assessment (TSPA) code, DOE sampled the propagation 

rate for localized corrosion on the waste package outer barrier using a log-uniform distribution 

in the range of 12.7 to 1,270 μm/yr [5 × 10 -4 to 5 × 10-2 in/yr] with a median value of 127 μm/yr 

[5 × 10-3 in/yr] (SAR Section 2.3.6.4.3.2). This range was based on corrosion testing of Alloy 22 

in aggressive environments, including 10 percent FeCl3 (Haynes International, 1997aa) and 

concentrated HCl (Haynes International, 1997ab). DOE compared the corrosion rate 

distribution sampled in the TSPA code to independently measured corrosion rates for 

similar, but less corrosion-resistant alloys, including Alloy C-276 and Alloy C-4 (SAR 

Section 2.3.6.4.4.2.2). DOE concluded that the measured corrosion rates fall within the 

bounds of the distribution sampled in TSPA code. 

Summary of NRC Staff’s Review for Localized Corrosion Propagation Rate 

The NRC staff reviewed the DOE approach, described in SAR Section 2.3.6.4, that established 

the distribution sampled in the TSPA code for the propagation rate of localized corrosion on the 

waste package outer barrier. The NRC staff determined that the distribution sampled in the 

TSPA code is based on corrosion data from studies that considered thermal and chemical 

conditions that were more aggressive than those expected in the repository. Also, DOE’s 

calculated range for the corrosion rate is consistent with the NRC staff independent 

measurements of the Alloy 22 localized corrosion rate (He and Dunn, 2005aa). On the 

basis of this information, the distribution from which DOE sampled the waste package outer 

barrier localized corrosion propagation rate in the TSPA code is reasonable because it will 

not underestimate the propagation rate. Localized corrosion at the rates DOE calculated 

would penetrate the 25-mm [0.98-in]-thick waste package outer barrier in approximately 20 to 

2,000 years, which is short relative to a 10,000-year period. Therefore, the NRC staff notes that 

uncertainties in the localized corrosion rate would not significantly affect the timing or magnitude 

of radionuclide release. 

Effects of Microorganisms on Localized Corrosion 

As DOE addressed in SAR Section 2.3.6.3.2.3, microorganisms in the repository may affect the 

corrosion processes on the waste package outer barrier such that the type and extent of 

corrosion in the presence of the microorganisms may be different from the corrosion in the 

absence of the microorganisms. Though DOE incorporated microbially influenced corrosion 

effects into its model abstraction for general corrosion of the waste package outer barrier (SAR 

Section 2.3.6.3.2.3), experimental observations indicate that microbially influenced corrosion 
4-39 

may also affect the localized corrosion behavior. In particular, a DOE study showed that small 

pits, or micropores, were observed on the surface of Alloy 22 corrosion specimens exposed in a 

borosilicate glass vessel with unsterilized Yucca Mountain tuff rock, whereas no such 

micropores were observed in sterilized conditions (Martin, et al., 2004aa). In DOE Enclosure 10 

(2009cl), DOE stated that the micropores Martin, et al. (2004aa) observed started to form 

during the first 17 months’ exposure, after which the size of the micropores was less than 1 μm 

[0.039 mil] in diameter. DOE further stated that the same specimens were observed after an 

additional 40 months’ exposure, after which there were more pores, but no significant increase 

in pore size compared to that measured after 17 months. DOE determined that, if the 

micropores were initiated pits, they quickly repassivated before they could propagate. 

Therefore, DOE concluded that it was appropriate to incorporate microbially influenced 

corrosion into its model abstraction for general corrosion of the waste package outer barrier, to 

be consistent with DOE observations in SNL Section 6.4.5 (2007al) that microbially influenced 

corrosion may enhance corrosion on the entire material surface. 

Summary of NRC Staff’s Review for Effects of Microorganisms on Localized Corrosion 

The NRC staff reviewed the information in DOE Enclosure 10 (2009cl). The DOE 

analysis that the pores may represent initiated pits that quickly repassivated is reasonable. 

Because the pores did not grow after repassivation, the NRC staff notes that the micropores 

are not indicative of localized corrosion that could significantly affect the timing or 

magnitude of radionuclide release from the waste package. The NRC staff also identified an 

independent report which confirmed that Alloy 22 is highly resistant to localized corrosion in 

microorganism-rich environments, including seawater, which also has low nitrate content 

(Aylor, et al., 1999aa). On the basis of this information, DOE’s exclusion of microbially 

influenced corrosion effects in the model abstraction for localized corrosion of the waste 

package outer barrier is reasonable. 

Abstraction and Integration 

DOE did not directly include and calculate the effects of localized corrosion of the waste 

package outer barrier in the TSPA code. Rather, DOE performed a Localized Corrosion 

Initiation Uncertainty Analysis, as described in SNL Appendix O (2008ag) and DOE Enclosure 1 

(2009dg), that calculated the fraction of waste packages in the repository that are susceptible to 

localized corrosion as a function of drip shield breach time (i.e., the time at which seepage water 

could contact the waste package). The Localized Corrosion Initiation Uncertainty Analysis 

implements the Localized Corrosion Initiation Submodel, as detailed in SNL Section 6.3.5.2.3 

(2008ag), which determines whether the environmental conditions in the drift will initiate 

localized corrosion, and gives this input to the TSPA code. The Localized Corrosion Initiation 

Submodel is similar to the TSPA code, but incorporates only those submodels that are needed 

to calculate localized corrosion initiation conditions. In particular, the Localized Corrosion 

Initiation Submodel uses information primarily from the 

 Engineered Barrier System Thermohydrologic Environment Submodel to determine the 

temperature and relative humidity history at the waste package 

 Drift Seepage Submodel to determine whether seepage occurs at a repository location 

 Engineered Barrier System Chemical Environment Submodel to determine the chemical 

composition of seepage water 
4-40 

 Seismic Ground Motion Damage Submodel to determine the time of drip shield plate 

failure due to seismic damage 

 Waste package and Drip Shield Degradation Submodel to account for corrosion in 

determining drip shield plate and waste package failure times 

In the Localized Corrosion Initiation Submodel, the repository was discretized into 

3,264 subdomains of equal area, at the center of which were 6 commercial spent nuclear 

fuel and 2 codisposal waste packages. The subdomains were distributed through the five 

percolation subregions. In the Localized Corrosion Initiation Submodel, localized corrosion 

could initiate because of the waste package corrosion potential or by salt separation. For each 

subregion, at every timestep in a realization, the Localized Corrosion Initiation Submodel 

compares the corrosion potential, as calculated by SAR Eq. 2.3.6-7, to the crevice repassivation 

potential, as calculated by SAR Eq. 2.3.6-6. If the corrosion potential was greater than or equal 

to the crevice repassivation potential in seepage conditions, the Localized Corrosion Initiation 

Submodel assumed that localized corrosion initiated at that subregion. Similarly, if the relative 

humidity at the waste package surface fell below the salt separation threshold humidity in 

seepage conditions, the Localized Corrosion Initiation Submodel assumed that localized 

corrosion initiated at that subregion. 

The Localized Corrosion Initiation Submodel calculated that, if the drip shield were breached at 

the time of the repository closure (i.e., no drip shield present), there is approximately 34 percent 

probability that localized corrosion will initiate on a given waste package surface (24 percent 

probability contribution by salt separation and 10 percent probability contribution by corrosion 

potential), as shown in DOE Enclosure 1, Figure 1 (2009dg). DOE calculated that as the time to 

drip shield breach increases, the probability of localized corrosion initiation decreases and is 

negligible if the drip shield fails beyond approximately 12,000 years after repository closure. In 

particular, DOE calculated that localized corrosion will not initiate by salt separation if drip shield 

breach occurs after approximately 1,000 years from the time of repository closure, because the 

relative humidity will remain above the threshold value. DOE also stated that changes in the 

repository environmental and chemical conditions (e.g., decreasing temperature) make initiation 

by corrosion potential less probable as the time to drip shield breach increases in its model. 

Given the results of the Localized Corrosion Initiation Uncertainty Analysis, DOE concluded that 

localized corrosion of the waste package outer barrier could affect the timing and magnitude of 

the release of radionuclides from the waste package only if the overlying drip shield plate was 

breached within approximately 12,000 years after repository closure. In regard to drip shield 

early failure, DOE assumed that localized corrosion under seepage conditions occurs on the 

waste packages located beneath the failed drip shield. Other than drip shield early failure, DOE 

modeled that no drip shield failure would occur within the approximately 12,000 years. 

Therefore, as shown in DOE Enclosure 1 (2009dg), DOE concluded that localized corrosion of 

the waste package outer barrier will have no significant effect upon either the timing or 

magnitude of radionuclide release calculated in the TSPA modeling cases. 

Summary of NRC Staff’s Review for Abstraction and Integration for Localized Corrosion 

The NRC staff reviewed the implementation and integration of the model abstraction for 

localized corrosion of the waste package outer barrier in the postclosure performance 

assessment. DOE has provided information for the NRC staff to understand how the 

conceptual model is implemented in the TSPA code and how the model inputs and outputs are 

integrated with other model components. The NRC staff determined that the model abstraction 
4-41 

is consistent with the design features of the waste package, including materials of construction 

and dimensions given in SAR Section 1.5.2.7. Further, the staff notes that, with respect to the 

parameters used in the model abstraction (i.e., corrosion potential, corrosion repassivation 

potential, relative humidity, and pit growth rate), DOE adequately justified the ranges of these 

parameters and accounted for uncertainty in the model abstraction. 

DOE’s analysis is reasonable that the consequence of drip shield breach within 12,000 years 

on the overall dose is negligible because the analysis does not underestimate the onset of 

seismic-induced breach. The NRC staff also notes that the probability of waste package 

localized corrosion initiation beyond 12,000 years after repository closure is low, even if drip 

shield failure allows seepage water to contact the waste package. In the Localized Corrosion 

Initiation Uncertainty Analysis, the probability for localized corrosion initiation decreases with 

increasing pH and increasing nitrate-to-chloride ion ratio. DOE showed that the pH and 

nitrate-to-chloride ion ratio of in-drift waters will generally be too high to initiate localized 

corrosion beyond 12,000 years after repository closure. The DOE model also shows that 

localized corrosion may initiate in low pH solutions or solutions with low nitrate-to-chloride ratio. 

DOE’s experimental data, however, showed that localized corrosion does not initiate at 90 °C 

[194 °F], even in corrosion test solutions with lower pH or lower nitrate-to-chloride ratio than 

waters expected to be present in drifts at such temperatures, including starting seepage waters 

used by DOE (SAR Section 2.3.5.5) and waters considered in the NRC staff’s independent 

analysis of in-drift water evolution (TER Section 2.2.1.3.3.3.2). As such, the NRC staff notes 

that waste package breach by localized corrosion is unlikely beyond 12,000 years after 

repository closure, even if drip shield failure allows seepage water to contact the waste 

package. The implementation of the TSPA model abstraction for localized corrosion of the 

waste package outer barrier is reasonable because it would not underestimate the timing or 

magnitude of radionuclide release to the accessible environment. 

NRC Summary of Conclusions for Localized Corrosion of the Waste Package 

Outer Barrier 

The NRC staff reviewed the DOE models for localized corrosion of the waste package outer 

barrier that were implemented in the TSPA code. DOE used appropriate experimental tests and 

applicable technical literature to provide support for the localized corrosion initiation and 

propagation models. For the first 12,000 years after repository closure, the NRC staff noted that 

waste package breach by localized corrosion is unlikely because the intact drip shields will 

prevent seepage waters from contacting the waste package (TER Sections 2.2.1.3.1.3.1 and 

2.2.1.3.2.6). For the time period beyond 12,000 years after repository closure, the DOE models 

show a low probability for localized corrosion initiation because the repository environment 

(i.e., temperature, pH, and chemical composition of seepage waters) will not support the 

initiation of localized corrosion. Therefore, DOE’s analytic models for localized corrosion 

of the waste package outer barrier in the postclosure performance assessment analyses 

are reasonable. 

2.2.1.3.1.3.2.3 Stress Corrosion Cracking of the Waste Package Outer Barrier 

Stress corrosion cracking generally refers to a process whereby cracks form in metals or alloys 

in a corrosive environment and under sustained tensile stresses. DOE presented data 

indicating that Alloy 22 is highly resistant to stress corrosion cracking in the environmental 

conditions (e.g., temperature, pH, and chemical constituents of seepage water brines) that are 

expected to occur in the repository, as detailed in SAR Section 2.3.6.5.2 and SNL Section 6.2 

(2007bb). Because of uncertainty regarding the long-term environmental conditions in the 
4-42 

repository, however, the DOE model in the TSPA code assumes that the repository 

environment supports stress corrosion cracking, such that sufficient residual tensile stress was 

the only criterion for stress corrosion cracking occurrence (SAR Section 2.3.6.5.1). In SAR 

Section 2.3.6.5, DOE evaluated stress corrosion cracking caused by residual stresses from 

waste package fabrication. In SAR Section 2.3.4.5, DOE also evaluated stress corrosion 

cracking caused by the residual stresses resulting from impacts to the waste package during 

seismic ground motions. This section of the TER includes NRC staff’s review of the DOE model 

abstractions for stress corrosion cracking of the waste package outer barrier. 

Conceptual Models 

The DOE models for stress corrosion cracking of the waste package outer barrier treat crack 

initiation (i.e., the formation of cracks on the waste package surface) and crack propagation 

(i.e., growth of cracks from the surface through the outer barrier) as distinct phenomena. In the 

TSPA code, DOE assumed that cracks initiated on areas of the waste package surface where 

the magnitude of the sustained tensile stress was greater than a threshold value, which DOE 

referred to as the residual stress threshold (RST) (SAR Section 2.3.6.5.2.1). Cracks initiated by 

this sustained stress were referred to as incipient cracks. In the DOE model, the residual 

stresses for crack initiation could result from fabrication processes, such as welding, or from 

impacts to the waste package during a seismic ground motion event. DOE stated that the 

concept of a threshold stress that must be exceeded for the onset of stress corrosion cracking is 

widely accepted in the technical literature (e.g., ASM International, 1987aa). In the TSPA code, 

DOE also assumed that waste package weld flaws (e.g., voids and slag inclusions) were 

initiated cracks, regardless of the magnitude of the residual stress (SAR Section 2.3.6.5.3.1). 

DOE used different conceptual models for the propagation of cracks initiated by fabrication 

stresses and weld flaws and those initiated by seismically induced stresses, respectively. 

With regard to cracks initiated by seismically induced stresses, DOE did not explicitly model 

crack propagation. Rather, DOE assumed that cracks instantaneously propagated through the 

wall at the time of initiation SAR Section 2.3.4.5.1.2.1. This assumption minimizes the time for 

through-wall propagation of cracks. With regard to cracks initiated by fabrication stresses and 

weld flaws, DOE assumed that the stress intensity factor at the tip of the initiated crack must be 

greater than a threshold value for the crack to propagate (SAR Section 2.3.6.5.3.2). DOE 

stated that the concept of a threshold stress intensity factor is consistent with the general 

understanding of crack fracture mechanics (Jones and Ricker, 1987aa; Sprowls, 1987aa). 

To calculate the rate of growth for cracks with a stress intensity factor greater than the 

threshold value, DOE used the slip-dissolution film-rupture (SDFR) model, as discussed in SAR 

Section 2.3.6.5.3.2 and SNL Section 6.4.2 (2007bb). In the slip-dissolution film-rupture model, 

crack growth is related to the rupture and subsequent reformation of the passive metal oxide 

film at the crack tip. DOE stated that several studies (Ford and Andresen, 1988aa; Andresen, 

1991aa; Andresen and Ford, 1994aa) used the slip-dissolution film-rupture model to accurately 

calculate crack growth rates in stainless steel and nickel-based alloys similar to Alloy 22 

(e.g., Alloys 182 and 600). In SAR Section 2.3.6.5.3.3, DOE described an alternative 

conceptual model for the crack growth rate: the coupled environmental fracture model. The 

coupled environmental fracture model is based on conservation of electrons involved in the 

corrosion process (Macdonald and Urquidi-Macdonald, 1991aa; Macdonald, et al., 1994aa). 

It incorporates the effects of oxygen concentration, flow rate, and the conductivity of the external 

environment and accounts for the effect of stress on crack growth. DOE did not use the 

coupled environmental fracture model in the TSPA calculation, because it calculated a slower 

crack growth rate than the slip-dissolution film-rupture model. 
4-43 

Summary of NRC Staff’s Review for Conceptual Models for Stress Corrosion Cracking 

The NRC staff reviewed DOE’s models for stress corrosion cracking initiation. DOE’s use of a 

threshold stress for the onset of stress corrosion cracking in the waste package outer barrier is 

reasonable because this concept is consistent with reports of stress corrosion cracking behavior 

in a range of passive alloys similar to Alloy 22 (e.g., ASM International, 1987aa). 

Phenomenologically, the NRC staff notes that the initiation of stress corrosion cracking in 

Alloy 22 is similar to initiation in other passive alloys involving repetitive rupture and 

regeneration of a passive film. The NRC staff also notes that, because weld flaws may be 

present in the waste package outer barrier at the time of emplacement, it is reasonable for DOE 

to model the flaws as initiated cracks. Therefore, the DOE conceptual models for crack initiation 

are reasonable. 

The NRC staff also reviewed DOE’s models for stress corrosion cracking propagation. 

Regarding the propagation of cracks initiated by seismically induced stresses, the NRC 

staff notes that the DOE model does not take credit for the possibility that initiated cracks 

could arrest before propagating through the barrier or the time it would take for cracks to 

pass through the barrier. Therefore, the DOE model for propagation of seismically induced 

cracks is reasonable because it will underestimate the time it takes for cracks to breach the 

waste package outer barrier. Regarding the propagation of weld flaws and cracks initiated 

by fabrication stresses, DOE’s use of a threshold stress intensity factor is consistent with 

the technical understanding of crack fracture mechanics. Further, DOE has validated the 

slip-dissolution film-rupture model predictions with experimental results (SAR Figure 2.3.6-29). 

Therefore, the use of slip-dissolution film-rupture model for calculating the crack growth 

rates is reasonable. The staff also notes that the slip-dissolution film-rupture model 

calculates higher crack growth rates than DOE measured using the reversing direct current 

measurement technique on compact-tension-type Alloy 22 fracture mechanics specimens (SAR 

Figure 2.3.6-34), or greater than was calculated by the alternative coupled environmental 

fracture model (Ford and Andresen, 1988aa). As such, the DOE models for propagation of weld 

flaws and cracks initiated by fabrication stresses are reasonable because they will not 

overestimate the time it takes for cracks to breach the waste package outer barrier. 

Stresses for Crack Initiation and Propagation 

The NRC staff reviewed the DOE approaches to establish residual stress threshold values for 

crack initiation and the threshold stress intensity factor for the propagation of weld flaws and 

cracks initiated by fabrication stresses. 

Residual Stress Threshold 

DOE performed laboratory tests to establish the value of the residual stress threshold for 

Alloy 22. DOE performed constant-load crack initiation tests (SAR Section 2.3.6.5.2.1.1), 

slow strain rate tests (SAR Section 2.3.6.5.2.1.2), and U-bend stress corrosion cracking 

initiation tests (SAR Section 2.3.6.5.2.1.3). These tests were performed for up to 5 years for 

Alloy 22 specimens with metallurgical conditions representative of waste package metallurgical 

conditions in the repository (i.e., welded, thermally aged, cold worked). The tests were 

performed in the temperature range of 25 to 165 °C [77 to 329 °F] in different brines, including 

basic simulated water, simulated dilute water, simulated concentrated water, and simulated 

acidified water, the compositions of which are shown in SAR Table 2.3.6-1. 
4-44 

For the constant-load crack initiation tests (SAR Section 2.3.6.5.2.1.1), DOE exposed Alloy 22 

specimens to basic simulated water (pH of 10.3) at 105 °C [221 °F] for up to 28,000 hours 

(approximately 3 years). The test specimens were subjected to tensile stress up to 2.1 times 

the at-temperature yield strength for as-received materials and 2.0 times the yield strength of 

the welded materials, which corresponds to approximately 95 percent of the ultimate tensile 

strength of Alloy 22 in the respective material conditions. DOE reported that no sample 

ruptured during the test, as shown in SAR Figure 2.3.6-28. For the slow strain rate testing 

(SAR Section 2.3.6.5.2.1.2), Alloy 22 specimens were exposed to simulated acidified water, 

basic simulated water, simulated concentrated water, and calcium-chloride-type brines over a 

range of temperatures, with and without applied potential (SAR Table 2.3.6-14). DOE stated 

that it did not observe stress corrosion cracking in most experimental conditions, though stress 

corrosion cracking was observed in simulated concentrated water with large applied anodic 

potentials. DOE concluded in SNL Section 6.2.1.3 (2007bb), however, that such potentials are 

not representative of repository conditions. For the U-bend stress corrosion cracking initiation 

tests (SAR Section 2.3.6.5.2.1.3), Alloy 22 specimens were tested in simulated dilute water, 

simulated concentrated water, and simulated acidified water for 5 years with no evidence of 

stress corrosion cracking initiation. 

Even though stress corrosion cracking of Alloy 22 was not observed in the experimental testing, 

DOE concluded that cracks may initiate at lower stresses on the repository time scale, yet would 

not be observed in short-term laboratory tests, as described in SNL Section 6.2.2 (2007bb). 

Therefore, DOE stated that there existed some uncertainty associated with the value of the 

residual stress threshold. Thus, to establish the residual stress threshold value for the TSPA 

code, DOE applied a safety factor of two to the maximum stress that Alloy 22 specimens 

withstood with no evidence of stress corrosion cracking initiation. As outlined in SNL 

Section 6.2.2 (2007bb), DOE determined that this maximum stress was 210 percent of the 

Alloy 22 at-temperature yield strength, as measured during constant-load crack initiation testing 

in basic simulated water. This approach established the upper bound for the residual stress 

threshold to be 105 percent of the Alloy 22 at-temperature yield strength. DOE stated that use 

of a safety factor of two is consistent with general engineering practice and has been used to 

establish the allowable long-term fatigue stress on engineering components (American Society 

of Mechanical Engineers, 1969aa). To further account for uncertainty in the residual stress 

threshold, DOE established 90 percent of the Alloy 22 at-temperature yield stress as a lower 

bound. Thus, in the Total System Performance Assessment (TSPA) code, DOE sampled the 

residual stress threshold from a uniform distribution between 90 and 105 percent of the Alloy 22 

at-temperature yield stress, as shown in SNL Table 6-3 (2007bb). 

In response to the NRC staff’s request for additional information that DOE assess the observed 

case of stress corrosion cracking initiation in simulated concentrated water, DOE provided new 

data in DOE Enclosure 1 (2009cj) from U-bend testing of as-welded and mill-annealed Alloy 22 

specimens at 165 °C [329 °F] in aerated simulated concentrated water (Andresen and Kim, 

2009aa). After 32,000 hours (approximately 3.5 years), no stress corrosion cracking was 

observed for stresses estimated to be at or slightly above the at-temperature yield strength of 

Alloy 22. DOE cited an additional study in which low strain rate crack initiation tests were 

performed on Alloy 22 specimens in simulated concentrated water at 86 and 89 °C [186.8 and 

192.2 °F] (Fix, et al., 2003aa). DOE reported that crack initiation did not occur until the tensile 

stress exceeded a value of 605 MPa [87.7 ksi], which is approximately 160 percent of the room 

temperature yield strength of Alloy 22. On the basis of this information, DOE concluded that the 

range of the residual stress threshold, given in SAR Section 2.3.6.5, was adequate. 
4-45 

DOE evaluated the expected fabrication-induced residual stresses during the postclosure period 

to determine whether such stresses could exceed the residual stress threshold. DOE stated 

that it plans to do a stress-relief heat treatment to mitigate the stresses in the waste package 

outer barrier after fabrication (SAR Section 1.5.2.7.1) following the standards specified in 

American Society of Mechanical Engineers Boiler and Pressure Vessel Code, Section III, 

Division 1, Subsection NC-4600 (American Society of Mechanical Engineers, 2001aa). 

DOE concluded that fabrication-induced residual stresses will not exceed the residual stress 

threshold for portions of the waste package that are heat treated. In the DOE fabrication 

process, however, the heat treatment takes place before the waste is placed in the waste 

package and the outer lid is welded onto the shell. Welding the outer lid onto the waste 

package shell may induce residual stresses in the region of the closure weld that the heat 

treatment process cannot mitigate, as described in SNL Section 6.5.3.1 (2007bb). Therefore, 

DOE concluded that the region of the waste package closure weld is the only part of the waste 

package outer barrier where fabrication-induced stresses could cause stress corrosion cracking. 

DOE plans to implement a process called low plasticity burnishing (a process whereby the 

material surface is plastically deformed to create a layer with compressive residual stress) to 

delay the initiation of incipient stress corrosion cracking (SAR Section 1.5.2.7.2.2). The current 

waste package design requires a compressive residual stress to a depth of at least 3 mm 

[0.12 in] below the weld surface (SAR Table 1.9-9, Design Control Parameter 03-17). 

DOE concluded that the initiation of stress corrosion cracking would be delayed by the time it 

would take for general corrosion to corrode through at least the 3-mm [0.12-in] burnished layer. 

DOE performed finite element analyses to calculate the residual stress profile of the weld, 

as detailed in SAR Section 2.3.6.5.2.3 and SNL Section 6.5.3.3 (2007bb), when the closure 

weld was plasticity burnished resulting in compressive stresses to a depth of 3 mm [0.12 in] 

below the weld surface. The analysis simulated multipass welds, with the residual stress 

represented as a function of welding parameters, thermal transients, temperature-dependent 

material properties, and elastic–plastic stress reversals. The DOE analyses indicated that 

the residual stress decays rapidly with increasing radial distance from the weld line and is 

negligible at a distance from the weld line approximately equal to the thickness of the waste 

package wall, as shown in SNL Figures 6-19 through 6-22 (2007bb). Given the rapid decay in 

weld-induced stress with increasing distance from the weld line, DOE assumed that initiation of 

stress corrosion cracking by fabrication-induced stress could only occur in patches representing 

the waste package closure weld in the TSPA model. These patches represent approximately 

2.67 percent and 2.95 percent of the total surface area for the commercial spent nuclear fuel 

and the codisposal waste packages, respectively, as outlined in SNL Section 6.3.5.1.2 

(2008ag). 

In the region of the closure weld, DOE calculated that radial stresses do not exceed the 

residual stress threshold through the entire thickness of the weld, but that hoop stresses can 

exceed the residual stress threshold at a depth of approximately 5 to 7.5 mm [0.20 to 0.30 in] 

below the weld surface, as described in SNL Section 6.5.5.2.2 (2007bb). In the TSPA code, 

DOE represents the hoop stress as a function of depth from the weld surface in SNL Eq. 64 

(2007bb). DOE also considered angular variability in the residual stress around 

the circumference of the waste package closure weld in SNL Eq. 6.3.5-6 (2008ag). On 

the basis of literature reports (e.g., Shack, et al., 1980aa), DOE calculated that the residual 

stress may have circumferential variation up to ± 2.5 ksi [± 17.24 MPa] from the mean stress, 

as detailed in SNL Section 6.5.6.1 (2007bb). SAR Figure 2.3.6-30 showed the angular 

variability in the residual hoop stress profile in the waste package closure weld. More generally, 

DOE identified literature reports (e.g., Mohr, 1996aa; Pasupathi, 2000aa) which indicated that 

welding and stress mitigation processes introduce uncertainty into the weld residual stress 
4-46 

profile. The reports indicated that the uncertainty range for residual stress may be between 

5 percent and 35 percent of the material yield strength. On the basis of the fabrication 

techniques and process controls planed for the waste package closure weld, DOE selected a 

three standard deviation uncertainty range of ±15 percent of the at-temperature yield strength of 

Alloy 22, as outlined in SNL Section 6.5.6.2 (2007bb). This is implemented in the TSPA model 

by applying a scaling factor to the residual stress. The scaling factor is sampled from a 

truncated (± 3 standard deviations) normal distribution where the mean is 0 and the standard 

deviation is 5 percent of the Alloy 22 at-temperature yield strength. SAR Figure 2.3.6-32 

showed the uncertainty in the residual hoop stress profile in the waste package closure weld. 

DOE compared the residual stress profile calculated by the finite element analysis to the 

residual stress experimentally measured by Woolf (2003aa) for plasticity-burnished Alloy 22 

simulated closure welds, as described in SNL Section 6.5.6.5 (2007bb). As shown in SNL 

Figure 6-60 (2007bb), Woolf (2003aa) measured compressive residual stress to a depth of more 

than 7 mm [0.28 in] from the weld surface. DOE concluded that the calculated residual stress 

profile implemented in the TSPA code underestimates the extent of stress mitigation by 

plasticity burnishing. 

Summary of NRC Staff’s Review for Residual Stress Threshold 

The NRC staff reviewed the DOE approach to establish the value of the residual stress 

threshold for the waste package outer barrier. The NRC staff notes that DOE used different 

types of stress corrosion cracking initiation tests, each of which is appropriate for measuring 

stress corrosion cracking susceptibility and is consistent with applicable standards, including 

ASTM G 30-94 (ASTM International, 1994aa); ASTM E 399-90, (ASTM International, 1991aa); 

ASTM G 129-00 (ASTM International, 2000ab); and ASTM G 49-85 (ASTM International, 

2000aa). With regard to the material conditions for the stress corrosion cracking initiation tests, 

DOE tested Alloy 22 specimens with microstructures that are representative of those 

expected for the waste package on the basis of the fabrication procedures set forth in SAR 

Section 1.5.2.7. With regard to the test solutions for the corrosion tests, DOE tested Alloy 22 

specimens in a range of solutions, including simulated acidified water, simulated concentrated 

water, simulated dilute water, and basic simulated water On the basis of information published 

by Chiang, et al. (2005aa, 20006aa) and Shukla, et al. (2006aa), the residual stress threshold 

for the waste package in the repository may depend on such factors as the pH and 

concentration of ionic species in water that contacts the waste package. Therefore, the NRC 

staff reviewed the corrosion test solutions to determine whether they are adequate to measure 

the residual stress threshold in repository conditions. The NRC staff notes that the corrosion test 

solutions are more chemically aggressive than waters expected to occur within repository drifts, 

including starting water compositions in the DOE near-field chemistry model described in SAR 

Section 2.3.5.5 and waters considered in the NRC staff’s independent analysis of in-drift water 

evolution, described in TER Section 2.2.1.3.3.3.2. As such, it is reasonable for DOE to measure 

the residual stress threshold on the basis of tests in these simulated brines. 

The NRC staff reviewed DOE's use of the data from the stress corrosion cracking tests to 

establish the range for the residual stress threshold. The NRC staff notes that stress corrosion 

cracking initiation was observed only on Alloy 22 in simulated concentrated water under applied 

potential. This condition is not representative of repository conditions, because experimental 

studies showed that Alloy 22 specimens submerged in simulated concentrated water underwent 

stress corrosion cracking only when the applied potential was higher than the corrosion 

potentials (Fix, et al., 2003aa; Chiang, et al., 2005aa, 2006aa; Shukla, et al., 2006aa). 

Therefore, DOE did not observe stress corrosion crack initiation in any condition representative 
4-47 

of the repository environmental conditions. As such, the NRC staff notes that reducing the 

stress that Alloy 22 withstood without evidence of stress corrosion cracking by a safety factor of 

2 is reasonable to establish the upper bound for the residual stress threshold to be 105 percent 

of the at-temperature yield stress. Moreover, a residual stress threshold lower bound of 

90 percent of the at-temperature yield stress appropriately quantifies the range of uncertainty 

associated with the value of this parameter. The distribution from which DOE sampled the 

residual stress threshold in the TSPA code is reasonable because it is unlikely to overestimate 

the stress at which stress corrosion cracking initiates in the waste package outer barrier. 

The NRC staff reviewed the DOE analysis of fabrication-induced stresses in the waste package 

outer barrier. The NRC staff notes that a heat treatment process that follows the standards 

specified in American Society of Mechanical Engineers Boiler and Pressure Vessel Code, 

Section III, Division 1, Subsection NC-4600 (American Society of Mechanical Engineers, 

2001aa) is consistent with nuclear industry practice to relieve fabrication-induced stresses in 

components fabricated with materials similar to Alloy 22. Therefore, DOE consideration in the 

TSPA that stress corrosion cracking caused by fabrication-induced stresses could only occur in 

the region of the waste package closure weld, which is not heat treated, is reasonable. 

The NRC staff also reviewed DOE’s calculation of the closure weld residual stress profile, as 

described in SAR Section 2.3.6.5.2.3. DOE’s finite element stress analyses were performed 

with a well-established methodology that is accepted in the technical literature (e.g., NRC, 

1977aa; Rybicki and Stonsifer, 1979aa). Further, the NRC staff notes that the DOE-calculated 

residual stress profile is consistent with literature reports which show that residual stresses in 

highly controlled welds tend to persist for only a short distance from the weld line (ASM 

International, 1993aa). The NRC staff also notes that DOE provided information that shows that 

plasticity burnishing is an effective stress mitigation technique in engineered components 

(Prevey and Cammett, 2001aa) and that the stresses DOE calculated are lower than measured 

values (Woolf, 2003aa) for plasticity burnished Alloy 22 welds. Finally, the uncertainty and 

variability in the residual stress profile implemented in the TSPA code is reasonable because 

the model is consistent with weld stress analyses reported in the technical literature (Mohr, 

1996aa; Pasupathi, 2000aa). Therefore, the residual stress profile for the waste package outer 

barrier closure weld used in the TSPA code is reasonable because it will not underestimate the 

residual stress. 

Threshold Stress Intensive Fracture 

DOE determined the numerical value of the stress intensity factor threshold using a crack 

blunting criterion, as detailed in SAR Section 2.3.6.5.1 and SNL Section 6.4.5 (2007bb). 

According to the crack blunting criterion, crack growth will arrest if the crack tip radius 

decreases, because the general corrosion rate at the sides of the crack is greater than the 

rate at which the crack tip is advancing (Andresen and Ford, 1994aa). DOE calculated the 

threshold stress intensity factor as a function of the Alloy 22 general corrosion rate and the 

repassivation slope, a parameter related to the rate at which Alloy 22 repassivates following 

a passive film rupture, as shown in SNL Eq. 19 (2007bb). DOE used a point value of 7.23 nm/yr 

[2.85 × 10-7 in] for the general corrosion rate of Alloy 22, on the basis of the 5-year corrosion 

data described in SAR Section 2.3.6.3. DOE determined the value of the repassivation slope by 

measuring the crack growth rate for fatigue precracked Alloy 22 compact tension specimens at 

110 °C [230 °F] in basic simulated water and 150 °C [302 °F] in simulated concentrated water, 

as outlined in SAR Section 2.3.6.5.2.4 and SNL Section 6.4.4.2 (2007bb). For these conditions, 

the measured values of the repassivation slope are shown in SAR Table 2.3.6-17. DOE 

considered epistemic uncertainty in the repassivation slope and, in turn, the threshold stress 
4-48 

intensity factor. In the TSPA code, DOE sampled the repassivation slope from a normal 

distribution. The mean threshold stress intensity factor was calculated as 6.62 MPa-m0.5 

[6.02 ksi-in0.5], with lower and upper bounds of 1.96 MPa-m0.5 [1.78 ksi-in0.5] and 15.38 MPa-m0.5 

[14.0 ksi-in0.5], respectively. DOE stated that this range corresponds to values reported in 

the technical literature (e.g., Jones, 1992aa) for other corrosion-resistant 

chromium-nickel-iron alloys. 

DOE calculated the stress intensity factor profile for the waste package closure weld to 

determine whether the stress intensity factor could exceed the threshold value to cause crack 

propagation during the postclosure period, as described in SAR Section 2.3.6.5.2.3 and SNL 

Section 6.5 (2007bb). DOE used an approach in which it calculated the stress intensity factor 

for a relatively simple crack geometry and given stress distribution, then modified the solution 

for the waste package closure weld with a geometry correction factor, as detailed in SNL 

Section 6.5.3.3.3 (2007bb). DOE represented a radially oriented crack in the closure weld with 

the idealized case of a semicircular crack in an infinite plate, as outlined in SNL Section 6-17 

(2007bb). The geometry correction factor was obtained by comparing the simplified solution to 

finite element analysis solutions for a number of crack sizes. Using this approach, DOE 

calculated the stress intensity factor profile for the plasticity-burnished waste package closure 

weld, as shown in SAR Table 2.3.6-16. Because DOE’s calculated stress intensity factor was 

a linear function of the residual stress, uncertainty and variability in the residual stress 

profile were also represented in the stress intensity factor profile used in the TSPA code. 

SAR Figure 2.3.6-30 showed the angular variability in the stress intensity factor profile for the 

closure weld, and SAR Figure 2.3.6-32 showed the uncertainty in the stress intensity factor 

profile in the waste package closure weld. 

Summary of NRC Staff’s Review for Threshold Stress Intensive Fracture 

The NRC staff reviewed the approach to calculate the threshold stress intensity factor for the 

waste package closure weld. The stress concentration at the tip of a crack generally decreases 

with increasing crack tip radius. In addition, the crack tip radius will increase if the crack 

sidewall general corrosion rate is higher than the crack tip advance rate. Therefore, the crack 

blunting criterion that DOE used is reasonable because it is consistent with the linear elastic 

fracture mechanics theory reported in the technical literature (e.g., Andresen and Ford, 1994aa). 

Further, the NRC staff notes DOE reasonably calculated the repassivation slope by measuring 

the crack growth rate for fatigue precracked Alloy 22 compact tension specimens because the 

crack growth rates were extremely low {less than 10−8 mm/s [3.94 x 10-10 in/s]} even though the 

stress intensity factor for the cracks was significantly higher than the sampled range for the 

threshold stress intensity factor in the TSPA code. 

The NRC staff also verified the DOE calculation of the stress intensity factor profile for the waste 

package closure weld. DOE’s modeling the radial crack in the waste package closure weld as a 

semicircular crack in an infinite plate is reasonable because the hoop stress in the weld region 

decreases rapidly with increasing distance from the weld line. Additionally, the variability and 

uncertainty in the stress intensity factor profile modeled in the TSPA code adequately reflects 

associated variability and uncertainty in DOE’s calculated residual stress profile because the 

stress intensity factor profile in the TSPA code parameters were determined from mean, lower, 

and upper bounds of calculated residual stress profiles. Therefore, the stress intensity factor 

used in the TSPA code for the waste package outer barrier closure weld is reasonable, because 

it will not underestimate the stress intensity factor. 
4-49 

Crack Size and Density 

The staff reviewed DOE’s approaches to calculate the size and density of cracks initiated 

by fabrication-induced stresses and weld flaws, and those initiated by seismically induced 

stresses, respectively. 

Cracks Initiated by Fabrication-Induced Stresses and Weld Flaws 

DOE’s analytic models assumed that the cracks initiated by fabrication-induced stresses 

exceeded the residual stress threshold and have a uniform size density. DOE assumed that it is 

energetically favorable for the cracks to have elliptical shape, as shown in SNL Figure 6.3-3 

(2007aj), with crack length (i.e., major axis of the ellipse) of 50 mm [1.97 in]. DOE selected this 

length because it calculated that weld-induced stresses can persist on both sides of the weld 

centerline up to a distance approximately equal to the nominal thickness of the waste package 

outer barrier {i.e., 25 mm [0.98 in]}. The crack opening width was calculated using a fracture 

mechanics equation derived from an analysis of the energy associated with crack free surfaces. 

In this manner, DOE calculated a crack opening width of 0.1956 mm [7.70 × 10-3 in]. Given the 

crack length of 50 mm [1.97 in] and width of 0.1956 mm [7.70 × 10-3 in], DOE calculated that the 

opening area of an individual incipient crack was 7.682 mm2 [1.19 × 10-2 in2

], which is assumed 

to be constant through the waste package wall (SNL, 2007aj). DOE stated that a crack of these 

dimensions would permit diffusive transport, but preclude advective transport by water [features, 

events, and processes (FEP) 2.1.03.10.0A; SNL (2008ac)]. Moreover, DOE assumed that the 

density of through-wall cracks is constrained by stress-field interactions in the area around the 

crack, which limit the ability of cracks in relative proximity to propagate through the waste 

package wall, as described in SNL Section 6.6.1 (2007bb). DOE’s analysis indicated that the 

minimum spacing between through-wall cracks is equal to the thickness of the waste package 

outer barrier, which is 25 mm [0.98 in] (Structural Integrity Associates, 2002aa). 

DOE used a different approach to model the size and density of weld flaw cracks in the 

waste package outer barrier closure weld. For the outer closure weld, DOE determined 

that the size and density of flaws would be small because of (i) highly controlled welding 

procedures that would limit flaw generation and (ii) extensive postweld nondestructive 

examination used to identify weld flaws, as detailed in SNL Section 6.3.1 (2007aa). 

DOE stated in SAR Section 1.5.2.7 that weld fabrication and inspection will follow the 

requirements of American Society of Mechanical Engineers Boiler and Pressure Vessel Code 

Section III, Division 1, Subsection NC (American Society of Mechanical Engineers, 2001aa). 

The American Society of Mechanical Engineers (ASME) Code specifies that flaws larger than 

1.6 mm [6.30 × 10-2 in] be detected and repaired, a requirement that is incorporated as a waste 

package Design Control Parameter [SAR Table 1.9-9, Design Control Parameter 03-17(b)]. 

To determine the size and density of flaws that may be expected in the waste package closure 

welds, DOE fabricated simulated welds (Smith, 2003aa; SAR Section 2.3.6.5.2.2). DOE stated 

that postweld nondestructive examination detected all flaws in the simulated welds than were 

larger than ASME Code allowed. 

DOE performed a statistical analysis using data from the simulated welds to derive 

probability distributions for the size and density of flaws to be sampled in the TSPA 

analysis, as outlined in SNL Section 6.3.1 (2007aa). DOE used a Bayesian approach, 

consistent with weld flaw analyses in the technical literature (e.g., American Nuclear 

Society/Institute of Electrical and Electronics Engineers, 1983aa). Following this approach, 

DOE determined that a Poisson distribution best represents the undetected weld flaw density. 

DOE calculated that after performing postweld nondestructive examination, the mean size of 
4-50 

flaws that would go undetected is 1.0 mm [3.94 × 10-2 in], with 5th and 95th percentile 

values of 0.07 and 2.6 mm [2.76 × 10-3 and 1.02 × 10-1 in], respectively, as described in SNL 

Appendix A (2007aa). DOE calculated that after the nondestructive examination, there 

would be a mean of approximately one weld flaw per 140 m3 [4.94 × 103

 ft3] of weld volume, 

with 5th and 95th percentile values of approximately one weld flaw per 56 m3 [1.98 × 103

 ft3] and 

one weld flaw per 264 m3 [9.32 × 103 ft3], respectively, as detailed in SNL Appendix A (2007aa). 

Given the expected closure weld volume, DOE calculated that there is about 84 percent 

probability that a waste package will have no weld flaws, 14 percent probability that a waste 

package has one flaw, and 2 percent probability that a waste package has two or more flaws 

(SAR Table 2.3.6-18). 

In the TSPA code, DOE calculated that only radially oriented flaws (i.e., those that make an 

angle of greater than 45° with respect to the weld line) are able to propagate because the 

primary stress component in the closure weld is the hoop stress (SAR Section 2.3.6.5.3.1). 

DOE determined that there is little driving force for the propagation of cracks that make an angle 

of less than 45° with respect to the primary stress direction, as outlined in SNL Section 6.3.4.3 

(2007bb). DOE analyzed the flaws in the simulated welds to calculate a probability distribution 

for the orientation of flaws in the closure weld, as described in SNL Section 6.3.1.5 (2007aa). 

Using the Bayesian approach, DOE calculated that 0.8 percent of weld flaws will be radially 

oriented such that they can propagate under a hoop stress. DOE concluded that this calculation 

was supported by the independent analyses of Shcherbinskii and Myakishev (1970aa), who 

reported that most (~99 percent) weld flaws are oriented within about ±13° from the weld line. 

In the TSPA code, DOE also assumed that only those weld flaws exposed to the environment 

by general corrosion during the postclosure period would be susceptible to propagation, as 

outlined in SNL Section 6.3.4.2 (2007bb). In the TSPA code, DOE calculated that 25 percent of 

weld flaws will be exposed and able to propagate based upon the approximate percentage of 

the waste package weld that would be removed by general corrosion during the postclosure 

period (SAR Section 2.3.6.3). On the basis of the small number of embedded weld flaws 

capable of propagation, DOE concluded that breach of the waste package outer barrier by weld 

flaw cracks is far less likely than breach by incipient cracks initiated where the residual stress is 

greater than the residual stress threshold, as outlined in SNL Section 6.3.5.1.3[a] (2008ag). 

Summary of NRC Staff’s Review for Cracks Initiated by Fabrication-Induced Stresses and 

Weld Flaws 

The NRC staff reviewed the DOE approaches to calculate the size and density of cracks 

initiated by fabrication-induced stresses and weld flaws in the waste package outer barrier. 

Regarding cracks initiated by fabrication-induced stresses, the NRC staff notes that reports in 

the technical literature indicate that crack propagation requires energy to create the new crack 

surfaces (Anderson, 2005aa). Absent external stresses on the waste package, the NRC staff 

notes that the energy for crack propagation would come from the internal stresses in the weld. 

Thus, crack propagation will mitigate the residual stress in the weld by creating new free 

surfaces, thereby causing cracks to narrow as they propagate through the waste package outer 

barrier. DOE assumed a constant crack opening area through the waste package wall and did 

not take credit for stress mitigation and crack narrowing in its model. Therefore, the opening 

area DOE calculated is reasonable because it does not underestimate the actual crack opening 

area. The NRC staff also notes that stress mitigation by crack propagation will constrain the 

density of through-wall cracks. As such, DOE’s calculated crack density is reasonable because 

it does not underestimate the actual crack density. 
4-51 

The NRC staff also reviewed DOE’s analysis of the size and density of flaws in the waste 

package closure weld. DOE’s use of data from the simulated welds to evaluate the flaws in the 

waste package closure weld is reasonable because the simulated welds were fabricated with 

similar materials, procedures, equipment, and postwelding nondestructive evaluation methods 

as will be applied for the actual waste package welds. Further, DOE’s use of a Bayesian 

approach to develop probability distributions for the size and density of undetected weld flaws is 

reasonable and consistent with NRC guidance (American Nuclear Society/Institute of Electrical 

and Electronics Engineers, 1983aa) because this approach is appropriate when there is little 

information available regarding the characteristics of flaws in Alloy 22 welds. The NRC staff 

also notes that the DOE analysis was consistent with NRC analyses of flaws in dry storage cask 

welds, as described in NRC Appendix B (2006ab). Finally, DOE assumed a constant crack 

opening area through the waste package wall and did not take credit for stress mitigation and 

crack narrowing in its model for weld flaws. On the basis of this information, the DOE 

distributions for weld flaw size and density used in the TSPA analyses are reasonable because 

these distributions will not underestimate these parameters. 

Cracks Initiated by Seismically Induced Stresses 

For stress corrosion cracks caused by impacts to the waste package during seismic ground 

motion, DOE sampled a parameter in the TSPA code called the crack area density, which is the 

product of the crack size and the crack density, as identified in SAR Section 2.3.4.5.1.4.1 and 

SNL Section 6.7.2 (2007bb). The crack area density is a unitless scalar measure that, when 

multiplied by the size of the damaged area on the waste package surface, gives the total open 

area of the through-wall cracks. For seismically induced stress corrosion cracking, DOE 

assumed that through-wall cracks have the same shape characteristics as cracks in the closure 

welds, as in SNL Figure 6.3-3 (2007aj). In contrast to the weld cracks, however, DOE 

considered uncertainty in the size and density for the cracks induced by seismic ground motion. 

DOE evaluated the uncertainty by calculating the crack area density using two conceptual 

models in which the crack size and crack density values were varied, as described in SNL 

Section 6.7.3 (2007bb). Both conceptual models use a regular hexagonal array of cracks on the 

waste package surface because this gives high effective crack density, as described in SNL 

Section 6.7.2 (2007bb). In the first conceptual model in SNL Section 6.7.3.1 (2007bb), cracks 

abutted tip-to-tip and the distance between parallel rows of cracks was the waste package wall 

thickness. In the second conceptual model in SNL Section 6.7.3.2 (2007bb), cracks could 

overlap (crack length was two times that in the first conceptual model) and the distance between 

crack centers was the wall thickness (crack number density was lower than that assumed in the 

first conceptual model). These resulted in SNL Eq.s 37 and 40 (2007bb) that DOE used to 

calculate the lower and upper bounds, respectively, for the crack area density sampled in the 

TSPA code. 

Using this approach, DOE calculated a lower bound for crack area density of approximately 

3.27 × 10-3 (i.e., stress corrosion cracking breached area is 0.327 percent of the waste 

package damaged area) and an upper bound of approximately 1.31 × 10-2 (i.e., stress 

corrosion cracking breached area is 1.31 percent of the waste package damaged area) 

(SAR Section 2.3.4.5.1.4.1). DOE considered an alternative conceptual model in which a 

single crack circumscribed the damaged area, as described in SNL Section 6.7.4 (2007bb). 

For this conceptual model, DOE calculated a crack area density of 7.22 × 10-3, which is within 

the bounds given by the hexagonal crack network models. DOE determined that the alternative 

conceptual model provided support for the crack area density range calculated by the primary 

conceptual models. Therefore, in the TSPA code, DOE samples the crack area density from a 

uniform distribution between the bounding values given by the hexagonal crack network models. 
4-52 

Summary of NRC Staff’s Review for Cracks Initiated by Seismically Induced Stresses 

The NRC staff reviewed the DOE approach to establish the value of the crack area density for 

seismically induced stress corrosion cracks. The NRC staff notes that reports in the technical 

literature indicate that crack propagation requires energy to create new crack surfaces 

(Anderson, 2005aa). Absent external stresses on the waste package, the energy for crack 

propagation would necessarily come from the residual stresses generated from impacts to the 

waste package. Thus, crack propagation will mitigate the seismically induced stresses by 

creating new free surfaces, thereby causing cracks to narrow as they propagate through the 

waste package outer barrier. The NRC staff notes that DOE assumed a constant crack opening 

area through the waste package wall and did not take credit for stress mitigation and crack 

narrowing in its model. Moreover, stress mitigation by crack propagation will constrain the 

density of through-wall cracks. As such, DOE’s calculated range for the crack area density is 

reasonable because it does not underestimate the value of this parameter. 

Abstraction and Integration 

For the Nominal and Seismic Ground Motion Modeling Cases in the Total System Performance 

Assessment (TSPA) code, the model abstraction for stress corrosion cracking of the waste 

package closure weld was implemented in the Waste Package and Drip Shield Degradation 

Submodel, as detailed in SNL Section 6.3.5.1 (2008ag). In the submodel, the waste package 

closure weld area is represented by an annulus that is 1 patch wide and has the same radius as 

the waste package, as shown in SNL Figure 6.3.5-4 (2008ag). This results in about 40 patches 

to model the waste package closure weld. The waste package general corrosion abstraction 

and stress corrosion cracking abstraction, respectively, are implemented independently on each 

of the patches. As each patch thinned by general corrosion, the submodel calculated the 

residual stress on the patch on the basis of the through-wall residual stress profile. At each 

realization time step, the submodel compared the residual stress on the patch to the sampled 

residual stress threshold. If the residual stress on the patch was greater than the residual stress 

threshold, the submodel assumed that stress corrosion cracking initiated. The submodel also 

distributed weld flaws among the patches on the basis of the probability distributions for the 

weld flaw size and density. To determine whether initiated cracks in the waste package 

closure weld could propagate, the submodel calculated the stress intensity factor at the crack 

tip on the basis of the through-wall stress intensity factor profile, and compared it to the 

sampled threshold stress intensity factor. If the stress intensity factor was greater than the 

threshold stress intensity factor, the submodel assumed that the crack would propagate. 

The crack growth rate was calculated using the slip-dissolution film-rupture model. A breached 

patch was assumed to have cracks with a size of 7.682 mm2 [1.19 × 10-2 in2

] and spacing of 

25 mm [0.98 in] (i.e., 6 cracks per patch). The output of the model was the time of waste 

package breach and the breach area. This output was provided to the Waste Form Degradation 

and Mobilization Model Component and the Engineered Barrier System Flow and Engineered 

Barrier System Transport Submodels. 

For the Nominal Modeling Case, DOE calculated that waste packages were not breached by 

stress corrosion cracking in the closure weld (i.e., less than 1 in 10-4 probability) for 

approximately 150,000 years after repository closure, and within 1 million years, a mean of 

approximately 50 percent of waste packages were breached [SAR Figure 2.1-10(a)]. Of those 

breached waste packages, DOE calculated that the mean fraction of breached area to total 

waste package surface area was less than 10-5 over 1 million years [SAR Figures 2.1-13(b) and 

2.1-15(b)]. DOE calculated similar results for stress corrosion cracking of the closure weld in 

the Seismic Ground Motion Modeling Case, as shown in DOE Figures 1–4 (2009bj). 
4-53 

The model abstraction for stress corrosion cracking caused by impacts during seismic 

ground motion was implemented in the TSPA code in the Seismic Ground Motion 

Modeling Case, as outlined in SNL Section 6.6 (2008ag). In the Seismic Ground Motion 

Modeling Case, the residual stress threshold was sampled from a uniform distribution between 

90 and 105 percent of the Alloy 22 at-temperature yield stress. Using the sampled residual 

stress threshold, DOE calculated the size of the waste package damaged area. The crack 

area density for the given damaged area was sampled from a uniform distribution, bounded by 

3.27 × 10-3 and 1.31 × 10-2. The product of the size of the damaged area and the crack area 

density gave the total open area of the stress corrosion cracking network. The output of 

the model was the time of waste package breach and the breach area. This output was 

provided to the Waste Form Degradation and Mobilization Model Component and the 

Engineered Barrier System Flow and Engineered Barrier System Transport Submodels. 

For stress corrosion cracking induced by impacts during seismic ground motion, DOE 

calculated that a mean of approximately 10 percent of commercial spent nuclear fuel waste 

packages were breached within about 250,000 years of repository closure and for the 

codisposal waste packages, a mean of approximately 40 percent were breached within about 

150,000 years of repository closure (DOE, 2009cj). For both waste package types, the fraction 

of the waste package surface consisting of open cracks was less than 10−3

, as shown in DOE 

Figures 7 and 8 (2009cj). DOE stated that the response of the respective waste package types 

is different because commercial spent nuclear fuel waste packages are generally not damaged 

by seismic ground motion until breached by another mechanism (e.g., stress corrosion cracking 

of the closure weld) that leads to degradation of the waste package internals. Conversely, 

codisposal waste packages are structurally weaker and can be damaged by seismic ground 

motion regardless of previous damage. For both waste package types, the probability of 

seismically induced stress corrosion cracking plateaus within 250,000 years after repository 

closure because drip shields collapse and impinge the waste packages. 

Summary of NRC Staff’s Review for Abstraction and Integration for Stress 

Corrosion Cracking 

The NRC staff reviewed the implementation and integration of the model abstraction for 

stress corrosion cracking of the waste package outer barrier in the postclosure performance 

assessment. DOE has provided information for the NRC staff to understand how the 

conceptual model is implemented in the TSPA code and how the model inputs and outputs are 

integrated with other model components. The model abstraction is consistent with the design 

features of the waste package, including materials of construction and dimensions given in 

SAR Section 1.5.2.7. Further, the NRC staff notes that, with respect to the parameters used in 

the model abstraction, DOE adequately justified the ranges of these parameters and 

accounted for uncertainty in the model abstraction. Therefore, DOE’s implementation of 

the model abstraction for stress corrosion cracking of the waste package outer barrier in the 

TSPA code is reasonable because it would not underestimate the timing or magnitude of 

radionuclide release to the accessible environment. 

NRC Summary of Conclusions for Stress Corrosion Cracking of the Waste Package 

Outer Barrier 

The NRC staff reviewed the DOE model abstraction for stress corrosion cracking of the waste 

package outer barrier that was implemented in the TSPA code. DOE used appropriate 

experimental tests and other independent technical literature to provide support for its model 
4-54 

abstraction. Therefore, DOE reasonably accounted for stress corrosion cracking of the waste 

package outer barrier in the TSPA code. 

2.2.1.3.1.3.2.4 Waste Package Early Failure 

In SAR Section 2.2.2.3, DOE defined early failure to be a through-wall penetration of the waste 

package caused by manufacturing- and handling-induced defects, at a time earlier than would 

be expected for a nondefective waste package. DOE assumed that a waste package 

undergoes early failure if it is emplaced in the repository with an undetected manufacturing- or 

handling-induced defect. On the basis of the processes associated with waste package 

manufacturing and handling, DOE calculated that the probability of waste package early failure 

is best represented in the TSPA code by a lognormal distribution with a mean of 1.13 × 10−4

 per 

waste package and an error factor of 8.17, as shown in SNL Table 6-7 (2007aa). The NRC staff 

reviewed the adequacy of this probability distribution in TER Section 2.2.1.2.2.4. This section 

addresses the NRC staff’s review of the implementation of this probability distribution in the 

TSPA code. 

Waste Package Early Failure Conceptual Model 

DOE’s conceptual model for early waste package failure is that the waste package with an 

undetected manufacturing- or handling-induced defect completely fails (i.e., is removed as a 

barrier to the flow of water) at the time of repository closure (SAR Section 2.3.6.6.1). DOE 

selected this representation because there are uncertainties associated with the timing and 

extent of breach for defective waste packages and a completely degraded waste package at the 

time of repository closure will not underestimate the timing and magnitude of radionuclide 

releases, as described in SNL Section 6.5.2 (2007aa). DOE concluded that this is a 

conservative representation of the early failure because the most likely consequence of 

improper waste package manufacturing or handling would be introduction of stress corrosion 

cracking, which tends to cause tight cracks that would limit the extent of radionuclide transport 

(SAR Section 2.3.6.6.4.1). 

Summary of NRC Staff’s Review for Waste Package Early Failure Conceptual Model 

The NRC staff reviewed DOE’s conceptual model for waste package early failure. The NRC 

staff notes that DOE attributed no barrier capability to the early failed waste package. The 

NRC staff noted, however, that consequences of manufacturing- or handling-induced defects 

(e.g., increased probability of stress corrosion cracking) would likely allow the waste package to 

maintain some barrier capability, which limits radionuclide releases. Because early failed waste 

packages in the DOE model have no barrier capability, the model will not cause DOE to 

underestimate the timing or magnitude of radionuclide releases. Therefore, the DOE 

conceptual model for waste package early failure analysis is reasonable. 

Abstraction and Integration 

The DOE model abstraction for early failure of the waste package was implemented in TSPA 

code in the Waste Package Early Failure Modeling Case, as detailed in SAR Section 2.4.2.1.5.2 

and SNL Section 6.4.2 (2008ag). This modeling case uses most of same modeling components 

and submodels as were implemented in the Nominal Modeling Case. In the Nominal Modeling 

Case, however, the Waste Package and Drip Shield Degradation Submodel provides the waste 

package and drip shield breached areas as a function of time to the Engineered Barrier System 

Flow and Transport Submodels and the Waste Form Degradation and Mobilization Model 
4-55 

Components. In the Waste Package Early Failure Modeling Case, the Waste Package and Drip 

Shield Degradation Submodel was replaced with the Waste Package Early Failure model, which 

simulated early failure by treating all patches on the failed waste package as breached at the 

time of repository closure. 

DOE projected that the dose consequence of a waste package early failure would depend 

primarily upon the type of waste package, the environmental conditions at the waste package 

(e.g., temperature and relative humidity), and whether the waste package was in seepage 

conditions. Therefore, the Waste Package Early Failure Modeling Case calculated the dose 

consequence of a single early failure of a commercial spent nuclear fuel and codisposal waste 

package in each of the five percolation subregions with and without seepage conditions. The 

TSPA code then calculated the expected dose using the early failure probability [sampled from 

the distribution given in SNL Table 7-1 (2007aa)], the distribution for the waste package type, 

and the seepage fraction for each percolation bin. 

DOE calculated that there is approximately 55.8 percent probability of no waste package early 

failures, approximately 22.4 percent probability of one early failure, approximately 9.6 percent 

probability of two early failures, and approximately 12.3 percent probability of three or more 

early failures in the repository, as outlined in SNL Table 6.4-1 (2008ag). Waste package early 

failure makes a small contribution to DOE’s calculated mean annual dose within approximately 

20,000 years following closure {less than 0.001 mSv [0.1 mrem]}, with a declining contribution 

thereafter (SAR Figure 2.4-18). 

Summary of NRC Staff’s Review for Abstraction and Integration of the Waste Package 

Early Failure Model 

The NRC staff reviewed the implementation of the waste package early failure model in the 

TSPA code. DOE has provided information for the NRC staff to understand how the conceptual 

model is implemented in the TSPA code and how the model inputs and outputs are integrated 

with other model components. The NRC staff determined that the model abstraction was 

consistent with the design features of the waste package, including materials of construction 

and dimensions given in SAR Section 1.5.2.7. Further, with respect to the parameters used in 

the model abstraction, DOE’s parameter ranges and accounting of uncertainty in the model 

abstraction are reasonable. Therefore, DOE’s implementation of the Waste Package Early 

Failure model abstraction in the TSPA code is reasonable because it would not underestimate 

the timing or magnitude of radionuclide release to the accessible environment. 

NRC Summary of Conclusions for Waste Package Early Failure 

The NRC staff reviewed the DOE model abstraction for early failure of the waste package outer 

barrier that was implemented in the TSPA code. DOE provided appropriate support for the 

model abstraction. Therefore, DOE reasonably accounted for waste package early failure in the 

TSPA analysis. 

2.2.1.3.1.4 NRC Staff Conclusions 

The NRC staff notes that the DOE description of this model abstraction for the degradation of 

engineered barriers is consistent with the guidance in the YMRP. The NRC staff also notes that 

the DOE technical approach discussed in this chapter is reasonable for use in the Total System 

Performance Assessment (TSPA). 
4-56 

2.2.1.3.1.5 References 

Ahn, T., H. Jung, X. He, and O. Pensado. 2008aa. “Understanding Long-Term Corrosion of 

Alloy 22 Container in the Potential Yucca Mountain Repository for High-Level Nuclear Waste 

Disposal.” Journal of Nuclear Materials. Vol. 379. pp. 33–41. 

American Nuclear Society/Institute of Electrical and Electronics Engineers. 1983aa. 

NUREG/CR–2300, “PRA Procedures Guide: A Guide to the Performance of Probabilistic Risk 

Assessments for Nuclear Power Plants.” Vols. 1 and 2. Washington, DC: NRC. 

American Society of Mechanical Engineers. 2001aa. ASME Boiler and Pressure Vessel Code. 

New York City, New York: American Society of Mechanical Engineers. 

American Society of Mechanical Engineers. 1969aa. Criteria of the ASME Boiler and Pressure 

Vessel Code for Design by Analysis in Sections III and VIII, Division 2. New York City, 

New York: American Society of Mechanical Engineers. 

Amy, P.S., C. Pantle, T.A. Else, and C. Neuwohner. 2002aa. “Humidity and Temperature 

Boundaries for Biofilm Formation in Yucca Mountain.” TR–02–002. Rev. 0. 

LSN DN200000748. Las Vegas, Nevada: University of Nevada. 

Anderson, T.L. 2005aa. Fracture Mechanics. 3rd Edition. Boca Raton, Florida: Taylor 

and Francis. 

Andresen, P.L. 1991aa. “Fracture Mechanics Data and Modeling of Environmental Cracking of 

Nickel-Base Alloys in High Temperature Water.” Proceedings of the CORROSION 91

Conference. Paper No. 44. Houston, Texas: NACE International. 

Andresen, P.L. and F.L. Ford. 1994aa. “Fundamental Modeling of Environment Cracking for 

Improved Design and Lifetime Evaluation in BWRs.” International Journal of Pressure Vessels 

and Piping. Vol. 59, Nos. 1–3. pp. 61–70. 

Andresen, P.L. and Y.J. Kim. 2009aa. “Final Report for October 1, 2007–September 30, 2008: 

Stress Corrosion Crack Initiation and Growth Measurements in Environments 

Relevant to High-Level Nuclear Waste Packages.” GE–GRC–Sandia–2008–1. 

DTN: MO0902SCCIGMFR.000. Niskayuna, New York: General Electric Global 

Research Center. 

Andresen, P.L. and Y.J. Kim. 2007aa. “Stress Corrosion Crack Initiation and Growth 

Measurements in Environments Relevant to High-Level Nuclear Waste packages: Final Report 

for October 1, 2006–September 30, 2007.” GE–GRC–Sandia–2007–3. Niskayuna, New York: 

General Electric Global Research Center. 

Andresen, P.L. and Y.J. Kim. 2006aa. “Stress Corrosion Crack Initiation and Growth 

Measurements in Environments Relevant to High-Level Nuclear Waste packages.” 

GE–GRC–Bechtel–2006–2. MOL.20061109.0070/MOL.20061108.0004. Niskayuna, 

New York: General Electric Global Research Center. 
4-57 

Ashida, Y., L. G. McMillion, and M. Misra. 2008aa. “Communication C: A Heated Electrode 

Test System for Studying Corrosion Behavior of Alloy 22.” Nuclear Waste Research: Siting, 

Technology and Treatment. A.P. Lattefer, ed. Hauppauge, New York: Nova Science 

Publishers, Inc. pp. 25–36. 

Ashida. Y., L.G. McMillion, and M.L. Taylor. 2007aa. “The Effect of Temperature Oscillation on 

the Passive Corrosion Properties of Alloy 22.” Electrochemistry Communications. Vol. 9, 

Issue 5. pp. 1,102–1,106. 

ASM International. 1993aa. “Welding, Brazing, and Soldering.” Vol. 6. Metals Handbook. 

Metals Park, Ohio: ASM International. 

ASM International. 1987aa. “Corrosion”. Vol. 13. Metals Handbook. 9th Edition. Metals Park, 

Ohio: ASM International. 

ASTM International. 2007aa. “Standard Practice for Maintaining Constant Relative Humidity by 

Means of Aqueous Solutions.” ASTM E104–02. West Conshohocken, Pennsylvania: 

ASTM International. 

ASTM International. 2003aa. “Standard Test Method for Conducting Cyclic Potentiodynamic 

Polarization Measurements for Localized Corrosion Susceptibility of Iron-, Nickel-, or 

Cobalt-Based Alloys.” ASTM G 61-86. West Conshohocken, Pennsylvania: 

ASTM International. 

ASTM International. 2003ab. “Standard Practice for Preparing, Cleaning, and Evaluating 

Corrosion Test Specimens.” ASTM G 1-03. West Conshohocken, Pennsylvania: 

ASTM International. 

ASTM International. 2000aa. “Standard Practice for Preparation and Use of Direct Tension 

Stress-Corrosion Test Specimens.” ASTM G 49-85. West Conshohocken, Pennsylvania: 

ASTM International. 

ASTM International. 2000ab. “Standard Practice for Slow Strain Rate Testing to Evaluate the 

Susceptibility of Metallic Materials to Environmentally Assisted Cracking.” ASTM G 129-00. 

West Conshohocken, Pennsylvania: ASTM International. 

ASTM International. 1999aa. “Standard Practice for Preparing, Cleaning, and Evaluating 

Corrosion Test Specimens.” ASTM G 1-90. West Conshohocken, Pennsylvania: 

ASTM International. 

ASTM International. 1998aa. “Standard Test Method for Conducting Potentiodynamic 

Polarization Resistance Measurements.” ASTM G 59-97. West Conshohocken, Pennsylvania: 

ASTM International. 

ASTM International. 1994aa. “Standard Practice for Making and Using U-Bend 

Stress-Corrosion Test Specimens.” ASTM G 30-94. Philadelphia, Pennsylvania: 

ASTM International. 

ASTM International. 1991aa. “Standard Test Method for Plane-Strain Fracture Toughness of 

Metallic Materials.” ASTM E 399-90. West Conshohocken, Pennsylvania: ASTM International. 
4-58 

Aylor, D.M., R.A. Haysz, R.M. Kain, and R.J. Ferrara. 1999aa. “Crevice Corrosion 

Performance of Candidate Naval Ship Seawater Valve Materials in Quiescent and Flowing 

Natural Seawater.” Proceedings of the CORROSION/99 Conference. Paper No. 329. 

Houston, Texas: NACE International. 

Brossia, C.S. and G.A. Cragnolino. 2004aa. “Effect of Palladium on the Corrosion Behavior of 

Titanium.” Corrosion Science. Vol. 46. pp. 1,693–1,711. 

Brossia, C.S. and G.A. Cragnolino. 2001ab. “Effect of Palladium on the Localized and Passive 

Dissolution of Titanium.” Proceedings of the Corrosion/2001 Conference. Paper No. 01127. 

Houston, Texas: NACE International. 

Brossia, C.S. and G.A. Cragnolino. 2000aa. “Effects of Environmental, Electrochemical, and 

Metallurgical Variables on the Passive and Localized Dissolution of Ti Grate 7.” 

CORROSION/2000. Paper No. 00211. Houston, Texas: NACE International. 

Brossia, C.S., L. Browning, D.S. Dunn, O.C. Moghissi, O. Pensado, and L. Yang. 2001aa. 

“Effect of Environment on the Corrosion of Waste package and Drip Shield Materials.” 

CNWRA 2001-003. San Antonio, Texas: CNWRA. 

Brown, A.D. 1976aa. “Microbial Water Stress.” Bacteriological Reviews. Vol. 40. 

pp. 803–846. 

BSC. 2007bu. “Yucca Mountain Project Engineering Specification Prototype Drip Shield.” 

000–3SS–SSE0–00100–000. Rev. 000. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004as. “General Corrosion and Localized Corrosion of the Drip Shield.” 

ANL–EBS–MD–000004. Rev. 02. AD 01, ACN 01, ACN 02, ERD 01. Las Vegas, Nevada: 

Bechtel SAIC Company, LLC. 

Chiang, K.-T., D.S. Dunn, and G.A. Cragnolino. 2006aa. “Combined Effect of Bicarbonate and 

Chloride Ions on Stress Corrosion Cracking Susceptibility of Alloy 22.” Proceedings of the 

CORROSION/2006 Conference. Paper No. 06506. Houston, Texas: NACE International. 

Chiang, K.-T., D.S. Dunn, and G.A. Cragnolino. 2005aa. “Effect of Groundwater Chemistry on 

Stress Corrosion Cracking.” Proceedings of the CORROSION/2005 Conference. Paper 

No. 05463. Houston, Texas: NACE International. 

Codell, R.B. and B.W. Leslie. 2006aa “Drip Shield Corrosion from Fluoride in Dents.” 

Proceedings of 11th International High-Level Radioactive Waste Management Conference, 

Las Vegas, American Nuclear Society. 

Costa, D. and P. Marcus. 1993aa. “Modification of Passive Films Formed on Ni-Cr-Fe Alloys 

With Chromium Content in the Alloy and Effects of Adsorbed or Segregated Sulphur.” 

Proceedings of the European Symposium on Modifications of Passive Film, Paris, France, 

February 15–17, 1993. P. Marcus, B. Barous, and M. Keddam, eds. London, England: 

Institute of Materials, Minerals, and Mining. pp. 17–25. 
4-59 

Cragnolino, G.A., D.S. Dunn, C.S. Brossia, V. Jain, and K.S. Chan. 1999aa. “Assessment 

of Performance Issues Related to Alternate Engineering Barrier System Materials and 

Design Options.” CNWRA 99-003. San Antonio, Texas: Center for Nuclear Waste 

Regulatory Analyses. 

Dixit, S., S. Roberts, K. Evans, T. Wolery, and S. Carroll. 2006aa. “General Corrosion and 

Passive Film Stability–FY05 Summary Report.” UCRL–TR–217393. Livermore, California: 

Lawrence Livermore National Laboratory. 

DOE. 2010ae. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.3.6.8), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.3.1, Set 2.” Letter (February 22) J.R. Williams to 

J.H. Sulima (NRC). ML100540266. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bj. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.4.4) Safety Evaluation Report Vol. 3, 

Chapters 2.2.1.4.1, 2.2.1.4.2, and 2.2.1.4.3, Set 1.” Letter (July 29) J.R. Williams to J.H. Sulima 

(NRC). ML092110472. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cj. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.6.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.1, Set 3.” Letter (May 7) J.R. Williams to J.H. Sulima (NRC). ML091280184. 

ML091280185. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cl. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.6.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.1, Set 2.” Letter (April 13) J.R. Williams to J.H. Sulima (NRC). ML091100634. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009dg. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.6.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.1, Set 2.” Letter (April 8) J.R. Williams to J.H. Sulima (NRC). ML090980537. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cm. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.6.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.1, Set 4.” Letter (September 10) J.R. Williams to J.H. Sulima (NRC). 

ML092540339. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cn. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.6.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.1, Set 1.” Letter (March 25) J.R. Williams to J.H. Sulima (NRC). 

ML090840553. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 
4-60 

Dunn, D.S., Y.-M. Pan, X. He, L.T. Yang, and R.T. Pabalan. 2006ab. “Evolution of Chemistry 

and Its Effects on the Corrosion of Engineered Barrier Materials.” The 30th Symposium on the 

Scientific Basis for Nuclear Waste Management Materials Research Society 2006 Fall Meeting, 

Boston, Massachusetts, November 27–December 1, 2006. Pittsburgh, Pennsylvania: Materials 

Research Society. 

Dunn, D.S., O. Pensado, Y.-M. Pan, R.T. Pabalan, L. Yang, X. He, and K.T. Chiang. 2005aa. 

“Passive and Localized Corrosion of Alloy 22—Modeling and Experiments.” Rev. 01. 

CNWRA 2005-002. San Antonio, Texas: Center for Nuclear Waste Regulatory Analyses. 

Dunn, D.S., G.A. Cragnolino, and N. Sridhar. 2000aa. “An Electrochemical Approach to 

Predicting Long-Term Localized Corrosion of Corrosion-Resistant High-Level Waste Container 

Materials.” Corrosion. Vol. 56. pp. 90–104. 

Elibiache, A. and P. Marcus. 1992aa. “The Role of Molybdenum in the Dissolution and the 

Passivation of Stainless Steels With Adsorbed Sulphur.” Corrosion Science. Vol. 33, No. 2. 

pp. 261–269. 

Else, T.A., C.R. Pantle, and P.S. Amy. 2003aa. “Boundaries for Biofilm Formation: Humidity 

and Temperature.” Applied and Environmental Microbiology. Vol. 69, No. 8. pp. 5,006–5,010. 

Evans, K.J., A. Yilmaz, S.D. Day, L.L. Wong, J.C. Estill, and R. Rebak. 2005aa. “Using 

Electrochemical Methods to Determine Alloy 22's Crevice Corrosion Repassivation Potential.” 

Journal of Metals. Vol. 57, No. 1. pp. 56–61. 

Evans, K.J., M.L. Stuart, R.A. Etien, G.A. Hust, J.C. Estill, and R.B. Rebek. 2005ab. 

“Long-Term Corrosion Potential and Corrosion Rate of Creviced Alloy 22 in Chloride Plus 

Nitrate Brines.” UCRL–CONF–216910. Livermore, California: Lawrence Livermore 

National Laboratory. 

Fix, D.V., J.C. Estill, G. A. Hust, K.J. King, S.D. Day, and R.B. Rebak. 2003aa. “Influence of 

Environmental Variables on the Susceptibility of Alloy 22 to Environmentally Assisted Cracking.” 

Proceedings of the CORROSION 2003/Conference. Paper No. 03542. Houston, Texas: 

NACE International. 

Fontana, M.G. and N.D. Greene. 1978aa. Corrosion Engineering. 2nd Edition. New York City, 

New York: McGraw Hill. 

Ford, F.P. and P.L. Andresen. 1988aa. “Development and Use of a Predictive Model of Crack 

Propagation in 304/316L, A533B/A508, and Inconel 600/182 Alloys in 288 ̊C Water.” 

Proceedings of the Third International Symposium on Environmental Degradation of Materials in 

Nuclear Power Systems—Water Reactors, Traverse City, Michigan, August 30–September 3, 

1988. G.J. Theus and J.R. Weeks, eds. Warrendale, Pennsylvania: The Metallurgical Society. 

pp. 789–800. 

Gray, J.J., J.R. Hayes, G.E. Gdowski, B.E. Viani, and C.A. Orme. 2006aa. “Inhibiting Effects of 

Nitrates on the Passive Film Breakdown of Alloy 22 in Chloride Environments.” Journal of the 

Electrochemical Society. Vol. 153, No. 5. pp. B156–B161. 
4-61 

Greenspan, L. 1977aa. “Humidity Fixed Points of Binary Saturated Aqueous Solutions.” 

Journal of Research of the National Bureau of Standards—A: Physics and Chemistry. 

Vol. 81A. pp. 89–96. 

Haynes International. 1997aa. “Hastelloy Alloy C-276.” Kokomo, Indiana: 

Haynes International. 

Haynes International. 1997ab. “Hastelloy C-22 Alloy.” Kokomo, Indiana: Haynes International. 

He, X. and D.S. Dunn. 2005aa. “Crevice Corrosion Penetration Rates of Alloy 22 in 

Chloride-Containing Waters–Progress Report.” CNWRA 2006-001. San Antonio, Texas: 

Center for Nuclear Waste Regulatory Analyses. 

He, X., B. Brettmann, and H. Jung. 2009aa. “Effects of Test Methods on Crevice Corrosion 

Repassivation Potential Measurements of Alloy 22.” Corrosion. Vol. 65. pp. 449–460. 

He, X., J.J. Noel, and D.W. Shoesmith. 2007aa. "Temperature Effects on Oxide Film 

Properties of Grade-7 Titanium." Corrosion. Vol. 63. pp. 781–792. 

He, X, D.S. Dunn, and A.A. Csontos. 2007ab. “Corrosion of Similar and Dissimilar Metal 

Crevices in the Engineered Barrier System of a Potential Nuclear Waste Repository.” 

Electrochimica Acta. Vol. 52. pp. 7,556–7,569. 

Horn, J., S. Martin, C. Carrillo, and T. Lian. 2005aa. “Microbial Effects on Nuclear Waste 

Packaging Materials.” UCRL–TR-213915. Rev 1. Livermore, California: Lawrence Livermore 

National Laboratory. 

Hua, F. and G. Gordon. 2004aa. “Corrosion Behavior of Alloy 22 and Ti Grade 7 in a Nuclear 

Waste Repository Environment.” Corrosion. Vol. 60, No. 8. pp. 764–777. 

Hunkeler, F. and H. Boehni. 1983aa. “Pit Growth Measurements on Stainless Steels.” 

Passivity of Metals and Semiconductors, Proceedings of the Fifth International Symposium on 

Passivity, Bombannes, France, May 30–June 3, 1983. M. Froment, ed. New York City, 

New York: Elsevier. pp. 655–660. 

Hur, D.H. and Y.S. Park. 2006aa. “Effect of Temperature on the Pitting Behavior and Passive 

Film Characteristics of Alloy 600 in Chloride Solution.” Corrosion. Vol. 62, No. 9. pp. 745–750. 

Ishikawa, H., A. Honda, and N. Sasaki. 1994aa. “Long Life Prediction of Carbon Steel 

Overpacks for Geological Isolation of High-Level Radioactive Waste.” Life Prediction of 

Corrodible Structures. R.N. Parkins, ed. Houston, Texas: NACE International. Vol. 1. 

pp. 454–483. 

Jones, D.A. 1996aa. Principles and Prevention of Corrosion. 2nd Edition. Upper Saddle River, 

New Jersey: Prentice Hall. 

Jones, R.H., ed. 1992aa. “Stress-Corrosion Cracking.” Materials Park, Ohio: 

ASM International. 

Jones, R.H. and R.E. Ricker. 1987aa. “Stress-Corrosion Cracking.” Metals Handbook, Vol. 13: 

Corrosion—9th Edition. Metals Park, Ohio: ASM International. pp. 145–163. 
4-62 

Jung, H. 2010aa. “A Review for Long-Term Persistence of Passive Film of Alloy 22 Under 

Potential YM Repository Environments.” Scientific Notebook No. 835. San Antonio, Texas: 

Center for Nuclear Waste Regulatory Analyses. 

Jung, H., T. Mintz, L. Yang and T. Ahn. 2008aa. “Long-Term Persistence of the Passive Film 

on Alloy 22 at Elevated Temperatures in the Potential Yucca Mountain Repository 

Environment.” ML090220175 and ML090220186. Washington, DC: NRC. 

Jung, H., T. Mintz, D.S. Dunn, O. Pensado, and T. Ahn. 2007aa. “A Review of the Long-Term 

Persistence of the Passive Film on Alloy 22 in Potential Yucca Mountain Repository 

Environments.” ML072880595. Washington, DC: NRC. 

Lee, S.G. and A.A. Solomon. 2006aa. “Localized Corrosion of Alloy C22 Nuclear Waste 

Canister Material Under Limiting Conditions.” Materials Science and Engineering. Vol. A 434. 

pp. 114–123. 

Lian, T., M.T. Whalen, and L.L. Wong. 2005aa. “Effects of Oxide Film on the Corrosion 

Resistance of Titanium Grade 7 in Fluoride-Containing NaCl Brines.” Proceedings of the 

Corrosion/2005, 60th Annual Conference and Exposition, 1945–2005, April 3-7, 2005. Paper 

No. 05609. Houston, Texas: NACE International. 

Lian, T., S. Martin, D. Jones, A. Rivera, and J. Horn. 1999aa. “Corrosion of Candidate 

Container Materials by Yucca Mountain Bacteria.” Proceedings of the CORROSION/1999 

Conference. Paper No. 476. Houston, Texas: NACE International. 

Lin C., B. Leslie, R. Codell, H. Arlt, and T. Ahn. 2003aa. “Potential Importance of Fluoride to 

Performance of the Drip Shield.” Proceedings of the American Nuclear Society 10th

International High-Level Radioactive Waste Management Conference, March 30–April 2, 2003, 

Las Vegas, Nevada. 

Lloyd, A.C., J.J. Noël, S. McIntyre, and S.W. Shoesmith. 2004aa. “Cr, Mo and W Alloying 

Additions In Ni and Their Effect on Passivity.” Electrochimica Acta. Vol. 49. pp. 3,015–3,027. 

Lloyd, A.C., D.W. Shoesmith, N.S. McIntyre, and J.J. Noel. 2003aa. “Effects of Temperature 

and Pote+B25ntial on the Passive Corrosion Properties of Alloys C22 and C276.” Journal of the 

Electrochemical Society. Vol. 150. pp. B120–130. 

Lorenzo de Mele, M.F. and M.C. Cortizo. 2000aa. “Electrochemical Behavior of Titanium in 

Fluoride-Containing Saliva.” Journal of Applied Electrochemistry. Vol. 30. pp. 95–100. 

Macdonald, D.D. and M. Urquidi-Macdonald. 1991aa. “A Coupled Environment Model for 

Stress Corrosion Cracking in Sensitized Type 304 Stainless Steel in LWR Environments.” 

Corrosion Science. Vol. B33 32, No. 1. pp. 51–81. 

Macdonald, D.D., M. Urquidi-Macdonald, and P.-C. Lu. 1994aa. “The Coupled Environmental 

Fractural Model—A Deterministic Method for Calculating Crack Growth Rates.” Proceedings of 

the CORROSION/1994 Conference. Paper No. 246. Houston, Texas: NACE International. 
4-63 

Marcus, P. 2001aa. “Long-Term Extrapolation of Passive Behavior: Proceedings From an 

International Workshop on Long-Term Extrapolation of Passive Behavior.” Presented at the 

Nuclear Waste Technical Review Board Meeting, Arlington, Virginia, July 19–20, 2001. Paris, 

France: Universite Pierre et Marie Curie. pp. 55–60. 

Marcus, P. and J.M. Grimal. 1990aa. “The Antagonistic Roles of Chromium and Sulfur in the 

Passivation of Ni-Cr-Fe Alloys Studies by XPS and Radiochemical Techniques.” Corrosion 

Science. Vol. 31. pp. 377–382. 

Marcus, P. and M. Moscatelli. 1989aa. “The Role of Alloyed Molybdenum in the Dissolution 

and the Passivation of Nickel-Molybdenum Alloys in the Presence of Adsorbed Sulfur.” Journal 

of the Electrochemical Society. Vol. 136, No. 6. pp. 1,634–1,637. 

Marcus, P. and H. Talah. 1989aa. “The Sulfur Induced Breakdown of the Passive Film and 

Pitting Studied on Nickel and Nickel Alloys.” Corrosion Science. Vol. 29. pp. 455–463. 

Marcus, P., H. Talah, and J. Oudar. 1988aa. “Breakdown of the Passive Film on Nickel and 

Nickel Alloys Induced by Sulfur.” Key Engineering Materials. Vols. 20–28, Issue 4. 

pp. 3,947–3,952. 

Marcus, P., A. Testier, and J. Oudar. 1984aa. “The Influence of Sulphur on the Dissolution and 

the Passivation of a Nickel-Iron Alloy—Electrochemical and Radiotracer Measurements.” 

Corrosion Science. Vol. 24, No.4. pp. 259–268. 

Marcus, P., I. Olefjord, and J. Oudar. 1984ab. “The Influence of Sulfur on the Dissolution and 

the Passivation of a Nickel-Iron Alloy—Sulfur Analysis by ESCA.” Corrosion Science. Vol. 24, 

No. 4. pp. 269–278. 

Marcus, P., J. Oudar, and I. Olefjord. 1980aa. “Studies of the Influence of Sulfur on the 

Passivation of Nickel by Auger Electron Spectroscopy and Electron Spectroscopy for Chemical 

Analysis.” Materials Science and Engineering. Vol. 42. pp. 191–197. 

Marsh, G.P., K.J. Taylor, and A.H. Harker. 1991aa. “The Kinetics of Pitting Corrosion of 

Carbon Steel Applied to Evaluating Containers for Nuclear Waste Disposal.” SKB TR–91–62. 

Stockholm, Sweden: Svensk Kärnbränsleförsörjning AB. 

Martin, S., J. Horn, and C. Carrillo. 2004aa. “Micron-Scale MIC of Alloy-22 After Long-Term 

Incubation in Saturated Nuclear Waste Repository Microcosms.” Proceedings of the 

CORROSION/2004 Conference. Paper No. 04596. Houston, Texas: NACE International. 

Mattsson, H. and I. Olefjord. 1990aa. “Analysis of Oxide Formed on Ti During Exposure in 

Bentonite Clay-I. The Oxide Growth.” Werkstoffe und Corrosion. Vol. 41, No. 7. Weinheim, 

Germany: VCH Verlagsgesellschaft mbH. pp. 383–390. 

McMillion, L.G., A. Sun, D.D. Macdonald, and D.A. Jones. 2005aa. “General Corrosion of 

Alloy 22: Experimental Determination of Model Parameters From Electrochemical Impedance 

Spectroscopy Data.” Metallurgical and Materials Transactions A. Vol. 36A. pp. 1,129–1,141. 

Mintz, T.M. and T.M. Devine. 2004aa. “Influence of Surface Films on the Susceptibility of 

Inconel 600 to Stress Corrosion Cracking.” Key Engineering Materials. Vols. 261–263. 

pp. 875–884. 
4-64 

Mintz, T. and X. He. 2009aa. “Modeling of Hydrogen Uphill Diffusion in Dissimilar Titanium 

Welds.” Proceedings of the CORROSION/2009 Conference. Paper No. 09430. Houston, 

Texas: NACE International. 

Miserque, F., B. Huet, G. Azou, D. Bendjaballah, and V. L’Hostis. 2006aa. “X-Ray 

Photoelectron Spectroscopy and Electrochemical Studies of Mild Steel FeE500 Passivation in 

Concrete Simulated Water.” Journal de Physique IV Proceedings. Vol. 136. pp. 89–97. 

Mohr, W.C. 1996aa. “Internal Surface Residual Stresses in Girth Butt-Welded Steel Pipes.” 

Residual Stresses in Design, Fabrication, Assessment and Repair, PVP. Vol. 321. New York 

City, New York: American Society of Mechanical Engineers. pp. 37–44. 

Montemor, M.F., M.G.S. Ferreira, M. Walls, B. Rondot, and M. Cunha Belo. 2003aa. “Influence 

of pH on Properties of Oxide Films Formed on Type 316L Stainless Steel, Alloy 600, and 

Alloy 690 in High-Temperature Aqueous Environments.” Corrosion. Vol. 59, No. 1. pp. 11–21. 

Mughabghab, S.F. and T.M. Sullivan. 1989aa. “Evaluation of the Pitting Corrosion of Carbon 

Steels and Other Ferrous Metals in Soil Systems.” Waste Management. Vol. 9, No. 4. 

Elmsford, New York: Pergamon Press. pp. 239–251. 

Mulford, S.J. and D. Tromans. 1988aa. “Crevice Corrosion of Nickel-Based Alloys in Neutral 

Chloride and Thiosulfate Solutions.” Corrosion. Vol. 44, No. 12. pp. 891–900. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2006ab. NUREG–1864, “A Pilot Probabilistic Risk Assessment of a Dry Cask Storage 

System at a Nuclear Power Plant.” Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

ML051360241. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

NRC. 1977aa. NUREG–0376, “Residual Stresses at Girth-Butt Welds in Pipes and Pressure 

Vessels.” Rev. 5. Washington, DC: NRC. 

Oka, Y.I., S. Mihara, and H. Miyata. 2007aa. “Effective Parameters for Erosion Caused 

by Water Droplet Impingement and Applications to Surface Treatment Technology.” Wear. 

Vol. 263. pp. 386–394. 

Orme, C.A. 2005aa. “The Passive Film on Alloy 22.” UCRL–TR–215277. Livermore, 

California: Lawrence Livermore National Laboratory. 

Pabalan, R.T. 2010aa. “Quantity and Chemistry of Water Contacting Engineered Barriers 

Integrated Subissue.” Electronic Scientific Notebook 930E. San Antonio, Texas: Center for 

Nuclear Waste Regulatory Analyses. 
4-65 

Pasupathi, V. 2000aa. “Documentation of Literature on Residual Stress Measurements.” 

Interoffice Correspondence (May 19) to G.M. Gordon. LV.WASTE PACKAGE.VP.05/00-070, 

With Enclosures. LSN DEN001312636. Las Vegas, Nevada: CRWMS M&O. 

Pedersen, K. and F. Karlsson. 1995aa. “Investigation of Subterranean Microorganisms: Their 

Importance for performance Assessment of Radioactive Waste Disposal.” SKB 95-10. 

Stockholm, Sweden: Swedish Nuclear Fuel and Waste Management Company. 

Pensado, O., D.S. Dunn, G.A. Cragnolino, and V. Jain. 2002aa. “Passive Dissolution 

of Container Materials—Modeling and Experiments.” CNWRA 2003-01. San Antonio, Texas: 

Center for Nuclear Waste Regulatory Analyses. 

Pourbaix, M. 1974aa. Atlas of Electrochemical Equilibria in Aqueous Solutions. Houston, 

Texas: NACE International. 

Prevey, P.S. and J. Cammett,. 2001aa. “Low Cost Corrosion Damage Mitigation and Improved 

Fatigue Performance of Low-Plasticity Burnished 7075-T6.” Journal of Materials Engineering 

and Performance. Vol. 10. pp. 548–555. 

Pulvirenti, A.L., K.M. Needham, D.S. Wong, M.A. Adel-Hadadi, A. Barkatt, C.R. Marks, and 

J.A. Gorman. 2003aa. “Fluoride Corrosion of Ti-Grade 7: Effects of Other Ions.” 

CORROSION/2003, 58th Annual Conference and Exposition, San Diego, California, 

March 16–20, 2003. Paper No. 03686. Houston, Texas: NACE International. 

Pulvirenti, A.L., K.M. Needham, M.A. Adel-Hadadi, A. Barkatt, C.R. Marks, and J.A. Gorman. 

2002aa. “Corrosion of Titanium Grade 7 in Solutions Containing Fluoride and Chloride Salts.” 

CORROSION/2002, 57th Annual Conference and Exposition, Denver, Colorado, April 7–11, 

2002. Paper No. 02552. Houston, Texas: NACE International. 

Rebak, R.B. 2005aa. “Factors Affecting the Crevice Corrosion Susceptibility of Alloy 22.” 

Proceedings of the CORROSION/2005 Conference. Paper No. 05610. Houston, Texas: 

NACE International. 

Rybicki, E.F. and R.B. Stonesifer. 1979aa. “Computation of Residual Stresses Due to 

Multipass Welds in Piping Systems.” Journal of Pressure Vessel Technology. Vol. 101. 

pp. 149–154. 

Sala, B., S. Chevalier, A. Gelpi, H. Takenouti, and M. Keddam. 1999aa. “Electrochemical 

Study of the Corrosion Processes of the Secondary Side of Steam Generators.” Proceedings of 

the 9th International Conference on Environmental Degradation of Materials in Nuclear Power 

Systems—Water Reactors. S.M. Bruemmer, F.P. Ford, and G.S. Was, eds. Warrendale, 

Pennsylvania: Materials Research Society. pp. 587–598. 

Sala, B., S. Chevalier, M. Organista, A. Gelpi, A. Stutzmann, and M. Dupin. 1998aa. “Study of 

the Corrosion Process of the Secondary Side of Steam Generators: An Electrochemical Study,” 

JAIF International Conference on Water Chemistry in Nuclear Power Plants, Water 

Chemistry ’98. Kashiwazaki City, Niigata: Japan Atomic Industrial Forum. p. 498–504. 

Sala, B., P. Combrade, A. Gelpi, and M. Dupin. 1996aa. “The Use of Tube Examinations and 

Laboratory Simulations to Improve the Knowledge of Local Environments and Surface 

Reactions in TSPs.” Control of Corrosion on the Secondary Side of Steam Generators. 
4-66 

R.W. Staehle, J.A. Gorman, and A.R. McIlree, eds. Houston, Texas: NACE International. 

pp. 483–497. 

Sala, B., R. Erre, and A. Gelpi. 1993aa. “Local Chemistry and Formation of Deposits on the 

Secondary Side of Steam Generators.” Proceedings of the 6th International Conference on 

Environmental Degradation of Materials in Nuclear Power Systems—Water Reactors. 

R.E. Gold and E.P. Simonen, eds. Warrendale, Pennsylvania: Materials Research Society. 

pp. 215–226. 

Schutz, R.W. 2005aa. “Corrosion of Titanium and Titanium Alloys.” ASM 

Handbook—Vol. 13B: Corrosion. Materials. Materials Park, Ohio: ASM International. 

Schutz, R.W. and J.S. Grauman. 1986aa. “Corrosion Behavior of Titanium and Other Alloys in 

Laboratory FGD Scrubber Environments.” Materials Performance. Vol. 25, No. 4. Houston, 

Texas: NACE International. pp. 35–42. 

Schutz, R.W. and D.E. Thomas. 1987aa. “Corrosion of Titanium and Titanium Alloys.” ASM 

Handbook—Corrosion: Vol. 13. Materials Park, Ohio: ASM International. Pp. 669–706. 

Scully, J.R., G. Ilevbare, and C. Marks. 2001aa. “Passivity and Passive Corrosion of 

Alloys 625 and 22.” SEAS Report No. UVA/527653/MSE01/103. Charlottesville, Virginia: 

University of Virginia, School of Engineering & Applied Science. 

Shack, W.J., W.A. Ellington, and L.E. Pahis. 1980aa. “Measurement of Residual Stresses in 

Type 304 Stainless Steel Butt Weldments.” EPRI NP–1413. Palo Alto, California: Electric 

Power Research Institute. 

Shan, X. and J. H. Payer. 2007aa. “Comparison of Ceramic and Polymer Crevice Formers on 

the Crevice Corrosion Behavior of Ni-Cr-Mo Alloy C-22.” Proceedings of the 

CORROSION/2007 Conference. Paper No. 07582. Houston, Texas: NACE International. 

Sharland, S.M., C.C. Naish, K.J. Taylor, and G.P. Marsh. 1994aa. “An Experimental and 

Modelling Study of the Localized Corrosion of Carbon Steel Overpacks for the Geological 

Disposal of Radioactive Waste.” Life Prediction of Corrodible Structures. R.N. Parkins, ed. 

Houston, Texas: NACE International. pp. 402–418. 

Shcherbinskii, V.G. and V.M. Myakishev. 1970aa. “Statistical Distribution of Welding Defects 

with Respect to Azimuth.” Translated from Defektoskopiya, No. 4. New York City, New York: 

Plenum Publishing. pp. 143–144. 

Shukla, P.K., D.S. Dunn, K.-T. Chiang, and O. Pensado. 2006aa. “Stress Corrosion Cracking 

Model for Alloy 22 In the Potential Yucca Mountain Repository Environment.” Proceedings of 

the CORROSION/2006 Conference. Paper No. 06502. Houston, Texas: NACE International. 

Smailos, E. 1993aa. “Corrosion of High-Level Waste Packaging Materials in Disposal Relevant 

Brines.” Nuclear Technology. Vol. 104. pp. 343–350. 

Smailos, E. and R. Köster. 1987aa. “Corrosion Studies on Selected Packaging Materials for 

Disposal of High Level Wastes.” Materials Reliability in the Back End of the Nuclear Fuel Cycle. 

Proceedings of a Technical Committee Meeting, Vienna, Austria, September 2–5, 1986. 

TECHDOC–421. Vienna, Austria: International Atomic Energy Agency. pp. 7–24. 
4-67 

Smith, D. 2003aa. “Weld Flaw Evaluation and Nondestructive Examination Process 

Comparison Results for High-Level Radioactive Waste Package Manufacturing Program.” 

TDR–EBS–ND–000007. Rev. 01. ACC: ENG.20030515.0003. Las Vegas, Nevada: 

Bechtel SAIC Company. 

SNL. 2009aa. “Unexpected Test Results—Heterogeneous Alloy 22 Oxide Thickness.” 

CR 12799. DEN001614731. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2009ab. “Unexpected Test Results—Residue on Subset of Alloy 22 Coupons.” 

CR 12868. DEN001614752. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ac. “Features, Events, and Processes for the Total System Performance 

Assessment: Methods.” ANL–WIS–MD–000026. Rev. 00. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007aa. “Analysis of Mechanisms for Early Waste package/Drip Shield Failure.” 

ANL–EBS–MD–000076. Rev. 00. ACN 01, ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007aj. “EBS Radionuclide Transport Abstraction.” ANL–WIS–PA–000001. Rev. 03. 

ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ak. “Engineered Barrier System: Physical and Chemical Environment.” 

ANL–EBS–MD–000033. Rev. 06. ERD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007al. “General Corrosion and Localized Corrosion of Waste package Outer 

Barrier.” ANL–EBS–MD–000003. Rev. 03. ACN 01, ERD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007bb. “Stress Corrosion Cracking of Waste package Outer Barrier and Drip Shield 

Materials.” ANL–EBS–MD–000005. Rev. 04. ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2004ab. “Evaluation of Potential Impacts of Microbial Activity on Drift Chemistry.” 

ANL–EBS–MD–000038. Rev. 01. ACN 02, ERD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

Sprowls, D.O. 1987aa. “Evaluation of Stress-Corrosion Cracking.” Metals 

Handbook—Corrosion: Vol. 13. 9th Edition. Metals Park, Ohio: ASM International. 

pp. 245–282. 

Structural Integrity Associates. 2002aa. “Structural Integrity Associates Support of Waste 

package Design for Year 2001.” LSN DEN001314737. San Jose, California: Structural 

Integrity Associates. 
4-68 

Thomas, D.E. and H.B. Bomberger. 1983aa. “The Effects of Chlorides and Fluorides on 

Titanium Alloys in Simulated Scrubber Environments.” Materials Performance. Houston, 

Texas: NACE International. pp 29–36. 

U.S. Nuclear Waste Technical Review Board. 2001aa. “Proceedings From the International 

Workshop on Long-Term Extrapolation of Passive Behavior Conference.” Arlington, Virginia, 

July 19–20, 2001. A.A. Sagues and C.A. Di Bella, eds. Arlington, Virginia: U.S. Nuclear Waste 

Technical Review Board. 

Wong, L., T. Lian, D.V. Fix, M. Sutton, and R. Rebak. 2004aa. “Surface Analysis of Alloy 22 

Coupons Exposed for Five Years to Concentrated Ground Waters.” Proceedings of the 

CORROSION 2004 Conference. Paper No. 04701. Houston, Texas: NACE International. 

Woolf, R. 2003aa. “Controlled Plasticity Burnishing (CPB) for Developing a Very Deep Layer of 

Compressive Residual Stresses in Rectangular Specimens of Alloy 22 for Yucca Mountain 

Nuclear Waste package Closure Weld.” SET Job No. 37. LSN DN2002365233. Cincinnati, 

Ohio: Surface Enhancement Technologies. 

Yang, L., D. Dunn, G. Cragnolino, X. He, Y.-M. Pan, A. Csontos, and T. Ahn. 2007aa. 

“Corrosion Behavior of Alloy 22 in Concentrated Nitrate and Chloride Salt Environments at 

Elevated Temperatures.” Proceedings of the CORROSION/2007 Conference. Paper No. 

077580. Houston, Texas: NACE International. 

Yang, L., R. Pabalan, and M. Juckett. 2006aa. “Deliquescence Relative Humidity 

Measurements Using an Electrical Conductivity Method.” Journal of Solution Chemistry. 

Vol. 35, No. 4. pp. 583–604. 
5-1 

CHAPTER 5 

2.2.1.3.2 Mechanical Disruption of Engineered Barriers 

2.2.1.3.2.1 Introduction 

This chapter of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s evaluation of the proposed Engineered Barrier System (EBS) the 

U.S. Department of Energy (DOE) described in its Safety Analysis Report (SAR), Section 2.3.4 

(DOE, 2008ab). The design aspects of the EBS were described in SAR Sections 1.3.4 and 

1.5.2, while the performance aspects were described in SAR Sections 2.1, 2.3.4, 2.3.5, 2.3.6, 

and 2.3.7. DOE stated that the following engineered barrier system features contribute to 

barrier performance: emplacement drifts, drip shields, waste packages, waste forms, waste 

form internals, waste package pallets, and emplacement drift inverts. According to DOE, the 

engineered barrier system features are designed to work together with the natural barriers by 

preventing or substantially reducing the release rate of radionuclides from the repository to the 

accessible environment. A disruption of the engineered barrier system components has the 

potential to affect their barrier performance. DOE anticipates that mechanical disruption of 

engineered barrier system components could generally result from external loads generated 

by accumulating rock rubble. Rubble accumulation can result from processes such as 

(i) degrading emplacement drifts due to thermal loads, (ii) time-dependent natural weakening of 

rocks, and (iii) effects of seismic events (vibratory ground motion or fault displacements). This 

TER chapter evaluates the performance of the various engineered barrier system components 

under a reasonable range of anticipated loading conditions. 

To estimate the timing and extent of rubble accumulation, rocks in the repository block (RB) 

need geologic characterization. DOE characterized the repository rock mass as consisting of 

two major rock types: lithophysal and nonlithophysal. Lithophysal rocks (approximately 

85 percent of the repository emplacement area) are characterized as relatively more deformable 

rocks with low compressive strength because of the voids of varying sizes contained within the 

rock. The nonlithophysal rocks (approximately 15 percent) are characterized as hard, strong, 

and jointed rocks. According to DOE, these two rock types are expected to behave differently 

under thermal and seismic loads and thus require different modeling approaches to account for 

different modes of failure (e.g., rock blocks separating from the mass and falling due to gravity, 

gradual unraveling or, tensile failure during vibratory loads). On the basis of geologic mapping 

and testing, DOE categorized the lithophysal rocks into five categories on the basis of rock 

mass qualities (to represent the variability in mechanical properties). DOE has conducted 

laboratory and in-situ testing on small and large rock samples and developed a range of input 

parameters for the numerical models. DOE has presented several approaches to estimate the 

timing and extent of degradation, including numerical modeling results. 

According to DOE, the functions of the drip shield are to prevent rocks from falling on the waste 

packages and to prevent water from contacting the waste package surface soon after 

emplacement when waste packages are still hot, thereby minimizing the potential for corrosion. 

The purpose of the waste package is to protect the waste form and isolate the radionuclides or 

slow down their rate of release to the accessible environment. To estimate the effects on timing 

and magnitude of radionuclide release, DOE analyses considered potential loads from seismic 

events and the resulting mechanical disruption of the engineered barrier system components. 

DOE considered gradual drift degradation due to thermal loads, time-dependent weakening, 

and seismic events as sources of generating loads from rubble accumulation on and around the 
5-2 

drip shields. However, DOE excluded the effects of drift degradation due to thermal loads and 

time-dependent weakening from its Total System Performance Assessment (TSPA) code. The 

NRC staff’s review of DOE’s technical bases for exclusion of features, events, and processes 

(FEPs) (FEP 2.1.07.02.0A, Drift Collapse) is presented in TER Section 2.2.1.2.1.3.2. The scope 

of this TER chapter is limited to reviewing how DOE considered the effects of seismic disruption 

(i.e., vibratory ground motion and fault displacement) and used the results in the Total System 

Performance Analysis. 

The U.S. Nuclear Regulatory Commission (NRC) staff’s review followed the guidance provided 

in the Yucca Mountain Review Plan (YMRP) (NRC, 2003aa). YMRP Section 2.2.1 provides 

guidance to the NRC staff on applying risk information throughout the review of the performance 

assessment. The NRC staff used DOE’s risk information derived from an initial review of DOE’s 

treatment of multiple barriers, as appropriate. The NRC staff’s review approach is to assess the 

DOE design and analyses of engineered barrier system components under anticipated 

demands generated by drift degradation due to seismic loads. For those cases in which the 

design capacities may be exceeded, the NRC staff examined the potential for continued 

functionality of the components under a range of anticipated conditions. On the basis of the risk 

insights developed, the NRC staff’s review focuses primarily on the seepage barrier functionality 

of the drip shield and the potential for loads from accumulated rubble to be transferred onto the 

waste package. In considering the range of possible loads and temperature conditions that can 

be generally anticipated during the repository life, the NRC staff takes into account uncertainty 

and variability in (i) rock characterization data, (ii) laboratory and in-situ test results, 

(iii) modeling approaches and conceptualization of failure modes, (iv) independent NRC staff’s 

verifications, and (v) professional judgment based on experience and an understanding of 

excavated rock behavior under thermal and seismic loads. 

2.2.1.3.2.2 Evaluation Criteria 

NRC staff’s assessment of model abstractions used in DOE’s postclosure performance 

assessment, including those considered in this chapter for mechanical disruption of engineered 

barriers is guided by criteria in 10 CFR 63.114 (Requirements for Performance Assessment) 

and 63.342 (Limits on Performance Assessments). The DOE Total System Performance 

Assessment is reviewed in TER Section 2.2.1.4.1. 

The regulations for performance assessment in 10 CFR 63.114 require that a 

performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment in 10 CFR 63.342, and evaluate in 
5-3 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

 The NRC staff’s evaluation of DOE’s inclusion or exclusion of FEPs is in TER 

Chapter 2.2.1.2.1. 10 CFR 63.114(a) sets forth requirements for performance 

assessment for the initial 10,000 years following disposal. 10 CFR 63.114(b) and 

63.342 set forth requirements for the performance assessment methods for the time 

from 10,000 years through the period of geologic stability, defined in 10 CFR 63.302 as 

1 million years following disposal. These sections require that through the period of 

geologic stability, with specific limitations, the DOE dose calculation is to 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

 Include in the performance assessment those features, events, and processes used in 

the performance assessment for the initial 10,000-year period 

This model abstraction of mechanical disruption of engineered barriers involves seismic and 

igneous activity. Thus, 10 CFR 63.342(a) and (b) also apply to this abstraction as these 

regulations require that DOE provide criteria for assessing the effects of seismic and igneous 

activity on the repository performance, subject to the probability limits in 10 CFR 63.342(a) and 

(b). Specific constraints on the analysis required for seismic and igneous activity analyses are 

given in 10 CFR 63.342(c)(1)(i) and 10 CFR 63.342(c)(1)(ii), respectively. 

The NRC staff’s review of the SAR and supporting information follows the guidance laid out in 

YMRP Sections 2.2.1.3.2, Mechanical Disruption of Engineered Barriers, as supplemented by 

additional guidance for the period beyond 10,000 years after permanent closure (NRC, 2009ab). 

The YMRP acceptance criteria for model abstractions provide guidance on information NRC 

staff could use to evaluate the performance assessment. Following the guidance, the NRC 

staff’s review of DOE’s abstraction of mechanical disruption of engineered barriers considered 

five criteria 

1. System description and model integration are adequate. 

2. Data are sufficient for model justification. 

3. Data uncertainty is characterized and propagated through the abstraction. 

4. Model uncertainty is characterized and propagated through the abstraction. 

5. Model abstraction output is supported by objective comparisons. 

10 CFR Part 63 specifies the use of a risk-informed approach for the review of a license 

application. The NRC staff review used the guidance provided by the YMRP, as supplemented 

by NRC (2009ab), to the extent reasonable for aspects of mechanical disruption of engineered 

barriers important to repository performance. The NRC staff considered all five criteria provided 

in the YMRP in its review of information provided by DOE. In the context of these criteria, only 

those aspects of the model abstraction that substantively affect the performance assessment 

results, as determined by the NRC staff, are discussed in detail in this chapter. The NRC staff’s 

determination is based both on risk information provided by DOE and on NRC staff’s knowledge 

gained through experience and independent confirmatory analyses. 
5-4 

2.2.1.3.2.3 Technical Evaluation 

2.2.1.3.2.3.1 Seismic and Fault Displacement Inputs for Mechanical Disruption of 

Engineered Barriers 

DOE investigated the geological, geophysical, and seismic characteristics of the Yucca 

Mountain region to obtain sufficient information to estimate how the site would respond to 

vibratory ground motions from earthquakes. In SAR Section 1.1.5.2, DOE provided its 

description of site seismology. DOE described its analysis of potential seismic hazards in SAR 

Section 1.1.5.2.4, the overall approach to developing a seismic hazard analysis for Yucca 

Mountain in SAR Section 2.2.2.1, and the conditioning (adaption or modification) of the ground 

motion hazard at Yucca Mountain in SAR Section 1.1.5.2.5.1. Additional information was 

provided in DOE responses to the NRC staff’s request for additional information (RAI) in DOE 

Enclosure 19 (2009ab) and DOE Enclosures 6, 7, and 8 (2009aq), and the references 

cited therein.

The DOE overall approach to developing a seismic hazard analysis for Yucca Mountain, 

including fault displacement hazards as described in SAR Section 2.2.2.1, involves the following 

three steps: 

1. Conducting an expert elicitation in the late 1990s to develop a probabilistic seismic 

hazard analysis (PSHA) for Yucca Mountain. This assessment included a probabilistic 

fault displacement hazard analyses (PFDHA) (CRWMS M&O, 1998aa; BSC, 2004bp). 

The probabilistic seismic hazard analysis was developed for a reference bedrock 

outcrop, specified as a free-field site condition with a mean shear wave velocity (VS) of 

1,900 m/sec [6,233 ft/sec] and located adjacent to Yucca Mountain. This value was 

derived from a Vs profile of Yucca Mountain with the top 300 m [984 ft] of tuff and 

alluvium removed, as provided in Schneider, et al., Section 5 (1996aa). 

2. Conditioning probabilistic seismic hazard analysis ground motion results to constrain the 

large low-probability ground motions to ground motion levels that, according to DOE, are 

more consistent with observed geologic and seismic conditions at Yucca Mountain, as 

provided in BSC ACN02 (2005aj). 

3. Modifying the conditioned probabilistic seismic hazard analysis results using 

site-response modeling. This accounts for site-specific rock material properties of 

the tuff, in and beneath the emplacement drifts, and the site-specific rock and soil 

material properties of the strata beneath the site. 

Probabilistic Seismic Hazard Analysis Methodology 

DOE conducted an expert elicitation on probabilistic seismic hazard analysis in the late 1990s 

(CRWMS M&O, 1998aa; BSC, 2004bp) on the basis of the methodology described in the 

Yucca Mountain Site Characterization Project (DOE, 1997aa). DOE stated that its probabilistic 

seismic hazard analysis methodology followed the guidance of the DOE-NRC-Electric Power 

Research Institute-sponsored Senior Seismic Hazard Analysis Committee (Budnitz, et al., 

1997aa). In SAR Section 2.2.2.1.1.1, DOE concluded that the methodology used for the 

probabilistic seismic hazard analysis expert elicitation is consistent with NRC expert elicitation 

guidance, which is described in NUREG–1563 (NRC, 1996aa). 
5-5 

To conduct the probabilistic seismic hazard analysis, DOE convened two panels of experts as 

described in SAR Section 2.2.2.1.1.1. The first expert panel consisted of six 3-member teams 

of geologists and geophysicists (seismic source teams) that developed probabilistic distributions 

to characterize relevant potential seismic sources in the Yucca Mountain region. These 

distributions included location and activity rates for fault sources, spatial distributions and 

activity rates for background sources, distributions of moment magnitude and maximum 

magnitude, and site-to-source distances. The second panel consisted of seven seismology 

experts (ground motion experts) who developed probabilistic point estimates of ground motion 

for a suite of earthquake magnitudes, distances, fault geometries, and faulting styles. These 

point estimates incorporated random uncertainties that were specific to the regional crustal 

conditions of the western Basin and Range. The ground motion attenuation point estimates 

were then fitted to yield the ground motion attenuation equations used in the probabilistic 

seismic hazard analysis. The two expert panels were supported by technical teams from DOE, 

the U.S. Geological Survey, and Risk Engineering Inc., which provided the experts with relevant 

data and information; facilitated the formal elicitation, including a series of workshops designed 

to accomplish the elicitation process; and integrated the hazard results. 

According to the DOE-NRC-Electric Power Research Institute-sponsored Senior Seismic 

Hazard Analysis Committee (Budnitz, et al., 1997aa), the basic elements of the probabilistic 

seismic hazard analysis process are (i) identification of seismic sources such as active faults or 

seismic zones; (ii) characterization of each of the seismic sources in terms of their activity, 

recurrence rates for various earthquake magnitudes, and maximum magnitude; (iii) ground 

motion attenuation relationships to model the distribution of ground motions that will be 

experienced at the site when a given magnitude earthquake occurs at a particular source; and 

(iv) incorporation of the inputs into a logic tree to integrate the seismic source characterization 

and ground motion attenuation relationships, including associated uncertainties. According to 

the Budnitz, et al. (1997aa) methodology, each logic tree pathway represents one expert’s 

weighted interpretations of the seismic hazard at the site. The computation of the hazard for all 

possible pathways results in a distribution of hazard curves that is representative of the seismic 

hazard at a site, including variability and uncertainty. 

NRC Staff’s Evaluation of DOE’s Probabilistic Seismic Hazard Analysis Methodology 

The NRC staff reviewed DOE’s probabilistic seismic hazard analysis (PSHA) methodology 

described in SAR Sections 1.1.5.2.4 and 2.2.2.1.1 using the guidance provided in the YMRP 

and NUREG–1563 (NRC, 1996aa). The NRC staff also evaluated the DOE probabilistic seismic 

hazard analysis development to ensure that it included the four basic elements described in 

Budnitz, et al. (1997aa). In addition, the NRC staff observed all expert elicitation meetings and 

reviewed summary reports of those meetings as they were produced. On the basis of this 

information, including the evaluation with respect to Budnitz, et al. (1997aa) and NRC staff’s 

direct observations of the expert elicitation process, the NRC staff notes that DOE’s elicitation 

for the probabilistic seismic hazard analysis is consistent with the framework for conducting an 

expert elicitation described in NUREG–1563 (NRC, 1996aa), as referenced in the YMRP. 

Probabilistic Seismic Hazard Analysis—Input Data and Interpretations 

During the expert elicitation, DOE’s seismic source teams considered a range of information 

from many resources including DOE, the U.S. Geological Survey, project-specific Yucca 

Mountain studies, and information published in the scientific literature. This information included 

(i) data and models for the geologic setting as summarized in BSC (2004bp); (ii) seismic 

sources and seismic source characterization, including earthquake recurrence and maximum 
5-6 

magnitude (BSC, 2004bp); (iii) historical and instrumented seismicity, as described in 

CRWMS M&O Appendix G (1998ab); (iv) paleoseismic data (Keefer, et al., 2004aa); and 

(v) ground motion attenuation (Spudich, et al., 1999aa). DOE also supported the probabilistic 

seismic hazard analysis with a broad range of data, process models, empirical models, and 

seismological theory (CRWMS M&O, 1998ab). The expert panels built their respective inputs to 

the probabilistic seismic hazard analysis on the basis of this information and information 

presented to the experts during the elicitation meetings (CRWMS M&O, 1998ab). The resulting 

set of hazard curves was intended to provide DOE with sufficient representation of the seismic 

hazard for use in the TSPA analysis. 

DOE expressed the probabilistic seismic hazard analysis (PSHA) curves in increasing levels 

of ground motion as a function of the annual probability that the ground motion will be 

exceeded. These curves were developed for bedrock conditions with a mean shear wave 

velocity (Vs) of 1,900 m/sec [6,233 ft/sec]. Such rocks are located adjacent to Yucca Mountain 

as described previously in the probabilistic seismic hazard analysis methodology subsection of 

this TER section. Estimates of uncertainty in the hazard curves are also included (see SAR 

Figure 1.1-74; e.g., hazard curves). The SAR provided probabilistic seismic hazard analysis 

results on horizontal and vertical components of peak acceleration (defined at 100 Hz); 

spectral accelerations (SAs) at frequencies of 0.3, 0.5, 1, 2, 5, 10, and 20 Hz; and peak ground 

velocity (PGV). 

NRC Staff’s Evaluation of Input Data and Interpretations 

The NRC staff reviewed DOE’s probabilistic seismic hazard analysis input data and 

interpretations as described in SAR Sections 1.1.5.2 and 2.2.2.1.1. The NRC staff notes 

that DOE adequately developed geological, geophysical, and seismological information to 

support the expert elicitation. This conclusion is based in part on the NRC staff’s evaluations 

of this information in NUREG–1762 (NRC, 2005aa). In NUREG–1762, the NRC staff 

concluded that the existing DOE information was consistent with site conditions at Yucca 

Mountain. This adequacy is also based on the NRC staff’s first-hand knowledge of the 

geology and seismic characteristics of the Yucca Mountain region, which includes more than 

a decade of independent geological and geophysical research and study (e.g., Ferrill, et al., 

1999ab, 1996aa; Stamatakos, et al., 2007aa, 1998aa,1997ab; Waiting, et al., 2003aa; Gray, 

et al., 2005aa). The resulting suite of ground motion hazard curves; horizontal and vertical 

components of peak acceleration (defined at 100 Hz); spectral accelerations at frequencies 

of 0.3, 0.5, 1, 2, 5, 10, and 20 Hz; and peak ground velocity is reasonable because it is 

consistent with NRC guidance outlined in Regulatory Guide 1.165 (NRC, 1997ab) and 

Regulatory Guide 3.73 (NRC, 2003ae). 

The NRC staff also reviewed additional geological, geophysical, and seismological 

information discovered since the elicitation was performed (Wernicke, et al., 2004aa). 

On the basis of the NRC staff’s detailed understanding of the Yucca Mountain geology, 

new geological and seismological information would not substantially alter the probabilistic 

seismic hazard analysis results. 

Conditioning of Ground Motion Hazard

DOE provided in SAR Section 1.1.5.2.5.1 the conditioning of ground motion hazard at the 

reference bedrock outcrop where the probabilistic seismic hazard analysis was developed. 

Since completion of the probabilistic seismic hazard analysis in 1998, several studies and 

reports, including ones from NRC staff (NRC, 1999aa), the Nuclear Waste Technical Review 
5-7 

Board Panel on Natural System and Panel on Engineered Systems (Coraddini, 2003aa), and 

DOE itself (BSC, 2004bj), questioned whether the very large ground motions the probabilistic 

seismic hazard analysis predicted at low annual exceedance probabilities (below ~10−6

/yr) were 

physically realistic. These ground motion values are well beyond the limits of existing 

earthquake accelerations and velocities from even the largest recorded earthquakes worldwide. 

They are deemed physically unrealizable because they require a combination of earthquake 

stress drop, rock strain, and fault rupture propagation that cannot be sustained without 

wholesale fracturing of the bedrock (Kana, et al., 1991aa). 

The NRC staff notes that overly conservative earthquake ground motions arise in the DOE 

study because the seismic hazard curves were constructed as unbounded lognormal 

distributions. In past practice, probabilistic seismic hazard curves were used to estimate ground 

motions with annual exceedance probability to 10−4

 or 10−5 (typical annual exceedance 

probability values for nuclear power plant design and safe shutdown earthquakes). 

For Yucca Mountain, however, the seismic hazard curves were extrapolated to estimate 

ground motions with annual exceedance probabilities as low as 10−8

 (SAR Section 1.1.5.2.5.1). 

At these low probabilities, the seismic hazard estimates are driven by the tails of the 

untruncated Gaussian distributions (the tail is not defined by the data, but by the assumed 

distribution) of the input ground motion attenuation models (Bommer, et al., 2004aa). 

As Anderson and Brune (1999aa) pointed out, overestimates of the hazards may also arise 

because of the way in which uncertainty in ground motion attenuation from empirical 

observations or theory is distributed between aleatory and epistemic uncertainties. 

To account for these large ground motions, DOE modified or conditioned the hazard using both 

a shear-strain-threshold approach and an extreme-stress-drop approach, as described in SAR 

Section 1.1.5.2.5.1. Rather than reconvene the probabilistic seismic hazard analysis expert 

elicitation and redo the probabilistic seismic hazard analysis, DOE chose to treat the issue as 

part of the ground response analysis. Accordingly, DOE’s second step in developing ground 

motion inputs for postclosure analysis, after the development of the probabilistic seismic hazard 

analysis, is to condition the ground motion hazard. This second step in the three-step DOE 

process includes information on the level of extreme ground motion that is consistent with the 

geological setting of Yucca Mountain. Conditioning of ground motion hazard is a unique study 

developed for the Yucca Mountain project. 

NRC Staff’s Evaluation of Conditioning Methodology 

The NRC staff reviewed DOE’s methods for conditioning its probabilistic seismic hazard 

analysis results in SAR Section 1.1.5.2.5.1; reviewed DOE’s responses to the NRC staff’s RAIs, 

DOE Enclosures 6 and 8 (2009aq); and notes the methods are reasonable because they are 

based on the shear-strain-threshold and extreme-stress-drop approaches, both of which are 

technically sound methods based on fundamental principles of physics and are widely accepted. 

Results of Probabilistic Seismic Hazard Analysis Conditioning 

The unconditioned hazard curve DOE developed, which is the annual probability of exceedance 

(APE) as a function of ground motion, is convolved with the distribution of extreme ground 

motion for the reference bedrock outcrop to produce the conditioned ground motion hazard of 

the same bedrock outcrop. The shear-strain-threshold conditioning has a marginal impact as 

compared to the extreme-stress-drop approach. For example, for an annual probability of 

exceedance of 10−8

, the shear-strain-threshold conditioned peak ground velocity hazard is 
5-8 

reduced from 1,200 cm/sec to about 1,100 cm/sec [472 to 433 in/sec] or about 10 percent; the 

Stress-drop-conditioned peak ground velocity hazard is reduced from 1,200 cm/sec to about 

480 cm/sec [472 to 189 in/sec] or about 60 percent, as identified in BSC Section A4.5.1 

(2008bl). The impact of conditioning at low probabilities is less significant and increases as the 

probability decreases (i.e., annual probabilities of exceedance of 10−5

, 10−6, 10−7, and 10−8) 

(SAR Section 1.1.5.2.5.1). SAR Figures 1.1-79 and 1.1-80 compared the unconditioned and 

conditioned peak ground accelerations (PGAs) and peak ground velocity (PGV) mean hazard 

curves for the reference bedrock outcrop. 

BSC Appendix A (2008bl) outlined the workshop proceedings, which included presentations, 

discussions, and assessments, that were conducted to develop the expert judgment. The 

stress drop data from the United States and other countries were used in the expert judgment. 

The parameter variability involved in the empirical ground motion attenuation relationship and 

numerical simulations of ground motions that the experts relied on was included in the 

conditioning. Variability in velocity profile, stress drop, source depth, and kappa (the site- and 

distance-dependent parameter representing the effect of intrinsic attenuation of the wave field 

as it propagates through the crust from source to the receiver) were considered in the modeling 

to map the stress drop into ground motion distribution. 

In response to NRC RAIs (DOE, 2009aq), DOE provided information showing DOE’s application 

of the two methods in series where the output of the extreme-stress-drop conditioning becomes 

the input of the shear-strain-threshold conditioning. In the RAI responses, DOE also clarified 

and updated the formulations for the two conditioning methods, as described in BSC 

Appendix A (2008bl). 

NRC Staff’s Evaluation of Results of Conditioning 

The conditioned hazard curves for postclosure Total System Performance Assessment (TSPA) 

calculations are appropriate because the conditioning methods follow basic mechanical, 

material, and seismological principles in which (i) the shear-strain-threshold approach is based 

on laboratory rock mechanics data and corroborated by numerical modeling and (ii) the 

extreme-stress-drop approach is based on the worldwide observations of stress drop from large 

earthquakes (SAR Section 1.1.5.2.5.1). The final conditioned ground motion levels at very low 

annual probability of exceedance (APE) continue to be very conservative when compared with 

the observed worldwide strong motion data, which include records from earthquakes much 

greater than those expected in the Yucca Mountain region. DOE assumed that the tectonic 

setting and therefore the stress drops of earthquakes from the existing faults at Yucca Mountain 

are not going to change significantly during the next million years. This assumption is 

reasonable given the basic tectonics in the Yucca Mountain region and provides the basis for 

the conditioning at very low annual probability of exceedance. 

Summary of NRC Staff’s Evaluation of Probabilistic Seismic Hazard Analysis 

The NRC staff reviewed SAR Sections 1.1.5.2 and 2.2.2.1 and DOE’s responses to the NRC 

staff’s request for additional information (RAIs) and notes that the methodologies, input data, 

interpretation of the probabilistic seismic hazard analysis, and conditioning of the probabilistic 

seismic hazard analysis results for the Yucca Mountain site are reasonable. DOE used 

appropriate methods for relying on the collective judgment of appropriate experts by following 

an established procedure to elicit and document the experts’ conclusions. DOE supported the 

expert elicitation program with sufficient technical and scientific information. 
5-9 

2.2.1.3.2.3.1.1 Seismic Site-Response Modeling 

To address the effects of earthquakes during the postclosure period, DOE performed 

site-response analysis, which incorporates the effects of the upper rock and soil layers on 

the input ground motion at the reference rock (the conditioned ground motion hazards 

discussed previously). 

Overall Approach to Site-Response Modeling 

In SAR Section 1.1.5.2.5.2 DOE discussed how the various types and thicknesses of rocks, 

alluvium, and soils that comprise the site would likely respond to earthquake ground motions. 

The results of site-response modeling include understanding and quantifying the amplification or 

damping factor of ground motion and how much the vertical-to-horizontal motion ratio varies 

from place to place. DOE used the site-specific ground motion curves that are consistent with 

the conditioned probabilistic seismic hazard analysis ground motion hazard curves. 

NRC Staff’s Evaluation of Site Response Modeling Approach 

The NRC staff reviewed DOE’s overall approach to site-response modeling using the guidance 

of NUREG/CR–6728 (McGuire, et al., 2001aa) and the YMRP. DOE’s adoption of Approach 3 

from NUREG/CR–6728 is a reasonable approach and is recommended by NUREG/CR–6728. 

The NRC staff also notes that the two frequency ranges (1–2 and 5–10 Hz) used in the 

calculations of input control motions are consistent with NRC guidance provided in NRC 

Regulatory Guide 1.165, Appendix C (NRC, 1997ab). 

On the basis of the NRC staff’s review described previously, DOE’s method used to perform 

seismic site-response modeling, which is based on the random vibration theory, is reasonable. 

The NRC staff notes that the random-vibration-theory-based point-source and one-dimensional 

equivalent-linear site-response model combined with Approach 3 from NUREG/CR–6728 and 

the conditioned hazard as input are appropriate for use in characterizing the seismic ground 

motions at Yucca Mountain, because it accounts for the site response adequately without 

underpredicting the response. DOE’s site-response model is reasonable for use in the 

performance assessment. 

Ground Motion Inputs 

DOE provided ground motion inputs developed for the repository block in SAR 

Section 1.1.5.2.6. For the site surface, 52 combinations of site properties were evaluated 

in the site-response modeling (SAR Section 1.1.5.2.6.1). These combinations were from two 

base case velocity profiles (south and northeast of the Exile Hill Fault splay), two base case sets 

of dynamic material property curves for tuff and alluvium separately, four values of alluvium 

thickness northeast of the fault splay, and three values of alluvium thickness south of the fault 

splay (resulting in a total of seven combinations). Each combination incorporated aleatory 

variability by averaging the amplification factors from 60 randomized velocity profiles and 

dynamic material property curves. 

Site-Specific Hazard Curves 

The seven combinations of alluvium and tuff hazard curves were combined into two sets: 

the northeast and south fault splay sets. The four and three combinations of hazard 

curves for four and three alluvium thicknesses were enveloped separately for south and 
5-10 

northeast of the fault splay. These two sets of hazard curves were enveloped again to 

produce mean horizontal and vertical hazard curves (BSC, 2008bl). The final mean horizontal 

and vertical hazard curves for peak ground accelerations (PGAs); 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 

and 3.3 seconds spectral acceleration (SA); and peak ground velocity were provided in BSC 

Figures 6.5.2-34 to 6.5.2-42 (2008bl) for the surface facilities area and BSC Figures 6.5.3-9 

to 6.5.3-16 (2008bl) for the repository block. The data for these plots are in 

DTN: MO0801HCUHSSFA.001, as identified in BSC Section 6.5.2.2 (2008bl). 

Earthquake Time Histories 

The repository block time histories for postclosure analyses were developed differently for 

annual probability of exceedance (APE) of 10−5

, 10−6, and 10−7 (SAR Section 1.1.5.2.6.2), where 

17 sets of time histories were developed: one horizontal (H1) component of each seed time 

history was scaled according to the peak ground velocity from site-response modeling and the 

other two components were scaled to maintain the inter-component variability of the seed time 

history (SAR Section 1.1.5.2.6.2). 

NRC Staff’s Evaluation of Ground Motion Inputs 

The processes and procedures DOE used to develop site-specific hazard curves, time histories, 

strain-compatible soil properties for the site, and the ground motions for postclosure analyses 

are reasonable. DOE used an averaging process to account for the data (velocity profiles and 

dynamic material properties) and site-response model uncertainties and enveloping process to 

accommodate the alluvium thickness change (spatial variability) when it developed the hazard 

curves. Then DOE followed the recommended (McGuire, et al., 2001aa) routine procedures in 

engineering seismology for ground motion inputs. The strain-compatible soil properties are the 

products of the previously described site-response analysis. 

2.2.1.3.2.3.1.2 Fault Displacement Hazard Analysis 

Fault displacement (the relative displacement between opposite sides of a fault) is a potential 

hazard to the underground facility because it could damage or shear drifts and/or waste 

packages, trigger rockfall within the drifts and shafts, degrade drift walls and ground-support 

systems, and degrade other engineered barrier system components. These hazards might 

affect the postclosure performance of the engineered barriers.

Probabilistic Fault Displacement Hazard Analyses—Input Data and Interpretations 

The probabilistic fault displacement hazard analyses integrated two data types: (i) known 

and/or documented faulting activity consisting of measurements of regional and local 

earthquakes, and measurements of fault displacements within the last ~1.8 million years and 

(ii) inferred potential faulting activity, on the basis of analysis of mapped geological faults, 

overall tectonic setting, and regional estimates of ongoing crustal strain. DOE analyzed 

100 earthquakes in the Basin and Range region to determine the relationships among the 

amounts and patterns of both principal and distributed fault displacements, the minimum 

magnitude at which an earthquake may produce surface faulting, and the maximum magnitude 

at which an earthquake does not displace the surface. 

For the largest mapped faults at Yucca Mountain, the probabilistic fault displacement hazard 

curves were largely based on the same detailed paleoseismic and earthquake data used to 

characterize these faults as potential seismic sources. The expert elicitation relied on both 
5-11 

anecdotal evidence and expert judgment to develop conceptual models of distributed faulting 

and to estimate the probabilities of secondary faulting of smaller faults and fractures in the 

repository (Youngs, et al., 2003aa; CRWMS M&O, 1998aa). 

DOE chose the following nine sites around Yucca Mountain as demonstration sites of the 

application of the probabilistic fault displacement hazard analyses, as identified in SAR 

Chapter 1, Table 1.1-67, p. 1.1-304: (i) Bow Ridge fault, (ii) Solitario Canyon fault, (iii) Drill Hole 

Wash fault, (iv) Ghost Dance fault, (v) Sundance fault, (vi) an unnamed fault west of Dune 

Wash, (vii) a location 100 m [328 ft] east of the Solitario Canyon fault, (viii) a location between 

Solitario Canyon fault and Ghost Dance fault, and (ix) a location within Midway Valley. These 

demonstration sites were selected to represent a range of faulting and related fault deformation 

conditions at the site, including large block-bounding faults, such as the Solitario Canyon and 

Bow Ridge faults; smaller mapped faults within the repository footprint, such as the Ghost 

Dance fault; and unmapped minor faults near the larger faults, fractured tuff, and intact tuff. 

Results of the probabilistic fault displacement hazard analyses (CRWMS M&O, 1998aa) show 

that, except for the Bow Ridge and Solitario Canyon faults, mean fault displacements are less 

than 1 m [3.28 ft] over the next 10 million years (SAR Table 2.2-15). Mean displacements for 

the demonstration sites within the current repository footprint [demonstration sites (v), (vii), and 

(viii) as identified in the previous paragraph] do not exceed 0.40 m [1.3 ft] in 10 million years. 

For a 10,000-year period, the mean displacements are calculated to be less than 0.01 m 

[0.03 ft] for all 9 demonstration sites (SAR Table 1.1-67). 

Individual fault displacement hazard curves were developed to characterize fault displacements 

at each of the nine demonstration sites. These fault displacement hazard curves are analogous 

to seismic hazard curves, in which increasing levels of fault displacements are computed as a 

function of the annual probability that those displacements will be exceeded. Example fault 

displacement curves for the nine demonstration sites were provided in SAR Figure 2.2-13. 

NRC Staff’s Evaluation of DOE’s Probabilistic Fault Displacement Hazard Analyses 

The NRC staff evaluated DOE’s input to the probabilistic fault displacement hazard analyses in 

the SAR and supporting documents. The NRC staff also conducted its independent analysis of 

slip tendency (Morris, et al., 1996aa) and faults within the Yucca Mountain region (Morris, et al., 

2004aa). The input data to the probabilistic fault displacement hazard analyses and their 

interpretation are appropriate because (i) the DOE approach used representative data, (ii) the 

methods used to interpret the data were rigorous, and (iii) the interpretations made are 

consistent with the regional characteristics. On the basis of the NRC staff’s review, as 

described previously, DOE’s methodology, input data, and interpretations of the probabilistic 

fault displacement hazard analyses are reasonable for use in the performance assessment. 

2.2.1.3.2.3.2 Fault Displacement Considerations in TSPA 

This section reviews the information provided in SAR Section 2.3.4.3 (and selected references) 

to evaluate the adequacy of the conceptual model of seismic fault displacement. DOE 

considered seismic fault displacement as one of the modeling cases in the TSPA. DOE 

assumed that fault displacement occurred concurrently with the ground motion during a low 

probability seismic event (SAR Section 2.3.4.5.5.1). DOE considered that only the waste 

packages located directly above faults were subject to damage from fault displacement. DOE 

expected the dose related to fault displacement to be a small fraction of the total dose for the 

seismic scenario class because damage from fault displacement affected a small fraction of the 
5-12 

engineered barrier system and damage occurred only for events with very low exceedance 

frequencies. DOE calculated the dose contribution from the seismic fault displacement on the 

basis of simplified calculations (SAR Section 2.4.2.2.1.2.2.2). The abstraction assumed waste 

package damage when fault displacement exceeded the available waste package clearance. 

To evaluate fault displacement, DOE assumed (i) the faults are perpendicular to the drift axis 

with the displacement being vertical; (ii) the fault displacement occurs at a discrete plane, 

creating a sharp discontinuity; and (iii) clearances are based on emplacement drifts that are fully 

collapsed at the time of the seismic event (SAR Section 2.3.4.5.5.2.1.1).

The potentially relevant features, events, and processes in DOE’s TSPA model were listed in 

SAR Table 2.2-1. In this abstraction, DOE evaluated and included FEP 1.2.02.03.0A, Fault 

Displacement Damages Engineered Barrier System Components. 

Conceptual Model 

Clearance 

DOE analyzed the clearances between the engineered barrier system components for intact 

and failed drip shield scenarios (SAR Section 2.3.4.5.5.2.1). For intact drip shield 

configurations, DOE defined the clearance as the interior height of the drip shield less the 

outside diameter of the waste package outer corrosion barrier without a pallet, to account for 

part of the substantial movement of the rubble (SAR Section 2.3.4.5.5.2.1.1). On the basis of 

this simplification, DOE used a maximum allowable displacement with drift collapse and an 

intact drip shield that varied from 67.3 to 96.9 cm [26.5 to 38.15 in] (SAR Table 2.3.4-52), 

according to the type of waste package. 

NRC Staff’s Evaluation of Clearance 

The NRC staff compared the values of DOE’s clearances in SAR Table 2.3.4-52 to 

the engineered barrier system geometry presented in SAR Figure 2.3.4-53 that showed the 

distances between the top of the waste packages and bottom of the drip shields {35.6 to 

68.6 cm [14 to 27 in]}. Additionally, the NRC staff considered the potential for the rubble to 

accommodate some of the fault displacement through compaction. On the basis of a 

confirmatory calculation, the NRC staff estimates that approximately 72–186 cm [28.3–73.2 in] 

of fault displacement can be accommodated through rubble compaction and the distance 

between the top of the waste package and the bottom of the drip shield crown. The values DOE 

used are adequate for their intended use because they fall within the range of potential 

displacements, on the basis of the design geometries. 

For failed drip shield configurations, DOE stated that no free space existed between the top of 

the waste package and bottom of the drip shield. DOE concluded that rubble movement will 

accommodate some amount of fault displacement due to rubble consolidation (SAR 

Section 2.3.4.5.5.2.1.2). DOE stated that fault displacement had to exceed one-quarter of the 

outer diameter of the outer corrosion barrier to cause waste package failure {from 43.7 to 

51.1 cm [17.2 to 20 in]} (SAR Tables 2.3.4-50 and 2.3.4-53). This approach is reasonable on 

the basis of a simplified, bounding calculation DOE presented in SNL Section 6.11.1.2 (2007ay). 

DOE showed that the potential porosity values for the rubble, if compacted, would 

accommodate 0.5–1.6 m [1.64–5.25 ft] of displacement. Accordingly, the low estimate of 

porosity, if compacted, generally accommodates the clearance DOE estimated. 
5-13 

Expected Movements and Number of Impacted Waste Packages 

DOE stated that the probability of events exceeding 0.1 cm [0.039 in] of displacement in the 

repository block was 10−5

 per year (SAR Section 2.3.4.5.5.1). DOE characterized the 

subsurface geologic repository operations area and determined that few faults were capable of 

producing movements greater than calculated clearances for events with a probability of 

exceedance of 10−8

 per year (SAR Table 2.3.4-55). SAR Table 2.3.4-59 showed that less than 

2 percent of the waste packages can potentially be impacted by a seismic faulting event with an 

annual exceedance frequency of 1 × 10−8

/yr to 3 × 10−8/yr. To mitigate the potential risk of 

faulting that could cause mechanical damage to the waste packages, DOE stated that waste 

packages would be placed 60 m [196.85 ft] from known, major faults (SAR Table 1.9.9, 

Design Control Parameter 01-05). The NRC staff compared the probability, location, and 

magnitude of potential seismic fault displacement events determined in the probabilistic seismic 

hazard analysis (reviewed in TER Section 2.2.1.3.2.3) to the values presented for use in the 

TSPA (SAR Table 2.3.4-55) model and noted the values are consistent. 

NRC Staff’s Evaluation of Number of Impacted Waste Packages 

On the basis of the low probability of occurrence, the limited number of faults that could impact 

the waste package via fault displacement, and the 60-m [196.85-ft] offset from the location of 

known faults capable of impacting the waste packages, DOE’s estimate of the number of waste 

packages that could be potentially impacted by seismic faulting events is reasonable. 

Damage to the Engineered Barrier System 

DOE sampled a uniform distribution of open areas of waste packages to model the open area of 

a waste package failed by fault displacement. This distribution has a lower bound of 0 m2

 [0 ft2] 

and an upper bound equal to the area of the waste package lid (SAR Section 2.3.4.5.5.4). DOE 

stated that the areas of the lids for the transportation, aging, and disposal (TAD) and codisposal 

(CDSP) waste package groups are 2.78 and 3.28 m2 [30 and 35 ft2

], respectively. (These areas 

were calculated using the waste package diameters provided in SAR Table 2.3.4-50.) 

NRC Staff’s Evaluation of Engineered Barrier System Damage 

The NRC staff notes the breached area distribution appropriately bounds the potential 

breach area expected in the fault displacement modeling case based on the following 

considerations. Given the uncertainty in the magnitude of displacement, the modeled breach 

area bounds the potential breach area, which could range from a plastically deformed waste 

package (with no open area) to one that has been completely sheared or sliced in half. 

Additionally, the average waste package is deemed to provide no barrier capability to crown 

seepage once approximately 4 percent {~1.5 m2 [~16 ft2

]} of the surface area is breached. 

Thus, the waste package barrier capability to prevent water from contacting the waste form 

decreases proportionally. (This aspect of DOE’s performance assessment is evaluated in TER 

Section 2.2.1.3.3.3.3.) 

To determine the impact of seismic faulting on drip shield failure, DOE assumed that the drip 

shield fails at the instant the underlying waste package is breached. This simplification is 

reasonable because the magnitude of a faulting event required to damage a waste package 

would damage the adjacent drip shield. The NRC staff notes the loss of drip shield barrier 

capability is appropriately bounding, in that the drip shield retains no barrier capability. 
5-14 

Summary of NRC Staff’s Evaluation of Fault Displacement 

DOE adequately represented the fault displacement modeling case in the Total System 

Performance Assessment analysis because 

 The clearances are reasonably bounding, when considering the potential for 

rubble compaction 

 The expected fault displacements are consistent with the site characterization data 

 The impact on the engineered barrier system appropriately bounds the 

potential consequences 

2.2.1.3.2.3.3 Seismically Induced Drift Degradation 

The NRC staff’s evaluation in this TER section focuses on DOE’s assessment of potential drift 

degradation due to seismic ground motions and use of the information to assess potential 

mechanical disruption of the engineered barriers. The NRC staff reviewed DOE’s information in 

SAR Section 2.3.4.4 and supporting documents (BSC, 2004al) that describe potential 

seismically induced degradation of emplacement drifts after permanent closure. DOE’s 

information included estimates of the amount of rubble accumulation in drifts, drip shield loading 

due to rubble, sizes of individual blocks that may strike the drip shield during a seismic event, 

and the associated impact velocity and location of the impact on the drip shield. The NRC 

staff’s evaluation focuses on the potential occurrence of rubble loading that is large enough to 

damage the drip shield and the time of occurrence of such rubble loading. The time of 

occurrence is important because the NRC staff’s review (TER Section 2.2.1.3.1) suggests that 

mechanical damage of the drip shield during approximately the first 12,000 years after 

repository closure could expose the waste package to aggressive chemical conditions that may 

support localized corrosion. DOE estimated the potential for rubble accumulation in drifts 

through process-level analyses of the effects of seismic ground motions on drift degradation. 

DOE chose the analysis approach by considering the mechanical behavior of two types of 

rocks (i.e., lithophysal and nonlithophysal) that constitute the repository block (SAR 

Section 2.3.4.4.8.3). The NRC staff’s review of DOE’s rock characterization information 

relevant to drift degradation modeling is summarized in TER Section 2.2.1.2.1.3.2. 

DOE’s Process-Level Modeling of Drift Degradation Due to Seismic Events 

According to DOE, the mechanical deformation of the nonlithophysal rock mass will be 

controlled predominantly by movements of rock blocks along existing fractures. DOE analyzed 

drift degradation in nonlithophysal rock by modeling motions of rock blocks on surfaces formed 

by existing fractures as described in SAR Section 2.3.4.4.4. DOE used the analyses to estimate 

the characteristics of individual rock blocks that may strike the drip shields during a seismic 

event (SAR Table 2.3.4-19) and the volumes of rubble that may accumulate in the drifts (SAR 

Tables 2.3.4-20 and 2.3.4-24). However, the NRC staff’s review focuses on potential rubble 

accumulation in the lithophysal rocks, which cover about 85 percent of the emplacement drifts. 

The potential for rock block impacts exists only in nonlithophysal areas (approximately 

15 percent of the emplacement drifts). DOE considered the failure mechanism by rock block 

impact and excluded it from the TSPA model (excluded FEP 1.2.03.02.0B as a result of 

low consequence). 
5-15 

For the lithophysal rock mass, DOE indicated that mechanical deformation of the rock will 

consist predominantly of rock material deformations aided by lithophysae and a high density of 

existing small-scale fractures. As described in SAR Section 2.3.4.4.5, DOE analyzed drift 

degradation in lithophysal rock by modeling potential fracturing of the rock through formation of 

new fractures and movement on existing fractures as dictated by the rock stress. DOE used the 

analyses to estimate potential rubble accumulation for drift sections in lithophysal rock but 

applied the results of the analyses to the entire repository. DOE stated that this approach would 

be bounding. 

To assess potential drift degradation in lithophysal rock, DOE used a model that focuses on 

estimating the rock mass volume that could break up along failure surfaces determined by the 

effects of the prevailing stress field on a diffuse network of small “incipient” fractures. DOE 

obtained the estimates by modeling the rock mass as an assemblage of polygonal blocks 

generated randomly using the Voronoi tessellation model, as identified in SAR 

Section 2.3.4.4.5.3 and BSC Sections 6.4.2.1 and 7.6.1 (2004al). The individual blocks can 

deform elastically or slide or separate at block contact surfaces. The blocks are initially attached 

together at contact surfaces and may slide or separate if the contact resistance is overcome by 

the prevailing stress. Thus, the contacts represent incipient fractures that could allow the blocks 

to detach from the assemblage if the prevailing stress permitted such detachment. Detached 

blocks could fall as a result of gravity or seismically induced force. DOE stated in BSC 

Section 6.4.2.1 (2004al) that the model could simulate rock deformation, stress changes, rock 

fracturing or breakage, and free fall of broken rock blocks. DOE explained that mechanical 

behavior of the model is influenced by the block size, block elastic parameters (Young’s 

modulus and Poisson’s ratio), contact elastic parameters (shear and normal stiffness), and 

contact strength parameters (tensile strength, cohesion, and friction). DOE set the values of the 

model parameters by calibrating the unconfined compressive strength and elastic stiffness of 

the model (i.e., block assemblage) against the unconfined compressive strength and elastic 

stiffness of the rock mass on the basis of laboratory test data, as outlined in BSC Section 7.6.1 

(2004al). DOE implemented the model in a two-dimensional universal distinct element code 

UDEC, as identified in BSC Section 3.1 (2004al), and used the code to calculate changes in 

drift profile and amount of rubble accumulation due to seismic events, and drip shield loading 

due to rubble. Additional details of the rock characterization, laboratory and field testing, 

numerical experiments for calibration, and field validation are summarized and reviewed in TER 

Section 2.2.1.2.1.3.2. The application of this model for estimating the amount of rubble 

accumulation and the corresponding loads that might act on the drip shields and other 

components of the engineered barrier system is addressed in the following sections. 

DOE’s Model for Estimating Rubble Formation Due to Seismic Events 

Consideration of the Two Rock Types in the TSPA Model 

DOE stated that the rockfall volume in the nonlithophysal zones is significantly less than in the 

lithophysal zones for the same peak ground velocity level because the nonlithophysal rock mass 

is significantly stronger than the lithophysal rock. This assessment is reasonable on the basis of 

a comparison of strength and mechanical properties of the two rock types provided in SAR 

Tables 2.3.4-16 and 2.3.4-17. According to DOE’s data (SAR Table 2.3.4-25), the mean rockfall 

volume in the lithophysal rock is a factor of 40 to 200 greater than the mean rockfall volume in 

the nonlithophysal rock for the 1.05 and 2.44 m/sec [3.44 and 8 ft/sec] peak ground velocity 

levels, respectively. On the basis of a review of DOE’s information, the NRC staff notes that the 

weaker lithophysal rock will usually fail before nonlithophysal rock during a seismic event; thus, 

the probability of rockfall for the lithophysal rock defines the upper bound for the probability of 
5-16 

rockfall in the nonlithophysal zones. DOE’s use of 30–120 m3

/m [320–1,280 ft3/ft] in the TSPA 

is reasonable for both rock types. The NRC staff verified that the rockfall volumes used for 

calculating the fraction of drift filled with rubble as a function of time presented in SAR 

Figure 2.1-14 are supported by model predictions of rubble volume calculated for lithophysal 

and nonlithophysal rocks (SAR Tables 2.3.4-23 and 2.3.4-24). Thus, DOE’s approach of using 

a single bounding range to represent rockfall in both rock types does not underestimate the 

volume of rockfall in the nonlithophysal region.

Drift Degradation From Seismic Events 

DOE indicated in SAR Section 2.3.4.4.5.4 that seismic ground motions could cause partial or 

complete collapse of drifts in lithophysal rock, resulting in various amounts of rubble 

accumulation for different ground motion magnitudes and mechanical categories of lithophysal 

rock (BSC, 2004al). To describe the effects of ground motion magnitude and lithophysal rock 

categories on potential rubble accumulation, DOE performed analyses for ground motion at 

peak ground velocity levels of 0.4, 1.05, and 2.44 m/s [1.31, 3.44, and 8 ft/sec], which, 

according to DOE, correspond to an annual frequency of exceedance of 10−4

, 10−5, and 10−6, 

respectively (SNL, 2007ay). DOE performed the analyses using 15 ground motion time history 

cases at each annual frequency of exceedance and 5 sets of values of mechanical properties 

representing the 5 lithophysal rock categories DOE defined in SAR Section 2.3.4.4.5.4 and BSC 

Section 6.4.2.2.2 (2004al). DOE concluded that (i) ground motion with an annual frequency of 

exceedance of 10−4

 will have negligible effects on drift degradation, (ii) ground motion with an 

annual frequency of exceedance of 10−6

 will cause complete drift collapse, and (iii) ground 

motion with an annual frequency of exceedance of 10−5

 could cause various degrees of 

collapse and varying amounts of rubble accumulation. DOE selected 15 analysis cases (SAR 

Table 2.3.4-23) to represent potential rubble accumulation due to seismic ground motions, as 

described in SNL Section 6.7.1.1 (2007ay).

DOE used the calculated rubble volumes from 11 of the 15 cases to develop relationships 

between rubble accumulation and the peak ground velocity of a seismic event, as 

described in SAR Figure 2.3.4-48 and SNL Section 6.7.1.2, Figure 6-57 (2007ay). 

SAR Section 2.3.4.4.8.3.1 stated that four cases calculated using rock mass Category 1 

properties were eliminated because DOE considered the rubble volumes from the four cases to 

be nonrepresentative. According to SAR Section 2.3.4.4.8.4, DOE used the resulting 

relationship between rubble volumes and peak ground velocity to estimate the amount of rubble 

accumulation due to a seismic event. DOE estimated the rubble accumulation due to multiple 

seismic events by adding the accumulations from the individual events, as identified in SAR 

Section 2.3.4.4.8.4 and SNL Section 6.7.1.4 (2007ay). The abstraction did not include 

weakening of the host rock due to previous events, because rapid filling of drifts in lithophysal 

units mitigates concerns about numerous seismic events slowly weakening the rock mass, as 

DOE explained in SNL Section 6.7.1.4 (2007ay). Therefore, to calculate the drift volume 

fraction filled with rubble in lithophysal rock areas after a sampled seismic event, DOE 

accumulated rockfall [using relationships based on SNL Table 6-30 and Figure 6-56 (2007ay)] 

and divided the accumulated volume by a number sampled from a uniform distribution between 

30 and 120 m3

/m [320 and 1,280 ft3/ft]. The uniform distribution of 30–120 m3/m [320–1,280 

ft3

/ft] represents DOE’s estimate of the volume of rockfall that would fill a drift with rubble, as 

described in SNL Section 6.12.2 (2007ay). SAR Figure 2.1-14 summarized DOE’s estimates of 

potential rubble accumulation due to seismic events during a 1-million year period. According to 

this figure, at 10,000 years, the volume of accumulated rubble in the drift could reach as much 

as 15 percent of the drift volume (with a mean value of approximately 4 percent). Similarly, the 

corresponding volume of accumulated rock rubble at 100,000 years reached about 60 percent 
5-17 

(with a mean value of about 32 percent). The volume of rubble per unit length of drift that is 

required to fill the drift was sampled for each epistemic realization in the TSPA analysis, and the 

sampled volume ranged uniformly between 30–120 m3

/m [320–1,280 ft3/ft]. 

To calculate the static load on a drip shield due to rubble, DOE multiplied the volume fraction of 

the drift filled with rubble (on the basis of the assessment of drifts in lithophysal rock) by the drip 

shield load of a fully collapsed drift, as outlined in SNL Section 6.12.2 (2007ay). The magnitude 

of drip shield loading due to rubble at a given time depends on the amount of rubble 

accumulation, shape of the rubble pile, and the amount of rubble loading transmitted to the drip 

shield for a given amount and shape of the rubble pile. DOE used the information to determine 

the potential occurrence of rubble loading large enough to damage the drip shield in lithophysal 

and nonlithophysal areas. 

NRC Staff’s Evaluation 

The NRC staff evaluated DOE’s assessment of potential drip shield loading due to rubble 

accumulation in lithophysal rock sections of emplacement drifts by considering the amount of 

rock accumulation and the shape of rubble piles. 

Amount of Rubble Accumulation 

The timing and amount of rubble accumulation in a drift due to seismic events depend on the 

occurrence of seismic ground motions large enough to cause rock failure around the drift 

opening. The occurrence of rock failure during a seismic event depends on the ground motion 

levels and the rock mass strength. The rock mass strength around an emplacement drift 

during a seismic event may be affected by any previous weakening of the rock due to 

thermal stress, time-dependent effects, or seismic ground motions. In SNL Section 6.7.1.4 

(2007ay), DOE’s abstraction of rubble accumulation due to seismic events did not include any 

effects of rock weakening, because, according to DOE, rapid filling of drifts in lithophysal units 

mitigates concerns about the effects of rock weakening. In response to an NRC staff’s request 

for additional information, DOE provided information in DOE Enclosure 1 (2010aa) that showed 

the following: (i) seismic ground motions with an annual frequency of exceedance of 10−4

 will 

have negligible effects on drift degradation if the host rock has not been affected by thermal 

stress; (ii) ground motions with an annual frequency of exceedance of 10−4

 that occur in the 

presence of thermal stress in the rock could cause some additional drift damage and rubble 

accumulation, as outlined in DOE Enclosure 1, Figure 3(a) (2010aa); and (iii) the amount of drift 

damage and rubble accumulation due to the seismic event increase if the event occurs after a 

long period of time-dependent weakening of the rock, as described in DOE Enclosure 1, 

Figure 3(b)(2010aa). 

DOE also showed, with appropriate technical basis, that the potential incremental rockfall due to 

(i) multiple events and (ii) combined effects of time-dependent weakening and seismic events 

was relatively small during the first 10,000 years, as detailed in DOE Enclosure 1, Figure 4 

(2010aa). The NRC staff obtained additional confidence in DOE’s conclusion as explained next, 

by comparing the analytical results to empirical mining industry data provided in DOE’s 

response to the NRC staff’s request for additional information, as identified in DOE Enclosure 1, 

Figure 5, (2010aa). DOE Enclosure 1, Figure 5 (2010aa) and BSC Figure 6-149 (2004al) 

summarized the caving potential of an excavated underground opening. Caving potential is 

expressed in terms of modified rock mass rating (a measure of rock quality and strength where 

a higher value indicates greater stability) and hydraulic radius [a dimension based on the 

geometry of the excavated opening; hydraulic radius is a measure of stability (i.e., a larger 
5-18 

hydraulic radius indicates a less stable opening, and hence a higher caving potential)]. The 

rock mass rating data for Yucca Mountain rocks (between 50 and 60) show the corresponding 

hydraulic radii needed to cause high caving potential, which is on the order of 25 to 35 m [82 to 

115 ft]. DOE analyses showed that the hydraulic radius of a degraded waste emplacement drift 

after 10,000 years of heating and time-dependent strength degradation coupled with a seismic 

ground motion would still be far less than the hydraulic radius of a degraded opening with high 

caving potential. Therefore, the following DOE conclusion is reasonable: while the incremental 

rockfall accumulation due to combined effects and cumulative effects could be considerable 

over the entire period of repository performance (hundreds of thousands of years), DOE in 

its TSPA analyses did not underestimate the amount of rubble accumulation for the initial 

10,000 years of repository performance. 

Shape of Rubble Piles 

The process-level model that DOE used to analyze drift degradation in lithophysal rock appears 

somewhat constrained because of the upper boundary. In BSC Figure 6-116 (2004al), the 

tessellated domain (a mosaic pattern used to represent the domain with discrete elements) of 

the model is set 10.25 m [33.6 ft] above the initial drift roof. The upper boundary of the potential 

degradation zone DOE used was 1.86 drift diameters above the initial drift roof. However, 

contours of block displacement magnitude intersected the upper boundary of the tessellated 

domain. The calculated displacement contours indicate that some additional displacement 

could occur outside this domain [e.g., BSC Figure 6-176 (2004al)]. Also, plots of the final 

position of the Voronoi blocks after an analysis [e.g., BSC Figures P-17, P-18, and P-24 

(2004al)] indicate blocks at the top of the model could be predicted to separate from the 

overlying elastic domain. Such a separation would suggest the caved zone might have 

extended higher if the model upper boundary had been higher. 

In response to an NRC staff’s request for additional information, DOE provided analyses to 

show that the rubble volume calculated using DOE’s model is insensitive to the size of the 

tessellated domain (DOE, 2010aa). DOE provided results from two models with the boundary 

of the tessellated domain at 8.25 and 13.25 m [27 and 43.5 ft], respectively, in DOE 

Enclosure 2, Section 1.3 (2010aa). DOE used the models to estimate the extent of caving 

needed to fill a drift with rubble if the prevailing mechanical conditions were to cause complete 

drift collapse. This was accomplished by artificially degrading the rock mass strength to zero in 

a quasi-static analysis. The results of these studies demonstrated that the patterns of 

calculated displacements and stresses did not change appreciably as a result of changing the 

size of the tessellated domain. The quasi-static analyses showed that the caved zone extended 

approximately one drift diameter above the drift irrespective of the size of the tessellated 

domain. DOE also provided calculations to show that potential caved zones due to seismic 

events will likely extend to much less than one drift diameter above the drift except for seismic 

events with an annual exceedance probability of 106

 or smaller. 

The NRC staff reviewed DOE’s analyses with the expanded boundaries. The additional 

analyses DOE used provided convincing evidence of continuity in the displacement field 

crossing the tessellated region, as described in DOE Enclosure 2, Figures 8 and 9 (2010aa). 

On the basis of the review of DOE’s responses to the NRC staff’s request for additional 

information, the NRC staff notes that the use of the original boundary did not significantly affect 

or limit the estimates of rubble accumulation. In addition, the results of the sensitivity analyses 

DOE provided demonstrated that with the expanded boundary of the tessellated region, the 

distressed zone is completely contained within the original (smaller) tessellated domain. Thus, 
5-19 

DOE’s conclusion is reasonable and rock rubble estimates resulting from seismic events are 

not underestimated. 

Rubble Loading Transmitted to the Drip Shield 

DOE used the results of the discontinuum model to estimate the potential drip shield loading 

due to rubble (SAR Figure 2.3.4-43). DOE also presented alternative bounding analytical 

approaches for estimating the potential static loading on the drip shields (continuous curves in 

SAR Figure 2.3.4-46). The analytical model estimates drip shield loading due to rubble using 

the dead weight of rubble. However, it is well established in the field of rock mechanics that 

loads transmitted to a drip shield from rubble could differ because of frictional resistance among 

broken pieces of rock rubble, between rubble and the drift wall, or between rubble and the sides 

of the drip shield. 

To justify using the loads from the numerical model, SAR Section 2.3.4.4.6.3.2 stated that 

DOE’s process-level model accounts for load transmission among rubble particles and to the 

drip shield. However, DOE in BSC Section P4 (2004al) identified factors that may affect load 

transmission within rubble and to the drip shield, including size and shape distribution of rubble 

particles, rubble compaction, and deformability of the drip shield and invert. DOE represented 

the rock mass in the process-level model as an assemblage of equidimensional polygonal 

blocks with a characteristic length of approximately 0.2 m [0.65 ft] (SAR Figure 2.3.4-40). SAR 

Section 2.3.4.4.5.3 and BSC Section 6.4.2.1 (2004al) stated that the model blocks are 

approximately the same size as potential lithophysal rock blocks. 

However, independent NRC staff’s analysis in Ofoegbu, et al., p. 4-16 (2007aa) of DOE’s 

fracture data suggests a wide range of rock block sizes and shapes for the lithophysal rock 

mass, which contrasts with the approximately uniform block size and shape that DOE uses in 

the rock mass model. Because the potential block size and shape distributions of rubble from 

the lithophysal rock mass could be different from the approximately uniform block sizes and 

shapes DOE uses in its drift degradation modeling, the effects of load transmission through 

rubble could be different from what DOE used in its analysis. Therefore, the NRC staff 

requested additional information from DOE to demonstrate that appropriate variations in block 

size and shape have been considered in the UDEC-Voronoi analyses of rockfall during seismic 

events. In response, the NRC staff received additional information in DOE Enclosure 1 

(2010ab) and demonstrated that the bulking factors obtained from UDEC calculations are below 

the lower end of the ranges for rock rubble because of the assumption that particles are of 

approximately equal size. This would result in overestimation of the loads acting on the drip 

shields. DOE conducted additional sensitivity analyses considering different block sizes and 

demonstrated that, for the range of bulking factors of interest, the average vertical pressure on 

the drip shield increased only by a small amount (small compared to the standard deviation). 

Therefore, on the basis of the parametric studies, DOE’s conclusion is reasonable and loads 

were not underestimated when using equidimensional polygons of characteristic length of 

approximately 0.2 m [0.65 ft]. 

Summary of NRC Staff’s Evaluation of Seismic Drift Degradation 

The NRC staff evaluated DOE’s assessment of potential degradation of emplacement drifts due 

to seismic events and estimates of drip shield loading resulting from rubble accumulation. The 

NRC staff notes that DOE 
5-20 

 Used alternative approaches including empirical, analytical, and numerical models and 

reasonable methodologies for estimating the timing and extent of drift degradation due to 

seismic events 

 Appropriately considered variability of properties and uncertainties in parameters in the 

supporting analyses 

 Used appropriate rock rubble loads to evaluate performance of engineered barriers 

 Justified the abstraction of seismic degradation parameters used for TSPA inputs 

2.2.1.3.2.3.4 Drip Shield Structural/Mechanical Performance in the Context of Its 

Seepage Barrier Function 

In DOE’s engineered barrier system, the drip shield is a freestanding structure that surrounds 

the waste package and rests on the crushed rock that forms the invert at the base of the drift. 

The drip shield is designed to protect the waste package from contact by seepage water and 

rockfall (SAR Section 2.3.4.5.1.1). The main structural elements of the drip shield are a 

framework consisting of a bulkhead and support beams (legs) that will be made of Titanium 

Grade 29. Plates of Titanium Grade 7 are welded onto the framework to form a full composite 

structure in response to mechanical loading (SAR Section 2.3.4.5.1.1; SAR Figure 2.3.4-56). 

Damage to the drip shield can occur from mechanical impacts of falling rocks, by loads from 

accumulated rock rubble that can be increased by seismic accelerations, and by corrosion 

processes. Through time, DOE expects that thinning of drip shield components will decrease 

the capacity of the drip shield to withstand loads and that the likelihood of the drip shield having 

experienced a potentially damaging load will increase (e.g., SAR Section 2.3.4.1). During the 

initial few thousand years following repository closure, temperatures within the drifts decrease 

from around 160 °C [320 °F] to below the boiling point. At these elevated temperatures, 

generalized corrosion could occur if water contacts the surface of the waste packages. Thus, if 

the barrier capability of the drip shield fails in the first 12,000 years following repository closure, 

seepage water could contact the waste package and lead to localized corrosion. DOE relied on 

the presence of the drip shield as a barrier to preclude significant occurrences of localized 

corrosion (e.g., SAR Section 2.1.2.2.6). 

Consistent with the guidance in the YMRP, the NRC staff focused its review on the risk 

significant aspects of the drip shield performance. On the basis of DOE’s reliance on the drip 

shield in the demonstration of multiple barrier performance (SAR Section 2.1.2.2.6), the NRC 

staff focused on evaluating the performance of the drip shield as a barrier to seepage during the 

early years following drift closure. 

For seepage to contact a waste package, openings must occur on the drip shield with sufficient 

size to permit the advective flow of water through the drip shield plates. Crack openings, such 

as those produced by stress corrosion , are too small to allow advective flow of water through 

the drip shield and are excluded from the performance assessment analysis, as described in 

SNL (2008ab) for FEP 2.1.03.10.0B. Openings large enough for advective water flow could 

potentially occur through (i) corrosion processes, (ii) impacts of large rock blocks causing 

puncture of the plates, (iii) physical separation between adjacent drip shield segments due to 

ground motions from seismic events, (iv) fault displacements, and (v) rupture by deformations 

that produce effective strains greater than the failure strains in the plates (SNL, 2007ap). 
5-21 

The NRC staff’s evaluation of the drip shield corrosion processes is presented in TER 

Section 2.2.1.3.1. In that review, the NRC staff determined that DOE used reasonable 

corrosion rates for titanium alloys. Thus, the timing and degree of drip shield component 

thinning due to corrosion is reasonable for use for mechanical analyses of the drip shield 

performance in this TER section. DOE excluded large-block impacts from the TSPA as part of 

the screening analysis for FEP 1.2.03.02.0B (SNL, 2008ab). The NRC staff reviewed the DOE 

screening arguments in TER Section 2.2.1.2.1 and noted that DOE had appropriately screened 

out this feature, event, and process from the performance assessment analysis. Thus, the NRC 

staff’s detailed review focused on DOE’s representation of processes affecting drip shield 

separations caused by seismic events or fault displacements, and on the potential for 

plate rupture. 

Separations from Seismic Shaking 

Unlikely or low probability seismic events can create ground motions that may cause adjacent 

drip shields to separate. Consequently, DOE assessed the potential for drip shield separations 

during seismic events, as described in SNL Section 6.7.3 (2007ay). DOE determined that 

ground motions large enough to cause potential drip shield separations also cause partial to 

complete collapse of the repository drifts. DOE determined that rockfall associated with drift 

collapse occurs during the first seconds of large seismic events. DOE modeled the effect of this 

rockfall on the ability of the drip shields to separate during seismic events and concluded that 

rockfall loads from partial drift collapse are sufficient to prevent horizontal separation of the drip 

shields, as outlined in SNL Section 6.7.3 (2007ay). While these models calculated that minor 

amounts of vertical separation might occur between the drip shield sections due to settling of 

the invert or framework damage, the 26-cm [10.24-in]-wide overlaps between the drip shield 

connectors prevent rockfall or seepage water from contacting the waste package through 

relatively small vertical separations. In FEP 1.2.03.02.0A (SNL, 2008ab), DOE concluded that 

seismically induced separations of drip shields can be excluded from the TSPA analysis on the 

basis of low probability. 

NRC Staff’s Review of Separations Caused by Seismic Shaking 

The NRC staff reviewed the information presented in SNL Section 6.7.3 (2007ay) and analyses 

in BSC Section 5.3.3.2.2 (2004bq). DOE used rockfall rubble loading conditions that were 

consistent with the results documented in BSC (2004al) and were appropriate for the seismic 

events modeled. The NRC staff notes that the DOE model uses the same approach to evaluate 

the dynamic response of drip shields as was used to evaluate the dynamic response of waste 

packages. This modeling approach was reasonable as documented in this TER chapter. The 

NRC staff reviewed the dynamic analyses in SNL Section 6.3.7.1 (2007ay) and notes that 

potential separations of the drip shield only occurred in an open drift that was subjected to 

5.35 m/sec [17.55 ft/sec] peak ground velocity ground motion. NRC staff determined that 

complete drift collapse is expected for large magnitude seismic events (BSC, 2004al). The 

NRC staff notes that the DOE approach of modeling an open drift (i.e., no rockfall rubble) would 

maximize the potential for drip shield separations to occur during seismic events, because the 

presence of rockfall rubble effectively pins the drip shield segments and restricts the ability for 

segments to separate. The NRC staff notes that the DOE modeling approach to represent drip 

shield kinematics during seismic events maximized the potential for separations to occur. DOE 

appropriately excluded drip shield separations from seismic events in the TSPA analysis. 
5-22 

Separations Caused by Fault Displacement 

DOE concluded that fault displacement occurs concurrently with the ground motion during 

low probability seismic events (SAR Section 2.3.4.5.5.1) and determined that only engineered 

barrier system components located directly above the moving faults are subject to damage. 

In the analysis of the effects of fault displacements on engineered barrier system performance, 

DOE assumed that the drip shield fails completely if fault displacements are sufficient to breach 

the underlying waste package (SAR Section 2.3.4.5.5.4). In this analysis, DOE assumed that 

all seepage water entering the drift passes through the failed drip shield, with no diversion of 

the water. 

NRC Staff’s Review of Separations Caused by Fault Displacement 

The NRC staff’s review of the fault displacement model with regard to waste package failure is 

presented in this TER chapter. DOE’s assumption of drip shield failure is reasonable for use in 

the fault displacement analysis, because the magnitude of a faulting event required to damage a 

waste package would be sufficient to damage the drip shield. DOE’s assumption that a 

damaged drip shield has no barrier capability is reasonable for use in the performance 

assessment, because this assumption provides a credible upper bound to the potential 

significance of fault displacement on drip shield performance. 

Plate Rupture by Deformation 

To affect performance significantly, the drip shield barrier must fail and allow advective water 

flow to contact the waste package during the first approximately 12,000 years of postclosure, 

when environmental conditions are calculated to support localized corrosion of the waste 

package (e.g., SAR Section 2.1.2). On the basis of the appropriate use of titanium alloy 

corrosion rates (i.e., TER Section 2.2.1.3.1), negligible thinning of the drip shield components is 

expected during the first 12,000 years of postclosure. Deformation of the drip shield plates can 

occur if the underlying framework buckles or collapses due to physical loading. Rupture of the 

drip shield plate can occur if the magnitude of effective strain on the plate exceeds the strain 

threshold for the Titanium Grade 7 plates (e.g., SAR Section 2.3.4.5.1.2.2). 

NRC Staff’s Evaluation of Plate Rupture 

The NRC staff’s evaluation of the potential for drip shield plate rupture focuses on DOE’s 

modeling approach to evaluate effective stresses in the plates following framework collapse and 

the basis to determine the location of potential ruptures on the drip shield. The NRC staff 

focused its review on plate ruptures in the crown area rather than the sides of the drip shields 

because the ruptures that occur on the sides of the drip shield have negligible potential to allow 

water contact with the waste package, whereas ruptures on the crown area of the drip shield are 

likely to allow seepage water to contact the waste package. 

Temperature Effects 

Mechanical analyses of drip shield performance are dependent on the material properties 

used in the numerical models. DOE used mechanical properties for the drip shield plates 

and framework derived from standard handbooks and manufacturer’s catalogs (SAR 

Section 2.3.4.5.1.3.1 and Table 2.3.4-28). DOE considered that a reference temperature of 

60 °C [140 °F] for these properties was appropriate, because this temperature is representative 

of most of the repository closure period. Although DOE recognized that temperatures as high 
5-23 

as 300 °C [572 °F] could potentially occur soon after repository closure, DOE considered that 

the duration of elevated temperatures was too short to warrant consideration for drip shield 

performance (SAR Section 2.3.4.5.1.3.1). 

DOE provided additional information to assess the potential effects of temperatures at or 

greater than 120 °C [248 °F] on titanium alloy material properties, as discussed in DOE 

Enclosure 7 (2009bp). During the first 650 years of repository closure, DOE concluded that drip 

shield temperatures could range from 120–300 °C [248–572 °F]. DOE expects the effect of this 

temperature increase would not affect titanium alloy properties significantly, because the 

likelihood for potentially damaging rockfall or seismic events is sufficiently low to preclude 

significance in the performance assessment. For temperatures below 120 °C [248 °F], DOE 

compared expected changes in materials properties (e.g., yield strength, tensile strength) to 

assess the effects of component thinning on the likelihoods of drip shield plate or framework 

failure. Using small rockfall loads, DOE concluded that changes in the titanium mechanical 

properties between 60–120 °C [140–248 °F] are a factor of 3 to 4 less than the corresponding 

percentage changes in component thicknesses that have no significant effect on fragility values. 

NRC Staff’s Evaluation of Temperature Effects 

The NRC staff reviewed the mechanical properties DOE used for titanium alloys at 60 °C 

[140 °F]. The NRC staff compared these values with values available in standard reference 

handbooks and noted that DOE used the appropriate mechanical material properties (e.g., yield 

stress and ultimate tensile strength) for drip shield performance at 60 °C [140 °F]. 

The NRC staff evaluated the rationale DOE provided to exclude consideration of temperatures 

greater than 120 °C [248 °F] on titanium material properties. The NRC staff notes that seismic 

events with <10−5

 annual probabilities of exceedance are required to produce appreciable 

amounts of rockfall and that annual probabilities of <10−6

 are required for reasonable 

likelihoods of complete drift collapse (SAR Section 2.3.4.4.5.4). The likelihood of appreciable 

drift collapse occurring in the first 650 years of closure is small. The NRC staff also conducted 

independent confirmatory analyses that evaluated the effects of increasing temperature from 

150 to 260 °C [302 to 500 °F] (Ibarra, et al., 2007aa). These analyses showed an 

approximately 20 percent decrease in drip shield capacity associated with this temperature 

increase. This decrease in capacity would not affect the performance significantly, because 

temperatures associated with this decrease would persist only for hundreds of years and loads 

associated with potential damage to the drip shield are unlikely to occur. 

The NRC staff determined that complete drift collapse from seismic events could occur during 

the first 12,000 years of repository closure and that the DOE information did not wholly address 

the uncertainties associated with rockfall load and temperature effects. Consequently, the NRC 

staff reviewed additional information provided in response to an RAI in DOE Enclosure 7 

(2009bp) to evaluate potential temperature effects at 120 °C [248 °F]. Using the methods 

DOE developed to evaluate 10 percent of rockfall loads, the NRC staff extended this approach 

to 100 percent of potential rockfall loads. Using the information in SAR Table 2.3.4-43, the 

NRC staff notes that an approximately 30 percent reduction in drip shield plate thickness 

minimally increases the likelihood of plate rupture from approximately 1 percent to 

approximately 5 percent. Although some strength properties show 30 percent variations from 

60 to 120 °C [140 to 248 °F], these variations are expected to have only a small to negligible 

effect on the likelihood of plate rupture for 100 percent collapsed drifts. This is because the 

titanium plate will have increased ductility and, thus, increased its ability to accommodate 

deformation without rupture under loads associated with unlikely seismic events. In addition, 
5-24 

the NRC staff considers the loads that could potentially increase the likelihood of plate rupture 

are associated only with earthquakes having <5 × 10−7

 annual likelihoods. Using insights from 

the TSPA, potential changes in the likelihood of plate rupture on the order of several percent 

would not affect the performance assessment significantly. The NRC staff notes that DOE’s 

sensitivity analyses as presented in DOE (2010ac) are reasonable for use to demonstrate that 

potential changes in the likelihood of plate rupture on the order of several percent would not 

affect the performance assessment significantly. DOE’s use of titanium alloy material properties 

at 60 °C [140 °F] is reasonable for use to evaluate postclosure repository performance, because 

uncertainties associated with potentially higher temperatures would not significantly affect the 

results of the performance assessment. 

Modeling Approach 

To evaluate drip shield plate capacity, DOE conducted numerical modeling of the drip shield 

under quasi-static and dynamic loading conditions. For the quasi-static analyses, DOE 

calculated rock rubble loads on the drip shield, multiplied these loads by the vertical component 

of peak ground acceleration, and modeled the drip shield response to these loads. DOE 

calculated the quasi-static loading conditions on the drip shield plate using FLAC3D, a 

three-dimensional finite-difference computer code. DOE calculated stresses and strains on 

one-half of the plate on the drip shield crown, which represents one segment between two 

framework bulkheads. 

DOE used rock rubble loads calculated from UDEC analyses of rockfall during seismic events 

(SAR Section 2.3.4.5.3.2). DOE evaluated two static loading configurations on the drip shield. 

One configuration used an average of six UDEC realizations for each modeled segment of the 

drip shield, which DOE used to consider spatial variability in the nonuniform load. The second 

configuration used a single UDEC realization, which DOE considered as representative of the 

highest loads on the drip shield crown (SAR Section 2.3.4.5.3.2.1). 

For each loading configuration, the vertical load was applied over the entire top surface of 

the plate and increased incrementally until a failure mechanism developed, as described in 

SNL Section 6.4.3.1.2 (2007ap). For each load increment, the model compared the residual 

tensile stresses or accumulated plastic strain against a failure criterion of 80 percent of the 

yield strength for Titanium Grade 7, as outlined in SNL Section 6.4.3.1.3 (2007ap). DOE 

concluded that plate failure occurred at the smallest applied load that exceeded either the stress 

or strain criterion. 

By uniformly increasing the static load in the UDEC model, DOE calculated that an intact 

drip shield plate has a capacity (i.e., limit load) of approximately 2,500 kPa [52,218 psf], 

which is approximately twice the calculated capacity of the drip shield framework (SAR 

Section 2.3.4.5.3.3.1). To determine the likelihood of drip shield plate failure, DOE integrated 

the annual likelihood of exceeding levels of ground acceleration with the likelihood of rupture for 

plates experiencing the loads corresponding to the level of ground acceleration. For intact drip 

shield plates and 100 percent rockfall load, DOE calculated that seismic events with annual 

probabilities of exceedance <5 × 10−7

 can lead to plate rupture on 1–7 percent of the drip 

shields (SAR Section 2.3.4.5.3.4). 

As an alternative to the quasi-static analyses, DOE also conducted dynamic analyses for drip 

shield plate capacity using the UDEC computer code (SAR Section 2.3.4.5.3.3.3). These 

analyses used a two-dimensional cross section of the drip shield surrounded by rock rubble. 

The dynamic analyses used vertical ground accelerations from time histories that DOE views as 
5-25 

representative of larger magnitude seismic events in the Yucca Mountain region. DOE applies 

these vertical accelerations to the basal boundary of the UDEC model, which allows the 

emplacement drift, rubble, and drip shield to interact dynamically for the modeled period of 

strong ground motion. DOE compared the results of the dynamic analyses with the quasi-static 

analyses and concluded that the quasi-static model underestimates the stability of the drip 

shield plates. DOE therefore concluded that the quasi-static approach is an appropriate basis to 

calculate the likelihoods of plate rupture, as the dynamic analysis would result in lower 

likelihoods of rupture (SAR Section 2.3.4.5.3.3.3). 

NRC Staff’s Evaluation of Modeling Approach 

The NRC staff reviewed the use of the FLAC3D computer code in the analyses of the drip shield 

plate capacity. The NRC staff reviewed the information in SNL Section 7.3.3.1 (2007ap) and 

determined that DOE appropriately compared the FLAC3D model results with an alternative 

approach used in structural mechanical analyses (i.e., LS-DYNA). In response to the NRC 

staff’s request for additional information (RAI), DOE provided the additional information in DOE 

Enclosure 8 (2009bp) to address the representation of nonlinear responses of materials. On the 

basis of review of this information, DOE provided appropriate support for use of the FLAC3D 

computer code to calculate drip shield plate performance. 

The NRC staff notes that the rock rubble static loads used in the models were consistent with 

the degraded drift configurations used elsewhere in SAR Section 2.3.4.5 for seismic events, and 

that the UDEC analysis representation of this rubble resulted in bulking factors that were 

appropriate for the Topopah Springs lithophysal tuff. 

The NRC staff conducted independent confirmatory calculations using an alternative modeling 

approach to evaluate drip shield deformation from rock loading (Ibarra, et al., 2007aa). The 

NRC staff compared the deformation patterns determined from the independent model to the 

deformation patterns DOE determined using the dynamic modeling approach. This comparison 

showed that because the drip shield framework has lower capacity than the plates, deformation 

is most likely to occur on the legs of the drip shield and not on the crown. The NRC staff notes 

that the quasi-static analyses provide a reasonable basis to determine the likelihood of drip 

shield plate rupture from seismically accelerated rock rubble loads. 

Drip Shield Framework Deformation 

DOE calculated the likelihood of drip shield framework failure using the same approach as 

implemented for the drip shield plate analyses (SAR Section 2.3.4.5.3.3.2). These analyses 

determined that the drip shield framework has approximately half the bearing capacity as 

compared to the drip shield plates and that buckling of the drip shield legs results from 

exceeding the bearing capacity. DOE also determined that if the drip shield becomes tilted after 

the framework buckles, the drip shield connector plate and connector guide provide a physical 

barrier that will divert seepage from the crown to the sides of the drip shield, as outlined in SNL 

Section 6.7.3.2 (2007ay). 

DOE postulated that if one segment of a drip shield collapsed more extensively than adjacent 

segments, localized stresses may lead to rupture of the drip shield plates along the crown. 

DOE considered the likelihood of isolated segment collapse to be low, because rubble loads are 

expected to be relatively uniform and the rigidity of the drip shield is expected to effectively 

transfer loads to the adjacent segments (DOE, 2010ac). Thus, DOE expects complete collapse 

of the drip shield when loads exceed the design capacity. Nevertheless, DOE analyzed 
5-26 

stress-strain relationships for a partially collapsed drip shield and determined that plate rupture 

would occur if vertical displacements between adjacent segments exceeded approximately 

19 cm [7.5 in] (DOE, 2010ac). DOE concluded that such displacements between adjacent 

segments are unlikely to occur, because the structure of the drip shield will effectively transfer 

stress from a deforming segment onto the adjacent segments. This stress transfer leads to a 

progressive collapse of adjacent drip shield segments, rather than isolated collapse and 

potential tearing of a single segment (DOE, 2010ac). 

NRC Staff’s Evaluation of Drip Shield Framework Deformation 

The NRC staff conducted independent confirmatory calculations using an alternative modeling 

approach to evaluate drip shield deformation from rock loading (Ibarra, et al., 2007aa). These 

calculations confirmed that buckling of the drip shield framework is expected in the legs. The 

NRC staff evaluated the drip shield design DOE provided (e.g., SAR Figure 1.3.4-15). On the 

basis of the low volumes of seepage water potentially contacting the drip shield, the NRC staff 

noted that the drip shield connector plate and connector guide adequately divert seepage water 

from the crown area if the drip shield is tilted due to framework buckling. Thus, uncertainties in 

the drip shield framework capacity would not affect the potential for seepage water to contact 

the waste package through tilting of the drip shield due to buckling of the drip shield legs. 

The NRC staff notes that potential underestimation of the drip shield framework capacity would 

result in early transitions to a damage state (i.e., SAR Section 2.3.4.5.4.1, Idealized Damage 

State 2) where the waste package is pinned by the collapsed drip shield. As a result, the waste 

package would be restrained from movement during large magnitude seismic events and have 

a reduced potential for stress corrosion cracking. For unrestrained motion during seismic 

events, as would occur when the drip shield is intact, up to 4 percent of the waste package 

surface area can be damaged sufficiently for stress corrosion cracks (SCC) to develop (SAR 

Section 2.3.4.5.2.1.4.2). In contrast, a collapsed drip shield localizes the potential damaged 

area on the waste package and results in an approximately order-of-magnitude decrease in the 

potential for stress corrosion cracking (SAR Section 2.3.4.5.4.3.2.1). The NRC staff noted that 

potential uncertainties that result in DOE overestimating drip shield framework capacity are not 

significant to performance, because increasing the ability of the drip shield framework to 

withstand seismic loads increases the potential for larger amounts of waste package damage, 

and potential radionuclide releases, through SCC. 

The NRC staff reviewed the DOE modeling approach for evaluating plate response during 

partial drip shield framework collapse and notes that DOE used appropriate physical parameters 

and reasonable geometries to evaluate stress-strain relationships (DOE, 2010ac). The NRC 

staff notes that (i) the DOE modeling approach (using the FLAC3D and LS-DYNA computer 

programs) was consistent with standard practice for determining stress-strain relationships and 

(ii) DOE’s failure criteria were appropriately implemented. 

The NRC staff notes the following DOE assumption is reasonable: relatively uniform rock-rubble 

loads should occur on a drip shield during seismic events, on the basis of the relatively uniform 

characteristics of the expected rock rubble and because most drifts are wholly collapsed during 

potentially damaging, very low probability seismic events. Nevertheless, the NRC staff 

recognizes that DOE represented heterogeneity in rubble load for some calculations for 

evaluating drip shield fragility [e.g., SNL Section 6.4.3.2.2.2 (2007ap)]. Thus, some potential 

exists for localization of rubble loads on the drip shield during very low probability, large 

magnitude seismic events. As a consequence, the potential exists for the rubble load to be 

greater on some segments of a drip shield than on adjacent segments. However, in the context 
5-27 

of how these results are used in DOE’s performance assessment, a more detailed consideration 

of the potential nonuniform load distribution is not likely to significantly affect the overall 

structural performance of the drip shield frame. Therefore, the NRC staff notes that such 

variations have minor effects on the overall seepage barrier performance of drip shields. 

On the basis of the review of the drip shield design and the expected response of the drip shield 

as a composite structure, significant stress redistribution would occur to adjacent drip shield 

segments, if loading was localized on an individual segment. The NRC staff notes that 

differential collapse of the drip shield would most likely involve at least several adjacent 

segments, which would be sufficient to prevent localized strains from exceeding the failure 

strain of the drip shield plates. The NRC staff recognizes that there is uncertainty in the amount 

of vertical displacement that the drip shield plates can accommodate {i.e., 18.2 cm [7.17 in] 

between two bulkheads} before the failure strain criterion derived from an assumed differential 

displacement of 19.8 cm [7.8 in] might be exceeded, as described in DOE Enclosure 9 

(2010ac). At the same time, the NRC staff notes that the analysis DOE provided likely 

overestimates failure potential because (i) the boundary conditions on the bulkheads are 

assumed to be fixed (which overestimates stresses) and (ii) the three longitudinal stiffeners are 

neglected (which underestimates the overall stiffness of the composite structure). 

To evaluate the potential significance of the uncertainty in the barrier capability of the drip shield 

plates during seismic events, the NRC staff used insights from the TSPA analysis for intrusive 

igneous events (SAR Section 2.4.2.2.1.2.3). In that analysis, DOE considered that an igneous 

intrusive event removed all barrier capabilities from the drip shield and the waste package, and 

made all waste available for dissolution and transport. Using an approximately 10−8

 average 

annual probability of occurrence, DOE calculated a probability-weighted igneous intrusive 

dose equivalent of less than 0.001 mSv/yr [0.1 mrem/yr] for the 10,000-year period (SAR 

Section 2.4.2.2.1.2.3.1). In DOE (2009aa) and SNL Appendix P (2008ag), DOE also showed 

that increasing the average annual probability of occurrence to 10-7 increases the expected 

annual dose equivalent to less than 0.006 mSv/yr [0.6 mrem/yr]. 

 

In comparison to an igneous intrusive event, seismic events have limited potential to create 

openings in drip shield plates that are large enough to permit advective water inflow. In 

addition, only a limited range of potential seepage waters has compositions that support 

potential localized corrosion processes (TER Section 2.2.1.3.3), in-drift conditions can support 

potential localized corrosion processes for only a limited period of time, and potential openings 

that could result from localized corrosion are small (TER Section 2.2.1.3.1). Thus, potential 

releases from uncertainties in drip shield performance under seismic conditions would be 

appreciably smaller than what could be expected for an igneous intrusive event. Given that an 

igneous intrusive event (which essentially removes all engineered barrier system capabilities) 

contributes less than a dose equivalent of 0.006 mSv/yr [0.6 mrem/yr] to the total effective dose 

calculation, uncertainties related to the drip shield barrier performance during seismic events 

are expected to cause insignificant variations in the total annual dose. Thus, DOE has 

appropriately accounted for the performance of the drip shield barrier function in the 

performance assessment, because uncertainties in the DOE evaluation would not affect the 

results of the performance assessment significantly. 

Summary of NRC Staff’s Evaluation of Drip Shield Performance

DOE relies on the drip shields as effective barriers to advective water flow or rock rubble 

impacts on the waste package. The NRC staff reviewed the information DOE presented 

relevant to the barrier capability of the drip shield and notes the following: 
5-28 

 DOE appropriately identified potential processes that may lead to openings in the drip 

shield that affect barrier capabilities. 

 DOE used reasonable models and information to demonstrate that potential openings 

from horizontal or vertical displacements during seismic events would not affect 

performance significantly. 

 DOE reasonably assumed that fault displacements sufficient to damage a waste 

package remove all barrier capabilities from the associated drip shield. 

 DOE appropriately evaluated the potential for ruptures in the drip shield plates during 

the first 12,000 years of closure by taking a conservative approach. 

 DOE appropriately determined that a small likelihood exists for such ruptures if 

earthquakes with annual probabilities of exceedance of <5 × 10−7

 occur. DOE 

appropriately implemented this likelihood of plate failure in the TSPA. 

 DOE reasonably demonstrated that uncertainties in this information would not affect the 

results of the performance assessment significantly. 

The NRC staff notes that DOE reasonably evaluated the barrier capabilities of the drip shield 

during mechanical deformation events and has appropriately incorporated the risk-significant 

aspects of this evaluation into the performance assessment calculations. 

2.2.1.3.2.3.5 Waste Package Mechanical/Structural Performance 

The DOE performance assessment includes information related to the mechanical disruption of 

engineered barrier abstractions and calculates waste package performance. DOE classified the 

waste package as important to waste isolation (SAR Table 2.1-1). DOE provided information on 

structural response of the waste package to mechanical disruption in SAR Section 2.3.4.5. The 

objective of this TER section is to evaluate whether reasonable technical bases have been 

provided for waste package abstractions used in DOE’s performance assessment.

DOE assessed potential waste package mechanical damage by performing detailed structural 

analyses. The results of these structural analyses were used as inputs to the 

seismic consequence abstractions (SCA). The SCA simulates mechanical interactions 

among the waste packages, the drip shield, the emplacement pallet, and/or accumulated 

rubble as a function of peak ground velocity. DOE calculated waste package damage as 

(i) stress corrosion cracks (SCCs) that may allow diffusive radionuclide releases and 

(ii) rupture and puncture areas that may allow advective radionuclide releases (reviewed in 

TER Section 2.2.1.3.4.3.5). 

The results of the seismic consequence abstractions are used as inputs to other process-level 

models and direct inputs to the TSPA. The waste package corrosion abstraction uses waste 

package breaches at the process level to initiate double-sided corrosion (reviewed in TER 

Section 2.2.1.3.1). Note that in this context, a breach is defined as any failure mechanism that 

penetrates the waste package (i.e., cracks, ruptures, and punctures). Waste package breaches 

also impact the chemistry inside the waste package (reviewed in TER Section 2.2.1.3.4). Stress 

corrosion crack area is used in the engineered barrier system transport abstraction to model a 
5-29 

pathway for diffusive radionuclide release (reviewed in TER Section 2.2.1.3.4). Waste package 

rupture or puncture area is used in the flux-splitting model to calculate water flux through the 

waste package (reviewed in TER Section 2.2.1.3.3). 

Information presented in SAR Table 2.1-3 suggests that seismic ground motion damage to the 

engineered barrier system components is an important mechanism that affects the engineered 

barrier system capability to perform its intended functions. DOE stated in DOE Enclosure 1 

(2009bl) that seismic-induced waste package damage is more significant in early times and 

that nominal failure processes are more significant at later times. According to DOE, 

seismic-induced stress corrosion cracking is the most probable waste package damage 

mechanism. The majority of commercial spent nuclear fuel (CSNF) and codisposal waste 

package failures due to seismic-induced stress corrosion cracking occur prior to drip shield 

plate/crown failure, as described in DOE Enclosure 1, Figures 5 and 6 (2009bl). 

As described next, DOE considered three idealized states of the engineered barrier system 

(SAR Section 2.3.4.5): 

1. Structurally stable drip shield state (intact drip shield)—when the waste packages are 

free to move and may be damaged due to impacts with other engineered barriers during 

seismic events 

2. Drip shield framework failure state (collapsed drip shield)—when the drip 

 shield–waste package interactions during seismic events may damage the 

waste package outer barrier 

3. Drip shield plates failure state—when the waste package is surrounded by and in direct 

contact with rubble and may be damaged due to waste package–rubble interactions 

during seismic events 

As DOE detailed in DOE Enclosure 1, Figure 1 (2009bl), nominal stress corrosion cracking 

in a commercial spent nuclear fuel waste package would initiate between 200,000 and 

300,000 years, when the timeframe is dependent on the drip shield performance (reviewed in 

TER Section 2.2.1.3.2.6). This would happen after the beginning of Idealized State 2. The 

commercial spent nuclear fuel waste packages cannot move as freely in Idealized State 2 as in 

Idealized State 1, thereby reducing the potential for seismic induced stress corrosion cracking. 

For the three idealized states, DOE considered two waste package failure modes. 

1. The first failure mode is referred to as “the residual stress failure mode” in this TER 

section. The waste package damage is expressed in terms of the waste package 

outer corrosion barrier surface area that may be susceptible to stress corrosion cracking. 

It is defined as an area with the residual stresses exceeding one of three residual stress 

threshold values: 90, 100, and 105 percent of the Alloy 22 yield stress (reviewed in 

TER Section 2.2.1.3.1.3.2.3). 

2. The second failure mode is referred to as “the tensile tearing failure mode” in this 

TER section. DOE used Alloy 22 ultimate tensile strain as a failure criterion to evaluate 

the waste package outer barrier tensile tearing (rupture and/or puncture) occurrence. 

For these two failure modes, DOE developed the abstractions using a three-part approach: 

(i) the rupture/puncture probability was defined as a function of peak ground velocity and the 
5-30 

effective tensile stress limits; (ii) the probability of a nonzero damaged area was defined as a 

function of peak ground velocity and the residual stress threshold damage; and (iii) for 

nonzero damaged area cases, a conditional probability distribution for the magnitude of the 

conditional damaged area was defined as a function of peak ground velocity and the residual 

stress threshold. 

DOE’s analyses results indicate greater mechanical damage potential to the waste package 

during Idealized State 1. However, the NRC staff reviewed the fundamental aspects 

of damages in all three idealized states and their abstractions. The review presented in 

this section is organized around these major topics considering the context of DOE’s 

performance assessment. 

Idealized State 1: Waste Package Structural Response with Structurally Stable 

Drip Shield 

Modeling Assumptions and Approach 

In SAR Section 2.3.4.5.2.1, DOE provided information on waste package structural response for 

the Idealized State 1 where the drip shield is structurally stable. DOE considered that dynamic 

impacts of the waste package on the rest of the engineered barrier system components may 

lead to waste package damage and rupture of the outer corrosion barrier. DOE evaluated the 

movement of and damage to waste packages resulting from seismic loads. The following three 

cases of impacts were considered using numerical models: (i) impacts between waste 

packages, (ii) impacts between the waste package and the emplacement pallet, and (iii) impacts 

between the waste package and the drip shield (SAR Section 2.3.4.5.2.1). DOE analyzed the 

transportation and disposal (TAD) and the codisposal (CDSP) waste packages for three waste 

package conditions where the drip shield is expected to remain functional and structurally stable 

(SAR Section 2.3.4.5.2.1). The three conditions are (i) 23-mm [0.91-in]-thick outer corrosion 

barrier with intact internals; (ii) 23-mm [0.91-in]-thick outer corrosion barrier with degraded 

internals; and (iii) 17-mm [0.67 in]-thick outer corrosion barrier with degraded internals. (DOE 

modeled a waste package with degraded internals as the waste package outer corrosion 

barrier only.) 

The NRC staff reviewed these three waste package conditions DOE analyzed using guidance in 

YMRP Section 2.2.1.3.2.2. As mentioned earlier, DOE considered 23- and 17-mm [0.91- and 

0.67-in] waste package outer corrosion barriers. These represent corrosion thinning of 2.4 and 

8.4 mm [0.09 and 0.33 in], respectively, from the initial 25.4-mm [1-in] outer corrosion barrier 

thickness and correspond to a timeframe of approximately 340,000 and 1.2 million years after 

emplacement (reviewed in TER Section 2.2.1.3.1.3.2.1). The time duration DOE considered 

covers the period of interest (i.e., 1 million years) for the mechanical disruption of engineered 

barriers. Therefore, DOE appropriately addressed uncertainties in the waste package 

conditions and environmental effects on the waste package components. Consideration of 

uncertainty was accomplished through appropriate reductions of the waste package outer 

corrosion barrier thickness and degradation of the waste package internals. 

Using guidance in YMRP Section 2.2.1.3.2.2, the NRC staff reviewed the material proprieties of 

the engineered barrier system components DOE incorporated into the numerical models. The 

NRC staff compared the values of these mechanical properties with the information available 

in the open literature (American Society of Mechanical Engineers, 2001aa) and determines 

that DOE used appropriate values of the mechanical properties for engineered barrier 

system components. 
5-31 

The NRC staff notes that DOE did not include the waste package damage potential for impact 

between the waste package and drip shield in the seismic damage abstractions. DOE’s 

decision was based on the observations of the waste package damage from the analyses of 

impacts between waste packages (SAR Section 2.3.4.5.2, p. 2.3.4-131). DOE concluded that 

the waste package areas damaged from a side impact on a flat elastic surface were zero or very 

small. These damaged areas were significantly less than the damaged areas from end impacts, 

as described in SNL Table 6-13 (2007ay). DOE stated that the waste package side impacts 

on a flat elastic surface are representative of the waste package impacts on the drip shield 

side wall. 

NRC Staff’s Evaluation of Modeling Assumptions and Approach 

The NRC staff reviewed this assumption using YMRP Section 2.2.1.3.2 and noted that DOE’s 

assumption is well supported and the results from impacts between waste packages bound 

the results for side, or lateral, impacts between the waste package and the drip shield. Thus, 

DOE’s basis for stating that the waste package damage due to side impacts with a drip 

shield caused by seismic events was bounded by considering the end impacts between 

waste packages. 

DOE also stated that vertical impacts between the waste package and the drip shield would 

have a small contribution to the total waste package damage (SNL, 2007ay). DOE concluded 

that the impact loads on the waste package would be distributed over a large contact area of the 

drip shield bulkheads and stiffeners. DOE further concluded that vertical impacts between the 

waste package and the drip shield surrounded by rubble would be similar to impacts between 

waste packages, which also result in small damaged areas. Therefore, DOE concluded that the 

impact damage between waste packages is representative of the waste package damage from 

vertical impacts between the waste package and drip shield. 

The NRC staff reviewed these assumptions and noted that the vertical impact between 

the waste package and the drip shield would be similar to impacts between the waste 

packages and the pallets. The NRC staff also reviewed information presented on the frequency 

of the vertical impacts between the waste package and the drip shield in SNL Section 6.4.5 

(2007ay). On the basis of this information, in 17 realizations of kinematic analyses at peak 

ground velocity levels of 1.05 and 2.44 m/sec [3.44 and 8 ft/sec], the number of impacts 

between the waste package and the drip shield bulkheads was less than 10 and at peak ground 

velocity levels of 4.07 m/sec [13.35 ft/sec] the number of impacts increased to 48. Note that the 

annual probability of exceedance of a seismic event associated to the peak ground velocity of 

4.07 m/sec [13.35 ft/sec] is on the order of 10−8

. Because the frequency of occurrence for the 

vertical impacts between the waste package and the drip shield is low, the waste package 

damage due to these impacts would be small when compared to the waste package damage 

from waste package to waste package and waste package to pallet impacts. Therefore, the 

assumption DOE used would not significantly affect TSPA results. 

To estimate waste package damage and rupture potential, DOE developed a two-part 

calculation process using numerical models developed in the computer code LS-DYNA 

(Livermore Software Technology Corporation, 2003aa). 

First, large-scale kinematic analyses were performed to determine the impact parameters for 

multiple waste packages in an emplacement drift. The parameters included locations and time 

of impacts, relative velocity and impact angles, and forces between the impacting bodies. DOE 
5-32 

used 17 ground motion time histories at peak ground velocity levels of 0.4, 1.05, 2.44, and 

4.07 m/sec [1.31, 3.44, 8, and 13.35 ft/sec] in these analyses. The NRC staff reviewed 

these values using YMRP Section 2.2.1.3.2 and notes that the ground motions used are 

consistent with the values presented on the bounded hazard curve in SAR Figure 2.3.4-18. 

The large-scale kinematic calculations presented in the SAR consider a “string” of multiple 

waste packages. A combination of TAD and codisposal waste packages in a section of an 

emplacement drift was considered. For these analyses, DOE considered a partially or fully 

collapsed emplacement drift. The drip shield was considered to be in a structurally stable 

condition. Thus, the structurally stable drip shield provided the only restriction to the movement 

of the waste packages and the pallet. DOE recorded impacts for the central waste packages 

(three and two central waste packages for the TAD and codisposal configurations, respectively) 

in the total string of waste packages (SAR Section 2.3.4.5.2.1.3.1). 

Second, DOE carried out detailed finite elements analyses for estimating damage and rupture 

potential. Impacts between individual waste packages and between waste package and pallet 

were analyzed. DOE evaluated waste package damage over the range of impact parameters, 

including those determined from the large-scale kinematic analyses. Using the results of the 

detailed finite element analyses, DOE estimated the waste package damage and rupture 

potential for the multiple impacts modeled using the large-scale kinematic analyses. 

Using guidance in YMRP Section 2.2.1.3.2, the NRC staff reviewed the modeling approach 

DOE employed to evaluate the waste package response to vibratory ground motions while the 

drip shield is structurally stable. DOE followed established industry practice for performing finite 

element analyses (Bathe, 1996aa) of mechanical/structural components of the waste package. 

The NRC staff considers reasonable and appropriate the geometric representation of the waste 

package and its components and associated simplifications made to the waste package 

geometry. The finite element models were appropriately used for characterizing waste package 

damage as input to the TSPA calculations. Further, the results for the central waste packages 

from a string of waste packages would be representative and would not be affected by the 

model boundaries along the emplacement drift direction. 

DOE stated that the waste package pallet eventually fails as the stainless steel connector tubes 

lose their structural integrity (SAR Section 2.3.4.1). For the damage analyses, however, DOE 

made an assumption that the waste package pallet is intact. This assumption, according to 

DOE, would lead to greater damage to the waste package outer corrosion barrier during 

vibratory ground motion. As DOE explained, the reason for this conclusion is that higher 

magnitude stresses are generated when the waste package impacts a “relatively stiff pallet as 

opposed to the crushed tuff invert” (SAR Section 2.3.4.1, p. 2.3.4-10). However, DOE initially 

did not consider that loss of connector integrity could result in a different range of conditions for 

pallet pedestal orientations, impact locations, and impact frequencies. The NRC staff 

questioned whether the variability in these parameters may have exceeded the range DOE 

considered. The NRC staff’s review indicated that larger uncertainty in the pedestal orientation 

can potentially affect the calculated results. For example, impact locations, time of impact, 

relative velocity of the impacting bodies, angle of impacts, and forces between the impacting 

bodies could be affected. To clarify this question, DOE was requested to supplement the 

information presented in SAR Sections 2.3.4.5.2 and 2.3.4.5.4 to address whether such 

uncertainties would affect significantly the characteristics of waste package damage calculated 

in kinematic analyses. 

In response to the NRC staff’s request for additional information, DOE provided additional 

evaluation in DOE Enclosure 1 (2009bq) to demonstrate that the intact waste package pallet 
5-33 

assumption did not underestimate the potential for waste package damage in the kinematic 

analyses. DOE stated that at lower peak ground velocity levels, the waste package and the 

pallet pedestals would have limited relative motion. Therefore, if the stainless steel connector 

tubes were to lose structural integrity due to corrosion, the waste package damage would 

remain bounded by the results of the analyses with the intact waste package pallet. DOE stated 

that, at higher peak ground velocity levels and degraded connector tubes, the impact between 

the waste package and pallet would be characterized by one of three cases: the waste package 

impacts both pallet pedestals (Case 1), the waste package impacts one pallet pedestal 

(Case 2), and the waste package impacts only the invert (Case 3). 

For Case 1, DOE stated that the angles and locations of impacts would be similar to those used 

in the kinematic analyses with an intact pallet. For Case 2, DOE stated that the locations of the 

impacts would be toward the end of the waste packages, as the waste package would tend to 

slide off the remaining pedestal and onto the invert. In SNL Tables 6-49 and 6-50 (2007ap) 

DOE stated that, due to higher waste package stiffness at the waste package lid, the waste 

package would experience less damage for impacts near the waste package lids than in the 

middle of the waste package. DOE concluded that for Case 2, the waste package damage 

would be bounded by the results of the analyses for waste package impacts with an intact 

pallet. For Case 3, DOE stated that the waste package damage would be bounded by the 

results of the intact pallet. This result is due to the waste package experiencing less damage 

from impact forces distributed over a larger waste package area. Therefore, DOE concluded 

that the results of the analyses with an intact pallet would bound the waste package damage for 

the case of structural integrity loss from corroded stainless steel connector tubes. 

The NRC staff reviewed DOE’s responses to the staff’s RAI and noted that 

 The waste package damage from impacts with an intact waste package pallet would 

bound the waste package damage from impacts with two separated pallet pedestals 

because the contact area of impact would not change 

 The waste package damage from angular impacts with an intact waste package pallet 

would be representative for the waste package damage with a single pallet pedestal 

because such an impact causes more damage than other impacts 

 The waste package damage from impacts with an intact waste package pallet 

would bound the waste package damage with the invert because the area of impact 

would increase and, as a result, reduce the stresses in the waste package outer 

corrosion barrier 

DOE considered waste package damage from angular impacts with an intact waste package 

pallet and concluded that the waste-package-to-pallet impacts are likely to cause more waste 

package damage than other types of impacts. The NRC staff’s review considered that DOE did 

not address the potential change in the frequency of this type of impact due to pallet 

degradation. However, because of pallet degradation, the number of waste package angular 

impacts would not significantly increase. Due to the close proximity of the waste packages to 

each other, and other engineered barrier system components within drip shield boundaries, the 

likelihood of waste package angular impacts on a single pallet pedestal is only feasible at very 

high peak ground velocity levels. On the basis of DOE’s seismic hazard curves, the NRC staff 

notes that the annual probability of exceedance of a seismic event associated with such high 

peak ground velocity levels is very low. Therefore, the DOE modeling approach is appropriate 

for use in the TSPA calculations. 
5-34 

Residual Stress Failure Mode 

To analyze the residual stress failure mode, DOE calculated the total damaged area of the 

waste package. Total damaged area is defined as the sum of the areas of all outer corrosion 

barrier elements in which the stress exceeds a threshold stress level at the end of a simulation. 

The three residual stress threshold values used are 90, 100, and 105 percent of the yield 

strength. (The NRC staff’s evaluation of the residual stress threshold values DOE used to 

estimate the waste package damaged area is presented in TER Section 2.2.1.3.1.3.2.3.) 

DOE used results from the analyses of the impacts between waste packages and between the 

waste package and the pallet. Inputs for the TSPA calculations were prepared in the form of 

lookup tables that provided damaged area as a function of the impact parameters. According to 

the information provided in these lookup tables (SNL, 2007ap), the amount of damage for single 

impacts is largest for impacts between a waste package and a pallet. The damage increases 

with a decrease in the outer corrosion barrier thickness. The reported damage area for single 

impacts ranged from 0.002 to 14.333 percent of the total surface area for the TAD waste 

package and from 0.002 to 20.106 percent for the codisposal waste package. 

For the analyses with multiple waste packages, the amount of reported damage is largest for 

impacts between waste a package and a pallet. The damage increases with an increase in 

peak ground velocity levels and a decrease in the outer corrosion barrier thickness. The 

reported damage area ranged from 0.006 to 43.467 percent of the total surface area for the 

TAD waste package. The range for the codisposal waste package used by DOE was from 

0.006 to 19.585 percent of its surface area (SNL, 2007ap). 

NRC Staff’s Evaluation of Residual Stress Failure Mode 

The NRC staff reviewed the residual stress failure mode results for these analyses and 

considered DOE’s response to an RAI (DOE, 2009br) that addressed whether the intact 

waste package pallet assumption would not underestimate the potential for waste package 

damage in the kinematic analyses. DOE followed established industry practice in performing 

these finite element analyses, incorporated reasonable simplification and defensible 

assumptions, and used appropriate loading conditions to characterize the waste package 

damage. Therefore, the waste package damage results for the residual stress failure mode are 

reasonable for use as input to TSPA analyses. 

Tensile Tearing Failure Mode 

To analyze the tensile tearing failure mode, DOE assessed the rupture condition for a single 

impact. The maximum effective strain in the waste package outer corrosion barrier for the full 

time-history analyses was compared with the rupture tensile strain failure criterion (SAR 

Section 2.3.4.5.2.1.3.2). DOE demonstrated through detailed finite element calculations that the 

strain for a single impact in the outer corrosion barrier was always below the ultimate tensile 

strain for Alloy 22 (SAR Section 2.3.4.5.2.1.3.2). For multiple impacts modeled in the 

large-scale kinematic analyses, DOE stated that if an impact causes “severe” deformation, the 

additional large impacts to the deformed area have the potential to cause rupture. For both the 

TAD and the codisposal waste packages with intact internals, DOE stated that the overall 

deformation of the outer corrosion barrier resulting from multiple impacts was insignificant even 

at the largest impact velocities. Therefore, DOE concluded that no rupture would occur (SAR 

Section 2.3.4.5.2.1.3.2). 
5-35 

For the analyses with degraded internals, DOE considered that the deformation from low 

velocity impacts {peak ground velocity levels less than 1.05 m/sec [3.44 ft/sec]} was not severe 

enough to lead to rupture after multiple impacts. In addition, the deformation becomes very 

large as the impact velocity increases. For peak ground velocity levels of 1.05 m/sec 

[3.44 ft/sec] and higher, a second impact of equal or greater magnitude would potentially cause 

a rupture of the outer corrosion barrier. Therefore, for the peak ground velocity of 1.05 m/sec 

[3.44 ft/sec] and higher, which have a mean annual probability of exceedance of 10−5

, the waste 

package rupture probability exceeds zero. In some realizations of large-scale models for both 

the TAD and the codisposal waste package configurations with degraded internals and peak 

ground velocity levels of 2.44 m/sec [8 ft/sec] and higher, DOE calculated the probability of 

rupture equal to one. 

NRC Staff’s Evaluation of Tensile Tearing Failure Mode 

The failure criterion (i.e., ultimate tensile strain) DOE used to evaluate the waste package 

rupture occurrence from a single impact is consistent with industry practice and is widely used in 

the field of mechanical/structural engineering (American Society of Mechanical Engineers, 

2001aa). DOE relied on engineering judgment to determine whether multiple impacts to the 

waste package result in tensile rupture (SAR Section 2.3.4.5.1.4.2). If the degree of 

deformation from a single impact was judged significant by DOE, a second impact of equal or 

greater magnitude was judged sufficient to cause tensile rupture. However, DOE initially did not 

describe the magnitude of stress or strain on the outer corrosion barrier, the impact velocities 

that caused this damage, or the threshold beyond which such damage occurs. The NRC staff 

determined that the SAR did not explain how variations in these or other indicators of damage 

were considered in the expert judgment process and therefore requested additional information. 

In response to the NRC staff’s request for additional information, DOE provided information in 

DOE Enclosure 2 (2009bq) to demonstrate the acceptability of the methodology that involves 

engineering judgment used in the qualitative evaluation of waste package rupture probability for 

multiple impacts. DOE performed a quantitative evaluation of the waste package rupture 

probability. The analysis is based on maximum effective strain limit and assessment of tensile 

strain in the waste package outer corrosion barrier. Because the quantitative approach did not 

predict waste package rupture, DOE developed a qualitative approach. This approach was 

based on an assessment of the outer corrosion barrier deformation. The deformation results 

were used to estimate the waste package rupture probability for multiple impacts. In SNL 

Figures 6-31 through 6-36 (2007ap), DOE examined deformation shapes of the outer corrosion 

barrier to determine a deformed state that could cause rupture if a second large impact 

occurred. For the analyses at an impact velocity of 5 m/sec [16.4 ft/sec], DOE stated that the 

outer corrosion barrier developed deformations sufficient to cause rupture at a subsequent 

seismic event. DOE defined this state as a lower bound such that another impact of 5 m/sec 

[16.5 ft/sec] or higher would cause rupture of the waste package outer corrosion barrier. DOE 

used impact force values associated with impacts at 5 m/sec [16.4 ft/sec] as a threshold force to 

define zero probability of waste package rupture due to multiple impacts. DOE defined the force 

associated with impacts at 7 m/sec [23 ft/sec] as an “upper force peg point” and used this to 

interpolate and extrapolate probability of waste package rupture between zero and one. DOE 

concluded that this qualitative method would not underestimate waste package rupture 

probability, because the force threshold used as a lower bound was derived on the basis of 

less severe and more frequent waste package deformations at impact velocities of 5 m/sec 

[16.4 ft/sec] and higher. 
5-36 

This threshold value is reasonable for use because another impact of 5 m/sec [16.4 ft/sec] or 

higher would rupture the waste package outer corrosion barrier. The NRC staff notes the 

following. Waste-package-to-pallet impacts would lead to greater damage to waste packages 

than the waste-package-to-waste-package or the waste-package-to-drip-shield impacts. 

Further, for the waste-package-to-pallet impacts, the most damaging scenario is angular 

impacts at 6° angles into the middle of the TAD waste package with degraded internals (SNL, 

2007ap). The NRC staff could not determine the information on peak ground velocity levels that 

would trigger impact velocities of 5 m/s [16.4 ft/sec] or higher for the waste-package-to-pallet 

impacts. Therefore, the NRC staff was not able to estimate the probability of occurrence of 

seismic events triggering these impact velocities. However, DOE provided information on 

impact velocities of the drip-shield-to-waste-package impacts in SNL Tables 6-148 through 

6-150 (2007ap). On the basis of this information, impact velocities of 4 m/sec [13.12 ft/sec] or 

higher are likely to occur only for seismic events at peak ground velocity levels of 4.07 m/sec 

[13.35 ft/sec] or higher. The NRC staff estimated that for a given peak ground velocity level, the 

waste-package-to-pallet impacts would exhibit impact velocities similar to those observed for the 

drip-shield-to-waste-package impacts. Therefore, impact velocities of 5 m/sec [16.4 ft/sec] are 

likely to occur only for seismic events with an annual probability of exceedance of 10−8

 or lower. 

Thus, subsequent seismic events capable of triggering these large impact velocities are unlikely 

and therefore beyond consideration for TSPA analyses. Moreover, the NRC staff also notes 

that the waste package should have enough remaining capacity, after the first seismic event 

with impact velocities of 5 m/sec [16.4 ft/sec], to withstand subsequent seismic events at these 

impact velocities. This is based on DOE-provided numerical results indicating the waste 

package would not exhibit rupture for a single event at impact velocities of 10 m/sec 

[32.8 ft/sec], as described in SNL Table 6-63 (2007ap). Therefore, the qualitative methodology 

used to evaluate waste package rupture probability for multiple impacts based on a 5-m/sec 

[16.4-ft/sec] impact velocity threshold is reasonable for use and would not significantly affect the 

results of TSPA analyses. 

The NRC staff determines that DOE defined, in SNL Section 6.3.2.2.5 (2007ap), the maximum 

effective strain limit for the waste package rupture condition as 0.57 for uniaxial tension and 

0.285 for biaxial tension. For realizations where the maximum effective strain was less than 

0.285, DOE considered that rupture was not credible. When the maximum effective strain 

exceeded 0.285, the strain limit was multiplied by the triaxiality factor, resulting in an effective 

strain limit between 0.285 and 0.57. Finally, DOE evaluated the rupture condition on the basis 

of the newly computed strain limit. For some realizations, for which DOE concluded that the 

waste package did not rupture, the NRC staff noted that the computed maximum effective 

strains exceeded the effective strain limit [e.g., SNL Table 6-92 (2007ap)]. In response to the 

NRC staff’s request for additional information, DOE stated in DOE Enclosure 3 (2009bq) that for 

all realizations with computed effective strain in the outer corrosion barrier greater than the 

uniaxial tensile strain limit of 0.57, as outlined in SNL Tables 6-60, 6-90, and 6-92 (2007ap), 

the stress state is compressive. Therefore, according to DOE, under these conditions the waste 

package rupture would not occur. 

The NRC staff reviewed this information DOE submitted and notes exclusion of the waste 

package rupture is reasonable for these realizations of kinematic analyses. The NRC staff 

notes that a compressive state of stress would not lead to waste package rupture. Therefore, 

DOE’s waste package damage results for the tensile tearing failure mode are reasonable for 

use as inputs for abstraction in its TSPA model.
5-37 

Idealized State 2: Waste Package Structural Response Under Collapsed Drip Shield 

Modeling Assumptions and Approach 

DOE provided information on waste package structural response for the Idealized State 2 with a 

collapsed drip shield framework (SAR Section 2.3.4.5.4.3.2). DOE assessed deformations and 

stresses in the outer corrosion barrier of a TAD waste package loaded by a collapsed drip shield 

and the accumulated rubble. Outer corrosion barriers that were 17 and 23 mm [0.67 and 

0.91 in] thick with intact and degraded internals were assessed. DOE’s model represents the 

intact internals by the inner vessel, the TAD canister, and the fuel baskets with plates inside the 

canister. DOE assigned properties of Type 316 stainless steel to all internal components. The 

internals, which are assumed to be completely degraded, were represented by a material that 

can be considered to be similar to a weakly cohesive soil with no significant strength. This 

material fills the interior volume of the outer corrosion barrier to limit volume change to 

50 percent. 

DOE performed numerical analyses to assess the waste package structural response under a 

collapsed drip shield using the FLAC3D finite element models (SAR Section 2.3.4.5.4.3.2). In 

these analyses the drip shield was not explicitly modeled and was represented by bulkhead 

flanges that contact the waste package after collapse of the drip shield framework. DOE 

conducted these quasi-static analyses by applying vertical static loads to the drip shield 

bulkheads. The vertical loads were monotonically increased until pressures ranging from 500 to 

1,500 kPa [10,400 to 31,300 psf] were reached. DOE considered that the average vertical static 

pressure from lithophysal rockfall for a complete drift collapse exerted onto the drip shield is 

127 kPa [2,652 psf] (SAR Table 2.3.4-35). For the drip shield average vertical loading demand 

of 127 kPa [2,652 psf] (SAR Section 2.3.4.5.4.3.2.1), the maximum quasi-static pressures 

applied to the waste package are equivalent to peak ground accelerations (PGAs) in the range 

of about 3 to 9 g (“g” is acceleration due to gravity). DOE monitored the structural deformations 

and the residual stresses induced in the outer corrosion barrier as a function of the average 

vertical pressure exerted on the outer corrosion barrier by the drip shield bulkhead flanges. 

NRC Staff’s Evaluation of Modeling Approach and Assumptions 

Using guidance in YMRP Section 2.2.1.3.2, the NRC staff evaluated the modeling approach for 

Idealized State 2 that DOE employed to evaluate the waste package response under 

quasi-static loading under the collapsed drip shield. For characterizing waste package damage, 

DOE followed established industry practice for mechanical/structural performance assessment 

using finite element methods (Bathe, 1996aa). DOE used the modeling calculations and 

represented the waste package and the drip shield and their components’ geometries, including 

geometry simplifications, appropriately. 

Representing the drip shield by bulkhead flanges is reasonable because the contact between 

the collapsed drip shield and the waste package that may cause damage is likely to occur 

between the drip shield bulkhead flanges and the waste package outer corrosion barrier. For 

Idealized State 2 analyses, DOE assumed that drip shield components have zero contact 

angles (i.e., lie flat) on the waste package outer corrosion barrier when vertical loads are 

applied. However, DOE initially did not provide a basis to support that the drip shield 

components would have a zero contact angle with the waste package if the drip shield 

framework collapses. In addition, the initial information in the SAR did not address how 

uncertainties in contact angle that result from differential deformation of the drip shield 

(e.g., partial framework collapse) or tilting of the waste package (e.g., due to the waste package 
5-38 

emplacement pallet degradation) could affect the analyses for waste package damage. The 

NRC staff also considered that localization of stress from angular impacts may affect the 

localization of tensile strain in the outer corrosion barrier and, thereby, increase the likelihood of 

puncture or rupture (e.g., SAR Section 2.3.4.5.4.4.2). 

In response to the NRC staff’s request for additional information that addressed these issues, 

DOE provided additional information in DOE Enclosure 1 (2009br) to demonstrate the adequacy 

of its modeling approach. DOE provided waste package damage estimates that bounded waste 

package damage for angular impacts of the drip shield onto the waste package outer corrosion 

barrier. DOE stated that a partially collapsed drip shield could result in angular contact between 

the waste package outer corrosion barrier and the drip shield bulkhead. According to DOE, a 

partially collapsed drip shield does not completely lose its load-bearing capacity. DOE stated 

that a modeling approach that allows the drip shield to fully collapse onto the waste package 

(i.e., a modeling approach that produces a zero contact angle between the waste package outer 

corrosion barrier and the drip shield bulkheads) would overestimate the total load transferred to 

the waste package and, therefore, would overestimate waste package damage. 

The NRC staff reviewed this information and noted that the overall modeling approach DOE 

used is reasonable for use for the following two reasons. First, for the residual stress failure 

mode, although an angular impact of the drip shield onto the waste package outer corrosion 

barrier could result in localization of stresses, the stress concentration areas would also be 

reduced. This would reduce the waste package outer corrosion barrier surface area that may 

be susceptible to stress corrosion cracking. Second, for the tensile tearing failure mode, DOE’s 

analyses that do not consider an angular impact of the drip shield may underestimate the tensile 

tearing stresses. However, tensile rupture of the outer corrosion barrier would not occur for 

Idealized State 2, even for nonzero contact angles. This conclusion is based on the results of 

independent studies the NRC staff performed (Ibarra, et al., 2007aa,ab; Pomerening, et al., 

2007aa). These independent studies showed that given the high ductility of Alloy 22, the waste 

package outer corrosion barrier would not be breached for the loads considered in the Idealized 

State 2 and nonzero angular impact. Therefore, DOE’s modeling approach is reasonable for 

use, because it would not significantly affect the results of TSPA calculations. 

Residual Stress Failure Mode 

For the residual stress failure mode, DOE calculated the total damaged area as the sum of 

areas of all outer corrosion barrier elements (including interior and exterior surfaces) in which a 

single residual stress threshold of 90 percent of the Alloy 22 yield stress is exceeded (SAR 

Section 2.3.4.5.4.3.2). The NRC staff’s confirmatory evaluation of the residual stress 

threshold values to estimate the waste package damaged area is presented in TER 

Section 2.2.1.3.1.3.2.3. For the analyses with a 17- or 23-mm [0.67- or 0.91-in]-thick outer 

corrosion barrier with intact internals, DOE made the following observations: 

 The damaged area was less than 0.025 percent of the total outer corrosion barrier 

surface area for average vertical pressure up to 1,200 kPa [25,062 psf] 

 The maximum damaged area was approximately 0.3 percent or less of the total outer 

corrosion barrier surface area for the highest evaluated vertical pressure of 1,500 kPa 

[31,328 psf] 
5-39 

 For the analyses with degraded internals, the vertical pressure of about 660 and 

1,000 kPa [13,784 and 20,885 psf] may lead to a fully damaged waste package 

for 17- and 23-mm [0.67- and 0.91-in]-thick outer corrosion barriers, respectively 

 For vertical pressure of less than or equal to 350 kPa [7,309 psf], the waste package 

damaged area was less than 0.1 percent of the total area (SAR Figure 2.3.4-93) 

NRC Staff’s Evaluation of Residual Stress Failure Mode 

The NRC staff reviewed the residual stress failure mode results for these analyses using YMRP 

Section 2.2.1.3.2. DOE followed established industry practices in performing these finite 

element analyses, incorporated reasonable simplifications and assumptions, and used 

appropriate loading conditions to characterize the waste package damage. The NRC staff 

further notes that the results for the waste package damage DOE presented are technically 

defensible and therefore reasonable for use as input to the TSPA calculation. DOE’s results are 

consistent with earlier studies by the NRC staff related to deformation shapes, strain, and 

stresses (Ibarra, et al., 2007ab). 

Tensile Tearing Failure Mode 

For the tensile tearing failure mode, DOE provided information on the maximum stresses in the 

waste package outer corrosion barrier for three vertical pressure levels—486, 807, and 

1,483 kPa [10,150, 16,854, and 30,972 psf]. According to this information, the maximum 

stresses in the outer corrosion barrier did not exceed 420.4 MPa (SAR Figures 2.3.4-91 and 

2.3.4-92), which is below the Alloy 22 ultimate tensile strength of 786 MPa, as detailed in SNL 

Table 4-3 (2007ap). Therefore, DOE reasonably demonstrated that rupture of the waste 

package outer corrosion barrier would not occur at these three vertical load levels (SAR 

Section 2.3.4.5.4.3.2). 

NRC Staff’s Evaluation of Tensile Tearing Failure Mode 

The NRC staff reviewed the tensile tearing failure mode results for three vertical pressure 

levels analyses using YMRP Section 2.2.1.3.2 and, on the basis of the reasonableness of 

DOE’s assumptions related to material behavior, establishment of initial and boundary 

conditions for the abstraction models, and comparison with NRC staff’s independent studies 

(Ibarra, et al., 2007ab), noted that the results are reasonable. 

Collapsed Drip Shield Condition 

For Idealized State 2, with a collapsed drip shield framework, DOE concluded that 

 State 2 bounds the case with intact waste package internals [tensile strain calculations 

from dynamic loads due to rock rubble after drip shield plate failure (Idealized State 3, 

which is reviewed in the section to follow) 

 State 1 bounds State 2 for the case with degraded internals (the kinematic analyses for 

TAD waste packages) 

However, DOE initially did not present the model results for tensile strain of the waste package 

after drip shield collapse (SAR Section 2.3.4.5.4.4.1). In addition, DOE initially did not discuss 
5-40 

how free interactions between the waste package and drip shield, or dynamic interactions with 

rock rubble, appropriately bound localized tensile strains that could occur between a collapsed 

drip shield and the waste package. In response to the NRC staff’s request for additional 

information (DOE, 2009bs), DOE provided additional information intended to demonstrate that 

its results were bounding. DOE discussed how the free interactions between the waste 

package and drip shield and dynamic interactions with rock rubble appropriately bound localized 

tensile strains that could occur between a collapsed drip shield and the waste package. 

DOE performed a quantitative comparison of the maximum effective plastic strain results of 

(i) the kinematic analyses for impacts between the waste package and the pallet with degraded 

internals and (ii) the quasi-static analyses for the waste package with degraded internals loaded 

by a collapsed drip shield. On the basis of this comparison, DOE concluded that the maximum 

effective plastic strains from the kinematic calculations of impacts between a waste package 

and a pallet with degraded internals were greater. Thus, DOE concluded that the results 

bounded the effective plastic strains for the waste package with degraded internals loaded by a 

collapsed drip shield. In addition, DOE performed a quantitative comparison of the maximum 

effective plastic strain results of the kinematic analyses for the waste package surrounded by 

rubble and the quasi-static analyses for the waste package with its intact internals loaded by a 

collapsed drip shield. DOE concluded that the effective plastic strains from the calculations for 

the waste package surrounded by rubble were greater and, therefore, bounded the effective 

plastic strains for the waste package with intact internals loaded by a collapsed drip shield. 

NRC Staff’s Evaluation of Collapsed Drip Shield Condition 

The NRC staff reviewed this information and compared quantitative results of the effective 

plastic strain DOE provided. The effective plastic strain results of both kinematic analyses 

bound the results of quasi-static analyses. Therefore, DOE’s technical bases are reasonable for 

use and demonstrate that free interactions between the waste package and drip shield and 

dynamic interactions with rock rubble appropriately bound localized tensile strains that could 

occur between a collapsed drip shield and the waste package. Further, the NRC staff notes 

DOE appropriately represented waste package performance for the Idealized State 2 (waste 

package loaded by a collapsed drip shield framework) in the TSPA evaluation. 

Idealized State 3: Waste Package Structural Response in Direct Contact with Rubble 

Modeling Assumptions and Approach 

DOE provided information on waste package structural response for Idealized State 3 where the 

waste package is in direct contact with rock rubble (SAR Section 2.3.4.5.4.3.1). DOE 

considered the loads produced by the weight of the rock rubble and the amplification of these 

loads during vibratory ground motion. These loads may lead to waste package damage through 

stress corrosion cracking, or rupture and puncture of the outer corrosion barrier. To examine 

the waste package damage potential, DOE performed mechanical/structural analyses of the 

TAD waste package in direct contact with the rubble. Two waste package outer corrosion 

barrier thicknesses of 17 and 23 mm [0.67 and 0.91 in] with degraded internals were 

considered. The system was subjected to static loads and dynamic amplification from ground 

motions with peak ground velocity levels of 0.4, 1.05, 2.44, and 4.07 m/sec [1.31, 3.44, 8, and 

13.35 ft/sec] (SAR Section 2.3.4.5.4.1). These peak ground velocity values correspond to the 

ground motions presented on the bounded hazard curve in SAR Figure 2.3.4-18. 

DOE conducted two-dimensional seismic analysis of the waste package surrounded by rubble 

using the computer code UDEC. The UDEC model initially represented an intact emplacement 
5-41 

drift containing a waste package and pallet resting on the invert. The drift was allowed to 

collapse onto the waste package. Once static equilibrium was established, the model was 

subjected to ground motions and equilibrium was reestablished. DOE used a complete drift 

collapse simulation similar to the one used to assess potential drip shield framework buckling 

and drip shield plate rupture (SAR Section 2.3.4.5.3.2.1). The results included residual tensile 

stresses and effective tensile strains in the outer corrosion barrier. General observations on the 

deformed shapes of the outer corrosion barrier were also provided. DOE did not include the 

inner vessel, the TAD canister, or the fuel baskets in the waste package representation and only 

considered the degraded state of the waste package internals. DOE represented the degraded 

internals as a material similar to a weak cohesive soil with no significant strength. DOE stated 

that, for the geometrical representation used, the results of the TAD waste package provided a 

reasonable estimate of damage for the codisposal waste package. Therefore, separate models 

were not developed for the TAD and codisposal waste packages. 

DOE used a two-dimensional plane strain representation of the waste package and its 

components for dynamic analyses under rubble loads, as outlined in SNL p. 6-216 (2007ap). 

This simplification assumes that the waste package extends infinitely in the direction normal to 

the calculation plane and that the structural response of the waste package is not affected by its 

boundaries (i.e., waste package lids). In SNL Appendix D (2007ap), DOE compared results of 

two-dimensional and three-dimensional stress analyses, using uniform static loadings that are 

not representative of the dynamic loads associated with seismic events. Because of the higher 

rigidity of the waste package lid area, the NRC staff considered that the outer corrosion barrier 

area in the vicinity of the waste package lid potentially could be more susceptible to tensile 

tearing than an open cylinder. The NRC staff raised this as a question and submitted it as an 

RAI to DOE. 

In response to the staff’s RAI, DOE provided additional information in DOE (2009bt) to 

demonstrate that the use of a two-dimensional waste package representation in seismic 

analyses of the waste package surrounded by rubble did not underestimate waste package 

damage. DOE stated that the two-dimensional waste package representation had reduced 

stiffness because the waste package lids that provide additional structural support were not 

included. The two-dimensional waste package representation was chosen because 

it maximizes structural deformation of the outer corrosion barrier. Further, DOE stated that 

three-dimensional waste package analyses were performed to investigate the potential 

for failure of waste package lids and connections between the waste package lids and the 

waste package wall. DOE concluded that these analyses demonstrated that tensile rupture of 

the outer corrosion barrier would only occur when the outer corrosion barrier collapses due to 

the waste package wall buckling, as described in SNL Appendix D (2007ap). DOE stated that 

because the two-dimensional waste package representation underestimates the loading 

demands needed for an outer corrosion barrier collapse, DOE concluded this representation 

would not underestimate the potential for the waste package outer corrosion barrier 

tensile failure. 

NRC Staff’s Evaluation of Waste Package Performance Under Accumulated Rubble Using 

a Two-Dimensional Model 

The NRC staff reviewed the analyses and noted that DOE provided appropriate technical bases 

to show that the two-dimensional waste package representation in seismic analyses of the 

waste package surrounded by rubble did not underestimate the waste package damage for the 

residual stress failure mode and the waste package puncture probability. This is based on the 

following. The waste package damage for the residual stress failure mode and waste package 
5-42 

puncture probability are functions of waste package deformations. This is a conservative 

approach to analyzing the performance: because DOE used a two-dimensional waste 

package representation, the kinematic analyses of the waste package surrounded by rubble 

would overestimate the waste package deformations. As a result, the waste package 

damage for the residual stress failure mode and waste package puncture probability would 

also be overestimated. 

The NRC staff reviewed the kinematic analyses of the waste package surrounded by rubble 

analyses and noted that this modeling approach may underestimate the tensile tearing stress in 

the waste package outer corrosion barrier near the waste package lid. However, tensile rupture 

of the outer corrosion barrier in these locations is not likely to occur for the Idealized State 3 

loading scenario. This is based on the following. In Idealized State 3, DOE performed 

kinematic analyses for the same set of ground motion time histories used for the Idealized 

State 1 evaluation. However, in Idealized State 3, the waste package is surrounded by rubble 

and the dynamic impacts would be distributed over a larger waste package contact area than in 

Idealized State 1. Redistribution of impact loads would reduce the potential for high 

strain/stress concentration regions and subsequent reduction in waste package damage. The 

NRC staff independently determined that the largest distributed impact forces on the waste 

package in Idealized State 3 do not exceed the maximum forces evaluated in the kinematic 

analyses for Idealized State 1. Moreover, in Idealized State 1, DOE appropriately demonstrated 

that waste package rupture is not likely to occur. As a result, DOE’s assertion that waste 

package rupture is not likely to occur in Idealized State 3 is reasonable. Thus, the modeling 

approach DOE used is reasonable for use in the performance assessment because the model 

does not underestimate waste package damage for TSPA abstractions. 

Residual Stress Failure Mode 

For the residual stress failure mode, DOE concluded that the damaged area was generally a 

small percentage of the total waste package surface area. For the residual stress threshold of 

90 percent of the yield stress, the damaged area resulted in 0.2 percent of the total waste 

package outer corrosion barrier surface area. For the residual stress threshold of 105 percent 

of the yield stress, the damaged area was about 3 percent of the total outer corrosion barrier 

surface area. DOE stated that the increase in damaged area correlated with an increase in 

peak ground velocity levels and thinning of the outer corrosion barrier. 

NRC Staff’s Evaluation of Residual Stress Failure Mode 

The NRC staff reviewed the results of the residual stress failure mode using YMRP 

Section 2.2.1.3.2. DOE’s response to NRC staff’s request for additional information (DOE, 

2009bt) demonstrated that two-dimensional waste package representation maximizes structural 

deformation of the outer corrosion barrier. Because higher waste package deformations 

would lead to higher residual stresses in the waste package outer corrosion barrier, the results 

for the residual stress failure mode are reasonable because they do not underrepresent waste 

package damage. Therefore, DOE appropriately represented the waste package damage 

results for the residual stress failure mode as input to the TSPA code. 

Tensile Tearing Failure Mode 

For the tensile tearing failure mode, DOE concluded in SNL Section 6.5.1.4.1 (2007ap) that the 

probability of rupture for the TAD and codisposal waste packages surrounded by rubble for the 

17- and 23-mm [0.67- and 0.91-in]-thick outer corrosion barrier with degraded internals is zero. 
5-43 

DOE’s conclusion was based on the observation that, for all simulations, the maximum effective 

plastic strain was below the ultimate tensile strain of Alloy 22. 

For this idealized state, in addition to rupture probability, DOE calculated puncture probability of 

the waste package outer corrosion barrier. DOE considered that a severely deformed outer 

corrosion barrier may be punctured by the sharp edges of fractured or partially degraded 

internal components. DOE calculated a potential for puncture of the outer corrosion barrier. 

The calculation considered the reduction in the final cross-sectional area of a severely deformed 

outer corrosion barrier, as identified in SNL Section 6.5.1.4.1 (2007ap). DOE assumed that the 

probability of outer corrosion barrier puncture is zero until deformation of the waste package 

outer corrosion barrier is such that the diameter is reduced by 10 cm [4 in], as outlined in SNL 

p. 6-234 (2007ap). According to DOE, the puncture of the waste package outer corrosion 

barrier increased with increase in peak ground velocity and with decrease in the outer corrosion 

barrier thickness. Reported rupture probability ranges were from 0.01 to 0.82 for the 23-mm 

[0.91-in]-thick outer corrosion barrier with degraded internals and from 0.05 to 1.00 for the 

17-mm [0.67-in]-thick outer corrosion barrier. 

NRC Staff’s Evaluation of Tearing Failure Mode 

DOE used a failure criterion (i.e., ultimate tensile strain to evaluate the waste package rupture 

occurrence) that is consistent with accepted industry practice and is widely used in the field of 

mechanical/structural engineering (American Society of Mechanical Engineers, 2001aa). In 

SNL Section 6.5.1.2.2 (2007ap), DOE assessed the effective plastic stresses and strains of the 

final waste package configuration after reestablishing equilibrium. However, in the SAR, DOE 

did not explain whether effective stresses and strains were assessed at intermediate steps 

during the dynamic loading simulations. Because of the reversal of dynamic loading during 

modeled seismic events, the NRC staff questioned whether the effective plastic stresses and 

strains of final waste package configurations, after reestablishment of equilibrium, are consistent 

with the maximum effective plastic stresses and strains that occur during dynamic simulations. 

In response to the NRC staff’s request for additional information, DOE provided additional 

information in DOE Enclosure 2 (2009br) to demonstrate that using stresses and strains 

computed at the end of dynamic analysis is appropriate and does not underestimate damage to 

the waste package. DOE stated that, in the dynamic analyses of the waste package 

surrounded by rubble, the code cumulatively computes the effective plastic strain, and the 

plastic strains increase during the analyses’ time history. In addition, DOE stated that the 

effective plastic strain is larger than the effective strain for the analyses with strain reversals and 

loading/unloading transitions. Therefore, DOE concluded that the use of effective plastic strain 

value obtained at the end of dynamic analyses is appropriate to evaluate the waste package 

damage. DOE stated that this approach would not underestimate the waste package rupture 

probability. On the basis of the reasonableness of the modeling assumptions and initial and 

boundary conditions used in DOE’s analyses, DOE’s technical bases are reasonable for use. 

Specifically, the evaluation of the stresses and strains at the end of dynamic analysis would not 

underestimate waste package damage. Thus, the analyzed waste package damage results are 

reasonable for use for the tensile tearing failure mode. 

Puncture Probability 

In calculating the waste package puncture probability, DOE assumed that the probability of the 

waste package outer corrosion barrier puncture is zero until deformation reaches a preset value. 

A waste package diameter reduction of 10 cm [4 in] was selected as the preset limit, as 
5-44 

identified in SNL p. 6-234 (2007ap). Use of this assumption implies that the cross-sectional 

area of the outer corrosion barrier must decrease by 11 percent before the probability of 

puncture exceeds zero. This percentage decrease can be calculated from the ratio of the 

design basis waste package outer diameter to the waste package outer diameter reduced by 

10 cm [4 in]. For the highest peak ground velocity level used, DOE calculated the probability 

of the 17-mm [0.67-in]-thick outer corrosion barrier puncture to be 0.20. This implies that 

20 percent of the waste packages would be punctured during a seismic event at a 4.07-m/sec 

[13.35-ft/sec] peak ground velocity level. 

NRC Staff’s Evaluation of Puncture Probability 

On the basis of the information discussed previously, the NRC staff notes that if DOE assumed 

that the waste package puncture probability exceeds zero for any deformation of the outer 

corrosion barrier, the number of punctured waste packages during a seismic event at a 

4.07-m/sec [13.35-ft/sec] peak ground velocity level would only increase by 2 percent. 

Moreover, because the annual probability of exceedance for a 4.07-m/s [13.35-ft/sec] peak 

ground velocity level is 10−8

, this difference in the waste package puncture probability during the 

postclosure period would reduce even further. Therefore, DOE’s assumption for puncture 

probability is reasonable because it would not significantly affect TSPA results. 

In SAR Section 2.3.4.5.4.3.1.2 DOE stated that for a residual stress threshold of 90 percent of 

the yield stress, the damage area resulted in 0.2 percent of the total waste package outer 

corrosion barrier surface area. For a residual stress threshold of 105 percent of the yield stress, 

the damaged area was 3 percent. The NRC staff noted inconsistencies between this 

information and that provided in SAR Figure 2.3.4-89 and sought clarification. DOE provided 

the following clarification in its response to NRC staff’s RAI, as outlined in DOE Enclosure 4 

(2009bq): 

“The DOE agrees that the percentages cited in the fourth and fifth sentences in SAR 

Section 2.3.4.5.4.3.1.2 are inconsistent with SAR Figure 2.3.4-89. The DOE will correct the 

numerical values in the fourth and fifth sentences of SAR Section 2.3.4.5.4.3.1.2 to read 

as follows: 

If the residual stress threshold is 90% of the yield strength, the average damaged area is 

less than 1.2% of the total outer corrosion barrier surface area. If the RST is 105% of 

the yield strength, the average damaged area is less than 0.1% of the surface area. 

These numerical values are consistent with SAR Figure 2.3.4-89 and with the data in 

Mechanical Assessment of Degraded Waste Packages and Drip Shields Subject to Vibratory 

Ground Motion (SNL 2007ap, Table 6-163).” 

Summary of NRC Staff’s Evaluation of Waste Package 

Mechanical/Structural Performance 

The NRC staff reviewed DOE’s information related to the mechanical disruption of engineered 

barriers (MDEB) abstractions for the waste package in the DOE performance assessment. This 

review was performed using guidance in YMRP Section 2.2.1.3.2 taking into account the risk 

significance of the waste package in the context of the repository postclosure performance. The 

NRC staff notes the following.
5-45 

 For Idealized State 1, where the drip shield is structurally stable, DOE concluded that 

(i) dynamic impacts of the waste package with the rest of the engineered barrier system 

may cause damage to the waste package from end-to-end impacts between waste 

packages and between waste package and pallet and (ii) the extent of waste package 

damage for TSPA abstractions is a function of the waste package type, the waste 

package internals state, peak ground velocity levels, and the outer corrosion barrier 

thickness. DOE’s conclusions are consistent with the analyses presented in the SAR 

and other supporting documents, and DOE adequately represented waste package 

performance for Idealized State 1 in the TSPA analysis. 

 For Idealized State 2, with a collapsed drip shield framework, DOE concluded that (i) for 

the case with intact waste package internals, the waste package damage estimated for 

the Idealized State 3 is bounding and (ii) for the case with degraded internals, the waste 

package damage estimated for the Idealized State 1 is bounding. DOE’s conclusions 

are reasonable for use because DOE has demonstrated, by comparing the results for all 

three idealized states, that the extent of waste package damage for Idealized State 2 

bounds the waste package damage for Idealized States 1 and 3. 

 For Idealized State 3, where the waste package is in direct contact with rock rubble, 

DOE concluded that (i) a waste package with a 23-mm [0.91-in]-thick outer corrosion 

barrier and degraded internals will not be damaged under seismic events with peak 

ground velocities below 2.44 m/sec [8 ft/sec] and (ii) the waste package damage 

depends on the waste package outer corrosion barrier thickness and the peak ground 

velocity levels. These conclusions are consistent with the analyses presented in the 

SAR and other supporting documents. DOE’s waste package performance is 

reasonable for use for Idealized State 3 in the TSPA abstractions. 

The NRC staff further notes that the technical bases for TSPA waste package abstractions 

presented in the SAR are reasonable for use because 

 DOE appropriately addressed uncertainties in the waste package conditions and the 

environmental effects on the waste package components 

 For characterizing waste package damage, DOE followed established practice for 

mechanical/structural performance assessment 

 DOE used appropriate seismic loading conditions that are consistent with the values 

presented on the bounded hazard curve 

 To evaluate the waste package damage, DOE used failure criteria that are consistent 

with accepted industry practice and/or widely used criteria in the field of 

mechanical/structural engineering 

 For calculating the residual stress and establishing tensile tearing failure modes, DOE 

used analytical/numerical methods that are appropriate for the types of analyses 

In summary, DOE’s technical bases are reasonable for use for the waste package abstractions 

and these bases adequately represented waste package performance in the TSPA abstractions. 
5-46 

2.2.1.3.2.4 NRC Staff Conclusions 

NRC staff notes that the DOE description of this model abstraction for mechanical disruption 

of engineered barriers is consistent with the guidance in the YMRP. NRC staff also notes 

that the DOE technical approach discussed in this chapter is reasonable for use in the 

performance assessment. 

2.2.1.3.2.5 References 

American Society of Mechanical Engineers. 2001aa. ASME Boiler and Pressure Vessel Code. 

New York City, New York: American Society of Mechanical Engineers. 

Anderson, J.G. and J.N. Brune. 1999aa. “Probabilistic Seismic Hazard Analyses Without the 

Ergodic Assumption.” Seismological Research Letters. Vol. 70. pp. 19–28. 

Bathe, K.-J. 1996aa. Finite Element Procedures. Upper Saddle River, New Jersey: 

Prentice-Hall, Inc. 

Bommer, J.J., N.A. Abrahamson, F.O. Strasser, A. Pecker, P.-Y. Bard, H. Bungum, F. Cotton, 

D. Fäh, F. Sabetta, F. Scherbaum, and J. Struder. 2004aa. “The Challenge of Defining Upper 

Bounds on Earthquake Ground Motions.” Seismological Research Letters. Vol. 75. pp. 82–95. 

BSC. 2008bl. “Supplemental Earthquake Ground Motion Input for a Geologic Repository at 

Yucca Mountain, Nevada.” MDL–MGR–MG–000007. Rev. 00. ACN 01, ACN 02. Las Vegas, 

Nevada: Bechtel SAIC Company, LLC. 

BSC. 2005aj. “Peak Ground Velocities for Seismic Events at Yucca Mountain, Nevada.” 

ANL–MGR–GS–000004. Rev. 00. ACN 01, ACN 02. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2004al. “Drift Degradation Analysis.” ANL–EBS–MD–000027. Rev. 03. ACN 001, 

ACN 002, ACN 003, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bj. “Technical Basis Document No. 14: Low Probability Seismic Events.” Rev. 1. 

MOL 20000510.0175. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bp. “Characterize Framework for Seismicity and Structural Deformation at 

Yucca Mountain, Nevada.” ANL–CRW–GS–000003. Rev. 00. MOL20000510.0175. 

DOC20040223.0007. Las Vegas Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bq. “Mechanical Assessment of the Drip Shield Subject to Vibratory Motion and 

Dynamic and Static Rock Loading.” CAL–WIS–AC–000002. Rev. 00A. Las Vegas, Nevada: 

Bechtel SAIC Company, LLC. 

Budnitz, R.J., G. Apostolakis, D.M. Boore, L.S. Cluff, K.J. Coppersmith, C.A. Cornell, and 

P.A. Morris. 1997aa. NUREG/CR–6372, “Recommendations for Probabilistic Seismic 

Hazard Analysis: Guidance on Uncertainty and Use of Experts—Main Report.” Vol. 1. 

Washington, DC: NRC. 
5-47 

Coraddini, M.L. 2003aa. “Board Comments on February 24, 2003 Panel Meeting on Seismic 

Issues.” Letter (June 27) to Dr. Margaret S.Y. Chu, DOE, Office of Civilian Radioactive Waste 

Management. Washington, DC: United States Nuclear Waste Technical Review Board. 

CRWMS M&O. 1998aa. “Probabilistic Seismic Hazard Analyses for Fault Displacement and 

Vibratory Ground Motion at Yucca Mountain, Nevada.” WBS 1.2.3.2.8.3.6. Las Vegas, 

Nevada: CRWMS M&O. 

CRWMS M&O. 1998ab. “Synthesis of Volcanism Studies for the Yucca Mountain 

Site Characterization Project.” 3781MR1. MOL 19981207.0393. Las Vegas, Nevada: 

CRWMS M&O. 

DOE. 2010aa. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.2.4), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.2, Set 4.” Letter (January 29) J.R. Williams to J.H. Sulima (NRC). 

ML100290670. Washington, DC: DOE, Office of Technical Management. 

DOE. 2010ab. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.2.4), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.2, Set 4.” Letter (February 12) J.R. Williams to J.H. Sulima (NRC). 

ML100470767. Washington, DC: DOE, Office of Technical Management. 

DOE. 2010ac. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.3.4), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.2, Set 3.” Letter (January 28) J.R. Williams to J.H. Sulima (NRC). 

ML100290132. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009aa. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2.2.2), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.2.2, Set 2.” Letter (January 27) J.R. Williams to J.H. Sulima (NRC). 

ML090280281. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ab. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (February 23) J.R. Williams to J.H. Sulima (NRC). 

ML090550099. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009aq. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 1.1.10, 1.2.2, 1.1.5.2, and 1.1.5.3), 

Safety Evaluation Report Vol. 2, Chapter 2.1.1.1, Set 1.” Letter (January 12) J.R. Williams to 

C. Jacobs (NRC). ML090270750. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bl. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.1, 2.3.11, 2.4.3, and 2.4.4), Safety 

Evaluation Report Vol. 3, Chapters 2.2.1.4.1, 2.2.1.4.2, and 2.2.1.4.3, Set 1.” Letter (July 29) 

J.R. Williams to J.H. Sulima (NRC). ML091680648. Las Vegas, Nevada: DOE, Office of 

Civilian Radioactive Waste Management. 
5-48 

DOE. 2009bp. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.3.4.5.3.3.2), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.3.2, Set 1.” Letter (March 6) J.R. Williams to J.H. Sulima (NRC). 

ML090680836. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bq. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.3.4), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.2, Set 2.” Letter (November 24) J.R. Williams to J.H. Sulima (NRC). 

ML093360253. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009br. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.3.4), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.2, Set 2.” Letter (December 11) J.R. Williams to J.H. Sulima (NRC). 

ML093480212. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bs. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.3.4), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.2, Set 2.” Letter (December 15) J.R. Williams to J.H. Sulima (NRC). 

ML093500116. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bt. “Yucca Mountain—Response to Request for Additional information Regarding 

License Application (Safety Analysis Report Section 2.3.4), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.2, Set 2.” Letter (November 30) J.R. Williams to J.H. Sulima (NRC). 

ML093350040. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 1997aa. “Topical Report YMP/TR–002–NP: Methodology To Assess Fault 

Displacement and Vibratory Ground Motion Hazards at Yucca Mountain.” Rev. 1. Las Vegas, 

Nevada: DOE, Office of Civilian Radioactive Waste Management. 

Ferrill, D.A., J.A. Stamatakos, and D. Sims. 1999ab. “Normal Fault Corrugation: Implications 

for Growth and Seismicity of Active Normal Faults.” Journal of Structural Geology. Vol. 21. 

pp.1,027–1,038. 

Ferrill, D.A., J.A. Stamatakos, S.M. Jones, B. Rahe, H.L McKague, R. Martin, and A.P. Morris. 

1996aa. “Quaternary Slip History of the Bare Mountain Fault (Nevada) From the Morphology 

and Distribution of Alluvial Fan Deposits.” Geology. Vol. 24. pp. 559–562. 

Gray, M.B., J.A. Stamatakos, D.A. Ferrill, and M.A. Evans. 2005aa. “Fault-Zone Deformation 

in Welded Tuffs at Yucca Mountain, Nevada, U.S.A.” Journal of Structural Geology. Vol. 27. 

pp. 1,873–1,891. 

Ibarra, L., T. Wilt, G. Ofoegbu, and A. Chowdhury. 2007aa. “Structural Performance of Drip 

Shield Subjected to Static and Dynamic Loading.” San Antonio, Texas: CNWRA. 

Ibarra, L., T. Wilt, G. Ofoegbu, R. Kazban, F. Ferrante, and A. Chowdhury. 2007ab. “Drip 

Shield—Waste Package Mechanical Interaction—Progress Report.” ML072740136. San 

Antonio, Texas: CNWRA. 
5-49 

Kana, D.D., B.H.G. Brady, B.W. Vanznat, and P.K. Nair. 1991aa. NUREG/CR–5440, “Critical 

Assessment of Seismic and Geotechnical Literature Related to a High-Level Nuclear Waste 

Underground Repository.” Washington DC: NRC. 

Keefer, W.R., J.W. Whitney, and E.M. Taylor. 2004aa. “Quaternary Paleoseismology and 

Stratigraphy of the Yucca Mountain Area, Nevada.” U.S. Geological Survey Professional 

Paper 1689. Denver, Colorado: U.S. Geological Survey. 

Livermore Software Technology Corporation. 2003aa. LS-DYNA Users Manual. Ver. 970. 

Livermore, California: Livermore Software Technology Corporation. 

McGuire, R.K., W.J. Silva, and C.J. Costantino. 2001aa. NUREG/CR–6728, “Technical Basis 

for Revision of Regulatory Guidance on Design Ground Motions: Hazard- and Rick-Consistent 

Ground Motion Spectra Guidelines.” Washington, DC: NRC. 

Morris, A.P., D.A. Ferrill, D.W. Sims, N. Franklin, and D.J. Waiting. 2004aa. “Patterns of Fault 

Displacement and Strain at Yucca Mountain, Nevada.” Journal of Structural Geology. Vol. 26. 

pp. 1,707–1,725. 

Morris, A.P., D.A. Ferrill, and D.B. Henderson. 1996aa. “Slip-Tendency Analysis and Fault 

Reactivation.” Geology. Vol. 24. pp. 275–278. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

NRC. 2003ae. Regulatory Guide 3.73, “Site Evaluations and Design Earthquake Ground 

Motion for Dry Cask Independent Spent Fuel Storage and Monitored Retrievable Storage 

Installations.” Washington, DC: NRC. 

NRC. 1999aa. “Issue Resolution Status Report, Key Technical Issue: Structural Deformation 

and Seismicity.” Rev. 2. Washington, DC: NRC. 

NRC. 1997ab. Regulatory Guide 1.165, “Identification and Characterization of 

Seismic Sources and Determination of Safe Shutdown Earthquake Ground Motion.” 

Washington, DC: NRC. 

NRC. 1996aa. NUREG–1563, “Branch Technical Position on the Use of Expert Elicitation in 

the High-Level Radioactive Waste Program.” Washington, DC: NRC. 

Ofoegbu, G., R. Fedors, C. Grossman, S. Hsiung, L. Ibarra, C. Manepally, J. Myers, 

M. Nataraja, O. Pensado, K. Smart, and D. Wyrick. 2007aa. “Summary of Current 

Understanding of Drift Degradation and Its Effects on Performance at a Potential Yucca 

Mountain Repository.” Rev. 1. CNWRA 2006-02. ML071030115. San Antonio, 

Texas: CNWRA. 
5-50 

Pomerening, D., L. Ibarra, K. Hricisak, T. Wilt, K.T., Chiang, R. Kazban, and 

A. Chowdhury. 2007aa. “Experimental Tests on Drip Shield—Waste Package Mechanical 

Interaction—Progress Report.” San Antonio, Texas: CNWRA. 

Schneider, J.F., N.A. Abrahamson, and T.C. Hanks. 1996aa. “Ground Motion Modeling of 

Scenario Earthquakes at Yucca Mountain: Final Report for Activity 8.3.1.17.3.3.” 

MOL19980617.0477. Las Vegas, Nevada: Yucca Mountain Project. 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ap. “Mechanical Assessment of Degraded Waste Packages and Drip Shields 

Subject to Vibratory Ground Motion.” MDL–WIS–AC–000001. Rev. 00. ERD 01, ERD 02 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ay. “Seismic Consequence Abstraction.” MDL–WIS–PA–000003. Rev. 03. 

ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

Spudich, P., W.B. Joyner, A.G. Lindh, D.M. Boore, B.M. Margaris, and J.B. Fletcher. 1999aa. 

“SEA99: A Revised Ground Motion Prediction Relation for Use in Extensional Tectonic 

Regimes.” Bulletin of the Seismological Society of America. Vol. 89, No. 5. pp. 1,156–1,170. 

Stamatakos, J.A., D. Biswas, and M. Silver. 2007aa. “Supplemental Evaluation of Geophysical 

Information Used To Detect and Characterize Buried Volcanic Features in the Yucca Mountain 

Region.” San Antonio, Texas: CNWRA. 

Stamatakos, J.A., D.A. Ferrill, and K.P. Spivey. 1998aa. “Paleomagnetic Constraints on the 

Tectonic Evolution of Bare Mountain, Nevada. Geological Society of American Bulletin. 

Vol. 100. pp. 1,530–1,546. 

Stamatakos, J.A., C.B. Connor, and R.H. Martin. 1997ab. “Quaternary Basin Evolution and 

Basaltic Volcanism of Crater Flat, Nevada, From Detailed Ground Magnetic Surveys of the Little 

Cones.” Journal of Geology. Vol. 105. pp. 318–330. 

Waiting, D.J., J.A. Stamatakos, D.A. Ferrill, D.W. Sims, A.P. Morris, P.S. Justus, and 

K.I. Abou-Bakr. 2003aa. “Methodologies for the Evaluation of Faulting at Yucca Mountain, 

Nevada.” Proceedings of the10th International High-Level Radioactive Waste Management 

Conference, Las Vegas, Nevada, March 30–April 2, 2003. La Grange Park, Illinois: American 

Nuclear Society. pp. 377–387. 

Wernicke, B., J.L. Davis, R.A. Bennett, J.E. Normandeau, A.M. Friedrich, and N.A. Niemi. 

2004aa. “Tectonic Implications of a Dense Continuous GPS Velocity Field at Yucca Mountain, 

Nevada.” Journal of Geophysical Research. Vol. 109, No. B12404. p. 13. 

Youngs, R.R., W.J. Arabasz, R.E. Anderson, A.R. Ramelli, J.P. Ake, D.B. Slemmons, 

J.P. McCalpin, D.I. Doser, C.J. Fridrich, F.H. Swan III, A.M. Rogers, J.C. Yount, L.W. Anderson, 
5-51 

K.D. Smith, R.L. Bruhn, P.L. Knuepfer, R.B. Smith, C.M. dePolo, D.W. O’Leary, 

K.J. Coppersmith, S.K. Pezzopane, D.P. Schwartz, J.W. Whitney, S.S. Olig, and G.R. Toro. 

2003aa. “A Methodology for Probabilistic Fault Displacement Hazard Analyses (PFDHA).” 

Earthquake Spectra. Vol. 19, No. 1. pp. 191–219. 

6-1 

CHAPTER 6 

2.2.1.3.3 Quantity and Chemistry of Water Contacting 

Engineered Barriers and Waste Forms 

2.2.1.3.3.1 Introduction 

This Technical Evaluation Report (TER) chapter addresses those features, events, and 

processes (FEPs) included in the U.S. Department of Energy’s (DOE) abstraction1

 of the 

repository drift system that may alter the chemical composition and volume of water contacting 

the drip shield and waste package surfaces (NRC, 2005aa). DOE described this abstraction in 

Safety Analysis Report (SAR) Sections 2.3.5 and 2.3.7 (DOE, 2008ab). This TER chapter 

provides the U.S. Nuclear Regulatory Commission (NRC) staff’s evaluation of the abstraction of 

key FEPs that address the following topics: (i) the chemistry of water entering the drifts, (ii) the 

chemistry of water in the drifts, and (iii) the quantity of water in contact with the engineered 

barrier system.2

 These three abstraction topics provide input needed to model the features and 

performance of the engineered barrier system (e.g., drip shield and waste package) and their 

contributions to barrier functions. For example, in its SAR, DOE relied on corrosion tests that 

were conducted on waste package and drip shield materials under a range of geochemical 

environments. The range of aqueous testing environments was deduced from a range of 

potential starting water compositions (Topic i) and from knowledge of near-field and in-drift 

processes that alter these compositions (Topics ii and iii). 

2.2.1.3.3.2 Evaluation Criteria

The NRC staff’s review of model abstractions used in the DOE postclosure performance 

assessment, including those considered in this chapter for the quantity and chemistry of water 

contacting engineered barriers and waste forms, is guided by 10 CFR 63.114 (Requirements for 

Performance Assessment) and 63.342 (Limits on Performance Assessments). The DOE Total 

System Performance Assessment (TSPA) is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 

1

As used in the Technical Evaluation Report (TER), the term “abstraction” refers to the representation of the essential 

components of a process model into a suitable form for use in a total system performance assessment. A model 

abstraction is intended to maximize the use of limited computational resources while allowing a sufficient range of 

sensitivity and uncertainty analyses. An abstracted model is a model that reproduces, or bounds, the essential 

elements of a more detailed process model and captures uncertainty and variability in what is often, but not always, 

a simplified or idealized form. 

2

The abstraction of key features, events, and processes (FEPs) that address thermal-hydrologic processes affecting 

seepage rates are reviewed in TER Section 2.2.1.3.6; those that address corrosion processes affecting the drip 

shield and waste packages are reviewed in TER Section 2.2.1.3.1; and those that address the quantity and chemistry 

of water inside breached waste packages and the invert are reviewed in TER Section 2.2.1.3.4. Also, the review of 

the rationale for key FEPs that DOE has excluded from these abstractions is covered in TER Section 2.2.1.2.1. 
6-2 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment in 10 CFR 63.342, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is given in TER Section 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of geologic 

stability, defined in 10 CFR 63.302 as 1 million years following disposal. These sections require 

that through the period of geologic stability, with specific limitations, DOE 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

This model abstraction of the quantity and chemistry of water contacting engineered 

barriers and waste forms involves seismic and igneous activity. 10 CFR 63.342(a) and (b) 

address the assessment of the effects of seismic and igneous activity on repository 

performance, subject to the probability limits given in 10 CFR 63.342(a) and (b). Specific 

constraints on the seismic and igneous activity analyses are in 10 CFR 63.342(c)(1)(i) and 

(ii), respectively. 

In addition, for this model abstraction of the quantity and chemistry of water contacting 

engineered barriers and waste forms, 10 CFR 63.342(c)(2) further provides that DOE may 

assess climate change after 10,000 years by using a constant-in-time specification of the mean 

and uncertainty distribution for repository-average deep percolation rate for the period from 

10,000 to 1 million years. DOE elected to use this representation in its Safety Analysis Report 

(SAR). Thus, implementation of the specified representative percolation rate and its uncertainty 

distribution is reviewed for the post-10,000-year period. 

The NRC staff’s review of the SAR and supporting information follows the guidance provided in 

the Yucca Mountain Review Plan (YMRP) (NRC, 2003aa) Sections 2.2.1.3.3, Quantity and 

Chemistry of Water Contacting Engineered Barriers and Waste Forms, as supplemented by 

additional guidance for the period beyond 10,000 years after permanent closure (NRC, 2009ab). 

The YMRP acceptance criteria for model abstractions that provide guidance for the NRC staff’s 

evaluation of DOE’s abstraction of the quantity and chemistry of water contacting engineered 

barriers and waste forms are 
6-3 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

The NRC staff’s review used a risk-informed approach and the guidance in the YMRP, as 

supplemented by NRC (2009ab), to the extent reasonable for aspects of the quantity and 

chemistry of water contacting engineered barriers and waste forms important to repository 

performance. The NRC staff considered all five YMRP criteria in its review of information 

provided by DOE. In the context of these criteria, only those aspects of the model abstraction 

that substantively affect the performance assessment results, as determined by the NRC staff, 

are discussed in detail in this chapter. The NRC staff’s determination is based both on risk 

information provided by DOE and on the NRC staff’s knowledge gained through experience and 

independent confirmatory analyses. 

2.2.1.3.3.3 Technical Evaluation 

2.2.1.3.3.3.1 Chemistry of Water Entering Drifts 

The abstraction for the chemistry of water entering drifts uses site-specific and literature-derived 

information as inputs to DOE’s near-field chemistry model, which simulates chemical 

interactions of minerals in the Yucca Mountain host rocks with pore waters that percolate 

downward toward the repository. The model calculates (i) a water–rock interaction parameter 

that is used to predict initial seepage water compositions important to drip shield and waste 

package corrosion; (ii) radionuclide solubility [key parameters are pH, ionic strength (I), and 

concentrations of chloride (Cl-), nitrate (NO3), and fluoride (F-

)]; and (iii) the range of in-drift 

carbon dioxide partial pressures (pCO2). Important processes related to developing these 

parameters are discussed later in this chapter under the heading “Conceptual Model.” 

DOE used the results of its near-field chemistry model as inputs to other process-level models 

and direct inputs to the Total System Performance Assessment (TSPA) model. Potential 

seepage water compositions are used by the in-drift chemical and physical environment 

abstraction (reviewed in TER Section 2.2.1.3.3.3.2) and the waste package and drip shield 

corrosion abstraction at the process level (reviewed in TER Section 2.2.1.3.1). The range of 

in-drift pCO2 values the near-field chemistry model predicts is used to generate a lookup table in 

the TSPA model, which is sampled to provide inputs to the waste form degradation and 

mobilization abstraction (reviewed in TER Section 2.2.1.3.4). TER Section 2.2.1.3.6 evaluates 

the abstraction that addresses thermal-hydrologic processes affecting seepage rates, TER 

Section 2.2.1.3.1 evaluates the abstraction that addresses corrosion processes affecting the 

drip shield and waste packages, and TER Section 2.2.1.3.4 evaluates the abstraction that 

addresses the quantity and chemistry of water inside breached waste packages and the invert. 

In Safety Analysis Report (SAR) Table 2.1-3 and DOE (2009an), the chemistry of water flowing 

into drifts was recognized as important to the barrier capability of the emplacement drift, one 

component of the engineered barrier system. Water chemistry is important to performance 

because some seepage waters can have compositions that affect the corrosion of engineered 

materials. Localized corrosion of the waste package may occur if seepage waters of 

appropriate chemistry contact the waste package when the waste package temperature 

exceeds 23.4 °C [74.1 °F] (i.e., the ambient drift temperature prior to waste emplacement). 

The U.S. Nuclear Regulatory Commission (NRC) staff’s review in Technical Evaluation Report 
6-4 

(TER) Section 2.2.1.3.1 considers the conditions under which localized corrosion is not 

expected to occur. In the U.S. Department of Energy’s (DOE) system of engineered barriers, 

titanium drip shields prevent seepage water from contacting the waste package. DOE 

predicts that the drip shields will maintain their capability until compromised by mechanical 

or corrosive failure. The NRC staff’s review of SAR Section 2.3.6.8 notes (see TER 

Section 2.2.1.3.1.3.1) that appreciable fluoride in the seepage water is needed to chemically 

compromise (by corrosion) the integrity of the drip shield. At DOE’s predicted fluoride 

concentrations for nondisruptive scenarios, the drip shields will not appreciably corrode and will 

remain intact, as barriers to seepage contacting the waste package, during the first 

250,000 years following closure. In the DOE model, as long as the drip shields remain intact, 

only slow general corrosion of waste packages occurs and only diffusional release of 

radionuclides is possible. With intact drip shields, significant amounts of seepage 

water are unlikely to contact the waste packages during the first 40,000 years following 

closure, when temperatures and relative humidity values are expected to exceed the 

pre-waste-emplacement conditions. After 40,000 years seepage water chemistries are 

expected to return to pre-waste-emplacement compositions with dilute concentrations of 

dissolved components. The NRC staff’s review notes (TER Section 2.2.1.3.4) that these 

conditions will not significantly affect the mobility of radionuclides released from the waste 

package into the invert. 

Mechanical processes are the other means by which drip shield performance may be 

compromised. DOE excluded early drip shield failure due to partial or complete collapse 

of drifts due to thermal effects (FEP 2.1.07.02.0A) on the basis of “low consequence.” 

The rationale for excluding this specific FEP is reviewed in TER Section 2.2.1.2.1.3.2. The 

DOE performance assessment and the NRC staff’s review note (TER Sections 2.2.1.3.1.3.1 

and 2.2.1.3.2.6) that few drip shields suffer mechanical failure within 12,000 years after 

repository closure. This is important because DOE calculated that conditions in the drift 

(e.g., temperature, pH, seepage water chemistry) may support localized corrosion of the waste 

package if the drip shield fails and allows seepage water to contact the waste package within 

approximately 12,000 years after repository closure, as described in DOE Enclosure 11 

(2009dg). Beyond 12,000 years after repository closure, DOE calculated that there is a low 

probability for conditions in the drift to support localized corrosion of the waste package, if the 

drip shield fails and allows seepage water to contact the waste package, even given a 

somewhat elevated temperature of the waste package. The DOE model calculated that both 

the pH and nitrate-to-chloride ratio of water that may contact the waste package will generally 

be too high to initiate localized corrosion beyond 12,000 years after repository closure. 

Because of the limited potential for the chemistry of water entering the drifts to affect 

performance significantly, the NRC staff conducted a simplified review that focused on the 

fundamental aspects of this abstraction. This approach is consistent with Yucca Mountain 

Review Plan (YMRP) guidance for conducting a risk-informed, performance-based review. 

Thus, the NRC staff’s review focused on (i) the conceptual model, (ii) the initial range of pore 

water chemistries, (iii) the range of seepage water chemistries the near-field chemistry model 

predicted, and (iv) abstraction and integration. The review presented in this chapter is 

organized around these major topics and presented within the context of the DOE performance 

assessment evaluation. An assessment of the chemistry of seepage water that may contact the 

waste package or enter the invert during the time period when conditions (e.g., temperature, pH, 

seepage water chemistry) in the drift are affected by heat generated from radioactive decay of 

the waste is provided in TER Section 2.2.1.3.3.3.2. 
6-5 

Conceptual Model 

This section addresses the system description and model integration (focused on system 

description), and the data used for model justification. The U.S. Nuclear Regulatory 

Commission (NRC) staff reviewed the information provided in Safety Analysis Report (SAR) 

Section 2.3.5.3 (and relevant references) to evaluate the conceptual model of the chemistry of 

water entering the drifts. 

SAR Table 2.2-1 contains the features, events, and processes (FEPs) that the U.S. Department 

of Energy (DOE) believes are potentially relevant to the chemistry of water entering drifts. 

DOE evaluated and included the following FEPs in this abstraction: (i) Chemical characteristics 

of groundwater in the unsaturated zone (FEP 2.2.08.01.0B), (ii) Chemistry of water flowing 

into the drift (FEP 2.2.08.12.0A), and (iii) Chemical effects of magma and magmatic 

volatiles (FEP 1.2.04.04.0B). DOE evaluated and excluded (on the basis of low probability 

or low consequence) the following FEPs from this abstraction: (i) Hydrothermal activity 

(FEP 1.2.06.00A), (ii) Altered soil or surface water chemistry (FEP 1.4.06.01.0B), (iii) Rind 

(chemically altered zone) forms in the near-field (FEP 2.1.09.12.0A), and (iv) Re-dissolution 

of precipitates directs more corrosive fluids to waste packages (FEP 2.2.08.04.0A). The 

exclusion of these FEPs from this abstraction is reviewed in Technical Evaluation Report (TER) 

Section 2.2.1.2.1.3.2. Furthermore, DOE’s pre-10,000-year treatment of FEPs in this 

abstraction continues unchanged beyond the 10,000-year postdisposal period through the 

period of geologic stability. 

The DOE conceptual model describes the chemical evolution of water as it percolates vertically 

toward the repository drifts. In the model, the water flows through the Topopah Spring 

repository host rock, a homogeneous unit that is 200 m [656.2 ft] thick, with average rock and 

hydrologic properties derived from measurements from equivalent units in the Yucca Mountain 

vicinity. Pore waters percolating through the unsaturated zone are modeled as chemically 

evolving by dissolution of alkali feldspar, which makes up about 60 percent of the host rock. 

Because of alkali feldspar’s abundance, DOE assumed its dissolution represented host rock 

dissolution processes. The rate of feldspar dissolution increases as pore waters encounter 

elevated host rock temperatures that result from heat generated from radioactive waste decay. 

After pore waters flow through the Topopah Spring rock to a location above the repository, the 

model calculates a chemical composition by combining one of four initial pore water 

compositions with an amount of feldspar predicted to have dissolved, and assuming chemical 

equilibrium with the minerals calcite and amorphous silica. Cation exchange onto clays or 

zeolites is not considered explicitly. Gas phase carbon dioxide (CO2) concentrations are 

controlled by contributions from the CO2 present in the local aqueous phase, CO2 released from 

the evaporation of water (containing dissolved CO2), and the partial pressure of CO2 (10-3.5

atmospheres) in the atmosphere. Calcite precipitation and feldspar dissolution influence the 

aqueous phase concentration of CO2. Temperature also has a strong effect on CO2 because 

this gas partitions more strongly to the gas phase at elevated temperatures. 

The NRC staff compared DOE’s description of the near-field chemistry conceptual model with 

the NRC staff’s understanding of primary mineral dissolution and secondary mineral 

precipitation processes that control the chemical evolution of pore water as it percolates 

downward through the Yucca Mountain natural system. The NRC staff notes that while the 

included FEPs are very broad in nature, in abstracting them, DOE included the important 

chemistry-affecting processes and provided adequate technical bases for their inclusion in the 

abstracted model. 
6-6 

In addition, DOE considered the seismic and igneous intrusive scenarios in the abstraction 

of seepage water chemistry. The conceptual model for seepage water chemistry in the 

seismic scenario is the same as for the nondisruptive scenario described previously. For the 

igneous intrusive scenario, in which basaltic magma similar in composition to dikes found in 

the Yucca Mountain area fills much of the drifts, DOE considered the composition of seepage 

waters contacting the waste to be consistent with water that has reacted with basalt [BSC 

Section 6.3.1.3.5(a), (2005ad)]. DOE considered three basalt-influenced water compositions 

from large fractured basalt reservoirs (SAR Tables 2.3.7-10 and 2.3.7-11). 

On the basis of the review of this information and knowledge of likely basalt–water interactions, 

the NRC staff notes that the three basalt groundwaters have chemical compositions that span 

the range of variation that could potentially enter a breached waste package following an 

igneous event and are reasonable for use in model simulations. Additional discussion of 

basaltic magma and its influence on seepage water is in TER Section 2.2.1.3.10. DOE showed 

in BSC Sections 6.5(a) and 6.6(a) (2005ad) that in-package chemistry is relatively insensitive to 

incoming water composition, whether from seismic scenarios or the igneous intrusive scenario. 

Consequently, the chemistries affected by igneous events, seismically induced ground motion, 

or fault displacement are adequate for their intended use in the SAR. 

Initial Range of Pore Water Chemistries 

This section addresses the characterization and propagation of data uncertainty through the 

model abstraction, and support of the model abstraction output by objective comparisons. The 

U.S. Department of Energy (DOE) described input parameter development and parameter 

uncertainty in Safety Analysis Report (SAR) Sections 2.3.5.3.2.2.1 and 2.3.5.3.2.2.2. The 

U.S. Nuclear Regulatory Commission (NRC) staff reviewed the information provided in the SAR 

(and relevant references) to evaluate the model inputs for the chemistry of water entering the 

drift abstraction. This evaluation focused on evaluating the uncertainty in the range of initial pore 

water chemistry [especially pH, ionic strength (I), and concentrations of chloride (Cl) and nitrate 

(NO3)] and carbon dioxide partial pressures (pCO2). In SAR Section 2.3.5.5 DOE identified 

these parameters as important inputs to the abstractions that deal with drip shield and waste 

package corrosion. 

DOE’s near-field chemistry model considers four initial pore water compositions as inputs. DOE 

assumed these four compositions represent the range of pore water compositions expected for 

the entire Yucca Mountain repository. A multistep screening process, based on charge balance 

and partial pressure of carbon dioxide, was used to evaluate 90 pore water analyses from 

Yucca Mountain cores that DOE deemed to be sufficiently complete for use in the near-field 

chemistry model. Thirty-four pore water compositions were identified as meeting the charge 

balance criteria (± 10 percent) and as having been minimally affected by microbial alteration 

(thus suitable for further consideration). Statistical cluster analysis was performed on the 

34 samples, and 4 distinct groupings, or clusters, of samples were identified. The sample 

with the composition closest to the centroid of each cluster was selected as representative of 

each cluster.3

 

3

Clustering analysis is a standard method for finding clusters of data that are similar in some sense to one another. 

The members of a cluster are more like each other than they are like members of other clusters. The centroid 

represents the most typical case in a cluster. 
6-7 

The NRC staff evaluated the information provided in SAR Section 2.3.5.3.2.2.1 and DOE 

(2009ck). The 56 pore water compositions that the DOE screening process eliminated from 

consideration had a median chloride-to-nitrate ratio 5 times greater than the 34 previously 

mentioned samples. DOE attributed the high chloride-to-nitrate ratios of the screened-out 

samples to the loss of nitrate by microbial activity during sample storage. 

The NRC staff evaluated the support for the criteria used to screen the initial pore water 

compositions used as inputs to the near-field chemistry model (DOE, 2009ck). The NRC staff 

notes that while microbial activity during storage could result in high chloride-to-nitrate ratios, no 

direct evidence of such activity was presented by DOE. As a result, the NRC staff investigated 

the risk significance of any uncertainty that may have been introduced by excluding samples 

from the data set DOE used. The NRC staff did this by comparing the range and uncertainty in 

pore water compositions represented by DOE’s 34 included pore water compositions with the 

NRC staff understanding of the Yucca Mountain natural system obtained from independent 

analyses of unsaturated zone geochemical processes and field observations at Yucca Mountain 

(Pabalan, 2010aa). The independent analysis considered 156 pore water samples the 

U.S. Geological Survey (USGS) collected from the unsaturated zone of Yucca Mountain. These 

156 samples did not meet all of DOE’s screening criteria for inclusion in the near-field chemistry 

model. However, the NRC staff notes that the data for these samples are sufficiently complete 

for this analysis, because the samples were characterized for pH, ionic strength, and 

chloride-to-nitrate concentration ratio. Thirty-three samples were selected to represent the 

range and distribution of the 156 pore water composition data set. This sample set represents a 

larger spatial distribution than the DOE samples and also bounds and enlarges the range of 

composition and concentration that DOE’s 34 pore water compositions covered. The NRC 

analysis used StreamAnalyzer 2.0 and OLIAnalyzer 3.0 (Gerbino, 2006aa) thermodynamic 

software to simulate the evaporative evolution of pore seepage waters. The StreamAnalyzer 

software uses a different electrolyte solution thermodynamic model and a more comprehensive 

thermodynamic dataset than the EQ3/6 code DOE used. The results of the analysis indicated 

that evaporation of initially dilute pore waters forms brines with compositions that do not support 

localized corrosion of the waste package and general corrosion of the drip shield. On the basis 

of this analysis, the NRC staff notes that considering a range of pore water compositions 

broader than the range DOE used in its near-field chemistry abstraction does not significantly 

affect the performance of the drip shield and waste package. Consequently, the DOE range of 

initial pore water compositions represented by the 34 pore water samples, while not bounding, 

is reasonable for use in the DOE Total System Performance Assessment (TSPA) model. 

The Range of Seepage Water Chemistries Predicted by the Near-Field Chemistry Model 

This section addresses the data used for model justification, and the characterization and 

propagation of model uncertainty through the model abstraction. The U.S. Nuclear Regulatory 

Commission (NRC) staff reviewed the information provided in Safety Analysis Report (SAR) 

Section 2.3.5.3 (and relevant references) to evaluate the implementation of the conceptual 

model of the chemistry of water entering the drifts. As explained in TER Sections 2.2.1.3.1.3.1 

and 2.2.1.3.2.6, the NRC staff notes that the U.S. Department of Energy (DOE) conclusion that 

few drip shields will fail within 12,000 years after repository closure as a result of corrosion or 

mechanical failure is reasonable. For the period 12,000 to 40,000 years after repository 

closure, the NRC staff expects that heat generated from radioactive decay of the waste would 

continue to affect conditions in the repository (i.e., temperature, relative humidity, pH, and 

chemical composition of in-drift waters). TER Section 2.2.1.3.3.3.2 assesses the chemistry 

of seepage water during the time period 12,000 to 40,000 years after repository closure, 

when conditions in the repository are affected by heat generated from radioactive decay of 
6-8 

the waste. After 40,000 years, conditions in the repository are expected to return to the 

pre-waste-emplacement state. Consequently, the repository environment is not expected to 

alter the chemical compositions of seepage waters the near-field chemistry model predicted 

would enter the drift. As a result, this review focuses on the range of chemistries the near-field 

chemistry model predicted after 40,000 years. 

DOE used the near-field chemistry model to determine the potential chemical compositions of 

seepage waters entering the drifts during the thermal period and when pre-waste-emplacement 

conditions return. The model uses a decoupled approach where hydrological and thermal 

processes are calculated independently. Chemistry is loosely coupled to the thermal and 

hydrological processes through the dissolution of alkali feldspar. The chemical composition of 

potential seepage waters is calculated at a location above the repository and at the bottom of 

the model domain using the geochemical speciation and reaction path code EQ3/6. 

The NRC staff reviewed the information provided in SAR Sections 2.3.5.3.3.3 and 2.3.5.3.3.5 

(and relevant references) and DOE (2009ck) to evaluate the model support for the range of 

seepage water compositions the near-field chemistry model predicted. The NRC staff reviewed 

DOE’s comparison of the range of pH, chloride-to-nitrate ratio, and ionic strength values the 

near-field chemistry model predicted with the calculated values of pH, chloride-to-nitrate ratio, 

and ionic strength for the 34 pore water samples included in its abstraction. The comparison 

showed that the range of compositions the near-field chemistry model predicted for 40,000 

years and beyond, when the repository returns to pre-waste-emplacement conditions, are not 

significantly different than those of the 34 starting pore water samples DOE included in its 

near-field chemistry model. The NRC staff also compared the range of compositions the 

near-field chemistry model predicted with the 56 samples DOE screened out from its near-field 

chemistry model and also with the 33 pore water compositions that NRC staff selected to 

represent the 156 USGS Yucca Mountain unsaturated zone pore water samples. The results of 

this comparison show that the range of seepage water pH, chloride-to-nitrate ratio, and ionic 

strength values the DOE near-field chemistry model predicted encompasses most (greater than 

92 percent) of the pore water pH, chloride-to-nitrate ratio, and ionic strength values represented 

by the 34 screened in, 56 screened out, and 33 representative USGS samples. On the basis of 

this analysis, the NRC staff notes that the range of seepage water pH, chloride-to-nitrate ratio, 

and ionic strength values the DOE near-field chemistry model predicted under 

pre-waste-emplacement conditions is adequate for its intended use in the TSPA analysis. 

Abstraction and Integration 

This section addresses system description and model integration (focused on integration). The 

U.S. Nuclear Regulatory Commission (NRC) staff reviewed the information provided in Safety 

Analysis Report (SAR) Section 2.3.5.3.4 (and relevant references) to evaluate model integration 

and abstraction into the Total System Performance Assessment (TSPA) model of the chemistry 

of water entering the drifts. None of the results from the abstraction of the chemistry of water 

entering the drifts are directly used in the TSPA model. The near-field chemistry model 

provides inputs to the Engineered Barrier System Physical and Chemical Environment 

Abstraction Model. Results from the Engineered Barrier System Physical and Chemical 

Environment Abstraction Model are abstracted into the TSPA model. Both the Engineered 

Barrier System Physical and Chemical Environment Abstraction Model and the abstraction of 

results into the TSPA model are evaluated in TER Section 2.2.1.3.3.3.2. 
6-9 

The near-field chemistry model calculates the thermal field using the same modeling approach 

and assumptions as other unsaturated zone thermal-hydrologic models. The NRC staff has 

evaluated the information provided in SAR Section 2.3.5.3.3.2.6 and compared it with the 

multiscale thermal-hydrologic modeling (SAR Section 2.3.5.4.1) and the in-drift condensation 

modeling (SAR Section 2.3.5.4.2). The NRC staff notes that the modeling approach DOE used 

in the SAR is consistent with these related abstractions and in the assumptions, approach, and 

parameters used. 

Summary of NRC Staff’s Review of Chemistry of Water Entering Drifts 

Because of the limited potential for the chemistry of water entering drifts to affect drip 

shield and waste package performance significantly and the extent of DOE’s corrosion 

testing programs, the U.S. Nuclear Regulatory Commission (NRC) staff’s review focused on 

the fundamental aspects of this abstraction. The NRC staff reviewed the description of the 

near-field environment, the assumptions incorporated in the near-field chemistry model 

abstraction, the range of initial pore water compositions, the range of predicted seepage 

water compositions, and integration with other model abstractions. The NRC staff notes that the 

pre-10,000-year treatment of features, events, and processes (FEPs) in this abstraction 

continue unchanged beyond the 10,000-year postdisposal period through the period of geologic 

stability (defined as 1 million years). Therefore, DOE’s treatment of FEPs through the period of 

geologic stability is reasonable. 

In particular, considering the risk to performance, DOE appropriately described (i) the range of 

input data to the near-field chemistry model, (ii) important processes such as feldspar 

dissolution, and (iii) integration and consistency with other related model abstractions. The 

NRC staff further notes that the range of ambient temperature information passed to other 

abstractions, as well as the DOE corrosion testing program [e.g., pH, ionic strength (I) and 

concentrations of chloride (Cl) and nitrate (NO3)] and gas phase partial pressures of carbon 

dioxide (pCO2)], is reasonable. 

2.2.1.3.3.3.2 Chemistry of Water in the Drifts

The abstraction for the chemistry of water in the drifts receives information on input gas 

and water compositions from the near-field chemistry model. The main purpose of the 

U.S. Department of Energy (DOE) in-drift water chemistry abstraction is to predict the range of 

chemical compositions for seepage on the waste package or in the invert for a given set of 

temperature, relative humidity, and pCO2 conditions. This abstraction is implemented in the 

Total System Performance Assessment (TSPA) model in the form of lookup tables. These 

lookup tables enable the TSPA model to provide the parameters (and their uncertainties) 

needed to represent the chemical environment for the corrosion of waste package surfaces and 

for radionuclide transport in the invert. 

The in-drift chemistry abstraction is not used to provide input to drip shield corrosion 

modeling. Instead, DOE modeled general corrosion of the titanium drip shield using two 

corrosion rate values based on weight-loss data determined from long-term corrosion tests. 

The U.S. Nuclear Regulatory Commission (NRC) staff’s evaluation of the DOE drip shield 

general corrosion model abstraction is discussed in TER Section 2.2.1.3.1.3.1.1. In addition, 

TER Section 2.2.1.3.6 reviews the abstraction for thermal-hydrologic processes affecting 

seepage rates; TER Section 2.2.1.3.1 reviews the abstraction for corrosion processes affecting 

the drip shield and waste packages; and TER Section 2.2.1.3.4 reviews the abstraction for the 

quantity and chemistry of water inside breached waste packages and the invert. 
6-10 

According to DOE’s Safety Analysis Report (SAR) Table 2.1-3 and DOE (2009an), the 

chemistry of water in the drifts is important to the capability of the emplacement drift (a barrier in 

the engineered barrier system). For example, incorrect representation of the chemistry of the 

waters in the drift may influence the calculation of waste package corrosion and radionuclide 

transport in the invert and may thus lead to incorrect dose estimates. Key risk information 

considered in assessing this abstraction includes (i) input data to the In-Drift Precipitates/Salts 

Model; (ii) consideration of processes that strongly affect water chemistry, such as 

evaporation, condensation, and salt precipitation; and (iii) uncertainty propagation through the 

model abstraction. 

The following specific NRC staff’s insights from the DOE performance assessment are 

important in evaluating this abstraction: 

 During much of the thermal period, to about 12,000 years after repository closure, the 

drip shield is expected to prevent seepage water from contacting the waste package 

surface and greatly reduce the possibility of localized corrosion of the waste package. 

 With no seepage water contacting the waste package within 12,000 years after 

repository closure and relatively limited expected waste package corrosion, only 

diffusive, not advective, release of radionuclides from the waste package is 

considered possible. 

 For the period following 12,000 years after repository closure, DOE calculated that there 

is a low probability for the repository environment (i.e., temperature, pH, and chemical 

composition of in-drift waters) to support localized corrosion of the waste package even 

if the drip shield fails and allows seepage water to contact the waste package. 

 After 40,000 years, the temperature and relative humidity in the drifts will have returned 

to pre-waste-emplacement (or near pre-waste-emplacement) conditions and seepage 

water chemistry will also have returned to pre-waste-emplacement compositions with 

dilute concentrations of dissolved components. As a result of the low temperature, high 

relative humidity, and dilute seepage water chemistry, localized corrosion is not an 

important contributor to waste package degradation after 40,000 years. 

Through its FEP screening process, DOE excluded several processes that might have 

been important to the chemical evolution of water in the drifts, such as corrosion due to 

deliquescence (FEPs 2.1.09.28.0A and 2.1.09.28.0B). The rationale for excluding specific 

FEPs from the in-drift water chemistry abstraction is reviewed in TER Section 2.2.1.2.1.3.2. 

The NRC staff’s review focuses on evaluating (i) the conceptual model inclusion of important 

processes, (ii) data and model justification, (iii) data and model uncertainty, and (iv) model 

abstraction support. 

Conceptual Model and Important Processes 

This section addresses the system description and model integration. The NRC staff reviewed 

information that the U.S. Department of Energy (DOE) provided in Safety Analysis Report (SAR) 

Section 2.3.5.5 (and relevant references) and DOE (2009cv,cw) to evaluate the conceptual 

model used to characterize the in-drift chemical environment. This evaluation focused on 
6-11 

(i) features of the conceptual model and (ii) important processes, because these specific topics 

are important to the DOE abstraction(DOE, 2008ab; NRC, 2003aa). 

SAR Table 2.2-1 contains the features, events, and processes (FEPs) that DOE believes are 

potentially relevant to the chemistry of water in the drifts. DOE evaluated and included the 

following FEPs in the in-drift water chemistry abstraction: (i) Chemical characteristics of water 

in drifts (FEP 2.1.09.01.0A); (ii) Reduction-oxidation potential in drifts (FEP 2.1.09.06.0B); 

(iii) Reaction kinetics in drifts (FEP 2.1.09.07.0B); and (iv) Thermal effects on chemistry and 

microbial activity in the engineered barrier system (FEP 2.1.11.08.0A). DOE evaluated 

and excluded, on the basis of low probability or low consequence, the following FEPs in 

this abstraction: 

 Chemical effects of excavation and construction in the engineered barrier system 

(FEP 1.1.02.00.0A) 

 Undesirable materials left (in the repository) (FEP 1.1.02.03.0A) 

 Seismic-induced drift collapse alters in-drift chemistry (FEP 1.2.03.02.0E) 

 Chemical properties and evolution of backfill (FEP 2.1.04.02.0A) 

 Erosion or dissolution of backfill (FEP 2.1.04.03.0A) 

 Chemical effects of rock reinforcement and cementitious materials in the engineered 

barrier system (FEP 2.1.06.01.0A) 

 Chemical degradation of invert (FEP 2.1.06.05.0D) 

 Chemical effects at engineered barrier system component interfaces (FEP 2.1.06.07.0A) 

 Gas explosions in the engineered barrier system (FEP 2.1.12.08.0A) 

 Radiolysis (FEP 2.1.13.01.0A) 

 Complexation in the engineered barrier system (FEP 2.1.09.13.0A) 

 Microbial activity in the engineered barrier system (FEP 2.1.10.01.0A) 

 Gas generation (CO2, CH4, H2S) from microbial activity (FEP 2.1.12.04.0A) 

 Radiological mutation of microbes (FEP 2.1.13.03.0A) 

 Chemical effects of excavation and construction in the near-field (FEP 2.2.01.01.0B) 

The rationale for excluding these specific FEPs from the in-drift water chemistry abstraction is 

reviewed in TER Section 2.2.1.2.1.3.2. Furthermore, DOE’s pre-10,000-year treatment of FEPs 

in this abstraction continues unchanged beyond the 10,000-year postdisposal period through 

the period of geologic stability. 
6-12 

DOE explained its conceptual model for in-drift chemistry as follows: early in the postclosure 

period, drift wall temperatures higher than the boiling point of water will prevent seepage from 

occurring. After the drift wall temperatures fall below the boiling point of water and the rewetting 

process begins, seepage may occur, as local hydrologic conditions allow. Because waste 

package surface temperatures will still be elevated, seepage water falling on the drip shield, and 

on the waste package in the event of drip shield failure, will evaporate and concentrate. As 

waste package temperatures continue to decrease, relative humidity will increase to the point 

that wet conditions persist. Over time, further increases in relative humidity will suppress 

evaporation and result in progressively more dilute aqueous solutions on the waste package 

surface or in the invert. 

DOE’s model considers how the chemistry of seepage water will evolve after it enters the 

repository drifts. In its conceptual model, DOE considered the effects of seepage water 

evaporation, condensation, gas–water interaction, precipitation and dissolution of salts, and salt 

separation. DOE’s conceptual model describes in general terms how each of these processes 

influenced the chemistry of in-drift water. For example, seepage evaporation will cause the 

most soluble components to concentrate in the aqueous phase and minerals to precipitate. 

With precipitation, the relative concentrations of components remaining in solution will change. 

Salt separation may occur when seepage water flows downward over the drip shield or waste 

package surface while evaporation is occurring. During this process, spatial separation of 

chemical components could occur, transporting the more soluble aqueous components 

(e.g., NO3

–

) and leaving behind as precipitates the less soluble constituents (e.g., Cl–, as NaCl 

precipitate). Condensation of water, which would dilute the aqueous phase concentration, could 

occur when the in-drift relative humidity is high enough. To model the in-drift water chemistry, 

DOE used the In-Drift Precipitates/Salts Model, which is a process-level geochemical model that 

accounts for the effects of in-drift processes. The In-Drift Precipitates/Salts Model was 

implemented using the EQ3/6 geochemical code and a Pitzer thermodynamic database that 

was developed for use in the In-Drift Precipitates/Salts Model. The NRC staff notes that the 

DOE model considered the most risk- and performance-significant processes, on the basis of 

NRC independent understanding of processes affecting the evolution of in-drift water chemistry 

(e.g., Murphy, 1994aa; Browning, et al., 2004aa; Yang, et al., 2011aa). 

In natural systems, the chemical evolution of evaporating water generally is controlled by the 

high solubility of chloride and nitrate salt minerals relative to the moderate solubility of calcium 

sulfate and the low solubility of calcium carbonate minerals—a mechanism referred to as a 

chemical divide (Hardie and Eugster, 1970aa). Thus, evaporation of initially dilute natural 

waters at the Earth’s surface, such as in saline lakes, typically leads to the formation of one of 

three brine types, depending on the initial composition of the system: calcium chloride brine, 

alkaline carbonate brine, or high sulfate brine. DOE concluded the same brine types could 

occur within the drifts because in-drift brines would be produced by processes similar to those 

that occur at the Earth’s surface. 

DOE used several assumptions in its abstraction of in-drift water chemistry. For example, all 

aqueous and gas constituents are assumed to achieve and maintain local equilibrium. The 

NRC staff considers this assumption reasonable because the chemical reactions considered in 

the abstraction are fast relative to the modeling timeframe. Also, the seepage waters on the 

waste package surface are assumed to reach equilibrium with the relative humidity on the waste 

package surface. The NRC staff notes that this assumption is conservative because the 

temperature would be highest and the relative humidity would be lowest at the waste package 

surface, which would maximize seepage water evaporation and result in the highest brine 

concentration. In addition, DOE assumed that an aqueous solution is present for all 
6-13 

temperature and relative humidity conditions once seepage onto a waste package occurs. The 

NRC staff considers this a reasonable and conservative assumption given that an aqueous 

phase is necessary for corrosion of engineered barriers. This assumption is also supported by 

laboratory experiments in which dryout was not observed at temperatures up to 190 °C [374 °F], 

particularly for brines with Na–K–Cl–NO3 compositions (Rard, et al., 2006aa). DOE also 

assumed the chemical compositions of drift wall condensation and of condensation that 

penetrates a failed drip shield to be the same as seepage composition and to be benign. This 

assumption is reasonable because water that condenses on the drift wall or other surfaces likely 

will be dilute, and any increase in concentration due to chemical interaction with drift wall 

surfaces, in-drift gases, and dusts deposited on waste package and drip shield surfaces will be 

small relative to increases in concentration due to evaporation. 

In the salt separation abstraction, DOE assumed that the solution that forms during the salt 

separation process is chloride rich. The NRC staff considers this reasonable and conservative 

because a chloride-rich solution is more corrosive to the waste package material than a chloride 

plus nitrate solution. 

With the In-Drift Precipitates/Salts Model, DOE conducted a series of seepage 

evaporation/dilution analyses at discrete temperature, relative humidity, and pCO2 values. 

The analyses used as input the water compositions derived from the near-field chemistry 

model using 11 water–rock interaction parameters for each of the 4 representative pore 

water compositions—a total of 44 water compositions. DOE selected 3 temperatures for the 

analyses—30, 70, and 100 °C [86, 158, and 212 °F]—to cover the temperature range of interest 

while minimizing interpolation errors. At each temperature, the 44 waters were evaporated at 

3 pCO2 values: 10−2

, 10−3, and 10−4 bar; these pCO2 values were selected on the basis of the 

results from the near-field chemistry model. In a second set of EQ3/6 simulations, the waters 

were diluted by a factor of 100. The oxygen partial pressure (pO2) in all the simulations was set 

equal to the atmospheric value to represent oxidizing conditions in the drift. The NRC staff 

considers this assumption reasonable given that the ranges for expected conditions are 

consistent with those predicted by DOE’s performance assessment model and NRC staff’s 

independent modeling (e.g., Murphy, 1994aa; Browning, et al., 2004aa). NRC staff notes that 

the pCO2 values and pore water composition selection are reasonable, as discussed in TER 

Section 2.2.1.3.3.3.1. 

The seepage evaporation/dilution analysis results formed the basis for the DOE in-drift water 

chemistry abstraction, which was implemented in the Total System Performance Assessment 

(TSPA) code in the form of 396 lookup tables (simulation results for 4 representative pore 

waters × 11 water–rock interaction parameter values × 3 temperatures × 3 pCO2 values). The 

lookup tables represented the range of chemical compositions that potentially could be 

generated by evaporation or dilution of drift wall seepage or condensation, or by waters imbibed 

into the invert. The lookup tables enabled the TSPA code to provide the following parameters 

and their uncertainties for a given set of temperature, relative humidity, and pCO2 conditions: 

pH, ionic strength, Cl−

 and NO3

−

 concentrations, and the NO3

−

/Cl−ratio. These parameters are 

used in the TSPA model to represent the chemical environment for the corrosion of waste 

package surfaces and for radionuclide transport in the invert. 

To determine which set of lookup tables is used for the in-drift water composition (SAR 

Section 2.3.5.5.4.3), the TSPA model used the following four inputs: the starting water identity 

(Groups 1, 2, 3, or 4); the water–rock interaction parameter derived from the near-field 

chemistry model; the pCO2, which was derived from the near-field chemistry model; and the 

waste package surface temperature derived from the multiscale thermal-hydrologic model. 
6-14 

The specific water composition in the table was selected on the basis of the relative humidity at 

the waste package surface, which in turn was derived from the multiscale thermal-hydrologic 

model. For water–rock interaction parameters, temperatures, and pCO2 values that fell between 

the values listed in the lookup tables, DOE interpolated values from adjacent tables. 

DOE indicated that brine compositions resulting from seepage evaporation are most sensitive to 

the degree of water–rock interaction and to pCO2; temperature had a comparatively smaller 

effect. The degree of water–rock interaction (the amount of feldspar dissolved) had the greatest 

effect on pH. With increasing amounts of feldspar dissolved, all the waters DOE considered in 

the analysis evolved into carbonate-type brines because feldspar dissolution and secondary 

mineral precipitation consume calcium and magnesium ions and raise the pH and bicarbonate 

concentration. The observed relationship between degree of water–rock interaction and brine 

type is important because carbonate-type brines typically have chloride and nitrate 

concentrations that are not conducive to localized corrosion of the Alloy 22 waste package outer 

barrier material. DOE concluded that corrosive calcium and magnesium-chloride brines are not 

expected to form in the potential repository. 

The NRC staff compared DOE’s conceptual model with the NRC staff’s understanding of the 

Yucca Mountain natural system and independent analysis of in-drift processes (Browning, et al., 

2003aa; 2004aa). On the basis of this understanding and risk-informed independent analysis, 

the NRC staff notes that DOE incorporated the appropriate physical processes and couplings in 

its conceptual model for in-drift water chemistry. The included features, events, and processes 

(FEPs) and associated physical processes and couplings in the abstracted model are consistent 

with independent geochemical modeling and consider relevant processes and parameters. 

The DOE model calculated that for time periods beyond 12,000 years after repository closure, 

there is a low probability for the repository environment (i.e., temperature, pH, and chemical 

composition of in-drift waters) to support localized corrosion of the waste package even if the 

drip shield fails and allows seepage water to contact the waste package. The DOE model 

calculated that the pH and nitrate-to-chloride ion ratio in in-drift water will generally be too high 

to initiate localized corrosion in this time period. The NRC staff conducted independent analysis 

of in-drift water that may contact the waste package under the temperature and relative humidity 

conditions that may exist in the drift at 12,000 years after repository closure or later. The NRC 

analysis used StreamAnalyzer 2.0 and OLIAnalyzer 3.0 (Gerbino, 2006aa) thermodynamic 

software to simulate the evaporative evolution of seepage waters, using as input the 

compositions of USGS pore water samples discussed in TER Section 2.2.1.3.3.3.1. The 

results of the analysis indicate that brines resulting from evaporation of initially dilute pore 

waters do not support localized corrosion of the waste package at in-drift temperature and 

relative humidity conditions 12,000 years after repository closure or later. On the basis of this 

independent analysis, the NRC staff notes that the DOE results that indicate a low 

probability for localized corrosion initiation beyond 12,000 years after repository closure are 

reasonable, because the repository environment (i.e., temperature, pH, and chemical 

composition of seepage waters) would not support the initiation of localized corrosion 

(TER Section 2.2.1.3.1.3.2.2). 

Data and Model Justification 

This section addresses the data used for model justification. The NRC staff reviewed 

information that DOE provided in Safety Analysis Report (SAR) Section 2.3.5.5 (and relevant 

references) and in DOE (2009cw) to evaluate the data and model justification used to 

characterize the in-drift chemical environment. This evaluation focused on (i) the 
6-15 

thermodynamic database and (ii) support of the model by laboratory experiments and other 

corroborating sources, because these specific topics are important to the DOE abstraction 

(DOE, 2008ab; NRC, 2003aa). 

As indicated in the preceding section, the In-Drift Precipitates/Salts Model was implemented 

using the EQ3/6 geochemical code and a Pitzer thermodynamic database. The parameters in 

the database were obtained from a variety of thermodynamic data and solubility measurements 

reported in the scientific literature and synthesized into an internally consistent data set. DOE 

evaluated the principal temperature-dependent Pitzer parameters in the synthesized data set for 

their ability to reproduce the original source information. The NRC staff evaluated the 

comparisons of measured data and model results in SNL Appendix I (2007ao) and notes that 

the In-Drift Precipitates/Salts Model adequately reproduces the data used to construct the 

thermodynamic database. 

DOE also used several chemical data sets to support its parameter values and to build 

confidence in the In-Drift Precipitates/Salts Model. The data sets included (i) laboratory 

experiments designed to investigate the effects of evaporation on the chemical evolution of 

water compositions and environmental conditions relevant to the potential repository; (ii) a 

natural analog for evaporative concentration of seawater at the Morton Bahamas solar salt 

production facility on Great Inagua Island in the Bahamas; and (iii) compilations of solubility 

measurements in single, binary, and ternary salt systems from handbooks and published 

sources. DOE compared these data with results from the In-Drift Precipitates/Salts Model in 

SAR Section 2.3.5.5.3.3 and referenced documents. The NRC staff evaluated these 

comparisons of measured data and model results and verified that, in general, the parameters 

used in the Pitzer thermodynamic database are reasonably supported by laboratory 

experiments, natural analog research, process-level modeling, and scientific literature 

(e.g., Linke,1958aa,1965aa; McCaffrey, et al.,1987aa; Wolf, et al.,1989aa; Rosenberg, et al., 

1999aa, 1999ab; Alai, et al., 2005aa). However, the NRC staff determined that some model 

simulation results, such as those for individual and multisalt solutions, are inconsistent with 

experimental data. For example, although DOE reported that In-Drift Precipitates/Salts Model 

results for single, binary, and ternary salt saturation points and deliquescence relative humidities 

are generally within 20 percent of literature values, several In-Drift Precipitates/Salts Model 

results differ by 20 percent or more compared with experimental data. Although DOE asserted 

that the comparisons between literature and calculated values are favorable for individual and 

multisalt systems, figures provided for the single and multisalt systems show a mismatch 

between some calculated and experimental values, including a lack of similar trending between 

data sets. Furthermore, figures from SNL (2007ao) not included in the SAR show greater 

uncertainties than those included in the SAR. These figures show that as the complexity of the 

system increases, the dissimilarity between the values calculated by the In-Drift 

Precipitates/Salts Model and literature data appears to increase. However, the NRC staff notes 

that model justification is reasonable on the basis of propagation of uncertainty throughout the 

in-drift water chemistry abstraction (see next section), and because localized corrosion is not 

considered a contributor to waste package degradation after 12,000 years, because the in-drift 

water chemistry will not support localized corrosion of the waste package. 

The NRC staff also evaluated DOE’s baseline data used to justify the in-drift water chemistry 

abstraction. The NRC staff notes that the thermal, hydrological, and geochemical values used 

by DOE are justified. For example, the abstraction uses temperature and pCO2 values that are 

technically defensible and that account for uncertainties and variability in those parameters. 

DOE described how measured pore water chemistry was used, interpreted, and synthesized 

into the in-drift water chemistry abstraction. However, the NRC staff believes that DOE did not 
6-16 

consider the full range of natural system characteristics when DOE established the initial and 

boundary conditions for the in-drift water chemistry model. In particular, the four starting water 

compositions DOE used do not appear to represent the uncertainty in Yucca Mountain pore 

water composition. Nevertheless, as discussed in TER Section 2.2.3.3.3.1, NRC staff notes, 

on the basis of its understanding of near-field processes and from the results of its independent 

analyses, that incorporating a wider range of starting water compositions in the in-drift water 

chemistry abstraction will not significantly affect repository performance. Thus, the DOE 

baseline data justify the in-drift water chemistry abstraction given their intended use in DOE’s 

Total System Performance Assessment (TSPA) model. 

Data and Model Uncertainty 

This section addresses the characterization and propagation of data and model uncertainty 

through the model abstraction. The U.S. Nuclear Regulatory Commission (NRC) staff reviewed 

information that the U.S. Department of Energy (DOE) provided in SAR Section 2.3.5.5 (and 

relevant references) and in DOE (2009cw) to evaluate the data and model uncertainties used 

to characterize the in-drift chemical environment. This evaluation focused on (i) inclusion of 

uncertainty propagation in input data and (ii) uncertainty propagation throughout the In-Drift 

Precipitates/Salts Model, because these specific topics are important to the DOE abstraction 

(DOE, 2008ab; NRC, 2003aa). 

DOE identified that uncertainties in the In-Drift Precipitates/Salts Model result in uncertainties in 

the TSPA code values of pH, ionic strength, concentrations of Cl–

 and NO3

–

, NO3

–

/Cl– ratio, and 

deliquescence relative humidity. DOE evaluated these uncertainties using model–data 

comparisons. Uncertainty in pH was given particular consideration due to variances in methods 

of measuring pH (whether true activity of the hydrogen ion is taken into account) and because 

there is significant experimental error when measuring the pH of concentrated brines. For 

solutions with water activities approximately 0.75 or higher, pH uncertainty was determined 

indirectly through the uncertainty in total inorganic carbon concentration, which reasonably 

reflects uncertainty in pH for the near-neutral range. This carbon concentration was evaluated 

using data from evaporation experiments and on calcite or CO2 solubility. For more 

concentrated solutions with lower water activities, pH uncertainty was estimated on the basis of 

comparisons of calculated and measured pH in concentrated solutions. DOE evaluated the 

uncertainty in ionic strength by comparing values calculated using the In-Drift Precipitates/Salts 

Model with those derived from evaporation experiments. Uncertainties in the Cl–

 and NO3

–

concentrations and in the NO3/Cl–

 ratio were evaluated by comparing In-Drift Precipitates/Salts 

Model results with data from evaporation experiments and solubility measurements. 

DOE assessed the uncertainty in deliquescence relative humidity by comparing In-Drift 

Precipitates/Salts Model results with deliquescence relative humidity values reported in 

the literature. 

The NRC staff evaluated DOE’s characterization and propagation of uncertainty in the in-drift 

water chemistry abstraction. The NRC staff notes that uncertainties in the DOE conceptual 

model are considered and are consistent with available laboratory experiments and chemical 

data in published literature (e.g., McCaffrey, et al., 1987aa; Alai, et al., 2005aa). Specifically, 

uncertainties in pH, ionic strength, deliquescence relative humidity, and ionic concentrations 

derived from the In-Drift Precipitates/Salts Model are reasonably supported by laboratory 

evaporation experiments, solubility data, and deliquescence relative humidity data. 
6-17 

Model Abstraction Support 

This section addresses support for the model abstraction output by objective comparisons. The 

U.S. Nuclear Regulatory Commission NRC staff reviewed information that the U.S. Department 

of Energy (DOE) provided in Safety Analysis Report (SAR) Section 2.3.5.5 (and relevant 

references) to evaluate the support for the model abstraction used to characterize the in-drift 

chemical environment. This evaluation focused on (i) consistency with process-level modeling 

and (ii) consistency of output data ranges with independent data, because these specific topics 

are important to the DOE abstraction (DOE, 2008ab; NRC, 2003aa). 

In SAR Section 2.3.5.5.4.2.2, DOE described the approach it used to build confidence in the 

in-drift water chemistry model abstraction. For example, DOE abstracted the range of in-drift 

water chemistry in the form of lookup tables at discrete temperature and pCO2 values. DOE 

supported the abstraction approach by showing that the results derived by interpolation between 

lookup tables are within the stated model uncertainties for In-Drift Precipitates/Salts Model 

simulations at the actual temperature and pCO2 conditions tested. DOE provided additional 

support to its in-drift water chemistry model abstraction in DOE (2009cv). The NRC staff 

evaluated the DOE information and notes that the Total System Performance Assessment 

(TSPA) abstraction of in-drift water chemistry is consistent with process-level modeling. The 

NRC staff verified that the in-drift water chemistry abstraction is based on the same 

assumptions and approximations that are reasonable for process-level models of closely 

analogous natural or experimental systems. 

The NRC staff also verified that DOE used accepted and well-documented procedures to 

construct and test the numerical model that simulates the evolution of in-drift water chemistry. 

The thermodynamic database used in the In-Drift Precipitates/Salts Model was developed from 

a variety of literature sources and synthesized into an internally consistent data set, which was 

evaluated for its ability to reproduce the original source information. The In-Drift 

Precipitates/Salts Model was supported by comparison with laboratory and natural analog 

information. Further, the NRC staff compared the ranges of in-drift water chemistry (e.g., pH, 

Cl–

 and NO3

–

 concentrations, ionic strength) DOE tabulated in lookup tables with the ranges 

derived from an alternative modeling approach the NRC staff used in its Total-system 

Performance Assessment code (Leslie, et al., 2007aa). The NRC modeling approach used 

thermodynamic calculations to simulate the evaporation of seepage waters. The calculations 

were implemented using StreamAnalyzer 2.0 and OLIAnalyzer 3.0 (Gerbino, 2006aa) and used 

as input chemical compositions of Yucca Mountain unsaturated zone pore water samples the 

USGS reported (Yang, et al., 2003aa, 1998aa, 1996aa). The ranges in pH, Cl–

 and NO3

–

concentrations, and NO3

–

/Cl– ratio derived from the NRC approach are consistent with those 

derived from the DOE in-drift water chemistry abstraction. Thus, the NRC staff notes that the 

DOE abstraction output is supported by objective comparisons. 

Summary of NRC Staff’s Review of Chemistry of Water in the Drifts 

The U.S. Nuclear Regulatory Commission (NRC) staff reviewed the in-drift chemistry 

abstraction (including the description of the in-drift environment), the assumptions incorporated 

in the In-Drift Precipitates/Salts Model Abstraction, the Pitzer database for the In-Drift 

Precipitates/Salts Model, supporting data and experiments, and uncertainty propagation 

through the In-Drift Precipitates/Salts Model. The NRC staff notes that the pre-10,000-year 

treatment of features, events, and processes (FEPs) in this abstraction continue unchanged 

beyond the 10,000-year postdisposal period through the period of geologic stability (defined 
6-18 

as 1 million years). Therefore, DOE’s treatment of FEPs through the period of geologic 

stability is reasonable. 

In particular, on the basis of risk insight information, DOE appropriately (i) described input data 

to the In-Drift Precipitates/Salts Model; (ii) considered important processes such as evaporation, 

condensation, and salt precipitation; and (iii) propagated uncertainty through the model 

abstraction. The NRC staff notes that, for pre-waste-emplacement temperatures, the range of 

chemistry tabulated in the Total System Performance Assessment (TSPA) lookup tables 

(i.e., relative humidity; pCO2 conditions of pH, ionic strength, Cl−

 and NO3

−

 concentrations; and 

the NO3

−

/Cl− ratio) reasonably represents the potential chemistry of water contacting the surface 

of waste packages and radionuclide transport in the invert. The NRC staff also notes, on the 

basis of its independent analysis, that in-drift water chemistry is unlikely to initiate waste 

package localized corrosion in the time following 12,000 years after repository closure. 

2.2.1.3.3.3.3 Quantity of Water in Contact With the Engineered Barrier System 

The purpose for abstracting the quantity of water in contact with the engineered barrier system 

is to (i) determine seepage flux4

 rates through and around breached or intact waste packages 

and the drip shield and (ii) provide an estimate for partitioning of radionuclides exiting the 

engineered barrier system between unsaturated zone fractures and in the rock matrix. The 

engineered barrier system flow abstraction receives seepage flux approaching the drift wall from 

the drift seepage abstraction (BSC, 2004aa), condensation on the drift walls from the In-drift

Natural Convection and Condensation model abstraction (BSC, 2004aw), capillary wicking 

(imbibition flux) into the invert from the Multiscale Thermal-Hydrologic Model abstraction (BSC, 

2005ah), and the size and evaluation of corrosion openings on the waste packages from the 

WAPDEG Corrosion model abstraction (BSC, 2004bs). Finally, TER Section 2.2.1.3.6 reviews 

the abstraction that addresses thermal-hydrologic processes affecting seepage, TER 

Section 2.2.1.3.1 reviews the abstraction that addresses corrosion processes affecting the 

drip shield and waste packages, and TER Section 2.2.1.3.4 reviews the abstraction that 

addresses the quantity and chemistry of water inside breached waste packages and the invert. 

The review of this abstraction is important because missing, discontinuous, or misrepresented 

flow paths in the engineered barrier system, and misrepresentation of seepage through and 

capillary diversions around the breached drift and waste packages, may result in incorrect dose 

estimates. Key risk information used to assess this abstraction includes (i) seepage flux rate 

at the drift wall, (ii) timing of the failure of the drip shield, and (iii) fraction of the breached 

patch area on waste packages. As discussed in the previous paragraph, this key risk 

information, which affects the distribution and flux rate throughout the engineered barrier 

system, is computed outside of this flow abstraction and then passed into this flow abstraction. 

Furthermore, the relative magnitude of the imbibition flux from the host rock matrix into the 

invert, which is computed outside the engineered barrier system flow abstraction, and the 

flux into the invert ( that, in turn influences the fraction of radionuclides released from the 

engineered barrier system into unsaturated fractures and the rock matrix), which is computed 

by the engineered barrier system flow abstraction, are the key risk information propagated to 

the unsaturated zone transport abstraction in the Total System Performance Assessment 

(TSPA) model. 

 

4

Flux is the amount of water associated with a flow path at a given point along a pathway, at a given time. Flux rate 

is the amount of water per unit time. 
6-19 

The U.S. Nuclear Regulatory Commission (NRC) staff reviewed DOE’s model for engineered 

barrier system flow in Safety Analysis Report (SAR) Section 2.3.7.12 (and relevant references). 

The NRC staff’s review focused on evaluating (i) the conceptual model for flow paths and flux 

splitting throughout the intact and failed engineered barrier system components under both 

nominal and disruptive events; (ii) model integration of the engineered barrier system flow 

abstraction with other abstractions in the TSPA model, as well as information exchanges 

between the engineered barrier system flow abstraction and other abstractions; (iii) support for 

model parameters by available experimental data, and propagation of data uncertainties within 

the engineered barrier system flow abstraction and into other abstractions in the TSPA code; 

and (iv) analysis of model uncertainties through alternative model abstractions. 

Conceptual Model for the Engineered Barrier System Flow Paths and Flux Splitting 

This section addresses the system description and model integration (focused on system 

description/conceptual model). U.S. Nuclear Regulatory Commission (NRC) staff reviewed the 

information provided in Safety Analysis Report (SAR) Sections 2.3.7.12 and 2.2.1.2.1 (DOE, 

2009av) (and relevant references) and in DOE (2009ab) to evaluate the conceptual model of the 

quantity of water in the engineered barrier system. This evaluation focused on (i) the continuity 

and integration of flow paths and (ii) impacts of intact and breached engineered barrier system 

components on the engineered barrier system flow in the nominal case and disruptive events, 

because these specific topics are important to the U.S. Department of Energy’s (DOE) 

abstraction (DOE, 2008ab; NRC, 2003aa). 

Safety Analysis Report (SAR) Table 2.2-1 contains the features, events, and processes 

(FEPs) that DOE believes are potentially relevant to the quantity of water in contact with 

the engineered barrier system. DOE evaluated and included the following FEPs in this 

abstraction: (i) Capillary effects (wicking) in engineered barrier system (FEP 2.1.08.06.0A) 

and (ii) Unsaturated flow in the engineered barrier system (FEP 2.1.08.07.0A). DOE evaluated 

and excluded, on the basis of low probability or low consequence, the following features, 

events, and processes (FEPs) from this abstraction: (i) Advection of liquids and solids through 

cracks in the waste package (FEP 2.1.03.10.0A), (ii) Advection of liquids and solids through 

cracks in the drip shield (FEP 2.1.03.10.0B), (iii) Saturated flow in the engineered barrier system 

(FEP 2.1.08.09.0A), and (iv) Condensation on underside of drip shield (FEP 2.1.08.14.0A). 

Note that the rationale for excluding these specific FEPs from this abstraction is reviewed in 

TER Section 2.2.1.2.1.3.2. Furthermore, the U.S. Department of Energy’s (DOE) treatment 

of FEPs in the pre-10,000-year period in this abstraction continues unchanged beyond the 

10,000-year postdisposal period through the period of geologic stability. 

DOE’s engineered barrier system flow abstraction is based on a mass-conserving, flux-splitting 

algorithm involving eight potential unsaturated flow pathways in the engineered barrier system. 

The flow pathways and fluxes along these pathways are labeled F1 through F8 in SAR 

Figure 2.3.7-8 (DOE, 2009av). The upper wall of the drift forms the top boundary (along the 

F1 flow path), and the bottom part of the invert forms the lower boundary (along the F8 flow 

path) in the engineered barrier system abstraction. The abstraction calculates time-variant flux 

rates along unsaturated flow pathways across the engineered barrier system for the nominal 

case and disruptive events. 

DOE described the flow pathways and flux rates (F1–F8) in the engineered barrier system flow 

abstraction as follows (Figure 6-1): the F1 flow path accounts for the total dripping flux from a 

drift wall. The total dripping flux is the sum of the seepage flux arriving at the drift wall from 

above and the condensed water on drift walls (TER Section 2.2.1.3.6.3.4); these are direct 
6-20 

Figure 6-1. Potential Flow Pathways in the Engineered Barrier System 

(SAR Figure 2.3.7-8; DOE, 2008ab) 

inputs to the engineered barrier system flow abstraction. The F2 flow path accounts for the flux 

through partially failed patches of the drip shield formed by general corrosion. 

Localized corrosion of the drip shield is excluded from the performance assessment (TER 

Section 2.2.1.2.1.3). The F3 flow path accounts for the diversion of flux around the drip 

shield (computed as F3 = F1 – F2), which will drain directly into the invert. Although the 

diversion of flux around the drip shield is included in the construction of the engineered barrier 

system flow model, DOE did not implement the flux-splitting algorithm for drip shields in TSPA 

simulations, because the drip shields were modeled to be either all intact or failed, as 

described in SNL Section 6.1.1 (2007aj). The F4 flow path accounts for the flux through 

patches, formed as a result of general corrosion of the outer barrier of the waste packages. 

Localized corrosion of the outer barrier of waste packages is not considered important (TER 

Section 2.2.1.3.1.3.2.5). The F5 flow path accounts for the diversion of flux around waste 

packages (computed as F5 = F3 – F4), which will drain directly into the invert. The F6 flow path 

is the total flux entering the invert (computed as F6 = F4 + F3 + F5). The F7 flow path accounts 

for the imbibition flux from the host rock matrix into the invert and is a direct input to the 

engineered barrier system flow abstraction. The F8 flow path is the total flux from the invert to 

the unsaturated zone (computed as F8 = F6 + F7). Thus, the magnitude of fluxes in the 

engineered barrier system is determined by the flux rates at the drift wall, flow exchanges 

between the invert and the surrounding unsaturated fractured domain, and the size of corrosion 
6-21 

patches on the drip shield and waste packages, which are externally calculated. The rest of the 

fluxes in the engineered barrier system are computed on the basis of the mass-conserving, flux 

splitting algorithm. 

In addition to the nominal case, DOE addressed both the igneous intrusion and the seismic 

ground motion cases. The NRC staff confirmed that DOE showed, on the basis of TSPA 

simulation results, that these two disruptive modeling cases are the most significant contributors 

to the total dose for the 10,000- and 1-million-year simulations, as shown in SAR Figure 2.4-18 

(DOE, 2009av). Because the contribution of the other modeling cases (including drip shield 

early failure, waste package early failure, seismic fault displacement, and volcanic eruption) to 

the mean annual dose for the 10,000- and 1-million-year simulations are at least an order 

magnitude smaller than the contributions to the mean annual dose by the igneous intrusion and 

the seismic ground motion modeling cases, the other modeling cases are not discussed in this 

section. For the igneous intrusion scenario, the drip shield and waste packages entirely lose 

their integrity instantaneously at the time of the intrusive event, and all seepage water 

approaching the drift wall flows through the waste package, as described in SNL Section 6.1.1 

(2007aj). For the seismic ground motion scenario, after the drip shield fails, the water flow rate 

through a damaged waste package depends on the expected fraction of the waste package 

surface (which increases with time) that is breached by corrosion patches, as shown in SNL 

Figures 8.3-11(a) and 8.3-12(a) (2008ag) and DOE (2009an). 

The NRC staff reviewed the model conceptualization, mass-balance equations, and underlying 

assumptions of the engineered barrier system flow model abstraction and other relevant 

abstractions with which the engineered barrier system flow model abstraction exchanges data 

and information to assess DOE’s description of the engineered barrier system flow model 

abstraction and the underlying mass-conserving, flux-splitting algorithm. The NRC staff notes 

that DOE reasonably identified and described potential flow pathways and flow factors in 

estimating the quantity of water that could contact the engineered barrier system and waste 

forms on the basis of the mass-conserving, flux-splitting algorithm in the engineered barrier 

system flow abstraction. The engineered barrier system flow abstraction is reasonable because 

(i) the potential unsaturated flow pathways that DOE identified encompass all potential major 

flow pathways within the engineered barrier system, and between the engineered barrier system 

and the surrounding unsaturated fractured rocks; (ii) the impact of drip shield and waste 

package failures is addressed in the engineered barrier system flow abstraction by explicitly 

incorporating the failed or intact mode of the drip shield and incorporating externally computed 

temporal variations in the number of corrosion patches on waste packages; (iii) the impacts of 

the transient nature of dripping flux and flux exchange between the unsaturated zone and the 

invert are carried into the engineered barrier system flow abstraction through the F1 flow path 

and the F7 flow path, respectively; and (iv) loss of barrier capability of the engineered barrier 

system following an igneous event and gradual increase of the expected fraction of surface 

patches on waste packages, which account for increased water fluxes due to seismic ground 

motion (DOE, 2009an) are physically reasonable, and they are more risk significant as 

compared with the nominal case. 

Model Integration and Information Flow 

This section addresses the system description and model integration (focused on integration). 

U.S. Nuclear Regulatory Commission (NRC) staff reviewed the information provided in Safety 

Analysis Report (SAR) Section 2.3.7.12 (and relevant references) to evaluate the model 

integration and information exchange with the other abstractions. This evaluation focused 

on (i) integration and continuity of flow components in the engineered barrier system flow 
6-22 

abstraction and (ii) information on input to and output from the engineered barrier system flow 

abstraction, because these specific topics are important to the U.S. Department of Energy’s 

(DOE) abstraction (DOE, 2008ab; NRC, 2003aa). 

Input to the engineered barrier system flow abstraction includes: seepage flux into the drift 

from the drift seepage abstraction (BSC, 2004aa); condensation on the drift walls from the

In-drift Natural Convection and Condensation Model abstraction (BSC, 2004aw), which 

makes up the F1 flow path; imbibition flux into the invert; the F7 flow path from the Multiscale 

Thermal-Hydrologic Model abstraction (BSC, 2005aa); and patch size and its evolution from the 

WAPDEG corrosion model (BSC, 2004bs), which is used for calculating the F4 flow path. 

The F4 flow path determines the seeping or nonseeping condition in the waste package. 

Information on the seeping or nonseeping condition is used in the engineered barrier system 

radionuclides and colloid abstraction for determining the rate constant for irreversible 

attachment of plutonium and americium onto mobile corrosion product colloids (DOE, 2009ay). 

The U.S. Department of Energy’s (DOE) SAR showed the engineered barrier system 

Unsaturated Zone Interface Model (SNL, 2007aj) uses water fluxes along the F6 and F7 flow 

paths to calculate advective flow rates of radionuclides and colloid suspensions to be used in 

the unsaturated zone transport abstraction. The F6 flow path determines the water flux rate for 

radionuclides and colloid suspensions from the invert into the unsaturated zone fractures in a 

seeping environment. In a nonseeping environment, advective flux from the invert to 

unsaturated zone fractures is zero unless drift wall condensation is greater than zero. The 

F7 flow path provides the water flux for advective transport of radionuclides and colloid 

suspensions from the invert into the unsaturated zone matrix. Imbibition along the F7 flow path 

could provide a small advective flux into the unsaturated zone matrix in both seeping and 

nonseeping environments (DOE, 2009am). 

The NRC staff reviewed the model conceptualization, the mass-balance equations, and the 

underlying assumptions of the engineered barrier system flow model abstraction and other 

relevant abstractions with which the engineered barrier system flow model abstraction 

exchanges data and information to assess DOE’s description of the integration of the 

engineered barrier system flow model abstraction and the information exchange with other 

abstractions in the Total System Performance Assessment (TSPA) model. DOE reasonably 

described model integration and information flow between the engineered barrier system flow 

abstraction and other abstractions in the TSPA model because DOE (i) identified and integrated 

input to (the F1 and F7 flow paths) and output from (the F6 and F8 flow paths) the engineered 

barrier system flow abstraction, (ii) computed fluxes internally across breached engineered 

barrier system components (the F2 and F4 flow paths) on the basis of time-variant information 

from other abstractions, and (iii) computed fluxes internally without needing direct input from 

other abstractions. DOE identified the upstream abstractions (drift seepage abstraction and

In-drift Natural Convection and Condensation model), in-parallel abstractions (Multiscale 

Thermal-Hydrologic Model and WAPDEG corrosion model), and the downstream abstractions 

(engineered barrier system radionuclide transport abstraction), as well as the information 

exchanged among them through the F1, F2, F4, F6, F7, and F8 flow paths, under both seeping 

and nonseeping conditions. 

Data Support and Uncertainties 

This section addresses the data used for model justification, and the characterization and 

propagation of data uncertainty through the model abstraction. The U.S. Nuclear Regulatory 

Commission (NRC) staff reviewed the information provided in Safety Analysis Report (SAR) 
6-23 

Section 2.3.7.12 (and relevant references) to evaluate the supporting data and the 

characterization of uncertainties for the engineered barrier system flow abstraction. This 

evaluation focused on (i) experiments that address uncertainties associated with the number of 

drip points and flow rates; (ii) experimental data used to bound uncertainties associated with the 

engineered barrier system fluxes; and (iii) uncertainties propagated within the engineered 

barrier system flow abstractions and into the other abstractions in the TSPA code, because 

these specific topics are important to the U.S. Department of Energy (DOE) abstraction (DOE, 

2008ab; NRC, 2003aa). 

DOE relied on experimental data from the breached drip shield experiments, as outlined in SNL 

Section 6.3.2.3 (2007aj) and BSC (2003ag), to derive an equation to estimate flux through a 

breached drip shield (the F2 flow path). The equation is a function of the lateral spread angle of 

the rivulet flow on the drip shield, the number of corrosion breaches on the drip shield, the 

length of the breaches, and a sampled uncertainty factor. The uncertainty factor was bounded 

by DOE using data from the Breached Drip Shield experiment. DOE adopted the same 

equation to estimate the flux through a breached waste package (the F4 flow path). DOE 

identified that the only difference in implementing the equation for the drip shield and waste 

forms is that (i) the radius of the curvature of the waste package is less than that of the drip 

shield and (ii) the nominal patch size is smaller for a waste package than for the drip shield in 

the WAPDEG corrosion model (BSC, 2004bs). These differences affect the bounds for the 

uncertainty factor established for the drip shield and the waste package. DOE supported the 

abstraction for the flow through a breached drip shield and waste packages on the basis of data 

from the Breached Drip Shield experiment. However, in the TSPA code, the data support is 

used only for breaches on the waste package because the drip shields are modeled to be either 

all intact or failed, as detailed in SNL Section 6.1.1 (2007aj). 

The NRC staff notes that uncertainties associated with the seepage flow arriving at the drift wall 

are propagated into the engineered barrier system abstraction through the F1 flow path. 

Uncertainties associated with unsaturated flow in the host rock matrix are propagated into the 

engineered barrier system flow abstraction through the F7 flow path. DOE bounded the 

uncertainty factor used, as described in SNL Sections 7.1.1.1 and 7.1.1.2 (2007aj), for the 

calculations of the F4 flow path on the basis of data from the breached drip shield experiments 

(BSC, 2003ag). DOE assumed a uniform distribution for the uncertainty factors due to lack of 

supporting data for any statistical distribution. 

The NRC staff reviewed the Breached Drip Shield experiments to assess (i) the experiments 

designed to support the flux-splitting model conceptualization and (ii) the experimental data 

used to bound uncertainties in the engineered barrier system flow processes. The NRC staff 

notes that DOE used the data from the Breached Drip Shield experiments, in conjunction with 

simplified geometrical interpretations, as outlined in SNL Section 6.5.1.1.2 (2007aj), to 

characterize and bound the uncertainties associated with the F4 flow path (flux through a 

breached waste package). In particular, the NRC staff notes the following: (i) flow on the drip 

shield occurred as rivulets in the experiments, as expected from a physical standpoint, as 

a result of drips and splashes from a number of discrete drip points onto the drip shield; 

(ii) the experiments were run with the flow rate varying over 2 orders of magnitude {0.2–20 m3

/yr 

[52.8–5,283.4 gal/yr]}, covering a wide range of uncertainty in the flow rate; (iii) the experiments 

involved a wide range for drip points (1 to 90 drip points) directly above or away from patches to 

address uncertainties associated with drip locations (SAR Section 6.5.1.1.2.4); and (iv) the 

experiments provided the range for the splash angle and the effective drip shield, which were 

used for calculating uncertainties associated with the F4 flow path. During its review, the NRC 

staff calculated weighted seepage rates per waste package to be 0.01, 0.04, 0.05, and 
6-24 

0.08 m3

/yr [2.6, 10.6, 13.2, and 21.1 gal/yr] using the information in SAR Figure 2.3.3-47 (DOE, 

2009av) for the mean seepage rate per waste package during the present-day, monsoon, 

glacial, and from 10,000 to 1 million years, respectively. These computed seepage rates are 

lower than the seepage rates used in the Breached Drip Shield experiments; however, NRC 

staff notes that the lower seepage rates in the Breached Drip Shield experiments are 

conservative from the perspective of radionuclide transport in the engineered barrier system 

because higher water flux rates would result in lower radionuclide mass concentrations. 

Although upscaling and real-world repository conditions may introduce additional uncertainties, 

due to reasoning provided in (i) through (iv) in the previous paragraph, the NRC staff notes that 

the Breached Drip Shield experiments captured physical processes associated with the flow 

through the breached drip shield and waste packages. Hence, the use of the data from these 

experiments to bound uncertainties associated with the F4 flow path is reasonable. Because 

DOE implemented a mass-conserving flux-splitting algorithm in the engineered barrier system 

flow abstraction, and in light of the discussion in the previous section of this chapter on Model 

Integration and Information Flow (last paragraph), the NRC staff notes that (i) uncertainties 

associated with the drip flux and condensed water are propagated into the engineered barrier 

system flow abstraction through the F1 flow path, (ii) uncertainties associated with the number 

of patches on a breached drip shield and a waste package are propagated into the engineered 

barrier system flow abstraction through the F4 flow path, and (iii) uncertainties associated with 

flow conditions in the unsaturated zone around the invert are propagated into the engineered 

barrier system flow abstraction through the F7 flow path. Finally, the uncertainties associated 

with the engineered barrier system flow abstraction and data are propagated into the 

engineered barrier system radionuclide transport abstraction through the F6 flow path and the 

F8 flow path. Hence, data uncertainty is propagated within the engineered barrier system 

model abstraction and between the engineered barrier system model abstraction and other 

abstractions in the TSPA model. 

Model Support and Uncertainties 

This TER section addresses the characterization and propagation of model uncertainty through 

the model abstraction, and support of the model abstraction output by objective comparisons. 

U.S. Nuclear Regulatory Commission (NRC) staff reviewed the information provided in SAR 

Section 2.3.7.12.3.5 (and relevant references) to evaluate model support and uncertainties 

for the engineered barrier system flow abstraction. This evaluation focused on (i) the 

alternative conceptualizations; (ii) the justification for the inclusion or exclusion of the 

alternative conceptualizations; and (iii) comparison of model output with the results from other 

process-level models, because these specific topics are important to the U.S. Department of 

Energy’s (DOE) abstraction (DOE, 2008ab; NRC, 2003aa). 

DOE presented two alternative conceptualizations relevant to the engineered barrier system 

flow abstraction to characterize and propagate uncertainty through the model abstraction: the 

bathtub flow model and the dual-continuum invert flow model (SAR Section 2.3.7.12.3.5). 

The engineered barrier system flow abstraction is based on a flux-splitting algorithm that 

assumes a nonponding (no water accumulation) condition in the engineered barrier system. 

DOE alternatively tested a ponding condition through a bathtub model that allows water 

retention and accumulation in the waste package before being released to the engineered 

barrier system. DOE identified that a flow-through (nonponding) model is bounding for the 

bathtub (ponding) model in calculations of concentration and mass releases of radionuclides 

from the engineered barrier system due to the delays in releases in the bathtub case, when 
6-25 

(i) radionuclides are solubility rate limited or dissolution rate limited and the inflow rate is time 

invariant or (ii) radionuclides are dissolution rate limited and there is a step change in the inflow 

rate. DOE identified that the flow-through model is not bounding for the bathtub model when the 

inflow rate increases, because the flow-through model (with an increased volumetric water flow 

rate) would result in lower mass concentrations than the bathtub model (with a fixed, completely 

mixed bathtub storage volume). However, the total mass releases (unlike the mass 

concentrations) passed from the engineered barrier system model abstraction to the 

unsaturated transport abstraction would be identical for the flow-through and bathtub models. 

DOE discussed another alternative conceptualization in which the flow domain in the invert is 

characterized as a dual-continuum model, as opposed to the single-continuum model, in the 

engineered barrier system flow abstraction. In this alternative model, the flow domain is divided 

into intergranular and intragranular flow domains. As a result, the F8 flow path is redefined as 

the flux from the intragranular invert continuum to the unsaturated zone. DOE introduced an 

additional flow path, F9, that accounts for flux from the intergranular invert continuum to the 

unsaturated zone. DOE did not include this conceptualization in the TSPA model due to 

insufficient experimental data to validate diffusion when, in transport simulations, the water 

content is very low. 

The NRC staff reviewed DOE’s proposed alternative model conceptualizations to assess the 

rationale for their inclusion or exclusion in DOE’s Total System Performance Assessment 

(TSPA) model. The NRC staff notes that the exclusion of the bathtub model from the TSPA 

code is reasonable because the flow-through model, as implemented in the TSPA code, is 

bounding for the bathtub model when flow rates are constant. On the other hand, DOE noted 

that when the inflow rate increases and radionuclides are solubility rate limited, the difference in 

the performance of the bathtub model and the flow-through model is not critical to performance. 

DOE’s conclusion is reasonable because mass releases, rather than concentrations, of 

radionuclides are passed from the engineered barrier system to the unsaturated zone in the 

TSPA model and the mass of mobilized radionuclides (as a result of dissolution of waste forms) 

computed from the bathtub and flow-through models is identical in this case. The NRC staff 

further notes that the exclusion of the dual-continuum model from the TSPA code is reasonable 

because the spatial distribution of flow in the invert, or flow between and within the invert 

materials, is not significant in determining radionuclide releases from the engineered barrier 

system into the unsaturated fractures and unsaturated matrix in the TSPA model construction. 

Further, DOE showed that the engineered barrier system flux-splitting algorithm tends to 

overestimate the fraction of drift flow that enters the breached mock-up drip shield (F2/F1) in the 

Breached Drip Shield experiments, as shown in SNL Table 6.5-2 and Figure D–12(2007aj. On 

the basis of experimental data, DOE estimated that the fraction of drift flow that entered the 

breached drip shield ranged from 0.013 to 0.275 with a median value of 0.049. DOE also used 

the results from the Breached Drip Shield experiments to estimate the fraction of drift flow that 

enters breached waste packages. Using the flux-splitting model, DOE calculated that when 

approximately 4 percent of the waste package surface area is breached by general corrosion, 

10, 90, and 100 percent of the seepage flux approaching the (failed) drip shield from above 

enters into a breached waste package for the 5th, 50th, and 95th percentiles, respectively. In the 

TSPA model implementation, the average fraction of the breached waste package surface area 

in 1 million years is on the order of 10-3 for the nominal and disruptive modeling cases. DOE 

estimated that 0–11 percent (with a mean/median value of 5.5 percent) and 0–12 percent (with 

a mean/median value of 6 percent) of the seepage flux above the (failed) drip shield entered 

into a breached commercial spent nuclear fuel waste package for the nominal and seismic 

ground motion cases, respectively (DOE, 2009an). The NRC staff conducted similar 
6-26 

calculations for a breached codisposal waste package for the nominal and seismic ground 

motion cases by following the same procedure and using information DOE presented in Safety 

Analysis Report (SAR) Figure 2.1-17 (DOE, 2009av). The NRC staff’s calculations revealed 

that 0–12 percent (with a mean/median value of 6 percent) and 0–22 percent (with a 

mean/median value of 11 percent) of the seepage flux above the (failed) drip shield entered into 

a breached codisposal waste package for the nominal and seismic ground motion cases, 

respectively. The rest of the seepage flux was diverted around the breached waste package. 

The NRC staff notes that on average 5–11 percent of seepage flux entering into a damaged 

commercial spent nuclear fuel or codisposal waste package in TSPA calculations is consistent 

with breach flux rates (which had a median value of 5 percent of the inflow rate) obtained from 

Breached Drip Shield experiments. Thus, DOE provided adequate modeling support using 

experimental data and TSPA model output. 

Summary of NRC Staff’s Review of Quantity of Water in Contact With the Engineered 

Barrier System 

The U.S. Nuclear Regulatory Commission (NRC) staff reviewed the model conceptualization, 

mass-balance equations, the underlying assumptions of the engineered barrier system flow 

model abstraction and other relevant abstractions with which the engineered barrier system 

flow model abstraction exchanges data and information. The NRC staff also reviewed the 

Breached Drip Shield experiments DOE used to bound data uncertainties, and the alternative 

model conceptualizations DOE used to analyze model uncertainties. The NRC staff notes 

that the pre-10,000-year treatment of features, events, and processes (FEPs) in this abstraction 

continue unchanged beyond the 10,000-year postdisposal period through the period of geologic 

stability (defined as 1 million years). Therefore, DOE’s treatment of FEPs through the period of 

geologic stability is reasonable. 

The NRC staff notes that DOE adequately described (i) the engineered barrier system flow 

model abstraction involving failed and intact engineered barrier system components under the 

nominal case and disruptive events, (ii) input to and output from the engineered barrier system 

flow abstraction and the information exchange between the engineered barrier system flow 

abstraction and other abstractions in the Total System Performance Assessment (TSPA) code, 

(iii) data support for bounding uncertainties associated with fluxes through a breached drip 

shield and a waste package and their propagation across the engineered barrier system flow 

abstraction and into other abstractions in the TSPA code, and (iv) alternative model 

conceptualizations for analyzing model uncertainties. 

Finally, the NRC staff notes that the igneous intrusion and seismic ground motion scenarios are 

the most significant contributors to the total dose for the 10,000- and 1-million-year simulations. 

In the Total System Performance Assessment (TSPA) code implementation, the fraction of 

seepage water at the end of 1 million years represents the maximum seepage flux that entered 

into damaged waste packages. TSPA calculations indicated that at the end of 1 million years, 

on average 5.5 percent of the seepage water approaching the (failed) drip shield entered into 

breached commercial spent nuclear fuel waste packages under nominal and seismic scenarios. 

Similarly, at the end of 1 million years, on average 6 and 11 percent of the seepage water 

approaching the (failed) drip shield entered into a breached codisposal waste package under 

nominal and seismic scenarios, respectively. Thus, on average, only up to 11 percent of the 

seepage flux above the (failed) drip shield would be available for the advective transport of 

radionuclides and colloids in the waste form and corrosion products domains in the engineered 

barrier system radionuclide transport abstraction. In this abstraction, a zero or nonzero value of 
6-27 

the flux through a failed waste package determines the type of the transport mechanism for the 

radionuclides and colloids in the waste form and corrosion products domains (diffusive transport 

if the flux is zero; advective transport otherwise). Because, on average, not more than 

11 percent of the seepage water can enter a waste package under any circumstances, the 

NRC staff notes that breached waste packages consistently divert a large fraction (more 

than 89 percent) of drift seepage. However, as shown in Figure 6-1, this diverted flux around 

the failed waste package enters into the invert and is used to calculate F6, which determines 

the advective transport of radionuclides and colloids in the invert domain of the engineered 

barrier system radionuclide transport abstraction. Therefore, this implementation is 

reasonable, because advective transport of radionuclides in the invert is controlled by water 

fluxes in the invert. 

2.2.1.3.3.4 NRC Staff Conclusions 

The NRC staff notes that the DOE description of this model abstraction for the quantity and 

chemistry of water contacting engineered barriers and waste forms is consistent with the 

guidance in the YMRP. The NRC staff also notes that the DOE technical approach discussed in 

this chapter is reasonable for use in the Total System Performance Assessment (TSPA). 

2.2.1.3.3.5 References

Alai, M., M. Sutton, and S. Carroll. 2005aa. “Evaporative Evolution of a 

Na-Cl-NO3-K-Ca-SO4-Mg-Si Brine at 95 C: Experiments and Modeling Relevant to 

Yucca Mountain, Nevada.” Geochemical Transactions. Vol. 6. pp. 31–45. 

Browning, L., R. Fedors, L. Yang, O. Pensado, R. Pabalan, C. Manepally, and B. Leslie. 

2004aa. “Estimated Effects of Temperature-Relative Humidity Variations on the Composition of 

In-Drift Water in the Potential Nuclear Waste Repository at Yucca Mountain, Nevada.” 

Proceedings of the Materials Research Society. Symposium Proceedings 824. J.M. Hanchar, 

S. Stroes-Gascoyne, and L. Browning, eds. Warrendale, Pennsylvania: Materials Research 

Society. pp. 417–424. 

Browning, L., W. Murphy, C. Manepally, and R. Fedors. 2003aa. “Reactive Transport Model for 

the Ambient Unsaturated Hydrogeochemical System at Yucca Mountain, Nevada.” International 

Journal of Computers & Geosciences. Vol. 29, No. 3. pp. 247–263. 

BSC. 2005aa. “Analysis of Dust Deliquescence for FEP Screening.” ANL–EBS–MD–000074. 

Rev. 01. AD 01, ACN 001, ACN 002. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2005ad. “In-Package Chemistry Abstraction.” ANL–EBS–MD–000037. Rev. 04. 

ACN 01, AD 01, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2005ah. “Multiscale Thermohydrologic Model.” ANL–EBS–MD–000049. Rev. 03. 

AD 01, AD 02, ERD 01, ERD 02. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004aa. “Abstraction of Drift Seepage.” MDL–NBS–HS–000019. Rev. 01. AD 01, 

ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 
6-28 

BSC. 2004aw. “In-Drift Natural Convection and Condensation.” MDL–EBS–MD–000019. 

Rev. 00. ACN 01, ACN 02, AD 01, ERD 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2004bs. “WAPDEG Analysis of Waste Package and Drip Shield Degradation.” 

ANL–EBS–PA–000001. Rev. 02. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2003ag. “Atlas Breached Waste Package and Drip Shield Experiments: Breached Drip 

Shield Test.” TDR–EBS–MD–000025. Rev. 00. Las Vegas, Nevada: Bechtel SACI 

Company, LLC. 

DOE. 2009ab. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (February 23) J.R. Williams to J.H. Sulima (NRC). 

ML090550101. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009am. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.7, Set 1.” Letter (February 9) J.R. Williams to J.H. Sulima (NRC). 

ML090410352. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009an. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.1, Set 1.” Letter (February 6) J.R. Williams to J.H. Sulima (NRC). ML090400455. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009av. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 1. 

ML090700817. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2009ay. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.7), Safety Evaluation Report, Vol. 3, 

Chapter 2.2.1.3.4, Set 2.” Letter (May 12) J.R. Williams to J.H. Sulima (NRC). ML 091330282. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ck. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.5), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.3, Set 1.” Letter (April 30) J.R. Williams to J.H. Sulima (NRC). ML091210691. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cv. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.5), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.3), Set 1.” Letter (April 23) J.R. Williams to J.H. Sulima (NRC). ML091140343. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cw. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.5), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.3), Set 1.” Letter (April 16) J.R. Williams to J.H. Sulima (NRC). ML091100176. 

Washington, DC: DOE, Office of Technical Management. 
6-29 

DOE. 2009dg. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.6.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.1, Set 2.” Letter (April 8) J.R. Williams to J.H. Sulima (NRC). ML090980537. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

Gerbino, A. 2006aa. A Guide for Using the OLI Analyzers. Morris Plains, New Jersey: 

OLI Systems, Inc. 

Hardie, L.A. and H.P. Eugster. 1970aa. “Evolution of Closed-Basin Brines.” Mineralogical 

Society of America Special Paper. Vol. 3. pp. 273–290.

Leslie, B., C. Grossman, and J. Durham. 2007aa. “Total-system Performance Assessment 

(TPA) Version 5.1 Module Descriptions and User Guide.” Rev. 1. ML072710060. San Antonio, 

Texas: CNWRA. 

Linke, W.F. 1965aa. Solubilities, Inorganic and Metal-Organic Compounds. 4th Edition. 

Volume 2, K–Z. Washington, DC: American Chemical Society. 

Linke, W.F. 1958aa. Solubilities, Inorganic and Metal-Organic Compounds. 4th Edition. 

Volume 1, A–Ir. Washington, DC: American Chemical Society. 

McCaffrey, M.A., B. Lazar, and H.D. Holland. 1987aa. “The Evaporation Path of Seawater and 

the Coprecipitation of Br–

 and K+ With Halite.” Journal of Sedimentary Petrology. Vol. 57. 

pp. 928–937. 

Murphy, W.M. 1994aa. “Geochemical Models for Gas-Water-Rock Interactions in a Proposed 

Nuclear Waste Repository at Yucca Mountain, Nevada.” Proceedings of Site Characterization 

and Model Validation Conference (Focus ‘93), Las Vegas, Nevada, September 26–29, 1993. 

La Grange, Illinois: American Nuclear Society. pp. 115–121. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

ML051360241. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

Pabalan, R.T. 2010aa. "Quantity and Chemistry of Water Contacting Engineered Barriers 

Integrated Subissue." Electronic Scientific Notebook 930E. San Antonio, Texas: CNWRA. 

Rard, J.A., K.J. Staggs, S.D. Day, and S.A. Carroll. 2006aa. “Boiling Temperature and 

Reversed Deliquescence Relative Humidity Measurements for Mineral Assemblages in the 

NaCl + NaNO3 + KNO3 + Ca(NO3)2 + H2O System.” Journal of Solution Chemistry. Vol. 35. 

pp. 1,187–1,215. 
6-30 

Rosenberg, N.D., K.G. Knauss, and M.J. Dibley. 1999aa. “Evaporation of J13 Water: 

Laboratory Experiments and Geochemical Modeling.” UCRL–ID–134852. Livermore, 

California: Lawrence Livermore National Laboratory. 

Rosenberg, N.D., K.G. Knauss, and M.J. Dibley. 1999ab. “Evaporation of Topopah Spring 

Tuff Pore Water.” UCRL–ID–135765. Livermore, California: Lawrence Livermore 

National Laboratory. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007aj. “EBS Radionuclide Transport Abstraction.” ANL–WIS–PA–000001. Rev. 03. 

ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ao. “In-Drift Precipitates/Salts Model.” ANL–EBS–MD–000045. Rev. 03. ERD 01, 

ERD 02. Las Vegas, Nevada: Sandia National Laboratories. 

Wolf, M., O. Breitkopf, and R. Puk. 1989aa. “Solubility of Calcite in Different Electrolytes at 

Temperatures Between 10 and 60 C and at CO2 Partial Pressures of About 1 kPa.” Chemical 

Geology. Vol. 76. pp. 291–301. 

Yang, L., R. Pabalan, P. Shukla, M. Juckett, X. He, K.T. Chiang, H. Gonzalez, and T. Ahn. 

2011aa. “Corrosion of Alloy 22 Induced by Dust Deliquescence Brines.” San Antonio, Texas: 

CNWRA. 

Yang, I.C., Z.E. Peterman, and K.M. Scofield. 2003aa. “Chemical Analyses of Pore Water 

From Boreholes USW SD–6 and USW WT–24, Yucca Mountain, Nevada.” Journal of 

Contaminant Hydrology. Vol. 1, No. 878. pp. 1–20. 

Yang, I.C., P. Yu, G.W. Rattray, J.S. Ferarese, and J.N. Ryan. 1998aa. “Hydrochemical 

Investigations in Characterizing the Unsaturated Zone at Yucca Mountain, Nevada.” USGS 

Water Resources Investigations Report 98-4132. Denver, Colorado: U.S. Geological Survey. 

Yang, I.C., G.W. Rattray, and Y. Pei. 1996aa. “Interpretation of Chemical and Isotopic Data 

From Boreholes in the Unsaturated Zone at Yucca Mountain, Nevada.” USGS Water 

Resources Investigation Report 96-4058. Denver, Colorado: U.S. Geological Survey. 
7-1 

CHAPTER 7 

2.2.1.3.4 Radionuclide Release Rates and Solubility Limits 

2.2.1.3.4.1 Introduction 

This chapter addresses the U.S. Nuclear Regulatory Commission (NRC) staff’s evaluation of the 

U.S. Department of Energy’s (DOE) analytic models used in its Total System Performance 

Assessment (TSPA) computer program to simulate the processes that could result in water 

transport of radionuclides out of the engineered barrier system, including the waste package 

and the invert, and into the unsaturated zone (the rock mass directly below the repository 

horizon and above the water table). [As used in this Technical Evaluation Report (TER), the 

term “abstraction” refers to the representation of site characterization data; process-level 

models for features, events, and processes (FEPs); uncertainty and variability; and their overall 

integration (in a simplified manner) in the TSPA code.] These abstractions were described in 

Safety Analysis Report (SAR) Section 2.3.7 (DOE, 2009av) and in supporting documents, 

including DOE’s responses to the NRC staff’s requests for additional information. The objective 

of this review is to assess DOE’s models for radionuclide release rates out of the engineered 

barrier system. 

The engineered barrier system and the transport pathway within the drift (repository tunnel) are 

the initial barriers to radionuclide release. If a waste package is breached and water enters the 

waste package, the radionuclides contained in the package may be released from the 

engineered barrier system. The processes that could lead to radionuclide release are affected 

by the chemical characteristics of the water, which in turn are affected by the materials that 

interact with the water. The performance assessment analysis models radionuclide release 

rates from the engineered barrier system because these processes would significantly affect the 

timing and magnitude of transport for any radionuclide released from the repository. 

DOE identified five models it considered important for abstracting radionuclide releases from the 

engineered barrier system. The five models DOE identified and the associated sections in this 

chapter that address them are 

1. The in-package chemical and physical environment model (TER Section 2.2.1.3.4.3.1) 

used to establish the conditions under which waste forms degrade and radionuclides 

are mobilized 

2. The waste form degradation model (TER Section 2.2.1.3.4.3.2) used to calculate 

the rate at which the waste form degrades and the radionuclides become available 

for release 

3. The concentration limits model (TER Section 2.2.1.3.4.3.3) used to apply chemically 

based upper limits on dissolved concentrations of some radionuclides 

4. The availability and effectiveness of colloids model (TER Section 2.2.1.3.4.3.4) used to 

calculate the stabilities and concentrations of various types of colloids (small suspended 

particles that may mobilize radionuclides in water) 
7-2 

5. The engineered barrier system radionuclide transport model (TER Section 2.2.1.3.4.3.5) 

used to simulate radionuclide transport from the waste form, through the waste package, 

and out of the engineered barrier system 

The FEPs that are relevant to radionuclide release rates and solubility limits are listed in DOE’s 

SAR Section 2.3.7.2 and Table 2.3.7-1. The NRC staff evaluates the rationales for excluding 

relevant FEPs from the performance assessment model in TER Section 2.2.1.2.1.3.2. In that 

section, the NRC staff notes that DOE’s bases for the list of FEPs considered and excluded 

from the TSPA code analysis that are relevant to waste form behavior, solubility limits, colloidal 

transport, and radionuclide release rates are reasonable. 

In addition, the NRC staff notes (TER Sections 2.2.1.2.1.3.3 and 2.2.1.2.1.3.4) that DOE’s 

identification and screening of scenario classes considered all credible processes and events 

that could lead to radionuclide release. Evaluations of those FEPs included in the performance 

assessment are discussed under the five topical areas in this chapter. 

This chapter relies on the following information as inputs: (i) design details of the waste 

package, waste form, and internal components of the waste package; (ii) context for 

consideration of the barrier capabilities of the waste package and the drift; (iii) information on 

corrosion and mechanical failure of the drip shield and waste package, which may allow water 

into the waste package; and (iv) information on the rate of delivery of water to the waste 

package surface and the chemical characteristics of water that may enter the waste package. 

The output from the model of radionuclide release rates and solubility limits is used as input to 

the model for radionuclide transport in the unsaturated zone. The information the unsaturated 

zone model needs for calculating the movement of the radionuclides includes the rates and 

magnitudes of radionuclide release from the drift, including the characteristics of dissolved and 

colloidal species. Information from this model is also used for evaluating the barrier capability of 

the waste package interior, the waste form, and the drift below the waste package (e.g., the 

invert) and for supporting for the scenario analysis for the engineered barrier system. 

2.2.1.3.4.2 Evaluation Criteria 

The NRC staff’s review of model abstractions used in the DOE postclosure performance 

assessment, including those considered in this chapter for radionuclide release rates and 

solubility limits, is guided by 10 CFR 63.114 (Requirements for Performance Assessment) and 

63.342 (Limits on Performance Assessments). The DOE Total System Performance 

Assessment (TSPA) is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 
7-3 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment in 10 CFR 63.342, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is given in TER Chapter 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of geologic 

stability, defined in 10 CFR 63.302 as 1 million years following disposal. These sections require 

that through the period of geologic stability, with specific limitations, DOE 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

This model abstraction of radionuclide release rates and solubility limits involves seismic and 

igneous activity. 10 CFR 63.342(a) and 63.342(b) provide requirements for assessing the 

effects of seismic and igneous activity on repository performance, subject to the probability 

limits given in 63.342(a) and 63.342(b). Specific constraints on the seismic and igneous activity 

analyses are provided in 10 CFR 63.342(c)(1)(i) and (ii), respectively. 

The NRC staff’s review of SAR and supporting information follows the guidance laid out in the 

Yucca Mountain Review Plan (YMRP) Section 2.2.1.3.4 (NRC, 2003aa), Radionuclide Release 

Rates and Solubility Limits, as supplemented by additional guidance for the period beyond 

10,000 years after permanent closure (NRC, 2009ab). The YMRP acceptance criteria provide 

guidance for the NRC staff’s evaluation of DOE’s model abstraction of radionuclide release 

rates and solubility limits. These five criteria are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

The NRC staff’s review used a risk-informed approach and the guidance in the YMRP, as 

supplemented by NRC (2009ab), to the extent reasonable for aspects of radionuclide release 

rates and solubility limits important to repository performance. The NRC staff considered all 

five criteria provided in the YMRP in its review of information provided by DOE. In the context 

of these criteria, only those aspects of the model abstraction that substantively affect the 

performance assessment results, as determined by the NRC staff, are discussed in detail in 

this chapter. The NRC staff’s determination is based both on risk information provided by 
7-4 

DOE and on the NRC staff’s knowledge gained through experience and independent 

confirmatory analyses. 

2.2.1.3.4.3 Technical Evaluation

2.2.1.3.4.3.1 In-Package Chemical and Physical Environment 

This section details the NRC staff’s review of DOE’s abstraction and the Total System 

Performance Assessment (TSPA) implementation of in-package chemistry, as described in 

Safety Analysis Report (SAR) Section 2.3.7.5 and references cited therein. The in-package 

chemistry model estimates the water chemistry inside the breached waste packages and 

generates abstractions for pH, ionic strength, and fluoride concentration. Water chemistry 

inside the waste package (especially pH and ionic strength) is important to repository 

performance because it controls waste form degradation, radionuclide solubilities, and the 

suspension stabilities of colloids. 

The NRC staff’s review focused on aspects of the in-package chemistry model considered 

significant to risk, including conceptual design and implementation, data inputs, model 

limitations, sensitivity to environmental conditions, and model abstraction and support. Primary 

data inputs to the in-package chemistry model include (i) the compositions, surface areas, and 

degradation rates of waste forms and metal components in the waste package; (ii) incoming 

water chemistries; and (iii) the thermodynamic data used to calculate the stabilities of dissolved, 

aqueous, and gas phase species in the waste package. DOE used the sensitivity of the model 

to variations in environmental conditions (e.g., liquid influx rate, pCO2, and temperature) to 

determine the potential effects of disruptive events on model outputs. 

Conceptual Design and Implementation 

DOE’s in-package chemistry conceptual model consists of a batch reactor system composed of 

water, oxygen, carbon dioxide, waste forms, and metal alloys. The batch reactor system is in 

equilibrium with atmospheric conditions, and reactants degrade in the presence of water 

according to a rate determined by the physical properties and exposed surface area of each 

reactant. During the reactions, secondary mineral phases and metal (hydr)oxide corrosion 

products precipitate and water changes in composition and mass. The model simulates two 

water ingress conditions: (i) vapor influx, under which water vapor (simulated as pure water) is 

assumed to condense and react with internal waste package components, and (ii) liquid influx, 

under which seepage or “dripping” water (simulated as typical groundwater or drift wall 

condensate) enters a breached waste package, reacts with internal components, and then exits 

by advection. Vapor influx is included in the model because water films generated by vapor 

influx promote radionuclide diffusion, which is simulated in the engineered barrier system using 

a diffusion model (TER Section 2.2.1.3.4.3.5). 

DOE’s model considers both commercial spent nuclear fuel (SNF) and codisposal waste 

packages. Commercial spent nuclear fuel waste packages contain 21 pressurized water 

reactor fuel assemblies (21-PWR). Codisposal waste packages contain two DOE 

multicanister overpacks and two defense high-level waste canisters (2-MCO/2-DHLW). DOE’s 

model divides the waste packages into two domains: the waste form domain (Cell 1) and the 

corrosion products domain (Cell 2). Cell 1 of the codisposal waste packages is further divided 

into Cell 1a, represented by two high-level waste glass pour canisters (2–DHLW), and Cell 1b, 

represented by two multicanister overpack units containing N-Reactor fuel (2–MCO). 

Adsorption reactions are not simulated in the waste form cells, because the amount of iron 
7-5 

corrosion products is low compared to Cell 2 (i.e., the corrosion products domain). Adsorption 

reactions within Cell 2 are simulated in the engineered barrier system flow and transport model 

(TER Section 2.2.1.3.4.3.5). DOE excluded other features, events, and processes (FEPs) that 

could potentially impact in-package chemistry, such as in-package criticality, oxide wedging, 

radiolysis, and microbial activity on the basis of low probability or low consequence. The NRC 

staff evaluates DOE’s rationales for excluding these processes in Technical Evaluation Report 

(TER) Section 2.2.1.2.1.3.2. 

DOE used the geochemical reaction path equilibrium modeling code EQ6 to simulate interaction 

of water and materials in commercial spent nuclear fuel Cell 1 and codisposal Cells 1a and 1b 

(referred to collectively as waste form cells). For vapor influx, water is added to the waste 

form cells as one of the reactants at a rate corresponding to the maximum diffusion rate of 

vapor through openings in a breached waste package. For liquid influx, the solid-centered 

flow-through option of the EQ6 code is used to simulate the flow of source water into and 

through a constant-volume, well-mixed batch reactor. Under the flow-through option, an amount 

of source water is added to the reactor displacing an equal amount of water already equilibrated 

with the solid phases in the reactor. The water in the reactor then mixes completely, and the 

water, solids, and gases within the reactor reequilibrate. Kinetically controlled reactants are 

also added to the reactor prior to equilibrium to capture the case where the residence time 

within the reactor is sufficiently short that equilibrium cannot be reached with slowly degrading 

constituents (e.g., metal degradation). The ratio of water to reactants, which depends on liquid 

influx rate, is treated as a variable in the EQ6 model. At high liquid influx rates, the ratio is such 

that the materials of the water package are in contact with a volume of water equal to that of the 

void space. This case is referred to as the “bathtub” model and has the highest ratio of water to 

waste package materials. At low liquid influx rates, the ratio is such that the volume of water in 

contact with waste package materials is less than the void space. In BSC Section 6.6.1[a] 

(2005ad), DOE examined the impact of varying the water-to-reactants ratio in a sensitivity 

analysis to evaluate the effects of differing flow conditions on in-package water chemistry. 

The sensitivity analysis indicated a negligible effect on pH but a distinct effect on ionic strength 

(i.e., as the ratio of water to reactants is decreased to simulate low flow conditions, the ionic 

strength of the solution increases).

NRC Staff’s Review 

The NRC staff evaluated the modeling approach and the representation of the commercial 

spent nuclear fuel and codisposal waste packages used in the in-package chemistry conceptual 

design. DOE’s use of a thermodynamic equilibrium chemical approach of a flow-through, 

well-mixed batch reactor is reasonable because it represents in-package chemistry processes 

with sufficient flexibility to project the range of chemical conditions expected inside a breached 

waste package. Kinetics effects on chemical conditions inside the waste package are captured 

in the model by simulating a range of experimentally derived degradation rates for each waste 

form and metal component in the waste form cells. The effects of disruptive events on model 

outputs were evaluated through a sensitivity analysis to determine the effects of differing flow 

conditions on in-package water chemistry. The representation of commercial spent nuclear fuel 

and codisposal waste packages in the in-package chemistry model is consistent with waste 

package designs documented in SAR Section 1.5.2. 

The NRC staff evaluated the EQ6 modeling code. On the basis of this evaluation, the 

mathematical representation of geochemical processes in the EQ6 modeling code is reasonable 

because it is consistent with generally accepted approaches for simulating geochemical 

interactions among fluids, gases, and solid materials. The EQ6 code is suitable for predicting 
7-6 

the chemistry of in-package fluids because it addresses and simulates the geochemical 

processes that are important to in-package chemistry, including (i) interactions between 

codisposed waste, (ii) chemical effects of void space in the waste package, (iii) chemical 

characteristics of water in the waste package, (iv) oxidation-reduction potential in the waste 

package, (v) reaction kinetics in the waste package, and (vi) chemistry of water flowing into the 

waste package (SAR Table 2.3.7-1). 

Waste Form and Metal Alloy Compositions, Surface Areas, and Degradation Rates 

DOE derived input data for the in-package chemistry model from existing government 

design documents, standard reference material specification documents, and open 

literature information. The sources of input data to the in-package chemistry model were 

justified and documented in BSC Sections 4.1 and 4.1[a], Tables 4.1 and 4.1[a] (2005ad). 

The input data included the compositions, surface areas, and degradation rates of waste forms 

(e.g., pressurized water reactor or PWR fuel, high-level waste glass, and N-Reactor fuel) and 

material components of the waste form cells (e.g., stainless steels and aluminum alloys). 

NRC Staff’s Review 

The NRC staff evaluated the input data DOE used to define the surface areas and compositions 

of waste forms and material components in the waste form cells by reviewing existing 

government documents and open literature information, as discussed and referenced in this 

paragraph. The NRC staff verified that for each solid reactant in the waste form cells, 

the surface area available to react was calculated from dimensions of the internal components 

of a 21-pressurized water reactor (PWR) or 2-multicanister overpack (MCO)/2-DOE high-level 

waste (DHLW) package (SNL, 2007bm,bn). Also, the NRC staff reviewed the composition of 

the pressurized water reactor fuel used for commercial spent nuclear fuel (Cell 1) that was 

based on an initial enrichment of 2 wt% to 5 wt% U-235 and a burnup of 0 to 50 GWd/MTU 

(gigaWatt-days per metric ton of uranium) (BSC, 2003af). These enrichments bound the 

typical PWR fuel available for disposal. The composition of N-Reactor fuel (Cell 1b) was 

taken from DOE (2000aa), which contains detailed information on multicanister overpack 

compositions and dimensions. The NRC staff reviewed the composition of high-level waste 

glass (Cell 1a) that was derived from qualified data on the composition of high-level waste 

glasses from the Savannah River laboratory (Allison, 2004aa). The compositions of material 

components in the waste form cells (i.e., stainless steels and aluminum alloys) were based on 

the American Society for Testing and Materials standards. On the basis of the NRC staff’s 

evaluation of DOE’s input data, the NRC staff notes that DOE used reasonable data to define 

the surface areas and compositions of each solid reactant in the waste form cells because they 

are consistent with published and qualified data and are appropriate for use in process-level 

in-package chemistry model simulations. 

The NRC staff also reviewed the degradation rates DOE used. With the exception of the 

N-Reactor fuel, DOE selected degradation rates for waste forms and material components in 

the waste form cells on the basis of experimental measurements (BSC, 2004ae,ah,ai,ao). This 

approach is reasonable because DOE derived minimum, maximum, and basecase degradation 

rates for each solid reactant and captured the full range (i.e., uncertainty) of potential 

degradation rates in its analytic model. For the N-Reactor fuel, DOE assumed that the 

N-Reactor fuel degraded instantaneously upon waste package breach (SAR Section 2.3.7.8), 

which is conservative because this approach would maximize the effect of waste form 

degradation on in-package chemistry. The range of waste form and material degradation rates 

is reasonable for establishing initial and boundary conditions for in-package chemistry model 
7-7 

simulations because these ranges are consistent with experimental measurements or are based 

on conservative assumptions. 

Incoming Water Chemistry 

DOE incorporated a range of Yucca Mountain pore water and basalt water chemistries in 

developing the in-package chemistry model abstractions. For seepage water input for the 

nominal and seismic scenarios, DOE selected four Yucca Mountain pore water compositions 

(SAR Table 2.3.7-9). DOE also included J–13 well water chemistry as a potentially relevant 

seepage water because its composition is generally representative of water compositions in the 

saturated and unsaturated zones in the vicinity of Yucca Mountain (Harrar, et al., 1990aa). 

For seepage water input for the igneous intrusion case, DOE selected three groundwaters from 

large, fractured basalt reservoirs (SAR Tables 2.3.7-10 and 2.3.7-11). 

NRC Staff’s Review 

The NRC staff evaluated the chemistry of incoming waters used in the in-package 

chemistry model to simulate seepage water input. The NRC staff independently verified 

that the chemical compositions of the pore waters and the J–13 well water span the range 

of predominant water types found in the Topopah Spring welded tuff at Yucca Mountain, as 

described in SNL Section 6.6.5 (2007ak). DOE reasonably limited uncertainty in the initial 

chemistry of water entering the waste package by incorporating a range of Yucca Mountain 

pore water and basalt water chemistries in developing the in-package chemistry model 

abstractions. In BSC Sections 6.5(a) and 6.6(a) (2005ad) and in Safety Analysis Report 

(SAR) Figures 2.3.7-13 through 2.3.7-18, DOE showed limited sensitivity of in-package 

chemistry to the incoming water composition. Therefore, DOE used a reasonable range of 

incoming water chemistries to simulate seepage water input for developing the in-package 

chemistry model abstractions. 

Thermodynamic Database 

DOE used the thermodynamic database data0.ymp.R5 to execute EQ6 simulations. This 

database allows for the calculation of mineral and gas solubilities, the chemical state of 

dissolved species, and the dissolution rates of solids. Uncertainty in the data0.ymp.R5

thermodynamic database is implicit because it was constructed from the accumulation of a large 

number of experimental measurements or model estimations, each with its own associated 

uncertainty. To minimize this uncertainty, DOE examined experimental data and observations 

from natural analogs, as identified in BSC Section 7.4.3[a] (2005ad), and the results of 

sensitivity analyses, as detailed in BSC Section 6.6.11 (2005ad), to select appropriate 

secondary phase formation for use in process-level model simulations. 

NRC Staff’s Review 

The thermodynamic database data0.ymp.R5 is appropriate for the in-package chemistry model 

because it includes the elements that constitute the waste package, waste form, seepage, and 

gas compositions at the temperature expected in the drifts {25 °C [77 °F]} and the 

thermodynamic data on secondary mineral phases important to the in-package chemistry model 

(SNL, 2007at). The NRC staff also notes that DOE appropriately added thermodynamic data for 

several new mineral phases that could potentially affect model outputs. These data included 

nickel carbonate, nickel molybdate, and several uranium minerals that precipitate in UO2

degradation experiments and occur in the vicinity of natural UO2 ore deposits. The NRC staff 
7-8 

reviewed the experimental data, evidence from natural analogs, and results of sensitivity 

analyses used to guide secondary phase selection for use in process-level model simulations. 

On the basis of this review, DOE’s approach to limiting uncertainty in the EQ6 simulations 

associated with the data0.ymp.R5 database is reasonable because appropriate secondary 

minerals were chosen for use in developing the in-package chemistry abstractions. 

Model Limitations 

DOE addressed model limitations associated with the accumulation of water inside the waste 

package and the evolution of material component surface area and void space due to corrosion 

product buildup by implementing the following assumptions in the process-level EQ6 

simulations: (i) once a waste package is breached, the entire contents of the waste package 

are instantly exposed to oxygen, carbon dioxide, and water and (ii) secondary mineral formation 

and buildup inside the waste package do not reduce available void space in the waste package 

and do not reduce exposure of waste package internals to atmospheric gases and water 

(i.e., void volume and internal component surface areas are fixed and do not vary with time). 

NRC Staff’s Review 

The NRC staff evaluated the assumptions DOE used to address model limitations. The NRC 

staff notes that instantly exposing the entire contents of the waste package to atmospheric 

gases and water upon breach is reasonable and conservative because it increases the potential 

for the model to predict enhanced radionuclide release. Reducing exposure of waste forms to 

gases and water by filling void spaces with degradation products enhances waste isolation by 

limiting the impact of waste form degradation on solution chemistry. Therefore, fixing void 

volume and waste package component surface areas is conservative because it results in faster 

waste form degradation, which enhances the potential for radionuclide release and transport. 

On the basis of these reviews, the assumptions DOE used to address model limitations are 

reasonable because they will not result in an underestimation of risk. 

Environmental Conditions and Sensitivity Analyses 

DOE developed the abstractions for in-package chemistry by analyzing the results 

of process-level model simulations applied over the following range of environmental 

conditions: (i) a pCO2 range of 10-4 to 10-1.5 bars; (ii) a liquid influx rate of 0.1 to 1,000 L/yr 

[0.026 to 260 gal/yr]; (iii) a temperature range of 25 to 100 °C [77 to 212 °F]; and (iv) a relative 

humidity range for vapor influx of 95 to 100 percent. DOE performed sensitivity analyses to 

examine the effects of uncertain thermal-hydrologic-chemical input parameters on model 

outputs and approximate model uncertainty for propagation into the Total System Performance 

Assessment (TSPA) model. Parameters with significant effects on model outputs (e.g., pCO2

for pH and liquid influx rate and relative humidity for ionic strength) were incorporated as 

independent variables in the model abstractions. Within the TSPA code, the values of these 

independent variables are provided by other TSPA submodels (e.g., the engineered barrier 

system thermal-hydrologic environment submodel provides relative humidity, the engineered 

barrier system chemical environment submodel provides pCO2, and the engineered barrier 

system flow submodel provides liquid influx rate) (SNL, 2008ag). Parameters with smaller 

effects on model outputs (specifically, material degradation rates) were used to quantify model 

uncertainty for propagation to the TSPA model. 
7-9 

NRC Staff’s Review 

The NRC staff reviewed and evaluated the range of environmental conditions that was 

applied to process-level model simulations to develop the abstractions for in-package 

chemistry. The NRC staff notes that the applied range of pCO2 will bound the range of pCO2

that could potentially exist within emplacement drifts at Yucca Mountain. The applied range of 

liquid influx rate is appropriate because it bounds the range of flow rate used to characterize the 

uncertainty in seepage flux through a breached waste package (TER Section 2.2.1.3.3.3.3). 

Because liquid water, which is needed for the modeled reactions to take place, is precluded in 

DOE’s abstraction from entering the drift at temperatures above 100 °C [212 °F], the NRC staff 

notes that DOE’s model temperature range of 25 to 100 °C [77 to 212 °F] accounts for the 

temperature range expected inside breached waste packages. The NRC staff notes that the 

relative humidity range over which vapor influx conditions are simulated is reasonable because 

relative humidity less than 95 percent will result in few interconnected surface water films and 

negligible diffusion of radionuclides (SNL, 2007aj). On the basis of this review, the in-package 

chemistry model reasonably accounts for the range of environmental conditions that could 

reside inside breached waste packages under liquid and vapor influx conditions for the nominal 

and disruptive event scenario classes. 

The NRC staff evaluated the sensitivity analyses used to examine the effects of uncertain 

thermal-hydrologic-chemical input parameters on model outputs. The NRC staff verified 

that pCO2 had a significant effect on model outputs for pH, as detailed in BSC Section 6.6.3[a] 

(2005ad), and the liquid influx rate and relative humidity had significant effects on model 

outputs for ionic strength, as described in BSC Sections 6.6.4[a] and 6.5.2[a] (2005ad). The 

approach of incorporating these parameters as independent variables in the model abstractions 

(i.e., pCO2 for pH and liquid influx rate and relative humidity for ionic strength) is reasonable 

because it ensures adequate integration and coupling of thermal-hydrologic-chemical processes 

in the model abstractions. On the basis of review of DOE’s sensitivity analyses, the NRC staff 

notes that, after pCO2, relative humidity, and liquid influx rate, the material degradation rates 

had the greatest effect on model outputs, as outlined in BSC Section 6.6.5[a] (2005ad). 

Therefore, using the results of material degradation rate sensitivity analyses to quantify model 

uncertainty for propagation to the TSPA model is reasonable. 

Abstractions for pH 

DOE’s in-package chemistry abstractions for pH provide parameter distributions in the form of 

lookup tables for the TSPA code. Lower and upper pH limits for liquid and vapor influx in each 

waste form cell were quantified by simulated acid and base titrations over a range of pCO2 and 

ionic strength (SAR Figures 2.3.7-19 to 2.3.7-21). Abstracted pH ranges were defined by 

secondary oxides, the presence of which limits the range of in-package pH through solubility 

reactions. The lower pH limit was set by dissolution of trevorite (NiFe2O4), which accumulates 

as the steel degrades. The upper pH limit was set by dissolution of schoepite, which 

precipitates as UO2 fuel degrades and CO2 reaches equilibrium conditions. To capture the 

uncertainty, the pH values at any given pCO2 and ionic strength were assumed to be uniformly 

distributed between the pH limits established by the titration calculations. DOE supported 

estimated ranges for pH in the waste form cells by comparing predicted secondary mineral 

phases and pH ranges to observations from natural soils and groundwater, natural analogs, 

and/or laboratory experiments. 
7-10 

NRC Staff’s Review 

The NRC staff evaluated the modeling approach and information DOE used to generate 

and support the in-package chemistry abstractions for pH. The NRC staff verified that the 

solubility limit approach used to quantify lower and upper pH limits is consistent with 

accepted geochemical principles. The NRC staff evaluated information provided in DOE 

Enclosure 4 (2009ax) and notes that the choice of waste package design the model used 

(e.g., a 5-DHLW/DOE codisposal waste package containing five high-level waste glass 

containers versus the 2-MCO/2-DHLW codisposal waste package containing two high-level 

waste glass canisters) does not affect established pH limits, because the pH limits are based 

on buffering reactions that are not influenced by the total volumes and surface areas of material 

components in the waste form cells. In addition, on the basis of its review of information 

provided in BSC Sections 6.5[a], 6.6.3[a], and 6.6.5[a] (2005ad), the NRC staff notes that lower 

and upper pH limits defined for each waste form cell in the pH abstractions are reasonable 

because they are within the pH trends observed in time-dependent basecase EQ6 simulations 

at different incoming water chemistries and in sensitivity analyses at varying pCO2 values and 

material degradation rates. On the basis of open literature reviews, the NRC staff notes that the 

phases DOE predicted to form and control pH in the waste form cells (i.e., trevorite 

and schoepite) are consistent with the phases reported as alteration products in steel corrosion 

and UO2 degradation experiments, as well as in phases observed at natural analogs (Wang, 

et al., 2001aa; Da Cunha Belo, et al., 1998aa; BSC, 2004ah; Wronkiewicz, et al., 1996aa; 

Langmuir, 1997aa; Pearcy, et al., 1994aa). 

DOE’s abstracted pH ranges are consistent with the pH values measured in qualitatively 

similar soils and groundwaters and with pH ranges observed in UO2 degradation experiments 

(Hem, 1995aa; Wronkiewicz, et al., 1996aa). On the basis of the NRC staff’s evaluation, the 

solubility-controlling secondary oxide phases selected to quantify lower and upper pH limits in 

DOE’s pH abstractions and the abstracted pH limits are reasonable. 

Abstractions for Ionic Strength 

As with pH, DOE’s in-package chemistry abstractions for ionic strength provide parameter 

distributions in the form of lookup tables for the TSPA code. However, the manner by which 

ionic strength is abstracted differs under liquid and vapor influx conditions. 

Under liquid influx conditions, DOE derived abstractions for ionic strength from a series of 

time-dependent EQ6 simulations at different liquid influx rates. DOE approximated uncertainty 

in ionic strength on the basis of variation in ionic strength observed in material degradation rate 

sensitivity analyses. At low liquid influx rates, the model generates high ionic strengths in the 

waste form cells (SAR Figures 2.3.7-22 to 2.3.7-24) because low flow rates provide sufficient 

residence time for the buildup in solution of waste form and metal alloy degradation products. 

DOE supported high ionic strength predictions in the waste form cells by comparing predicted 

ionic strengths to observations from natural groundwater and laboratory experiments. 

For vapor influx conditions, DOE abstracted ionic strength as a function of relative humidity. 

At ionic strengths of 1 molal or less (relative humidity above ~98.5 percent), vapor influx was 

simulated using EQ6 and the B-dot equation to calculate activity coefficients to derive linear 

relationships between relative humidity and ionic strength (SAR Figure 2.3.7-25). When the 

ionic strength exceeded 1 molal (relative humidity at or below 98.5 percent), Pitzer calculations 

for simple salt solutions from the in-drift precipitates/salts model (SNL, 2007ao) were used to 

approximate the relationship between relative humidity and ionic strength, as described in BSC 
7-11 

Section 6.10.2.2[a] (2005ad). Uncertainty in ionic strength was derived from ionic strength 

variations observed in the Pitzer calculations. 

NRC Staff’s Review 

For liquid influx, the NRC staff reviewed the modeling approach and technical support DOE 

used to generate ionic strength abstractions. The derived ionic strength ranges, from output of 

time-dependent EQ6 simulations using the data0.ymp.R5 thermodynamic database, are 

appropriate because the ranges are calculated from a reasonably complete set of aqueous 

species at equilibrium. The sensitivity analyses results of liquid influx rate and material 

degradation rate on in-package ionic strength in BSC Sections 6.6.4[a] and 6.6.5[a] (2005ad) 

were reviewed. The variation in the liquid influx rate entering the waste package has a 

significant effect on ionic strength (i.e., ionic strength significantly increases as liquid influx rate 

decreases and significantly decreases as liquid influx rate increases). Therefore, deriving ionic 

strength as a function of liquid influx rate is reasonable because it provides a means for 

bounding in-package ionic strength over the entire range of flow conditions expected to enter a 

breached waste package. The variation in material degradation rates has a smaller effect on 

ionic strength. Therefore, approximating uncertainty in ionic strength as a function of material 

degradation rates is reasonable because it provides a means for bounding ionic strength on the 

basis of material components within the waste package. The NRC staff verified DOE’s 

supporting information showing that the relationship between low liquid influx rate and high ionic 

strength is consistent with the evolution of deep groundwater brines in Canadian Shield granite 

(Appelo and Postma, 1994aa) and the results of UO2 degradation experiments (Wronkiewicz, 

et al., 1996aa). 

For vapor influx, the NRC staff reviewed the modeling approach used to generate the ionic 

strength abstractions. DOE’s approach of deriving the ionic strength abstractions for vapor 

influx as a function of relative humidity is reasonable, because under vapor influx conditions, the 

water activity is controlled by relative humidity and ionic strength is strongly related to the water 

activity. At ionic strength values of 1 molal or less, deriving ionic strength from the output of 

EQ6 simulations using the data0.ymp.R5 thermodynamic database is appropriate because the 

values are calculated from a reasonably complete set of aqueous species at equilibrium. 

However, for solutions with ionic strengths greater than 1 molal, deriving ionic strength from the 

output of EQ6 simulations using the B-dot activity coefficient is inappropriate because 

precipitates may form at high ionic strength resulting in large uncertainties in calculated pH and 

ionic strength. The NRC staff evaluated DOE’s in-drift precipitates/salts model (SNL, 2007ao), 

which uses a Pitzer ion-interaction model to predict chemical conditions at high ionic strength. 

On the basis of this evaluation, the in-drift precipitates/salts model is reasonable for 

approximating the relationship between relative humidity and ionic strengths exceeding 1 molal. 

Abstraction for Fluoride Concentration 

DOE’s fluoride abstraction provides maximum fluoride values for discrete ionic strength intervals 

for each waste form cell. Although high-level waste glass may contain some fluoride, the major 

source of fluoride in breached waste packages is from liquid influx (i.e., incoming water). Under 

vapor influx conditions (where water vapor, simulated as pure water, is assumed to condense 

inside the waste package), there is no significant source of fluoride in the waste form cells and 

maximum fluoride concentration is set to zero. Therefore, the fluoride abstraction is only 

applicable under liquid influx conditions. At high ionic strengths, maximum fluoride values 

were selected on the basis of relationships between fluoride concentration and ionic 

strength observed in material degradation rate sensitivity analyses at various incoming 
7-12 

water compositions. At low ionic strength, maximum fluoride concentration was set to the 

maximum concentration observed in pore waters from the Topopah Spring welded tuff: 

4.8 mg/L [0.00025 molal] (SNL, 2007ak). 

NRC Staff’s Review 

The NRC staff evaluated the modeling approach and information DOE used to generate and 

support the in-package chemistry abstraction for fluoride. On the basis of its review, the NRC 

staff notes that the major source of fluoride in breached waste packages comes from liquid 

influx. At high ionic strengths, fluoride often concentrates as incoming water is consumed by 

degradation reactions, resulting in fluoride levels that tend to correlate with ionic strength. 

Therefore, the approach used to select maximum fluoride levels at high ionic strength is 

reasonable because it is based on relationships between fluoride concentration and ionic 

strength observed in material degradation rate sensitivity analyses at varying incoming water 

compositions. In addition, on the basis of its review of information provided in BSC Section 

6.10.3[a] (2005ad), the NRC staff notes that DOE’s maximum abstracted fluoride levels are 

set conservatively high when compared to fluoride levels observed in model output from the 

degradation rate sensitivity analyses at varying incoming water compositions. At low ionic 

strengths, the fluoride concentration either remains in the vicinity of the concentration of the 

incoming liquid or decreases due to mineral precipitation. Therefore, setting maximum fluoride 

concentrations to those observed in pore waters from the Topopah Spring welded tuff {i.e., 4.8 

mg/L [0.00025 molal]} is reasonable at low ionic strengths because this is the maximum 

possible fluoride concentration that can occur in the waste package without water loss due to 

degradation reactions. On the basis of this evaluation, the approach DOE used to set maximum 

fluoride levels is reasonable and will not result in an underestimation of risk. 

Summary of NRC Staff’s Review of In-Package Chemical and Physical Environment

The NRC staff determines that, in modeling the in-package chemical and physical environment, 

DOE appropriately incorporated design features of commercial spent nuclear fuel (SNF) and 

codisposal waste packages. DOE used reasonable conceptual and mathematical models and 

assumptions to simulate geochemical interactions between fluids, gases, and internal 

components of the waste package and generate abstractions for pH, ionic strength, and 

fluoride concentration. DOE used technically defensible data to establish initial and boundary 

conditions for model simulations. These data included the thermodynamic properties of solids, 

gases, and aqueous species; incoming water chemistries; and the compositions, surface areas, 

and degradation rates of waste forms and material components of the waste package. The 

NRC staff notes that model simulations were applied over the full range of environmental 

conditions that might be expected inside breached waste packages. DOE appropriately 

approximated model uncertainty for propagation into the TSPA code by performing sensitivity 

analyses to assess the effects of uncertain thermal-hydrologic-chemical parameters on model 

outputs. Parameters with significant effects on model outputs (pCO2 for pH and liquid influx rate 

and relative humidity for ionic strength) were incorporated as independent variables in the 

model abstractions. Parameters with smaller effects (material degradation rates) were used to 

quantify model uncertainty for propagation to the TSPA model. DOE provided support for the 

in-package chemistry pH abstractions by comparing predicted secondary mineral phase 

formation and estimated pH ranges to observations from natural soils and groundwater, natural 

analogs, and laboratory experiments. Support for the in-package chemistry ionic strength 

abstractions was provided by comparing predicted high ionic strengths to observations from 

natural groundwater and laboratory experiments. Therefore, on the basis of this review, DOE’s 
7-13 

abstraction and TSPA model implementation of the in-package chemical and physical 

environment are reasonable. 

2.2.1.3.4.3.2 Waste Form Degradation 

This section describes the NRC staff’s review of DOE’s abstraction and Total System 

Performance Assessment (TSPA) model implementation of radionuclide mobilization from 

waste form degradation. This radionuclide mobilization determines the quantity of radionuclides 

that may be transported by water from the solid waste form and eventually to the accessible 

environment. The waste form types include commercial spent nuclear fuel, high-level waste 

glass, and DOE spent nuclear fuel, as described in Safety Analysis Report (SAR) Section 1.5.1. 

Commercial spent nuclear fuel is composed of irradiated fuels from pressurized water reactors 

and boiling water reactors. High-level waste glass is made by melting high-level radioactive 

materials with silica and/or other glass-forming chemicals and then solidifying them. DOE spent 

nuclear fuel (including naval spent nuclear fuel) comes from a range of high-level waste 

generators, from noncommercial reactors, and from the use of radioactive material that 

encompasses a variety of fuel types. On the basis of the significance to risk in the performance 

assessment calculations, the NRC staff’s review focused on the inventory of radionuclides and 

radionuclide distribution in commercial spent nuclear fuel; degradation of commercial spent 

nuclear fuel; degradation of high-level waste glass; degradation of DOE spent nuclear fuel, 

naval spent nuclear fuel, and cladding; and associated model and data uncertainties, including 

waste form degradation under disruptive scenarios and microbial effects. Each waste form has 

its specific radionuclide inventory. In the nominal scenario, the waste form degrades as it 

dissolves after the cladding, if any, corrodes and fails in the aqueous environment. In the 

seismic or igneous scenarios, mechanically or thermally assisted degradation could also occur. 

For waste form degradation abstractions in the TSPA code, the input information includes the 

design description of the waste package, the waste form, the waste package internals, and 

in-package water chemistry and temperature. The output from this section includes waste form 

mobilization rates to assess engineered barrier system radionuclide transport. 

SAR Sections 2.3.7.1–2.3.7.4, 2.3.7.6–2.3.7.9, 2.4, and associated references summarized 

DOE’s model abstractions and related features, events, and processes (FEPs) related to the 

degradation of commercial spent nuclear fuel and cladding, high-level waste glass, and DOE 

spent nuclear fuel (including naval spent nuclear fuel). 

Inventory of Radionuclides and Radionuclide Distribution in Commercial Spent 

Nuclear Fuel 

The NRC staff’s review of DOE’s inventory data (SAR Section 1.5.1), in terms of weight, 

volume, and package design, for each waste form, is discussed in Technical Evaluation Report 

(TER) Sections 2.1.1.2.3.4.1, 2.1.1.2.3.4.2, and 2.1.1.2.3.5.1. More than 100 radionuclides may 

be collectively present in the waste package at the time of repository closure. Among them, a 

total of 32 isotopes of 18 elements were selected as important radionuclides to potential dose 

for scenario classes involving groundwater transport. 

NRC Staff’s Review 

The NRC staff’s evaluation of the radionuclide inventory incorporated DOE’s design features of 

the waste forms in the waste package (SAR Section 1.5.1). The design features include 

thermal loading, structural characteristics, radionuclide inventory, chemical composition, and 
7-14 

microstructural characteristics. The NRC staff independently evaluated (i) the total mass of the 

waste form and (ii) the long half-lived radionuclides that should be considered in the repository 

as summarized in NRC/Center for Nuclear Waste Regulatory Analyses (CNWRA®) reports 

(Leslie, et al., 2007aa; Jain, et al., 2004aa; Manaktala, 1993aa). In these independent 

evaluations, the NRC staff reviewed open literature information. DOE appropriately selected the 

total mass inventory and the long-lived radionuclides because this information is supported by 

data and models and a significant underestimation of dose exposure will not occur. 

The NRC staff evaluated DOE’s radionuclide inventory calculations by comparing the results 

with other published results and with independent calculated inventory histories for times from 

1 to 1 million years (Leslie, et al., 2007aa). The trends in the inventories were compared with 

those published elsewhere (e.g., Roxburgh, 1987aa). As expected, most radionuclide 

inventories decrease with increasing times, some remain relatively constant over long periods of 

time (those with long half-lives), and others increase with time (daughters in a decay chain). 

The NRC staff selected 43 radionuclides from hundreds of radionuclides present in commercial 

spent nuclear fuel using radionuclide screening processes. Screening criteria included 

half-lives, solubilities, and radiotoxicities of the radionuclides. Similar verifications were done for 

high-level waste glass and DOE (and naval) spent nuclear fuel. 

Most radionuclides, and essentially all of the rare earth and actinide radionuclides 

(e.g., plutonium isotopes), are retained in the UO2 matrix. Transition metals and fission 

products (e.g., technetium) are partly partitioned into metallic phases embedded in the matrix, 

spent nuclear fuel grain boundaries, and gap region (i.e., the interface between the pellets 

and the cladding). The NRC staff independently evaluated the distribution of radionuclides in 

the matrix and the accumulated radionuclides in the gap and grain boundaries. The NRC staff 

relied on open literature information and calculated radionuclide concentrations as a function of 

time, as earlier summarized in NRC/CNWRA reports (Leslie, et al., 2007aa; Jain, et al., 2004aa; 

Manaktala, 1993aa). The measured gap and grain boundaries in open literature include work 

by Bremier, et al. (2000aa), Gray (1999aa), Johnson and Tait (1997aa), and Lassmann, et al. 

(1995aa), and the changes in the radionuclide inventories were calculated using 

ORIGEN-ARP 2.00 (Bowman and Leal, 2000aa). 

DOE reasonably represented the radionuclide distribution in the spent nuclear fuel matrix, 

because the inventory was based on experimental data and standard analytical techniques the 

industry uses to establish the distribution of actinides, transition metals, and fission products, 

and is consistent with values documented in the open literature. 

Degradation of Commercial Spent Nuclear Fuel 

DOE reported that if the waste package and cladding are breached, oxidation and dissolution of 

the commercial spent nuclear fuel matrix may occur. If the temperature exceeds approximately 

100 °C [212 oF], solid-state oxidation or hydration will occur, depending on the relative humidity. 

Commercial spent nuclear fuel dissolves by oxidative reaction of the UO2 matrix in humid air or 

in solution at temperatures less than approximately 100 °C [212 oF]. Oxidation and hydration 

occur faster than dissolution. Oxidation, hydration, and dissolution can be preferentially 

enhanced along grain boundaries. In DOE’s commercial spent nuclear fuel degradation model 

(BSC, 2004ah), the high end of the dissolution rate range was obtained from tests in fast-flowing 

carbonate solutions. The low end of the dissolution rate range was obtained from commercial 

spent nuclear fuel rod segment tests under dripping groundwater conditions with precipitates 

deposited and failed cladding present. Un-irradiated UO2 was also tested because there is no 

significant difference between the dissolution rates of un-irradiated UO2 and commercial spent 
7-15 

nuclear fuel under air-saturated groundwater conditions. Data from long-term immersion and 

dripping water tests up to 8.7 years in duration were included in the model development. 

On the basis of these data analyses, DOE presented quantitative models and model parameter 

values for (i) the instantaneous release of radionuclides from the gap and grain boundaries and 

(ii) matrix dissolution inducing slow long-term radionuclide releases (SAR Sections 2.3.7.7, 

2.4.2.2, and 2.4.2.3). Mean fractional matrix dissolution rates were 5 × 10-4 to 6 × 10-3 year-1 at 

pH of 5.5–8.0 and a temperature range of 25–90 °C [77–194 °F] under wet conditions, 

according to DOE Enclosure 5 (2009an). Fission products and activation products were 

released with the matrix dissolution. Actinide releases may be controlled by solubility limits of 

dissolved radionuclides and also may be affected by colloids. DOE addressed uncertainties of 

its models and parameter values. 

In DOE’s TSPA model, high-solubility fission products and activation products (e.g., I-129 

and Tc-99) are released from the waste form at rates controlled by (i) the decay 

of radionuclide inventory of each waste package and (ii) waste package failure rate 

(e.g., SAR Section 2.4.2.2.3.2.1). The waste package failure rate is related in series to waste 

form dissolution rate and radionuclide diffusion rate. The slowest rate among the three controls 

the rate of release. DOE supported its model by stating that the dissolution rates of waste 

forms, including commercial spent nuclear fuel, are sufficiently faster (e.g., hundreds to a few 

thousand years) than the time intervals of each waste package failure. Low-solubility 

radionuclides (e.g., plutonium isotopes) are released at rates controlled mainly by the 

concentration limits of dissolved or colloidal species. 

NRC Staff’s Review of the Initial Condition of Spent Nuclear Fuel at Receipt 

The NRC staff notes that the model abstraction of the degradation of commercial spent nuclear 

fuel reasonably incorporates DOE’s waste form design features (which include thermal loading, 

structural characteristics, radionuclide inventory, chemical composition, and microstructural 

characteristics; SAR Section 1.5.1), in that the commercial spent nuclear fuel conditions at 

receipt will not be altered during transportation and interim storage, as outlined in DOE 

Enclosure 3 (2009ax). DOE appropriately assumed that the pressure of the residual water 

vapor inside the transportation, aging, and disposal canister would not be sufficiently high to 

degrade the commercial spent nuclear fuel matrix by grain boundary hydration. The NRC staff’s 

evaluation is based on DOE’s use of standard vacuum drying procedures in packaging waste. 

The vacuum drying lowers the residual water vapor, resulting in no matrix degradation by grain 

boundary hydration. In addition, the NRC staff notes from the staff’s literature review that 

potential matrix disintegration by high burnup in the range of ~60–65 MWD/kgU (e.g., Finch, et 

al., 1999aa; NRC, 2008aa) is not likely (Spino, et al., 2003aa). Also, in the TSPA model, 

release out of the waste package to the invert from spent nuclear fuel dissolution is insensitive 

to the initial condition. The release is mainly controlled by either (i) the radionuclide inventory of 

each waste package and waste package failure rate or (ii) concentration limits. Therefore, DOE 

presented reasonable technical arguments that the pressure of residual water vapor inside the 

transportation, aging, and disposal canister would not be sufficiently high to disintegrate the 

commercial spent nuclear fuel matrix by grain boundary hydration, and the residual water vapor 

will not cause the release of radionuclides out of the waste package to the invert from spent 

nuclear fuel dissolution to be underestimated. 
7-16 

NRC Staff’s Review of Releases From the Matrix and From the Gap and Grain Boundaries 

The NRC staff evaluated DOE’s processes and modeling for matrix dissolution and radionuclide 

release from the gap and grain boundaries. The NRC staff reviewed open literature information 

for this evaluation, including results from the NRC staff’s independent modeling (NRC, 2008aa; 

Leslie, et al., 2007aa; Jain, et al., 2004aa). The NRC staff notes that DOE reasonably 

concluded that the release of high-solubility radionuclides (e.g., I-129 and Tc-99) will be at the 

same rate of oxidative UO2 matrix dissolution, whereas release of low-solubility radionuclides 

(e.g., plutonium isotopes) may be limited by solubility. The DOE conclusions are consistent with 

laboratory test results (Wilson and Gray, 1990aa). 

The literature (Shoesmith, 2000aa) suggests that the UO2 matrix would dissolve oxidatively 

in the oxidizing environment expected at the proposed Yucca Mountain repository. This is 

consistent with the alteration process of natural analog uraninite (BSC, 2004ah). DOE’s 

mathematical models are empirical. DOE identified important environmental and commercial 

spent nuclear fuel parameters controlling the dissolution rate. Those parameters included 

oxygen partial pressure, carbonate concentration, temperature, pH, and the surface area of the 

matrix contacted by water. These parameter values were obtained from accelerated test results 

in oxidizing environments, with consideration of data uncertainties. These parameters and their 

values are reasonable, because they were derived from tests that are based on standard 

practice. In addition, the NRC staff performed independent evaluations (Leslie, et al., 2007aa) 

that are consistent with DOE’s results. DOE reasonably assumed that radionuclide release 

from the gap and grain boundaries is rapid because these radionuclides are not atomically 

bound in the matrix. Therefore, the NRC staff notes that DOE conservatively presented the 

conceptual and mathematical models for radionuclide releases that are faster than those 

expected to occur from matrix degradation and from the gap and grain boundaries. 

NRC Staff’s Review of Environmental Conditions Inside a Waste Package 

On the basis of DOE’s in-package chemistry models (BSC, 2005ad) and the NRC staff’s 

independent analyses (Leslie, et al., 2007aa; NRC, 2008aa, 1996ab), the NRC staff notes that 

DOE accounted for the range of environmental conditions expected inside the breached waste 

packages. DOE based the commercial spent nuclear fuel degradation model on pure carbonate 

solutions in the concentration range of 2 × 10−4

 to 2 × 10−2 mol/L. This is conservative because 

carbonate solutions lead to faster matrix dissolution in a waste package (NRC, 2008aa). The 

NRC staff’s independent review and analysis of open literature information (Leslie, et al., 

2007aa; NRC, 2008aa, 1996ab) suggest that dissolved constituents, such as calcium and silica, 

may reduce the dissolution rate. Slow dripping groundwater, partial protection by failed 

cladding, and iron corrosion products may also reduce the dissolution rate. As explained in 

DOE Enclosure 16 (2009ax), fast spent nuclear fuel dissolution in pure carbonate solutions may 

cause more actinide release associated with colloids. The NRC staff reviewed DOE’s derived 

dissolution processes of spent nuclear fuel in terms of the rates of dissolution and colloid 

formation of actinides, such as plutonium. On the basis of DOE’s conservative approach using 

carbonate solutions, the NRC staff’s independent review of dissolution data in open literature 

(NRC, 2008aa, 1996ab), and the TSPA’s dose consequence, the magnitude of this increased 

actinide release is not significant, because realistic matrix dissolution rates are lower. In the 

Total System Performance Assessment (TSPA) model, however, for high-solubility 

radionuclides, such as technetium, the release rate is controlled by the radionuclide inventory of 

each waste package and waste package failure rate. In this rate-controlled TSPA model, the 

slow realistic dissolution rates may result in increased release rates because multiple waste 

package failures at low dissolution rates may have a greater contribution to the release. The 
7-17 

NRC staff’s confirmatory analyses of the effects of multiple waste package failures suggest that 

the magnitude of the increase in release is minimal and insignificant. The NRC staff compared 

the release at different dissolution rates per unit area assuming spherical spent nuclear fuel 

particles from single- or multiple-remnant-failed waste packages at slow realistic dissolution 

rates. On the basis of this comparison, the NRC staff notes that, within the range of a factor of 

10 decrease in dissolution rates (NRC, 2008aa), the addition of release by slow dissolution from 

remnant failed waste packages is insignificant. 

NRC Staff’s Review of Alternative Models for Matrix Dissolution 

In BSC Section 6.4.2 (2004ah), DOE presented an electrochemical model and a 

surface complexation model as alternative models for the dissolution of the commercial spent 

nuclear fuel. The electrochemical model describes the process of the matrix dissolution by 

electric current flow under oxidizing conditions, and the surface complexation model describes 

the dissolution process by carbonate complexation. On the basis of its review, the NRC staff 

notes that DOE’s model results for dissolution rate are consistent with the basecase results 

within the uncertainty limits. In addition, the NRC staff performed independent assessments of 

the processes involved in these two alternative models by reviewing open literature information 

(NRC, 2008aa, 1996ab). On the basis of its literature review, the NRC staff notes that DOE’s 

basis and justification for the alternative models are reasonable. DOE’s results are consistent 

with the NRC staff’s independent evaluations (NRC, 2008aa, 1996ab). 

Degradation of High-Level Waste Glass 

DOE conceptually modeled high-level waste glass as being congruently dissolved for glass 

constituent elements and radionuclides at relative humidity greater than or equal to 44 percent. 

At lower relative humidity, the glass dissolution rate is set to zero. Dissolution kinetics were 

considered to be chemically controlled by dissolved orthosilicic acid (H4SiO4). As glass reacts 

with solution and reaches saturation with respect to mineral phases, precipitation occurs on the 

glass surface. DOE’s model was supported by dissolution studies conducted with a wide range 

of borosilicate glass compositions under various environmental conditions. On the basis of the 

data available from DOE’s tests, DOE presented a quantitative model and model parameter 

values for the high-level waste glass dissolution process. 

NRC Staff’s Review 

The model abstraction of the degradation of high-level waste glass incorporates DOE’s waste 

form design features, which include thermal loading, structural characteristics, radionuclide 

inventory, chemical composition, and microstructural characteristics (SAR Section 1.5.1). The 

NRC staff evaluated the processes and modeling that DOE presented for the dissolution of 

high-level waste glass. The NRC staff’s evaluation was based on open literature information 

originally compiled by Leslie, et al. (2007aa). 

The NRC staff notes that DOE reasonably assumed that the release of high-solubility 

radionuclides (e.g., I-129 and Tc-99) will be at the same rate of matrix dissolution, especially at 

repository relevant pH 5–8 in diluted in-package water chemistry at lower temperatures of 

25-90 °C [77–194 °F] after waste package failure (BSC, 2005ad; Leslie, et al., 2007aa). 

The NRC staff notes that the basis for this assumption is that higher solubility radionuclides will 

not tend to form immobile precipitates. As addressed in Technical Evaluation Report (TER) 

Section 2.2.1.3.4.3.1, the NRC staff notes that DOE appropriately assessed this environmental 

condition (BSC, 2005ad). DOE considered both sodium- and calcium-based pore waters and 
7-18 

data on the matrix dissolution under immersion, dripping groundwater, and vapor conditions. 

The data from these conditions were consistent with each other, and some accelerated tests 

such as fast-flowing water tests were also used to determine model parameter values. The 

NRC staff notes that the condition of mild aqueous chemistry used in the assessment would 

persist for the dissolution because the waste package would fail after several ten (for stress 

corrosion cracking) to hundred (for general corrosion) thousand years. DOE’s assertion that 

release of low-solubility radionuclides (e.g., plutonium isotopes) may be limited by solubility is 

reasonable. These two release modes and models are consistent with laboratory data 

presented by DOE and in the literature for borosilicate high-level waste glass in simulated 

Yucca Mountain in-package water, as described in BSC (2005ad), under immersion, dripping 

groundwater, and vapor conditions. 

DOE reasonably included in mathematical models important environmental and high-level 

waste glass parameters controlling the dissolution rate. The mathematical models are 

reasonable because they are consistent with an independent assessment of the dissolution 

rate based on open literature data (Leslie, et al., 2007aa). DOE quantitatively modeled the 

release rate of radionuclides as a function of surface area of glass contacted by water, 

intrinsic glass dissolution rate (i.e., release rate of boron as an indicator), pH, activation energy 

for temperature dependence, and the extent of orthosilicic acid saturation in solution with the 

glass. The dissolution rates in acidic and alkaline regimes of pH were separately modeled. 

At 100–250 °C [212–482 °F], a fixed pH was used. The mean fractional dissolution rates are 

2 × 10-5 to 4 × 10-3 year-1 at pH of 5.5–8.0 and temperature of 25–90 °C [77–194 °F] under wet 

conditions, according to DOE Enclosure 5 (2009an). 

DOE obtained model parameters and data uncertainties from suitable data, as documented in 

SAR Section 2.3.7.9. The equation used by DOE to calculate the area of glass surface 

contacted by water as glass dissolves accounts for (i) an increase in surface area from thermal 

and mechanical cracking, water access and reactivity with water and (ii) a loss in the surface 

area due to dissolution, as detailed in DOE Enclosure 2 (2009ax) and DOE Enclosure 2 

(2009cz). The increased surface area leads to increased release of radionuclides out of the 

waste package to the invert from high-level waste glass dissolution. In DOE’s model this 

increasing factor of surface area is expressed as “exposure factor.” The NRC staff notes that 

model or data uncertainties for other processes were appropriately discussed with respect to the 

distributions of model parameters (e.g., the extent of orthosilicic acid saturation) and 

conservatisms. DOE’s uncertainty assessment is reasonable, because the uncertainties of 

parameter values did not significantly affect the radionuclide release out of the waste package. 

Model support was also discussed with respect to long-term field tests and natural analog basalt 

glass. The NRC staff considers basalt glass to be an appropriate analog to high-level waste 

glass because of similarities in composition, reaction conditions, and transformation of the glass 

matrix during alteration into a range of minerals. Jantzen, et al. (2008aa) reviewed and 

analyzed existing field tests of high-level waste glass buried for ~24 years; this review further 

supports the SAR model and data for high-level waste glass dissolution. Jantzen, et al. 

(2008aa) showed superior or equivalent performance of this burial glass in unsaturated and 

saturated sediments, compared to saturated accelerated laboratory tests that are the basis for 

DOE’s model. 

Literature data and discussions on cracking (or pitting) of various glasses in more aggressive 

solutions (Pulvirenti, et al., 2006aa; Morgenstein, et al., 1999aa) did not show significant 

increase in dissolution rates. The test solutions used by Pulvirenti, et al. (2006aa) and 

Morgenstein, et al. (1999aa) were aggressive, unlike those expected in the repository, and 

many glasses considered were not based on borosilicates intended for disposal in the proposed 
7-19 

repository. DOE also assessed the volume occupied by porosity in the altered layer during the 

glass hydration. The porosity may increase the glass volume in a confined canister, which may 

create stress that further fractures the glass. The calculated porosity volume was close to the 

elemental mass percentage of soluble elements in the glass. Therefore, DOE considered that 

isovolumetric hydration would occur. For high-solubility radionuclides in TSPA, the release rate 

is controlled by the radionuclide inventory of each waste package and waste package failure 

rate because the dissolution rate is faster, as discussed for the degradation of commercial spent 

nuclear fuel. The NRC staff determines that the increased dissolution (even with further 

cracking by any means) associated with data or model uncertainties would not be rate 

controlling in the release. The waste form dissolution rate is related in series to the waste 

package failure rate and the radionuclide diffusion rate. The slowest among the three rates, the 

waste package failure rate in this case, controls the release rate. 

Therefore, DOE’s conceptual and mathematical models for radionuclide releases from highlevel waste glass degradation are reasonable. The NRC staff also notes that DOE reasonably 

accounted for the range of environmental conditions expected inside breached waste packages, 

because DOE’s in-package water chemistry models (BSC, 2005ad) were developed using 

current analytic techniques and thermodynamic approaches accepted in the technical 

community. In addition, the NRC staff’s independent models (Leslie, et al., 2007aa) predicted 

similar conditions considering immersion, dripping groundwater, and vapor environments. 

As an alternative model for high-level waste glass dissolution, DOE presented a dissolution 

model without considering the extent of orthosilicic acid saturation (i.e., affinity) (BSC, 2004ai). 

On the basis of its review of open literature information (e.g., Leslie, et al., 2007aa; BSC, 

2004ai), the NRC staff notes that the alternative model is conservative and consistent with 

available data/analyses and current scientific understanding. 

Degradation of DOE and Naval Spent Nuclear Fuel, and Cladding 

DOE divided its spent nuclear fuel into 34 distinct forms. Except for naval spent nuclear fuel, 

these DOE spent nuclear fuel types were modeled as degrading instantaneously upon waste 

package breach. Commercial spent nuclear fuel waste packages were used to represent the 

naval spent nuclear fuel waste packages for all scenario classes, because radionuclide release 

rates from the naval spent nuclear fuel waste packages were predicted to be considerably lower 

than from commercial spent nuclear fuel waste packages (BSC, 2004ao). Data uncertainties 

were discussed with respect to the conservatisms used. 

NRC Staff’s Review 

DOE reasonably assumed that DOE spent nuclear fuel will degrade instantaneously, because 

this assumption would not underestimate the radiological consequences. The NRC staff notes 

that DOE’s approach to model the naval spent nuclear fuel as commercial spent nuclear fuel is 

reasonable. This modeling assumption would not underestimate the radiological 

consequences, because the naval spent nuclear fuel is more robust and would release less 

radionuclides (BSC, 2004ao). This is also supported by the TSPA model. For high-solubility 

radionuclides, the release rate is controlled mainly by the inventory of each waste package and 

waste package failure rate; for low-solubility radionuclides, the release rate is controlled mainly 

by the concentration limits of dissolved species or colloids. The TSPA abstraction provides 

results consistent with output from detailed process-level models and/or empirical observation of 

DOE spent nuclear fuel characteristics (DOE, 2003ad). 
7-20 

The assumption of instantaneous degradation for DOE spent nuclear fuel may result in greater 

and faster colloidal release in a shorter period, a possibility addressed in DOE Enclosure 1 

(2009ax) and DOE Enclosure 1 (2009cz). This is similar to the case of instantaneous 

degradation of the waste form under igneous intrusive scenarios as described in the following 

subsection (Other Model and Data Uncertainties – Waste Form Degradation under Disruptive 

Scenarios). More realistically, after the instantaneous degradation, the waste form would be 

altered into other solid forms, such as further oxidized UO2, other oxide metal compounds, or 

hydrolysis or precipitation products (SNL, 2008ak, 2007ag), before being slowly dissolved, as 

described in DOE Enclosure 1 (2009cz). DOE considered colloid formation during the 

instantaneous degradation and subsequent alteration of the waste form. The NRC staff notes 

that model support for the quantitative information on colloid concentrations during these 

processes is reasonable. As outlined in DOE Enclosure 1 (2009ax), DOE Enclosure 1 

(2009cz), and DOE Enclosure 1 (2009db), DOE showed that faster dissolution does not 

necessarily result in greater plutonium colloid concentration in the bulk solution, because the 

majority of the plutonium remains in a residue (altered) layer at the reaction front. 

DOE assumed that the zircaloy and stainless steel commercial spent nuclear fuel cladding failed 

upon emplacement. Therefore, degradation of commercial spent nuclear fuel cladding was not 

included in the TSPA analysis. The effect of the naval spent nuclear fuel structure on the 

release and transport of radionuclides was treated separately from other DOE spent nuclear fuel 

types in the assessment. DOE spent nuclear fuel was conservatively assumed to degrade 

instantaneously. The naval spent nuclear fuel degrades more slowly than the commercial spent 

nuclear fuel, and therefore the naval spent nuclear fuel can be represented by commercial spent 

nuclear fuel waste packages in TSPA analysis. DOE documented the technical basis for 

cladding behavior in the repository briefly because DOE conservatively assumed that the 

zircaloy and stainless steel commercial spent nuclear fuel cladding failed at time of 

emplacement. This assumption is conservative because it does not underestimate the barrier 

performance of the fuel cladding. 

Other Model and Data Uncertainties 

Waste Form Degradation Under Disruptive Scenarios 

DOE stated that under the seismic scenario class, stress corrosion cracking of the waste 

package would occur earlier than waste package failure in the nominal case. Seismic response 

(motion and rockfall) could damage the drip shield and waste package, resulting in earlier stress 

corrosion cracking. The waste form dissolves in the diffused-in water vapor through the stress 

corrosion cracks, as in the nominal case. The igneous scenario class includes eruptive and 

intrusive events. In the volcanic eruption case, the impacted waste form was transported to the 

surface; this case does not involve groundwater transport and is not discussed in this Technical 

Evaluation Report (TER) chapter. In the igneous intrusive case, the waste form was assumed 

to be rapidly altered at expected elevated temperatures and made available to groundwater. 

NRC Staff’s Review 

The NRC staff reviewed the degradation models implemented in the Total System 

Performance Assessment (TSPA) code abstraction to confirm that they provide consistent 

results with the output from the detailed process-level models and/or empirical observations 

on the characteristics of commercial spent nuclear fuel and high-level-waste glass, as 

described in this TER section. DOE presented the bounding assumption regarding radionuclide 

release from all waste forms under igneous intrusive conditions in Safety Analysis Report (SAR) 
7-21 

Section 2.3.11.3.2.4. DOE assumed that all waste forms instantaneously degrade to be 

mobilized for release. These instantaneous degradation models are conservative and 

bounding in terms of soluble radionuclide release. As discussed earlier, in the TSPA model 

the release from the failed waste package is insensitive to the fast degradation rate of waste 

form. The release rate is controlled mainly by the inventory of each waste package and 

waste package failure rate for high-solubility radionuclides, and concentration limits of 

dissolved species or colloids for low-solubility radionuclides. The assumption of instantaneous 

degradation under igneous intrusive conditions may result in more and faster colloidal release 

in a shorter period, as addressed in DOE Enclosure 1 (2009ax) and DOE Enclosure 1 (2009cz). 

For the nominal scenario case, DOE presented concentrations of 1.2 × 10!8

 to 6.2 × 10!4 g/L 

[1.2 × 10!5

 to 6.2 × 10!1 ppm (part per million)] for irreversible colloids from commercial spent 

nuclear fuel, as detailed in DOE Enclosure 16 (2009ax), and 2.7 × 10!6

 to 1.4 × 10!5 g/L 

[2.7 × 10!3

 to 1.4 × 10!2 ppm] for high-level-waste glass colloids (SAR Section 2.3.7.11.3). 

DOE’s instantaneous degradation assumption is conservative because the waste form can be 

altered into other solid forms, such as oxidized UO2, other oxide metal compounds, or hydrolysis 

or precipitation products (SNL, 2008ak, 2007ag), before being slowly dissolved, as detailed in 

DOE Enclosure 1 (2009cz). 

DOE considered colloid formation during the instantaneous degradation and subsequent 

alteration of the waste form. The model support for the quantitative information on colloid 

concentrations during these processes is reasonable. In DOE Enclosure 1 (2009ax), DOE 

Enclosure 1 (2009cz), and DOE Enclosure 1 (2009db), DOE showed that faster dissolution 

does not necessarily result in greater plutonium colloid concentration in the bulk solution, 

because the majority of the plutonium remains in a residue (altered) layer at the reaction front. 

Microbial Effects 

DOE did not specifically address the microbial effects potentially affecting the dissolution of 

spent nuclear fuel and high-level waste glass. However, DOE’s models indirectly incorporate 

the effects to the extent that the models are consistent with natural analog and field test results. 

The NRC staff notes that the analog data or field test results could have been affected by the 

potential presence of microbial effects, compared with the laboratory test results. 

NRC Staff’s Review 

The NRC staff reviewed the waste form degradation that was partially tested in solutions under 

projected repository conditions, along with field tests and analog studies. These aqueous 

environments, especially field test or analog environments, may have contained organic 

byproducts or microbes. DOE’s models also include the characteristics of natural analogs of the 

waste form or field test results. No indication of microbe effects (e.g., lowering pH) were 

reported from these literature data (BSC, 2004ah, 2004ai). Therefore, DOE’s approach to the 

effects of organic byproducts or microbes on the waste form degradation is reasonable. 

Screening arguments are also found in excluded FEP 2.1.02.10.0A, Organic/Cellulosic 

Materials in Waste (SNL, 2008ab). Excluded features, events, and processes are not explicitly 

discussed in this chapter. The NRC staff notes that the technical basis for DOE’s exclusion of 

features, events, and processes is reasonable in TER Section 2.2.1.2.1.3.2. 

Integration in the Engineered Barrier System Radionuclide Transport Abstraction

The waste form mobilization abstraction provides radionuclide inventory, mobilized 

radionuclides, and waste form colloids to the engineered barrier system radionuclide transport 
7-22 

model. The NRC staff notes that DOE has appropriately deployed the waste form mobilization 

using GoldSim in the waste form domain. The NRC staff confirmed that the GoldSim results are 

consistent with DOE’s description and quantitative assessments of waste form lifetimes in the 

waste form domain. 

Summary of NRC Staff’s Review of Waste Form Degradation 

DOE appropriately incorporated design features of the waste form. The design features include 

thermal loading, structural characteristics, radionuclide inventory, chemical composition, and 

microstructural characteristics. The NRC staff reviewed DOE’s proposed dissolution processes 

of waste forms and notes that the following DOE conclusions are reasonable: (i) the dissolution 

process of commercial spent nuclear fuel is based on oxidative dissolution of the UO2 matrix 

and (ii) the dissolution process of high-level-waste glass is based on orthosilicic acid release. 

The NRC staff also notes that the models for DOE spent nuclear fuel dissolution are bounded 

by the assumption of instantaneous dissolution and that modeling naval spent nuclear fuel as 

commercial spent nuclear fuel is conservative. In the models, DOE reasonably includes 

important environmental and waste form parameters controlling the dissolution rate (e.g., pH, 

temperature, solution chemistry, cracking, and hydration). DOE’s models are reasonable and 

were supported by laboratory and/or field test results. DOE obtained sufficient data to vary 

environmental and waste form parameters. The model uncertainties were assessed with 

alternative models; the data uncertainties were assessed with sufficient data obtained under 

various environmental and waste form conditions. The TSPA model considers uncertainties 

associated with (i) conservative, fast dissolution of commercial spent nuclear fuel under nominal 

conditions and (ii) the instantaneous dissolution of all waste forms under igneous conditions and 

DOE spent nuclear fuel under nominal conditions. DOE appropriately implemented its TSPA 

code with the model abstraction of radionuclide mobilization from the waste form. 

2.2.1.3.4.3.3 Concentration Limits 

This section discusses the NRC staff’s review of DOE’s abstraction and Total 

System Performance Assessment (TSPA) model implementation of dissolved radioactive 

element (radioelement) concentration limits, as described in Safety Analysis Report (SAR) 

Section 2.3.7.10. For significant periods of time in the performance assessment model, 

concentration limits exert strong controls on the concentrations in water of dose-important 

radioelements—particularly neptunium and plutonium—and, thus, on the release rates of those 

elements’ isotopes from the engineered barrier system. These limits are based on chemical 

equilibrium relationships between the dissolved element and solid substances containing the 

element. The abstraction calculates, on the basis of water chemistry, maximum concentrations 

that limit how much of the total mass of a radioelement may remain dissolved in solution in the 

waste form domain, the corrosion products domain, and the invert (the drift floor, consisting of 

crushed rock). In the waste form domain, the radionuclide concentration calculated from the 

waste form degradation abstraction is reduced if it, along with the concentrations of other 

isotopes of the same element, exceeds the calculated concentration limit. The concentration 

limit comparison is also implemented for the radionuclide transport abstraction in the 

corrosion products domain and the invert. In each case, the remaining radionuclide mass is 

retained in the domain as a precipitated mass that is available for re-dissolution whenever 

the concentration is below the concentration limit. The inputs to the concentration limits 

abstraction are geochemical characteristics in the domain of the water from the in-package 

chemistry abstraction (waste form domain; TER Section 2.2.1.3.4.3.1) or the engineered 

barrier system radionuclide transport abstraction (corrosion products domain and invert; TER 

Section 2.2.1.3.4.3.5), and gas from the engineered barrier system chemical environment 
7-23 

abstraction (TER Section 2.2.1.3.3). The outputs from the TSPA model abstraction are the 

concentration limits used in the engineered barrier system radionuclide transport abstraction 

(TER Section 2.2.1.3.4.3.5). The actual application of the concentration limits and retention of 

the precipitated mass is calculated in the GoldSim computer code, as outlined in GoldSim 

Technology Group, pp. 253–255 (2006aa). Radionuclide Solubility, Solubility Limits, and 

Speciation in the Waste Form and Engineered Barrier System is an included FEP 

encompassing the abstraction evaluated in this section. The related excluded features, events, 

and processes are addressed in TER Section 2.2.1.2.1.3.2. 

To evaluate DOE’s abstractions of radioelement concentration limits, the NRC staff 

reviewed SAR Section 2.3.7, the analysis model report on concentration limits (SNL, 2007ah), 

and DOE’s responses to the NRC staff’s requests for additional information (DOE, 2010aj; 

2009ax,ay,cz,da,db,dc). The NRC staff also relied on the technical literature on solubility limits 

and the application of solubility limits in performance assessments, the NRC staff’s independent 

solubility limit evaluations (e.g., Murphy and Codell, 1999aa; Mohanty, et al., 2003aa), and the 

NRC staff’s independent laboratory studies (e.g., Prikryl, 2008aa). 

Overall Abstraction Approach 

DOE’s abstraction for concentration limits calculates concentration limits for plutonium, 

neptunium, uranium, thorium, americium, tin, and protactinium using lookup tables that define 

values (in mg/L, a unit that is approximately equivalent to parts per million, or ppm) as functions 

of pH and fCO2 (i.e., CO2 fugacity). For radium, the value is specified as a constant that 

depends on the range in which the pH value falls. For technetium, carbon, iodine, cesium, 

strontium, selenium, and chlorine, no concentration limit is applied; this abstraction, therefore, 

does not affect their release rates from the engineered barrier system. 

For plutonium, neptunium, uranium, thorium, americium, tin, and protactinium, DOE determined 

the concentration limit value from the lookup table for each timestep in a realization. 

Uncertainty is incorporated into the abstraction by sampling two uncertainty terms that are then 

added to (or subtracted from) the value derived from the lookup table. No further uncertainty is 

applied to the determined radium value. Concentration limits are treated the same for nominal 

and disruptive events, with the exception of the uranium abstraction in the igneous intrusive 

case (see uranium discussion in the Concentration Limits Parameters section). 

NRC Staff’s Review 

DOE’s overall approach to impose concentration limits is appropriate because it is consistent 

with standard thermodynamic geochemical principles and uses consistent and reasonable 

assumptions. The use of pure phase solubilities to constrain radioelement concentrations at the 

source is an accepted approach in performance assessments for radioactive waste disposal 

(e.g., Nuclear Energy Agency, 1997aa; Leslie, et al., 2007aa). When faced with uncertainty 

regarding the solid phase with which to model a solubility limit, DOE reasonably chose the solid 

phase that would result in higher dissolved concentrations (e.g., hydrated PuO2 instead of 

anhydrous PuO2). 

Chemical Environment for Concentration Limits 

For the waste form domain, DOE’s in-package chemistry abstraction provides pH, ionic 

strength, and fluoride concentration to the concentration limits abstraction at each timestep, and 

CO2 fugacity (fCO2) is obtained from the engineered barrier system chemical environment 
7-24 

abstraction. The same ionic strength and fCO2 are used in the corrosion products domain, but 

pH is calculated from a formula that involves fCO2 and the aqueous uranium concentration; this 

pH abstraction is based on the competitive surface complexation model discussed in Technical 

Evaluation Report (TER) Section 2.2.1.3.4.3.5. In the corrosion products domain, according to 

DOE Enclosure 3 (2009ay) and DOE Enclosure 2 (2009da), the abundant mass of products of 

stainless steel corrosion controls pH to a relatively narrow range of 7.0 to 8.4. For the invert, 

when there is no flow from the waste package, pH, ionic strength, and fCO2 are obtained from 

the engineered barrier system chemical environment abstraction; when there is advective flow 

out of the waste package, according to DOE Enclosure 7 (2009ax), pH and ionic strength in the 

invert are the same as in the corrosion products domain. 

NRC Staff’s Review 

The NRC staff notes that DOE used appropriate tools to model concentration limits, including 

the important geochemical parameter inputs (e.g., pH and fCO2) affecting the solubility model 

outputs. Concentration limits in the waste form domain are functions of chemical parameters 

developed by the in-package chemistry model (TER Section 2.2.1.3.4.3.1). Chemical conditions 

for concentration limits in the corrosion products domain were reasonably modeled using the 

surface complexation model, and the results of that model were supported by comparison with 

DOE’s independent modeling efforts in DOE Enclosure 2 (2009da). 

An abundant secondary phase, such as steel corrosion products, can have an important 

influence on pH buffering in an environment with such high solid-to-water ratios. The NRC staff 

questioned DOE concerning the choices of stainless steel corrosion rates and their effects on 

in-package pH in the performance assessment. In DOE Enclosure 2 (2009db), DOE provided 

information that (i) supported its selection of the uncertainty range for the stainless corrosion 

rate on the basis of laboratory data and (ii) showed that plutonium isotope release rates, which 

are sensitive to the pH-dependent plutonium solubility limit, are insensitive to the stainless steel 

corrosion rate. The NRC staff verified DOE’s analysis by reviewing corrosion rate data in the 

literature (Beavers and Durr, 1990aa; BSC, 2004ae; Glass, et al., 1984aa; McCright, et al., 

1987aa). In addition, DOE showed in DOE Enclosure 3 (2009db) that conservatisms in (i) the 

treatment of the timing of radionuclide release after waste package breach, (ii) assumptions 

regarding flow within the waste package, and (iii) the lower pH range meant that DOE was 

unlikely to overestimate the effectiveness of stainless steel corrosion products in controlling pH. 

The NRC staff notes that this is reasonable, in that the abstraction would not result in 

underestimation of radionuclide release rates, and is consistent with the NRC staff’s 

understanding of the abstraction. 

On the basis of this review, DOE’s TSPA integration of the concentration limit abstraction and its 

accounting for the range of geochemical environments expected in the waste package and 

invert are reasonable. 

Concentration Limits Parameters 

DOE calculated concentration limits for plutonium, neptunium, uranium, thorium, americium, tin, 

protactinium, and radium (using barium as an analog) assuming pure-phase solubility at 

equilibrium with solution using the geochemical modeling code EQ3NR. The solubility models 

were conducted at a range of pH, fCO2 (CO2 fugacity), and fluoride values. For plutonium, 

neptunium, uranium, thorium, americium, tin, and protactinium, the pH and fCO2 dependencies 

were incorporated into the lookup tables, and the fluoride sensitivity was applied through an 

uncertainty term. SAR Section 2.3.7.10 described the technical bases for the limiting minerals 
7-25 

selected for the solubility models. For most of the modeled elements, equilibrium with 

atmospheric oxygen was assumed; this assumption was modified for plutonium in the waste 

package. As mentioned previously, DOE did not apply a concentration limit for technetium, 

carbon, iodine, cesium, strontium, selenium, and chlorine. 

NRC Staff’s Review 

DOE’s use of no concentration limits for technetium, carbon, iodine, cesium, strontium, 

selenium, and chlorine is consistent with the lack of a strong technical basis for concentration 

limits and is conservative because it does not underestimate the radiological consequences of 

release of isotopes of these seven elements. Application of a concentration limit can only 

reduce the dissolved concentration of an element; therefore, ignoring this process can only 

increase radionuclide release rates or leave them unchanged. 

The NRC staff notes that DOE used, in its chemical speciation and solubility models, 

appropriate thermodynamic data that had been evaluated for this purpose and were based on 

extensive, scholarly, international reviews. More detailed concentration limit evaluations for 

each element are in the following paragraphs.

Plutonium 

On the basis of DOE’s TSPA code dose modeling results, plutonium is a risk-significant 

radioelement. DOE’s plutonium concentration limits abstraction is based on equilibrium 

geochemical modeling, but differs from other abstractions in that equilibrium with atmospheric 

oxygen was not assumed. Atmospheric oxygen would impose higher redox potentials that tend 

to lead to higher calculated dissolved plutonium concentrations. The NRC staff therefore 

focused on DOE’s adjusted-Eh model, which assumes lower Eh than would be imposed by 

atmospheric oxygen. (Eh is a measure of a solution’s oxidation potential, which may be 

described as the tendency of the solution to convert dissolved elements to higher oxidation 

states.) The adopted Eh-pH relationship for plutonium solubility models is more oxidizing than 

the line bounding a compilation of data from waters in contact with atmosphere, as outlined in 

SNL (2007ah), DOE Enclosure 9 (2009ax), and DOE Enclosure 7 (2009cz). For the typical pH 

and CO2 conditions in the waste package (SAR Figures 2.3.7-19 to 2.3.7-21), the sampled 

plutonium solubility limit in the TSPA model ranges approximately from 0.006 to 0.5 mg/L 

(SAR Figure 2.3.7-29); uncertainty terms extend this range as much as ± two orders of 

magnitude (e.g., SAR Figure 2.3.7-38). 

NRC Staff’s Review 

On the basis of information DOE provided and on an independent NRC staff’s review of the 

scientific literature (e.g., Rai, et al., 1999aa; Neck, et al., 2007aa), the plutonium solubility 

abstraction was reasonably developed using Eh-pH conditions that bound the in-package 

environment. As illustrated in SNL (2007ah), DOE Enclosures 8 and 9 (2009ax), DOE 

Enclosures 5 and 6 (2009cz), and DOE (2010aj), nearly all compared plutonium 

experimental data lie within two standard deviations of the uncertainty in the pH-dependent 

solubility relationship. DOE appropriately supported the plutonium model results by showing 

that most calculated concentrations will be higher than the results from spent nuclear fuel 

dissolution tests. 
7-26 

Neptunium 

DOE identified neptunium as an important risk contributor. In general, DOE modeled neptunium 

solubility limits assuming equilibrium with atmospheric oxygen, but used two different solid 

phases depending on conditions. The controlling solid phase for the invert is the oxidized phase 

Np2O5. The choice of the neptunium-controlling mineral in the waste package (waste form and 

corrosion products domains)—NpO2 or Np2O5—depends on the corrosion status of the steel 

components. DOE stated that local reducing conditions during steel corrosion would promote 

precipitation of reduced NpO2 over oxidized Np2O5. DOE noted that the literature suggests that, 

in the presence of reducing materials, NpO2 is an appropriate solubility-limiting solid phase 

under most modeled conditions. DOE chose Np2O5 to limit neptunium concentration in the 

absence of reductants. A sodium neptunium carbonate is modeled at the high pH margin of the 

water chemistry range. For the typical pH and CO2 conditions in the waste package (SAR 

Figures 2.3.7-19 to 2.3.7-21), the sampled neptunium solubility limit in the Total System 

Performance Assessment (TSPA) analysis ranges approximately from 0.02 to 11 mg/L for NpO2

and from 0.3 to 180 mg/L for Np2O5 (SAR Figure 2.3.7-30); uncertainty terms extend this range 

to more than an order of magnitude above and below those ranges (e.g., SAR Figure 2.3.7-39). 

NRC Staff’s Review 

DOE reasonably chose NpO2 and Np2O5 as solubility-limiting solids, depending 

on oxidation-reduction conditions. In support of its neptunium model, DOE showed that the 

range of calculated neptunium concentrations, as a function of pH, exceeds the majority of 

concentrations from spent nuclear fuel dissolution tests. Furthermore, the mean-value curves 

for concentration limit versus pH are higher than all the spent nuclear fuel dissolution test 

concentration values (SAR Figure 2.3.7-39). In DOE Enclosure 12 (2009ax), DOE compared 

the model to project and literature data that, in general, suggested higher neptunium solubility 

limits but are within the 2σ model uncertainty incorporated in its analysis. DOE showed that the 

experiments that yielded especially high neptunium concentrations measured solubilities of 

metastable hydrous neptunium oxides, rather than the lower solubility anhydrous neptunium 

oxides expected on longer repository time scales. Therefore, the highest measured neptunium 

concentrations could be excluded from development of neptunium concentration limits for 

performance assessment. In addition, DOE considered an alternative conceptual model 

involving neptunium incorporation in secondary uranyl minerals resulting from spent nuclear fuel 

dissolution. DOE excluded the alternative conceptual model because of inadequate technical 

bases for its inclusion in performance assessment, which is consistent with a similar analysis 

the NRC staff previously made (Pickett, 2005aa). Consequently, the NRC staff notes that 

DOE’s neptunium model is reasonable. 

Uranium 

DOE produced two different lookup tables for uranium, depending on the particular chemical 

environment. In most cases for commercial spent nuclear fuel packages, the hydrated uranyl 

oxide schoepite is the solubility-limiting solid. For the typical pH and CO2 conditions in a 

commercial spent nuclear fuel waste package (SAR Figure 2.3.7-19), the sampled uranium 

solubility limit in the TSPA code ranges approximately from 1 to 100 mg/L [ppm], as described in 

SNL Figure 6.7-1 (2007ah). The associated uncertainty terms extend this range as much as ± 

an order of magnitude; for example, as detailed in SNL Figure 7-6 (2007ah). For codisposal 

packages in all scenarios and all packages in the igneous intrusion scenario, and for the invert 

in all scenarios, the uranyl silicate sodium-boltwoodite and Na4UO2(CO3)3 are included in the 

solubility models. For the typical pH and CO2 conditions in a codisposal waste package (SAR 
7-27 

Figures 2.3.7-20 and 2.3.7-21), the sampled uranium solubility limit in the TSPA code ranges 

approximately from 1 to more than 10,000 mg/L (SAR Figure 2.3.7-31). The associated 

uncertainty terms extend this range as much as ± an order of magnitude; for example, as 

illustrated in SNL Figure 7-6 (2007ah). 

NRC Staff’s Review 

The NRC staff notes that DOE chose reasonable solubility-limiting solid phases for the uranium 

solubility limit model. Secondary uranyl minerals such as uranophane (Prikryl, 2008aa) were 

conservatively excluded in favor of schoepite and sodium-boltwoodite. These excluded 

minerals could control uranium to even lower concentrations under certain chemical conditions. 

Laboratory and natural analog studies DOE cited (SNL, 2007ah) support these choices. 

A relatively soluble sodium uranyl carbonate was modeled at highest pH and fCO2. DOE 

appropriately compared model results with uranium concentrations measured in spent nuclear 

fuel dissolution tests; this comparison shows that most measured uranium concentrations plot 

near or below the model solubility limit curves, as shown in SNL Figure 7-6 (2007ah). 

Therefore, DOE’s uranium model is reasonable. 

Thorium, Americium, Protactinium, Radium, and Tin 

In developing lookup tables for the other modeled actinides—thorium, americium, and 

protactinium—DOE chose solubility-limiting phases on the basis of available data from the 

literature and the Yucca Mountain program (SNL, 2007ah). For thorium, the limiting solid was 

ThO2(am), the solubility model for which produced values more consistent with experimental 

measurements than models for the lower solubility, crystalline solid ThO2. [In mineral formulas, 

“(am)” indicates that this is an amorphous, rather than orderly crystalline, solid. Amorphous 

solids tend to have higher solubilities than the corresponding crystalline forms.] The americium 

model used AmOHCO3, which was identified as the controlling solid in Yucca Mountain program 

studies conducted under appropriate conditions. Protactinium was treated by analogy with 

neptunium and thorium; that is, the Np2O5 model was adopted for protactinium, with wide 

uncertainty terms accounting for the possibly lower concentrations if behavior was similar to 

thorium. The model for tin [addressed in SNL (2007ah) but not in the SAR] was also developed 

using available literature data and considerations of uncertainties; the selected controlling solid 

in the tin model was SnO2(am). DOE used barium as a radium analog for solubility calculations 

due to their similar chemical properties. DOE constructed a pH-dependent, stepwise radium 

solubility limit on the basis of the model results. 

NRC Staff’s Review 

DOE’s concentration limits abstractions for the actinides thorium, americium, and protactinium 

are reasonable because they were based on solubility models constructed using appropriate 

solubility-limiting phases. The thorium model was supported by comparison with experimental 

solubility data that were independent of those used to develop the model. DOE supported the 

americium model by showing that the uncertainty range of predicted concentrations exceeded 

all americium concentrations measured in spent nuclear fuel dissolution experiments. Although 

the independent solubility data used to support the protactinium model were sparse, DOE 

additionally supported the model by noting that studies show protactinium solubility to be 

consistently lower than neptunium, which DOE used for modeling protactinium. The tin 

concentration limit abstraction is reasonable, because DOE based it on appropriate solubility 
7-28 

models and corroborated the results by comparison to an independent modeling study. In 

addition, the choice of the tin solubility-limiting solid SnO2(am) is reasonable because it is more 

soluble than the other considered tin mineral cassiterite (SnO2). 

The NRC staff notes that DOE’s radium concentration limit abstraction is reasonable. 

The chemical analogy to barium is supported by literature observations (cited by DOE) 

showing their similar geochemical behaviors. DOE appropriately compared the adopted 

values to literature data on radium solubility, showing that the abstracted limits are 

similar to or higher than published model and experimental values. In addition, radium 

is expected to be coprecipitated in sulfates of other alkaline earth elements, particularly 

barium (e.g., Zhu, 2004aa), such that dissolved radium would be constrained to very 

low concentrations. The pure-phase radium solubility model is, therefore, reasonable 

and conservative. 

Uncertainty 

In the concentration limits abstraction, DOE addressed uncertainty in (i) thermodynamic data 

supporting the solubility models and (ii) the effects of fluoride ion concentration. These 

uncertainties are applied as additional sampled terms added or subtracted to the lookup table 

values, with a pH-dependent coefficient applied to the sampled fluoride uncertainty term. 

These thermodynamic and fluoride uncertainty terms—with normal and triangular distributions, 

respectively—are sampled once per realization for each element. 

NRC Staff’s Review 

DOE appropriately accounted for uncertainty in thermodynamic constants by using sampled 

uncertainty terms that were based on literature thermodynamic studies DOE cited. Regarding 

model uncertainty, DOE explicitly accommodated the potential effects of fluoride on solubilities 

in uncertainty terms for the actinide abstractions. Uncertainty in chemical effects on solubility 

limits is applied implicitly by the in-package chemistry abstraction (TER Section 2.2.1.3.4.3.1).

Integration in the Engineered Barrier System Radionuclide Transport Abstraction 

The dissolved concentration limits abstraction provides maximum concentration values for each 

element to the engineered barrier system radionuclide transport model, with different values 

provided for the waste form domain, corrosion products domain, and the invert. 

NRC Staff’s Review 

DOE appropriately deployed the concentration limits using the GoldSim code in each domain, 

because the implementation accounts for the different chemical conditions. The NRC staff 

confirmed expected behavior by inspecting plots of modeled dissolved concentrations in the 

engineered barrier release domains in the TSPA analysis (e.g., DOE, 2009dc). On the basis of 

the NRC staff’s review of the plots, solubility limits in the TSPA model were within the ranges 

appropriate for the given domain, as would be predicted by DOE’s lookup tables and modeled 

chemical conditions. In addition, the radioelement concentration ranges either coincided with or 

were below the corresponding concentration limits. 
7-29 

Summary of NRC Staff’s Review of Concentration Limits 

The NRC staff notes that DOE’s abstraction of radioelement concentration limits is appropriately 

integrated into the engineered barrier system radionuclide release and transport abstraction. 

The solubility limit models reasonably account for the engineered barrier system design, the 

response of system components to changing in-package conditions, and the effects of those 

responses on the chemical environment. DOE used equilibrium geochemical models, with 

reasonably chosen solubility-limiting solid phases, to construct concentration limits lookup 

tables. Data supporting the various element abstractions are reasonable. Model support is 

reasonable; DOE compared model results to appropriate laboratory data on solubility limits and 

concentrations during waste form dissolution studies. DOE reasonably propagated model and 

data uncertainty through the abstractions. 

2.2.1.3.4.3.4 Availability and Effectiveness of Colloids 

This section describes the NRC staff’s review of DOE’s abstraction and Total System 

Performance Assessment (TSPA) model implementation of the type, stability, and mass 

concentration of colloid suspensions in the engineered barrier system, as described in Safety 

Analysis Report (SAR) Section 2.3.7.11 and references cited therein. Colloid suspensions 

inside the engineered barrier system are important to repository performance, because if they 

are stable and exist at sufficiently high concentrations, they could enhance transport of 

radionuclides that are reversibly or irreversibly associated with them. (In this chapter, the term 

“irreversible colloids” refers to colloids with radionuclides irreversibly, or permanently, attached 

to them. The term “reversible colloids” refers to colloids to which radionuclides may attach and 

detach reversibly.) 

On the basis of the NRC staff’s evaluation of the TSPA code results, the NRC staff notes that 

the igneous intrusion and seismic ground motion modeling cases are potentially the most 

significant to risk with respect to long-term repository performance. The NRC staff verified that 

irreversible colloids are modeled in the TSPA code as independent species, separate from 

reversible colloids and dissolved ionic species. The NRC staff verified that radionuclides that 

are reversibly bound onto colloids can move back into solution and, hence, become part of 

dissolved radionuclide mass even if reversible colloids are unstable and settle out. Therefore, 

the NRC staff’s review focused specifically on processes and features that limit the stability and 

mass concentrations of irreversible colloid suspensions [including plutonium-rich zirconium 

oxide (commercial spent nuclear fuel) colloids, glass waste form colloids, and iron oxide 

colloids] in the engineered barrier system under igneous intrusion and seismic ground motion 

modeling cases. 

The engineered barrier system colloid model calculates the mass concentrations of reversible 

and irreversible colloid suspensions in the waste package, corrosion products, and invert 

domains of the engineered barrier system on the basis of temporal variations in aqueous 

chemical characteristics (pH and ionic strength), flow rates, and failure status of the engineered 

barrier system components under nominal and disruptive modeling cases. 

Inputs to the engineered barrier system colloid mass concentration abstraction, 

described in SNL Section 1.1 (2008ak), include in-package ionic strength and pH from 

the in-package chemistry abstraction (TER Section 2.2.1.3.4.3.1), and in-drift ionic strength 

and pH from the engineered barrier system physical and chemical environment abstraction 

(TER Section 2.2.1.3.3). Mass concentrations of colloidal suspensions are used to calculate 
7-30 

colloid-assisted radionuclide transport in the engineered barrier system radionuclide transport 

abstraction (TER Section 2.2.1.3.4.3.5). 

 

Colloid Types and Radionuclides Associated With Colloids in the Engineered 

Barrier System 

Colloids are 1 to 2-μm [4 to 8 × 10-5-in]-sized particles; have the potential to facilitate transport 

of highly sorbing, low solubility radionuclides; and may allow radionuclide concentrations in 

water above their solubility limit. In the TSPA code, colloids in the engineered barrier system 

are formed by degradation of waste package internals and waste forms and also exist as 

groundwater colloids in seepage water. DOE used the engineered barrier system colloid 

abstraction in the TSPA code to determine the stability and mass concentrations of reversible 

and irreversible colloid suspensions in the waste form, corrosion products, and invert domains 

of the engineered barrier system (SAR Section 2.3.7.11). 

The engineered barrier system colloid model abstraction focuses on the following five colloid 

suspension types: (i) glass waste form colloids, (ii) plutonium-rich zirconium oxide commercial 

spent nuclear fuel colloids, (iii) oxidized uranium colloids derived from the spent nuclear fuel, 

(iv) iron oxide colloids, and (v) groundwater colloids. The conceptual model identifies two types 

of radionuclide attachment to colloids (i) reversible (glass waste form colloids, oxidized uranium 

colloids, iron oxide colloids, and groundwater colloids), in which the radionuclides are reversibly 

(temporarily) sorbed onto colloid surfaces, and (ii) irreversible (glass waste form colloids, 

commercial spent nuclear fuel colloids, and iron oxide colloids), in which the radionuclides are 

permanently attached to or embedded in the colloid structure, as detailed in SNL Section 6.3.1 

(2008ak) and SNL Section 6.3.4.4 (2007aj). Although glass waste form colloids, oxidized 

uranium colloids, iron oxide colloids, and groundwater colloids are considered in the waste 

form domain, iron oxide colloids are excluded from the waste form domain, as outlined in 

SNL Section 6.5.2.5 (2007aj) and DOE Enclosure 2 (2009ay). As shown by the data in SNL 

Tables 6.3.7-62, 6.3.7-63, 6.3.7-64, and 6.3.7-66 (2008ag), the sampled stable glass waste, 

commercial spent nuclear fuel, oxidized uranium, and groundwater colloid concentration ranges 

are 0.0004 to 2 mg/L [ppm], 0.000015 to 0.6 mg/L, 0.001 to 200 mg/L, and 0.001 to 200 mg/L, 

respectively, in all the engineered barrier system domains. As shown in SNL Table 6.3.7-65 

(2008ag), the sampled stable iron oxide colloid concentration range is 0.001 to 30 mg/L [ppm] in 

the corrosion products and invert domains. 

In the engineered barrier system colloid model abstraction, two radioelements (plutonium and 

americium) are modeled to permanently attach onto iron oxide colloids or be irreversibly 

embedded in glass waste form and commercial spent nuclear fuel colloids. As described in 

SNL Section 6.3.12.1 (2008ak), five radioelements (plutonium, americium, thorium, 

protactinium, and cesium) are modeled to reversibly sorb onto glass waste form colloids, eight 

radioelements (plutonium, americium, thorium, protactinium, cesium, tin, neptunium, and 

radium) reversibly sorb onto oxidized uranium colloids, three radioelements (plutonium, thorium, 

and neptunium) reversibly sorb onto iron oxide colloids, and four radioelements (uranium, 

neptunium, tin, and radium) reversibly sorb onto groundwater colloids. The irreversible colloids 

(and their associated masses of plutonium and americium) are modeled as independent 

species, separate from the dissolved plutonium and americium masses. 

NRC Staff’s Review 

The NRC staff’s review verified that DOE relied on laboratory and field-scale experimental 

results published in technical articles found in peer-reviewed journals to determine 
7-31 

representative waste form and groundwater colloid types, and types of radionuclides reversibly 

and irreversibly associated with these colloids in the engineered barrier system colloid model 

abstraction, as detailed in SNL Section 6.3 (2008ak). DOE considered both reversible and 

irreversible colloids in the abstraction and noted the uncertainties associated with the mass 

concentrations, the stability of these colloid types, and their upscaling and applicability to 

the Yucca Mountain site. DOE constructed an empirical ionic strength threshold 

versus pH curve on the basis of existing experimental data in the open literature to account 

for uncertainties in the colloid stability. If the computed ionic strength for the in-package 

(or in-drift) environment is below the ionic strength threshold, then the colloids are stable in 

the corresponding environment. DOE constructed empirical cumulative (uncertainty) 

distributions for the mass concentration of colloids by bounding the distributions using 

experimental data. DOE addressed the uncertainty in the mass concentration of colloids by 

randomly sampling the mass concentration of stable colloids from the corresponding 

uncertainty distributions. The NRC staff notes that DOE provided sufficient experimental 

evidence from the literature and provided adequate technical justifications for the choice of 

colloid types and the uncertainty ranges for their mass concentrations in the engineered barrier 

system model abstraction. 

Excluded Colloid Processes 

In the engineered barrier system colloid model abstraction, colloidal filtration, thin-film 

straining (retardation of colloid transport when colloid dimensions exceed the water film 

thickness), gravitational settling of colloids, and sorption of colloids on stationary surfaces and 

onto an air–water interface were excluded due to associated uncertainties, and exclusion of 

these processes was considered to be conservative. The abstraction did not include 

biocolloids, because of low microbial activity and negligible mass concentrations of such colloids 

in comparison to groundwater colloids. 

NRC Staff’s Review 

The NRC staff notes that DOE reasonably excluded colloidal filtration, straining, 

gravitational settling of colloids, and sorption of colloids on stationary surfaces and onto an 

air–water interface, as detailed in SNL Section 5.8 (2008ak) and SNL Section 5.7 (2007aj). 

Exclusion of these processes results in higher modeled radionuclide releases, and 

hence exclusion of these processes is conservative. Moreover, DOE’s exclusion of 

biocolloidal (e.g., viruses, bacteria, spores, or other microorganisms) transport is reasonable 

because the low humic substance concentrations at the Yucca Mountain site will not support 

high biocolloid concentrations. 

Importance of Colloids to Risk Under Disruptive Events 

The NRC staff’s review of the importance of colloids to risk focused on two disruptive 

scenario modeling cases: igneous intrusion and seismic ground motion. DOE showed, 

through its analyses, that these two modeling cases contribute the most to the total mean 

annual dose for 10,000 and 1 million years after repository closure (SAR Figure 2.4-18). 

These modeling cases are therefore useful for examining the potential significance to risk 

of colloids. Pu-242 is the most important contributor to the overall total mean dose after 

200,000 years (SAR Figure 2.4-20). Under the nominal scenario, the maximum Pu-242 activity 

due to irreversible colloids is about 30 percent of the total Pu-242 activity leaving the engineered 

barrier system; under the seismic scenario, it is only about 18 percent of the total Pu-242 activity 

leaving the engineered barrier system. The maximum Pu-242 release rate leaving the 
7-32 

engineered barrier system due to irreversible colloids is 2.5 percent of the total release rate 

under the igneous intrusion modeling case, as described in SNL Section P18.3 (2008ag) and 

DOE Enclosure 5 (2009an). DOE concluded that, for plutonium in the engineered barrier 

system, colloid-facilitated radionuclide transport is less effective than dissolved phase 

radionuclide transport, as shown in SAR Figures 2.1-20 and 2.1-23 and SNL Table A–2, 

p. A–130 (2008ad). 

In DOE’s igneous intrusion modeling case, the drip shield and waste packages entirely fail; 

hence there is no distinction between seep and no-seep cases, and water chemistries and 

colloid stability remain nearly constant once the temperature drops below the boiling point 

and water flow to the waste form is established. For the igneous intrusion modeling case, 

smectite colloids (derived from high-level waste glass or from the tuff host rock) and oxidized 

uranium colloids (derived from spent nuclear fuel degradation) are stable in the engineered 

barrier system, but commercial spent nuclear fuel colloids are completely unstable in the 

corrosion products domain, according to DOE Enclosure 3 (2009ay). 

For the igneous intrusion case, unstable and settled commercial spent nuclear fuel colloids in 

the corrosion products domain can be as important as the stationary corrosion products for the 

retention of Pu-242, as described in DOE Figure 1.1-26 (2009dc), DOE Enclosure 1 (2009dd), 

and DOE Enclosure 3 Figure 1 (2009ay), due to a narrow range of pH, 7 to 8.4. The narrow pH 

range is illustrated in DOE Enclosure 3 (2009ay) and supported in DOE Enclosure 2 (2009da). 

The contribution of suspended iron oxide colloids to Pu-242 mass in the corrosion products 

domain is about eight orders of magnitude smaller than the Pu-242 mass removed from 

inventory by the settled unstable commercial spent nuclear fuel colloids at 200,000 years for the 

igneous intrusion modeling case, as shown by a representative realization in DOE Figure 1.1-26 

(2009dc). Therefore, DOE concluded that iron oxide colloids are insignificant for plutonium 

mobility when commercial spent nuclear fuel colloids sequester Pu-242. In contrast, DOE 

Enclosure 1 (2009da) shows that, in 62 percent of realizations in the igneous intrusion case, 

commercial spent nuclear fuel colloids are unstable in the waste form domain, but iron oxide 

colloids are stable in the corrosion products domain. Therefore, DOE concluded that 

commercial spent nuclear fuel colloids have a negligible effect on Pu-242 waste package 

mobility in these cases. For a representative realization for these conditions, Pu-242 mass 

irreversibly associated with iron oxide colloids is three orders of magnitude lower than the 

Pu-242 mass in the dissolved phase and about seven orders of magnitude lower than the 

sorbed Pu-242 mass on stationary corrosion products, as shown in DOE Enclosure 1 Figure 7 

(2009da). DOE stated that this observation indicates insignificant effects of iron oxide colloids 

on Pu-242 waste package releases. 

For the seismic ground motion case, DOE assessed that damage on waste packages is mainly 

due to patch failures by general corrosion. Unlike the igneous intrusion modeling case, colloid 

concentrations are sensitive to seep versus no-seep environments after corrosion patches 

develop on the waste packages, and the ionic strengths of water and pH vary in time. For the 

seismic ground motion case, the ionic strength of waters in the engineered barrier system 

depends on the relative humidity when the water flux is less than 0.1 L/yr [0.026 gal/yr] under 

the condition of complete filling of the drift with rubble; otherwise, ionic strength depends on the 

chemistry of the advective flux through corrosion patches. The ionic strengths of seep water 

also correlate with the rubble-filling status, as shown in DOE Enclosure 3, Figures 17, 18, 23, 

and 24 (2009ay). 

For the seismic ground motion modeling case, DOE noted that diffusive transport 

(under no-seep conditions) through the engineered barrier system and the water chemistry 
7-33 

(pH and ionic strength) could largely limit colloid-facilitated transport, as shown in Safety 

Analysis Report (SAR) Section 2.4.2.2.3.2.2 and DOE Enclosure 3 (2009ay). 

The Total System Performance Assessment (TSPA) model results indicate that for the 

seismic ground motion case, initially high ionic strength leads to unstable colloid suspensions. 

After the waste packages are breached in both seep and no-seep conditions, the ionic 

strength (which depends on the relative humidity during this stage) drops to a level where 

smectite and oxidized uranium colloids become stable in the codisposal packages and largely 

stable (in more than 95 percent of realizations) in the commercial spent nuclear fuel waste 

packages. These colloids are stable for the remainder of the simulation. As was the case for 

the igneous intrusion modeling case, commercial spent nuclear fuel colloids are unstable in the 

corrosion products and invert domains for the seismic ground motion modeling case, as shown 

in DOE Enclosure 3, Figures 6, 29, and 30 (2009ay). Stability of colloid suspensions, except for 

the iron oxide colloids, is similar for both the seep and no-seep cases. Iron oxide colloids are 

stable in the corrosion products domain only when seep water enters the waste package 

through corrosion patches at later times, whereas they are largely unstable (in more than 

95 percent of realizations) under the no-seep conditions, as shown in DOE Enclosure 3, Figures 

15 and 16 (2009ay). 

NRC Staff’s Review 

DOE reasonably concluded that iron oxide colloids are insignificant contributors to Pu-242 

releases from the corrosion products domain, because Pu-242 is either (i) largely sorbed onto 

stationary corrosion products, (ii) associated with settled and unstable commercial spent nuclear 

fuel colloids, or (iii) in the dissolved phase. 

The NRC staff reviewed the TSPA model results for the disruptive modeling cases (highly 

significant to risk) to evaluate processes and features that could limit availability and transport of 

colloid suspensions in the waste form, corrosion products, and invert domains of the engineered 

barrier system, as detailed in DOE Enclosure 3 (2009ay). The NRC staff notes that DOE 

appropriately identified, described, and quantified the distinct processes and features that 

control stable colloid concentrations in the engineered barrier system (e.g., considering barrier 

capability of engineered barrier system components, the impact of seep and no-seep 

environments) for both the igneous intrusion and the seismic ground motion modeling cases. 

In addition to the simpler, long-term constant geochemical conditions considered in the igneous 

modeling case, the seismic ground motion modeling case demonstrated the effects of temporal 

variability in the stability and mass concentrations of colloids under both seep and no-seep 

conditions, as a function of patch failure developments on waste packages and resulted 

changes in ionic strength, pH, and relative humidity in the engineered barrier system. 

The NRC staff conducted independent, simplified, confirmatory calculations on the 

effectiveness of iron oxide colloids in facilitating Pu-242 releases in the igneous intrusion 

modeling case. The igneous intrusion modeling case was chosen for independent calculations 

because (i) chemical conditions and colloid stability remain unchanged throughout the entire 

simulation after a relatively short cooling period (less than 1,000 years), (ii) this modeling case 

dominates the long-term total mean annual dose, and (iii) that dose is dominated by Pu-242 

after 200,000 years. Using information provided by DOE and used for Pu-242 in the TSPA 

model, the NRC staff’s confirmatory calculations show that the ratio of (i) the plutonium 

attachment rate to iron oxide colloids to (ii) the plutonium attachment rate to stationary corrosion 

products is 2 × 10-7 (Pickett, 2010aa). These calculations showed that plutonium attachment to 

stationary corrosion products is much faster than attachment to iron oxide colloids. 
7-34 

As discussed in Technical Evaluation Report (TER) Section 2.2.1.3.4.3.5, DOE showed in DOE 

Enclosure 8 (2009ay) that reversible, kinetic plutonium sorption onto stationary corrosion 

products can be approximated as an equilibrium process. Therefore, the rate of plutonium 

desorption from the stationary corrosion products is approximately equal to the rate of sorption. 

On the basis of this observation and the NRC staff’s confirmatory calculation summarized in the 

previous paragraph, the NRC staff notes that the rate of irreversible plutonium sorption to iron 

oxide colloids is many orders of magnitude slower than the rate of plutonium desorption from 

stationary corrosion products to solution. Therefore, any transfer of dissolved plutonium to iron 

oxide colloids would be instantaneously compensated by desorption from the stationary 

corrosion products—which contain the majority of plutonium mass in the corrosion products 

domain—to maintain the quasi-equilibrium relationship. On the basis of its calculation, the NRC 

staff notes that irreversible sorption of plutonium to iron oxide colloids cannot substantially 

deplete dissolved plutonium. Sorption to stationary corrosion products is more important to 

plutonium release from the corrosion products domain than are iron oxide colloids. This result is 

consistent with DOE’s conclusion that iron oxide colloids are not significant for Pu-242 releases 

from the engineered barrier system, as shown for representative realizations in DOE Figures 

1.1-24 and 1.1-26 (2009dc) and DOE Enclosure 1, Figures 5 and 7 (2009da). 

For codisposal packages in disruptive scenarios, the NRC staff compared the plutonium release 

effectiveness of high-level waste glass colloids against dissolved plutonium. Results of a 

representative igneous intrusion modeling case realization in DOE Enclosure 1, Figures 1.1-29 

and 1.1-30 (2009dc) showed dissolved Pu-242 being released from the engineered barrier 

system more than 10 times faster than Pu-242 associated with high-level waste glass colloids. 

In addition, the mean plutonium solubility limit in the corrosion products domain in the TSPA 

model, which the NRC staff estimated as 10-5 g/L [0.01 ppm] from DOE data presented in SNL 

Table 6.5-1 (2007ah), is about 10 times higher than the mean concentration of plutonium 

associated with high-level waste glass colloids, which the NRC staff calculated as 1.2 × 10-6 g/l 

[0.0012 ppm] from data presented in SNL Table 6-4 (2008ak). The NRC staff notes, on the 

basis of these confirmatory calculations, that high-level waste glass colloids are, in general, less 

effective than the dissolved phase in effecting plutonium release from codisposal packages. 

The NRC staff considered (i) the insignificance of iron oxide colloids for Pu-242 releases 

from the corrosion products domain; (ii) the relatively shorter residence times for colloids 

and radionuclides in the waste form domain than in the corrosion products domain; and 

(iii) the presence of nearly 60–70 percent less steel corrosion product mass in the waste 

form domain than in the corrosion products domain, as described in DOE Enclosure 2 

(2009ay), and notes that excluding iron oxide colloids from the waste form domain will not be 

significant to risk. 

Reduction in colloid mass concentration under no-seep environments (which leads to diffusiondominant colloid migration) is reasonable, because the size of the colloids is a few orders of 

magnitude larger than the ions of aqueous radioelements, and the colloids therefore will 

encounter higher diffusional resistance. Colloids diffuse much more slowly than dissolved ionic 

species, such that colloid-associated release under no-seep conditions is negligible. 

In summary, for the reasons identified previously, DOE’s analysis of colloids under disruptive 

igneous and seismic events is reasonable. 
7-35 

Data Support and Uncertainty Propagation for Colloid Transport Abstraction for the 

Engineered Barrier System 

Mass Concentration of Colloids 

DOE used experimental and scientific literature data to support its colloid mass concentrations 

model. Uncertainties associated with the mass concentrations and stability of glass waste form 

colloids in the TSPA model rely on results from drip and immersion tests for degradation of 

alkali borosilicate glasses, as detailed in SNL Section 6.3.2.2 (2008ak). Experimental data were 

used to bound plutonium mass concentrations associated with zirconium oxide colloids formed 

from commercial spent nuclear fuel, as described in SNL Section 6.3.2.4 (2008ak). 

Uranophane colloids are used as representative colloids for oxidized uranium colloids formed 

from defense and commercial spent nuclear fuel. 

SNL Section 6.3.2.6 (2008ak) described an empirical cumulative distribution for the mass 

concentrations of groundwater colloids that was adopted for uranophane colloid suspensions, 

because both colloid suspensions display a similar stability profile. DOE relied on bench-scale 

experiments using a carbon-steel miniature waste package in bathtub and flow-through 

configurations. These experiments used water chemically similar to well water near Yucca 

Mountain to induce corrosion and subsequently to determine the geometric mean concentration 

of iron oxide colloids. Empirical cumulative distributions of colloid mass concentrations were 

constructed, on the basis of laboratory-scale experimental data, to address uncertainties in 

colloid mass concentrations, as detailed in SNL Section 6.3 (2008ak). DOE adopted colloid 

concentrations in groundwater at the Yucca Mountain site for colloid concentrations in seepage 

water entering breached waste packages. DOE collected colloid data from nine different 

sources and fitted them to a cumulative mass distribution to address uncertainties. 

NRC Staff’s Review 

With regard to DOE’s colloidal mass concentration model, the NRC staff verified that DOE relied 

on data from laboratory experiments published in peer-reviewed technical journals to determine 

the range for mass concentrations of reversible and irreversible colloids. The NRC staff 

reviewed (i) how DOE addressed uncertainties associated with how closely geochemical and 

hydrogeological conditions were represented in these experiments and (ii) how DOE upscaled 

laboratory results to the field scale at the Yucca Mountain site. DOE acknowledged 

uncertainties associated with the mass concentrations of colloid suspensions due to, among 

other things, measurements, experimental factors, and upscaling of experimental data and 

observations to repository scale and conditions (SAR Section 2.3.7.11.2). To be conservative, 

DOE set the upper bound for mass concentrations of iron oxide colloids to be larger than natural 

iron oxide colloid concentrations in groundwater (SAR Section 2.3.7.11.2). The NRC staff notes 

that, in the absence of field data, the use of laboratory data is reasonable, because DOE 

incorporated associated uncertainties (through sampling from uncertainty distributions) in 

determining the mass concentration of waste form and iron oxide colloids. For mass 

concentrations of groundwater colloids, DOE appropriately employed existing field data. DOE 

kept the pH and ionic strength range, which was based on experimental and/or literature data, 

wide enough in colloid stability analyses to cover expected stability conditions at the Yucca 

Mountain site. 

The NRC staff’s review verified that DOE addressed uncertainties in mass concentrations by 

sampling them from empirically constructed cumulative mass distributions obtained from 

experimental data. DOE (i) adequately propagated data uncertainty by constructing empirical 
7-36 

mass concentration distribution functions for each colloid type using relevant experimental data 

and sampling the mass concentration for a particular colloid suspension type and (ii) adequately 

addressed model integration and information exchange between the engineered barrier system 

abstraction and other abstractions. 

In-Package and In-Drift Stability of Colloids 

In Total System Performance Assessment (TSPA) code calculations, in-package and 

in-drift stability of colloid suspensions is determined by the ionic strength of the 

seepage water and pH. DOE constructed an empirical ionic strength threshold versus pH 

curve using experimental data specific to each colloid suspension type and using the 

Derjaguin-Landau-Verwey-Overbeek theory. In-package and in-drift pH and ionic strengths 

and dissolved radionuclide concentrations are computed outside the engineered barrier system 

abstraction and then fed into the empirical ionic strength threshold versus pH curve in the 

engineered barrier system abstraction, as outlined in SNL Section 6.5 (2008ak). For stability 

calculations, DOE modeled glass waste form colloids and groundwater colloids as smectite, 

plutonium-rich zirconium oxide colloids as zirconium oxide, oxidized uranium oxide colloids as 

uranophane, and iron oxide colloids as hematite, as described in SNL Section 6.3.1 (2008ak). 

If the colloid suspensions are stable, their mass concentrations are sampled from empirical 

distribution functions specific to the colloid type (constructed from experimental data). If a 

colloid type is unstable, the mass concentration is set to a low nonzero value, selected such that 

the colloid mass is too low to have any impact on radionuclide release and transport. In the 

case of groundwater colloids in the waste package, the initial concentration is set to 0 mg/L until 

flow begins in the waste package (SNL, 2008ak). 

NRC Staff’s Review 

The NRC staff’s review of the in-package and in-drift colloidal stability verified that DOE 

constructed empirical relations (on the basis of experimental data in the literature) for each 

colloid suspension type that related the ionic strength threshold to pH to determine stability of 

colloidal suspension in the waste package and in the drift. These empirical relations were 

constructed on the basis of the Derjaguin-Landau-Verwey-Overbeek model. DOE 

acknowledged that these empirical relations were purely mathematical fits and had no physical 

meaning, as stated in SNL Section 6.3.2.4 (2008ak), although they are driven by experimental 

data. The engineered barrier system model abstraction computes the ionic strength of the 

in-package fluid (or in-drift seepage fluid) and compares it against the ionic strength threshold 

read from the ionic strength threshold versus pH curve. If the in-drift (or in-package) fluid ionic 

strength exceeds the ionic strength threshold, then the corresponding colloidal suspensions 

become unstable in the abstraction, as detailed in SNL Section 6.3.2 (2008ak). The stability 

calculations for colloid suspensions are reasonable, because (i) the Derjaguin-Landau-VerweyOverbeek theory has been commonly used in the literature to determine stability of relatively 

dilute colloid suspensions [DOE provided references to support its common use in SNL Section 

6.3.2 (2008ak)], (ii) the empirical relations for ionic strength versus pH were constructed on the 

basis of experimental data, and (iii) DOE addressed temporal variations in in-package and indrift water chemistry in colloid stability calculations. 

Radionuclide Mass Sorption on Colloid Suspension 

DOE referred to previously published data to determine surface area for reversible glass 

waste and groundwater colloids, uranophane colloids, and iron oxide colloids in SNL 
7-37 

Sections 6.3.2.3.1, 6.3.2.7, and 6.3.12.2 (2008ak), respectively. This information is used in 

calculating sorbed radionuclide mass on colloid suspensions. 

NRC Staff’s Review 

The NRC staff’s review verified that DOE determined the range of specific surface area for 

reversible colloid suspension based on open literature experimental data and sampled from this 

range to address data uncertainties. On the basis of the NRC staff’s confirmatory review of the 

literature data cited in SNL (2008ak) and DOE’s use of sampled specific surface area to account 

for uncertainties, the NRC staff notes that the uncertainty distributions and literature data DOE 

provided for specific surface area are reasonable. 

Kinetic Attachment Rates for Plutonium and Americium Onto Iron Oxide Colloids 

In the engineered barrier system abstraction, plutonium and americium are modeled to be 

irreversibly attached onto iron oxide colloids. As described in SNL Section 6.3.12.2 (2008ak), 

DOE constructed an uncertainty distribution function for the attachment rate constant for iron 

oxide colloids on the basis of data from sorption experiments. DOE noted that the attachment 

rate is sampled from an experimentally supported lognormal uncertainty distribution under a 

no-seep condition or a condition where colloid suspensions are unstable in the corrosion 

products domain. Otherwise, the maximum of the sampled rate constant from a lognormal 

uncertainty distribution and the computed rate constant using the target flux out ratio are used, 

as detailed in SNL Section 6.5.2.4.6 (2007aj). 

NRC Staff’s Review 

The NRC staff reviewed the basis of DOE’s description for the attachment rate calculations 

and notes that DOE used attachment rates sampled from an experimentally supported 

uncertainty distribution for modeling irreversible attachments of plutonium and americium 

onto iron oxide colloids. The model favors attachment onto iron oxide colloids by 

implementing the target flux out ratio if the computed attachment rate remains within the 

experimentally determined range for the attachment rate; otherwise, the sampled attachment 

rate is used and the target flux out ratio is not implemented. This modeling feature is 

conservative, because it would allow more radionuclides to be transported from the corrosion 

products domain to the invert by stable iron oxide colloids (TER Section 2.2.1.3.4.5). The NRC 

staff further notes that because iron oxide colloids do not play a significant role in dose 

calculations (as elaborated in the previous section), the method chosen for irreversible 

attachment rate calculations is not important to dose calculations. 

Alternative Conceptual Model Consideration 

DOE considered two alternative conceptual models with respect to colloids: the first uses 

different mechanisms to generate glass colloids and the second focuses on air–water limitations 

on the releases of particles from weathered waste form surfaces under unsaturated conditions, 

as identified in SAR Section 2.3.7.11.3.2 and SNL Section 6.4 (2008ak). DOE did not 

implement these alternative models in the TSPA. 

NRC Staff’s Review 

The NRC staff’s review verified that DOE used a conceptual model in the TSPA for irreversible 

and reversible colloidal transport on the basis of a set of mass-balance equations. The NRC 
7-38 

staff notes that a mathematical framework for this conceptual model is consistent with colloid 

transport models in the literature (e.g., Corapcioglu and Jiang, 1993aa; van de Weerd, et al., 

1998aa). DOE does not have sufficient experimental data to fully evaluate the aforementioned 

two alternative conceptual models. It is reasonable that the two alternative conceptual models 

were excluded from TSPA, because the technical basis for including and quantifying the models 

is insufficient for use in a performance assessment calculation. 

DOE considered the bathtub flow model as an alternative conceptual model to the flow-through 

model, which is implemented in TSPA, in simulating water flow and radionuclide transport 

in a breached waste package. As discussed in Technical Evaluation Report (TER) 

Sections 2.2.1.3.3.3.3 and 2.2.1.3.4.3.5, the flow-through model is bounding to the bathtub 

model for flow and radionuclide transport simulations. DOE did not discuss potential 

implications of the bathtub flow model on the colloid transport in the engineered barrier system. 

This was reasonable, because if the commercial spent nuclear fuel colloids are unstable in the 

corrosion products domain during pulse periods, large fractions of commercial spent nuclear 

fuel colloids irreversibly associated with Pu-242 would be removed from the inventory, and 

hence, the implementation of bathtub model in TSPA would underestimate Pu-242 releases 

from the engineered barrier system. 

Summary of NRC Staff’s Review of Availability and Effectiveness of Colloids

DOE adequately described the engineered barrier system colloid transport model abstraction 

and its integration with other abstractions used in the TSPA code. The mathematical framework 

and the underlying conceptualization for the reversible and irreversible colloids and colloid 

stability analyses are consistent with models in the scientific literature. The absence of other 

conceptualizations used in the TSPA code is reasonable, given that sufficient data were not 

available to support alternative models. The abstraction appropriately propagates uncertainties 

through laboratory-data- or field-data-based cumulative distributions for mass concentrations, 

stability, and transport parameters. 

DOE used disruptive modeling cases that are most significant to risk (igneous intrusion and 

seismic ground motion modeling cases) to show the ineffectiveness of colloid-assisted 

radionuclide releases from the engineered barrier system in comparison to radionuclide 

releases in a dissolved phase. On the basis of the NRC staff’s evaluation of the TSPA 

code results and the NRC staff’s independent confirmatory analyses, the NRC staff notes that 

the limitation on stable colloid masses is the main reason for the insignificance of 

colloid-assisted radionuclide transport in both seep and no-seep environments. Moreover, 

diffusive release by colloids is limited due to size effects, making colloid transport further 

ineffective under no-seep environments. The NRC staff notes that dissolved radionuclides will 

be more significant than colloid-associated radionuclides to radionuclide release and transport 

and, therefore, to dose. 

2.2.1.3.4.3.5 Engineered Barrier System Radionuclide Transport 

This section details the NRC staff’s review of DOE’s abstraction and Total System 

Performance Assessment (TSPA) implementation for radionuclide transport in the 

engineered barrier system, as described in Safety Analysis Report (SAR) Section 2.3.7.12 

and references cited therein (particularly SNL, 2007aj). The abstraction estimates the 

rate of movement of radionuclides from degraded waste forms to the unsaturated zone 

and provides radionuclide fluxes (rates of mass transfer) versus time to the unsaturated 

zone transport abstraction (TER Section 2.2.1.3.7). Major inputs to the abstraction include 
7-39 

the flow conditions inside the engineered barrier system (TER Section 2.2.1.3.3.3.3), the 

chemical conditions inside the engineered barrier system (TER Section 2.2.1.3.4.3.1), 

waste form degradation rates (TER Section 2.2.1.3.4.3.2), dissolved concentration limits 

(TER Section 2.2.1.3.4.3.3), and colloid parameters (TER Section 2.2.1.3.4.3.4). 

DOE’s abstraction for radionuclide transport in the engineered barrier system is highly 

significant to risk because large masses of plutonium and other dose-significant actinides are 

retained in the engineered barrier system in DOE’s TSPA calculations. For example, in DOE 

(2009dc), DOE provided results for a representative realization of the igneous intrusion 

modeling case showing that approximately 8,000 kg [17,600 lb] of Pu-242 is permanently 

immobilized in the engineered barrier system for one percolation subregion. In the same 

realization and subregion, approximately 30,000 kg [66,000 lb] of Np-237 is retained on the 

waste package corrosion products at 100,000 years; Np-237 is released from the engineered 

barrier system slowly enough that more than 1,000 kg [2,200 lb] remained at 1 million years. 

On the basis of the importance to the abstraction, the NRC staff’s review focused on model 

framework and process conceptualization within the TSPA code implementation, representation 

of diffusion, sorption on stationary corrosion products, colloid-facilitated transport, and 

reasonableness and consistency of TSPA code results. The abstraction contains several 

included features, events, and processes. Excluded features, events, and processes are 

discussed in TER Section 2.2.1.2.1.3.2. 

Model Framework and Process Conceptualization 

Overall Conceptualization 

DOE based the abstraction and TSPA implementation on one-dimensional mass transport 

through three computational domains (i) waste form domain, (ii) corrosion products domain, and 

(iii) invert domain. The waste form domain contains a single computational cell representing a 

porous rind of degraded waste form for the commercial spent nuclear fuel packages. The waste 

form domain for codisposal packages comprises a computational cell representing high-level 

waste glass upstream of a cell representing DOE spent nuclear fuel. Corrosion products formed 

from the degradation of steel waste packages and package internals are represented in the 

corrosion products domain. The invert domain is assumed to be in close contact to the waste 

package and composed of crushed tuff material. A fourth domain, the invert-unsaturated zone 

interface, facilitates transfer of the radionuclide mass from the engineered barrier system 

transport model to the unsaturated zone transport model. 

DOE conceptualized the transport pathway as a flow-through model in which water flows 

vertically through a degraded waste package. DOE considered an alternative conceptual model 

in which the outlet for water is not on the underside of the waste package. In this bathtub 

model, water would fill the partially intact waste package until it reaches a spill point 

corresponding to a breach on the side of the waste package. In a variant of the bathtub model, 

the stored water and dissolved radionuclides would be suddenly released when a second 

breach develops on the underside of the waste package. 

NRC Staff’s Review 

The major structure of the transport abstraction, which is based on a one-dimensional spatial 

discretization, reasonably represents the transport pathway in the engineered barrier system 

with sufficient flexibility to represent the range of expected conditions and processes. 
7-40 

DOE has shown that the flow-through model provides an upper bound on radionuclide transport 

in the absence of a sudden release based on considerations of water residence times, as 

discussed in SNL Section 6.6.1.2.3 (2007aj). A sudden release of water stored in the waste 

package in the bathtub scenario could create a short-duration, high-intensity pulse in 

radionuclide release from the engineered barrier system. However, several mechanisms 

mitigate the effects of such a pulse by dispersing it in time. The pulse from a single package 

would be dispersed by physical processes such as sorption and dispersion in the engineered 

barrier system and the lower geological barrier. Radionuclide concentrations in the pulsed 

water would, therefore, become lower as the water moves from the engineered barrier system 

and through the natural barriers. Moreover, the pulses from individual realizations would be 

distributed in time, and the effect of any one pulse on the mean dose would be greatly reduced 

because the combined, averaged effects of time-distributed pulses would be similar to the effect 

of continuous flow-through releases. On the basis of its review of this information, DOE’s 

choice of the flow-through conceptualization over the bathtub model is reasonable. 

Transport Model Framework 

In DOE’s abstraction, dissolved radionuclides and radionuclides sorbed onto the five types of 

mobile colloids described in TER Section 2.2.1.3.4.3.4 are transported by diffusion and, if water 

is flowing in the engineered barrier system, advection. Advective velocities for colloids are 

identical to the water velocity. Advective transport of selected dissolved radionuclides is slowed 

by sorption onto stationary corrosion products. Solubility limits on the dissolved radionuclide 

concentrations are also imposed. 

NRC Staff’s Review 

On the basis of understanding developed during previous interactions (e.g., MacKinnon, 

2008aa) and the NRC staff’s experience with modeling similar systems, the NRC staff considers 

the represented diffusive and advective processes to be the dominant processes for transport. 

The NRC staff is unaware of other processes that might be expected to significantly contribute 

to transport. Thus, the processes and couplings represented in the abstraction are reasonably 

complete and do not underestimate the release of radionuclides. 

Transport Under Disruptive Events 

DOE’s TSPA implementation of engineered barrier system transport is similar for the disruptive 

and nominal modeling cases, although conditions within the engineered barrier system (input for 

the abstraction) may be different following disruptive events. Most importantly, DOE assumed 

instant degradation of the waste forms and advective conditions within the engineered barrier 

system following an igneous intrusion event. 

NRC Staff’s Review 

DOE’s model reasonably assumed that under an igneous intrusion event, the waste forms are 

instantaneously degraded and are transported under advective conditions. These assumptions 

for igneous intrusion events provide an upper bound on engineered barrier system transport. 
7-41 

Model Abstraction and TSPA Model Results 

In response to the NRC staff’s questions regarding consistency between the model abstraction 

and the TSPA code calculated results, DOE provided additional information on the mass 

retained in and flux out of each computational domain for key representative radionuclides using 

single representative realizations of the igneous intrusion and nominal modeling cases (DOE, 

2009da,dc). The NRC staff used the results for commercial spent nuclear fuel and the igneous 

intrusion modeling case to evaluate consistency between the results of the TSPA analyses and 

the conceptual process models DOE described. For I-129, 99.96 percent of the initial inventory 

is transported out of the engineered barrier system in the first TSPA timestep following the 

intrusion event. Release of Np-237 is significantly delayed but not eliminated by precipitation 

and sorption onto stationary corrosion products. For example, about 14 percent of the initial 

Np-237 inventory is released from the engineered barrier system in the first 40,500 years 

following the igneous intrusion event; after 1 million years the released fraction is 88 percent of 

the initial inventory (including ingrowth). 

For Pu-242, DOE provided information for two realizations: one with stable waste form colloids 

in the waste form domain, described in DOE (2009dc), and one with unstable waste form 

colloids in the waste form domain, described in DOE Enclosure 1 (2009da). For the realization 

with stable waste form colloids in the waste form domain, which according to DOE Enclosure 1 

(2009dd) is representative of about 38 percent of realizations, about 11 percent of the initial 

inventory is released from the engineered barrier system in the first 204,000 years. Nearly all of 

the remaining Pu-242 mass is retained in the corrosion products domain irreversibly associated 

with permanently immobilized (settled) waste form colloids in this realization. In DOE’s 

realization results with unstable waste form colloids in the waste form domain, which is 

representative of 62 percent of realizations, precipitation of plutonium-bearing minerals and 

sorption onto stationary corrosion products delay release but do not permanently sequester 

Pu-242, similar to Np-237. 

NRC Staff’s Review 

The NRC staff notes that rapid transport of I-129 following the igneous intrusion event is 

consistent with the conceptual process model for highly soluble, nonsorbing species, for which 

no significant engineered barrier system retention processes are represented. The NRC staff 

notes that the analyzed TSPA abstraction results for Np-237 release are consistent with the 

conceptual process models because dissolved neptunium concentrations are limited by 

precipitation of neptunium-bearing minerals and by strong sorption onto stationary corrosion 

products in this realization. The NRC staff notes that the mechanism by which Pu-242 is 

retained in the corrosion products domain is consistent with the process conceptualization 

when waste form colloids are calculated to be stable in the waste form domain and unstable 

in the corrosion products domain. Pu-242 behavior, when waste form colloids are unstable in 

the waste form domain, is also consistent with the process conceptualization. 

Summary of Diffusion Models 

The various analytical models used to simulate diffusive transport in the Total System 

Performance Assessment (TSPA) computer code are summarized next. 

DOE calculated diffusion coefficients for dissolved radionuclides as the product of tortuosity 

(the effect of flow path shape in a porous medium) and species-dependent free-water diffusion 

coefficients. The diffusion coefficients were adjusted for temperature. The tortuosity was 
7-42 

empirically related to porosity and liquid saturation using standard models. DOE based diffusion 

coefficients for colloids on the Stokes-Einstein equation, which accounts for temperature and 

particle size. The mathematical representation of diffusion and the approach for relating 

diffusion coefficients to porosity, water content, and temperature are reasonable, in that they are 

based on established models in the peer-reviewed scientific literature and are applied to 

conditions within their valid range. 

For the no-dripping situation, DOE calculated liquid water content from relative humidity using 

empirical adsorption isotherms. This information is needed to establish diffusion coefficients, 

which are dependent on water content. DOE developed a diffusion model for these conditions 

and conducted a literature review on data relevant to predicting water content on the basis of 

relative humidity, as described in SNL Section 6.3.4.3 (2007aj). DOE compared the output of 

the abstraction for adsorbed water content versus relative humidity with literature data for 

adsorption on goethite, hematite, Cr2O3, and NiO. DOE showed that, within uncertainty bounds, 

the output of the abstraction is consistent with the experimental data for relative humidity of 

about 0.42 and greater, which is the range of relative humidity in which diffusion may be 

significant, as outlined in SNL p. 7-22 (2007aj). DOE also compared the model output to the 

results of independent modeling studies. The NRC staff reviewed DOE’s data, model 

development, and model corroboration and notes that the water adsorption isotherm model for 

diffusion under no-dripping conditions is reasonable because it was derived using applicable 

literature data and was corroborated. 

The mass of steel corrosion products is needed to establish the liquid water content, which DOE 

calculated as a function of time from the degradation of steel internals of the waste package. 

The NRC staff compared the corrosion rates for stainless and carbon steel used in this 

abstraction with literature values and additional information DOE provided in DOE Enclosure 2 

(2009db) and noted that the uncertainty distributions are reasonable. Stainless steel corrosion 

rates are appropriate for representing diffusion because a possible overestimation of the 

corrosion rate at the lower end of the range would be conservative for simulating diffusion. The 

upper end of the corrosion rate is consistent with the data and would not result in an 

underestimate of liquid water content (and, therefore, diffusion coefficient). The NRC staff also 

reviewed DOE’s use of design information in establishing the corrosion product mass and notes 

that the appropriate information on waste package design was used. 

For the dripping situation, DOE assumed the porous materials were saturated with liquid water. 

This assumption is reasonable because it provides an upper bound with respect to diffusive 

transport; in general, contaminants will diffuse faster in a fully saturated medium than in a 

partially saturated medium. 

DOE used data on diffusion in crushed tuff material to develop uncertainty distributions for 

invert diffusion coefficients. Uncertainty in the diffusion coefficients and invert porosity are 

explicitly represented. The NRC staff notes that the supporting experiments are representative 

of the expected conditions in the invert and that DOE’s uncertainty distributions are thus 

appropriately developed. 

DOE compared the output of the invert diffusion coefficient model with two sets of experimental 

results. DOE’s abstraction predicts larger diffusion coefficients than the experimentally 

determined values. The abstraction is reasonable because it produces an upper bound of the 

diffusion coefficient and the diffusive transport of radionuclides in the invert. 
7-43 

DOE considered two alternative conceptual models related to diffusion in the invert. One of 

these alternative conceptual models is based on a dual-continuum representation of diffusion. 

DOE’s selection of the single-continuum model over the dual-continuum model is reasonable 

because little intergranular moisture is expected in the invert except in the dripping situation, in 

which case diffusion is expected to be minor compared with advection. (Little moisture would 

imply negligible intergranular diffusion, and the adopted single-continuum model provides an 

upper bound on diffusive transport.) DOE’s other alternative conceptual model considers 

alternative relationships between diffusion coefficients and moisture content at low moisture 

content. The NRC staff notes that, given significant gaps in the data that would support this 

alternative conceptual model, DOE reasonably did not adopt this model and adopted a 

relationship that overestimates diffusion coefficients at low water content and is consistent with 

available data. 

In DOE’s TSPA model, radionuclide mass enters the unsaturated zone after leaving the invert. 

Because the mass flux out of the invert is partly the result of diffusion, radionuclide 

concentrations in the unsaturated zone are needed to obtain estimates of the mass flux. DOE 

modeled a portion of the unsaturated zone in the engineered barrier system/unsaturated zone 

interface to calculate the diffusive fluxes into the unsaturated zone transport model. The 

engineered barrier system/unsaturated zone interface is a network of computational cells 

representing a local region of the unsaturated zone just below a drift. Hydrological 

conditions in the engineered barrier system/unsaturated zone interface are established 

similarly to the unsaturated zone transport model (TER Section 2.2.1.3.7). A zero concentration 

boundary condition is used at the lower boundary of the interface zone. Because of this 

assumption, DOE’s use of an engineered barrier system/unsaturated zone interface zone to 

couple the engineered barrier system and unsaturated zone abstractions produces an upper 

bound on diffusive transport into the unsaturated zone, and therefore use of the interface zone 

is reasonable.

Sorption on Corrosion Products 

In DOE’s abstraction, radionuclides enter the corrosion products domain from the 

waste form domain both in solution and associated with montmorillonite, plutonium, 

zirconium, and uranophane colloids. The transport of selected radionuclides in the 

abstraction is retarded by sorption onto stationary corrosion products (SAR Section 2.3.7.12). 

DOE treated the corrosion products domain as a single mixing cell containing a homogenous 

porous medium with no preferential flow paths, on the basis of a conceptualization of 

degraded waste form disseminated within a corrosion product mass. The NRC staff notes 

that DOE’s conceptualization and TSPA implementation as a mixing cell provide reasonable 

representations of radionuclide release because a disseminated radionuclide source 

supports the underlying assumption of uniform radionuclide concentrations within the 

represented volume.

In DOE’s abstraction, corrosion product surface area is used to calculate the volume 

of adsorbed water and the mass of radionuclides sorbed onto corrosion products. 

DOE assumed a mixture of hydrous ferric oxide and goethite for calculating corrosion 

product surface area without considering the aging of hydrous ferric oxide to more crystalline 

iron oxides with lower surface area. DOE summarized additional sensitivity information in DOE 

Enclosure 6 (2009ay) showing that the result of the abstraction is not significantly affected by 

the assumed relative abundance of hydrous ferric oxide. NRC staff reviewed the sensitivity 

analysis and notes that the abstraction appropriately represents uncertainty in the corrosion 

product surface area. 
7-44 

The abstraction calculates the corrosion product surface area as a function of time using an 

uncertainty distribution of stainless steel corrosion rates based on literature data. On the basis 

of additional information DOE provided in BSC (2004ae) and DOE Enclosure 2 (2009db) and an 

independent literature review, the range of stainless steel corrosion rates that were used to 

simulate growth of the corrosion products that provide the sorption substrate is reasonable 

because it is consistent with literature corrosion rate data (Beavers and Durr, 1990aa; Glass, et 

al., 1984aa; McCright, et al., 1987aa). 

DOE modeled sorption on corrosion products for uranium, neptunium, thorium, americium, and 

plutonium using a surface complexation model to develop effective distribution coefficients (Kds) 

taking into account the chemical conditions. A surface complexation model simulates 

equilibrium attachment of dissolved ions onto solid surfaces and incorporates the chemical 

complexity of the system. Other radioelements that are tracked in the unsaturated zone 

transport abstraction (TER Section 2.2.1.3.7)—such as cesium, protactinium, radium, 

selenium, strontium, tin, technetium, iodine, chlorine, and carbon—are assumed in DOE’s 

engineered barrier system transport abstraction to not sorb onto stationary corrosion products. 

Nickel is included in the surface complexation model to represent competition for sorption sites, 

but the sorbed mass of nickel is not explicitly tracked for transport purposes. 

The surface complexation model is not directly incorporated in DOE’s TSPA model abstraction, 

but the distribution coefficients developed from the surface complexation model are directly 

applicable to the transport of uranium, neptunium, and thorium, which undergo rapid and 

reversible sorption (SNL, 2007aj). Kinetic reversible sorption is modeled for americium and 

plutonium sorption on stationary corrosion products. The forward sorption rate constant in this 

case is sampled from an uncertainty distribution. The desorption rate constants are then 

calculated from this forward rate and the Kds calculated using the surface complexation model. 

To develop the Kd distributions used in the TSPA model abstraction, DOE carried out the 

surface complexation models using the PHREEQC geochemical software the U.S. Geological 

Survey developed. To represent parameter uncertainty in the surface complexation model, 

DOE conducted about 5,000 PHREEQC simulations, each with a unique combination of surface 

properties and aqueous chemistry parameters as inputs. DOE then analyzed the PHREEQC 

simulation results using multiple regressions to produce functions that calculated actinide 

sorption as a function of key geochemical properties. These functions provide the Kd values 

used in the TSPA model abstraction of sorption to stationary corrosion products. DOE’s 

approach is consistent with the NRC staff’s independent evaluations of radionuclide sorption 

(e.g., Leslie, et al., 2007aa, and references therein). The NRC staff notes that DOE 

considered uncertainty for appropriate key geochemical properties, such as pH and pCO2, and 

surface properties related to sorption site concentration such as site density, surface area, and 

solid mass. 

DOE compared the calculated surface complexation model results from almost 

5,000 simulations to recent U.S. Environmental Protection Agency (EPA) compilations of soil 

Kds obtained in the laboratory (SAR Section 2.3.7.12.3.4; SNL, 2007aj). DOE compared values 

over a pH range from 6 to 9 (SAR Section 2.3.7.12.3.4), and although the ranges are broad, 

there was general agreement between DOE’s estimated values and the EPA compilation over 

the pH range considered. DOE provided reasons for potential discrepancies at pH <7, but the 

NRC staff observed that there was a consistent bias of calculated Kd values to the high end of 

the EPA ranges at pH >8. For example, for moderately sorbing radioelements such as 

neptunium and uranium, the calculated Kd values for 8 < pH < 9 were about one to three orders 
7-45 

of magnitude above the lower limit for the EPA compilation at similar pH. In DOE Enclosure 9 

(2009ay), DOE provided additional experimental data that show actinides sorb more strongly 

onto hematite (an iron oxide) than onto clays and silicate minerals that tend to be more common 

in the soils assessed in the EPA compilation. In addition, analyses in DOE Enclosure 3 

(2009da) show a narrower range with a lower maximum Kd value is generated in the TSPA 

model for all five radionuclides simulated using the surface complexation model. In effect, this 

TSPA approach reduces the maximum mass of actinides retained by sorption to stationary 

corrosion products from what might be expected from the surface complexation model results 

alone, increasing radionuclide concentration in the water and, all else being equal, increasing 

dose. Although there are differences, DOE’s sorption model results are of the same order of 

magnitude with respect to actinide-iron oxyhydroxide sorption coefficients reported in the 

scientific literature. The NRC staff notes that this consistency supports the reasonableness of 

the Kd values used in DOE’s TSPA model abstraction.

DOE used several different approaches in developing its final TSPA abstraction for sorption to 

stationary corrosion products and considered several different surface complexation model 

approaches before selecting the diffuse-layer model, as outlined in DOE Enclosure 3 (2009da). 

This modeling approach is widely used in the technical community, and DOE discussed its 

advantages and disadvantages relative to other surface complexation models. The NRC staff 

notes that the surface complexation model as DOE implemented it is consistent with established 

geochemical modeling principles, uses available experimental data to constrain chemical 

parameters, and is thus reasonable. 

In SNL Section 6.5.2.4.2 (2007aj), DOE stated that aqueous thermodynamic data were 

propagated through the surface complexation model, but did not explicitly consider uncertainty 

in equilibrium constants for surface complexation constants used in the surface complexation 

model. The exclusion of this source of uncertainty is reasonable because the effects of these 

uncertainties on calculated radionuclide sorption would be small given the large excess number 

of available sorption sites as compared to aqueous radionuclide concentrations in all 

realizations. The large number of sorption sites in the surface complexation model for the 

corrosion products domain leads to the calculation of very high actinide Kd values, such that any 

additional uncertainty from surface complex thermodynamic data would have an insignificant 

effect on the transport model results. As described previously, DOE provided a reasonable 

evaluation of the uncertainties associated with parameters that control the number of sorption 

sites, such as surface area, mass of corrosion products, and site density. 

DOE’s abstraction assumes a single-rate, first-order kinetic model for plutonium and americium 

sorption. The forward rate constants for sorption of americium and plutonium onto corrosion 

products were estimated from plutonium sorption experiments in SNL (2008ak). Although 

experiments on plutonium sorption onto iron oxide colloids have been shown to be inconsistent 

with a single-rate model (SNL, 2007aj; Painter, et al., 2002aa), DOE provided additional 

information in DOE Enclosure 8 (2009ay) showing that sorption in the corrosion products 

domain is approximately an equilibrium process because water residence times are long 

compared with characteristic times for sorption. The single-rate kinetic sorption model is 

reasonable because the near-equilibrium condition for sorption makes details of the kinetic 

model irrelevant. 

Colloid-Facilitated Transport 

In DOE’s abstraction, the transport of selected radionuclides may be enhanced by the five 

colloid types described in Technical Evaluation Report (TER) Section 2.2.1.3.4.3.4. DOE’s 
7-46 

mathematical models for colloid-assisted transport with reversible and irreversible sorption, with 

and without kinetic limitations, are consistent with approaches established in the scientific 

literature (e.g., Corapcioglu and Jiang, 1993aa) and the NRC staff’s independent analyses 

(Cvetkovic, et al., 2004aa; Painter and Cvetkovic, 2006aa). Therefore, DOE’s mathematical 

models for colloid-assisted transport in the engineered barrier system are reasonable. Because 

of limited data about colloid retardation or physical straining in the engineered barrier system, 

the NRC staff notes that DOE appropriately excluded these retention processes when 

considering colloid migration in the engineered barrier system. 

DOE’s abstraction considers plutonium to be irreversibly associated with plutonium and 

zirconium colloids that originate from commercial spent nuclear fuel and montmorillonite colloids 

originating from defense high-level waste glass waste. Uranium is irreversibly associated with 

the uranophane colloids that originate in spent nuclear fuel. These three colloid types and 

the associated radionuclides originate in the waste form. Selected radionuclides (isotopes 

of americium, cesium, protactinium, plutonium, radium, tin, and thorium) are allowed to sorb 

reversibly and without kinetic limitations onto uranophane colloids and groundwater or 

waste-derived montmorillonite colloids. The empirical sorption model represents equilibrium, 

site-limited sorption with competition for sites. Although the assumed equilibrium distribution 

coefficients for plutonium are higher than the supporting data, the abstraction is reasonable 

because kinetically limited desorption is generally needed to significantly enhance transport for 

the range of colloid concentrations considered (Cvetkovic, et al., 2004aa; Painter and Cvetkovic, 

2006aa) and kinetic limitation is not modeled or expected for these colloid types. The NRC staff 

notes that the assumed equilibrium sorption model is consistent with current understanding of 

sorption on silicate minerals and is thus reasonable. 

In DOE’s abstraction, uranium, neptunium, and thorium sorb reversibly and without kinetic 

limitations to corrosion product colloids. DOE calculated equilibrium distribution coefficients 

(Kds) for these elements using the same surface complexation modeling approach as for 

sorption on stationary corrosion products. As described in the previous subsection, the surface 

complexation model simulates equilibrium attachment of dissolved ions onto solid surfaces 

(in this case, the surfaces of mobile colloids), incorporating the chemical complexity of the 

system. DOE’s abstraction does not include corrosion product colloids in the waste form 

domain. Kinetic irreversible sorption onto corrosion product colloids is modeled for 

americium and plutonium in the corrosion products domain. The NRC staff notes that DOE’s 

mathematical model for colloid-associated radionuclide transport with kinetic transfers 

is consistent with DOE’s process conceptualization, models established in the literature 

(e.g., Corapcioglu and Jiang, 1993aa), and the NRC staff’s independent analyses (Cvetkovic, 

et al., 2004aa; Painter and Cvetkovic, 2006aa). 

In DOE’s abstraction, the forward rate constants for americium and plutonium are not sampled 

directly. Instead, the fraction of total mobile radionuclide mass exiting the corrosion products 

domain that is associated with colloids (the target flux out ratio) is sampled from a uniform 

distribution with a range of 0.9 to 0.99. An analytical inverse solution is then used to calculate 

the forward rate corresponding to the sampled ratio. In the model abstraction, the computed 

forward rate constant and other parameters computed from the inverse solution are further 

compared against physically allowed ranges. If any computed value is outside its allowed 

range, the corresponding maximum or minimum value is used in the forward model for 

colloid-assisted transport in place of the sampled ratio. The approach based on a target flux-out 

ratio is reasonable because, for conditions under which colloids are stable and could potentially 

affect transport, this approach tends to enhance radionuclide release over that expected from a 

mechanistic process model. 
7-47 

DOE provided information suggesting that colloids will not significantly enhance transport of 

radionuclides in the engineered barrier system, because commercial spent nuclear fuel-derived 

colloids are expected to be unstable in the corrosion products domain and because 

radionuclide concentrations associated with iron oxide and high-level waste glass waste form 

colloids are expected to be smaller than dissolved concentrations. DOE’s conclusion is 

reasonable because (i) DOE’s analyses showed that commercial spent nuclear fuel-derived 

colloids are not expected to be stable in the pH ranges expected in the corrosion products 

domain (DOE, 2009ay); (ii) the NRC staff’s confirmatory calculations (TER Section 

2.2.1.3.4.3.4) show that iron oxide colloids exert negligible control on dissolved plutonium 

concentrations; and (iii) the mean sampled plutonium concentration associated with high-level 

waste glass colloids is an order of magnitude lower than the mean plutonium solubility limit 

(TER Section 2.2.1.3.4.3.4). 

Reasonableness and Consistency of TSPA Results for Engineered Barrier System 

Radionuclide Releases 

The NRC staff performed simplified confirmatory analyses to assess whether DOE’s Total 

System Performance Assessment (TSPA) results for the engineered barrier system radionuclide 

releases are consistent with DOE’s abstractions. As discussed in the following paragraphs, the 

NRC staff used hand calculations (Painter, 2010aa; Pickett, 2010aa) to estimate peak expected 

total-repository engineered barrier system release rates for Tc-99, Pu-242, and Np-237 using 

DOE-provided information, and then compared these releases with DOE-provided values.1

 The 

NRC staff’s confirmatory calculations focused on the igneous intrusion and seismic ground 

motion modeling cases because these cases result in the largest release rates from the 

engineered barrier system. These estimates are based on advection and diffusion of dissolved 

radionuclides and neglect transport of radionuclides associated with colloids. As discussed in 

TER Section 2.2.1.3.4.3.4, transport in the engineered barrier system is not significantly 

enhanced by colloids, because of low colloid concentrations in the corrosion products 

domain. The NRC staff’s simplified calculation estimates are presented only for selected 

cases for which representative DOE results were available for comparison. Results are 

presented for waste package release rates (i) of Pu-242 and Np-237 in the igneous intrusion 

modeling case, on the basis of control of the dissolved concentration limited by solubility (TER 

Section 2.2.1.3.4.3.3) or corrosion product sorption (this TER Section 2.2.1.3.4.3.5), and (ii) of 

Tc-99 in the seismic ground motion modeling case, on the basis of release rate control by waste 

package failure rate. 

DOE provided information showing that engineered barrier system releases of low-solubility, 

sorbing radionuclides (e.g., plutonium and neptunium) are mainly controlled by processes within 

the corrosion products domain because waste form dissolution and invert transport processes 

are fast relative to transport within the corrosion products domain. In the TSPA analyses, the 

important dose contributions from plutonium and neptunium isotopes result from the igneous 

intrusion modeling case (SAR Section 2.4.2.2.1.1.3), in which all waste packages fail and 

releases of these radionuclides are controlled by advection modified by sorption and 

precipitation of radionuclide-bearing minerals. The NRC staff performed simplified estimates 

to confirm DOE’s release calculations for Pu-242 and Np-237 for the igneous intrusion 

 

1

The NRC staff’s calculations are estimates only, using parameters and probabilities DOE provided in the SAR, 

supporting reports, and associated responses to requests for additional information. Because simplifications are 

involved, precise agreement between the NRC staff calculations and DOE’s results was not expected; rather, 

reasonable agreement of the release rates within an order of magnitude was evaluated. 
7-48 

modeling case in the engineered barrier system. The NRC staff assumed that these release 

rates for the corrosion products domain are controlled by advection and either (i) precipitation 

of solubility-limiting minerals or (ii) sorption onto corrosion products. The solubility limit (TER 

Section 2.2.1.3.4.3.3) is a chemically based maximum value for the dissolved concentration of 

an element, in the absence of sorption. If, however, there is capacity for sorbing the dissolved 

element onto solid surfaces, the dissolved concentration may not reach the solubility limit and 

could be controlled to a lower value by sorption. Because both solubility and sorption are viable 

processes in the corrosion products domain, the process that limits the dissolved concentration 

to the lowest value would provide the better simplified estimate of release rate. The NRC staff, 

therefore, used simplified calculations to estimate release rates controlled by both potential 

limits on dissolved concentration—solubility and sorption—and selected the lower value for 

comparison to DOE’s results. 

Using information provided by DOE, the NRC staff estimated a peak mean, repositorywide, 

sorption-limited Pu-242 engineered barrier system release rate of 7.9 g/yr [0.017 lb/yr] 

(Painter, 2010aa; Pickett, 2010aa). The NRC staff calculated a solubility-limited Pu-242 

release rate of 1.2 g/yr [0.0026 lb/yr] (Painter, 2010aa; Pickett, 2010aa), which is lower than 

the sorption-limited rate and is, therefore, taken as the estimated value. 

DOE provided representative commercial spent nuclear fuel package release results for 

percolation subarea 3 (which includes approximately 40 percent of the waste packages) 

and a single realization from the igneous intrusion case in DOE Enclosure 1, Figure 2 (2009da). 

For Pu-242, the peak corrosion products domain release from subarea 3 is approximately 

30 g/yr [0.07 lb/yr] at 100,000 years, conditional on an igneous event having occurred at 

10,000 years. Scaling that value to the full repository and multiplying by the probability of 

having a single igneous intrusion event in 1 million years (1.7 percent) gives a value of 1.3 g/yr 

[0.0029 lb/yr]. This DOE result is consistent with the NRC staff’s confirmatory estimate of 

1.2 g/yr [0.0026 lb/yr]. 

The NRC staff calculated an estimated peak mean, repositorywide, sorption-limited release 

rate for Np-237 of 3.0 g/yr [0.0066 lb/yr] for the igneous intrusion modeling case (Painter, 

2010aa; Pickett, 2010aa). DOE provided representative results for Np-237 release from the 

corrosion products domain in percolation subarea 3 and a single realization of the igneous 

intrusion modeling case in DOE Figure 1.1-12 (2009dc). That realization assumes that an 

igneous intrusion event occurs. Thus, DOE’s calculated peak release rate of approximately 

200 g/yr [0.4 lb/yr] is weighted by the probability of an igneous event in 1 million years 

(1.7 percent) and scaled to the full repository to compare with the NRC staff’s peak mean 

estimate. The result of that calculation is 8.5 g/yr [0.019 lb/yr] for Np-237 release for the 

igneous intrusion modeling case. This DOE result is in reasonable agreement with the NRC 

staff’s simplified calculation estimate of 3.0 g/yr [0.0066 lb/yr]. 

The NRC staff calculated an NpO2 solubility-limited neptunium engineered barrier system 

release rate of 12 g/yr [0.026 lb/yr] (Painter, 2010aa, Pickett, 2010aa). This result is higher than 

the sorption-limited rate and was therefore not used for comparison; as discussed previously in 

this section, the lower calculated rate is more appropriate for comparisons. A rate calculated 

using the Np2O5 solubility model (applicable after all in-package steel has corroded) would be 

even higher and was therefore not used for comparison. 

For high-solubility radionuclides that are weakly sorbing or nonsorbing (e.g., technetium), 

radionuclide-specific engineered barrier system releases in the seismic ground motion and 

nominal modeling cases are controlled primarily by the waste package failure rate because 
7-49 

dissolution rates of the waste form and transport of these radionuclides through the engineered 

barrier system are sufficiently fast compared with typical intervals between package failures 

(TER Section 2.2.1.4.1.3.3.1.2). Using the information provided by DOE, the NRC 

staff calculated a peak mean, repositorywide release rate for Tc-99 of 22 g/yr [0.049 lb/yr] 

for the seismic ground motion modeling case (Painter, 2010aa; Pickett, 2010aa). DOE provided 

plots of mean cumulative Tc-99 engineered barrier system release in SAR Figure 2.1-24. From 

the slopes of these plots, the NRC staff estimated that the release rate peaks at approximately 

12 g/yr [0.026 lb/yr] for the period between 10,000 and 100,000 years. This peak mean rate 

estimated from DOE’s Tc-99 release information is in reasonable agreement with the NRC 

staff’s simplified calculated estimate of 22 g/yr [0.049 lb/yr] (Painter, 2010aa; Pickett, 2010aa). 

Summary of NRC Staff’s Review of Engineered Barrier System Radionuclide Transport 

The NRC staff notes that, in modeling the transport of radionuclides in the engineered barrier 

system, DOE adequately described the system and models used, applied appropriate 

conceptual models, and considered alternative conceptual models. DOE used appropriate 

mathematical models to represent transport in the engineered barrier system. Transfer of 

information between the radionuclide transport abstraction and other TSPA code abstractions 

was consistently implemented. Relevant design information for the waste package was 

incorporated. DOE used appropriate data to establish model parameters and to represent 

uncertainty. Intermediate results of the abstraction were compared to independent information. 

Release rates DOE’s TSPA model produced for radionuclide transport in the engineered 

barrier system vary significantly by radionuclide and modeling case. The engineered barrier 

system does not significantly delay transport of soluble, nonsorbing radionuclides such as Tc-99 

and I-129, and the waste package failure rates control the engineered barrier system 

release rates for those radionuclides. Transport of low-solubility, sorbing radionuclides such as 

Np-237 and Pu-242 is significantly slower and is generally controlled by sorption onto stationary 

corrosion products and precipitation of radionuclide-bearing minerals in the corrosion products 

domain. Colloid-assisted transport is not significant compared with transport of dissolved 

radionuclides, because of limited colloid concentrations in the engineered barrier system. 

TSPA code results for engineered barrier system release rates DOE provided are consistent 

with the NRC staff’s simplified confirmatory calculations. Because the staff’s simplified 

confirmatory calculations are consistent with DOE’s TSPA code results, the NRC staff notes that 

the TSPA results are reasonable. 

2.2.1.3.4.4 NRC Staff Conclusions 

The NRC staff notes that the DOE description of this model abstraction for radionuclide release 

rates and solubility limits is consistent with the guidance in the YMRP. The NRC staff also 

notes that the DOE technical approach discussed in this chapter is reasonable for use in the 

Total System Performance Assessment (TSPA). 

2.2.1.3.4.5 References 

Allison, J.M. 2004aa. “Request for Referenceable Information on High-Level Waste (HLW) 

Radionuclide Inventories in Support of Preparation of the Yucca Mountain Project License 

Application, JCP–0445, 1/28/04.” Memorandum (February 26, 0303040661, with attachment) to 

J. Authur, III, OCRWM. MOL20040317.0265. Aiken, South Carolina: DOE, Savannah River 

Operations Office. 
7-50 

Appelo, C.A.J. and Postma, D. 1994aa. Geochemistry, Groundwater and Pollution. Brookfield, 

Vermont: A.A. Balkema. 

Beavers, J.A. and C.L. Durr. 1990aa. Container Corrosion in High Level Nuclear Waste 

Repositories. Columbus, Ohio: Cortest Columbus, Inc. 

Bowman, S.M. and L.C. Leal. 2000aa. NUREG/CR–0200, “ORIGEN-ARP: Automatic 

Rapid Process for Spent Fuel Depletion, Decay, and Source Term Analysis.” 

ORNL/NUREG/CSD–2/V1/R6. Oak Ridge, Tennessee: Oak Ridge National Laboratory. 

Bremier, S., C.T. Walker, and R. Menzel. 2000aa. “Fission Gas Release and Fuel Swelling at 

Burn-Ups Higher Than 50 Mwd/kgU.” Proceedings of the Fission Gas Behavior in Water 

Reactor Fuels. Cadarache, France, September 26–29, 2000. Paris, France: Nuclear Energy 

Institute. pp. 93–106. 

BSC. 2005ad. “In-Package Chemistry Abstraction.” ANL–EBS–MD–000037. Rev. 04. 

ACN 01, AD 01, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004ae. “Aqueous Corrosion Rates for Waste Package Materials.” 

ANL–DSD–MD–000001. Rev. 01. ACN 001, ERD 01, ERD 02. Las Vegas, Nevada: 

Bechtel SAIC Company, LLC. 

BSC. 2004ah. “CSNF Waste Form Degradation: Summary Abstraction.” 

ANL–EBS–MD–000015. Rev. 02. ACN 02. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2004ai. “Defense HLW Glass Degradation Model.” ANL–EBS–MD–000016. Rev. 02. 

ACN 001, ERD 001, ERD 002. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004ao. “DSNF and Other Waste Form Degradation Abstraction.” 

ANL–WIS–MD–000004. Rev. 4. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2003af. “PWR Assembly End-Effect Reactivity Evaluation.” CAL–UDC–NU–000006. 

Rev. 00, with 01 errata. MOL20010412.0158. DOC20031014.0007. Las Vegas, Nevada: 

Bechtel SAIC Company, LLC. 

Corapcioglu, M.Y. and S. Jiang. 1993aa. “Colloid-Facilitated Groundwater Contaminant 

Transport.” Water Resources Research. Vol. 29, No. 7. pp. 2,215–2,226. 

Cvetkovic, V., S. Painter, D. Turner, D. Pickett, and P. Bertetti. 2004aa. “Parameter and Model 

Sensitivities for Colloid-Facilitated Transport on the Field Scale.” Water Resources Research. 

Vol. 40. doi:10.1029/2004WR003048. 

Da Cunha Belo, M., M. Walls, N.E. Hakiki, J. Corset, E. Picquenard, G. Sagon, and D. Noel. 

1998aa. “Composition, Structure, and Properties of Oxide Films Formed on the Stainless 

Steel 316L in a Primary Type PWR Environment.” Corrosion Science. Vol. 40, Nos. 2 and 3. 

pp. 447–463. 

DOE. 2010aj. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.3.7), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.3.4, Set 1.” Letter (February 24) J.R. Williams to 

J.H. Sulima (NRC). ML100560258. Washington, DC: DOE, Office of Technical Management. 
7-51 

DOE. 2009an. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.1, Set 1.” Letter (February 6) J.R. Williams to J.H. Sulima (NRC). ML090400455. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009av. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 1. 

ML090700817. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2009ax. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.7), Safety Evaluation Report, Vol. 3, 

Chapter 2.2.1.3.4, Set 1.” Letter (May 5) J.R. Williams to J.H. Sulima (NRC). ML091260473. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ay. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.7), Safety Evaluation Report, Vol. 3, 

Chapter 2.2.1.3.4, Set 2.” Letter (May 12) J.R. Williams to J.H. Sulima (NRC). ML 091330282. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cz. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.3.7), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.3.4, Set 1 and Set 2.” Letter (June 26) J.R. Williams to 

J.H. Sulima (NRC). ML091770582. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009da. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.7), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.4, Set 3.” Letter (September 11) J.R. Williams to J.H. Sulima (NRC). 

ML092600883. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009db. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.7), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.4, Set 4.” Letter (October 16) J.R. Williams to J.H. Sulima (NRC). 

ML093200320. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009dc. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.7), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.4, Set 2.” Letter (May 28) J.R. Williams to J.H. Sulima (NRC). ML091480752. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009dd. “Yucca Mountain—Supplement Response to Request for Additional Information 

Regarding License Application (Safety Analysis Report Section 2.3.7), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.3.4, Set 2.” Letter (September 4) J.R. Williams to J.H. Sulima (NRC). 

ML092470427. Washington, DC: DOE, Office of Technical Management. 

DOE. 2003ad. “Review of DOE Spent Nuclear Fuel Release Rate Test Results.” 

Rev. 0. DOE/SNF/REP–073. Idaho Falls, Idaho: Idaho National Engineering and 

Environmental Laboratory. 

DOE. 2000aa. DOE/SNF/REP–056. “N Reactor (U-Metal) Fuel Characteristics for 

Disposal Criticality Analysis.” Rev. 0. TIC247956. Washington, DC: DOE, Office of 

Environmental Management. 
7-52 

Finch, R.J., E.C. Buck, P.A. Finn, and J.K. Bates. 1999aa. “Oxidative Corrosion of Spent UO2 

Fuel in Vapor and Dripping Groundwater at 90 °C.” 22nd Symposium on Scientific Basis for 

Nuclear Waste Management, 1998 Material Research Society Fall Meeting, Boston, 

Massachusetts, November 30–December 4, 1998. Symposium Proceedings Vol. 556. 

Warrendale, Pennsylvania: Materials Research Society. p. 431. 

Glass, R.S., G.E. Overturf, R.E. Garrison, and R.D. McCright. 1984aa. “Electrochemical 

Determination of the Corrosion Behavior of Candidate Alloys Proposed for Containment of High 

Level Nuclear Waste in Tuff.” Report UCID–20174. Livermore, California: Lawrence Livermore 

National Laboratory. 

GoldSim Technology Group. 2006aa. GoldSim Contaminant Transport Module User’s Guide. 

Version 4.0. Issaquah, Washington: GoldSim Technology Group. 

Gray, W.J. 1999aa. “Inventories of Iodine-129 and Cs-137 in the Gaps and Grain Boundaries 

of LWR Spent Fuels.” Proceedings of the Materials Research Society. Symposium 

Proceedings 556. Warrendale, Pennsylvania: Materials Research Society. pp. 487–494. 

Harrar, J.E., J.F. Carley, W.F. Underwood, and E. Raber. 1990aa. “Report of the Committee 

to Review the Use of J-13 Well Water in Nevada Nuclear Waste Storage Investigations.” 

UCID–21867. ACC:NNA 19910131.0274. Livermore, California: Lawrence Livermore 

National Laboratory. 

Hem, J.D. 1995aa. Study and Interpretation of the Chemical Characteristics of Natural Water. 

3rd Edition. Geological Survey Water-Supply Paper 2254. Washington, DC: U.S. Government 

Printing Office. 

Jain, V., G. Cragnolino, and L. Howard. 2004aa. “A Review Report on High Burnup Spent 

Nuclear Fuel—Disposal Issues.” ML 042860337. San Antonio, Texas: CNWRA. 

Jantzen, C.M., D.I. Kaplan, N.E. Bibler, D.K. Peeler, and M.J. Plodinec. 2008aa. “Performance 

of a Buried Radioactive High-Level Waste (HLW) Glass after 24 Years.” Journal of Nuclear 

Materials. Vol. 378. pp. 244–256. 

Johnson, L.H. and J.C. Tait. 1997aa. “Release of Segregated Nuclides From Spent Fuel.” 

SKB Technical Report 97-18. Stockholm, Sweden: Swedish Nuclear Fuel and Waste 

Management Company. 

Langmuir, D. 1997aa. Aqueous Environmental Geochemistry. Upper Saddle River, 

New Jersey: Prentice Hall. 

Lassmann, K., C.T. Walker, J. ven de Larr, and F. Lindstrom. 1995aa. “Modeling the High 

Burnup UO2 Structure in LWR Fuel.” Journal of Nuclear Materials. Vol. 226. pp. 1–8. 

Leslie, B., C. Grossman, and J. Durham. 2007aa. “Total-system Performance Assessment 

(TPA) Version 5.1 Module Descriptions and User Guide.” Rev. 1. ML072710060. San Antonio, 

Texas: CNWRA. 
7-53 

MacKinnon, R.J. 2008aa. “Engineered Barrier System (EBS) Transport Model Abstractions.” 

Presentation at DOE/NRC Technical Exchange on Total System Performance Assessment 

(TSPA) for Yucca Mountain, Las Vegas, Nevada, April 4, 2008. LSN:DEN001594514. 

Washington, DC: NRC. 

Manaktala, H. 1993aa. “Characteristics of Spent Nuclear Fuel and Cladding Relevant 

to High-Level Waste Source Term.” CNWRA 93-006. ML 040200340. San Antonio, 

Texas: CNWRA. 

McCright, R.D., W.G. Halsey, and R.A. Van Konynenburg. 1987aa. “Progress Report on the 

Results of Testing Advanced Conceptual Design Metal Barrier Materials Under Relevant 

Environmental Conditions for a Tuff Repository.” Report No. UCID–21044. Livermore, 

California: Lawrence Livermore National Laboratory. 

Mohanty, S., G. Adams, and R. Pabalan. 2003aa. “The Role of Solubility as a Barrier to 

Radionuclide Release.” Proceedings of the 10th International High-Level Radioactive Waste 

Management Conference, Las Vegas, Nevada, March 30–April 2, 2003. La Grange Park, 

Illinois: American Nuclear Society. pp. 938–945. 

Morgenstein, M.D., C.L. Wicket, and A. Barkatt. 1999aa. “Consideration of Hydration-rind 

Dating of Glass Artefacts: Alteration Morophologies and Experimental Evidence of 

Hydrogeochemical Soil-Zone Pore Water Control.” Journal of Archaeological Science. 

Vol. 26. pp. 1,193–1,210. 

Murphy, W.M. and R.C. Codell. 1999aa. “Alternate Source Term Models for Yucca Mountain 

Performance Assessment Based on Natural Analog Data and Secondary Mineral Solubility.” 

Proceedings of the Materials Research Society, November 30–December 4, 1998, Boston, 

Massachusetts. Symposium Proceedings 556. D.J. Wronkiewicz and J.H. Lee, eds. 

Warrendale, Pennsylvania: Materials Research Society. pp. 551–558. 

Neck, V., M. Altmaier, A. Seibert, J.I. Yun, C.M. Marquardt, and T. Fanghänel. 2007aa. 

“Solubility and Redox Reactions of Pu(IV) Hydrous Oxide: Evidence for the Formation of 

PuO2+x(s, hyd).” Radiochimica Acta. Vol. 95. pp. 193–207. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2008aa. NUREG–1914, “Dissolution Kinetics of Commercial Spent Nuclear Fuels in the 

Potential Yucca Mountain Repository Environment.” ML 083120074. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

NRC. 1996ab. NUREG–1564, “Long-Term Kinetic Effects and Colloid Formation in the 

Dissolution of LWR Spent-Fuel.” ML 073100056. Washington, DC: NRC. 

Nuclear Energy Agency. 1997aa. “Lessons Learnt from Ten Performance Assessment 

Studies.” Paris, France: Organisation for Economic Co-operation and Development, Nuclear 

Energy Agency. 
7-54 

Painter, S. 2010aa. “Radionuclide Transport Analysis for Yucca Mountain.” Electronic 

Scientific Notebook 318E. San Antonio, Texas: CNWRA. 

Painter, S. and V. Cvetkovic. 2006aa. “Effect of Kinetic Limitations on Colloid-Facilitated 

Transport at the Field Scale.” 11th International High-Level Radioactive Waste Management 

Conference, Global Progress Toward Safe Disposal, Las Vegas, Nevada, April 30–May 4, 2006. 

LaGrange Park, Illinois: American Nuclear Society. 

Painter, S., V. Cvetkovic, D. Turner, and D. Pickett. 2002aa. “Significance of Kinetics for 

Sorption on Inorganic Colloids: Modeling and Experimental Interpretation Issues.” 

Environmental Science and Technology. Vol. 24. pp. 228–234. 

Pearcy, E.C., J.D. Prikryl, W.M. Murphy, and B.W. Leslie. 1994aa. “Alteration of Uraninite 

From the Nopal I Deposit, Peña Blanca District, Chihuahua, Mexico, Compared to Degradation 

of Spent Nuclear Fuel in the Proposed U.S. High-Level Nuclear Waste Repository at Yucca 

Mountain, Nevada.” Applied Geochemistry. Vol. 9. pp. 713–732. 

Pickett, D.A. 2010aa. “Confirmatory Calculations of Radionuclide Release Rates and Colloid 

Transport Supporting Yucca Mountain Review.” Electronic Scientific Notebook 1052E. San 

Antonio, Texas: CNWRA.

Pickett, D.A. 2005aa. “Effects of Spent Nuclear Fuel Uranyl Alteration Phases on Radionuclide 

Dissolved Concentration Limits.” San Antonio, Texas: CNWRA. 

Prikryl, J.D. 2008aa. “Uranophane Dissolution and Growth in CaCl2–SiO2(aq) Test Solutions.” 

Geochimica et Cosmochimica Acta. Vol. 72. pp. 4,508–4,520. 

Pulvirenti, A.L., S.J. Eddy, T.M. Calabrese, M.A. Adel-Hadadi, A. Barkatt, and 

M.E. Morgenstein. 2006aa. “Interaction of Iron Containing Silicate Glasses with Aqueous 

Salt Solutions.” Physics and Chemistry of Glasses: European Journal of Glass Science 

and Technology Part B. Vol. 47, No. 1. pp. 47–57. 

Rai, D., N.J. Hess, A.R. Felmy, D.A. Moore, M. Yui, and P. Vitorge. 1999aa. 

“A Thermodynamic Model for the Solubility of PuO2(am) in the Aqueous 

K+

-HCO3

−

-CO3

2−

-OH−-H2O System.” Radiochimica Acta. Vol. 86. pp. 89–99. 

Roxburgh, I.S. 1987aa. Geology of High-Level Nuclear Waste Disposal: An Introduction. 

New York City, New York: Chapman and Hall. 

Shoesmith, D.W. 2000aa. “Fuel Corrosion Processes under Waste Disposal Conditions.” 

Journal of Nuclear Materials. Vol. 282. pp. 1–31. 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ad. “Postclosure Nuclear Safety Design Bases.” ANL–WIS–MD–000024. Rev. 01. 

ACN 01, ERD 01, ERD 02. Las Vegas, Nevada: Sandia National Laboratories. 
7-55 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ak. “Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: 

Abstraction and Summary.” MDL–EBS–PA–000004. Rev. 03. ERD 01. Las Vegas, Nevada: 

Sandia National Laboratories. 

SNL. 2007ag. “Dike/Drift Interactions.” MDL–MGR–GS–000005. Rev. 02. ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ah. “Dissolved Concentration Limits of Elements with Radioactive Isotopes.” 

ANL-WIS–MD–000010. Rev. 06. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007aj. “EBS Radionuclide Transport Abstraction.” ANL–WIS–PA–000001. Rev. 03. 

ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ak. “Engineered Barrier System: Physical and Chemical Environment.” 

ANL–EBS–MD–000033. Rev. 06. ERD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007ao. “In-Drift Precipitates/Salts Model.” ANL–EBS–MD–000045. Rev. 03. ERD 01, 

ERD 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007at. “Qualification of Thermodynamic Data for Geochemical Modeling of 

Mineral—Water Interactions in Dilute Systems.” ANL–WIS–GS–000003. Rev. 01. Las Vegas, 

Nevada: Sandia National Laboratories. 

SNL. 2007bm. “Total System Performance Assessment Data Input Package for Requirements 

Analysis for DOE SNF/HLW and Naval SNF Waste Package Overpack Physical Attributes Basis 

for performance Assessment.” Rev. 00. TDR–TDIP–ES–000003. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007bn. “Total System Performance Assessment Data Input Package for Requirements 

Analysis for TAD Canister and Related Waste Package Overpack Physical Attributes Bases for 

Performance Assessment.” Rev. 00. TDR–TDIP–ES–000006. Las Vegas, Nevada: Sandia 

National Laboratories. 

Spino, J., J. Cobos-Sabate, and F. Rousseau. 2003aa. “Room-Temperature Microindentation 

Behaviour of LWR-Fuels, Part 1: Microhardness.” Journal of Nuclear Materials. Vol. 322. 

pp. 204–216. 

van de Weerd, H., A. Leijnse, and W.H. van Riemsdijk. 1998aa. “Transport of Reactive 

Colloids and Contaminants in Groundwater: Effect of Nonlinear Kinetic Interactions.” Journal of 

Contaminated Hydrology. Vol. 32. pp. 313–331. 

Wang, X.Y., Y.S. Wu, L. Zhang, and Z.Y. Yu. 2001aa. “Atomic Force Microscopy and X-Ray 

Photoelectron Spectroscopy Study on the Passive Film for Type 316L Stainless Steel.” 

Corrosion Science. Vol. 57, No. 6. pp. 540–546. 
7-56 

Wilson, C.N. and W.J. Gray. 1990aa. “Measurement of Soluble Nuclide Dissolution 

Rates From Spent Fuel.” Proceedings of the Materials Research Society. Symposium 

Proceedings 176. V.M. Oversby and P.W. Brown, eds. Warrendale, Pennsylvania: Materials 

Research Society. pp. 489–498. 

Wronkiewicz, D.J., K.J. Bates, S.F. Wolf, and E.C. Buck. 1996aa. “Ten-Year Results From 

Unsaturated Drip Tests With UO2 at 90°C: Implications for the Corrosion of Spent Nuclear 

Fuel.” Journal of Nuclear Materials. Vol. 238, No. 1. pp. 78–95. 

Zhu, C. 2004aa. “Coprecipitation in the Barite Isostructural Family: 2. Numerical 

Simulations of Reactions and Mass Transport.” Geochimica et Cosmochimica Acta. 

Vol. 68. pp. 3,339–3,349. 
8-1 

CHAPTER 8 

2.2.1.3.5 Climate and Infiltration 

2.2.1.3.5.1 Introduction

This chapter of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s evaluation of the U.S. Department of Energy’s (DOE) representation 

of climate and infiltration, as presented in DOE’s Safety Analysis Report (SAR) Section 2.3.1 

(DOE, 2008ab) and supporting information. DOE considers the reduction of water flux from 

precipitation to net infiltration to be a barrier capability for the Upper Natural Barrier. Because of 

the generally vertical movement of percolating water through the unsaturated zone in the DOE 

representation of the natural system, water entering the unsaturated zone at the ground surface 

(infiltration) is the only source for deep percolation water in the unsaturated zone at and below 

the proposed repository. 

DOE used the term “net infiltration” to define the volumetric flux of water passing below the 

active plant root zone, but often refers to net infiltration simply as “infiltration.” DOE also 

used the term net infiltration to refer both to the output of the net infiltration model (SAR 

Section 2.3.1) and to the top boundary condition of the unsaturated zone model (SAR 

Section 2.3.2). This distinction is important because the average values from the net infiltration 

model differ from those used as net infiltration at the top boundary of the site-scale unsaturated 

zone model. NRC staff evaluates the former in the present section and the latter in TER 

Section 2.2.1.3.6.3.2. 

Climate and infiltration are treated differently in DOE’s performance assessment for the first 

10,000 years following permanent closure of the repository and the period from 10,000 to 

1 million years. For the first 10,000 years, DOE used paleoclimate records for the region to 

predict future climatic conditions and uses these predictions as input for estimating future net 

infiltration. In addition, DOE used the climate predictions to scale groundwater fluxes in the 

saturated zone portion of the performance assessment for this period. DOE described its 

approach for scaling the groundwater flux in SAR Section 2.3.9; the NRC staff’s review of 

groundwater flux in the saturated zone is in TER Section 2.2.1.3.8. For the period from 

10,000 years to 1 million years after permanent closure, 10 CFR 63.342(c)(2) allows DOE 

to consider long-term-average deep percolation flux at the proposed repository horizon 

instead of explicitly predicting climate and infiltration. DOE chose to use the prescribed 

deep percolation flux in its performance assessment for the post-10,000-year period. In TER 

Section 2.2.1.3.6.3.2, NRC staff evaluates DOE’s use of the prescribed average deep 

percolation flux at the proposed repository horizon for post-10,000-year performance 

assessment calculations. 

This TER chapter provides the NRC staff’s evaluation of DOE’s consideration of climate and 

infiltration in the first 10,000 years after closure in DOE’s Total System Performance 

Assessment calculations. NRC staff reviewed the DOE technical bases, input data, models, 

and net infiltration results. The NRC staff used its understanding of relative risk within the 

repository system to inform its review, by focusing on those aspects that are most significant for 

repository performance. The NRC staff considered how the flux of water through the 

unsaturated zone affects seepage, release of radionuclides from the engineered barrier system, 

and radionuclide transport through the natural system (evaluated in TER Sections 2.2.1.3.6.3.4, 

2.2.1.3.4.3.5, and 2.2.1.3.7, respectively) in determining the significant aspects of DOE’s net 
8-2 

infiltration model. On the basis of the downstream uses of climate and infiltration calculations, 

staff focused its review on DOE’s estimates of the magnitude, spatial distribution, and 

uncertainty of net infiltration over the next 10,000 years. 

2.2.1.3.5.2 Evaluation Criteria

NRC staff’s review of model abstractions used in the DOE postclosure performance 

assessment, including those considered in this chapter for climate and infiltration, is guided by 

10 CFR 63.114 (Requirements for Performance Assessment) and 63.342 (Limits on 

Performance Assessments). The resulting DOE Total System Performance Assessment 

(TSPA) is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment in 10 CFR 63.342, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is given in TER Chapter 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of geologic 

stability, defined in 10 CFR 63.302 as 1 million years following disposal. These sections require 

that through the period of geologic stability, with specific limitations, DOE 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

This model abstraction includes changes in future climate and the effect of such changes on 

infiltration of water. 10 CFR 63.342(c)(2) provides requirements for assessing the effects of 

climate change. DOE has chosen to assess the effects of climate change for the period after 

10,000 years following disposal by using constant-in-time deep percolation rates in its 
8-3 

performance assessment. The NRC staff’s evaluation of DOE’s use of these deep percolation 

rates is in TER Section 2.2.1.3.6.3.2. 

In addition,10 CFR 63.305 provides the following requirements for characteristics of the 

reference biosphere as used in this abstraction for climate and infiltration: 

 Features, events, and processes that describe the reference biosphere are consistent with 

present knowledge of the conditions in the region surrounding the Yucca Mountain site 

[10 CFR 63.305(a)]. 

 DOE should not project changes in society, the biosphere (other than climate), or human 

biology or increases or decreases of human knowledge and technology; all analyses should 

assume that all of those factors are constant as they are at the present time 

[10 CFR 63.305(b)]. 

 Factors related to the geology, hydrology, and climate should vary based upon cautious but 

reasonable assumptions of the changes in these factors that could affect the Yucca 

Mountain disposal system during the period of geologic stability, consistent with the 

requirements for performance assessments specified at 10 CFR 63.342 

[10 CFR 63.305(c)]. 

 Biosphere pathways are consistent with arid or semi-arid conditions [10 CFR 63.305(d)]. 

The requirements of 10 CFR 63.305 apply to the abstractions for climate and infiltration to the 

extent that the characteristics of the reference biosphere affect climate and infiltration. 

The NRC staff’s review of the SAR follows the guidance laid out in the Yucca Mountain Review 

Plan (YMRP) (NRC, 2003aa) Section 2.2.1.3.5, Climate and Infiltration, as supplemented by 

additional guidance for the period beyond 10,000 years after permanent closure (NRC, 2009ab). 

The YMRP acceptance criteria for model abstractions that provide guidance for the NRC staff’s 

evaluation of DOE’s abstraction of climate and infiltration are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

The NRC staff review used a risk-informed approach and the guidance in the YMRP, as 

supplemented by NRC (2009ab), to the extent reasonable for aspects of climate and infiltration 

important to repository performance. The NRC staff considered all five YMRP criteria in its 

review of information provided by DOE. In the context of these criteria, only those aspects of 

the model abstraction that substantively affect the performance assessment results, as 

determined by the NRC staff, are discussed in detail in this chapter. The NRC staff’s 

determination is based both on risk information provided by DOE, and on NRC staff’s 

knowledge gained through experience and independent analyses. 
8-4 

2.2.1.3.5.3 Technical Evaluation

The review of the technical information DOE provided for climate and infiltration over the 

next 10,000 years is divided into three subsections within this TER section. The first 

subsection reviews DOE’s identification and description of features and processes for 

climate and infiltration. The second subsection focuses on the climate data, future-climate 

model, and climate predictions. The third subsection addresses DOE’s description of net 

infiltration processes, models, and estimates of net infiltration at Yucca Mountain using the 

future climate predictions. 

2.2.1.3.5.3.1 Identification of Features and Processes 

In this section, NRC staff evaluates the DOE identification and description of processes 

important for estimating climate and net infiltration. This section addresses the system 

description and model integration. DOE’s overall screening of FEPs is reviewed in TER 

Section 2.2.1.2.1. 

DOE used regional and site characteristics to develop conceptual models for climate and net 

infiltration at Yucca Mountain. The natural features of topography and surficial soils of the 

Upper Natural Barrier were identified as important to waste isolation in SAR Section 2.1.2.1. 

On the basis of field observations, synthesis of data, and modeling over more than two 

decades, DOE indicated (SAR Section 2.3.1.1) that the features and processes important to the 

capability of the Upper Natural Barrier are (i) climate change, (ii) climate modification increases 

recharge, (iii) precipitation, (iv) topography and morphology, (v) rock properties of host rock and 

other units, (vi) surface runoff and evapotranspiration, (vii) infiltration and recharge, 

(viii) fractures, and (ix) fracture flow in the unsaturated zone. 

The following summary is based on the information in SAR Section 2.3.1.1 and illustrates how 

DOE integrated these features and processes in its conceptual models of climate and 

infiltration. DOE described the present climate at Yucca Mountain as semi-arid, with low annual 

precipitation. DOE expects the climate to change over the next 10,000 years, remaining 

semi-arid but with changes in precipitation patterns and rates. DOE recognized that surface 

temperature and vegetation will also vary with changes in climate. Evapotranspiration 

(the combination of evaporation and plant transpiration) removes a large portion of the annual 

precipitation that infiltrates into the soil. In this environment, evapotranspiration is strongly 

influenced by temperature and low atmospheric relative humidity. DOE conceptualized that net 

infiltration events occur in pulses during and for a short period following some of the larger or 

longer duration precipitation events. Evapotranspiration continually dries the soil between 

precipitation events. DOE considered snow as providing a source of delayed infiltration during 

snowmelt events. In DOE’s conceptual model, runoff and the soil’s water-holding capacity 

limit the magnitude of net infiltration pulses, but runoff from one area may subsequently 

infiltrate downstream. 

DOE conceived that soil, fractures, and bulk rock hydraulic properties affect the rate of water 

movement below the root zone, with a competition between downward flow and upward 

movement of water by evapotranspiration. In the DOE conceptual model, water flows quickly 

into the rock below shallow soil in areas where the bedrock is fractured or has a highly 

permeable matrix. Such rapid flow limits the effect of evapotranspiration. Surface water runoff, 

influenced by topography and surface morphology, spatially redistributes the flux of water. This 

process may reduce net infiltration in some areas (e.g., high on hillslopes) and increase net 

infiltration in others (e.g., washes and channels). DOE recognized that lateral movement of 
8-5 

water below the surface, termed interflow, is known to spatially redistribute water, but the DOE 

conceptual model does not consider interflow to be significant at Yucca Mountain. For the 

semi-arid climate of Yucca Mountain, the overall water balance in DOE’s model is dominated by 

precipitation and evapotranspiration, with infiltration and runoff representing relatively small 

portions of the balance. 

DOE implemented its conceptualization in (i) a climate model that predicts future climatic 

states, (ii) climatic input data for each climate state, and (iii) an infiltration model linked to a 

surface-water-routing algorithm for runoff. The infiltration and routing algorithms are integrated 

into the Mass Accounting System for Soil Infiltration and Flow (MASSIF) model. DOE described 

 A climate model for predicting climate over the next 10,000 years that uses Earth-orbital 

parameters and paleoclimatic data from the southwestern United States covering the 

past approximately 800,000 years 

 Climatic input data for each climate state that uses recorded meteorological data from 

local, regional, and western U.S. stations 

 Submodels of the infiltration model that consider precipitation, evapotranspiration, 

snowmelt, runoff and run on, and infiltration 

DOE used site characterization data, as available, to develop inputs for the MASSIF model 

(SAR Section 2.3.1.3). Where sparse or no site observations are available, other information 

from scientific literature or other sites was used to develop inputs. 

DOE reasonably identified and included features and processes in its climate models that are 

important for estimating future climatic conditions at Yucca Mountain. This is supported by 

a comparison of the information provided by DOE with staff’s knowledge of past climates in 

the southwest United States, including the Yucca Mountain region, obtained from literature 

reviews and independent analysis (NRC, 2005aa; Stothoff and Musgrove, 2006aa; Stothoff 

and Walter, 2007aa). NRC staff compared DOE’s description of infiltration, and the 

incorporation of features and processes, with NRC staff’s understanding of near-surface 

features and processes at Yucca Mountain obtained from literature reviews, field observations, 

and independent analysis (NRC, 2005aa; Stothoff, 2008aa,ab, 2009aa). Because DOE’s 

description of infiltration and incorporation of features and processes are consistent with those 

from other sites and with staff’s independent analyses (see previous), DOE reasonably 

identified and included in its overall conceptual model features and processes important for net 

infiltration at Yucca Mountain. 

2.2.1.3.5.3.2 Climate

This section contains NRC staff’s review of the DOE approach and results for predicting 

climate states for the next 10,000 years, and for predicting climatic conditions within each of 

the climate states. The NRC staff evaluated the performance assessment calculations 

representing the first 10,000 years of repository performance, to assess DOE’s net infiltration 

estimates. In its performance-based review, NRC staff focused on identifying whether the data, 

models, and results represent climate and the uncertainty of predicting future climate conditions. 

Because DOE chose to use the range and distribution of average deep percolation specified in 

10 CFR Part 63 for the period from 10,000 to 1 million years after closure, DOE did not provide 

information on climate or meteorology during this period. Therefore, the NRC staff did not 

evaluate explicit models for climate or meteorology for the post-10,000-year period in its TER. 
8-6 

The NRC staff evaluates the DOE approach to representing deep percolation during the period 

from 10,000 to 1 million years after closure in TER Section 2.2.1.3.6.3.2. 

The NRC staff evaluates DOE’s approach to modeling climate during the first 10,000 years 

by separately considering DOE’s approach to estimating long-term-average climate 

(TER Section 2.2.1.3.5.3.2.1) and DOE’s approach to estimating daily weather parameters 

given a long-term-average climate state (TER Section 2.2.1.3.5.3.2.2). These two sections 

separate climatic considerations into long and short time scales, respectively. 

2.2.1.3.5.3.2.1 Climate Change for the Next 10,000 Years 

This section addresses data for model justification, and characterization and propagation of data 

uncertainty, for DOE’s climate prediction for the 10,000 years following repository closure. 

DOE predicted climate states covering the next 10,000 years using paleoclimate proxies from 

regional records and the understanding of orbital variations as the principal drivers of Earth 

climate over the past several million years. DOE’s main paleoclimate proxies are layered 

mineral precipitates from Devils Hole and fossils preserved in continuously layered lake 

sediments from Owens Lake. Owens Lake, a present-day playa, and Devils Hole, a water-filled 

cave, are both within 140 km [87 mi] of Yucca Mountain. Cores from both sites record past 

cyclic changes in regional climate, between glacial and interglacial phases, and are generally 

consistent with other global climate proxy records. 

From these records, DOE derived three representative states for future climate. DOE predicted 

these three climates and the timing of step changes by (i) identifying the past point in time in 

the Devils Hole record that is equivalent to the present moment within the glacial cycle, 

(ii) identifying the same equivalent point in the Owens Lake sediment sequence, (iii) identifying 

the sediment sequence corresponding to the 10,000 years following the equivalent point, and 

finally, (iv) attributing climate states to the sediment sequence (SAR Section 2.3.1.2.3.1.1). 

 

The climate-sequence timing DOE described is based on two Earth-orbital parameters, which 

are recognized as climate forcing functions operating over geologic time scales: orbital 

eccentricity, with a period of approximately 100,000 years, and precession of the equinoxes, 

with a period of approximately 23,000 years (SAR Section 2.3.1.2.1.2.3). Values for these 

orbital parameters can be calculated to high precision from astronomical relations. DOE used 

oxygen isotope ratio (*18O) records in Devils Hole vein calcite, dated using uranium-series 

methods, to relate the orbital parameters to past glacial stages. In the SAR, DOE explained that 

obliquity, another recognized orbital forcing with a period of approximately 41,000 years, was 

not used in its model, because no consistent relationship was shown between obliquity and the 

Devils Hole *18O record. The SAR asserted that groups of 4 eccentricity cycles, totaling 

approximately 400,000 years, provide analogous repetitions of glacial cycles. DOE built 

confidence in the selection of the first 10,000 years of the cycle as an analog for the next 

10,000 years at Yucca Mountain by showing that the last 400,000-year cycle was similar to the 

previous 400,000-year cycle (400,000 to 800,000 years ago). 

DOE constructed past climates for particular glacial stages (SAR Section 2.3.1.2.1.2.4) using 

the Forester, et al. (1999aa) analysis of ostracode occurrences in lake sediment obtained from 

composite core OL-92, drilled in 1992 at Owens Lake, California, together with observed flows 

in Owens River for wet years. Ostracodes are microfossils, with different species having 

different environmental preferences for salinity and temperature. In DOE’s analysis, the 

ostracode-based salinity of the paleo-Owens Lake serves as a proxy for annual precipitation. 
8-7 

DOE used the abundance of five different species to infer compatible climatic parameters of 

temperature and seasonality and then developed future-climate parameters from meteorological 

stations with long records in locations where those species currently exist. DOE built 

confidence in the climate estimators using diatom records from the same core samples. 

DOE’s procedure yielded three representative climates for the first 10,000 years after 

closure: (i) modern (present-day) climate for the first 600 years, (ii) monsoonal climate for 

1,400 years, and (iii) glacial transitional climate for the remaining 8,000 years. Relatively 

speaking, these three climates can be described as hot and dry, hot and wet, and cool and 

wet, although all are classified as arid or semi-arid climates. DOE calculated sample-average 

mean annual precipitation values for the monsoon and glacial transition climates that were 

1.59 and 1.63 times the sample-average mean annual precipitation for the present-day climate 

(SAR Tables 2.3.1-2 through 2.3.1-4), and nominal mean annual temperatures for the monsoon 

and glacial transition climates were 0.9 °C [1.6 °F] warmer and 5.5 °C [9.9 °F] cooler, 

respectively, than the nominal mean annual temperature for the present-day climate using 

values from SNL Tables F-22 through F-24 and Eq. F-47(a) (2007az). DOE’s representation of 

a monsoonal climate also exhibited a shift in seasonality, with summer convection storms 

making up a larger fraction of its annual total precipitation than for either the present-day or 

glacial-transition climates. 

The NRC staff notes that DOE provided a reasonable description of its approach of using orbital 

cycles covering the past 800,000 years integrated with available paleoclimatic data to develop 

the timing and duration of climates over the next 10,000 years. The NRC staff compared the 

DOE description with staff’s understanding (e.g., Stothoff and Walter, 2007aa) of paleoclimatic 

data and approaches for projecting future climates based on paleoclimatic information. On the 

basis of this understanding, the NRC staff notes that DOE incorporated features and processes 

important for using paleoclimate reconstructions in projecting future climates. The paleoclimatic 

information DOE used to develop the timing and duration of climates over the next 10,000 years 

is reasonably representative of Yucca Mountain because DOE obtained the paleoclimatic data 

from the region near Yucca Mountain. 

DOE considered the two primary uncertainties in the data sets used to forecast future 

climate at Yucca Mountain to be the standard deviation associated with the Devils Hole 

ages and the uncertainty of the timing of climate change implied by the Devils Hole record 

(SAR Section 2.3.1.2.2.1.4). DOE also considered the two primary uncertainties in model 

forecasts of future climates to include (i) uncertainty in the location of the past–present 

equivalency point in the Owens Lake record, (ii) uncertainty arising from the chaotic nature of 

the climate system, and (iii) uncertainty in selecting a particular past climate sequence to 

forecast the future (SAR Section 2.3.1.2.3.2). 

From the DOE-identified primary uncertainties and the downstream uses of the DOE 

future-climate model, NRC staff identifies three specific aspects of the DOE future-climate 

model where uncertainties may have a potential effect on repository performance: 

(i) uncertainty in timing and duration of climate states used for performance assessment 

calculations, (ii) uncertainty in climatic conditions during the post-thermal-pulse period when 

temperatures near drifts drop below boiling (dominated by the glacial-transition climate 

state in the DOE future-climate model), and (iii) uncertainty in climatic conditions from 

anthropogenic activities. 
8-8 

Uncertainty in Timing and Duration of Climate States 

DOE used three climate states, each with a constant climate, to represent millennial-scale 

temporal variations found in the paleorecord. Uncertainty in the timing of transitions between 

the projected climate states could potentially impact estimates of future infiltration, which in turn, 

may affect unsaturated-zone flow, seepage rates, thermal histories, and radionuclide transport. 

The NRC staff notes that the timing for the transition between the monsoon and 

glacial-transition climate states has a minor impact on performance assessment results for the 

following reasons: 

 The mean annual precipitation values for these two climate states are similar. 

DOE calculated sample-average mean annual precipitation values for the monsoon 

and glacial-transition climate states that were 1.59 and 1.63 times the sample-average 

mean annual precipitation for the present-day climate state (SAR Tables 2.3.1-2 

through 2.3.1-4). 

 The mean annual infiltration values for these two climate states are similar. DOE 

calculated weighted-average mean annual infiltration values over the repository footprint 

for the monsoon and glacial-transition climate states of 15.88 and 21.25 mm/yr, 

respectively, as shown in DOE Enclosure 1, Table 1 (2010ai). The weighted-average 

glacial-transition mean annual infiltration was 1.34 times larger than the 

weighted-average monsoon mean annual infiltration. 

 The consequence of a much larger difference in mean annual infiltration has little 

consequence for performance. DOE stated that concurrently increasing areal-average 

mean annual infiltration by a factor of 2.39 for the monsoon state and 1.81 for the 

glacial-transition state has little effect on performance assessment results, as described 

in DOE Enclosure 5 (2009bo). 

The NRC staff notes that the timing of the transition between the present-day and monsoon 

climate states, as set by DOE at 600 years following repository closure, has low consequence in 

performance assessment calculations for the following reasons: 

 The present-day climate state predominantly corresponds to above-boiling conditions 

within emplacement drifts, and SAR Section 2.3.3.1 asserted that seepage into drifts is 

not expected to occur where rock above the repository exhibits above-boiling 

temperatures or dryout conditions. DOE estimated that above-boiling conditions within 

emplacement drifts may persist for several hundred to more than 1,000 years, 

depending on emplacement drift location (SAR Section 2.3.3.3.3.1). 

 An early onset of a high-infiltration (monsoon or glacial transition) climate may 

reduce the duration of the thermal period, but it could not lengthen the duration of 

the post-thermal-pulse period affected by seepage by more than 600 years out of 

the first 10,000 years of performance. 

 A delayed onset of a high-infiltration climate after the present-day climate state would 

result in smaller estimates of mean annual infiltration than are incorporated in the 

performance assessment. 
8-9 

The NRC staff notes that the potential for an early transition to a full-glacial climate state has 

low probability of occurring during the first 10,000 years after closure and low consequence if 

the transition occurs after 10,000 years. This is based on DOE’s estimated return to a 

full-glacial climate state 30,000 years (SAR Section 2.3.1.2.1.2.3) after permanent closure. On 

the basis of on the paleoclimatic data from the region around Yucca Mountain, it is unlikely that 

the full-glacial climate would occur during the first 10,000 years after permanent closure. 

Furthermore, an earlier return of the full-glacial climate up to 20,000 years sooner than the DOE 

prediction would not change flux rates, because DOE used the prescribed deep percolation flux 

rate for the post-10,000-year period. 

The glacial-transition climate represents 8,000 years of constant climate in the DOE model. 

Because millennial-scale fluctuations in climate are reflected in the paleo-records, NRC staff 

evaluated this model using a constant climate for 8,000 years instead of an alternative 

representation reflecting millennial-scale variations in climate. The NRC staff notes that the use 

of millennial-average net infiltration rates in performance assessment calculations, rather than 

the constant climate that DOE used for the final 80 percent of the 10,000-year performance 

period, has low consequence for performance for the following reasons: 

 DOE included extreme events in developing the millennial-average net infiltration rates. 

DOE included expected variability in annual precipitation over a 1,000-year period in the 

net infiltration calculations (SAR Section 2.3.1.3.3) (e.g., including the calculated wettest 

year within each 1,000-year sequence). 

 Using DOE information from its performance assessment sensitivity analyses, NRC staff 

notes in TER Section 2.2.1.3.6.3.2 that DOE’s performance assessment results would 

not be significantly affected by including temporal variability of percolation about the 

long-term-average percolation flux within a climate state into performance assessment 

calculations. Because net infiltration is closely linked to percolation, DOE performance 

assessment calculations would not be strongly affected by fluctuations in climate that 

lead to infiltration flux varying about the long-term-average infiltration flux. 

In summary, DOE adequately represents uncertainty in the timing and duration of climate 

states for performance assessment because DOE showed that changes in the representation 

for the timing and duration of the climate states have a low consequence for performance 

assessment calculations. 

Uncertainty in Climatic Conditions During the Post-Thermal-Pulse Period 

DOE used the glacial-transition climate state to represent climate during the final 80 percent of 

the first 10,000 years of repository performance. Of the three climate states that DOE used 

within the first 10,000 years of performance, the NRC staff considers the glacial-transition 

climate state to have the largest potential for affecting repository performance because this 

climate state has the longest duration, and seepage into emplacement drifts is least affected by 

the thermal pulse during this climate state. During the thermal pulse, above-boiling conditions 

and evaporation reduce the flux of water reaching drifts (SAR Section 2.3.3.1). In evaluating the 

DOE approach to representing uncertainty in the glacial-transition climate state, the NRC staff 

considers (i) methodology that DOE used to estimate future climatic conditions, (ii) parameters 

that DOE found contribute most heavily to uncertainty in downstream applications, (iii) available 

estimates for climate change from the last glacial maximum, and (iv) representation of 

intermediate climate fluctuations using a constant climate for a climate state. 
8-10 

DOE first considered the presence and absence of indicator species within Owens Lake to infer 

changes in climatic conditions relative to present-day climate, then estimated compatible 

climatic conditions from present-day locations where the same indicator species currently exist. 

DOE represented uncertainty regarding climatic conditions using upper and lower bounds for 

mean annual precipitation and mean annual temperature for each climate state. To account for 

the uncertainties in translating paleoclimatic indicators into meteorological records, DOE 

selected several meteorological stations to represent each of the bounding climatic conditions at 

an elevation of 1,524 m [5,000 ft] at Yucca Mountain (SAR Section 2.3.1.2.3.1.2). The criteria 

for selecting present-day meteorological stations, outlined in SAR Section 2.3.1.2.3.1.2, include 

(i) presence of Owens Lake indicator species, (ii) mean annual temperature, (iii) rain-shadow 

effects, (iv) position of the polar front, and (v) length of observational record. Further, DOE 

selected meteorological stations such that (i) the climate states have larger mean annual 

precipitation values for upper bounds than for lower bounds and (ii) the upper-bound mean 

annual precipitation values are larger than the present-day observations for both the monsoonal 

and glacial transition climate states. 

DOE concluded that mean annual precipitation is one of the two parameters that 

control uncertainty in MASSIF estimates of net infiltration for all climate states (SAR 

Section 2.3.1.3.3.2.2). Further, DOE based the selection of representative meteorological 

stations on the station record length and on observations that are sensitive to mean annual 

temperature and precipitation seasonality (i.e., ostracode species) without using criteria based 

on specific values of mean annual precipitation. Uncertainty in mean annual precipitation is a 

dominant source of uncertainty in the DOE net infiltration estimates, and the DOE procedure for 

selecting representative meteorological stations yields relatively large uncertainty in mean 

annual precipitation. 

DOE selected meteorological stations for the glacial-transition climate with average observed 

annual precipitation between 207 and 241 mm/yr [8.1 and 9.5 in/yr] for the lower bound and 

between 419 and 455 mm/yr [16.5 and 17.9 in/yr] for the upper bound (SAR Table 2.3.1-6). 

For comparison, meteorological stations at Yucca Mountain have observed mean annual 

precipitation between 183 and 213 mm/yr [7.2 and 8.4 in/yr], averaging 199 mm/yr [7.8 in/yr]. 

Accordingly, the mean upper- and lower-bound mean annual precipitation estimates for the 

glacial-transition climate state are approximately 2.2 and 1.2 times the average observed 

present-day precipitation of 199 mm/yr [7.8 in/yr] at Yucca Mountain. 

DOE does not expect that a full glacial climatic state would occur within the next 30,000 years, 

and the SAR did not estimate climatic conditions for a full glacial climatic state. NRC staff has 

nonetheless reviewed published estimates for mean annual precipitation during the last 

glacial maximum in the region surrounding Yucca Mountain, as detailed in Stothoff and Walter, 

Section 2.2 (2007aa). Several of the published estimates listed in Stothoff and Walter (2007aa) 

quantitatively considered the effects of mean annual precipitation on a water balance. Such 

quantitative estimates inferred changes in mean annual precipitation and mean annual 

temperature by considering elevation changes for plant species that have known environmental 

preferences, hydrologic balances for paleolakes, extent of glacial advances, and regional 

groundwater balances. Among these estimates, the largest estimated value for mean annual 

precipitation at the last glacial maximum suggests that mean annual precipitation was 1.9 times 

larger than present-day mean annual precipitation at Yucca Mountain. 

The NRC staff notes that DOE reasonably represents uncertainty in the magnitude of mean 

annual precipitation change during the glacial-transition climate state. The basis for this is that 

the upper bound of mean annual precipitation values that DOE used to represent the upper 
8-11 

bound of mean annual precipitation during the glacial-transition climate state is substantially 

larger than published quantitative estimates for mean annual precipitation during the last glacial 

maximum in the region surrounding Yucca Mountain. The NRC staff further notes that DOE 

reasonably represents mean annual temperature and precipitation seasonality during the 

glacial-transition climate state because DOE based the values on indicators in the region 

surrounding Yucca Mountain that are sensitive to these factors. 

Uncertainty in Climatic Conditions From Anthropogenic Activities 

DOE stated that the predicted modern climate is based on “climate records that implicitly 

include effects of modern society over the duration of historical record” SNL, Section 6.2, 

FEPs 1.4.01.00.0A and 1.4.01.02.0A (2008ab) and DOE, Enclosure 8 (2009cr). Uncertainty in 

the incorporation of anthropogenic effects on climate predictions used as input for net infiltration 

estimates is twofold. First, monsoonal and glacial-transition climate analog sites are derived 

from interpretation of the paleoclimatic record (e.g., Owens Lake ostracode and diatom 

observations). However, current levels of greenhouse gases (i.e., dominantly CO2 but including 

other gases) are elevated beyond any levels indicated in paleoclimate records covering the past 

800,000 years. Second, the effect of the global climatic changes on Yucca Mountain climate is 

uncertain. To address these uncertainties, DOE described consequences to infiltration 

estimates caused by likely projections of climate change in the desert Southwest considering 

anthropogenic influences. 

In DOE Enclosure 8 (2009cr), DOE considered projected climate changes in the desert 

Southwest, described by the International Panel on Climate Change (Christensen, et al., 

2007aa) to assess potential consequences of anthropogenic climate change on repository 

performance. Projected regional climate change estimates indicate the desert Southwest is 

likely to see temperature increases that are higher than average global warming and annual 

precipitation that is likely to decrease in the next century. DOE, Enclosure 8 (2009cr) described 

the projected regional climate changes as having potential consequences, including 

 Improved repository performance under warmer and drier conditions, because warmer 

temperatures and decreased precipitation lead to decreased net infiltration 

 Insignificant effects on repository performance under a warmer and wetter climate 

or early onset of monsoon conditions induced by anthropogenic climate change, 

because most of the repository would be above boiling during the first 600 years after 

closure [DOE considered climate changes Christensen, et al. (2007aa) projected to be 

similar to but smaller than that represented by the shift from modern climate to the 

monsoonal state] 

 Improved repository performance if anthropogenic climate change caused a delay in the 

onset of the glacial-transition climate because net infiltration under the cool and wet 

glacial-transition climate state is higher than would occur for earlier climate states. 

The NRC staff notes that DOE adequately bounded the effects of anthropogenic climate change 

in its performance assessment calculations because DOE showed that (i) net infiltration is not 

consequential to repository performance within the first 600 years after repository closure and 

(ii) net infiltration under credible projected climate changes would be overestimated by using the 

climate states already used for performance assessment calculations. 
8-12 

Summary of Conclusions Regarding Climate Change for the Next 10,000 Years 

In summary, DOE adequately represents future climate uncertainty because (i) DOE provided a 

reasonable description of the approach and data used to represent climate states, and 

furthermore, that those climate states and their uncertainties are representative of Yucca 

Mountain during the first 10,000 years after closure, (ii) the DOE representation of the timing 

and duration of the climate states has a low consequence for performance assessment 

calculations, (iii) DOE used upper-bound values for mean annual precipitation during the 

glacial-transition climate state (post-thermal pulse) that are more extreme than the largest 

available published estimate for the last glacial maximum, and (iv) DOE projected changes 

to climate stemming from anthropogenic activities that are either not consequential to 

repository performance or are bounded by the climate states used for performance 

assessment calculations. 

2.2.1.3.5.3.2.2 Local Spatial and Temporal Variation of Meteorological Conditions 

In this section, NRC staff evaluates the DOE model for climatic and meteorological conditions 

during each climate state (i.e., climate conditions at short time scales). This section addresses 

input data characterization and uncertainty. 

DOE represented meteorological conditions for each climate state using sampled 1,000-year 

sequences of daily estimates for total precipitation and temperature extremes, representing 

conditions at a reference elevation of 1,524 m [5,000 ft]. The MASSIF infiltration model 

subsequently estimates precipitation and temperature variability over each day using the 

daily values. DOE represented spatial variation by projecting the daily precipitation and 

temperature values to the infiltration-model cells using elevation-dependent lapse rates. 

DOE considered precipitation rates with up to a 1,000-year recurrence period in generating 

the 1,000-year sequence. DOE selected 10 representative 1-year sequences out of each 

1,000-year sequence to estimate long-term-average net infiltration. DOE used a water year 

representation, initiating each 1-year sequence on October 1 to capture the cycle of winter 

precipitation and large summer potential evapotranspiration. Each simulation was initiated with 

conditions representing extended summer evapotranspiration. DOE stated that the wettest 

years were sampled to ensure the disproportionate influence of wet years was captured for net 

infiltration estimates. 

NRC staff reviewed DOE’s representation of spatial and temporal variability in meteorological 

parameters for estimating net infiltration in the two following sections. 

Spatial Variability in Meteorological Parameters 

DOE considered the effect of elevation on meteorological parameters by adjusting the estimated 

daily values for precipitation and temperature extremes according to regional patterns in mean 

annual precipitation and mean annual temperature. The NRC staff notes that the dependency 

of meteorological parameters on elevation provides the only spatial variability of those 

parameters across the site. NRC staff notes, however, that DOE’s approach of representing 

spatial variability in precipitation and temperature for the purpose of providing 

long-term-average net infiltration estimates as a boundary condition to the site-scale 

unsaturated zone flow model is reasonable for the following reasons: 

 The size of the relevant washes is small relative to the size of typical 

precipitation events. Precipitation patterns during individual precipitation events 
8-13 

are likely to be relatively spatially uniform within a few kilometers [a few miles], 

especially during large storms and the frontal storms that are predominant in the cooler 

periods of the year with small potential evapotranspiration. For example, maximum 

observed 24-hour precipitation ranged between 78.5 and 87.1 mm [3.1 and 3.4 in] 

(SAR Table 1.1-23) for the 10 meteorological stations within 5 km [3 mi] of the repository 

footprint in the largest 24-hour event ever recorded with onsite meteorological stations 

(September 21 to 22, 2007). The repository footprint has an area of 4.6 km2

 [1.8 mi2] and 

includes more than a dozen washes. The NRC staff notes, on the basis of the smaller 

size of individual washes compared to the larger size of large frontal storms, that 

precipitation patterns are likely to be relatively spatially uniform within individual washes 

above the proposed repository footprint. 

 To estimate long-term average net infiltration, DOE assumes the washes are 

hydrologically independent (i.e., there is no lateral flow between washes). On the 

basis of site topography and drainage system (SAR Figure 1.1-5), the NRC staff notes 

that the washes within the repository footprint can reasonably be considered 

hydrologically separate. 

 To estimate long-term average net infiltration, DOE assumed a given meteorological 

sequence is equally applicable to each wash in the model domain. This is a reasonable 

assumption because each small wash is likely to exhibit similar frequencies of 

meteorological patterns over long periods of time due to close proximity. 

 DOE cited regional studies in SNL Sections 6.4.11 and 6.4.5.3 (2007az) indicating 

that mean annual precipitation and mean annual temperature are correlated with 

elevation even though local topography can modify the relationship. DOE derived the 

precipitation lapse rate used for infiltration calculations from meteorological stations in 

the Yucca Mountain region, as outlined in SNL Appendix F.2 (2007az). The NRC 

staff confirmed that the DOE precipitation lapse rate is comparable to other regional 

relationships, within the bounds of uncertainty, over an elevation difference typical of 

the repository footprint. Because DOE showed that the infiltration model results, 

other model results, and observational evidence from other locations all exhibit a 

systematic trend of larger net infiltration as mean annual precipitation increases 

(SAR Section 2.3.1.3.4), the NRC staff notes that DOE reasonably considers the 

systematic elevation-dependent variation in mean annual precipitation on net infiltration 

for calculating long-term average net infiltration. 

 DOE derived the temperature lapse rate used for infiltration calculations from a textbook 

value for the dry adiabatic lapse rate, representing an upper bound representation, as 

detailed in SNL Appendix C.1.4 (2007az). Although the temperature lapse rate may 

overestimate the regional lapse rate, DOE showed in SNL Section 7.1.4 (2007az) that 

the infiltration model results are not sensitive to the temperature lapse rate. Thus the 

temperature lapse rate for calculating long-term-average net infiltration is reasonable. 

In summary, the DOE approach of spreading a single, generated meteorological time history 

throughout the model domain using elevation-dependent lapse rates for calculating boundary 

condition fluxes for the site-scale unsaturated zone flow model is reasonable. This approach is 

unlikely to systematically bias the calculated areal-average long-term-average net infiltration. 

This is further based on the observations that (i) the individual washes within the repository 

footprint have relatively small areas compared to typical storms, (ii) the individual washes within 
8-14 

the repository footprint are hydrologically independent with respect to lateral flow, and 

(iii) systematic trends in the meteorological parameters that most strongly affect net infiltration 

are incorporated in the infiltration calculations. 

Temporal Variability in Meteorological Parameters 

DOE represented temporal variability of meteorological conditions using sampled daily values 

for precipitation, temperature minimum and maximum, and wind speed. On days with 

precipitation, DOE subdivided the daily calculation into two parts, representing storm and 

nonstorm conditions, and used wet-day instead of dry-day temperature values. DOE described 

the statistical parameters characterizing these meteorological components as varying 

sinusoidally over the year. DOE considered the representation of temporal variability adequate 

because measured regional and Yucca Mountain site data were used to develop precipitation 

and temperature sequences. 

The NRC staff notes that DOE’s representation of cool-season (winter) precipitation 

is a risk-significant aspect of temporal variability of meteorological conditions, because 

warm-season precipitation has a disproportionately small effect on net infiltration. This is based 

on the NRC staff’s literature surveys and independent confirmatory investigations in Stothoff 

and Walter, Section 3.6 (2007aa) and Stothoff (2008aa). In particular, the NRC staff notes 

measurements and analyses indicate that approximately 10 percent of recharge at Mount 

Charleston (in the Spring Mountains, southeast Nevada) has the isotopic signature of summer 

precipitation, which represents approximately 30 percent of the annual precipitation 

(Winograd, et al., 1998aa). 

Accordingly, the NRC staff compared DOE’s mathematical representation of precipitation in 

SNL Appendix F (2007az) with summary observations from meteorological stations DOE used 

to represent mean winter and summer precipitation for potential future climate states. In the 

analysis (Stothoff, 2010aa, Section 3), the NRC staff noted that several statistical properties 

from the observed precipitation data sets fell close to statistical properties of DOE’s precipitation 

representation. The NRC staff recognizes that there is uncertainty in estimating mean annual 

precipitation from observations; for example, average precipitation totals from 1994–2006 

for five Yucca Mountain Project meteorological stations, reported in SAR Tables 1.1-10 

through 1.1-12, 1.1-15, and 1.1-18, differ on average by approximately 7 percent from values 

for 1993–2004 reported in SNL Table 6.1-4 (2006aa). The NRC staff notes that DOE’s 

representation of precipitation is reasonable for calculating daily precipitation for net 

infiltration because the statistical model has seasonal patterns for precipitation values 

comparable to observations. 

The NRC staff compared DOE’s representation for temperature as sinusoidally varying during 

the year to observations from meteorological stations in Nevada, Utah, California, and Arizona, 

as described in Stothoff Figures 5-8 and 5-09 (2008aa). The amplitude and seasonality DOE 

represented is comparable to the observations. The NRC staff considers net infiltration 

calculations to be relatively sensitive to temperature on days with precipitation because 

evaporation during precipitation affects the amount of water infiltrating during an event. 

Calculations of net infiltration, however, are relatively insensitive to temperature fluctuations on 

days without precipitation because it typically takes weeks for evapotranspiration rates to 

remove the soil moisture from a large storm. The NRC staff notes that DOE considered 

temperature in daily meteorological sequences because DOE used a representation for 

temperature that has amplitude and seasonality comparable to observations, and because DOE 

considered separate parameterizations for wet and dry days. 
8-15 

2.2.1.3.5.3.3 Net Infiltration

In this section, NRC staff evaluates DOE’s model for net infiltration during the 10,000 years 

following repository closure. The NRC staff evaluates the downstream uses of the net 

infiltration results and the effect of uncertainty in net infiltration on DOE performance 

assessment calculations. The focus of the NRC staff’s assessment is on those aspects of 

DOE’s net infiltration model that are most important for repository performance. 

In evaluating repository performance with respect to unsaturated flow (see TER 

Sections 2.2.1.3.6.3.2 and 2.2.1.3.6.3.4), the NRC staff identified systematic changes in 

seepage as the dominant performance-affecting consequence of spatial and temporal 

variability in percolation fluxes within the unsaturated zone below the Paintbrush Tuff 

nonwelded (PTn). Also in those TER sections, the NRC staff noted that the DOE model is 

relatively insensitive to other aspects such as spatial variability in deep percolation, local flow 

focusing, decadal-to-centennial climatic variability, episodic deep percolation pulses, and 

calibration of net infiltration uncertainty. Because seepage and percolation closely track net 

infiltration, the NRC staff considers areal-average net infiltration the dominant 

performance-affecting feature of the infiltration model with respect to DOE’s performance 

assessment calculations. 

DOE also used the areal-average net infiltration results in performance assessment calculations 

related to the saturated zone, adjusting saturated zone groundwater fluxes for future, wetter 

climates using net infiltration (SAR Section 2.3.8). The NRC staff notes, however, that 

repository performance is more sensitive to changes in seepage than changes in saturated 

zone groundwater fluxes. Seepage directly affects radionuclide releases, so uncertainty in 

seepage directly affects the uncertainty in calculated dose. Uncertainty in groundwater flux 

rates has a much smaller effect on uncertainty in dose calculated by the DOE performance 

assessment because (i) nonsorbing radionuclides (e.g., Tc-99) are transported through the 

saturated zone in a small fraction of the performance period regardless of uncertainty in 

groundwater flux rates and (ii) uncertainties in transport rates for sorbing radionuclides are 

dominated by uncertainties in aspects other than groundwater flux, such as sorption 

characteristics. The following subsections contain the NRC staff evaluation of the technical 

bases for DOE’s models and inputs used to estimate areal-average net infiltration. 

Submodels for Net Infiltration 

This subsection addresses model integration, justification, and uncertainty. 

DOE used a water balance approach to integrate processes and features acting at and near the 

ground surface, to a depth at the bottom of the root zone. A water balance approach requires 

that the supply of water (precipitation and run on) at any location is equal to the sum of other 

components (e.g., evapotranspiration, change in water storage, runoff, and net infiltration). 

In the water balance, uncertainty in precipitation and evaporation, the largest components, can 

affect estimation of the much smaller component of net infiltration. DOE described the 

development and integration of features and processes into conceptual and numerical models in 

SAR Sections 2.3.1.3.1 and 2.3.1.3.2 and SNL Sections 6.3 and 6.4 (2007az). DOE separates 

the water balance model into the key MASSIF elements of 

 Climate and meteorology, using daily precipitation, temperature, and snowmelt 
8-16 

 Subsurface water movement and storage, using a one-dimensional vertical soil 

water balance 

 Surface runoff and run on, using topography-based flow routing 

 Evapotranspiration, using the FAO-96 approach (Allen, et al., 1998aa) modified for 

natural vegetation 

 Reference evapotranspiration, using the FAO Penman-Monteith method 

(Allen, et al., 2005aa) 

MASSIF comprises linked submodels for the identified processes and routines to manipulate 

geographically distributed input data into the formats required for the calculations. The 

geographically distributed input data, which are defined at each 30-m [98-ft] pixel across the 

Yucca Mountain area, include soil and rock hydrologic properties, topography, vegetation 

factors, and climate information (e.g., precipitation and temperature). 

In MASSIF, net infiltration is defined as the water that moves below the active zone where 

evaporation and plant uptake are significant processes. DOE assumed that the active zone 

does not penetrate the bedrock, so that water passing into the bedrock becomes net infiltration. 

DOE considered this assumption to be conservative with respect to the magnitude of net 

infiltration, because plant roots, especially in areas with thin soil cover, develop in bedrock 

fractures and thus would reduce net infiltration by taking up water for transpiration. 

DOE described in SNL Section 6.2.3 (2007az) six criteria used for selecting the infiltration model 

components: (i) the model and components should be consistent with the overall project 

purpose, (ii) the model component complexity should be consistent with the available input data, 

(iii) model components should be consistent with other model components, (iv) the model 

should be computationally efficient, (v) the model should be accessible and open, and (vi) the 

model and model components should demonstrate reasonable predictive capability. DOE 

described cases in the literature where the algorithms and approaches in submodels have been 

utilized at other locations in semi-arid areas. DOE explained that these criteria are motivated by 

the large spatial and temporal scales being modeled, the limited objectives of the infiltration 

model, and the need for numerous simulations to assess sensitivities and address multiple 

climate scenarios. DOE further explained that the infiltration model is not intended to describe 

the detailed spatial and temporal character of water movement. 

The NRC staff has experience in evaluating the features, processes, and models used for arid 

zone hydrology gained from two decades of interactions with DOE and from documenting the 

NRC staff’s independent modeling (Stothoff, 2008aa; Stothoff and Walter, 2007aa). On the 

basis of knowledge gained through this experience, the NRC staff notes that DOE reasonably 

described the technical basis for the infiltration conceptual model and the associated 

mathematical model in SAR Section 2.3.1.3 and supporting documents. Furthermore, the 

MASSIF submodels are reasonable for their intended use because the algorithms and 

approaches (i) are used in the scientific community, (ii) are reasonable for the spatial and 

temporal scales described in DOE’s six criteria for the infiltration model (see previous paragraph 

for DOE’s criteria), and (iii) consider downstream uses. The DOE submodels are consistent 

with staff’s experience regarding approaches for modeling net infiltration in arid environments 

(i.e., Stothoff and Musgrove, 2006aa; Stothoff, 2008aa). 
8-17 

To build confidence in the water balance approach for one-dimensional water storage and 

movement, DOE compared results with both measured data and results from an alternative 

model solving the Richards equation for unsaturated flow (SAR Sections 2.3.1.3.4.1 and 

2.3.1.3.4.2). The Richards equation includes capillary effects, unlike the water balance 

approach in MASSIF. The DOE comparisons focused on the MASSIF submodels for water 

storage, evapotranspiration, and one-dimensional vertical movement of water. The measured 

lysimeter data were from two locations, the Reynolds Creek Experimental Watershed in 

New Mexico and the Nevada Test Site. DOE also compared MASSIF results with results 

from the Richards-equation-based models for one-dimensional, stylized problems with 

varying soil and plant root depths. The NRC staff reviewed the DOE comparisons of 

MASSIF and Richards-based-equation model results with measured data and the stylized 

one-dimensional problem. On the basis of this review, the NRC staff notes that the water 

storage and evapotranspiration submodels in MASSIF adequately represent both the measured 

data and the Richards-equation-based model results. The staff notes that the MASSIF and the 

Richards-equation-based model have similar responses, and that the two numerical models 

track the observations to a quantitatively similar degree of accuracy. 

In another comparison with measured data, DOE contrasted MASSIF results against 

measurements from streamflow gauges in three subwatersheds at Yucca Mountain for 

several storm events, as outlined in SAR Section 2.3.1.3.4.1 and SNL Section 7.1.3 (2007az). 

SAR Figure 2.3.1-46 illustrated that the timing and magnitude of measured and modeled runoff 

are reasonably well matched with a particular set of input properties that lie within the 

uncertainty range considered in the net infiltration model. The input properties used to match 

streamflow observations represent the nominal properties with soil hydraulic conductivity 

adjusted to increase upland runoff and enhance channel infiltration. DOE indicated that local 

variations within the watersheds may have also been a factor in the comparison. On the basis 

of the adjustments DOE made to match streamflow observations, the uncertain input parameter 

distributions used for performance assessment calculations may, in the NRC staff’s view, create 

a bias toward overestimating the fraction of MASSIF-calculated total net infiltration that upland 

infiltration contributes relative to channel infiltration. 

The NRC staff recognizes, however, that uncertainty in runoff only affects the distribution of 

infiltration and does not affect the areal-average infiltration; it is this latter factor that is of greater 

importance to repository system performance. Thus, the surface runoff submodel provides a 

reasonable basis for predicting runoff over the entire repository for the next 10,000 years 

because (i) the surface runoff submodel algorithms are commonly used; (ii) DOE showed that, 

with appropriate input parameters, the MASSIF surface runoff submodel is capable of providing 

a reasonable match to observed runoff during storm events within representative 

subwatersheds; (iii) the DOE results are comparable to an independent, alternative model for 

runoff and infiltration (Woolhiser, et al., 2000aa, 2006aa); (iv) the uncertainty in net infiltration 

stemming from uncertain runoff is small; and (v) the DOE performance assessment calculations 

are not sensitive to different representations of spatial variability in net infiltration, as described 

in DOE, Enclosure 4 (2009cr). A more general evaluation of uncertainty in spatial variability is 

in a subsequent section of TER 2.2.1.3.5.3.3 entitled “Net Infiltration Results.” 

Input Parameters 

This subsection addresses characterization of data and propagation of data uncertainty. 

NRC staff understands that most net infiltration within the proposed repository footprint occurs in 

shallow soil as a pulse over a few days to weeks following large precipitation events during 
8-18 

periods of low potential evapotranspiration. This understanding is based in part on the DOE 

documentation of its infiltration modeling. Further in this regard, the NRC staff has experience 

with infiltration processes and modeling at Yucca Mountain (Stothoff, 2008aa, and references 

therein). During the short intervals with large net infiltration pulses, which dominate the 

long-term-average net infiltration, the flux of water passing into the bedrock in shallow soil can 

dominate evapotranspiration in the water balance. Accordingly, the NRC staff focused its 

review in this section on aspects of the site affecting rapid transmission of pulses to bedrock in 

shallow soil; in particular, soil water storage. 

Available soil water storage during a precipitation event depends on soil depth, soil 

water-holding capacity, and the antecedent soil moisture content (i.e., how dry the soil 

column is prior to the event). Soil and bedrock hydraulic properties affect the rate of soil 

water movement toward and into the bedrock. In DOE’s representation, water drains into the 

bedrock once the water storage capacity of the overlying soil layers is exceeded, thereby 

avoiding evapotranspiration. The drainage rate into the bedrock is controlled by the layer, 

soil or bedrock, with the smaller value bulk permeability. Using analyses reported in SNL 

Section 7.1.4 (2007az), DOE stated that net infiltration estimates are not sensitive to uncertainty 

in bulk bedrock permeability, in part because of the limited spatial extent where bedrock controls 

the drainage rate. Therefore, NRC staff’s review focuses on the soil depth, soil water-holding 

capacity, and changes to hydrologic input properties in future climates. Soil water-holding 

capacity depends on the soil unsaturated hydraulic properties. 

Areas with thin soil cover are particularly important because DOE identified infiltration as most 

readily occurring in shallow soil. Areas with shallow, or thin, soils comprise 70 percent of the 

unsaturated model domain (Soil Depth Class 4, SAR Section 2.3.1.3.2.1.3) and appear to cover 

an even larger fraction of the repository footprint (SAR Figure 2.3.1-19). In DOE Enclosure 5 

(2009cr), DOE identified soil depth in areas with shallow soil cover as the most important 

hydrologic property input for the infiltration model, with model results approximately as sensitive 

to uncertainty effective soil depth as uncertainty in precipitation. The effective soil depth is 

defined as the single soil depth value that, if applied everywhere, yields the same areal-average 

net infiltration as the actual soil-depth distribution. 

Soil Depth Class 4 represents soil depths between 0 and 0.5 m [0 and 1.6 ft] and corresponds 

to eolian deposits with various mixtures of entrained rock on hillslopes and ridgetops. DOE 

sampled a single sampled effective soil depth value to characterize Soil Depth Class 4 for each 

realization and assigned the value to every grid cell representing that class for the 

corresponding simulation. DOE used two datasets to support its effective soil depth distribution 

for this class: (i) 35 site observations recorded as point measurements ranging from depths of 

0 to 3 m [0 to 9.8 ft], with a recommended median of 0.25 m [0.82 ft], as described in SNL 

Table 6.5.2.4-2(a) (2007az), and (ii) 8 site observations recorded as general site characteristics 

at locations such as drill pads. DOE described the measurements as approximately lognormally 

distributed and, on the basis of geometric and arithmetic means of the two sets of observations, 

derived bounds on effective soil depth ranging from 0.1 to 0.5 m [0.33 to 1.6 ft]. DOE also 

analyzed 56 NRC soil depth measurements (Fedors, 2007aa) obtained from site visits focusing 

on the thin soils of the east-trending upper washes over the southern half of the repository 

footprint. DOE described the available NRC staff’s measurements as approximately following a 

lognormal distribution. 

For performance assessment calculations, DOE described the effective soil depth in Soil 

Class 4 as equally likely for any value between the upper and lower bounds. In selecting the 

uniform statistical distribution, DOE considered the difficulty in measuring soil depth, uncertainty 
8-19 

in the mean of the observations, and uncertainty in how soil depth affects net infiltration, as 

detailed in DOE, Enclosure 5 (2009cr). In the same document, DOE stated that sensitivity 

results in SNL Appendix H (2007az) indicate that calculated areal-average net infiltration is 

approximately linearly dependent on effective soil depth for Soil Class 4, and that shallow soil 

depths are not underrepresented in the effective soil depth distribution for Soil Depth Class 4. 

From this, DOE concluded that use of a uniform distribution for effective soil depth does not 

underestimate average net infiltration. 

NRC staff notes that DOE adequately characterized the statistical properties of the observations 

for use in the net infiltration modeling from two perspectives. First, DOE provided reasonable 

bounds on the uncertainty in effective soil depth in Soil Depth Class 4. A shallower extreme 

would require that more than half of the area with shallow soil has a soil depth less than 0.1 m 

[0.33 ft], which, in contrast to observations, implies extensive exposures of bare rock in the 

infiltration model domain; a deeper extreme would reduce net infiltration. Second, the NRC 

staff’s confirmatory field investigations (Stothoff, 2008ab) indicate that comparable topographic 

locations on hillslopes and ridgetops have similar soil depths across the repository footprint, 

consistent with the DOE description of an eolian source for the fine component of the soil. 

NRC staff considered the effect of uncertainty in the soil depth distribution for Soil Depth Class 4 

on the estimate of net infiltration. As described in SNL Table 6.5.2.4-2(a) and Section 7.2.4(a) 

(2007az) and DOE, Enclosure 5 (2009cr), DOE used a uniform distribution, while noting that the 

measured data may better fit a lognormal distribution. To assess the DOE representation of soil 

depth uncertainty, the NRC staff used DOE’s sensitivity analyses for fixed aleatory uncertainty 

under present-day and glacial transition climate states, as outlined in SNL Figures H-3, H-4, 

H-11, and H-12 (2007az), to estimate the consequence of decreasing median soil depth. Using 

this alternative representation for how uncertainty might be distributed, the NRC staff’s analysis 

suggested areal-average net infiltration would increase by 43 to 61 percent under the 

present-day climate state and by 29 to 38 percent under the glacial transition climate state if 

mean soil depth decreased from 0.3 m to 0.2 m [0.98 ft to 0.65 ft] for Soil Depth Class 4 

(Stothoff, 2010aa, Section 3). DOE, Enclosure 5 (2009bo) showed that increasing average net 

infiltration over the ambient flow model domain by percentages greater than 61 percent, due to 

uncertainty in the probability weights of the Generalized Likelihood Uncertainty Estimation 

methodology, did not strongly affect DOE’s performance assessment calculations. Because the 

increase (29 to 61 percent) in net infiltration calculated by the NRC staff using an alternative 

representation of the uncertainty of soil depth distribution is significantly smaller than the 

increase DOE estimated for uncertainty in net infiltration due to the Generalized Likelihood 

Uncertainty Estimation weights, the NRC staff notes that uncertainty in soil depth distribution of 

Soil Depth Class 4 is also not important to performance. The NRC staff notes that DOE 

reasonably represents effective soil depth in Soil Depth Class 4. This is because the bounds on 

the uncertainty distribution for net infiltration are sufficiently wide to cover the effects of 

uncertainty in soil depth. 

Water-holding capacity is calculated from soil hydraulic properties (i.e., porosity and water 

retention characteristics). DOE utilized a pedotransfer function derived for Hanford soils to 

relate Yucca Mountain soil texture to hydraulic properties for each soil group in the infiltration 

model domain. In the MASSIF model, larger water-holding capacity values result in smaller 

values of net infiltration. Because the hydrologic property relationship to soil texture may be 

different for soils from Hanford, Washington, compared to that for soils at Yucca Mountain, 

DOE, Enclosure 6 (2009cr) compared water-holding capacity used in MASSIF with two 

estimates made for local Yucca Mountain soils. The first set covers soils in Nye County, and 

the second set covers soils from the Yucca Mountain area but not used in the MASSIF model. 
8-20 

DOE, Enclosure 6 (2009cr) stated that the estimates of water-holding capacity used in MASSIF 

are smaller than those estimated for the other two sets of soils. On the basis of its review of this 

information, NRC staff notes that the development of average water-holding capacity values for 

soils from the Hanford-based pedotransfer function does not lead to underestimation of 

water-holding capacity for Yucca Mountain soils. 

NRC staff considered the performance consequence of the DOE assumption that all geologic 

and geographic parameters in the infiltration model remain the same over the transition from 

dryer to wetter climates during the next 10,000 years. DOE Enclosure 2 (2009cr) described 

changes to soil depth, soil hydraulic properties, and bulk bedrock permeability under future 

climates that may include (i) greater chemical soil profile development and enhanced 

weathering of bedrock at the interface with soil, (ii) relatively larger soil depths and different soil 

depth distributions, and (iii) relatively smaller amounts of caliche filling bedrock fractures at the 

soil/bedrock interface. In DOE Enclosure 2 (2009cr), DOE described the potential 

consequences as either inconsequential or beneficial to repository performance: 

 Projected changes to soil depth and soil hydraulic properties would tend to reduce the 

estimates of net infiltration. The NRC staff notes that DOE Enclosure 2 (2009cr) 

description of projected changes is consistent with current understanding of geomorphic 

responses to climate change in the desert Southwest [e.g., Bull, Section 2.5 (1991aa)]. 

 Where bedrock permeability values are greater than soil permeability, DOE 

considered the effect of a change in modeled bedrock properties as either 

inconsequential to net infiltration (if bedrock permeability increased) or to reducing 

infiltration (if bedrock permeability became smaller than the soil permeability). For more 

than half the ambient site-scale unsaturated zone modeling domain, DOE Enclosure 2 

(2009cr) stated that bedrock permeability is greater than soil permeability. 

 In the remaining area, DOE considered net infiltration as having a potential to increase 

only in the realizations where sampled bedrock permeability is smaller than soil 

permeability in the present climate, and only if bedrock permeability increases under a 

future climate. The NRC staff notes that this potential exists in less than half of the 

modeling domain for approximately half the realizations. The NRC staff calculated an 

upper-bound estimate for areal-average net infiltration that is 1.33 times larger than the 

DOE-calculated value, conservatively assuming that (i) half the area in half the 

realizations has low bedrock permeability and zero infiltration, (ii) all DOE-calculated net 

infiltration occurs in the remaining area with high bedrock permeability, (iii) increased 

bedrock permeability results in an upper bound of the areal-average net infiltration from 

the high bedrock permeability, (iv) the entire area has high bedrock permeability in all 

realizations, and (v) projected changes to soil depth and soil hydraulic properties do not 

result in lower net infiltration. The NRC staff notes that this upper-bound increase in 

areal-average net infiltration is less than the increase that DOE provided in DOE 

Enclosure 5 (2009bo) and is not significant to repository performance. 

NRC staff notes that DOE’s approach to maintain constant, but uncertain, soil depth and 

hydraulic properties and bulk bedrock permeability over the initial 10,000 years of performance 

is not likely to lead to consequential increases in areal-average net infiltration, because most 

expected changes to these properties would tend to reduce net infiltration in DOE’s model. 

Further, DOE showed that upper-bound estimates for potential increases in net infiltration do not 

significantly change performance assessment results. 
8-21 

Net Infiltration Results 

This subsection addresses propagation of uncertainty and support of model output for DOE’s 

estimates of net infiltration for each climate state. Effects of net infiltration model results on 

repository performance are considered in how the infiltration model output is used in the 

unsaturated flow model, including seepage and unsaturated zone transport. NRC staff 

reviewed the net infiltration results in the context of average ratio of infiltration to precipitation, 

values of areal-average net infiltration, and spatial and temporal distribution of net infiltration. 

DOE represents uncertain inputs to the MASSIF model with Monte Carlo sampling, using 

40 realizations of selected hydraulic properties and climate characteristics to estimate net 

infiltration uncertainty for a climate state (SAR Section 2.3.1.3.3). For each realization, DOE 

calculated a process-level map of mean annual net infiltration by (i) creating a synthetic weather 

history representing 1,000 years, (ii) selecting 10 water years (a water year is October 1 through 

September 30 of the following year) out of the 1,000-year history to represent the range of dry to 

wet years, (iii) calculating total net infiltration for each water year using MASSIF, and 

(iv) averaging the 10 water-year net infiltration maps. DOE selected 4 of the 40 equally likely 

process-level mean annual net infiltration maps to represent the uncertainty in infiltration by 

(i) calculating areal-average net infiltration for each map, (ii) ranking the average values from 

low to high, and (iii) selecting the 4th, 12th, 20th, and 36th ranked map to represent the 10th, 30th, 

50th, and 90th percentile ranking. The 12 maps of net infiltration are output provided to the 

unsaturated zone model for use as top boundary flux for the first 10,000 years. Because DOE 

used a standard approach for propagation of data uncertainty and conservatively assumed each 

stochastic realization was equally likely to occur, DOE reasonably propagated uncertainty in 

climate and hydrologic parameter inputs in development of the net infiltration maps. 

DOE adjusted 4 of the 12 upper-boundary net infiltration maps developed for the first 

10,000 years after closure to represent the probability distribution for deep percolation at the 

repository horizon for the post-10,000-year period. DOE selected the four upper-boundary net 

infiltration maps with the largest areal-average net infiltration within the repository footprint for 

the scaling procedure. The NRC staff reviews the procedure and technical basis for developing 

the post-10,000-year unsaturated zone model upper-boundary net infiltration maps in TER 

Section 2.2.1.3.6.3.2. 

In NRC staff’s analysis, three primary aspects of the response of net infiltration to climate 

may affect the performance of the proposed repository: (i) central tendency and uncertainty 

in areal-average time-averaged net infiltration, (ii) spatial variability in time-averaged net 

infiltration, and (iii) temporal changes in net infiltration. The NRC staff identified systematic 

changes in seepage as the dominant performance-affecting consequence of spatial and 

temporal variability in percolation fluxes below the PTn (TER Section 2.2.1.3.6.3.2). Through 

evaluation of DOE sensitivity analyses, the NRC staff notes in TER Section 2.2.1.3.6.3.2 that 

DOE’s performance assessment results are not strongly affected by any of the following factors: 

(i) systematic changes in seepage arising from different representations of local flow focusing, 

(ii) long-term climatic variability, (iii) episodic infiltration pulses, or (iv) calibration of infiltration 

uncertainty. On the basis of this, the NRC staff also notes that spatial or temporal variability in 

net infiltration has little effect on performance assessment unless such variability results in a 

systematic change in areal-average net infiltration above the repository of comparable or 

greater magnitude than considered in DOE’s calibration of infiltration uncertainty. 

NRC staff compared the average reduction in precipitation that becomes net infiltration 

calculated for Yucca Mountain (SAR Tables 2.3.1-2 through 2.3.1-4) with reductions for 
8-22 

other sites reported in the general literature. For Yucca Mountain, the ratio of calculated 

areal-average net infiltration to mean areal-average precipitation ranges from 0.022 to 0.154 

for the present-day climate state, 0.023 to 0.191 for the monsoon climate state, and 0.047 to 

0.166 for the glacial transition climate state. These areal-average estimates represent the 

entire MASSIF model domain. The NRC staff notes that these ratios are consistent with 

infiltration estimates from other arid and semi-arid sites in Nevada and the western United 

States with comparable precipitation rates [e.g., Stothoff and Musgrove (2006aa); SNL 

Section 7.2.1.2 (2008az)]. On the basis of the comparison of DOE estimates for Yucca 

Mountain with other sites in Western United States, NRC staff notes that the DOE estimates of 

areal-average net infiltration and its uncertainty are supported by information from relevant 

analog sites. 

Using NRC staff’s independently developed infiltration model and extensive independent 

staff field and modeling confirmatory investigations related to infiltration at Yucca Mountain 

(e.g., Stothoff, 2009aa, 2008ab, 1999aa, 1997aa, 1995aa; Stothoff, et al., 1999aa; Groeneveld, 

et al., 1999aa; Woolhiser, et al., 2000aa, 2006aa; Stothoff and Musgrove, 2006aa; Stothoff and 

Walter, 2007aa), the NRC staff further assessed DOE’s calculated uncertainty regarding 

areal-average net infiltration estimates for the climate states used in the DOE performance 

assessment. The NRC staff’s Infiltration Tabulator for Yucca Mountain (Stothoff, 2008aa) is an 

independent numerical model to estimate the uncertainty in net infiltration at Yucca Mountain. 

The Infiltration Tabulator for Yucca Mountain uses the same starting site characteristics of soil 

and bedrock maps and bedrock matrix hydraulic properties as the MASSIF model. The 

Infiltration Tabulator for Yucca Mountain differs from MASSIF in its computational approaches; 

conceptual models for water redistribution in subsurface overland flow and evapotranspiration; 

and input parameters for soil depths, soil and bedrock hydraulic properties, and topography. 

The NRC staff compared outputs from the DOE and NRC models and notes that the models 

produce comparable near-lognormal distributions for calculated areal-average net infiltration 

when using similar uncertainty in input parameters. Similarly, both models show that soil 

thickness and the soil retention characteristics are the two dominant factors (after precipitation) 

controlling net infiltration. DOE’s model and the independent NRC model have generally 

comparable representations for uncertainty in areal-average net infiltration. 

On the basis of these comparisons, the NRC staff notes that (i) each set of 4 net infiltration 

maps used to represent a climate state in the first 10,000 years after closure falls within or 

above the range of estimates represented by other sites with comparable arid climatic 

conditions and (ii) DOE’s uncertainty distribution reasonably represents uncertainty in 

areal-average net infiltration. 

In support of its consideration of spatial variability in DOE’s net infiltration in the vicinity of the 

proposed repository footprint, DOE provided an analysis in DOE Enclosure 4 (2009cr) that 

considers the consequences on performance from a variant property set that favors focused 

(channel) infiltration instead of distributed infiltration. The variant property set was based on 

simulations of observations in Pagany Wash. DOE considered the consequences of both 

spatial and temporal aspects of the variant property set with the infiltration model from two 

washes, stating that the consequences of a focused infiltration pattern are insignificant to 

repository performance calculations. The base case and variant simulations yield similar 

calculated values of areal-average net infiltration, with somewhat less net infiltration within 

the repository footprint for maps with larger total net infiltration, as described in SNL, 

Table 7.1.3.2-1(a) (2007az). DOE explained that the similarity in areal-average net infiltration 

arises from conservation of mass, in that the water infiltrating into channels in the variant 
8-23 

case would have otherwise infiltrated into hillslopes without the enhanced runoff. In other 

words, redistributing water through overland flow does not appreciably increase areal-average 

net infiltration in DOE’s model. NRC staff notes that the uncertainty in areal-average 

net infiltration above the repository related to different spatial patterns of infiltration is 

small relative to other sources of uncertainty that DOE considered. DOE showed 

that a reasonable alternative representation of spatial variability did not appreciably increase 

areal-average net infiltration. 

In TER Section 2.2.1.3.5.3.2, NRC staff notes that DOE’s representations of temporal variability 

in climate and meteorology are both reasonable for the intended use. The following discussion 

considers temporal variability in DOE’s net infiltration estimates with respect to temporal 

resolution of net infiltration calculations and systematic effects on net infiltration. 

The NRC staff considers the dominant way that temporal resolution may affect net infiltration 

estimates is in the partitioning between infiltration and runoff during precipitation events. 

This is because runoff calculations may be sensitive to the detailed representation of 

precipitation during storm events when the soil cannot accept all of the precipitation. By using 

a daily timestep in the net infiltration model simulations, DOE elected not to consider peak flow 

rates within a day, either as subsurface water movement or as runoff, as detailed in SNL 

Section 6.2.3 (2007az). As a consequence of not considering peak flow rates within a day, 

the spatial patterns of net infiltration may be affected by differences in runoff calculations. 

DOE supported the selected timestep by illustrating in SNL Appendix H (2007az) and DOE 

Enclosure 4 (2009cr) that calculated areal-average net infiltration has little sensitivity to 

uncertainty in the relationship between daily precipitation and precipitation duration. Because 

the treatment of temporal resolution of precipitation affects the partitioning between infiltration 

and runoff—which in turn affects the spatial patterns of infiltration—the NRC staff compared the 

uncertainty of different spatial patterns of infiltration with the other sources of uncertainty that 

DOE incorporated into the performance assessment. DOE reasonably represented temporal 

resolution in net infiltration calculations because (i) uncertainty in areal-average net infiltration 

above the repository related to different spatial patterns of infiltration is small relative to other 

sources of uncertainty that DOE considered and (ii) NRC staff noted in TER Section 

2.2.1.3.6.3.2 that spatial variability itself does not significantly affect performance. 

In summary, DOE’s representation of net infiltration for the first 10,000 years is reasonable for 

use in performance assessment calculations. This is because the range and uncertainty of 

areal-average infiltration within the repository vicinity is consistent with (i) available information 

on infiltration in arid environments and (ii) NRC staff’s independent modeling (Stothoff and 

Walter, 2007aa). Further, because saturated zone model results are less sensitive to 

uncertainty in net infiltration estimates than unsaturated zone model results, staff’s evaluations 

regarding net infiltration results for unsaturated zone flow and transport also hold for the 

saturated zone. Additionally, available information suggests that DOE’s representation of 

spatial and temporal variability in net infiltration is likely to have a small influence on release, 

transport, and expected dose calculations compared to areal-average infiltration. 

2.2.1.3.5.4 NRC Staff Conclusions

The NRC staff notes that the DOE description of this model abstraction for climate and 

infiltration is consistent with the guidance in the YMRP, as supplemented by guidance in NRC 

(2009ab). NRC staff also notes that the DOE technical approach discussed in this chapter is 

reasonable for use in the Total System Performance Assessment (TSPA). 
8-24 

2.2.1.3.5.5 References

Allen, R.G., I.A. Walter, R.L. Elliott, T. Howell, D. Itenfisu, and M. Jensen. 2005aa. The ASCE 

Standardized Reference Evapotranspiration Equation. Reston, Virginia: American Society of 

Civil Engineers. 

Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998aa. “Crop Evapotranspiration: 

Guidelines for Computing Crop Water Requirements.” FAO Irrigation and Drainage Paper 56. 

Rome, Italy: Food and Agriculture Organization of the United Nations. 

Bull, W.B. 1991aa. Geomorphic Responses to Climate Change. New York City, New York: 

Oxford University Press. 

Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K. Kolli, 

W.-T. Kwon, R. Laprise, V. Magana Rueda, L. Mearns, C.G. Menendez, J. Raisanen, A. Rinke, 

A. Sarr, and P. Whetton. 2007aa. In Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the 4th Assessment Report of the Intergovernmental Panel 

on Climate Change. S. Solomon, D. Quin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, 

M. Tignor, and H.L. Miller, eds. Cambridge, United Kingdom and New York City, New York: 

Cambridge University Press. 

DOE. 2010ai. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.3.1), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.3.5, Set 1 and (Safety Analysis Report Sections 2.3.2 

and 2.3.3), Safety Evaluation Report Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (February 2) 

J.R. Williams to J.H. Sulima (NRC). ML100340034. Washington, DC: DOE, Office of 

Technical Management. 

DOE. 2009bo. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.2 and 2.3.3), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (June 1) J.R. Williams to J.H. Sulima (NRC). 

ML091530403. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cr. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.5, Set 1.” Letter (June 24) J.R. Williams to J.H. Sulima (NRC). ML0918308480 

and ML0918300710. Washington, DC: DOE, office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

Fedors, R. 2007aa. “Soil Depths Measured at Yucca Mountain During Site Visits in 1998.” 

Note (January 9) R. Fedors to J. Guttmann (NRC). ML063600082. Washington, DC: NRC. 

Forester, R.M., J.P. Bradbury, C. Carter, A.B. Elvidge-Tuma, M.L. Hemphill, S.C. Lundstrom, 

S.A. Mahan, B.D. Marshall, L.A. Neymark, J.B. Paces, S.E. Sharpe, J.F. Whelan, J.F. Wigand, 

and P.E. Wigand. 1999aa. “The Climatic and Hydrologic History of Southern Nevada 

During the Late Quaternary.” USGS Open-File Report 98-635. Denver, Colorado: 

U.S. Geological Survey. 
8-25 

Groeneveld, D.P., S.A. Stothoff, and R. Fedors. 1999aa. “Weedy Brome Grasses and Their 

Potential Effect on the Infiltration and Recharge Rates in the Vicinity of Yucca Mountain, 

Nevada.” San Antonio, Texas: CNWRA. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007az. “Simulation of Net Infiltration for Present-Day and Potential Future Climates.” 

MDL–NBS–HS–000023. Rev. 01. AD 01, ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2006aa. “Data Analysis for Infiltration Modeling: Extracted Weather Station Data 

Used To Represent Present-Day and Potential Future Climate Conditions in the Vicinity of 

Yucca Mountain.” ANL–MGR–MD–000015. Rev. 00. ACN 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

Stothoff, S.A. 2010aa. “Infiltration and Unsaturated Zone Confirmatory Analyses.” Electronic 

Scientific Notebook 1005E. ML111110877. San Antonio, Texas: CNWRA. 

Stothoff, S.A. 2009aa. “Subsurface Observations Related to Infiltration at Yucca Mountain.” 

ML093160095. San Antonio, Texas: CNWRA. 

Stothoff, S.A. 2008aa. “Infiltration Tabulator for Yucca Mountain: Bases and Confirmation.” 

CNWRA Report 2008-001. ML082350701. San Antonio, Texas: CNWRA. 

Stothoff, S.A.. 2008ab. “Compilation of Prelicensing Field Observations Related to Infiltration at 

Yucca Mountain and Future-Climate Analogs.” ML082000174. San Antonio, Texas: CNWRA. 

Stothoff, S.A. 1999aa. “Infiltration Abstractions for Shallow Soil Over Fractured Bedrock in a 

Semiarid Climate.” ML061370364. San Antonio, Texas: CNWRA. 

Stothoff, S.A. 1997aa. “Sensitivity of Long-Term Bare Soil Infiltration Simulations to Hydraulic 

Properties in an Arid Environment.” Water Resources Research. Vol. 22, No. 4. pp. 547–558. 

Stothoff, S.A. 1995aa. NUREG/CR–6333, “BREATH Version 1.1—Coupled Flow and Energy 

Transport in Porous Media: Simulator Description and User Guide.” Washington, DC: NRC. 

Stothoff S. and M. Musgrove. 2006aa. “Literature Review and Analysis: Climate and 

Infiltration.” CNWRA Report 2007-002. ML063190115. San Antonio, Texas: CNWRA. 
8-26 

Stothoff, S. and G. Walter. 2007aa. “Long-Term-Average Infiltration at Yucca Mountain, 

Nevada: Million-Year Estimates.” CNWRA Report 2007-003. ML072760607. San Antonio, 

Texas: CNWRA. 

Stothoff, S.A., D. Or, D.P. Groeneveld, and S.B. Jones. 1999aa. “The Effect of Vegetation on 

Infiltration in Shallow Soils Underlain by Fissured Bedrock.” Journal of Hydrology. Vol. 218. 

pp. 169–190. 

Winograd, I.J., A.C. Riggs, and T.B. Coplen. 1998aa. “The Relative Contributions of Summer 

and Cool-Season Precipitation to Groundwater Recharge, Spring Mountains, Nevada, USA.” 

Hydrogeologic Journal. Vol. 6, No. 1. pp 77–93. 

Woolhiser, D. R.W. Fedors, R.E. Smith, and S.A. Stothoff. 2006aa. “Estimating Infiltration in 

the Upper Split Wash Watershed, Yucca Mountain, Nevada.” Journal of Hydrologic 

Engineering. Vol. 11, No. 2. pp 123–133. 

Woolhiser, D.A., S.A. Stothoff, and G.W. Wittmeyer. 2000aa. “Estimating Channel Infiltration 

From Surface Runoff in the Solitario Canyon Watershed, Yucca Mountain, Nevada.” Journal of 

Hydrologic Engineering. Vol. 5, No. 3. pp. 240–249. 
9-1 

CHAPTER 9 

2.2.1.3.6 Unsaturated Zone Flow 

2.2.1.3.6.1 Introduction

This chapter of the Technical Evaluation Report (TER) addresses the U.S. Department of 

Energy’s (DOE’s) abstraction of groundwater flow in that portion of the repository system 

above the water table. DOE presented this information in its Safety Analysis Report (SAR) 

of June 3, 2008 (DOE, 2008ab), and subsequent update of February 19, 2009 (DOE, 2009av). 

Although information from other sections of the SAR is cited in the review of the unsaturated 

zone flow abstractions, the primary SAR Sections used are 2.3.2 (Unsaturated Zone Flow), 

2.3.3 (Water Seeping into Drifts), and 2.3.5.4 (In-Drift Thermohydrological Environment). 

The proposed Yucca Mountain repository site has up to 400 m [1,300 ft] of variably saturated 

rock between the ground surface and the repository, and at least 200 m [650 ft] between the 

repository and the underlying water table (SAR Sections 2.1.1.1 and 2.1.1.3). Water percolating 

through the unsaturated zone may enter the drifts, thereby providing the means to interact with 

and potentially corrode the waste packages. Water percolating through the unsaturated zone 

below the repository also provides flow pathways for transporting radionuclides downward to the 

water table. Once radionuclides pass below the water table, they may move laterally within the 

saturated zone to the accessible environment. In this chapter, the term “unsaturated zone flow” 

includes not only flow processes in the host rock under ambient and thermally perturbed 

conditions, but also in-drift hydrological processes related to flow through natural rubble and 

in-drift convection and condensation. Unsaturated flow both above and below the repository 

horizon is addressed in this chapter. 

The unsaturated zone plays a role in two of the DOE-defined barriers: the Upper Natural Barrier 

and the Lower Natural Barrier (SAR Section 2.3.2). These barriers are reviewed in TER 

Sections 2.2.1.1.3.2. Together with Climate and Infiltration (reviewed in TER Section 2.2.1.3.5), 

processes in the unsaturated zone above the repository comprise the Upper Natural Barrier. 

They influence system performance through the amount of water reaching the Engineered 

Barrier System and their control on hydrological conditions in the drift. In DOE’s model of the 

nominal scenario, the Quantity and Chemistry of Water Contacting Engineered Barriers and 

Waste Forms (reviewed in TER Section 2.2.1.3.3), Degradation of Engineered Barriers 

(reviewed in TER Section 2.2.1.3.1), and Radionuclide Release Rates and Solubility Limits 

(reviewed in TER Section 2.2.1.3.4) abstractions use in-drift liquid water, relative humidity, and 

temperature to assess the potential for corrosion of waste packages, release of waste, and 

transport to the natural system. In the disruptive scenarios of seismic and igneous intrusion 

(reviewed in TER Sections 2.2.1.3.2 and 2.2.1.3.10), DOE’s model uses the flux of water to 

assess the movement of radionuclides to the natural system below the repository. The portion 

of the unsaturated zone below the repository is part of the Lower Natural Barrier. The 

magnitude and distribution of flux in the unsaturated zone below the repository are used to 

determine the flow pathways for Radionuclide Transport in the Unsaturated Zone (reviewed in 

TER Section 2.2.1.3.7). The unsaturated zone below the repository links the repository 

Engineered Barrier System to the Saturated Zone Flow and Transport System (reviewed in TER 

Sections 2.2.1.3.8 and 2.2.1.3.9) and ultimately to the biosphere in the accessible environment 

(reviewed in TER Sections 2.2.1.3.12 to 2.2.1.3.14). 
9-2 

The purpose of this chapter is to review DOE estimates of the 

 Magnitude and distribution of the mass flux of water (percolation) moving through the 

unsaturated zone and reaching the drift 

 Amount and distribution of liquid water seeping into the drift, contacting the engineered 

barriers (i.e., drip shield), and becoming available to carry radionuclides out of the drift 

and into the natural environment 

 Environmental conditions inside the drift (i.e., temperature, relative humidity, and 

moisture redistribution and condensation) 

 Magnitude and distribution of flux in the unsaturated zone below the repository as is 

important for transport of radionuclides 

2.2.1.3.6.2 Evaluation Criteria

The U.S. Nuclear Regulatory Commission (NRC) staff’s review of the model abstractions used 

in DOE’s postclosure performance assessment, including those considered in this chapter for 

unsaturated zone flow, is guided by 10 CFR 63.114 (Requirements for Performance 

Assessment) and 63.342 (Limits on Performance Assessments). 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment, and evaluate in sufficient detail 

those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is given in TER Section 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of geologic 

stability, defined in 10 CFR 63.302 as 1 million years following disposal. These sections require 

that through the period of geologic stability, with specific limitations, DOE 
9-3 

 Use performance assessment methods consistent with the performance assessment 

methods used to demonstrate compliance for the initial 10,000 years following 

permanent closure [10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

For this model abstraction of unsaturated zone flow, 10 CFR 63.342(c)(1) provides additional 

requirements for assessing the effects of seismic and igneous activity on the repository 

performance, subject to the probability limits in 10 CFR 63.342(a) and (b). Specific constraints 

on the seismic and igneous activity analyses are in 10 CFR 63.342(c)(1)(i) and (ii), respectively. 

In addition, for this model abstraction of unsaturated zone flow, 10 CFR 63.342(c)(2) addresses 

the assessment of climate change after 10,000 years by using a constant-in-time specification 

of the mean and uncertainty distribution for repository-average deep percolation rate for the 

period from 10,000 to 1 million years. DOE elected to use this representation in its SAR. Thus, 

implementation of the specified representative percolation rate and its uncertainty distribution is 

reviewed for the post-10,000-year period. 

The NRC staff’s review of the SAR and supporting information follows the guidance provided 

in the Yucca Mountain Review Plan (YMRP) (NRC, 2003aa) Section 2.2.1.3.6, Unsaturated 

Zone Flow, as supplemented by additional guidance for the period beyond 10,000 years 

after permanent closure (NRC, 2009ab). The YMRP acceptance criteria for model 

abstractions that provide guidance for the NRC staff’s evaluation of DOE’s abstraction of 

unsaturated zone flow are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

The NRC staff’s review used a risk-informed approach and the guidance in the YMRP, as 

supplemented by NRC (2009ab), to the extent reasonable for aspects of the unsaturated zone 

flow important to repository performance. The NRC staff considered all five YMRP criteria in its 

review of information provided by DOE. In the context of these criteria, only those aspects of 

the model abstraction that substantively affect the performance assessment results, as 

assessed by the NRC staff, are discussed in detail in this chapter. The NRC staff’s assessment 

is based both on risk information provided by DOE and on NRC staff knowledge gained through 

experience and independent analyses. 

2.2.1.3.6.3 Technical Evaluation 

The review of the technical information DOE provided for unsaturated zone, seepage, and 

in-drift hydrological conditions in this section is divided into six subsections. The first subsection 

is an overview of the DOE description of processes and models, and a summary of results for 

the entire unsaturated zone flow area of review. The overview (TER Section 2.2.1.3.6.3.1) 

provides context for and reviews the integration between models and results separately 

evaluated in the remaining five subsections (TER Sections 2.2.1.3.6.3.2–2.2.1.3.6.6) within the 

unsaturated zone. The remaining five subsections follow a natural flow through the unsaturated 
9-4 

zone system: (i) ambient flow above the repository, (ii) thermohydrology, (iii) ambient and 

thermal seepage, (iv) in-drift hydrologic conditions, and (v) ambient flow below the repository. 

2.2.1.3.6.3.1 Integration Within the Unsaturated Zone 

In this section, the NRC staff’s review covers a range of processes and features occurring at 

widely disparate spatial and temporal scales within the Upper and Lower Natural Barriers and, 

to a lesser extent, within the Engineered Barrier System. Within this area of review, the NRC 

staff evaluates aspects of repository performance pertaining to 

 Unsaturated zone flow fields (SAR Section 2.3.2) above and below the repository 

 Seepage into drifts (SAR Section 2.3.3) 

 Hydrological aspects of the in-drift environment (SAR Section 2.3.5); in particular, the 

Multiscale Thermal-Hydrologic Model (SAR Section 2.3.5.4.1), the In-Drift Condensation 

Model (SAR Section 2.3.5.4.2), and the thermohydrologic response to the range of 

design thermal loadings (SAR Section 2.3.5.4.3) 

The NRC staff evaluates the DOE treatment of interactions between liquid fluxes and 

engineered barriers inside drifts (i.e., drip shields, waste packages, and inverts) in TER 

Sections 2.2.1.3.3 and 2.2.1.3.4. 

DOE’s unsaturated zone flow models receive input from and provide output to several areas of 

review. SAR Section 2.3.1 provided spatially distributed net infiltration rates for the different 

predicted future climates for use as the top boundary flux of models in the unsaturated zone. 

For outputs, in-drift liquid-phase water, relative humidity, and temperature were used for 

abstractions of (i) chemistry for the in-drift environment (SAR Sections 2.3.5.3, 2.3.5.5), 

(ii) corrosion of engineered components (SAR Section 2.3.6), and (iii) waste form degradation 

and in-drift transport (SAR Section 2.3.7) in the Total System Performance Assessment (TSPA). 

In SAR Section 2.3.5, feedback from thermal-hydrological-chemical models in the host rock 

during the thermal period provided information on the perturbation of hydrological properties 

caused by emplaced waste. Output flow fields from the ambient unsaturated zone 

mountain-scale model (SAR Section 2.3.2), along with radionuclide flux from the Engineered 

Barrier System (SAR Section 2.3.7), were then used by Radionuclide Transport in the 

Unsaturated Zone abstraction (SAR Section 2.3.8). 

This TER section focuses on higher level issues common to each of the unsaturated zone flow 

models, including (i) integration among those models, (ii) representative flow reduction through 

the mountain, and (iii) propagation of uncertainty in performance assessment calculations. 

Integration of Unsaturated Zone Flow Models 

DOE represented water and heat transfer in the unsaturated zone using a variety of 

process-level models covering features and processes at a range of scales from millimeters to 

kilometers [fraction of inches to miles]. In addition to models that provide direct input, DOE 

used additional models to support aspects of conceptual model assumptions and parameter 

input. The models require different computational grids, different data needs, and different 

model support. Because the different models overlap in terms of features, processes, inputs, 

and outputs, the NRC staff reviewed the DOE integration between models. Specifically, the 
9-5 

NRC staff evaluated the spatial continuity of percolation flux (consistent propagation of high and 

low flux patterns) and quantification of barrier capability through the mountain. 

The following list of models, inputs, and outputs used in performance assessment 

calculations provides context to the parts of TER Section 2.2.1.3.6.3. NRC staff evaluation 

in the remainder of TER Section 2.2.1.3.6.3 parallels this list, which follows the flow of water 

through the mountain. 

Ambient Site-Scale Unsaturated Flow Model (SAR Section 2.3.2)

 Receives top flux boundary condition from net infiltration model 

 Creates set of flow fields for TSPA 

— Above-repository flux distribution to the Multiscale Thermal-Hydrologic Model 

— Below-repository flow field for unsaturated zone transport (see last item in 

this list) 

 Reviewed by NRC staff in TER Sections 2.2.1.3.6.3.2 (Above Repository Only) 

Multiscale Thermal-Hydrologic Model (SAR Section 2.3.5.4)

 Composed of a set of five linked process-level thermal and thermohydrology models 

 Creates a set of thermal response abstractions that provides 

— In-drift temperature and relative humidity for chemistry of seepage and corrosion 

of engineered barriers abstractions 

— Deep percolation field for seepage and chemistry models 

— Flux from host rock into invert for Engineered Barrier System transport 

 Reviewed by NRC staff in TER Section 2.2.1.3.6.3.3 

Ambient and Thermal Seepage Models (SAR Section 2.3.3)

 Process-level models used to create seepage abstractions for TSPA that provide 

— Seepage fraction (number of waste packages getting wet) to Engineered Barrier 

System transport 

— Seepage flux to Engineered Barrier System transport 

— Temperature threshold at drift wall, above which no seepage occurs 

 Reviewed by NRC staff in TER Section 2.2.1.3.6.3.4 
9-6 

In-Drift Convection and Condensation Models (SAR Section 2.3.5.4)

 Convection model provides dispersion coefficients to condensation model 

 Condensation model provides flux rate and distribution to Engineered Barrier 

System transport 

 Reviewed by NRC staff in TER Section 2.2.1.3.6.3.5 

Site-Scale Unsaturated Flow Below the Repository (see first item in this list)

 Provides flow field to unsaturated zone transport model 

 Reviewed by NRC staff in TER Section 2.2.1.3.6.3.6 

The NRC staff reviews repository performance with respect to water and heat transfer within 

the unsaturated zone by separately evaluating the individual process models and their 

abstractions (TER Sections 2.2.1.3.6.3.2 through 2.2.1.3.6.3.6) and the overall integration 

among models (the present section). The NRC staff recognizes that it may not be practical to 

use one process-level model to represent the entire suite of features and processes in the 

unsaturated zone for performance assessment calculations. The NRC staff also recognizes that 

linking decoupled models is a standard practice for modeling complex systems at a wide 

expanse of scales, when important dependencies from upstream to downstream models are 

adequately reflected in linked models. On the basis of staff knowledge of general modeling 

practices and staff review of the information that links (is passed between) separate DOE 

models in TER Sections 2.2.1.3.6.3.2 through 2.2.1.3.6.3.6, the NRC staff notes that the 

ensemble of DOE models is a reasonable approach for representing the unsaturated system for 

the performance assessment. 

NRC staff reviewed the information passed between the unsaturated zone models. DOE did not 

strictly follow continuity between models that relate to the transfer of water from the natural 

system into the emplacement drifts and back to the natural system. However, spatial continuity 

was approximately followed between DOE’s larger site-scale models and smaller drift-scale 

seepage models. DOE used five percolation bins to maintain continuity of flow above, through, 

and below drifts for both ambient and thermal periods. Spatial detail is lost in the progression 

from the net infiltration model to the site-scale models and the seepage model, but use of the 

percolation bin approach ensures that high and low seepage zones correspond generally with 

high and low percolation zones above and below drifts. The NRC staff evaluations of spatial 

variability, upscaling, downscaling, and other linkages between models, as appropriate, are 

included in TER Sections 2.2.1.3.6.3.2 through 2.2.1.3.6.3.4. In those TER sections, NRC staff 

notes that DOE’s treatment of spatial variability is reasonable for performance assessment 

because the inclusion of more detailed variation did not significantly increase calculated dose. 

On the basis of the NRC staff review of the DOE technical bases, the NRC staff notes that the 

flux of water is adequately integrated between the unsaturated zone models and between the 

natural system and the Engineered Barrier System. 

NRC staff review also considered the DOE implementation of barrier capabilities, represented 

by changes to percolation flux rates as water moves through the mountain from the ground 

surface into the drifts and onto the water table. Table 9-1 illustrates the quantitative reduction in 

flux from the ground surface to water entering the drift using flux averages over the repository 

footprint. Flux values in the table are from DOE Enclosure 1, Tables 1, 5, and 8 (2010ai), and 
9-7 

seepage fraction values are from SAR Tables 2.1-6 to 2.1-9. The table also provides the 

component of the Upper Natural Barrier, primary features or processes, and the relevant SAR 

section for each step of the flux reduction. 

DOE presented seepage flux values as volumetric flux over the area of a waste package, 

calculated by multiplying seepage flux values determined in units of volumetric flux per unit area 

by a waste package footprint that is 5.1 m [17 ft] long and 5.5 m [18 ft] wide (i.e., drift width). 

To maintain consistent units of flux for this table, the NRC staff divided the DOE-provided 

seepage flux values by the same scaling factor. DOE Enclosure 1, Tables 5 and 8 (2010ai) 

used million-year simulation results to provide the seepage flux and fraction values for the 

10,000-year period, the latter of which may differ slightly from the simulation results used in 

TSPA calculations for 10,000-year dose estimates. Flux values of net infiltration through deep 

percolation retain the significant figures DOE presented. In Table 9-1, seepage fraction is that 

portion of the drifts where dripping is predicted to occur (also called the seeping environment). 

The table includes DOE values for its nominal case (no disruptive events) and seismic ground 

motion scenario. In its igneous intrusion scenario (not included in the table), DOE 

conservatively assumed that seepage processes at the drift wall do not act as a barrier. 

Condensation rates are similarly not included in Table 9-1, because these fluxes are short lived. 

In DOE’s model these have no effect on performance, because drip shields are predicted to 

remain intact beyond the several thousand years to 10,000 years of the thermal period. The 

Table 9-1. Quantitative Reduction in Flux From the Ground Surface to Water Entering the 

Drift Using Flux Averages Over the Repository Footprint 

Precipitation 

mm/yr* 

Net 

Infiltration 

mm/yr*

Unsaturated Zone 

Site-Scale Top 

Boundary Net 

Infiltration 

mm/yr* 

Deep 

Percolation 

mm/yr* 

Seepage 

Repository 

Footprint 

Flux 

mm/yr* 

Fraction 

of Area 

Component of 

Upper Natural 

Barrier 

— Topography 

and Soils — Unsaturated Zone Unsaturated Zone 

Primary Feature 

or Processes 

Semiarid 

Climate 

Evapotranspiration, 

Runoff, 

Infiltration 

Uncertainty in Net 

Infiltration — Capillary Diversion 

and Vapor Barrier 

Section of SAR 2.3.1 2.3.1 2.3.2 2.3.2 2.3.3 

Thermal Period† — — — — 0 0 

At 10,000 yearsI

Nominal 

296.7 38.88 21.37 21.74 

2.0 

{6.4}' 0.31 

At 10,000 yearsI

Seismic 

2.3 

{7.4}' 0.31 

Post-10,000 

years, Nominal — — — 31.83 

3.4 

{8.5}' 0.40 

Post-10,000 

years, Seismic 

15 

{22}' 0.69 

*Units: 25.4 mm/yr = 1 in/yr 

HThermal period defined by drift wall temperature > 100 °C [212 °F] (SAR Section 2.3.3.3.4). 

IValues of precipitation and percolation for initial 10,000 years are for glacial transition climate. 

'Average flux for seeping environment is in brackets. 
9-8 

NRC staff noted in TER Sections 2.2.1.3.1.3.1 and 2.2.1.3.2.6 that DOE reasonably supported 

that drip shields are expected to remain intact for at least 12,000 years and thus will be able to 

divert all water away from waste packages. Representative thermal effects (peak waste 

package temperature, drift wall temperature, duration of drift wall temperatures at or above 

boiling) caused by waste package emplacement were summarized in SAR Tables 2.3.5-7 and 

2.3.5-8 and are not reflected in Table 9-1. 

NRC staff used the list of average flux values in Table 9-1 as a first-order continuity check for 

net infiltration, deep percolation, and seepage in DOE’s process models. The first-order 

continuity check the NRC staff considered in its evaluation is an evaluation of the magnitude of 

change in flux as new processes and features are introduced in the DOE models for the 

unsaturated zone. NRC staff used DOE’s glacial transition climate to represent the first 

10,000 years because (i) the monsoon and glacial transition climates have approximately 

equal flux rates and DOE’s climate model represents these periods as encompassing most 

(94 percent) of the first 10,000 years of performance and (ii) DOE described seepage as 

precluded by above-boiling conditions at the repository horizon during the preceding climate 

state. From the entries in Table 9-1, NRC staff calculates that deep percolation is 7 percent of 

precipitation on average for the glacial transition climate in DOE’s performance assessment 

model. This reduction comes from the two process-model steps that DOE used in its 

performance assessment: (i) net infiltration calculations, using the Mass Accounting System for 

Soil Infiltration and Flow (MASSIF) infiltration model, and (ii) net infiltration uncertainty 

calibration, using the unsaturated zone model top boundary net infiltration. 

For the first 10,000 years of performance 

 The net infiltration calculated by DOE for the first 10,000 years is a small fraction 

(approximately 13 percent) of total precipitation. The NRC staff notes in TER 

Section 2.2.1.3.5 that the net infiltration estimates are adequately supported because 

the estimates are generally consistent with infiltration for areas throughout the desert 

Southwest of the United States with similar climates, and with the NRC staff independent 

analyses conducted over the past 15 years. 

 DOE compared subsurface observations with simulations based on the site-scale 

ambient unsaturated flow model to calibrate the uncertainty in net infiltration, leading 

to a further reduction of 45 percent in the percolation flux. This further reduction of net 

infiltration flux is reasonable because DOE showed that the reduction did not lead to an 

underestimate in dose (see TER Section 2.2.1.3.6.3.2). 

The average seepage flux calculated by DOE is approximately 10 percent of average 

percolation. Using this value, DOE calculated that 31 percent of waste package 

locations would become wet. NRC staff notes in TER Section 2.2.1.3.6.4 that an 

underestimate of seepage has a low consequence for repository performance, because 

DOE predicts that the drip shields remain intact and prevent seepage water from 

contacting the waste packages well beyond 10,000 years. 

For the post-10,000-year period, NRC staff’s review includes the seismic ground motion and 

igneous intrusion modeling scenarios, because these two scenarios contribute most to DOE’s 

predicted dose. In particular 
9-9 

 Deep percolation values used for the post-10,000-year period adequately reflect the 

prescribed values (see TER Section 2.2.1.3.6.3.2) 

 The average seepage fraction for the seismic ground motion modeling case (69 percent) 

is sufficiently large that increases are not expected to significantly increase expected 

dose (see TER Section 2.2.1.3.6.3.4) 

 The average seepage flux estimated for the seismic ground motion modeling case is 

sufficiently large (47 percent of percolation) such that larger values would not 

significantly affect expected dose (see TER Section 2.2.1.3.6.3.4) 

 DOE represented emplacement drifts as not forming a barrier with respect to seepage 

for the igneous intrusion modeling case; therefore, seepage is not underestimated (see 

TER Section 2.2.1.3.6.3.4) 

On the basis of these considerations, the NRC staff notes that the average values of flux 

reduction are consistent with staff understanding of the features, processes, barrier capabilities 

of the unsaturated zone, and sensitivity to repository performance. 

In summary, the NRC staff notes that at a high level, flux variability is adequately incorporated 

throughout the unsaturated zone flow models discussed previously. Flux variability was 

evaluated both in terms of spatial continuity of high and low flux zones and in terms of 

reductions in average flux through the mountain. Because continuity of flux through the 

mountain and average reductions in flux are reasonable, the DOE collection of linked process 

models and abstractions for site-scale unsaturated flow and seepage is reasonable for its 

intended use in performance assessment. Detailed NRC staff review of repository performance 

with respect to DOE results for net infiltration, percolation, seepage, and condensation is given 

in TER Sections 2.2.1.3.5.3 and 2.2.1.3.6.3.2 through 2.2.1.3.6.3.6. 

Propagation of Uncertainty in Performance Assessment 

NRC staff evaluates propagation of uncertainty in DOE performance assessment calculations 

by examining (i) the technical basis for selecting parameters and uncertainties for sensitivity 

analyses and (ii) the DOE sensitivity analysis for the selected parameters with respect to TSPA 

intermediate results and calculated doses. 

DOE conducted a series of analyses to determine the sensitivity of model outputs to input 

parameter uncertainties in its performance assessment calculations (SAR Section 2.4.2.3.3.3). 

DOE treated a subset of parameters in the unsaturated zone and Engineered Barrier System as 

uncertain in the analyses. These parameters relate to infiltration (linked to percolation), 

seepage into the drift, host rock thermal and hydrogeologic parameters, and in-drift thermal 

processes that affect the release of radionuclides. In TER Sections 2.2.1.3.5.3.3 and 

2.2.1.3.6.3.2–2.2.1.3.6.3.6, the NRC staff evaluated (i) the analyses used to identify uncertain 

parameters carried into performance assessment calculations and (ii) the technical basis for 

describing uncertain parameters when evaluating individual models and abstractions. In each 

of these sections, the NRC staff noted that, for use in the corresponding abstraction, DOE 

addressed model and parameter uncertainty in performance assessment calculations. On the 

basis of staff’s analyses of NRC Appendix D (2005aa), NRC staff notes that DOE identified a 

reasonable set of the most important parameters to include in performance assessment 
9-10 

calculations of dose. Because the uncertainty for the individual models is reasonable, DOE 

adequately propagated uncertainty throughout the unsaturated zone. 

Further confidence that DOE reasonably propagated uncertainty in its estimation of dose is 

based on the DOE estimate of the importance of the unsaturated zone compared to Engineered 

Barrier System components. DOE evaluated the sensitivity of intermediate results and 

expected mean annual dose as calculated by TSPA with respect to the selected parameters. 

In general, the DOE analyses identified key uncertain input parameters associated with waste 

package failure as the dominant factors affecting performance (SAR Table 2.4-12); saturated 

zone transport and net infiltration were also identified as key uncertain input parameters under 

some scenarios. 

The DOE analyses consistently identified uncertainty in net infiltration, which is closely related to 

percolation fluxes within the unsaturated zone, as significantly affecting uncertainty in 

intermediate results (e.g., drift seepage, drift wall temperatures, radionuclide releases from the 

Engineered Barrier System, unsaturated zone radionuclide transport rates) and expected mean 

annual doses (SNL, 2008ag). The DOE analyses also identified (i) relatively smaller 

contributions to uncertainty in seepage into drifts arising from uncertainty in host rock 

permeability and capillary strength and (ii) contributions to uncertainty in in-drift temperature and 

relative humidity from host rock thermal conductivity, as outlined in SNL Section K4 (2008ag). 

The NRC staff examined the DOE sensitivity analyses with respect to intermediate results and 

expected mean annual dose by comparing the sensitivity results with the DOE description of the 

physical processes governing barrier function as represented in the models used for 

performance assessment. The NRC staff’s review focused on parameters that DOE identified 

as systematically affecting either intermediate results or expected mean doses. On the basis of 

the NRC staff comparison, the NRC staff notes that the DOE sensitivity analysis is consistent 

with the DOE description of the physical processes embodied in models used for performance 

assessment calculations with respect to net infiltration, percolation fluxes, thermal responses in 

the host rock, seepage, and in-drift temperature and humidity. 

The NRC staff’s review of the DOE procedures for propagating uncertainty in 

performance assessment considers the reasonableness of overall performance assessment 

results. This is based in part on the NRC staff review of the performance assessment 

calculations (TER Section 2.2.1.4.1). DOE’s rankings of key parameter inputs are consistent 

with its representation of engineered barrier characteristics and are derived from a variety of 

approaches for sensitivity analyses, as described in SNL Appendix K9 (2008ag). The rankings 

suggest that failure and release mechanisms are more important to repository performance than 

natural system factors of the unsaturated zone, because of the longevity of the engineered 

barriers. NRC staff gained confidence in the reasonableness of DOE rankings of key parameter 

inputs on the basis of a comparison with NRC staff’s parameter rankings derived from 

uncertainty analyses performed using independent methods and models (NRC, 2005aa). 

On the basis of these considerations, DOE has adequately propagated data uncertainty 

associated with infiltration; seepage into the drift; host rock thermal and hydrogeologic 

parameters; and in-drift thermal, chemical, and mechanical processes that affect the release of 

radionuclides in its performance assessment. 
9-11 

2.2.1.3.6.3.2 Ambient Mountain-Scale Flow Above the Repository

DOE represented the unsaturated zone and Engineered Barrier System using a hierarchy of 

far-field, near-field, and in-drift models. DOE uses the term far-field models to focus on features 

and processes of the natural system sufficiently distant from the repository to be unaffected by 

excavation and emplacement of waste. Similarly, near-field models focus on features and 

processes in the region affected by excavation and emplacement of waste (these terms are 

further described in the glossary). In-drift models focus on features and processes inside the 

disposal drift. DOE used its site-scale unsaturated zone flow model to represent far-field 

ambient mountain-scale flow from the ground surface to the water table (i.e., above, within, and 

below the proposed repository). In DOE documentation, site-scale and mountain-scale flow are 

interchangeable terms referring to the large scale of the computational grid, and ambient flow is 

the percolation flux that occurs without the flow-diverting effects of drifts and waste-produced 

thermal boiling fronts. DOE used output from the site-scale unsaturated zone flow model to 

account for far-field effects. This TER section evaluates repository performance with respect to 

ambient water flow within the upper unsaturated zone between the ground surface and the 

proposed repository horizon, focusing on the site-scale unsaturated zone flow model and an 

intermediate-scale model that links mountain-scale flow to seepage models. The NRC staff 

evaluates repository performance related to the effects of drift openings and thermal 

perturbation on flow patterns within and below the proposed repository horizon in TER 

Sections 2.2.1.3.6.3.3 through 2.2.1.3.6.3.6. 

In its review, NRC staff considered the site-scale unsaturated-zone flow model from two 

perspectives: (i) in the context of flow within the unsaturated zone as a whole and (ii) in the 

context of the DOE-defined upper and lower natural barriers. The NRC staff evaluation in this 

TER section focuses on aspects of repository performance primarily related to the upper natural 

barrier, in particular aspects of flow that affect seepage. The evaluation presented in TER 

Section 2.2.1.3.6.3.6 addresses aspects of repository performance primarily related to the lower 

natural barrier; in particular, aspects of flow that affect transport. The present section also 

considers some aspects of repository performance that apply to the entire unsaturated zone, 

such as estimation of parameter value sets for models. This TER section also describes 

the NRC staff evaluation of aspects of the repository performance specifically related to 

DOE’s implementation of the prescribed percolation values during the period from 10,000 to 

1 million years after permanent closure. 

Conceptual Model 

The DOE conceptual model for flow in the unsaturated zone is based on the primary 

hydrogeologic units within a column (SAR Section 2.3.2.2.1). The uppermost, the Tiva Canyon 

welded unit, features vertical episodic and fracture-dominated flow strongly influenced by 

episodic infiltration pulses. The underlying Paintbrush Tuff nonwelded (PTn) unit exhibits 

essentially vertical matrix-dominated flow with a strong potential for dampening and smoothing 

flows, thereby buffering lower units from episodic and localized infiltration pulses. The 

repository host horizon is within the Topopah Spring welded (TSw) unit, which features 

essentially vertical fracture-dominated flows in equilibrium with decadal-average net infiltration. 

In the TSw, mountain-scale flow patterns are controlled by mountain-scale infiltration patterns 

and fine-scale flow patterns are controlled by the TSw rock properties. The DOE conceptual 

model for the units underlying the repository host horizon [Calico Hills nonwelded (CHn) and 

Crater Flat undifferentiated (CFu) units] is discussed and evaluated in detail in TER 

Section 2.2.1.3.6.3.6. 
9-12 

DOE adequately described its conceptual model of the physical phenomena affecting 

unsaturated flow under ambient conditions because DOE described (i) the features of the site 

geology that affect unsaturated zone flow; (ii) the most important processes affecting the 

performance of the upper natural barrier, such as lateral diversion, spatial variability, and 

temporal variability; and (iii) the body of laboratory and field data supporting the conceptual 

model. The NRC staff gained additional confidence that DOE’s description of its conceptual 

model of the physical phenomena was reasonable by comparing the DOE description of the 

ambient-flow conceptual model with the NRC staff understanding of the Yucca Mountain natural 

system, obtained from extensive prelicensing experience and independent analyses of 

unsaturated zone flow processes at Yucca Mountain (e.g., NRC, 2005aa). 

Implementation of the Conceptual Model 

DOE used several models to represent various aspects of flow within the upper unsaturated 

zone, each considering different processes and scales. Because several DOE abstractions 

consider flows in the upper unsaturated zone, and multiple downstream models depend on 

the calculated flows, the NRC staff considered how the flow abstractions interact with the 

other calculations affecting repository performance by (i) separately evaluating individual 

flow abstractions, (ii) evaluating downstream uses of the flow abstractions for consistency 

between abstractions, and (iii) evaluating the effects of the abstractions on performance 

assessment calculations as a whole. In this TER section, the NRC staff evaluates the 

ambient mountain-scale flow model, focusing on the linkages between the model and both 

upstream models (i.e., the infiltration model) and downstream models (e.g., the Multiscale 

Thermal-Hydrologic Model and seepage models). 

In performing a risk-informed, performance-based review of DOE’s representation of ambient 

flow in the upper unsaturated zone, the NRC staff considered the DOE-defined function of the 

Upper Natural Barrier to prevent or substantially reduce water seeping into emplacement drifts. 

On the basis of the function of the barrier, the NRC staff identified several aspects of the DOE 

abstractions as having primary importance for representing flow in the upper unsaturated zone. 

These aspects include 

 Integration of flow models; in particular, integration between the mountain and seepage 

scales, because integration directly affects modeling approaches in upstream and 

downstream models 

 Mountain-scale flow patterns within the upper unsaturated zone, because this 

characteristic affects the amount of water diverting away from the repository as a whole 

 Site-scale unsaturated zone flow model parameters, because model properties may 

affect flow patterns and thereby affect the rate of water seepage into drifts or the thermal 

response of the natural system 

 Systematic effects of infiltration uncertainty on percolation fluxes, because percolation 

rates at the repository horizon directly affect the rate of water seepage into drifts, and 

percolation rates below drifts directly affect the rate of radionuclide transport from the 

Engineered Barrier System to the water table 

 Temporal and spatial variability; in particular, flow patterns at the drift walls, because 

these flow characteristics affect the rate and patterns of water seepage into drifts 
9-13 

The following sections describe the NRC staff evaluation of these aspects of repository 

performance with respect to ambient flow in the upper unsaturated zone. 

Integration of Flow Models 

The NRC staff considered the DOE approach to integrating the site-scale unsaturated zone flow 

model representation of ambient flow above the repository with (i) the MASSIF infiltration model 

(SAR Section 2.3.1), (ii) the Multiscale Thermal-Hydrologic Model (SAR Section 2.3.5.4), 

(iii) seepage models, and (iv) ambient flow below the repository. The NRC staff identified the 

integration of ambient flow above the repository with seepage models as the most risk 

significant among these for the following reasons: 

 The MASSIF net infiltration model, site-scale unsaturated zone flow model, and 

Multiscale Thermal-Hydrologic Model are decoupled models that are linked in only one 

direction (see TER Section 2.2.1.3.6.3.1); the site-scale unsaturated zone flow model 

seamlessly considers ambient flow above and below the repository, and the linkages 

locally conserve mass. 

 Performance assessment calculations are sensitive to seepage, which is in turn 

sensitive to the representation of fluxes near emplacement drifts at scales smaller than 

represented by the site-scale unsaturated zone flow model. 

The NRC staff notes that DOE adequately integrated the site-scale unsaturated zone 

flow model representation of ambient flow above the repository with the MASSIF infiltration 

model and the Multiscale Thermal-Hydrologic Model and ambient flow below the repository 

because the linkages locally conserve mass and the overlapping input properties are generally 

consistent between the models. The NRC staff reviewed input properties that are not consistent 

(e.g., hydraulic properties of bedrock near the ground surface) between the infiltration and 

site-scale ambient unsaturated flow models. On the basis of NRC staff expertise with 

developing input property sets for the different needs of infiltration and unsaturated zone models 

(Stothoff and Walter, 2007aa; Manepally, et al., 2004aa) and knowledge of scaling of properties 

between different spatial scales, the NRC staff notes the differences are not important or are 

technically justified. 

In considering integration between models, the NRC staff identified a potential concern with 

integration between the infiltration and seepage models (even though the two models are not 

directly linked), because (i) the MASSIF calculations are performed at a finer spatial and 

temporal resolution than the site-scale unsaturated zone flow model, (ii) modeled spatial and 

temporal variability in flow is reduced in the transfer of information between the two models, 

and (iii) spatial and temporal variability in flow near emplacement drifts affects the DOE 

seepage calculations. 

The NRC staff evaluates DOE’s integration between flow at the mountain and seepage 

scales, also considering integration between infiltration and seepage, in the remainder of 

this subsection. The NRC staff evaluates the DOE approach to modeling seepage in TER 

Section 2.2.1.3.6.3.4. 

DOE used a hierarchical approach to link percolation fluxes within the repository horizon at the 

mountain scale to seepage, progressing from mountain-scale flow to the smaller scales of 

intermediate- or drift-scale flow and fine-scale flow (SAR Section 2.3.3.2). The site-scale 
9-14 

unsaturated zone flow model, which calculates mountain-scale flow, represents flow averaged 

over an area approximately corresponding to the combined area of a drift and the pillar between 

drifts. Intermediate-scale flow represents average flow over an area approximately 

corresponding to the width of a drift. Fine-scale flow represents flow at scales smaller than a 

drift wall. DOE considered all three scales in calculating average seepage into drifts. 

DOE represented both intermediate-scale and fine-scale flow statistically, as described in SAR 

Section 2.3.3.2.3 and DOE Enclosure 4 (2009bo). DOE derived an abstraction for the statistical 

distribution of intermediate-scale fluxes from numerical model calculations considering 

hydraulic-property heterogeneity in the densely welded TSw unit above the proposed repository 

using model parameters based on field observations. DOE provided simulation results 

demonstrating that the statistical patterns of predominantly vertical unsaturated zone flow at the 

intermediate scale stabilize within a short vertical distance from the top boundary and geological 

unit changes, using several representations of the top boundary flux (SAR Section 2.3.3.2.3.5). 

DOE also performed sensitivity studies considering alternative statistical representations of 

intermediate-scale variability. The NRC staff evaluates the sensitivity studies in a subsequent 

subsection (Temporal and Spatial Variability), noting that DOE showed that the dose 

consequences from alternative statistical representations of intermediate-scale variability 

(including potential links between infiltration and seepage) are not significant to performance. 

The NRC staff notes that DOE adequately integrated mountain-scale flow and seepage for the 

following reasons: 

 The procedure DOE used to pass flow from the mountain scale to the intermediate scale 

to the fine scale does not propagate flow diversion near the drift wall to larger scales, so 

seepage calculations use upper-bound estimates for local percolation flux. 

 In performance assessment calculations, DOE used statistical distributions of relevant 

hydrological input parameters at a smaller scale that conserve fluxes calculated at the 

larger scale (e.g., mountain-scale flow model to intermediate-scale model, 

intermediate-scale model to seepage model). 

 DOE used site observations to derive the statistical distributions for rock properties 

used as input to the intermediate-scale model linking the mountain-scale and 

seepage models. 

 The performance assessment calculations are not strongly sensitive to the 

representation for intermediate-scale fluxes. DOE showed that, for a given 

areal-average seepage flux, calculated peak cumulative areal-average radionuclide 

releases would not be underestimated using the performance assessment procedure, 

outlined in DOE Enclosures 2 and 4 (2009bo). Further, DOE showed that alternative 

statistical representations for the intermediate-scale flux linking mountain-scale and 

seepage fluxes do not significantly affect the performance assessment (see the 

subsection Temporal and Spatial Variability in the Upper Unsaturated Zone in this 

TER section). 

Mountain-Scale/Site-Scale Flow Patterns 

The site-scale unsaturated zone flow model represents mountain-scale flow as steady state in 

equilibrium with climatic conditions, with water flowing from the upper boundary to the water 
9-15 

table without lateral inflow or outflow. The NRC staff identified the key uncertainties in 

mountain-scale flow as (i) the magnitude of areal-average percolation flux through the repository 

footprint and (ii) the spatial patterns of percolation flux through the repository footprint. The 

NRC staff evaluates the site-scale model separately for the Upper Natural Barrier portion of the 

unsaturated zone, above the proposed repository (this section), and the Lower Natural Barrier 

portion of the unsaturated zone, below the repository (TER Section 2.2.3.6.3.6). The NRC staff 

evaluates the DOE representation for the spatial patterns of percolation flux in this section and 

evaluates the DOE representation for the magnitude of areal-average percolation flux through 

the repository footprint, which is determined by net infiltration, in a subsequent subsection 

(Infiltration Uncertainty). 

DOE represents spatial variability in percolation fluxes as dominated by intermediate- and 

fine-scale variability in the seepage calculations for performance assessment. In the 

preceding section of this chapter (Integration of Flow Models), the NRC staff notes that DOE 

integrated mountain-scale flow and seepage. DOE accounts for alternative intermediate- and 

fine-scale spatial patterns of percolation fluxes within the repository footprint (and as a logical 

consequence acceptably accounting for alternative mountain-scale spatial patterns that 

preserve the areal-average percolation within the repository footprint). Therefore, the NRC 

staff’s review of the site-scale unsaturated zone flow model representation of mountain-scale 

flow patterns focuses on DOE’s representation of potential systematic mountain-scale flow 

diversion above the proposed repository horizon. 

DOE showed that the site-scale unsaturated zone flow model calculates mountain-scale spatial 

patterns of percolation fluxes, combining matrix and fracture flows, that are essentially vertical 

from the ground surface through the repository horizon within the repository footprint, in 

accordance with the DOE conceptual model, as shown in SNL Figures 6.1-2 through 6.1-5 and 

6.6-1 through 6.6-4 (2007bf). DOE numerical analyses indicate that substantial lateral 

diversion (associated with the PTn unit) away from the repository footprint may occur, which 

would reduce the amount of water passing through the footprint at the repository horizon (SAR 

Section 2.3.2.2.1.1). 

The NRC staff notes that DOE adequately considered uncertainty in ambient mountain-scale 

flow patterns above the repository horizon in performance assessment calculations for the 

following reasons: 

 The site-scale unsaturated zone flow model represents mountain-scale percolation 

fluxes as consistent with the DOE conceptual model and NRC staff understanding of the 

physical system. Further, DOE showed that alternative flow patterns with the same 

areal-average percolation within the repository footprint do not significantly affect 

repository performance. 

 DOE showed that alternative representations for flow above the repository horizon tend 

to increase lateral diversion out of the repository footprint. Logically, diverting 

percolation from the repository footprint would reduce percolation fluxes through the 

repository horizon and thereby reduce calculated seepage estimates. 

Site-Scale Unsaturated Zone Model Parameters 

The NRC staff’s risk-informed, performance-based review of the site-scale unsaturated zone 

model parameters includes consideration of (i) the technical bases for the parameters and 

(ii) the use of the parameters in the site-scale unsaturated zone model and downstream models 
9-16 

used for performance assessment calculations. DOE does not base input parameters for the 

downstream seepage models on site-scale unsaturated zone model parameters; the NRC staff 

evaluates the seepage model input in TER Section 2.2.1.3.6.3.4. 

DOE’s site-scale unsaturated zone flow model represents the stratigraphy at Yucca Mountain 

using 32 homogeneous material property layers, with the same set of material properties 

assigned to every grid cell in a property layer (SAR Section 2.3.2.4.1.2.2). The material 

property layers were developed from geologic layers described in the Geologic Framework 

Model. DOE justified the use of layerwise homogeneous material properties on numerical 

simulations by comparing outputs from simulations with different levels of heterogeneity, 

concluding that similarities in fracture flux patterns and tracer transport times show 

that heterogeneities within units have only a minor effect on site-scale flow processes 

(SAR Section 2.3.2.4.1.1.4). DOE developed some model parameters directly from site 

observations (e.g., porosity) and some from model calibration to field measurements of 

saturation, potential, pneumatic pressure, and perched water elevations. DOE used a set of 

in-situ observations not used for model calibration (e.g., calcite, Carbon-14, and strontium) to 

build confidence in the flow model (SAR Section 2.3.2.5.1). DOE further used a set of in-situ

observations not used for either model calibration or for building confidence in the flow model 

(i.e., chloride and temperature observations) to calibrate the uncertainty in infiltration 

(SAR Section 2.3.2.4.1.2.4.5). 

The NRC staff notes from review of the SAR and supporting documentation, summarized in the 

preceding paragraph, that (i) the model parameters are based on site-specific information and 

(ii) DOE clearly documented the procedures and bases for developing the parameters. 

The DOE performance assessment explicitly considered uncertainty in model parameters 

arising from uncertainty in infiltration flux, using a separate set of calibrated properties for the 

10th, 30th, 50th, and 90th percentiles of the infiltration map uncertainty distribution. DOE ensured 

that the flow fields resulting from each set of calibrated properties featured predominantly 

vertical flow from the ground surface through the repository horizon within the repository 

footprint, in accordance with the DOE conceptual model. DOE did not propagate the full 

range of parameter uncertainties into the TSPA on the basis that sensitivity analyses 

demonstrating that the model results, including performance-affecting results such as flow 

pathways, are insensitive to the parameter values in the range that DOE considered (SAR 

Section 2.3.2.4.2.2). 

The NRC staff notes that DOE adequately considers site-scale unsaturated zone model 

parameter uncertainty with respect to the Upper Natural Barrier representation in performance 

assessment calculations for the following reasons: 

 The downstream feed for the site-scale unsaturated zone flow model as used in 

performance assessment calculations (percolation flux at the base of the PTn unit) 

is approximately equal to net infiltration under predominantly vertical flow. 

 The Multiscale Thermal-Hydrologic Model (TER Section 2.2.1.3.6.3.3) and the near-field 

chemistry model (TER Section 2.2.1.3.3) use hydraulic parameters based on the 

site-scale unsaturated zone model. 
9-17 

Infiltration Uncertainty 

The NRC staff evaluates infiltration uncertainty by considering (i) the technical bases for the 

DOE approach and (ii) consequences of the approach in performance assessment calculations. 

DOE addressed uncertainty in infiltration estimates both derived from the net infiltration model 

using input parameter uncertainty and from infiltration uncertainty from deep subsurface 

observations. The NRC staff evaluates the latter type of infiltration uncertainty in this section. 

DOE also referred to the results from incorporating this uncertainty as the unsaturated zone 

top (upper) boundary net infiltration to distinguish it from results derived from the net 

infiltration model. 

For each climate state in the first 10,000 years after repository closure, DOE selected four 

infiltration maps calculated by the MASSIF infiltration model, out of the 40 equally likely 

realizations for the climate state (SNL, 2007az), to represent the uncertainty in infiltration. 

Initial probabilities of 0.2, 0.2, 0.3, and 0.3 were assigned to the four selected maps under each 

climate state, on the basis of the percentile rankings for the infiltration maps. Because the 

site-scale unsaturated zone model uses larger grid cells than the MASSIF model, DOE created 

upper boundary net infiltration maps for the site-scale unsaturated zone model grid. DOE 

transferred the infiltration values for each site-scale unsaturated zone model grid cell by 

accumulating infiltration fluxes from nearby MASSIF grid cells. For the post-10,000-year climate 

state, DOE assigned weights to the upper boundary net infiltration maps to be consistent with 

the draft rule describing deep percolation in the post-10,000-year period. The NRC staff 

evaluates DOE’s treatment of net infiltration during the post-10,000-year period in a subsequent 

subsection (Post-10,000-Year Approach). 

DOE found that model predictions for temperature and chloride distributions in the unsaturated 

zone, using models based on the site-scale unsaturated flow model, did not reflect average 

measured quantities in the unsaturated zone. DOE used a version of the Generalized 

Likelihood Uncertainty Estimation methodology (Beven and Binley, 1992aa) to update the initial 

probability weights assigned to each flow field (SAR Section 2.3.2.4.1.2.4.5). The Generalized 

Likelihood Uncertainty Estimation methodology uses likelihood functions to revise initial 

probability weights on the basis of differences between field observations and numerical model 

predictions. A likelihood function is a statistical model that is used to describe how likely a 

population represents observed values. DOE updated the probability weights using 

temperature observations from 5 boreholes and chloride observations from 12 boreholes, the 

Exploratory Studies Facility, and the Enhanced Characterization for the Repository Block 

Cross Drift. DOE used four different likelihood functions to compare the model predictions 

with observations. 

SAR Section 2.3.2.4.1.2.4.5.5 reported estimated final weighting factors of 0.619, 0.157, 0.165, 

and 0.0596 for the 10th, 30th, 50th, and 90th percentile upper boundary net infiltration maps, 

respectively, by combining the analyses considering temperature and chloride data. These 

final weights, when applied to the upper boundary condition infiltration maps of the ambient 

site-scale unsaturated zone model, result in weighted-average infiltration over the repository 

footprint of 8.46, 16.00, and 21.37 mm/yr [0.33, 0,63, and 0.84 in/yr] under present-day, 

monsoon, and glacial transition climate states as recorded in DOE Enclosure 1, Table 1 

(2010ai). For comparison, the initial weights applied to the upper boundary condition 

infiltration maps result in weighted-average infiltration over the repository footprint of 17.31, 

38.12, and 38.88 mm/yr [0.68, 1.50, and 1.53 in/yr] for the same climate states, also recorded in 

DOE Enclosure 1, Table 1 (2010ai). The values using the initial weights are larger than the 
9-18 

Generalized Likelihood Uncertainty Estimation-weighted infiltration by factors of 2.0, 2.4, 

and 1.8, respectively. 

The NRC staff notes from review of the SAR and supporting documentation, summarized in the 

preceding paragraphs, that (i) the representation of infiltration uncertainty is based on 

site-specific information and (ii) DOE documented the procedures and bases for developing the 

infiltration uncertainty representation. 

DOE identified several uncertainties in interpreting field observations using the temperature 

and chloride models (SAR Section 2.3.2.4.1.2.4.5.2). These uncertainties may affect the 

calculated probability weights for the assigned flow fields. DOE also showed that there is 

uncertainty associated with the likelihood functions used to determine the weights by showing 

that the calculated weights varied depending on the selection of a likelihood function (SAR 

Tables 2.3.2-25 through 2.3.2-27). 

DOE considered expected doses in the first 10,000 years of performance for the seismic ground 

motion and igneous intrusion scenarios, which collectively account for approximately 97 percent 

of the calculated peak expected mean annual dose, as described in DOE Enclosure 5 (2009bo). 

DOE compared the performance assessment dose calculations of expected doses with and 

without the Generalized Likelihood Uncertainty Estimation weighting, finding that the original 

weighting scheme (without the Generalized Likelihood Uncertainty Estimation procedure) results 

in a 29 percent greater peak expected dose than the Generalized Likelihood Uncertainty 

Estimation weights. DOE concluded that, because the calculated doses are so similar, 

adjusting the infiltration uncertainty with the Generalized Likelihood Uncertainty Estimation 

procedure does not significantly affect performance assessment results. 

The NRC staff notes that DOE’s performance assessment calculations adequately represent the 

uncertainty in net infiltration for the following reasons: 

 The NRC staff notes in TER Section 2.2.1.3.5 that DOE’s representation of net 

infiltration obtained from the MASSIF net infiltration model is reasonable for use in 

performance assessment calculations. 

 The NRC staff notes in a preceding subsection (Integration of Flow Models) that the 

MASSIF net infiltration model results are adequately integrated with the site-scale 

unsaturated zone flow model. 

 DOE showed that the consequence of adjusting net infiltration uncertainties using the 

Generalized Likelihood Uncertainty Estimation procedure is inconsequential to repository 

performance. 

Temporal and Spatial Variability in the Upper Unsaturated Zone 

DOE described a primary role for the upper unsaturated zone (i.e., above the proposed 

repository) as strongly dampening and smoothing episodic infiltration pulses, to the extent that 

flows below the PTn within the proposed repository footprint are essentially steady, in 

equilibrium with the long-term climate. This is a screening argument for FEP 2.2.07.05.0A, 

Flow in the Unsaturated Zone (UZ) from Episodic Infiltration (SNL, 2008ab). DOE identified the 

consequence of this dampening effect as reducing time-averaged seepage rates into intact or 

degraded drifts, with the reduction effect becoming less significant as the drift degrades in 

DOE Enclosure 2 (2009an). 
9-19 

The NRC staff evaluates DOE’s treatment of temporal and spatial variability by considering the 

potential consequences arising from partial breaches in the barrier capability represented by the 

upper unsaturated zone. The NRC staff identified effects on cooling, transport, and seepage as 

potential consequences resulting from a partial breach. The NRC staff focused on the 

consequences with respect to seepage, because in the DOE performance assessment 

calculations (i) conduction is the dominant thermal transport mechanism within the host rock, 

(ii) water moves from the repository horizon to the water table quickly relative to the 

performance period, and (iii) changes in percolation flux may have a nonlinear effect on 

seepage and release calculations. The NRC staff recognizes that temporal and spatial 

variability in percolation flux may nonlinearly affect release calculations in counteracting ways, 

because seepage may disproportionately increase with percolation. However, because of 

protection offered by the drip shield and waste package barriers, release rates may increase 

less than proportionately with seepage. 

The NRC staff identified potential concerns with the DOE representation of temporal and spatial 

variability in percolation flux at the repository horizon for use in performance assessment 

stemming from (i) long-term (decadal to centennial) fluctuations around the mean climate, 

(ii) drift-scale percolation flux variability, and (iii) short-term (episodic or seasonal) fluctuations in 

deep percolation fluxes. DOE may not have considered the full range of uncertainty for these 

processes in its performance assessment calculations. 

NRC staff considered the effects of DOE’s representation of long-term fluctuations about the 

mean climate by considering the consequences on seepage. DOE considers decadal to 

centennial variability in percolation likely to occur below the PTn because the PTn has a finite 

storage capacity, but expects that the increase in calculated average seepage would be small if 

decadal to centennial variability was explicitly included. DOE showed that fluctuations in 

percolation flux of 20 and 50 percent about the mean (i.e., coefficients of variation of 0.2 and 

0.5) yielded a systematic increase in Generalized Likelihood Uncertainty Estimation-weighted 

seepage of approximately 2.7 and 17 percent, respectively, under glacial transition conditions, 

as outlined in DOE Enclosure 1 (2009cc). Using an analysis demonstrating that a systematic 

increase in Generalized Likelihood Uncertainty Estimation-weighted seepage by a factor of 

approximately 2.5 has a negligible effect on the expected dose, as described in DOE 

Enclosure 5 (2009bo), DOE concluded by analogy that the smaller systematic increases in 

seepage induced by decadal to centennial climatic fluctuations also would have a negligible 

effect on performance assessment results. DOE reasonably represented long-term (decadal to 

centennial) climatic fluctuations in performance assessment calculations because DOE showed 

that such fluctuations have a negligible effect on the expected dose. 

The NRC staff gains added confidence by using the approach in DOE Enclosure 5 (2009bo), 

along with independent analysis described in Stothoff Section 3 (2010aa) that considered a 

more extreme range of decadal- to centennial-scale fluctuations. Using a coefficient of variation 

of 0.8, which roughly corresponds to infiltration variability over a glacial cycle, as shown in 

Stothoff and Walter Table 4-2 (2007aa), the increase in seepage is smaller than the systematic 

increase in seepage that is attributed to the Generalized Likelihood Uncertainty Estimation 

approach, as detailed in DOE Enclosure 1 (2009cc). 

DOE relied on numerical simulations to justify the representation of spatial variability in 

performance assessment calculations and to show that episodic flow is expected to be rare 

below the PTn. In these simulations, the NRC staff notes that DOE’s calculated spatial 

variability in percolation is dependent on the estimated heterogeneity of input parameters, which 

DOE did not explicitly tie to all sources of spatial variability potentially indicated by site 
9-20 

observations. DOE described several mechanisms to explain why some field evidence, such as 

modern tritium observations below the PTn, may not completely support the DOE assumptions 

related to drift-scale variability and episodic flow. These mechanisms include transport through 

faults, episodic flow, spatially variable infiltration patterns, and heterogeneous hydrologic 

properties, as outlined in DOE Enclosure 1 (2009cc). 

To evaluate uncertainty in spatial variability, the NRC staff considered possible sources of 

spatial variability in percolation between mountain and drift scale that may not have been 

incorporated into the DOE models, such as intermediate-scale structural and stratigraphic 

features. The NRC staff considered the performance consequence of alternative 

representations of spatial variability using DOE’s analyses reflecting intermediate-scale 

percolation flux variability. In those DOE analyses, the effect on seepage was considered for 

several alternative statistical relationships for the effect of rock heterogeneity on flow focusing, 

as shown in SNL Section 6.8.2, Case 6 (2007bf), ranging from no intermediate-scale focusing 

(Case 6a) to flow focusing that is more extreme than used for performance assessment 

calculations (Case 6c). DOE characterized the Case 6c distribution, which has a maximum 

flow-focusing factor more than six times larger than the distribution used in performance 

assessment calculations, as unrealistically extreme to represent rock heterogeneity, as 

described in DOE Enclosure 4 (2009bo). NRC staff treated Case 6c distribution as a 

surrogate that bounds spatial distribution not included in the DOE model. DOE showed that 

incorporation of the Case 6c distribution yields a minor increase in expected dose for the 

million-year seismic ground motion modeling case, as shown in DOE Enclosure 1 (2009cx). 

DOE described the seismic ground motion modeling case as the dominant contributor to 

calculated expected dose. Because DOE showed that the calculated peak cumulative 

areal-average releases are not strongly affected by an extreme increase in spatial variability, 

DOE adequately represented spatial variability between mountain and drift scales for 

performance assessment calculations. 

The NRC staff evaluated the consequences of DOE’s representation of flow as steady at the 

repository horizon, instead of episodic, by considering the field and modeling information DOE 

presented as a whole. DOE represented the PTn unit as a barrier with a strong potential to 

dampen episodic pulses below the PTn. This conceptual model was supported by numerical 

modeling results interpreting chloride, temperature, and radioisotope data obtained from within 

and below the PTn. DOE estimated that approximately 1 percent of the repository is affected by 

fast pathways, predominantly in faults (SAR Section 2.3.2.2.1.1), and argued that these fast 

pathways are not necessarily a consequence of episodic flow, but instead would represent 

spatial variability. The NRC staff notes that the information DOE presented (i) shows that the 

PTn has the potential to strongly dampen episodic pulses below the PTn and (ii) suggests that 

any areas at the repository horizon that exhibit episodic pulses are likely to require a 

combination of flow focusing and fast pathways through the PTn to overcome the PTn 

dampening potential. As a consequence, DOE’s data and modeling results suggest that, if 

episodic pulses do penetrate the PTn, relatively few waste packages are likely to be affected by 

these episodic pulses. Therefore, the NRC staff notes that the effect of episodic percolation can 

be evaluated by an analysis of the effect of the spatial extent of fast pathways that breach the 

barrier capability of capillary diversion at the drift (i.e., seepage becomes equal to percolation). 

The NRC staff considered potential consequences of episodic flow using information in DOE 

Enclosure 1, Tables 4 and 5 (2010ai) to perform a calculation that assumes an extreme 

representation of the extent of episodic flow, as described in Stothoff Section 3 (2010aa). In the 

NRC staff analysis, staff assumes that (i) episodic flow completely avoids the capillary barrier 

and becomes seepage, (ii) episodic flow pathways occur under the largest fluxes, and (iii) the 

area experiencing fast pathways is five times larger than DOE estimated. Each of these 
9-21 

assumptions conservatively represents an aspect of the DOE seepage abstraction. Under 

these assumptions, all of DOE’s Percolation Bin 5 (highest flux bin) is modeled as 

completely ineffectual at preventing seepage (seepage rates are equal to percolation rates). 

For comparison, seepage rates are less than 7 percent of infiltration rates for Percolation 

Bin 5 in the DOE model under glacial-transition conditions. With these assumptions, NRC 

staff calculates an areal-average seepage rate 2.4 to 3 times larger than DOE used in 

performance assessment calculations, which is less than the 3.9 times increase in total 

seepage that DOE calculated in its analysis of the Case 6c flow-focusing distribution. As 

previously stated, DOE showed that this order of increase in total seepage does not significantly 

increase calculated dose. 

The NRC staff notes that repository performance is not strongly affected by the DOE 

representation of episodic flow for performance assessment calculations because (i) DOE 

showed that repository performance is not strongly affected by increased seepage from 

increased infiltration or increased spatial variability and (ii) NRC independent analysis of an 

extreme case showed that the increase in seepage that might occur from episodic flow results in 

a similarly small increase in seepage. 

In summary, DOE reasonably represented temporal and spatial variability within the upper 

unsaturated zone because repository performance with respect to expected dose is not strongly 

affected by alternative representations. 

Post-10,000-Year Approach 

DOE chose not to explicitly model climate or infiltration for the post-10,000-year period, and 

instead used a distribution of percolation rates. To derive its distribution, DOE (i) selected four 

of the upper boundary condition net infiltration maps used to represent specific climate states in 

the first 10,000 years of performance; (ii) modified these maps into four new maps to achieve 

areal-average deep percolation flux target values within the repository footprint, at the 

infiltration-map probability values used in the first 10,000 years after closure; and (iii) created 

four site-scale unsaturated zone model flow fields on the basis of these boundary conditions 

(SAR Section 2.3.2.4.1.2.4.2). 

DOE used the percolation distribution in proposed 10 CFR Part 63 (SAR 

Section 2.3.2.4.1.2.4.2) because the final rule was not promulgated until a few months 

before DOE submitted the SAR. Reflecting the difference between the draft and final 

percolation distributions, the mean percolation in the final rule is 16 percent larger than that 

in the proposed rule. In DOE Enclosure 6 (2009cb), DOE performed sensitivity analyses 

that showed no significant affect on repository performance when using the distribution from 

the proposed rule instead of that from the final rule. 

The NRC staff notes that DOE reasonably considered the prescribed deep percolation rates and 

distributions for the following reasons: 

 DOE evaluated repository performance using a probability distribution of areal-average 

deep percolation fluxes that adequately reflects the prescribed distribution. 

 The upper boundary condition net infiltration maps and deep percolation fluxes for the 

post-10,000-year period are based on FEPs considered in the first 10,000 years of 

performance consistent with NRC (2009ab). 
9-22 

 DOE represents infiltration uncertainty in the first 10,000 years of performance and in the 

post-10,000-year period using a consistent approach. 

The NRC staff evaluated the spatial distribution of net infiltration results for the post-10,000-year 

period by considering the potential effects of features and processes affected by climate 

change, such as alterations in soil depth, soil profile development, and changes in caliche 

volume. The NRC staff discusses in TER Section 2.2.1.3.5 the influence of both temporal and 

spatial aspects of features and processes on the spatial distribution of net infiltration results. 

Therefore, the effect of changes to properties over time can be evaluated in terms of alternative 

spatial distribution of net infiltration. The NRC staff noted, as described in the previous section 

of this chapter, that DOE’s performance assessment calculations would not be strongly affected 

by including systematic changes in seepage resulting from alternative representations of spatial 

variability, long-term climatic variability, and episodic fluctuations during the first 10,000 years of 

performance. The NRC staff reasons that these effects would be no more important in the 

post-10,000-year period because (i) these effects have a lesser effect on DOE’s seepage model 

calculations as percolation fluxes increase, and the specified percolation fluxes in the 

post-10,000-year period are generally larger than those used during the first 10,000 years and 

(ii) these effects have a lesser effect on seepage for degraded drifts, and DOE includes drift 

degradation from accumulated seismic events in performance assessment calculations for the 

post-10,000-year period. 

Summary 

The NRC staff reviewed the model conceptualization, the underlying assumptions of the 

ambient site-scale unsaturated zone flow model and other relevant abstractions with which the 

ambient site-scale unsaturated zone flow model exchanges data and information, and the 

alternative model conceptualizations DOE used to analyze model uncertainties. The NRC staff 

notes that 

 DOE considered the important flow-affecting processes and provided adequate technical 

bases for their inclusion in the abstracted ambient site-scale unsaturated zone flow 

model and downstream abstractions used in performance assessment calculations 

 DOE integrated intermediate-scale and fine-scale flow processes to represent the Upper 

Natural Barrier 

 DOE represented ambient mountain-scale percolation fluxes above the repository 

horizon and ambient drift-scale percolation fluxes at the repository horizon 

 DOE represented spatial and temporal variability of model parameters and 

boundary conditions to estimate flow paths and percolation flux with respect to the 

Upper Natural Barrier 

 DOE provided sufficient data for ambient site-scale unsaturated zone model justification 

 DOE characterized and propagated ambient site-scale unsaturated zone model 

parameter uncertainty in process-level and performance assessment models with 

respect to the Upper Natural Barrier 
9-23 

 DOE considered percolation during the period from 10,000 to 1 million years after 

permanent closure 

2.2.1.3.6.3.3 Thermohydrologic Effects of Waste Emplacement 

DOE represented the unsaturated zone and Engineered Barrier System using a hierarchy of 

far-field, near-field, and within-drift models to account for thermal effects due to emplacement. 

DOE used a conceptual and numerical model, the Multiscale Thermal-Hydrologic Model, to 

represent near-field and in-drift thermohydrologic conditions. DOE used input from the 

site-scale unsaturated zone flow model to account for far-field effects and output from the 

Multiscale Thermal-Hydrologic Model in downstream models that estimate the effects of in-drift 

thermohydrologic conditions on thermal seepage, quantity and chemistry of water contacting 

engineered barriers and waste forms, degradation of engineered barriers, and radionuclide 

release rates and solubility limits. This TER section describes the NRC staff evaluation of the 

effects of waste emplacement on near-field and in-drift thermohydrologic conditions. The NRC 

staff evaluated repository performance related to the effects of thermal load on (i) seepage, 

(ii) quantity and chemistry of water contacting engineered barriers and waste forms, 

(iii) degradation of engineered barriers and radionuclide release rates, and (iv) solubility limits in 

TER Sections 2.2.1.3.6.3.4, 2.2.1.3.3, 2.2.1.3.1, and 2.2.1.3.4, respectively. 

DOE passed Multiscale Thermal-Hydrologic Model output to several downstream models in its 

performance assessment calculations. DOE used drift-wall temperature to switch between two 

limiting conditions for seepage, assuming that (i) no seepage occurs where drift-wall 

temperature is greater than 100 °C [212 °F] {the boiling temperature of water at the repository 

elevation is approximately 96 °C [205 °F]} and (ii) seepage occurs at the ambient rate for lower 

drift-wall temperatures (SAR Section 2.3.3.4). DOE modeled waste-package corrosion rates as 

depending on waste-package temperature and relative humidity, and temperature-dependent 

chemistry of seeping water (SAR Section 2.3.6). DOE modeled a diffusive-release pathway 

forming within failed waste packages once a continuous liquid film forms at elevated 

waste-package relative humidity levels. DOE used invert saturation, invert temperature, and 

imbibition fluxes into the invert to estimate properties and fluxes affecting released radionuclide 

transport from the waste package to the host rock (SAR Section 2.3.7). 

The DOE repository design basis places limits on the (i) peak waste package temperature 

{300 °C [572 °F] for 500 years, followed by 200 °C [392 °F] for 9,500 years} to reduce the 

potential for degradation of Alloy 22 waste packages; (ii) peak postclosure drift wall temperature 

{200 °C [392 °F]} to reduce thermal effects on drift stability; and (iii) peak mid-pillar temperature 

{96 °C [205 °F]} to facilitate drainage of percolation water between emplacement drifts (SAR 

Section 2.3.5.4.3; SAR Table 1.3.1-2). DOE’s analysis indicated that the peak temperature 

limits can be accomplished through thermal loading criteria for waste packages (SAR 

Section 1.3.1.2.5). DOE used the Multiscale Thermal-Hydrologic Model to show that these 

design basis temperature limits can be achieved using (i) a stylized postclosure reference case 

based on expected waste package receipts over the emplacement period and (ii) two estimated 

limiting waste streams developed with different management options (SAR Section 1.3.1.2.5). 

In evaluating repository performance with respect to near-field and in-drift thermohydrologic 

conditions, the NRC staff reviews (i) the DOE conceptual model, (ii) the process-level 

implementation of the DOE conceptual model, (iii) data support and propagation of uncertainty, 

(iv) abstraction of the process-level model into performance assessment calculations, and 

(v) use of the model outputs in downstream models. The NRC staff’s review of these topics 

focuses on aspects of repository performance that affect (i) duration of above-boiling 
9-24 

temperatures at the drift wall, (ii) waste-package temperatures, (iii) in-drift humidity after 

onset of seepage, and (iv) seepage fluxes into inverts. These aspects of the Multiscale 

Thermal-Hydrologic Model are outputs that are used as input for downstream models that 

calculate seepage, corrosion of waste packages, and release pathways within the Engineered 

Barrier System. The NRC staff review also focuses on the DOE representation of collapsed 

drifts, because burial of waste packages by an insulating rubble layer could result in elevated 

waste-package temperatures. 

Conceptual and Implemented Numerical Models 

NRC staff compared the DOE description of the conceptual model for thermohydrologic effects 

of waste emplacement provided in SAR Sections 2.3.3.1.1 and 2.3.5.4 (and selected 

references) with the NRC staff understanding of the Yucca Mountain natural system, obtained 

from extensive prelicensing experience and independent analyses of thermohydrologic 

processes at Yucca Mountain (e.g., NRC, 2005aa; Painter, et al., 2001aa; Manepally, et al., 

2004aa). The NRC staff notes that DOE adequately described its conceptual model of the 

thermohydrologic effects of waste emplacement, because DOE described (i) the features of the 

site geology and engineered barriers that affect thermohydrologic processes and (ii) the most 

important thermohydrologic processes and parameters affecting the performance of the upper 

natural barrier, such as in-drift temperature and relative humidity, host rock drying and 

rewetting, thermal conduction, and thermal radiation. 

DOE implemented the conceptual model into the four submodels of the Multiscale 

Thermal-Hydrologic Model. The submodels were linked through the mathematical 

principle of superposition, representing different aspects of coupled thermohydrologic 

processes at different spatial scales (SAR Section 2.3.5.4.1.3.1). These submodels 

consider (i) three-dimensional mountain-scale conduction, (ii) two-dimensional drift-scale 

thermal-hydrology in cross sections (“chimneys”) perpendicular to the drift axis, (iii) links 

between the mountain-scale and chimney submodels, and (iv) effects of discrete waste 

packages. On the basis of the descriptions supplied in SAR Section 2.3.5.4 and SNL (2008aj), 

the NRC staff notes that, with respect to each submodel, DOE (i) reasonably documented the 

procedures used to develop and support the submodel, (ii) developed the submodels of the 

Multiscale Thermal-Hydrologic Model in accordance with the corresponding conceptual model 

for the submodel, and (iii) used standard numerical approaches for the thermohydrologic 

processes considered in the submodel. The NRC staff notes that while DOE did not 

transparently describe the overall Multiscale Thermal-Hydrologic Model methodology linking the 

submodels, it reasonably showed that the linked submodels of the Multiscale 

Thermal-Hydrologic Model adequately represent thermohydrologic processes using 

comparisons to alternative models. The NRC staff gained confidence in the completeness 

and representativeness of the DOE description of the technical basis for the Multiscale 

Thermal-Hydrologic Model, as shown in SAR Section 2.3.5.4 and SNL Section 6.2 (2008aj), 

by comparing the technical basis with the NRC staff understanding of numerical simulation 

approaches, obtained from extensive prelicensing experience and independent analyses of 

thermohydrologic processes at Yucca Mountain (e.g., NRC, 2005aa; Painter, et al., 2001aa; 

Manepally, et al., 2004aa). 

DOE used its Drift Scale Test to validate the conceptual model underlying the drift-scale 

thermal-hydrologic submodel, as described in SAR Section 2.3.5.4.1.3.3 and SNL Section 7.4 

(2008aj), and used its Large Block Test, shown in SNL Section 7.3 (2008aj), to build confidence 

in the ability of the Multiscale Thermal-Hydrologic Model to predict thermal-hydrologic 

processes in the host rock. DOE (i) simulated the thermohydrologic behavior observed in the 
9-25 

Drift Scale Test and Large Block Test using the same modeling techniques included in the 

thermohydrologic submodel of the Multiscale Thermal-Hydrologic Model; (ii) compared modeled 

and observed temperature, relative humidity, and liquid-phase saturation values; and 

(iii) concluded that the differences were within the parametric uncertainty of Multiscale 

Thermal-Hydrologic Model results. On the basis of these considerations, the NRC staff notes 

that DOE showed that thermohydrologic processes observed in these heater tests are 

adequately included in Multiscale Thermal-Hydrologic Model. 

DOE identified several assumptions and limitations associated with linking the submodels 

(SAR Section 2.3.5.4.1.3.1), including restrictions related to mountain-scale and along-drift 

convection. DOE considered alternative conceptual models by comparing Multiscale 

Thermal-Hydrologic Model results with those of (i) an east-west cross section from a 

smeared-heat-source mountain-scale model; (ii) a three-dimensional, mountain-scale, 

nested-grid thermal-hydrologic model for a three-drift test case; and (iii) a three-dimensional, 

pillar-scale model with different axial in-drift vapor transport assumptions (SAR 

Section 2.3.5.4.1.3.3). The NRC staff notes that (i) DOE provided reasonable information 

to assess the model implementation uncertainty, because these comparisons range from 

mountain-scale to detailed pillar-scale simulations, and (ii) DOE used a modeling approach 

that conservatively represents the effects of in-drift vapor transport on in-drift relative humidity, 

because in-drift axial transport would tend to systematically delay rewetting compared to the 

Multiscale Thermal-Hydrologic Model results. Therefore, DOE reasonably showed that the 

Multiscale Thermal-Hydrologic Model results are comparable to alternative conceptual models 

in the intended thermal regime for repository operation. DOE assumed that mobilized water 

predominantly moves perpendicular to the axis of the emplacement drifts within the host rock. 

NRC staff notes this is consistent with the intended use of the Multiscale Thermal-Hydrologic 

Model under the design thermal regime for repository operation, because mobilized water can 

drain within the mid-pillar region without systematic mountain-scale lateral redistribution of water 

when boiling conditions are not reached throughout the pillar. 

In summary, DOE adequately provided the technical bases for the individual submodels 

and the linkages among the submodels supporting the intended use of the Multiscale 

Thermal-Hydrologic Model under the design thermal regime for repository operation, as 

discussed previously. 

Abstraction of the Multiscale Thermal-Hydrologic Model for TSPA 

The Multiscale Thermal-Hydrologic Model calculates time-dependent thermal-hydrologic 

variables for each of the 8 waste packages simulated for the 3,264 subdomains, each 

representing a 20-m [66-ft] segment of an emplacement drift. DOE referred to this as the 

comprehensive set of Multiscale Thermal-Hydrologic Model outputs. DOE performed the 

calculations for 7 parameter uncertainty cases, representing 12 combinations of infiltration 

uncertainty and thermal conductivity uncertainty, as described in the next subsection. DOE 

mapped each of the 3,264 subdomains to one of the five percolation bins abstracting the 

effects of seepage. 

DOE provided the comprehensive set of Multiscale Thermal-Hydrologic Model outputs for 

waste package temperature and relative humidity for different waste package types and 

drift-wall temperature, for all Multiscale Thermal-Hydrologic Model subdomains in each 

percolation bin, as input to the Waste Package Degradation Model Component, the Drift 

Seepage Submodel, and the Drift Wall Condensation Submodel within the TSPA model, 

as shown in SNL Section 6.3.2.2 (2008ag). These downstream models use Multiscale 
9-26 

Thermal-Hydrologic Model outputs to calculate waste package failure rates. The NRC staff 

notes that DOE abstracts Multiscale Thermal-Hydrologic Model output for these downstream 

models because the entire set of process-level output is provided to these downstream models. 

DOE also abstracted the Multiscale Thermal-Hydrologic Model results by selecting a single 

representative codisposal waste package and commercial spent nuclear fuel waste package for 

each percolation bin. DOE used the codisposal and commercial spent nuclear fuel waste 

packages with peak waste package temperature and drift-wall boiling duration closest to the 

median values to represent thermohydrologic conditions for all waste packages in the 

percolation bin. DOE provided the time-dependent output values of waste package surface 

temperature and relative humidity, drift wall temperature, invert temperature, invert saturation, 

and flux into the invert for the selected representative waste packages to the Waste Form 

Degradation and Mobilization Model Component, the Engineered Barrier System Flow and 

Transport Model Component, the Engineered Barrier System Chemical Environment 

Submodel, and the Drift Wall Condensation Submodel within the TSPA model, as outlined 

in SNL Section 6.3.2.2 (2008ag). These downstream models use the Multiscale 

Thermal-Hydrologic Model outputs to calculate radionuclide release rates from failed waste 

packages and radionuclide transport within the Engineered Barrier System. DOE provided 

analyses in SNL Section 7.3.4.3.1 (2008ag) comparing estimates of cumulative radionuclide 

releases from a single failed waste package using the representative location with estimates 

calculated using the comprehensive set of Multiscale Thermal-Hydrologic Model output. These 

analyses showed essentially identical cumulative release of representative radionuclides from 

the Engineered Barrier System after both 10,000 and 1 million years. DOE reasonably 

abstracted Multiscale Thermal-Hydrologic Model process-level results in performance 

assessment calculations with respect to these downstream models because DOE showed that 

the abstraction used for performance assessment calculations does not lead to an 

underestimate of radionuclide releases from the Engineered Barrier System. 

To provide further confidence in the abstraction process, the NRC staff considered whether 

uncertainty in heterogeneity was adequately incorporated into the comprehensive set of 

Multiscale Thermo-Hydrologic Model results. The NRC staff used systematic changes to the 

duration of boiling as a key thermal-hydrologic measure of performance consequence, because 

a shorter average boiling duration would result in earlier seepage and an earlier onset of 

enhanced release from failed waste packages. DOE identified host rock thermal conductivity 

and percolation flux as the dominant parameters responsible for variability and uncertainty in 

simulated thermal hydrologic conditions (SAR Section 2.3.5.4.1.3.2), but did not explicitly 

represent local heterogeneity in host rock thermal conductivity or drift-scale percolation flux 

variability in Multiscale Thermal-Hydrologic Model calculations. The NRC staff expects that 

local variability in temperature would be increased if such heterogeneity were included. 

Because local variability logically leads to approximately the same number of waste 

packages experiencing temperature increases and decreases, and therefore does not 

systematically shorten the average boiling duration, the NRC staff gained confidence that DOE 

represented uncertainty and variability in input parameters for the abstraction of Multiscale 

Thermal-Hydrologic Model results. 

Data Support and Uncertainty Propagation in the Multiscale Thermal-Hydrologic Model 

NRC staff reviewed the information provided in SAR Section 2.3.5.4.1.2 and selected 

references to evaluate the DOE supporting data and characterization of uncertainties in the 

Multiscale Thermal-Hydrologic Model. Because of the importance of the following three topics 

in performance assessment calculations, the NRC staff focuses its review on (i) input 
9-27 

parameters that significantly affect Multiscale Thermal-Hydrologic Model results, (ii) data 

support for uncertainty bounds in input parameters, and (iii) uncertainty propagation in 

the Multiscale Thermal-Hydrologic Model. 

Multiscale Thermal-Hydrologic Model simulations require input to describe the Engineered 

Barrier System and natural barriers (SAR Section 2.3.5.4.1.2.2). DOE identified the 

design control parameters and associated design constraints in SAR Table 2.2-3. DOE 

derived Engineered Barrier System parameters from the design information. DOE based 

natural barrier parameters on the site-scale unsaturated zone flow model (evaluated in TER 

Section 2.2.1.3.6.3.3), including hydrologic properties of the unsaturated zone, natural system 

geometry, and percolation fluxes below the PTn. 

DOE explicitly represented the drift, drip shield, and invert components of the Engineered 

Barrier System {SNL Section 4[a] (2008aj)}. DOE screened out representing the 

thermohydrologic behavior of other engineered components potentially affecting performance 

(e.g., rock bolts and associated boreholes used for ground support) on the basis of low 

consequence for performance assessment calculations. The NRC staff evaluates DOE’s 

screening arguments with respect to FEP 1.1.01.01.0B (Influx through Holes Drilled in Drift Wall 

or Crown) and 2.1.06.04.0A (Flow Through Rock Reinforcement Materials in Engineered Barrier 

System) in TER Section 2.2.1.2.1.1, finding both screening arguments reasonable, based on the 

small effect on performance assessment calculations. DOE described the design information 

representing engineered features in the Multiscale Thermal-Hydrologic Model as consistent with 

the design of subsurface structures, systems, and components (SAR Section 2.3.5.4.1.2.1). 

In SNL Section 4.1 (2008aj), DOE described repository subsurface and waste package design 

information as obtained from controlled sources and based on the current repository design. 

NRC staff notes that DOE adequately represented the input parameters describing engineered 

components with respect to the thermohydrologic parameters the Multiscale Thermal-Hydrologic 

Model calculated because DOE (i) identified the Engineered Barrier System components 

predominantly influencing in-drift thermohydrologic conditions, (ii) included these components in 

the Multiscale Thermal-Hydrologic Model, (iii) reasonably screened out other components, and 

(iv) described the included Engineered Barrier System components using parameter values 

consistent with the current repository design. 

DOE identified host rock thermal conductivity and percolation flux as the dominant parameters 

responsible for variability and uncertainty in simulated thermal-hydrologic conditions (SAR 

Section 2.3.5.4.1.3.2). NRC staff evaluated the adequacy of percolation flux in TER 

Section 2.2.1.3.6.3.2; incorporation of uncertainty in percolation is described in the paragraphs 

that follow. DOE considered the uncertainty in thermal conductivity of the host rock using a 

geostatistical model supported by laboratory measurements and core samples to constrain and 

condition the geostatistical model (BSC, 2004bf). DOE extracted host rock thermal conductivity 

values from the geostatistical model, evaluated the influence of thermal conductivity on peak 

waste package temperatures and duration above boiling, and assigned weights for 

implementation in TSPA, as outlined in SNL Section 6.2.13.3[a] (2008aj). DOE averaged the 

thermal properties of nonrepository units to facilitate computational efficiency, using a sensitivity 

analysis to show that the averaging does not affect the Multiscale Thermal-Hydrologic Model 

results, as shown in SNL Section 6.2.13.4[a] (2008aj). The percolation flux applied at the top 

boundary in the thermohydrologic submodel is the percolation flux at the base of the PTn 

unit calculated in the site-scale unsaturated zone flow model for the nominal 10th, 30th, 50th, 

and 90th percentile scenarios. The uncertainty in percolation flux in the site-scale unsaturated 

zone flow model at the mountain scale is propagated consistently in the Multiscale 
9-28 

Thermal-Hydrologic Model. The NRC staff notes that DOE reasonably propagated the 

uncertainty in host rock thermohydrologic properties with respect to observations into Multiscale 

Thermal-Hydrologic Model input, because DOE appropriately identified the dominant 

thermohydrologic property affecting uncertainty and used reasonable methods to propagate the 

uncertainty from the available laboratory and field data into the Multiscale Thermal-Hydrologic 

Model input. 

DOE propagated uncertainty in host rock thermal conductivity and percolation flux into 

simulations using 7 of the 12 combinations of 10th, 30th, 50th, and 90th percentile flux 

scenarios with low, mean, and high thermal conductivities as input (SAR Section 2.3.5.4.1.3.2). 

For each of the remaining five combinations, DOE used the results from one of the seven 

simulations as a surrogate on the basis of similarity in boiling duration, as described in SAR 

Section 2.3.5.4.1.3.2 and SNL Section 6.2.12.3[a] (2008aj). DOE propagated the full set of 

12 combinations into performance assessment calculations. The NRC staff notes that 

DOE reasonably propagated uncertainty in thermal-hydrologic conditions using the key 

performance-affecting result (duration of drift-wall boiling) to select surrogate simulations and 

considering the full range of the key thermal-hydrologic parameters. 

In summary, the NRC staff notes that DOE reasonably propagated uncertainty in 

thermohydrologic parameters into performance assessment calculations because 

 DOE identified the key performance-affecting thermohydrologic parameters and factors 

 DOE adequately propagated uncertainty from observations into Multiscale 

Thermal-Hydrologic Model input 

 DOE represented uncertainty in the key performance-affecting parameters using 

appropriate combinations of the uncertain parameters 

Use of Multiscale Thermal-Hydrologic Model Results in Downstream Models 

NRC staff evaluated predictions of thermohydrologic conditions in part by considering how DOE 

used Multiscale Thermal-Hydrologic Model results in downstream models. The downstream 

models using Multiscale Thermal-Hydrologic Model results include (i) thermal seepage, 

(ii) quantity and chemistry of water contacting engineered barriers and waste forms, 

(iii) degradation of engineered barriers, and (iv) radionuclide release rates and solubility limits. 

The NRC staff evaluates these downstream models in TER Sections 2.2.1.3.6.3.4, 2.2.1.3.3, 

2.2.1.3.1, and 2.2.1.3.4, respectively. 

On the basis of the NRC staff review, the NRC staff notes that DOE properly used the 

Multiscale Thermal-Hydrologic Model results and parameters in downstream models because 

they are generally used in a manner consistent with Multiscale Thermal-Hydrologic Model 

assumptions. DOE chose to not represent the full range of local variability in Multiscale 

Thermal-Hydrologic Model results in some downstream model calculations. On the basis 

of DOE sensitivity analyses [e.g., SNL Section 6.3 (2008aj)], the NRC staff notes that 

DOE reasonably represented local variability in Multiscale Thermal-Hydrologic Model results 

(e.g., variability in drift wall temperature, waste package temperature and relative humidity, 

imbibition fluxes) because DOE showed that local variability on radionuclide releases minimally 

affects duration or magnitude of radionuclide releases. 
9-29 

Uncertainty in Thermal Loading 

DOE described the loading strategy implemented in the Multiscale Thermal-Hydrologic Model 

as a stylized postclosure reference case based on expected waste package receipts over the 

emplacement period (SAR Section 1.3.1.2.5). DOE considered the actual future waste stream 

to be uncertain and considered flexibility in emplacement strategies necessary to manage 

acceptance of a wide spectrum of waste streams (SAR Section 1.3.1.2.5). For example, the 

design heat load DOE described in SNL (2008ai) updated the heat load used for Multiscale 

Thermal-Hydrologic Model calculations (SAR Section 2.3.5.4.1). In SAR Section 2.3.5.4.3, DOE 

described how temperature estimates using the design heat load compared with the 

temperature estimates from the Multiscale Thermal-Hydrologic Model results. DOE stated that 

it would develop an emplacement drift plan [SAR Section 1.3.1.2.5; DOE Enclosure 1 (2009ct)] 

for each drift, or set of drifts, that will (i) provide specific information, such as waste 

characteristics, waste package emplacement locations, and ventilation duration, and 

(ii) describe how preclosure and postclosure performance criteria will be met using the selected 

emplacement strategy. 

DOE showed that management approaches using temperature index functions representing 

three- and seven-package segments are capable of achieving performance targets for 

mid-pillar, drift-wall, and waste-package peak temperatures (SAR Section 2.3.5.4.3) for the 

two estimated limiting waste streams. The management approaches utilize extended duration 

of ventilation beyond 50 years or different surface handling facilities and aging capacities. 

DOE concluded that (i) only minor modifications to the TSPA model inputs are needed to 

represent the anticipated range of thermal loading; (ii) the geomechanical, hydrogeologic, 

and geochemical system responses for the two estimated limiting waste streams are each 

within the range of applicability for the respective models, as shown in SNL Section 6.4 

(2008ai); and (iii) the changes in system responses arising from future waste streams 

different than the reference case do not significantly affect the screening justifications for 

excluded FEPs or the modeling basis for included FEPs, as outlined in SNL Section 6.5 

(2008ai). The NRC staff notes that DOE showed that practical management strategies can be 

devised to achieve postclosure design basis targets in intact drifts using surface aging (limited 

to the 50-year emplacement period) and flexibility in ventilation duration beyond the minimum 

length of 50 years. 

DOE expects that, under bounding assumptions, peak waste package temperatures for some 

waste packages may exceed the design basis temperature by nearly 100 °C [180 °F] if drifts 

were to collapse within the first 90 years after closure (SAR Section 2.3.5.4.3, SAR 

Figure 2.3.5-37). DOE considered several mechanisms for drift collapse, including 

seismic-induced ground motion, thermally induced stresses, and gravitational stresses. DOE 

screened out all mechanisms for drift collapse other than seismic-induced ground motion 

(FEP 2.1.07.02.0A, Drift Collapse) on the basis of low consequence (SNL, 2008ab). The NRC 

staff, in TER Section 2.2.1.2.1.3.2 (FEP 2.1.07.02.0A), notes that DOE provided an adequate 

basis for the screening decision. DOE screened in mechanisms of drift degradation from 

seismic-induced ground motion, including in-drift temperature and relative humidity 

consequences from seismic-induced drift collapse. DOE performed a bounding probabilistic risk 

analysis considering uncertainty in seismic events and the key thermohydrologic parameter 

(thermal conductivity). This risk analysis used methods and assumptions consistent with DOE’s 

performance assessment calculations to calculate a probability of approximately 1 in 10,000 that 

the hottest waste package in the stylized postclosure reference case exceeded the 300 °C 

[572 °F] waste package temperature design basis because of drift collapse during the first 

10,000 years after closure, as described in SAR Section 2.3.5.4.3, SNL Section 6.3.17[a] 
9-30 

(2008aj), and SNL Section 6.4.2.5 (2008ai). DOE screened out consideration of peak waste 

package temperatures exceeding the established temperature limits in performance 

assessment calculations on the basis of the probabilistic calculation and additional information 

describing the nature of the bounding assumptions (SAR Section 2.3.5.4.3). DOE adequately 

justified screening out the risk from Engineered Barrier System temperatures exceeding the 

established temperature limits as a result of drift collapse because DOE (i) used appropriate 

methods consistent with performance assessment calculations to develop risk estimates and 

(ii) applied an approximate screening criterion for the probability of occurrence. 

The NRC staff recognizes that actual waste sent to Yucca Mountain, flexible emplacement 

strategies, and natural system and modeling factors may increase or decrease estimated peak 

temperatures relative to the stylized reference case [e.g., SNL Figure 6.4.2-28 (2008ai)], which 

may result in locally different thermal regimes compared to the performance assessment and 

screening calculations. DOE stated that it would develop an emplacement drift plan prior to 

waste emplacement that specified for each drift, or set of drifts, the (i) waste characteristics, 

(ii) waste package emplacement locations, (iii) ventilation duration, and (iv) how preclosure and 

postclosure performance criteria will be met using the selected emplacement strategy, as 

described in SAR Section 1.3.1.2.5 and DOE Enclosure 1 (2009ct). To confirm the heat load is 

in the range analyzed in the SAR, DOE should provide emplacement loading plans before 

emplacement begins. 

Summary 

In this TER section, the NRC staff evaluated the in-drift and near-field thermohydrologic 

conditions estimated using the Multiscale Thermal-Hydrologic Model (SAR Section 2.3.5.4.1). 

With respect to the Multiscale Thermal-Hydrologic Model, DOE adequately described 

the (i) conceptual models, (ii) implementation, (iii) performance assessment abstraction, 

(iv) data used to derive inputs, and (v) uses of output in performance assessment 

calculations. DOE adequately considered (i) conceptual model uncertainty, (ii) alternative 

conceptual models, and (iii) data variability and uncertainty with respect to the application of the 

Multiscale Thermal-Hydrologic Model for performance assessment calculations. On the basis of 

the previous discussion, the NRC staff notes that DOE adequately integrated the Multiscale 

Thermal-Hydrologic Model with downstream models in performance assessment calculations. 

The NRC staff notes that DOE adequately demonstrated the capability for devising 

emplacement loading strategies that achieve postclosure design basis targets. To confirm the 

heat load is in the range analyzed in the SAR, DOE should provide emplacement loading plans 

before emplacement begins. 

2.2.1.3.6.3.4 Ambient and Thermal Seepage Models

This section contains the NRC staff review of DOE’s model and results for water seeping into 

drifts. Seepage into drifts encompasses a subset of processes in the unsaturated flow system 

that occurs in the vicinity of the drift wall. DOE described seepage as a component of the 

second feature, the unsaturated zone above the repository, within the Upper Natural Barrier 

(SAR Section 2.1.2.1). DOE considered seepage (SAR Section 2.3.3) separately from 

unsaturated zone flow (SAR Section 2.3.2) because of the smaller scale of analysis needed for 

the processes important for seepage and, consequently, the need for a different set of data and 

models to produce results for use in the performance assessment. 

DOE strictly defined seepage as liquid water that drips from the drift ceiling and thus could 

potentially contact engineered barrier components. Two primary processes provide barrier 
9-31 

capability in DOE’s seepage model: capillary diversion of liquid water around large openings 

(drifts in this case) and vaporization in the host rock that creates a dry zone around the drifts 

(SAR Section 2.1.2.1). Capillary forces may make drifts barriers to flow by inducing water to 

laterally flow (divert) around the large opening. During the thermal period, the vaporization 

barrier refers to the boiling of water in the host rock and migration of the resultant vapor to 

locations away from the heat source. In the DOE abstraction, the resultant creation of a dryout 

zone surrounding a drift leads to elimination of liquid flux at the drift wall. 

Three inputs are provided to the seepage abstraction from other areas of the natural systems. 

First, the Multiscale Thermal-Hydrologic Model passes the distribution of percolation rates 

across the repository to the seepage abstraction (SAR Section 2.3.5.4.1). The values 

used are consistent with those from the ambient, site-scale unsaturated zone model (SAR 

Section 2.3.2). Second, the Multiscale Thermal-Hydrologic Model passes the temperature 

history for the drift wall to the seepage abstraction (SAR Section 2.3.5.4.1). Third, the 

thermal-mechanical model abstraction (SAR Section 2.3.4) passes the accumulated amount of 

rubble to the seepage abstraction, which uses it to reflect the degradation state of drift openings 

for seepage calculations. 

The DOE seepage abstraction passes two outputs to the Engineered Barrier System 

models: the seepage rate and the fraction of drift segments where liquid water seeps into 

drifts. Drift segments can be thought of as waste package locations. The total dripping 

flux (SAR Section 2.3.3), which is the sum of the seepage and condensation flux (SAR 

Section 2.3.5.4.2), is the flux of liquid water leaving the drift wall and contacting 

engineered components. NRC staff reviews DOE estimates of condensation flux in TER 

Section 2.2.1.3.6.3.5. 

In its review of the DOE seepage estimates, NRC staff considered how and to what extent 

seepage affects performance. The fraction of waste package locations getting wet and seepage 

flux in those wet areas are both passed directly from the DOE seepage abstraction model to the 

Engineered Barrier System models. The condition of the drip shields (intact or degraded) plays 

a critical role in the DOE performance assessment. When intact, drip shields prevent liquid 

water from contacting the waste package. In this case, seepage has no influence on the 

release of radionuclides, and transport rates out of failed waste packages are constrained to 

the slower diffusive rate of radionuclide movement rather than the faster advective rate. 

When degraded, drip shields do not divert all water away from waste packages. In this 

case, the Engineered Barrier System models use seepage estimates, which may influence the 

(i) corrosion of engineered components; (ii) number of waste packages contacted by water; and 

(iii) dissolution, mobilization, and transport of radionuclides to the unsaturated zone below the 

drifts. NRC staff reviews these three areas, which include processes and features from the drip 

shield to the invert/host rock interface, in TER Sections 2.2.1.3.1, 2.2.1.3.3, and 2.2.1.3.4. 

Consistent with a risk-informed approach and NRC guidance (NRC, 2003aa), the 

remainder of this seepage section focuses on review of the information and bases 

DOE provided for the (i) development of the ambient seepage abstraction; (ii) capillary 

diversion for intact drifts; (iii) capillary diversion for degraded drifts; (iv) seepage fraction, 

which is the fraction of repository that is in the seeping environment; (v) spatial variability of 

flow; and (vi) thermal seepage. 
9-32 

Development of Ambient Seepage 

This section reviews DOE’s description of seepage processes, field tests, and measured data 

and how they are incorporated into the seepage abstraction. 

DOE stated that capillary diversion of liquid water around large openings is the dominant 

seepage process providing barrier capability during the ambient period (SAR Section 2.1.2.1). 

In the context of seepage, DOE defined the thermal period as the time when the drift wall 

temperature exceeds 100 °C [212 °F]. Thus, DOE used the ambient seepage model to estimate 

water flux entering the drift from approximately the first few thousand years through a million 

years (SAR Section 2.3.3.1). 

For ambient seepage, DOE described (i) the theoretical treatment of seepage into circular 

openings in porous media; (ii) its choice of a fracture-only, stochastic, porous-media continuum 

seepage model; and (iii) field tests at Yucca Mountain used to calibrate seepage models (SAR 

Section 2.3.3.1). Because drifts are approximately circular in cross section, DOE drew on the 

theoretical treatment derived from an analysis in Philip, et al. (1989aa) of water diversion around 

large circular openings in homogeneous porous media. Because water is more likely to drip 

from fractures than from matrix, and to simplify the numerical models, DOE developed seepage 

models that include only the fracture network as the porous media. DOE described field tests at 

Yucca Mountain and observations from analog sites that illustrated the capability to divert water. 

DOE implemented a seepage approach predicated on continuum models on the basis of the 

Richards equation for granular porous media and the representative element volume 

assumption. Flow in fractures and through the fracture networks, however, may not satisfy 

these assumptions. For example, the density of fractures with flowing water is small relative 

to the grid size of the seepage model. Also, in addition to capillary-based flow in small-aperture 

fractures, flow regimes in fractures also include adsorbed films, sliding drops, rivulet flow, 

stable thick films, and unstable (laminar or turbulent) films (Ghezzehei, 2004aa); none of 

these would follow classical Richards-equation-based models. Instead of directly modeling 

these complexities, DOE’s approach calibrated the seepage model to field injection tests that 

would inherently incorporate small-scale processes and flow regimes in fractures. DOE 

models solve the Richards equation (Richards, 1931aa) for saturated-unsaturated flow through 

porous materials, with the van Genuchten–Mualem relations describing the capillary 

pressure and relative liquid permeability in the fracture continuum as a function of liquid 

saturation (van Genuchten, 1980aa). Because DOE showed that flow below the PTn can be 

approximated as steady state (evaluated by NRC staff in TER Section 2.2.1.3.6.3.2) and 

because field injection tests were performed, NRC staff believes that hysteresis is not an 

important process to incorporate into the model. 

DOE used two separate numerical seepage models: one for calibration to field tests and the 

other to generate ambient seepage abstraction lookup tables for the performance assessment 

model. Injection tests at Yucca Mountain, as described in BSC Section 6.2 (2004av), form the 

basis of the DOE calibrated seepage model that was designed specifically for the injection test 

domains (SAR Section 2.3.3.2.3.3). The key parameter for estimating seepage is the 

unsaturated zone property of capillary strength. It is the inverse of the van Genuchten " term 

(van Genuchten, 1980aa) and reflects the ability of the fracture continuum to offset gravity for 

water dripping into drifts. DOE conceptually separated percolating water reaching the drift 

ceiling into (i) water diverted by capillarity, which remains in the host rock; (ii) water dripping 

from ceiling, which is defined as the seepage flux; and (iii) water entering the drift but not 

dripping, which includes along-wall flow and evaporation. Because the capillary strength 
9-33 

parameter is calibrated from the injection tests, it implicitly includes the effects of evaporation, 

along-wall flow, and capillary diversion. 

The seepage abstraction was developed using the second seepage model that kept the same 

grid characteristics as the seepage calibration model, but was designed for the geometry of 

emplacement drifts (SAR Section 2.3.3.2.3.4). DOE used the second seepage model to 

generate two tables: one for intact drifts and one for collapsed drifts for the seismic modeling 

cases. These tables covered a wide range of percolation rates, and permeability and 

capillary strength parameter values. To estimate seepage at any location using the 

abstraction, DOE sampled capillary strength and permeability from uncertainty distributions 

(SAR Section 2.3.3.2.4.1) and used a spatially dependent local percolation rate. 

DOE adjusted the local percolation flux input for the seepage lookup table by a flow-focusing 

factor (SAR Section 2.3.5.4.1). Flow-focusing factors increase local percolation in some areas 

and decrease it in other areas, but the flow-focusing factor does not modify the total flux over 

the entire area. DOE used the flow-focusing factor to incorporate intermediate-scale 

heterogeneity (e.g., nonvertical small faults) that might lead to convergence or divergence of 

flow in the rock layers immediately above drifts. Spatially variable net infiltration and other 

large-scale heterogeneities from the ambient site-scale unsaturated flow model were 

propagated to the Multiscale Thermal-Hydrologic Model, and thus were automatically brought 

into the seepage abstraction. DOE described intermediate scale as falling between the grid 

scale of the ambient site-scale unsaturated flow model {approximately on the order of 100 m 

[330 ft]} and several drift diameters. For spatial variability below the scale of several drift 

diameters, DOE incorporated heterogeneity directly into the seepage numerical model input. 

For the seismic ground motion, seismic fault displacement, and igneous intrusion modeling 

cases, DOE predicted changes to the drift opening that might lead to changes in seepage rate 

and distribution. To account for changes in the drift wall caused by seismic events that lead to 

changes in dripping, DOE utilized the second seepage table for collapsed drifts. The degree of 

drift degradation controls the switch from the intact to the collapsed seepage table. For 

lithophysal rocks only, values from both tables are obtained and some intermediate value is 

calculated on the basis of scaling to the volume of rubble detached from the drift ceiling. For 

nonlithophysal rocks, accumulated rockfall above a specified threshold causes the seepage to 

be set equal to the percolation rate. The two seismic modeling cases are treated slightly 

differently. For the seismic ground motion modeling case, all drifts are shifted to a degraded 

state. For the seismic fault displacement, only a small number of drifts and waste package 

sections are affected by the seismic event. As with the seismic scenario for nonlithophysal 

rocks, the DOE seepage abstraction for the igneous scenario is simplified by neglecting the 

effect of capillary diversion. 

NRC staff compared DOE’s description of ambient seepage processes, and the incorporation of 

those features and processes into models and abstractions, with NRC staff’s understanding of 

seepage-related features and processes at Yucca Mountain obtained from field observations 

and independent analyses (NRC, 2005aa; Leslie, et al., 2007aa; Basagaoglu, et al., 2007aa; 

Or, et al., 2005aa). On the basis of this understanding, NRC staff notes that DOE adequately 

described features and processes at Yucca Mountain and that DOE included the important 

features and processes in its conceptual and numerical models for ambient seepage for the 

nominal, seismic, and igneous intrusion scenarios. 
9-34 

Capillary Diversion around Intact Drifts

DOE described the effectiveness of the unsaturated zone in the Upper Natural Barrier in terms 

of two metrics: seepage flux and seepage fraction (SAR Section 2.1.2.1.6). This subsection 

focuses on the seepage flux, which is calculated as the amount of water percolating through the 

host rock above the drift that the capillary diversion process does not divert around the drift by 

capillary diversion process. Capillary strength is the key parameter in the DOE seepage model 

for estimating the amount of water diverted around drifts, both because of the sensitivity of 

seepage estimates to this input parameter and because of the uncertainty in estimating 

representative values of this parameter. Percolating water (i) drips from the drift ceiling 

(seepage); (ii) flows laterally around the drift in the host rock; (iii) enters the drift, but flows along 

the drift wall; or (iv) enters the drift in the gas phase (vapor flux). DOE defined seepage as only 

the water dripping from the drift ceiling. 

NRC staff reviewed the information provided in the SAR to evaluate the adequacy of DOE’s 

estimate of seepage during the ambient period considering data and model support. NRC 

staff relied on metrics based on consequence to performance in its evaluation of DOE 

information provided for the capability of capillary diversion to reduce the flux of water entering 

drifts. This review approach was driven by NRC staff’s difficulty in assessing the effect of 

uncertainties in DOE’s model, due to (i) alternative interpretations of injection tests used to 

calibrate the seepage model, as described in DOE Enclosure 4 (2009ct) and Or, et al. (2005aa); 

(ii) representativeness of the locations of injection tests for estimating distribution of capillary 

strength parameter, as described in DOE Enclosures 2 and 3 (2009ct); (iii) lack of literature 

support for capillary strength parameter because of grid dependency and nonstandard inclusion 

of other processes in this term; (iv) alternative conceptual model for water entering drifts 

suggested by observations in the East-West Cross Drift Passive Test, as shown by Salve and 

Kneafsey (2005aa) and DOE Enclosure 1 (2009bo); and (v) lack of quantitative support from 

analogs, and problems and inconsistencies from other analogs [e.g., lithophysae as outlined in 

DOE Enclosure 6 (2009ct) and ancient artifact sites]. 

To evaluate seepage rates for performance assessment, NRC staff notes that the 10,000-year 

and million-year periods may be considered separately. NRC staff evaluates seepage rates for 

the million-year period in its review of the seepage for degraded drifts in the next subsection. 

Average seepage estimates during the first 10,000 years are not significantly affected by 

degraded drifts, because of the low probability of occurrence of seismic and igneous events that 

lead to collapsed drifts. 

For the first 10,000 years after closure, NRC staff notes that underestimates of seepage are not 

important to 10,000-year performance. This is based on the NRC staff evaluations in TER 

Sections 2.2.1.3.1.3.1 and 2.2.1.3.2.6 that note corrosion and mechanical processes do not 

degrade drip shields during the first 12,000 years after repository closure. Intact drip shields 

divert all seepage from contacting waste packages. 

Capillary Diversion for Degraded Drifts 

NRC staff reviewed the DOE approach and estimate of seepage flux that account for the 

disruptive modeling cases of seismic ground motion, seismic fault displacement, and igneous 

intrusion. In the DOE model, capillary diversion remains the predominant barrier for seismically 

degraded drifts. This evaluation focuses on seepage in lithophysal units reflecting seismically 

degraded drifts. 
9-35 

To address drift collapse, DOE developed a collapsed drift seepage table similar to the intact 

drift seepage lookup table. The table was developed using an enlarged drift opening, with 

an 11-m [36-ft] instead of 5.5-m [18-ft] diameter. DOE selected a perfectly circular 11-m 

[36-ft]-diameter drift opening, as outlined in BSC Section 6.6.3 (2004be), based on inspection 

of simulation results from rock mechanics modeling, as described in BSC Appendix R (2004al). 

NRC staff notes that DOE’s calculated increase in seepage for the collapsed drift seepage table 

is solely due to the larger opening and does not reflect irregular drift shapes, as illustrated in 

BSC Appendix R (2004al). NRC staff believes that both the size and shape of a drift affect 

capillary diversion. The irregular shape of degraded drifts may lead to increased values of 

seepage. NRC staff evaluations that follow indicate that uncertainty of seepage due to the 

irregular shape of collapsed drifts is not significant to performance. 

A tiered abstraction was used to account for the degree of drift degradation (SAR 

Section 2.3.3.4.1.1). For nonlithophysal rock, seepage estimates from the intact seepage 

table were used with estimated accumulated rubble less than 0.5 m3

 per meter of drift length 

[5.4 ft3

 per foot of drift length]. Otherwise, seepage was set to the percolation rate, including 

adjustments from the sampled focusing factor. For lithophysal rock, the intact seepage table 

was used for accumulated rubble less than 5 m3

 per meter of drift length [54 ft3 per foot of drift 

length]. For accumulated rubble greater than 60 m3

 per meter of drift length [650 ft3 per foot of 

drift length], the collapsed drift seepage table was used. Seepage was interpolated from the 

entries in both the intact and collapsed drift seepage tables for intermediate values of rubble 

accumulation {between 5 and 60 m3

 per meter of drift length [54 and 650 ft3 per foot of drift 

length]} in lithophysal rock. 

NRC staff notes that DOE estimates of seepage for seismic ground motion, seismic fault 

displacement, and igneous intrusion modeling cases during the first 10,000 years are 

reasonable for performance assessment calculations, because of low probability of occurrence 

for disruptive events. For the seismic fault displacement modeling case, only a small number of 

waste packages can be affected by each event, as outlined in SNL Section 6.1.2.3.4 (2008ag). 

Therefore, uncertainty in seepage rates has little effect on total estimated dose. For the igneous 

intrusion modeling case and for seismically degraded drifts in nonlithophysal rock, seepage is 

not likely to be underestimated, because DOE conservatively sets seepage rate equal to 

percolation rate. 

For the million-year period, DOE’s model determined disruption by seismic ground motion to be 

the most important contributor to dose estimates. To evaluate DOE’s estimates of seepage rate 

for the million-year period, NRC staff considered the following: 

 DOE calculated an average seepage rate for the million-year period to be 49 percent of 

percolation rate, shown in DOE Table 8 (2010ai). On average, seepage is unlikely to be 

greater than percolation (i.e., NRC staff knows of no process that focuses water to drifts 

on an average basis for the repository). Hence, doubling the seepage rate so that 

seepage is equal to percolation is a maximum bound. Information from sensitivity 

analyses in DOE Enclosure 1 (2009cx) illustrated that increases in seepage flux by 

factors slightly larger than two do not significantly affect estimates of total dose. 

 The NRC staff expects that degraded drifts provide some measure of barrier 

capability for seepage, though the percentage of percolation is uncertain. This 

expectation is based on NRC staff’s understanding of the physics of flow at sharp 

boundaries and modeling efforts completed during the prelicensing period (Basagaoglu, 

et al., 2007aa; Leslie, et al., 2007aa; Or, et al., 2005aa). The NRC staff models 
9-36 

generate seepage rates that are approximately the same as the DOE estimates 

for the million-year period, thereby providing NRC staff confidence in DOE estimates 

of seepage. 

Therefore, DOE’s treatment of capillary diversion and estimates of seepage for the million-year 

period are reasonable for performance assessment. 

Seepage Fraction 

DOE described the effectiveness of the unsaturated zone in the Upper Natural Barrier in 

terms of two metrics: seepage flux and seepage fraction (SAR Section 2.1.2.1.6). This 

subsection focuses on the latter. DOE defined seepage fraction as the number of drift 

segments where seepage occurs, divided by the total number of drift segments (SAR 

Section 2.3.3.1). DOE defined drift segment as an approximation to the average waste 

package length including the gap between waste packages. Therefore, seepage fraction is 

essentially the same as fraction of waste packages getting wet. The NRC staff’s evaluation of 

the DOE estimates of seepage fraction focuses on the adequacy of average values used for the 

million-year period when the seismic ground motion and igneous intrusion modeling cases 

dominate estimates of dose. Estimates of seepage fraction for the first 10,000 years after 

closure are not important, because drip shields are expected to remain intact for this entire 

period (TER Sections 2.2.1.3.1.3.1 and 2.2.1.3.2.6) and will divert all seeping water away from 

waste packages. 

Conceptually, DOE defined seepage fraction as the area portion of the repository where 

dripping is expected to occur, using the footprint of a waste package (drift diameter and waste 

package length) to define a seepage area. Thus, seepage fraction is linked to the number of 

waste packages that would get wet if no drip shields were present, divided by the total number 

of waste packages. The remainder of the repository is the nonseeping environment, where the 

flux of liquid water potentially dripping in a waste package location is set to zero in the 

DOE abstraction. 

In the DOE abstraction, seepage fraction is important because releases of radionuclides in the 

seeping environment are transported by advection. In that portion of the repository where the 

liquid flux is zero, any released radionuclides are transported by diffusive processes out of the 

waste package, which are slow compared to advective transport rates. Releases in the 

nonseeping environment rely on transport by diffusion along stagnant water films. Therefore, 

determination of the threshold at which seepage occurs can impact radionuclide transport. 

DOE used the seepage model to predict seepage at all locations. At locations where calculated 

seepage was less than some small rate, DOE set the value to zero in the performance 

assessment. Because the seepage fraction is sensitive to the selection of a value for the 

seepage threshold, DOE Enclosure 5 (2009ct) described sensitivity analyses showing that 

reducing the seepage threshold value to zero led to a negligible change in performance. 

In the abstraction, seepage fraction is fixed at a constant value for any particular TSPA 

realization. To determine the constant value, DOE selected the highest calculated 

seepage fraction that would occur at any time during the simulation period (excluding effect 

of igneous events). This value of seepage fraction was then applied throughout the simulation. 

Separate TSPA realizations were run for the 10,000-year (using a 20,000-year simulation 

period) and million-year calculations. DOE provided average values for TSPA realizations in 

SAR Tables 2.1-6 through 2.1-9. DOE estimated an average seepage fraction of 0.10 for the 
9-37 

first 10,000 years when seismic and igneous scenarios do not influence seepage. Similarly, 

DOE estimated an average seepage fraction of 0.69 for the million-year period when the 

seismic ground modeling case is the dominant dose contributor. Igneous intrusion and 

seismic fault displacement scenarios do not influence the seepage fraction used in the 

million-year calculation, because (i) the abstraction for igneous intrusion sets seepage fraction 

to one after the occurrence of an igneous event on the basis of SAR Section 2.3.11.6.5 and 

DOE Enclosure 8 (2009ct), although probability for an igneous event is low for any particular 

realization, and (ii) seismic fault displacement only affects a small number of drifts. 

NRC staff notes that seepage fractions are not underestimated on the basis of the following: 

 For the 10,000-year period, DOE adequately demonstrated that drip shields are 

estimated to remain intact well beyond the first 10,000 years (TER Sections 2.2.1.3.1.3.1 

and 2.2.1.3.2.6). Thus, no liquid is predicted to reach the waste packages, because 

drip shields divert all water regardless of whether the drift segment is a seeping or 

nonseeping environment. Therefore, the average value, and any uncertainty, in 

the value of seepage fraction are not important for performance during the first 

10,000 years. 

 For the million-year period, the NRC staff notes that the calculated seepage fractions 

cannot increase much before reaching the bounding value of one (i.e., all waste 

packages get wet). Increasing the seepage fraction from 0.69 to the maximum of 1 at 

most results in a 43 percent increase in dose, which is not significant to performance 

results when total dose is low. 

The NRC staff expects that seepage fraction during the million-year period will likely be less 

than the bounding case of one (i.e., all waste packages get wet). The potential for a seepage 

fraction to be less than one is uncertain, but any value less than one provides further 

confidence in the reasonableness of DOE’s results. NRC staff expects seepage fraction to be 

less than one because 

 DOE observations during a natural seepage event in the South Ramp of the Exploratory 

Studies Facility tunnel, which started in February 2005 and continued for several 

months, supported a seepage fraction significantly smaller than one. Results of a DOE 

simulation using its seepage model qualitatively reproduced the seepage fraction 

deduced from observations in the tunnel (SAR Section 2.3.3.4.3). 

 NRC staff’s analysis of site features that may reflect the spacing of flowing fracture 

suggests the seepage fraction is likely less than one (Basagaoglu, et al., 2007aa). 

NRC staff’s analysis suggests that the average seepage fraction is uncertain, but like 

DOE’s estimate, is likely less than one. The features considered in the NRC staff 

analysis include (i) fracture spacing, including long through-going fractures; (ii) structural 

features such as highly fractured zones and faults; and (iii) spacing of features with 

secondary mineralization. 

Representation of Spatial Variability 

NRC reviewed the representation and propagation of spatial variability across the repository to 

determine whether DOE’s model underestimated seepage. DOE incorporated spatial variability 

at several levels in developing its seepage results for performance assessment, including 
9-38 

(i) integration of variability from upstream model results, (ii) variability of permeability and 

capillary strength in the seepage model, (iii) incorporation of a flow-focusing factor, and 

(iv) abstraction of spatial variability for performance assessment. The key aspect for NRC 

staff’s evaluation of adequacy of spatial variability reflected in the performance assessment is 

the upscaling of results to five percolation bins for the entire repository. 

DOE incorporated spatial variability in the seepage by 

 Integrating aspects of spatial variability related to net infiltration and large-scale 

heterogeneities from the ambient site-scale unsaturated-flow model (and propagated 

to the Multiscale Thermal-Hydrologic Model) directly into the seepage model through the 

input of percolation distribution across the repository. This aspect of variability is 

evaluated in TER Section 2.2.1.3.6.3.2. 

 Incorporating spatial variability and uncertainty in permeability and capillary strength 

directly into the seepage model used to create the seepage lookup tables. This 

incorporated variability at the scale of several drift diameters. Permeability was 

stochastically varied across the seepage model grid. Capillary strength was treated as 

an upscaled parameter for the model domain. 

 Incorporating a flow-focusing factor in the seepage abstraction that addressed the 

possibility of convergence or divergence of flow in the rock layers above drifts. The 

flow-focusing factor represents intermediate-scale heterogeneity, which DOE described 

as falling between the grid scale of the ambient site-scale unsaturated flow model 

{approximately on the order of 100 m [330 ft]} and several drift diameters (seepage 

model grid). Flow-focusing factors increased local percolation in some areas and 

decreased it in other areas, but the total flux over the entire area remained constant in 

the DOE performance assessment. The resulting values of flow-focusing factors reflect 

spatial variability and range from 0.116 to 5.016, as outlined in SNL Section 6.6.5.2.3 

(2007bk). To provide confidence in estimates of the distribution of flow-focusing factors, 

DOE performed additional modeling exercises using different assumptions for 

calculating the focusing factor (SAR Section 2.3.3.2.3.7.6). Results of alternative 

flow-focusing distributions led DOE to use a narrower range for the distribution of 

flow-focusing factors. 

 Using five percolation bins, and thus five seepage histories, to address spatial variability 

in the performance assessment. The use of average seepage histories for a percolation 

bin represents an upscaling of spatial variability. 

Because DOE upscaled seepage estimates for its performance assessment to five percolation 

bins, thus losing details of spatial variability, NRC staff considered whether the upscaling was 

important to performance. If the upscaling of spatial variability does not lead to underestimates 

of dose, then the representation and supporting basis of detailed spatial variability are not 

important for performance. DOE used five percolation bins to separate the repository into areas 

of similar percolation rates. The areas of any one bin are not necessarily contiguous. The 

binning of percolation rates roughly ensured spatial continuity of flow zones above and below 

the repository (i.e., high percolation and thus high seepage zones correspond with high flow 

zones for transport below the repository). DOE Enclosure 2 (2009bo) compared calculated 

release results using the five percolation flux bins with results using the 3,264 locations. The 

analysis showed that the two approaches have similar time histories of radionuclide release, but 
9-39 

the bin approach tended to estimate larger repository-wide cumulative release of radionuclide 

mass to the lower unsaturated zone at 10,000 years and 1 million years after closure. 

Comprehensive model results, however, predicted higher doses at intermediate times during the 

first 10,000 years because of radionuclides that are solubility limited and have intermediate to 

high sorption coefficients, as shown in DOE Enclosure 2, Figures 2 and 3 (2009bo). However, 

higher predicted dose for the comprehensive model compared to results from the percolation 

bin approach is not significant, because drip shields are estimated to be intact well beyond the 

first 10,000 years. DOE adequately represented spatial variability of percolation and seepage 

flux because DOE showed that the upscaled percolation bin approach in the TSPA abstraction 

does not underestimate cumulative radionuclide release rates compared to estimates derived 

from representations with detailed spatial variability. 

Thermal Seepage 

DOE described two important features created by the thermal pulse that affect seepage into 

drifts: the dryout zone around a drift and a reflux zone at the outer edge of the dryout zone. 

DOE’s abstraction for thermal seepage sets seepage to zero for drift wall temperatures 

exceeding 100 °C [212 °F]. This temperature threshold for seepage is the focus of NRC staff’s 

evaluation of thermal seepage in the following paragraphs. NRC staff reviewed the description 

of features and processes incorporated into the conceptual and numerical models that DOE 

used to develop the seepage threshold of thermal seepage. Considering uncertainty derived 

from observations used to develop the thermal seepage abstraction, staff focused its evaluation 

on the impact to performance. 

DOE described the predominant seepage barrier capability for the thermal period as the 

elimination of liquid flux at the drift wall due to the dryout zone. DOE referred to this as the 

vaporization barrier (SAR Section 2.1.2.1.6.2). Flow diversion due to capillary forces (capillary 

diversion) remains a relevant process at all temperatures. DOE indicated that this vapor barrier 

would eliminate liquid water reaching the drift wall at temperatures exceeding 100 °C [212 °F] 

(SAR Section 2.3.3.4). In the DOE thermohydrological characterization, two-phase flow (liquid 

and vapor) in the host rock occurs at the outer edge of the dryout zone. Referred to as the 

reflux zone or heat pipe, because of increased heat transfer, evaporated liquid water rises and 

condenses in a continuous cycle. This zone of elevated liquid saturation above the dryout zone 

can serve as a supply of water added to the local percolation that potentially may breach the 

dryout zone as focused flow in large fractures, possibly reaching the drift wall and dripping into 

the drift. 

DOE separated the thermal evolution into three regimes: dryout, transition, and low temperature 

(SAR Section 2.3.5.4.1.1.3). DOE asserted that no water enters the drift during the dryout 

period, and seepage may occur during the transition period and continue into the lower 

temperature period transitioning to ambient temperature conditions. DOE defined the dryout 

period as the time when drift wall temperatures are estimated to exceed 100 °C [212 °F]. DOE 

eliminated seepage into drifts at a threshold value of 100 °C [212 °F] for intact and 

partially degraded drifts, but no threshold was implemented for fully collapsed drifts in the 

seismic scenario. Drift wall temperature is provided to the thermal seepage abstraction from 

the Multiscale Thermal-Hydrologic Model, which incorporates host rock heat transport, 

dryout, and rewetting (presented in SAR Section 2.3.5.4; reviewed by NRC staff in TER 

Section 2.2.1.3.6.3.3). 

DOE derived the seepage threshold value of 100 °C [212 °F] from process-level 

thermohydrological modeling exercises to evaluate the possibility of preferential flow 
9-40 

breaching the dryout zone under different realistic and bounding flow conditions. DOE 

described the thermal seepage model as a dual-continuum (matrix and fracture) 

representation with coupled heat and mass transport. The model necessarily uses a different 

property set than that used for the fracture-only continuum models for ambient seepage. DOE 

assumed that hydrologic properties need not incorporate the effect of thermal-mechanical and 

thermal-chemical processes. This assumption is based on results of DOE’s thermal-mechanical 

and thermal-hydrological-chemical modeling of the heated field tests, which suggest that 

changes to the flow patterns are smaller than the variability and uncertainty already 

considered for seepage. In addition, DOE indicated these changes may be transient and 

likely would disappear with the decay of the thermal pulse. Generally, the modeling exercises 

included pulses of water applied to a single fracture and assessment of whether the pulse 

would evaporate before reaching the drift ceiling. Thermal aspects of the numerical model 

were supported by field and laboratory observations from thermal tests DOE performed. 

However, hydrological aspects, particularly preferential flow, are difficult to observe or 

measure in field tests. 

NRC staff compared DOE’s description of thermohydrological features and processes during 

the thermal period, which is summarized in the previous paragraphs, with staff’s knowledge and 

expertise (NRC, 2005aa; Green, et al., 2008aa, and references contained therein) gained from 

observations and modeling of field and laboratory thermal tests. On the basis of this 

comparison, NRC staff notes that DOE acceptably described features and processes important 

for thermal seepage and developed a framework to adequately incorporate those features and 

processes into the thermal seepage model. 

DOE stated that the value of 100 °C [212 °F] for the thermal seepage threshold temperature at 

the drift wall, being several degrees above the ambient boiling point {96.3 °C [205 °F]}, accounts 

for modeling uncertainties and the possibility of a heat pipe occurring near the drift wall (SAR 

Section 2.3.3.3.4). To determine whether the seepage threshold adequately accounted for 

uncertainty, NRC staff considered field test observations reflective of temperatures at which 

preferential flow may have occurred. 

NRC staff noted that observations at thermal field tests could indicate preferential flow 

occurring in the dryout zone where measured temperatures exceeded 100 °C [212 °F] 

(Green, et al., 2008aa). Although DOE provided reasonable explanations for the 

observations, NRC staff believes the difficulty in collecting relevant observations and the 

uncertainty in interpretations of observations both support a larger uncertainty factor than 

reflected by the DOE estimate of the seepage threshold temperature value. DOE Enclosure 7 

(2009bo) provided information on drip shield integrity during the thermal period and noted 

that intact drip shields will divert any dripping water away from waste packages. On the basis 

of DOE’s prediction that the drip shields will remain intact and divert all seeping water away 

from waste packages throughout the thermal period, DOE’s treatment of data uncertainty and 

model support for the 100 °C [212 °F] thermal seepage threshold are reasonable for 

performance assessment. 

Summary 

The NRC staff has reviewed the information DOE provided and notes that the performance and 

treatment of the uncertainty for the seepage rate and fraction are reasonable because they are 

consistent with the technical justification provided for the model abstractions and the barrier 

capabilities. In evaluating seepage rates for performance assessment, NRC staff notes that the 

10,000-year and million-year periods may be considered separately. In particular 
9-41 

 For the first 10,000 years after closure, mean values of seepage fraction and rate and 

their uncertainty are not important for performance assessment, because drip shields 

are predicted to remain intact well beyond 10,000 years. Intact drip shields divert 

seeping water away from waste packages. 

 The thermal seepage abstraction that shows no seepage occurring when the drift wall 

temperature exceeds 100 °C [212 °F] is reasonable because drip shields remain 

intact well beyond the thermal period and thus divert any seeping water away from 

waste packages. 

 For the period from 10,000 years to 1 million years, DOE’s seepage and dose 

calculations are dominated by the seismic ground motion seepage scenario. Average 

seepage fraction (69 percent) and rate (49 percent of percolation) are reasonable 

because reasonable increases accounting for uncertainty would not significantly affect 

performance assessment calculations and dose results. 

 Seepage estimates for the igneous intrusion and seismic fault displacement 

modeling cases are reasonable because DOE uses conservative assumptions for 

estimating seepage. 

2.2.1.3.6.3.5 In-Drift Convection and Moisture Redistribution

This section contains NRC staff evaluation of DOE models, data, and results representing 

in-drift convection and moisture redistribution. Condensation flux, which results from moisture 

redistribution via vapor movement, is added to the seepage flux to obtain the total flux of water 

that may reach the engineered barrier components. 

In SAR Section 2.3.5.4.2.1 and SNL Section 6.1 (2007bl), DOE described in-drift convection 

and moisture redistribution as driven by temperature differences between the waste package, 

drift wall, and other engineered components. In the DOE conceptual model, decay heat from 

emplaced waste will create large temperature differences, both radially and axially within a drift. 

The temperature differences will produce buoyancy-driven natural convective flow of air inside 

the drift opening that will increase heat transfer and redistribute moisture. Convective air flow 

will cause water evaporation at warmer locations in the host rock and subsequent transport by 

in-drift convection to cooler locations where it may condense on cooler surfaces. DOE 

described the ensemble of evaporation, convective moisture redistribution, and condensation on 

cooler surfaces inside the drift as the cold trap phenomenon. 

The in-drift convection and condensation models provide two outputs. First, the convection 

model provides support for the effective thermal conductivity used in the thermohydrological 

model, reviewed in TER Section 2.2.1.3.6.3.3. Second, the condensation model, using 

dispersion coefficients calculated from the convection model, provides probability and flux of 

condensation to performance assessment. Condensation is added to the dripping flux to obtain 

a total seepage flux entering drifts. Condensation is linked to DOE’s abstraction for chemistry of 

liquid water contacting engineered barrier components (reviewed in TER Section 2.2.1.3.3) and 

to flux of water in the invert, which influences radionuclide transport (reviewed in TER 

Section 2.2.1.3.4). 
9-42 

The NRC staff evaluation of the convection and condensation models and results are divided 

into two parts. The following two subsections contain NRC staff’s evaluation of (i) in-drift heat 

transfer and convection and (ii) moisture redistribution and condensation. 

2.2.1.3.6.3.5.1 In-Drift Heat Transfer and Convection 

NRC staff reviews the DOE conceptual model for in-drift heat transfer and implementation of the 

numerical model in this section. The review considers the adequacy of the heat transfer model 

to estimate representative dispersion coefficients and effective thermal conductivity. 

In-drift heat exchange processes involve conduction, convection, radiation, and phase-change 

(latent) heat transfer (SAR Section 2.3.5.4.2.3.1). Heat transfer processes reduce temperature 

differences created by emplacing heated waste packages in a drift (SAR Section 2.3.5.4.2.3). 

Though the heat transfer model generally will be referred to here as the convection model, 

radiative and conductive heat transfer processes are also included in DOE’s model 

and analyses. 

DOE implemented the convection model using the commercial computational fluid dynamics 

solver FLUENT®, a code commonly used in industry and academia. DOE set up FLUENT® to 

solve the steady-state form of the Navier-Stokes equation for selected times during the thermal 

pulse with a model domain that is divided into a large number of computational cells. DOE 

models incorporate, as appropriate, the complex arrangement of engineered components and 

take advantage of vertical axial symmetry to reduce computational effort. Radiation and 

conduction are included, but latent heat transfer is excluded because DOE showed in SNL 

Sections 6.3.7.2.4 and 6.3.5.1.2 (2007bl) that it does not significantly affect overall heat transfer 

and convection. Independent NRC experiments and analyses (Fedors, et al., 2004aa) similarly 

show latent heat transfer is a small component of overall heat transfer. For radiation and 

conduction, DOE used standard textbook heat transfer models and relevant parameter values 

(e.g., Incropera and DeWitt, 2002aa; Kreith and Bohn, 2001aa). 

NRC staff focused its review of the heat transfer on the convection aspect of the DOE model 

because convection most directly affects moisture redistribution and is the most complex of the 

heat transfer processes in the drifts. Asymmetric geometry inside the drift leads to complex 

convective flow patterns at different scales. SNL Sections 6.1 and 6.2 (2007bl) used numerical 

models at local and drift scales to represent heat transfer at scales ranging from large-scale 

heat transfer along drifts (center to repository edge) to small-scale heat transfer across 

boundary layers at solid-air interfaces. The DOE local-scale model emphasizes cross-sectional 

patterns in its simulations of temperature gradients between the waste package, drip shield, and 

drift wall. The DOE drift scale models address temperature gradients between the hot 

repository center and cooler edges. Model support was provided by DOE laboratory convection 

experiments and other experiments in the general literature using similar geometries (Kuehn 

and Goldstein, 1978aa). 

NRC staff notes that DOE has adequately incorporated convective patterns in its conceptual 

and numerical models for convection. NRC staff notes that analysis at two different scales is a 

generally accepted scientific technique for simulating large systems covering a wide range of 

scales and incorporating complex geometries. Furthermore, NRC staff notes that DOE 

adequately described and incorporated the important heat transfer processes and engineering 

design features in developing its conceptual and numerical convection models. Independent 

NRC experiments and analytic modeling results (Das, et al., 2007aa; Green and Manepally, 

2006aa; Manepally, et al., 2007ab) are consistent with DOE’s assessments. 
9-43 

In its two- and three-dimensional convection models, DOE used dimensions and physical 

properties of waste packages, drip shield, invert, and heat loads consistent with upstream 

models, as shown in SAR Sections 2.3.5.4.2.2 and 1.3.2 and SNL Section 4.1 (2007bl), and 

used values for physical properties of fluids and solids derived from standard thermal textbooks 

(i.e., Incropera and DeWitt, 1996aa; Kreith and Bohn, 2001aa). DOE assumed that convection 

is based on pure air (i.e., without water vapor) and showed that this assumption would slightly 

underestimate in-drift vapor transport, as outlined in SNL Section 6.1.3.2.1 (2007bl). 

Independent NRC staff analyses similarly used and justified this assumption (Fedors, et al., 

2004aa). On the basis of this assumption, DOE used a neutrally buoyant tracer gas in the 

simulations and calculated the dispersion coefficients using the resulting concentration 

gradients. Independent analyses reported in Fedors, et al. (2004aa) support use of this 

assumption. On the basis of these considerations, NRC staff notes that DOE adequately 

represented physical parameters for solids and fluids and geometrical parameters in convection 

flow analyses. 

NRC staff considered DOE’s use of the convection model results to estimate dispersion 

coefficients for the condensation model. DOE stated that dispersion coefficients are dependent 

on a number of factors, including axial drift wall temperature variation, convective flow pattern, 

presence of drip shields, and time, as outlined in SNL Section 6.2.7 (2007bl). DOE calculated 

dispersion coefficients at two locations in the simulated drift and at discrete timesteps during the 

thermal pulse. DOE addressed uncertainty in the dispersion coefficient using parametric 

studies and bounding analyses, as described in SNL Sections 6.1.7 and 6.2.7 (2007bl). NRC 

staff reviewed the representativeness and uncertainty of the calculated dispersion coefficients 

for their intended usage with the condensation model. In particular, DOE did not address some 

uncertainty in the convection model output, including (i) the basis for the representative 

dispersion coefficients being spatially constant, rather than varying along drifts; (ii) justification 

for selecting the two specific locations in the analog 71-m [233-ft] drift for calculating dispersion 

coefficients and how the values are representative of an entire drift length; and (iii) the effect of 

the revised heat load scenario (SNL, 2008ai) on calculations. Although the DOE convection 

model is reasonable for calculating dispersion coefficients, the approach for estimating 

representative values from the model output may not be fully supported for use in the 

condensation model, because of the three aspects of uncertainty in representative dispersion 

coefficients described previously. However, NRC staff notes that the choice of dispersion 

coefficient values is not important for repository performance. This is supported by the NRC 

staff evaluations noting that drip shields remain intact for 12,000 years (TER Sections 

2.2.1.3.1.3.1 and 2.2.1.3.2.6) and thus prevent direct contact of condensation with waste 

packages during the first 2,000 years, when DOE maintains condensate formation is significant. 

NRC staff also reviewed DOE’s use of the convection model results to derive estimates of 

effective thermal conductivity for the porous media representation of air gaps in the 

thermohydrologic submodel of the Multiscale Thermal-Hydrologic Model. DOE computed an 

effective thermal conductivity from output of the convection model between (i) drip shield and 

drift wall and (ii) drip shield and waste package, as shown in SNL Table 6.4.7-3 (2007bl). The 

calculated values supported the Francis, et al. (2003aa) correlations used in the Multiscale 

Thermal-Hydrologic Model submodels, as described in SNL Appendix I[a] (2007aj). NRC staff 

notes use of the convection model to estimate effective thermal conductivity is reasonable 

because it follows a widely accepted, technical approach used in engineering analyses (Kuehn 

and Goldstein, 1976aa, 1978aa) and the inputs are reasonable for the purpose of the model. 
9-44 

In summary, DOE provided reasonable support for estimates of in-drift heat transfer because (i) 

the convection model is based on a reasonable technical approach, (ii) inputs are supported by 

textbooks values, (iii) data and model uncertainty were addressed with parametric studies and 

bounding analyses, and (iv) model inputs and results are supported by laboratory experiments. 

NRC staff also notes the approach DOE used to estimate thermal conductivity is reasonable, 

but the support for dispersion coefficients relied on drip shield integrity. 

2.2.1.3.6.3.5.2 Moisture Redistribution and Condensation 

NRC staff reviewed the information DOE presented to support estimates of condensation flux in 

drifts. DOE described the conceptual, numerical, and abstraction models for moisture transport 

and condensation in SAR Section 2.3.5.4.2. Treatment of data and model uncertainty was 

described in SAR Sections 2.3.5.4.2.2 and 2.3.5.4.2.3.3. Considering the support DOE 

provided for models and results, NRC focused its evaluation on the consequence of 

condensation flux on repository performance. 

Condensation Approach 

This section reviews the DOE description of the conceptual, numerical, and abstracted models 

used to estimate condensate rate for the performance assessment model. 

Moisture redistribution and condensation inside the drift is also referred to as the cold trap 

process. The process involves water evaporation from hotter locations, convection to cooler 

locations, and the condensation of vapor on cooler surfaces. DOE considered surface 

condensation, which requires direct contact of the convecting gas-phase with a cooler surface, 

but did not provide information on the potential effects of dust or volumetric condensation 

(Cussler, 1995aa) in its conceptualization. DOE predicted that condensation will only occur on 

the drift wall because the drift wall will be cooler than the drip shield, waste package, or invert at 

each axial position along any drift. DOE added condensation flux to the dripping rate to obtain a 

total seepage rate contacting engineered barrier components and reaching the invert and, thus, 

affecting advective radionuclide transport rates to the natural system. 

DOE described the evolution of moisture transport and condensate formation using three 

stages controlled by drift wall temperature (SAR Section 2.3.5.4.2.1). In Stage 1, the initial 

cooling stage, the drift wall temperature exceeds boiling along the entire length of the 

emplacement drift. DOE stated that no condensate formation takes place during the initial 

stage. In Stage 2, the intermediate cooling stage, the drift wall temperature exceeds boiling in 

most of the drift, but the end of the drift (repository edge) is below the boiling temperature. For 

the intermediate stage, DOE performed a bounding analysis to calculate condensation flux 

occurring on codisposal waste packages at cooler locations. In Stage 3, the final cooling stage, 

the drift wall temperature is below boiling along the entire length of the drift. In the DOE 

abstraction, condensation occurs at both codisposal and spent nuclear fuel waste package 

locations, but all condensation ceases at 2,000 years. Results for process-level models for the 

intermediate and final cooling stages provide the basis for the abstraction model used in the 

performance assessment. 

For the intermediate cooling stage, DOE estimated condensation using a three-dimensional, 

pillar-scale thermal-hydrological model (SAR Section 2.3.5.4.2.4). This is an alternative 

conceptual model supporting the thermohydrological results the Multiscale Thermal-Hydrologic 

Model calculated (SAR Section 2.3.5.4.1.3.3; reviewed in TER Section 2.2.1.3.6.3.3). 

Described as a bounding approach, DOE used a range of dispersion coefficients and 
9-45 

percolation values in the three-dimensional, pillar-scale model to determine that condensation 

occurs on codisposal waste packages, but not on spent nuclear fuel waste packages. 

For the final cooling stage, DOE used a one-dimensional analytical moisture transfer model to 

estimate condensation occurrence and flux when drift wall temperatures along the entire length 

of the drift are below boiling. The DOE network model calculates quantity of condensate at a 

given location along the drift (SAR Section 2.3.5.4.2.3.1) for specified percolation rates and 

thermal input. The one-dimensional model is based on a diffusion-type equation and uses 

values of dispersion coefficients calculated by the convection model as an effective 

diffusion-type parameter. Conductive heat transfer in host rock is based on an analytical 

mountain-scale conduction model, as outlined in SNL Section 6.3.5.1.1 (2007bl), and in-drift 

heat transfer between components is calculated on the basis of correlations derived from simple 

systems and reported in open literature (Raithby and Hollands, 1985aa; Kuehn and Goldstein, 

1976aa; Burmeister, 1993aa). DOE considered the supply of water for evaporation at drift 

walls to be bounded by the sum of capillary-pumping flow and local percolation flux intercepted 

by the emplacement drift footprint. The NRC staff considers the control parameter that 

commits DOE to an unheated open length at the ends of emplacement drifts (SAR Section 1.9, 

Control Parameter Number 01-18, Table 1.9-9) to be an important design feature that was 

integrated into the convection and condensation models. This design feature allows axial 

convection to convey a portion of the moisture beyond the last waste package before 

condensation would occur. 

DOE implements the abstraction of condensation in the performance assessment using a 

three-step process (SAR Section 2.3.5.4.2.4). First, DOE used the process-level condensation 

models to generate a set of results for different parametric variations that account for dispersion 

coefficient, percolation rates, invert assumptions, and temporal variation of heat load. Second, 

DOE developed a set of regression curves that establishes a functional relationship between 

percolation flux, probability of condensation, and condensate mass. Third, DOE used the 

regression curves for each percolation subregion to determine the occurrence (fraction of area) 

and magnitude of condensation. DOE added condensate flux directly to dripping flux to obtain a 

total flux of water entering drifts. 

In DOE’s model, condensation within emplacement drifts would be altered if a disruptive event 

occurs during the thermal period. The DOE abstraction sets condensation to zero once an 

igneous intrusion or drift collapse event occurs (SAR Section 2.4.2.3.2.1.12.3), because these 

processes would fill drifts with rock and substantially reduce air gaps. However, such events 

have low probability and DOE expects drifts to remain intact throughout the first 2,000 years, as 

described in DOE Enclosure 7 (2009ct). 

DOE provided a transparent description of conceptual, numerical, and abstracted models and 

parameter inputs. This is supported by staff expertise gained from prelicensing interactions with 

DOE (NRC, 2005aa) and from independent laboratory experiments and numerical modeling of 

convection, vapor transport, and condensation in drift analogs (Das, et al., 2007aa; Manepally, 

et al., 2007ab). 

Condensation Results 

The DOE condensation model results can be summarized as follows: 

 During the intermediate cooling period, all codisposal waste package locations receive 

condensation dripping from the drift ceiling at rates 8 to 35 times greater than the mean 
9-46 

seepage rate [calculated from SAR Table 2.1-11 and DOE Table 7 (2010ai)]. DOE 

conservatively applied condensation to all codisposal waste package locations, but no 

spent fuel waste package locations receive any condensation. 

 During the final cooling stage (after approximately 1,500 years), mean condensation 

rates are less than 1 percent of mean seepage rate, and condensation only occurs at a 

small fraction of locations for both codisposal and spent fuel waste packages, as shown 

in SAR Tables 2.1-10 and 2.1-11 and DOE Tables 6 and 7 (2010ai). 

The average condensation rate in a percolation bin is calculated by multiplying the fraction of 

waste package locations receiving condensation times the condensation flux rate, which is then 

added to the seepage rate (SAR Section 2.3.3) rate to obtain a total dripping rate. 

NRC staff considered two types of information DOE provided to gain confidence in the 

condensation model and results used in the performance assessment. First, in developing the 

conceptual model, DOE stated that observations of vapor movement and condensation in 

response to small thermal gradients in the East-West Cross-Drift indicate the importance of the 

cold trap process in the repository (SAR Section 2.3.5.4.2). Although no quantitative estimate 

could be made from observations in the East-West Cross Drift, the NRC staff believes the 

observations from near-ambient conditions point to possible condensation in emplacement drifts 

both prior to and well beyond the 2,000-year cutoff used in the DOE condensation abstraction. 

Second, to provide confidence in model results, DOE included evaluations by two university 

professors in the model validation section of SNL Section 7.6 (2007bl). 

On the basis of its evaluation of condensation estimates on the consequence for repository 

performance, the NRC staff notes that condensation estimates are not important for repository 

performance. This is derived from different considerations for different time periods following 

repository closure. During the first 2,000 years after permanent closure, the presence of the 

intact drip shield ensures that the condensate will not directly contact waste packages. During 

this period, DOE asserts that drip shields will be sufficiently warm that any condensation will 

occur on drift walls, above or away from drip shields. On the basis of the evaluation of drip 

shield corrosion and mechanical degradation processes, NRC staff notes in TER Sections 

2.2.1.3.1.3.1 and 2.2.1.3.2.6 that drip shields are likely to remain intact for at least 12,000 years, 

which is well beyond the thermal period DOE defined for condensation. 

NRC staff notes that even if condensation continued past the 2,000-year limit DOE specified in 

the TSPA, it will not significantly affect repository performance for the following reasons: 

 For condensation during the period from 2,000 years to 12,000 years after closure, 

condensation rates are not important, because the drip shields will remain intact and 

thus divert water from the waste package. Note that the DOE condensation rate 

estimate is zero during this period, though NRC staff considers the possibility that 

evaporation and condensation rates are nonzero based on observations in the 

East-West Cross-Drift Test. 

 For condensation between 12,000 years and 255,000 years after closure, NRC staff 

expects the total amount of water entering drifts would be the same regardless of the 

mechanism for water entering drifts. The total flux of water approaching a drift limits the 

total water entering a drift regardless of whether that water flux is due to dripping or to 

evaporation and condensation. NRC staff notes in TER Section 2.2.1.3.6.3.4.1 that the 

seepage percentage is already high for this period and that further increases would 
9-47 

minimally affect performance. In addition, NRC staff suggests that any change in the 

fraction of waste package locations getting wet because of condensation, rather than 

dripping, will not adversely affect performance, because DOE already set the seepage 

fraction to be a high value (TER Section 2.2.1.3.6.3.4.1) for this period. 

 Beyond 255,000 years, DOE predicts that drifts will be collapsed (SAR Section 2.1.2.2.6) 

and axial convection along drifts will no longer occur. If portions of the drifts collapsed, 

allowing for small convection cells and potential condensation, NRC staff expects the 

fraction of waste packages that get wet to remain the same or decrease. 

Summary 

On the basis of its consideration of DOE-provided information on in-drift convection, moisture 

transport, and condensation abstraction, the NRC staff notes that DOE acceptably described 

and represented convection along drifts to estimate dispersion coefficients and support 

estimates of effective thermal conductivity in the Multiscale Thermal-Hydrologic Model. The 

NRC staff notes that, for the condensation abstraction and results, condensate estimates are 

not important for performance during the first 2,000 years, because drip shields are expected to 

remain intact. NRC staff notes in TER Sections 2.2.1.3.1.3.1 and 2.2.1.3.2.6 that drip shields 

are expected to remain intact and divert water away from waste packages for 12,000 years. 

Drip shield performance is evaluated in TER Sections 2.2.1.3.1 and 2.2.1.3.2. After the drip 

shield becomes degraded, NRC notes that condensation flux estimates would not change the 

total flux of water entering drifts, because dripping rates would decrease in response. 

2.2.1.3.6.3.6 Ambient Mountain-Scale Flow—Below the Repository

NRC staff’s evaluation of the flow field in the unsaturated zone below the repository considers 

how the flow magnitudes and patterns affect radionuclide transport. Flow above the repository 

is evaluated in TER Section 2.2.1.3.6.3.2, and flow below the repository is evaluated in this 

section. The site-scale unsaturated flow model provides flow fields both above and below the 

repository for different climates (SAR Section 2.3.2.3). In TER Section 2.2.1.3.6.3.2, NRC staff 

evaluates the use of site characterization data, development of a conceptual model, calibration 

procedure, and confidence building exercise and validation for this model. The following factors 

influence aspects of the ambient site-scale flow fields that are relevant to the flow fields below 

the repository: (i) the CHn influences flow in the southern and northern portions of the 

repository footprint, (ii) the active fracture model (AFM) influences the fracture-to-matrix flux, 

and (iii) the uncertainty of flow fields influences transport. Output of the ambient site-scale flow 

model (i.e., flow patterns, water saturations, and flow rates) is direct input to the radionuclide 

transport abstraction, which NRC staff reviews in TER Section 2.2.1.3.7. 

Evaluation of the adequacy of flow fields below the repository is separated into three parts: 

(i) NRC staff reviews the DOE description of the conceptual model for flow below the repository, 

(ii) NRC staff evaluates information and observations supporting flow features in the southern 

vitric CHn zone and in the northern zeolitic CHn zone, and (iii) NRC staff evaluates how 

uncertainty in flow fields can affect repository performance regarding radionuclide transport. 

2.2.1.3.6.3.6.1 Flow Model Conceptualization 

DOE described aspects of the flow below the repository in SAR Section 2.3.2.2.1.4 and how 

these aspects are related to the hydrogeologic units (SAR Table 2.3.2-2) below the repository. 

The hydrogeologic units include 
9-48 

 TSw (Topopah Spring welded tuff); dominantly fracture flow 

 CHn (Calico Hills nonwelded hydrologic units); 

— Calico Hills Formation; nonwelded vitric and zeolitic zones 

— Prow Pass Tuff and top of Bullfrog Tuff; devitrified and zeolitic horizons 

 CFu (Crater Flats undifferentiated units); varied degree of welding 

 Fault zones crossing all hydrologic units 

DOE described flow in the first layer underlying the repository, the TSw, as occurring dominantly 

through fractures (SAR Section 2.3.2.2.1.3). DOE assumed steady-state flow was based on 

dampening of episodic infiltration pulses by the overlying PTn unit (SAR Section 2.3.2.2.1.2; 

reviewed by NRC staff in TER Section 2.2.1.3.6.3.2). Percolating water moves approximately 

vertically from the ground surface through the proposed repository to the base of the TSw. 

Below the TSw, DOE described flow patterns in the CHn that differ markedly between the 

northern and southern portions of the repository footprint (SAR Section 2.3.2.2.1.4). The CHn is 

the only unit where lateral variation has been incorporated into the ambient site-scale 

unsaturated flow model. DOE described portions of the originally vitric CHn layer as altered to 

zeolites, which strongly modifies hydraulic properties. The distribution of alteration is described 

as increasing with depth and increasing to the north and east across the repository footprint 

(SAR Section 2.3.2.2.1.4). Below the southern portion, DOE expects flow in the vitric CHn to be 

dominated by matrix flow, because matrix permeability is higher than percolation rates. DOE 

expects little fracture flow where the CHn is unaltered. Below the northern portion, where most 

of the CHn has been altered to zeolites, perched water occurs in overlying units due to low 

permeability of the zeolitic tuff. DOE described that perched water affected performance by 

causing lateral flow to faults and fast vertical flow and transport down to the groundwater table 

(saturated zone). Because flow through the matrix of vitric CHn units is much slower than flow 

through fractures and faults, DOE predicted travel times in the southern portion to be much 

longer than in the northern portion of the repository (SAR Section 2.3.8.1). 

Little information was available to support DOE model estimates of flow patterns in the 

underlying Prow Pass, Bullfrog, and CFu Tuffs. DOE described these units as layers 

of devitrified, zeolitic, welded, and nonwelded tuff (SAR Table 2.3.2-2). In SAR 

Section 2.3.2.2.1.4, DOE noted that these units comprise a small volume of rock above 

the water table. With contrasting hydraulic properties for layers in Prow Pass, Bullfrog, and 

CFu Tuffs, percolating waters would be expected to switch between fracture- and 

matrix-dominated flow several times between the repository horizon and the water table, with 

the matrix layers controlling sorption and travel time. NRC staff notes that DOE adequately 

described the flow magnitudes and paths below the repository consistent with the staff analysis 

(Leslie, et al., 2007aa; NRC, 2005aa). 

2.2.1.3.6.3.6.2 Flow Features Below Southern and Northern Portions of Repository 

NRC staff reviewed the support DOE provided for flow features below the repository that may 

affect performance. Flow patterns below the repository can be described in terms of water 

velocity, which together with water saturation is directly tied to transport travel times. Flow 

patterns can be separated into three horizons starting at the repository and proceeding 
9-49 

downward: (i) fracture flow in welded tuffs of the TSw, (ii) influence of nonwelded tuffs of the 

CHn, and (iii) flow in the variably welded tuffs below the CHn. DOE described flow through the 

TSw as primarily vertical and rapid because of the pervasive fracture network, but described 

rock alteration in the underlying CHn as causing different flow patterns in the southern and 

northern portions of the repository footprint (SAR Section 2.3.2.2.1.4 and supporting 

documents). In the southern portion, travel times are slow and sorption potential is high due to 

flow predominantly through the matrix of the unaltered, vitric CHn. This is contrasted with fast 

travel times for transport in the northern portion of the repository where fracture and fault flows 

dominate, because the low permeability of the altered, zeolitic CHn led to the formation of 

perched water. Below the CHn, alternating layers of tuff with differing degrees of welding are 

host to the present-day and expected future water table. 

There is limited access to, and thus limited direct observations of, these units because of their 

depth below the ground surface and below the existing tunnel and drift. Therefore, NRC staff 

considered how uncertainty caused by sparse data may affect performance of the repository. 

To accomplish this, the NRC staff reviewed support for the conceptual model and estimation of 

parameter values of the numerical model deemed important for flow patterns below the 

repository. These aspects include (i) fracture-matrix flow in the TSw immediately below the 

repository, (ii) properties and distribution of vitric CHn in the southern portion below the 

repository, (iii) influence of zeolitic CHn on perched water below the northern portion of the 

repository, and (iv) uncertainty of flow patterns below the CHn. 

Flow in Welded Topopah Spring Tuff 

NRC staff reviewed the basis provided for flow patterns through the fracture network of the 

welded tuffs of the TSw immediately below the repository. The review here focuses on support 

for the hydrologic properties of the fracture network, including support for the conceptualization 

and estimation of parameter values for the AFM. 

DOE utilized air permeability and fracture data (SAR Section 2.3.2.3.3.2) as prior information 

for calibration of fracture hydrological properties. DOE assumed that fractures follow the 

van Genuchten–Mualem constitutive relations for saturation, water potential, and relative 

permeability, adjusted by the AFM. Support for the AFM parameter values is discussed later in 

this chapter. DOE based fault hydrological properties on air permeability measurements (SAR 

Section 2.3.2.3.3.3) and integrated these properties into the transition of one-dimensional 

calibration values to three-dimensional values across the site. DOE did not include sorption on 

fracture surfaces (SAR Section 2.3.8.1) and represented flow through welded-tuff fractures and 

faults as fast [e.g., tens to hundreds of years; SAR Figure 2.3.8-49 and DOE Enclosure 6 

(2009an)]. The NRC staff notes that DOE represented uncertainty in the fracture and fault input 

parameters of permeability, water retention, and relative permeability because alternative 

representations would either insignificantly reduce radionuclide travel times or improve 

repository performance. 

Estimation of parameter values for the AFM may affect performance because the AFM controls 

the flux of water from fractures into the surrounding matrix. Increasing the flux of water moving 

from fractures to matrix increases the movement of radionuclides into the matrix, where slower 

travel times and increased sorption occurs relative to the fracture continuum. Therefore, NRC 

staff reviewed the basis and uncertainty of parameter values used for the AFM. 

DOE described and implemented the AFM of Liu, et al. (1998aa) in the site-scale unsaturated 

flow model to capture the effects of gravity-driven fingering flow through a limited number of 
9-50 

water-conducting or active fractures. In the site-scale unsaturated zone flow model, DOE kept 

layerwise AFM parameters constant in TSPA realizations, but varied the values with infiltration 

uncertainty scenario, as shown in SAR Tables 2.3.2-8 through 2.3.2-11 and SNL Section 6.5.6 

(2008an). DOE estimated the AFM parameters for the three-dimensional model by calibrating 

one-dimensional flow simulations with field data (SAR Section 2.3.2.4.1.2.3.2). DOE adjusted 

the calibrated AFM parameter values to induce perching for model layers with observed 

perched water, thereby forming a fast pathway for water to flow into faults and bypass the 

underlying low-permeability and high-sorption units. The DOE AFM model is reasonable for 

representing flow patterns below the repository because DOE showed it was capable of 

reproducing field-injection test data and natural geochemical and isotopic observations. 

Additional confidence in this evaluation is derived from independent NRC staff analysis and 

modeling studies (Basagaoglu, et al., 2009aa). 

NRC evaluated the support DOE provided for representative estimates of the AFM parameter 

values used in performance assessment calculations. NRC staff considered the (i) uncertainty 

from using one-dimensional model sensitivity analyses to estimate three-dimensional behavior 

for a three-dimensional model result and (ii) uncertainty in interpretations of observations from 

natural isotopes, fracture coatings, and forced-injection field tests caused by multiple processes 

affecting transport of natural and injected tracers. DOE estimation of AFM parameter values 

is reasonable because DOE provided results of sensitivity analyses that showed no 

significant changes to the flow fields for reasonable ranges of the AFM parameter values 

(SAR Section 2.4.2.3.1.7 and references cited therein). 

NRC staff also considered integration between the unsaturated flow and transport for the AFM 

parameters. DOE used different values and uncertainty distributions for the AFM parameter 

values for flow and transport simulations. DOE used fixed values of the gamma parameter in 

the AFM in flow simulations (SAR Tables 2.3.2-8 to 2.3.2-11, 2.3.2-13, and 2.3.2-21 to 2.32-24). 

DOE, however, treated the same parameter as uncertain in transport simulations by 

probabilistically sampling from a distribution of values (SAR Section 2.3.8.4.5.2). From a 

conceptual perspective, DOE, following Zhou, et al. (2007aa), suggested that advective flow 

occurs mostly in large-scale fractures, but transport through matrix diffusion takes place in both 

small- and large-scale fracture networks in real heterogeneous fractured continua. Thus, 

conceptually, there may be additional contributions to the active fracture-matrix interfacial area, 

across which diffusive mass transfer occurs, by small-scale fracture networks that do not 

contribute to large-scale flows. The effect of transport in small-scale fractures of a network is 

not explicitly addressed in the DOE algorithm, as specifically shown in SNL Eq. C–40 (2008an), 

but the effect is incorporated in the sampling of gamma for transport calculations. NRC staff 

reviews the distribution of gamma parameter values for transport in TER Section 2.2.1.3.7.3.2.3. 

Because DOE provided a reasonable basis for using different values for the AFM parameter in 

the flow model compared to those in the transport model, NRC staff notes the flow and transport 

models are adequately integrated in regard to the AFM. 

Influence of CHn in Southern Portion of Repository 

DOE described the CHn in the southern portion of the repository as unaltered, though 

some zeolitic alteration is present and increases with depth. DOE identified the unaltered, 

or vitric, zones of the CHn as an important component of the Lower Natural Barrier in terms 

of water travel times and the unit’s capability for delaying radionuclide movement (SAR 

Section 2.1.2.3.1). Because the travel times through the matrix of the vitric CHn are longer 

than in other units above and below the CHn where fast fracture flow may dominate, transport 

through the vitric CHn dominates the travel times of the entire sequence of hydrogeologic units 
9-51 

below the southern portion of the repository to the water table. Therefore, NRC staff included 

the uncertainty of the hydrologic properties and the spatial distribution of the vitric CHn in 

its review. 

DOE characterized flow in the vitric CHn unit as matrix flow dominant (i.e., little or no fracture 

flow) due to the unit’s relatively high matrix permeability and porosity (SAR Section 2.3.2.2.1.4). 

DOE provided information from boreholes near the repository and from the Busted Butte analog 

site to support its characterization of the vitric CHn below the repository. Busted Butte was a 

field experiment DOE performed to support the importance of capillarity and matrix-dominated 

flow in the CHn vitric tuff using several injection tracer tests (SAR Section 2.3.2.3.2.4). Noting 

that the CHn at Busted Butte is a distal portion of the CHn found at Yucca Mountain, as 

described in BSC Appendix H (2004av), DOE provided a lithologic and mineralogic comparison 

of the CHn near the repository with the units at Busted Butte, but did not provide a hydrologic 

comparison. Therefore, the NRC staff reviewed measured and calibrated values of hydraulic 

conductivity and porosity from the boreholes near Yucca Mountain and the Busted Butte site. 

The NRC staff notes that porosity values do not significantly differ between the two sites. The 

NRC staff noted, however, that hydraulic conductivity does differ between the sites. The 

NRC staff analysis indicates uncertainty in hydraulic conductivity is two orders of magnitude 

larger or smaller than the values used in the performance assessment, when considering 

measured values in different layers and scale effects for calibrated values. Values used in 

the NRC staff analysis are in (i) SNL Tables 7-8, 7-9, 7-13, and 7-14 (2007bj); (ii) SAR 

Table 2.3.2-3; (iii) BSC Table H-3 (2004av); and (iv) Flint Table 7 (1998aa). The NRC staff 

notes that differences between the borehole information at Yucca Mountain and Busted Butte 

are not important because 

 If hydraulic conductivity of the CHn matrix near the repository footprint is several orders 

of magnitude smaller than estimated, NRC staff expects matrix flow to dominate in the 

vitric CHn unit because the matrix can still accommodate typical percolation rates. 

 If the hydraulic conductivity of the CHn matrix is larger than estimated by DOE, travel 

time and sorption would not be significantly different. 

Because the uncertainty in the range indicated by the Busted Butte experiment is not important 

to performance, the NRC staff notes that DOE estimates of hydrologic properties of the vitric 

CHn are reasonable for performance assessment. 

DOE described the spatial distribution of the CHn both laterally and vertically in the southern 

portion of the repository footprint. DOE indicated there were few available data to constrain the 

spatial distribution of vitric and zeolitic zones below the repository. As a result, uncertainty 

about spatial variability of the CHn units (i.e., vitric versus zeolitic) may increase with depth and 

distance from the repository footprint (SAR Section 2.3.2.3.5.3). In its analysis, DOE 

incorporated data from surface mapping and 23 boreholes spread in and around Yucca 

Mountain, as described in BSC Section 6.2.3 (2004bt). Six of these boreholes lie within or near 

the edge of the repository footprint. 

To assess the reasonableness of using a small number of boreholes to represent the extent of 

the vitric CHn, the NRC staff evaluates the uncertainty of the spatial distribution of the vitric tuff 

by considering potential alternative representations. NRC staff notes that (i) the contact 

between the zeolitic and vitric zones of the CHn is not well constrained because of large 

distances between boreholes, and the horizontal and vertical complexity of the contact itself and 

(ii) by fixing the distribution of CHn vitric and zeolitic zones in the site-scale unsaturated flow 
9-52 

model grid, DOE did not quantify uncertainty in the extent of the vitric unit in performance 

assessment calculations. The NRC staff built confidence in its review of the spatial distribution 

of vitric tuff using a bounding estimate for the performance consequence resulting from the 

presence of vitric tuff. Because of slower flows and greater sorption in matrix-dominated flows 

through the vitric tuff, which DOE represents as underlying approximately half of the repository, 

the vitric tuff provides a greater barrier capability than elsewhere. In the alternative 

representation, the NRC staff assumed that the southern vitric portion performed similarly to the 

northern zeolitic portion, thereby substantially reducing the performance capability of the vitric 

portion. If the entire repository was represented by the generally rapid travel times and minimal 

sorption DOE ascribed to the northern zeolitic half of the repository footprint, then dose would at 

most increase by a factor of two. With this extreme bounding assumption, alternative vitric 

distributions would either reduce calculated dose (if the vitric area were larger) or would 

increase by no more than a factor of two (if no vitric unit was present). This bounding analysis 

provides confidence that uncertainty in the location of the vitric/zeolitic contact, and thus the 

spatial distribution of the vitric unit, has small consequence for performance relative to the DOE 

model. Therefore, using a performance metric, NRC notes that the small number of boreholes 

available to constrain the distribution of the vitric CHn is reasonable. The NRC staff compared 

DOE’s description, integration, and interpolation of sparse borehole data with staff’s 

knowledge of site characteristics, as outlined in Leslie, et al. Section 6.4 (2007aa) and NRC 

Section 5.1.3.6.4 (2005aa). The NRC staff notes that DOE reasonably represents the estimated 

areal extent and vertical variations because DOE represents the contact between vitric and 

zeolitic units as consistent with geological conceptualizations considering the interplay between 

faulting and stratigraphic dip of the layers. 

Influence of CHn in Northern Portion of Repository 

DOE described released radionuclides in the northern portion of the repository as starting out in 

the welded units of the TSw; proceeding vertically, predominantly in the fracture system to 

perched water above the zeolitic zones of the CHn; predominantly bypassing the 

low-permeability, high-sorptivity zeolitic zones by rapidly moving laterally to faults in the perched 

water body; and finally rapidly moving vertically within faults to the water table (SAR 

Section 2.3.2.2.1.4). DOE did not include sorption on fracture surfaces (SAR Section 2.3.8.1) 

and represented flow through welded-tuff fractures and faults as fast [e.g., tens to hundreds 

of years; SAR Figure 2.3.8-49 and DOE Enclosure 6 (2009an)]. DOE stated that neglecting 

sorption on fracture surfaces and representing fast flow in fractures and faults were 

conservative assumptions for estimating dose in the performance assessment. In this section, 

the NRC staff reviews the DOE treatment of model uncertainty for (i) perching of water and 

(ii) extent of the zeolitic unit that causes the perching to ensure that the DOE representations 

did not lead to underestimates of dose. 

DOE implemented a permeability-barrier conceptual model for perched water (SAR 

Section 2.3.2.4.1.2.4.4) in which sufficient local percolation flux, poorly interconnected and 

conductive fractures, and locally low vertical and horizontal permeabilities contribute to the 

occurrence of perched water. DOE incorporated the conceptual model for flow in the perched 

water by adjusting the calibrated model parameters for the layers where perched water has 

been observed in the field. DOE identified a reasonable alternative model for perching of water 

whereby slow vertical flow through the zeolitic portions of the CHn unit occurs at rates much 

smaller than percolation (SAR Section 2.3.2.4.2.1.3). Of the two reasonable perched water 

models, DOE selected the one that provides the smallest barrier capability. Therefore, the NRC 

staff notes the permeability-barrier conceptual model adequately represents field conditions for 

performance assessment. 
9-53 

DOE constrained the spatial extent zeolitic CHn, and thus perched water, in the site-scale 

unsaturated zone model using borehole data. Through hydraulic testing and interpretation of 

borehole observations, DOE suggested that the volume and extent of the perched-water bodies 

at Yucca Mountain may vary greatly (SAR Section 2.3.2.2.2.4). The extent of perched water is 

inversely related to the extent of vitric CHn, which NRC staff reviews in the previous subsection 

on the southern portion of the repository. NRC considered the possibility that DOE 

underestimated the extent of perched water because this would lead to overestimates of 

average travel times, and thus potentially lead to underestimates of dose. NRC staff notes that 

it is unlikely that the extent of perched water is significantly underestimated because of 

geospatial constraints provided by the locations of boreholes and DOE’s reasonable, but 

conservative, placement of the zeolitic contact with the vitric CHn. 

In summary, DOE reasonably represented the influence of the CHn unit in the northern portion 

of the repository footprint because alternative models for the cause of perched water and the 

extent of perched water would either increase travel times or insignificantly reduce travel times. 

Flow Patterns Below the CHn 

DOE provided sparse observations related to hydrologic properties and flow patterns below the 

CHn in the northern and southern repository areas traversing the Prow Pass, Bullfrog, and CFu 

Tuffs. In its review of flow patterns below the CHn unit, NRC staff considered uncertainty 

caused by the sparseness of observations and data. 

NRC staff reviewed the hydrologic characterization of layers below the CHn. To estimate the 

hydrologic properties of the lowermost layers to calibrate the site-scale unsaturated flow model, 

DOE supplemented the available information and observations with analog data from the PTn 

and TSw (SAR Section 2.3.2.3.5.3). NRC staff considered the effect on uncertainty on the basis 

of sparse data available from scattered boreholes that reach deep enough to cross these units. 

NRC staff notes that uncertainty in hydrologic properties and flow patterns in the Prow Pass, 

Bullfrog, and CFu Tuffs does not significantly affect calculations of dose, because (i) flow paths 

in the northern portion of the repository bypass these units, (ii) travel time in the southern 

portion of the repository is dominated by transport time in the Calico Hill nonwelded vitric tuff 

horizons, and (iii) assigned hydraulic properties lead to more focusing of flow and thus increase 

transport velocities through the highly sorbing matrix units in DOE’s flow model. 

NRC staff considered model support for the spatial distribution of zones of focused flow 

potentially provided by the pattern of water table temperatures. The NRC staff expects that 

large-scale zones of focused flow may depress the geothermal gradient in the unsaturated zone 

and perturb the temperature at the water table. Temperatures at the water table might reflect 

large-scale flow features, such as (i) localized and high flux rates predicted by the unsaturated 

zone model in faults; (ii) low flux reaching the water table below zeolitic rocks, which 

predominate in the northern half of the repository; or (iii) flux rates focused by decreasing areal 

extent of vitric CHn with depth, which predominates in the southern half of the repository. 

Alternative interpolations of water table temperature were presented in the SAR Figure 2.3.2-37, 

SNL Figure 6.3.1-7 (2008ag), and Sass, et al. (1988aa). Because the distribution of water table 

temperatures in any of the interpolations was not consistent with the large-scale spatial 

distribution of percolation in the DOE site-scale unsaturated zone flow model, DOE suggested 

that water table temperature was not a sensitive indicator to percolation rate, as outlined in DOE 

Enclosure 1 (2009cy). DOE suggested that multiple factors make it difficult to interpret potential 

relationships between temperature and percolation, including (i) uncertainty in ground surface 
9-54 

temperature, (ii) thickness of the unsaturated zone, (iii) uncertainty in thermal conductivity of 

unsaturated zone units, (iv) influence of vertical groundwater flow in the saturated zone, and 

(v) uncertainty in the deep subsurface heat flux. 

The locations of zones of focused flow are fixed in the DOE model. Because no other support 

was available to support predictions of the spatial distribution of flow reaching the water table, 

the NRC staff considered the consequence of uncertainty in the location of focused flow in the 

lower part of the unsaturated zone. DOE showed with sensitivity analyses that the exact 

locations of radionuclide release from the unsaturated zone to the saturated zone are not 

important in the performance assessment (SAR Section 2.3.2.3.5.4). Because the radionuclide 

release distribution is not important to performance, the distribution of focused flow also is not 

important to performance. On the basis of the DOE sensitivity analyses and the fact that less 

focusing would improve performance, the NRC staff notes that DOE reasonably accounted for 

the sparseness of data in model development for flow paths below the repository. 

NRC staff reviewed water table position because of its effect on unsaturated zone transport 

length. Future, wetter climates affect flow below the repository in two ways: increased flow 

rates and water table rise. The increased percolation rates during future climates are evaluated 

as part of the site-scale unsaturated flow model in TER Section 2.2.1.3.6.3.2. The present-day 

water table is located in the dipping layers of the Prow Pass, Bullfrog, and Crater Flat Tuffs. 

Mineralogical and geochemical evidence suggests the water table occurred at higher elevations 

in the past. DOE found no evidence supporting higher perched water elevations underneath the 

repository; therefore, NRC only considered the uncertainty of a higher water table. DOE 

accounted for water table rise under future, wetter climate conditions by raising the location to a 

uniform 850-m [2,790-ft] elevation, which is approximately 120 m [394 ft] higher than the 

present-day estimate for the water table position. DOE stated (SAR Section 2.3.2.5.2) this rise 

in the water table is significantly greater than indicated by geologic evidence, which includes 

mineralogic alteration and isotopic ratios in secondary minerals, and flow modeling exercises 

with increased precipitation and recharge. Because a greater rise in the water table elevation 

reduces the transport path length for the unsaturated zone, and thus shortens travel times and 

reduces potential sorption capacity of the unsaturated zone, NRC notes that DOE acceptably 

incorporated the effects of future, wetter climates on flow paths below the repository. 

2.2.1.3.6.3.6.3 Adequacy of Flow Fields for Transport 

NRC staff reviewed the effect of uncertainty of flow fields on transport through the unsaturated 

zone. DOE passed the flow fields below the repository to the Unsaturated Zone Transport 

(SAR Section 2.3.8.5) portion of the DOE performance assessment. Overall, DOE considered 

advection to be the most important transport process in the unsaturated zone because the rate 

of water movement in the unsaturated zone largely controls radionuclide travel times, as 

outlined in SNL Section 6.1.2.1 (2007bj) and DOE Enclosure 6 (2009an). DOE also identified 

matrix diffusion and sorption as highly important for moderately to strongly sorbing 

radionuclides, particularly radionuclides with a short half-life that pass through a matrix unit, as 

described in DOE Enclosure 6 (2009an). DOE identified matrix diffusion and sorption as more 

important in the southern half of the repository because of the control from matrix transport in 

the CHn vitric facies, and more important for the 10,000-year period than the million-year period, 

as outlined in DOE Enclosure 6 (2009an). On the basis of these DOE assessments, the NRC 

staff focuses its review on the flow fields with respect to transport for nonsorbing and 

moderately to strongly sorbing dissolved radionuclides. NRC staff evaluates the repository 

performance with respect to unsaturated zone transport, including colloidal transport, in 

TER Section 2.2.1.3.7.3.2.4. 
9-55 

Flow path differences between the northern and southern portions of the repository influence 

the travel times of nonsorbing and sorbing radionuclides. DOE provided model results (SAR 

Figures 2.3.8-36 and 2.3.8-49) that exhibited the presence of three predominant types of 

transport pathways. NRC staff describes these as (i) fast transport for fracture releases occurs 

in the northern half of the repository, with mean travel times of years to centuries; (ii) moderately 

slow transport pathways for both matrix and fracture releases go through the southern half of 

the repository, with mean travel times of centuries to millennia; and (iii) slow transport through 

the matrix for radionuclides released into the matrix of the TSw tuff with mean travel times of 

millennia, with a small percentage transferring to the fracture system and reaching the water 

table more rapidly. The DOE ambient site-scale unsaturated zone model includes perching 

below the repository horizon in the northern half of the repository. In the DOE implementation, 

perching diverts fracture waters into faults and thereby creates a large difference in travel times 

for the northern and southern halves of the repository. 

 

NRC staff first considered nonsorbing radionuclides. In DOE Enclosure 1 (2009am), several 

single-realization simulations with 30th and 50th percentile infiltration maps demonstrated 

transport properties for the unsaturated zone. Using DOE Enclosure 1, Table 4 (2009am), DOE 

calculated that total activity released from the unsaturated zone in the first 10,000 years after 

closure is 73 percent of total activity released from the Engineered Barrier System for Tc-99 

(a nonsorbing radionuclide) in the northern half of the proposed repository and 78 percent in 

the southern half under the seismic ground motion scenario. DOE calculated that total Tc-99 

activity released from the unsaturated zone in the first million years after closure is at least 

98 percent of that released from the Engineered Barrier System for the igneous intrusion and 

seismic ground motion scenarios, regardless of release location. 

The NRC staff notes that DOE reasonably represents flow in the unsaturated zone for 

nonsorbing radionuclides because the DOE transport model represents radionuclide transport 

processes through the unsaturated zone as not substantially reducing the activity of nonsorbing 

radionuclides released from the Engineered Barrier System. 

The NRC staff next evaluates the extent to which the unsaturated zone flow fields affect DOE’s 

performance assessment with respect to moderately to strongly sorbing radionuclides. DOE 

classified the porous matrix as either zeolitic, devitrified, or vitric and assigned all three 

classifications with sorptive capability. DOE explained that sorbing radionuclides are 

preferentially released to the fracture system as a result of sorption within the invert, as outlined 

in DOE Enclosure 1, Section 1 (2009am). As a result of the DOE release and flow models, 

which route fracture waters through the perched zone and into faults draining to the water table 

in the northern half of the repository, sorbing radionuclides predominantly bypass the matrix in 

the north. In the DOE flow model, both matrix and fracture waters pass into the matrix of the 

permeable and sorbing vitric CHn unit in the south. 

The NRC staff notes that, for transport calculations of sorbing radionuclides in the northern 

portion of the proposed repository, DOE’s conceptual model for perching represents a 

conservative approach for flow from the repository horizon to the water table, because almost all 

fracture waters and some matrix waters experience short travel times from the top of the 

perched zone to the water table. For the southern portion of the repository, DOE showed that 

reasonable changes to the hydraulic properties of the vitric CHn matrix will not significantly 

change the flow regime (SAR 2.3.8.5.2.2). The NRC staff notes that reasonable increases or 

decreases in hydraulic conductivity of the vitric CHn would minimally affect travel times of 

sorbing radionuclides. The NRC staff notes that, for transport calculations of sorbing 
9-56 

radionuclides in the southern portion below the proposed repository, DOE’s model represents a 

reasonable approach because the CHn vitric units have large matrix saturated hydraulic 

conductivity values so that matrix-dominated flows are likely. 

2.2.1.3.6.3.6.4 Summary 

On the basis of evaluations of the northern and southern portions of the proposed repository 

footprint, the NRC staff notes that the range of flow fields generated from DOE’s site-scale flow 

model adequately represents model and data uncertainty for performance assessment 

calculations. In particular, the NRC staff notes that (i) the resulting flow fields are unlikely to 

overestimate radionuclide travel times from the proposed repository to the water table and 

(ii) different parameter value sets either would minimally affect travel times or would increase 

travel times. Because the flow fields are directly used as input in the transport model 

abstraction, NRC staff notes integration between the unsaturated flow and transport 

abstractions is reasonable. The NRC staff evaluates DOE’s overall approach to transport 

modeling in TER Section 2.2.1.3.7. 

2.2.1.3.6.4 NRC Staff Conclusions

NRC staff notes that the DOE description of this model abstraction for unsaturated zone flow is 

consistent with the guidance in the YMRP. NRC staff also notes that the DOE technical 

approach in the areas discussed in the preceding section is reasonable for use in the Total 

System Performance Assessment. 

2.2.1.3.6.5 References 

Basagaoglu, H., S. Succi, C. Manepally, R. Fedors, and D. Wyrick. 2009aa. “Sensitivity of the 

Active Fracture Model Parameter to Fracture Network Orientation and Injection Scenarios.” 

Hydrogeology Journal. Vol. 17. pp. 1,347–1,358. 

Basagaoglu, H., K. Das, R. Fedors, R. Green, C. Manepally, S. Painter, O. Pensado, 

S. Stothoff, J. Winterle, and D. Wyrick. 2007aa. “Seepage Workshop Report.” ML072980846, 

ML172980851, and ML072980850. San Antonio, Texas: CNWRA. 

Beven, K. and A. Binley. 1992aa. “The Future of Distributed Models: Model Calibration and 

Uncertainty Prediction.” Hydrological Processes. Vol. 6, No. 3. pp. 279–298. 

BSC. 2004al. “Drift Degradation Analysis.” ANL–EBS–MD–000027. Rev. 03. ACN 001, 

ACN 002, ACN 003, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004av. “In-Situ Field Testing of Processes.” ANL–NBS–HS–000005. Rev. 03. 

ACN 01, ACN 02, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004be. “Seepage Model for PA Including Drift Collapse.” MDL–NBS–HS–000002. 

Rev. 3. ACN 001. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bf. “Thermal Conductivity of the Potential Repository Horizon.” 

MDL–NBS–GS–000005. Rev. 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bt. “Mineralogic Model (MM3.0) Report.” MDL–NBS–GS–000003. Rev. 01. 

Las Vegas, Nevada: Bechtel SAIC Company, LLC. 
9-57 

Burmeister, L.C. 1993aa. Convective Heat Transfer. 2nd Edition. New York City, New York: 

John Wiley & Sons, Inc. 

Cussler, E.L. 1995aa. Diffusion Mass Transfer in Fluid Systems. 2nd Edition. New York City, 

New York: Cambridge University Press. 

Das, K., S. Green, and C. Manepally. 2007aa. “FLOW–3D YMUZ2 Version 1.0 Users Manual.” 

ML073030286. San Antonio, Texas: CNWRA. 

DOE. 2010ai. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.3.1), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.3.5, Set 1 and (Safety Analysis Report Sections 2.3.2 

and 2.3.3), Safety Evaluation Report Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (February 2) 

J.R. Williams to J.H. Sulima (NRC). ML100340034. Washington, DC: DOE, Office of 

Technical Management. 

DOE. 2009am. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.7, Set 1.” Letter (February 9) J.R. Williams to J.H. Sulima (NRC). 

ML090410352. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009an. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.1, Set 1.” Letter (February 6) J.R. Williams to J.H. Sulima (NRC). ML090400455. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009av. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 1. 

ML090700817. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2009bo. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.2 and 2.3.3), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (June 1) J.R. Williams to J.H. Sulima (NRC). 

ML091530403. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cb. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 5.” Letter (June 5) J.R. Williams to J.H. Sulima (NRC). 

ML091590581. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cc. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.2, Table 2.2-5), 

Safety Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 5.” Letter (August 12) J.R. Williams to 

J.H. Sulima (NRC). ML092250006. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ct. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.2, 2.3.3, and 2.3.5), Safety Evaluation 

Report Vol. 3, Chapter 2.2.1.3.6, Set 2.” Letter (July 20) J.R. Williams to J.H. Sulima (NRC). 

ML0920204130 and ML0920204141. Washington, DC: DOE, Office of Technical Management. 
9-58 

DOE. 2009cx. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Sections 2.3.2 and 

2.3.3), Safety Evaluation Report Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (December 14) 

J.R. Williams to J.H. Sulima (NRC). ML093490398. Washington, DC: DOE, Office of 

Technical Management. 

DOE. 2009cy. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.2, 2.3.3, and 2.3.5), Safety Evaluation 

Report Vol. 3, Chapter 2.2.1.3.6, Set 2.” Letter (August 3) J.R. Williams to J.H. Sulima (NRC). 

ML092160370 and ML092160371. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

Fedors, R., S. Green, D. Walters, G. Adams, D. Farrell, and S. Svedeman. 2004aa. 

“Temperature and Relative Humidity Along Heated Drifts With and Without Drift Degradation.” 

ML042160472. San Antonio, Texas: CNWRA. 

Flint, L.E. 1998aa. “Characterization of Hydrogeologic Units Using Matrix Properties, Yucca 

Mountain, Nevada.” Water-Resources Investigation Report 97-4243. Denver, Colorado: 

U.S. Geological Survey. 

Francis, N.D., Jr., S.W. Webb, M.T. Itamura, and D.L. James. 2003aa. “CFD Modeling 

of Natural Convection Heat Transfer and Fluid Flow in Yucca Mountain Project (YMP).” 

SAND 2002-4179. ACC:MOL20030906.0165. Albuquerque, New Mexico: Sandia 

National Laboratories. 

Ghezzehei, T.A. 2004aa. “Constraints for Flow Regimes on Smooth Fracture Surfaces.” 

doi:10.1029/2004WR003164. Water Resources Research. Vol. 40. p. W11503. 

Green, S. and C. Manepally. 2006aa. “Software Validation Report for FLOW3D® Version 9.0.” 

ML063050289. San Antonio, Texas: CNWRA. 

Green, R.T., C. Manepally, R.W. Fedors, and M.M. Roberts. 2008aa. “Examination of Thermal 

Refluxing in In-Situ Heater Tests.” ML083030097 San Antonio, Texas: CNWRA. 

Incropera, F.P. and D.P. DeWitt. 2002aa. Fundamentals of Heat and Mass Transfer. 

5th Edition. New York City, New York: John Wiley & Sons, Inc. 

Incropera, F.P. and D.P. DeWitt. 1996aa. Fundamentals of Heat and Mass Transfer. 

4th Edition. New York City, New York: John Wiley & Sons, Inc. 

Kreith, F. and M.S. Bohn. 2001aa. Principles of Heat Transfer. 6th Edition. Pacific Grove, 

California: Brooks/Cole. 

Kuehn, T.H. and R.J. Goldstein. 1978aa. “An Experimental Study of Natural Convection Heat 

Transfer in Concentric and Eccentric Horizontal Cylindrical Annuli.” Journal of Heat Transfer. 

Vol. 100. pp. 635–640. 

Kuehn, T.H. and R.J. Goldstein. 1976aa. “An Experimental Study and Theoretical Study of 

Natural Convection in the Annulus Between Horizontal Concentric Cylinders.” Journal of Fluid 

Mechanics. Vol. 74, Part 4. pp. 695–719. 
9-59 

Leslie, B., C. Grossman, and J. Durham. 2007aa. “Total-system Performance Assessment 

(TPA) Version 5.1 Module Descriptions and User Guide.” Rev. 1. ML072710060. San Antonio, 

Texas: CNWRA. 

Liu, H.H., C. Doughty, and G.S. Bodvarsson. 1998aa. “An Active Fracture Model for 

Unsaturated Flow and Transport in Fractured Rocks.” Water Resources Research. Vol. 34. 

pp. 2,633–2,646. 

Manepally, C., S. Green, F. Viana, and R. Fedors. 2007ab. “Evaluation of In-Drift Heat 

Transfer Processes.” ML071070508. San Antonio, Texas: CNWRA. 

Manepally, C., A. Sun, R. Fedors, and D. Farrell. 2004aa. “Drift-Scale Thermohydrological 

Process Modeling—In-Drift Heat Transfer and Drift Degradation.” CNWRA 2004-05. 

ML042160447. San Antonio, Texas: CNWRA. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

ML051360241. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

Or, D., M. Tuller, and R. Fedors. 2005aa. “Seepage Into Drifts and Tunnels in Unsaturated 

Fractured Rock.” doi:10.1029/2004WR003689. Water Resources Research. Vol. 41. 

p. WR05022. 

Painter, S., C. Manepally, and D.L. Hughson. 2001aa. “Evaluation of U.S. Department of 

Energy Thermohydrologic Data and Modeling Status Report.” ML043080474. San Antonio, 

Texas: CNWRA. 

Philip, J.R., J.H. Knight, and R.T. Waechter. 1989aa. “Unsaturated Seepage and Subterranean 

Holes: Conspectus and Exclusion Problem for Circular Cylindrical Cavities.” Water Resources 

Research. Vol. 25, No. I. pp. 16–28. 

Raithby, G.D. and K.G.T. Hollands. 1985aa. “Natural Convection.” Handbook of Heat Transfer 

Fundamentals. 2nd Edition. W.M. Rohsenow, J.P. Hartnett, and E.N. Ganic, eds. New York 

City, New York: McGraw-Hill. 

Richards, L.A. 1931aa. “Capillary Conduction of Liquids Through Porous Mediums.” Physics. 

Vol. 1. pp. 318–333. 

Salve, R. and T.J. Kneafsey. 2005aa. “Vapor-Phase Transport in the Near-Drift Environmental 

at Yucca Mountain.” Water Resources Research. Vol. 41. p. W01012. 

Sass, J.H., A.H. Lachenbruch, W.W. Dudley, Jr., S.S. Priest, and R.J. Munroe. 1988aa. 

“Temperature, Thermal Conductivity, and Heat Flow Near Yucca Mountain, Nevada: Some 

Tectonic and Hydrologic Implications.” USGS Open-File Report 87-649. Denver, Colorado: 

U.S. Geological Survey.
9-60 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ai. “Postclosure Analysis of the Range of Design Thermal Loadings.” 

ANL–NBS–HS–000057. Rev. 00. ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2008aj. “Multiscale Thermohydrologic Model.” ANL–EBS–MD–000049. Rev. 03. 

ADD 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008an. “Particle Tracking Model and Abstraction of Transport Processes.” 

MDL–NBS–HS–000020. Rev. 02. ADD 02. Las Vegas Nevada: Sandia National Laboratories. 

SNL. 2007aj. “EBS Radionuclide Transport Abstraction.” ANL–WIS–PA–000001. Rev. 03. 

ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007az. “Simulation of Net Infiltration for Present-Day and Potential Future Climates.” 

MDL–NBS–HS–000023. Rev. 01. AD 01, ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007bf. “UZ Flow Models and Submodels.” MDL–NBS–HS–000006. Rev. 03. ACN 01, 

ERD 01, ERD 02, ERD 03. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007bj. “Radionuclide Transport Models Under Ambient Conditions (U0060).” 

MDL–NBS–HS–000008. Rev. 02. CAN 002, AD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007bk. “Abstraction of Drift Seepage.” MDL–NBS–HS–000019. Rev. 01. Las Vegas, 

Nevada: Sandia National Laboratories. 

SNL. 2007bl. “In-Drift Natural Convection and Condensation.” MDL–EBS–MD–000001. 

Rev. 00. ADD 01. Las Vegas, Nevada: Sandia National Laboratories. 

Stothoff, S.A. 2010aa. “Infiltration and Unsaturated Zone Confirmatory Analyses.” Electronic 

Scientific Notebook 1005E. ML111110877. San Antonio, Texas: CNWRA. 

Stothoff, S. and G. Walter. 2007aa. “Long-Term-Average Infiltration at Yucca Mountain, 

Nevada: Million-Year Estimates.” CNWRA Report 09-003. ML072760607. San Antonio, 

Texas: CNWRA. 

van Genuchten, M.Th. 1980aa. “A Closed-Form Equation for Predicting the Hydraulic 

Conductivity of Unsaturated Soils.” Soil Science Society of America Journal. Vol. 44. 

pp. 892–898. 

Zhou, Q., H.H. Liu, F.J. Molz, Y. Zhang, and G.S. Bodvarsson. 2007aa. “Field-Scale Effective 

Matrix Diffusion Coefficient for Fractured Rock: Results From Literature Survey.” Journal of 

Contaminant Hydrology. Vol. 93, Nos. 1–4. pp. 161–187. 
10-1 

CHAPTER 10 

2.2.1.3.7 Radionuclide Transport in the Unsaturated Zone 

2.2.1.3.7.1 Introduction

This chapter provides the U.S. Nuclear Regulatory Commission (NRC) staff’s evaluation of the 

U.S. Department of Energy’s (DOE’s) representation of radionuclide transport in the unsaturated 

zone. This is a key component in DOE’s performance assessment for the proposed geologic 

repository at Yucca Mountain, Nevada, as identified in its Safety Analysis Report (SAR) 

Figure 2.3-2 (DOE, 2008ab). DOE’s performance assessment analysis included the flow of 

water from precipitation falling on Yucca Mountain, its migration as groundwater through the 

unsaturated zone above and below the repository, and the flow of groundwater in the saturated 

zone to the accessible environment. Exposure to radionuclides in groundwater extracted by 

pumping is one of the principal pathways for radiological exposures to the reasonably maximally 

exposed individual and for releases of radionuclides into the accessible environment. 

Therefore, as required by 10 CFR 63.114, the performance assessment analysis included 

radionuclide transport in the unsaturated zone among those model components that significantly 

affect the timing and magnitude of transport for any radionuclides released from the repository. 

In SAR Section 2.3.8, DOE (i) described the features, events, and processes (FEPs) that DOE 

included to model the transport of radionuclides in groundwater in the unsaturated zone below 

the repository and (ii) provided the technical basis for DOE’s implementation (or abstraction)1

 of 

the unsaturated zone transport model in the Total System Performance Assessment (TSPA) 

model. The NRC staff’s evaluation focuses on the following processes, detailed in subsequent 

sections of this chapter, that DOE included in its SAR Section 2.3.8 as important for 

radionuclide transport in the unsaturated zone: (i) advection, because most of the radionuclide 

mass is carried through the unsaturated zone by water flowing downwards to the water table; 

(ii) sorption, because sorption in porous media in the southern half of the repository area has 

the largest overall effect on slowing radionuclide transport in the unsaturated zone; (iii) matrix 

diffusion in fractured rock, because matrix diffusion coupled with sorption slows radionuclide 

transport in the northern half of the repository area; (iv) colloid-associated transport, because 

radionuclides attached to colloids may travel relatively unimpeded through the unsaturated 

zone; and (v) radioactive decay and ingrowth, because these processes affect the quantities of 

radionuclides released from the unsaturated zone over time. The NRC staff’s review of DOE’s 

technical basis for excluding other FEPs is addressed in the Technical Evaluation Report (TER) 

Section 2.2.1.2.1 (Scenario Analysis). 

DOE’s radionuclide transport model abstraction for the unsaturated zone utilizes information on 

the magnitude and patterns of groundwater flow in the unsaturated zone and the flux of 

radionuclides released from the waste forms and engineered barrier systems (EBS). In turn, the 

unsaturated zone radionuclide transport abstraction provides information about the mass flux of 

radionuclides released to the saturated zone.

2.2.1.3.7.2 Evaluation Criteria

The NRC staff’s assessment of model abstractions used in DOE’s postclosure performance 

assessment, including those considered in this chapter for radionuclide transport in the 

 

1

The term “abstraction” is defined as a representation of the essential components of a process model into a suitable 

form for use in a Total System Performance Assessment. Model abstraction is intended to maximize the use of 

limited computational resources while allowing a sufficient range of sensitivity and uncertainty analyses. 
10-2 

unsaturated zone, is guided by provisions in 10 CFR 63.114 (Requirements for Performance 

Assessment) and 10 CFR 63.342 (Limits on Performance Assessments). The resulting DOE 

Total System Performance Assessment (TSPA) is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry of the 

surface and subsurface from the site and the region surrounding Yucca Mountain 

[10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration 

processes of engineered barriers that would adversely affect performance of the 

natural barriers, consistent with the limits on performance assessment, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff evaluation of inclusion or exclusion of FEPs is given in TER Chapter 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of geologic 

stability, defined in 10 CFR 63.302 as 1 million years following disposal. These sections require 

that through the period of geologic stability, with specific limitations, DOE 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose during the initial 10,000 years following permanent 

closure [10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

The NRC staff review of the SAR and supporting information follows the guidance in the Yucca 

Mountain Review Plan (YMRP) (NRC, 2003aa) Section 2.2.1.3.7, Radionuclide Transport in the 

Unsaturated Zone, as supplemented by additional guidance for the period beyond 10,000 years 

after permanent closure (NRC, 2009ab). The YMRP acceptance criteria for model abstractions, 

which provide guidance for the NRC staff’s evaluation of DOE’s abstraction of radionuclide 

transport in the unsaturated zone, are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 
10-3 

Consistent with a risk-informed approach, NRC staff review used the guidance provided by the 

YMRP, as supplemented by NRC (2009ab), to the extent reasonable for aspects of radionuclide 

transport in the unsaturated zone important to repository performance. The NRC staff 

considered all five YMRP criteria in its review of information provided by DOE. In the context of 

these criteria, only those aspects of the model abstraction that substantively affect the 

performance assessment results, as determined by the NRC staff, are discussed in detail in this 

chapter. The NRC staff’s determination is based both on risk information provided by DOE and 

on NRC staff knowledge gained through experience and independent confirmatory analyses. 

2.2.1.3.7.3 Technical Evaluation

The NRC staff reviewed information in SAR Section 2.3.8 and references therein that described 

how DOE predicted the transport of radionuclides in the unsaturated zone below the repository. 

The NRC staff’s technical evaluation focused on how DOE (i) represented transport-related 

geological, hydrological, and geochemical features of Yucca Mountain in the unsaturated zone 

transport abstraction, (ii) integrated the transport abstraction with other TSPA model 

components, and (iii) established the technical basis for modeling the major, risk-significant 

transport processes in DOE’s process-level models and in the unsaturated zone radionuclide 

transport abstraction. 

2.2.1.3.7.3.1 System Description and Model Framework

DOE used the Yucca Mountain site data in analyzing the downward flow of water in the 

unsaturated zone, through fractures, major faults, and rock matrix from the repository drifts to 

the water table. DOE used the same analytical framework for its modeling of unsaturated zone 

transport of radionuclides as for its site-scale unsaturated zone flow model (SAR Section 2.3.2): 

a three-dimensional representation of layered volcanic tuff units with specified geological and 

hydrological properties, in which water and radionuclides move through fractures in the rock, 

through the rock matrix, and between fractures and matrix. Major faults, which DOE assumed 

to provide fast transport pathways through the unsaturated zone, are represented in the model 

framework separately by a model with limited fracture–matrix interaction, as documented in SNL 

Section P21 (2008ag). 

DOE simulated the transport of radionuclides as (i) dissolved species and as (ii) attached to 

mobile, colloid-sized particles. These two modes of transport are subject to various physical 

and chemical processes that affect radionuclide transport rates. DOE’s conceptual model 

addresses how each of the transport-affecting processes influences the rate at which 

radionuclides travel through the unsaturated zone relative to the rate that water travels (SAR 

Section 2.3.8.2). 

NRC Staff’s Review 

The NRC staff compared DOE’s description of the unsaturated zone transport model 

framework in SAR Section 2.3.8 and references therein with the NRC staff’s understanding 

of the Yucca Mountain natural system, obtained from extensive prelicensing field observations 

and independent analysis of the unsaturated zone transport processes, as identified in NRC 

Section 5.1.3.7 (2005aa) and Leslie, et al. (2007aa). The NRC staff notes that DOE’s 

conceptual model appropriately includes FEPs that are expected to affect radionuclide transport 

in the unsaturated zone over the period of geologic stability (i.e., 1 million years). The NRC staff 

notes that DOE has provided a system description for radionuclide transport in the unsaturated 

zone because (i) DOE appropriately identified the Yucca Mountain site characteristics that 
10-4 

produce vertical and lateral unsaturated zone groundwater flow pathways, lateral variability 

within layers (e.g., differences in the properties of the Calico Hills tuff in the southern part of the 

repository area compared to the northern area), and the presence of fault zones; (ii) DOE 

appropriately used Yucca Mountain site characterization data to develop geologic and 

hydrologic parameter values for specific rock units or to define ranges of values for these 

properties to address uncertainty about the natural variability of the system; and (iii) DOE 

adequately identified how and where the features of the unsaturated zone beneath the 

repository contributed to barrier capability in the performance assessment, as detailed in SAR 

Sections 2.3.8.1 and 2.1.2.3 and references therein and DOE Enclosure 1 (2009am). DOE 

adequately represented radionuclide transport in fracture-dominated and matrix-dominated 

unsaturated zone flow paths by using a model framework that, overall, is consistent with the 

conditions and assumptions of site-scale flow processes at Yucca Mountain. 

2.2.1.3.7.3.1.1 Model Integration for the TSPA Code 

DOE represented unsaturated zone transport as a model abstraction that simulates the 

transport of dissolved radionuclides and colloid-associated radionuclides through the 

unsaturated zone beneath the repository. This model generates breakthrough curves at the 

water table for the 27 aqueous species and 12 colloidal species that DOE determined were the 

most representative and risk significant (SAR Sections 2.3.8.6, 2.3.7.4.1.2, and 2.3.8.5.4). DOE 

simulated radionuclide transport in the abstraction with a residence-time particle-tracking 

technique in an external process model, FEHM, as identified in SNL Section 3.6 (2008ag). 

FEHM simulates flow and transport in three dimensions through fractured and porous rock. The 

three-dimensional volume through which the water and radionuclides travel is subdivided into a 

three-dimensional grid of cells, each of which is assigned fracture and matrix properties specific 

to each cell’s spatial location. The particle-tracking technique determines the amount of time 

that a particle spends in each cell of the model and determines, on the basis of flow field 

information, which cell (fracture or matrix) the particle travels to next. 

DOE integrated the unsaturated zone radionuclide transport abstraction with three other TSPA 

model components (SAR Figure 2.3.8-2): the EBS radionuclide transport abstraction (SAR 

Section 2.3.7.12), the site-scale unsaturated zone flow model (SAR Section 2.3.2.4.1), and the 

saturated zone radionuclide transport abstraction (SAR Section 2.3.9.3). Using flow conditions 

and concentration gradients at the boundary between the EBS and the unsaturated rock 

beneath the repository, the EBS transport abstraction calculates the radionuclide mass flux that 

enters the unsaturated zone fractures and rock matrix (SAR Section 2.3.7.12.3.2). Because the 

radionuclides travel more slowly in the rock matrix than in fractures (SAR Figure 2.3.8-49), DOE 

identified release of radionuclides from the EBS into the rock matrix as being a significant 

barrier mechanism (SAR Section 2.3.8.5.4). Accordingly, the NRC staff has evaluated DOE’s 

technical basis for the flux-splitting submodel that calculates the release of radionuclides from 

the EBS into the rock matrix and into the fractures (TER Section 2.2.1.3.7.3.1.2). 

DOE’s site-scale unsaturated zone flow model passes flow field information to the unsaturated 

zone transport abstraction, such that radionuclide transport through the fractures and rock 

matrix in the model grid depends on the percolation (downward flow) fluxes provided by the 

flow fields. In particular, the flow fields generated by the site-scale unsaturated zone flow 

model provide the transport abstraction with spatial distributions of fracture-to-fracture, 

matrix-to-matrix, fracture-to-matrix, and matrix-to-fracture flow rates and moisture contents in 

the three-dimensional model framework, as detailed in SNL Section 6.3.9.2 (2008ag). During a 

calculated TSPA realization, the unsaturated zone transport abstraction receives flow field 

information from a sequence of up to four steady-state flow fields associated with different 
10-5 

climate states (i.e., present-day, monsoon, glacial-transition, and post-10,000-year period) to 

account for future changes in percolation flux at specified points in time (SAR Section 2.3.8.5.3). 

In DOE’s unsaturated zone transport abstraction model, the elevation of the water table beneath 

Yucca Mountain increases at the transition from the present-day climate state to a future, wetter 

climate state. The water table remains at the higher elevation for the remainder of the 

realization, effectively shortening the modeled thickness of the unsaturated zone transport path 

[SNL Section 6.4.8 (2008an); SNL Section 6.3.9.3 (2008ag)]. All other features of the 

unsaturated zone transport model grid and the sampled values of model parameters for the 

unsaturated zone transport abstraction remain constant throughout a TSPA realization. 

The output of the unsaturated zone transport abstraction calculation provides time-dependent 

radionuclide mass flux as input to the saturated zone transport abstraction water table. The 

unsaturated zone transport abstraction groups the radionuclide mass fluxes into four collection 

regions and transfers the grouped mass fluxes to the saturated zone transport abstraction, 

which then initiates radionuclide transport in the saturated zone at an arbitrarily selected 

location for each of the four regions (SAR Section 2.3.8.5). 

NRC Staff’s Review 

The NRC staff evaluated the information DOE provided in SAR Section 2.3.8 and in SNL 

Sections 6.3, 6.4, and 6.5 (2008an) and references therein about the development of 

the unsaturated zone transport abstraction and its integration with related model abstractions 

in TSPA calculations. The NRC staff verified, by examining results in SAR Section 2.4 and SNL 

Section 7.1[a] (2008ag), that the suite of radionuclides DOE used for the unsaturated zone 

transport calculations was consistent with the radionuclides that DOE’s radionuclide 

screening analysis identified for transport in groundwater pathways, as provided in SNL 

Sections 6.2 and 6.3 (2007au). 

The NRC staff compared DOE’s integration of the unsaturated zone transport abstraction (SAR 

Section 2.3.8.5 and references therein) and the site-scale unsaturated zone flow model (SAR 

Section 2.3.2.4.1 and references therein). The NRC staff notes that DOE’s representation of 

radionuclide transport in fracture-dominated and matrix-dominated unsaturated zone flow paths 

used well-documented and peer-reviewed FEHM modeling approaches (e.g., Doughty, 1999aa; 

Robinson, et al., 2003aa) that were consistent with the conditions and assumptions of the 

site-scale unsaturated zone flow model. In addition, DOE’s integration of the unsaturated zone 

transport abstraction and the site-scale unsaturated zone flow model is reasonable because 

DOE’s unsaturated zone transport abstraction used a model framework, technical bases, model 

properties, and assumptions that were consistent with the site-scale unsaturated zone flow 

model. The NRC staff’s review of the site-scale unsaturated zone flow model is documented in 

TER Section 2.2.1.3.6. 

The NRC staff evaluated DOE’s integration of the unsaturated zone transport abstraction (SAR 

Section 2.3.8.5 and references therein) and the saturated zone transport abstraction (SAR 

Section 2.3.9.3 and references therein). The NRC staff notes that DOE’s system description 

and model integration are reasonable for the unsaturated zone transport abstraction because 

DOE adequately described how its model assumptions about the transfer of radionuclides from 

the fractures and rock matrix of the unsaturated zone to fracture-dominated flow paths in the 

saturated zone were consistent with expected differences in site characteristics, flow model 

properties, and differences in scale between the unsaturated zone and the saturated zone 

transport paths. 
10-6 

2.2.1.3.7.3.1.2 Engineered Barrier System—Unsaturated Zone Boundary Condition 

The release of radionuclides from the EBS is simulated in DOE’s EBS transport abstraction and 

is used as input to the unsaturated zone transport abstraction. In DOE’s model for the EBS, 

radionuclides are transported out of breached waste packages by advection (flow) or by 

diffusion, travel through the crushed tuff invert, and exit into the unsaturated rock at the base of 

the repository drift (SAR Section 2.3.7.12.3.2 ). At the model exit boundary, the EBS transport 

abstraction uses a submodel, which DOE termed the EBS–unsaturated zone interface 

submodel [SNL Section 6.5.2.6 (2007aj)], to distribute the radionuclides between the fractures 

and the rock matrix, according to modeled flow conditions and concentration gradients at the 

boundary. As stated in DOE Enclosure 6 (2009an), the overall result of the calculations in the 

EBS–unsaturated zone interface submodel is that most radionuclides released from waste 

packages in seeping drifts are transferred by advection into fractures, and most radionuclides 

released from nonseeping drifts are transferred by diffusion into the rock matrix. 

In SAR Section 2.3.8.5.4 and DOE Enclosure 9 (2009am), DOE identified the initiation of 

transport in the low permeability rock matrix beneath the drifts as a mechanism that 

significantly delays the transport of radionuclides in the unsaturated zone. Accordingly, 

the NRC staff reviewed the technical basis for the submodel that DOE used to integrate 

the EBS transport abstraction and the unsaturated zone radionuclide transport abstraction 

in TSPA calculations. DOE provided information about the flux-splitting model in SAR 

Section 2.3.7.12.3.2; SNL Sections 6.5.2.5. and 6.5.2.6 (2007aj); SNL Sections 6.3.8 and 

7.7.1[a] (2008ag); and DOE Enclosures 1, 9, 11, and 12 (2009am). The NRC staff has 

separately reviewed the technical basis and model properties for DOE’s EBS transport 

abstraction and model properties in TER Section 2.2.1.3.4.3.5. 

NRC Staff’s Review 

In the EBS–unsaturated zone interface submodel, DOE represents the near-field 

unsaturated zone as a localized, two-dimensional vertical array of overlapping fractures 

and rock matrix beneath the repository drift. The NRC staff compared DOE’s model 

descriptions in SAR Sections 2.3.7.12.3.2 and 2.3.8.4.1 and verified that DOE’s conceptual 

model for the EBS–unsaturated zone interface submodel was consistent with the modeling 

approach for fracture and matrix cells that DOE used for the unsaturated zone transport 

abstraction. The NRC staff also compared the conceptual basis of the EBS–unsaturated zone 

interface submodel with an independent model that relied on percolation flux and unsaturated 

matrix conductivity to simulate flux splitting, as identified in Leslie, et al. Chapter 11 (2007aa). 

The NRC staff confirms that DOE’s approach resulted in releases to fractures and rock matrix 

that were similar to those for the alternate conceptual model. The NRC staff notes that DOE’s 

flux-splitting model was adequately integrated with the unsaturated zone transport abstraction 

and the EBS transport abstraction because (i) DOE developed fracture and matrix water 

saturation and fluxes for the EBS–unsaturated zone interface submodel that were consistent 

with flow fields calculated by the unsaturated zone flow model, as identified in SNL Section 

6.5.2.6 (2007aj); (ii) DOE chose values for rock properties and radionuclide transport 

parameters (e.g., sorption coefficients and effective matrix diffusion coefficients) in the 

submodel that were based on unsaturated zone transport processes and appropriate model 

properties for the Topopah Spring tuff subunits from DOE’s unsaturated zone transport model; 

and (iii) DOE used transport properties for the crushed tuff invert for the EBS–unsaturated zone 

interface submodel that were based on an appropriate set of data from DOE’s EBS 

transport model. 
10-7 

2.2.1.3.7.3.2 Unsaturated Zone Radionuclide Transport Processes 

In DOE’s unsaturated zone transport abstraction, the migration of radionuclides through the 

unsaturated zone is influenced by the transport-affecting processes of advection and dispersion, 

sorption, matrix diffusion, and colloid-associated radionuclide transport, as well as radioactive 

decay and ingrowth (SAR Section 2.3.8.1). Four of these processes—advection, dispersion, 

matrix diffusion, and colloidal transport—are transport mechanisms that move radionuclides 

from one location to another. In contrast, sorption may delay the transport of a radionuclide by 

attachment to stationary surfaces such as the rock matrix. Radioactive decay removes a 

radionuclide permanently from the system. Ingrowth is the replacement of a decayed 

radionuclide with a newly formed (daughter) nuclide, which may have different radioactivity and 

transport properties than the parent. 

2.2.1.3.7.3.2.1 Advection and Dispersion 

In DOE’s unsaturated zone transport model and abstraction, advection refers to the transport of 

radionuclides, as either dissolved or colloid-associated phases, by the bulk movement of water. 

Overall, DOE considered advection to be the most important transport process in the 

unsaturated zone because, as DOE stated in SNL Section 6.1.2.1 (2007bj), the rate of water 

movement largely controls radionuclide travel times in the unsaturated zone. DOE coupled the 

advective transport of radionuclides with the bulk movement of water in fractures, in the rock 

matrix, and between fractures and matrix, using the groundwater flow rates and flow paths 

supplied by the site-scale unsaturated zone flow model, as detailed in SAR Section 2.3.8.5.2.1 

and SNL Section 6.3.9.2 (2008ag). Because the unsaturated zone flow model predicts that 

water flows through the unsaturated zone at different rates in different rock units, the advective 

radionuclide transport rates vary correspondingly at different locations in the unsaturated zone. 

For example, in the fracture-dominated northern part of the repository area, DOE’s unsaturated 

zone transport model predicts generally fast advective transport of radionuclides due to high 

modeled flow rates in fractures and faults. In the southern part of the repository area, advective 

transport of radionuclides in the unsaturated zone is slower due to low flow rates in the 

matrix-dominated flow system of the Calico Hills vitric tuff units (SAR Section 2.3.8.5.4). 

The NRC staff reviewed information DOE provided about advection in SAR Section 2.3.8 and 

references therein. Because advective radionuclide transport in DOE’s unsaturated zone 

transport abstraction depends directly on the flow field calculations supplied by DOE’s 

unsaturated zone flow model abstraction, the NRC staff notes that the technical basis for the 

site-scale unsaturated zone flow model also provides the technical basis for DOE’s 

representation of advective radionuclide transport in the unsaturated zone transport abstraction. 

On the basis of its technical evaluation of DOE’s site-scale unsaturated zone flow model in TER 

Section 2.2.1.3.6, the NRC staff notes that DOE has provided a reasonable technical basis for 

modeling radionuclide transport by advection in the unsaturated zone transport abstraction. 

DOE described dispersion as a spreading plume of dissolved radionuclides caused by localized 

differences in flow conditions, as identified in SAR Section 2.3.8.2.2.1 and SNL Section 6.3.9.1 

(2008ag). As stated in SNL Section 4.1.6, AD01 (2008an), DOE did not identify dispersion as 

an important transport-affecting process at the scale of the unsaturated zone transport model. 

However, DOE chose to include a simple fixed-value longitudinal dispersion term in the 

transport model to support numerical analyses of breakthrough curves at the water table (SAR 

Section 2.3.8.5.2.2). To estimate a value for the dispersion term, DOE used results from 
10-8 

saturated zone flow and transport tests at Yucca Mountain that were comparable in scale to 

site-scale unsaturated zone flow and transport paths (SAR Section 2.3.8.5.2.2). 

The NRC staff reviewed the information DOE provided about dispersion in SAR Section 2.3.8 

and references therein. From a risk-informed perspective, DOE provided an adequate technical 

basis for dispersion in the unsaturated zone transport abstraction because DOE provided 

adequate mathematical examples, field observations, and process-level modeling results to 

support DOE’s statement that dispersion did not appreciably affect radionuclide travel times in 

the unsaturated zone transport calculations. In addition, DOE’s representation of dispersion as 

a transport process was consistent with DOE’s conceptual model of fracture and matrix flow 

conditions at Yucca Mountain; DOE estimated the value of the dispersion term appropriately 

from site-specific field tests that were representative of the expected scale of dispersion in the 

unsaturated zone transport abstraction; and DOE addressed data and model uncertainty for the 

dispersion term with simplifying assumptions that were appropriate for the minor effect of 

dispersion on radionuclide transport through the unsaturated zone. 

2.2.1.3.7.3.2.2 Sorption 

Sorption is a general term for chemical and physical processes that transfer a fraction of 

dissolved species to the surface of a solid phase. Depending on specific properties of the 

dissolved species, solid, and liquid, some dissolved species will sorb more readily onto solids 

than others will, and some will not sorb at all. DOE modeled sorption of dissolved radionuclide 

species in the unsaturated zone rock matrix but assumed that there was no sorption on fracture 

surfaces, except for those portions of the model framework that are designated as fault zones, 

as identified in SAR Section 2.3.8.5.2.3 and DOE Enclosure 2 (2009am). DOE also included 

sorption in modeling colloid-associated radionuclide transport, as discussed separately (TER 

Section 2.2.1.3.7.3.2.4). 

In DOE’s unsaturated zone transport model, sorption of radionuclides onto immobile rock 

surfaces slows the transport rate of radionuclides through the rock relative to the flow rate of 

water, a delaying effect that is called retardation (SAR Section 2.3.8.2.2.2). Sorption potentially 

can retard the transport of moderately or strongly sorbing radionuclides in the unsaturated zone 

for thousands of years or longer, contributing more significantly to unsaturated zone barrier 

capability than any other retardation process, as identified in DOE Enclosure 6 (2009an). 

In contrast, sorption of radionuclides onto mobile colloids, instead of onto immobile rock 

surfaces, may decrease the overall retardation effect. 

DOE represented sorption in the unsaturated zone rock matrix with a sorption coefficient (Kd), 

an empirically determined or modeled value that represents the ratio of the sorbed-phase 

radionuclide concentration to the dissolved-phase radionuclide concentration. Low or zero 

values of Kd indicate that little or no sorption occurs; higher values indicate moderate or strong 

sorption, and therefore retardation. Factors that influence Kd values include the radionuclide 

chemistry and dissolved-phase concentration; the solution pH and major ion water chemistry; 

the temperature of the system; and the physical and chemical properties of the solid phase, 

including its surface area. Retardation by sorption is expressed in transport calculations by a 

retardation factor that depends on the value of the sorption coefficient and the physical 

properties (porosity and density) of the solid medium through which the radionuclide is 

transported. Retardation calculations assume that Kd does not vary with changes in 

radionuclide concentration, sorption and desorption reactions are fast relative to the flow rate, 

and bulk chemical composition of the water is constant (Davis and Curtis, 2003aa; Langmuir, 

1997aa; Davis and Kent, 1990aa). In the unsaturated zone transport model and abstraction, 
10-9 

DOE assumes that four radioelements are nonsorbing (carbon, chlorine, iodine, and 

technetium) and assigns a fixed value of Kd = 0 for each. For the remaining 11 radioelements 

modeled in unsaturated zone transport calculations (americium, cesium, neptunium, plutonium, 

protactinium, radium, selenium, strontium, thorium, tin, and uranium), DOE developed ranges 

and statistical distributions of Kd values for each radioelement and for each modeled rock unit 

from a combination of empirical data, process modeling, and professional judgment, as 

summarized in SAR Table 2.3.8-2. DOE detailed the Kd selection process in SNL 

Appendices A, B, I, and J and Addendum 1 (2007bj) and in DOE Enclosure 3 (2009am). 

In terms of the barrier capability of the lower unsaturated zone (SAR Section 2.1.2.3), DOE 

attributed a higher overall importance to sorption than to any other transport process, as 

identified in DOE Enclosure 6 (2009an). Accordingly, the NRC staff has conducted a detailed 

review of the information DOE provided about sorption in SAR Section 2.3.8 and references 

therein, with a particular focus on (i) how DOE obtained data for the sorption model, (ii) how 

DOE addressed data and model uncertainty, and (iii) how DOE supported the sorption model as 

implemented in performance assessment calculations. 

To obtain estimates of Kd values for sorption modeling, DOE grouped the various rock units 

below the repository into three rock types that have different sorption characteristics—zeolitized 

tuff, devitrified tuff, and vitric tuff (SAR Section 2.3.8.3.1). DOE measured sorption data from 

batch experiments that used site-specific crushed tuff samples and saturated zone water 

samples from two wells (J-13 and UE-25 p#1). DOE chose these water chemistries to bracket 

the major ion chemistry observed in pore waters and perched zone waters in the unsaturated 

zone, as provided in SAR Section 2.3.8.3.1 and SNL Section A4 (2007bj). DOE provided 

summaries of major ion chemistry (e.g., calcium, sodium, bicarbonate) for unsaturated zone 

pore waters, sampled by extraction from rock cores, and perched waters sampled by wells in 

locally saturated regions above the regional water table and compared the reported ranges with 

the two waters used in the sorption experiments, as identified in SNL Section A4 (2007bj). The 

NRC staff notes that for the purpose of estimating radionuclide sorption coefficients, DOE’s use 

of the J-13 and UE-25 p#1 water chemistries adequately bounded the ranges reported for 

unsaturated zone water chemistries for major ions such as sodium, calcium, and bicarbonate at 

Yucca Mountain. For the long-lived actinides (americium, neptunium, plutonium, and uranium), 

DOE further characterized the effects of variability in geochemistry and mineral surface area 

using a non-electrostatic surface complexation modeling approach described in Davis, et al. 

(1998aa) and SNL Addendum 1 and Appendix A, Sections A7 and A8 (2007bj). This modeling 

approach is similar to independent models the NRC staff developed (e.g., Turner, et al., 

2002aa) to examine the effects of broader chemistry ranges on several radionuclides. 

In some cases, DOE supplemented the experimental and modeling sorption data with data 

from peer-reviewed scientific literature and reports prepared by other agencies {e.g., SNL 

Section A1[a] (2007bj)}. In the TSPA model, DOE sampled Kd values from the specified ranges 

to account for experimental uncertainty and variability in geologic conditions, including water 

chemistry and rock type, as detailed in SAR Table 2.3.8-2; SNL Appendices A, B, I, and J and 

Addendum 1 (2007bj) and DOE Enclosure 3 (2009am). 

DOE identified mineral surface area and particle size as potential sources of data uncertainty 

related to the use of crushed tuff in experiments. The general DOE approach to addressing this 

uncertainty was to use batch experiments for a range of particle sizes and to bias the minimum 

and maximum limits for the Kd distributions toward lower (weaker sorption) values, as 

documented in DOE Enclosure 3, Table 1.1.2-1 (2009am). DOE referenced studies both 

from within and outside the DOE program in SNL Section 6.1.3.1 (2007bj). These studies 

indicated the effects of particle size on sorption are typically small except for the very fine 
10-10 

(e.g., clay-sized) fraction. DOE’s approach is reasonable because the approach properly 

identified potential sources of data uncertainty on the basis of site- and radionuclide-specific 

data and propagated the uncertainty through the unsaturated zone transport model abstraction. 

In addition, DOE’s use of surface complexation modeling to extend the limited chemical 

conditions in the batch crushed tuff experiments supported DOE’s technical basis for the upper 

and lower limits of sorption coefficients for the targeted actinides. 

DOE addressed data uncertainty by obtaining empirical sorption data that assessed Kd

variability as a function of time, radioelement concentration, atmospheric composition, water 

composition, particle size, and temperature. Although most of the data were gathered from 

batch sorption experiments, DOE also performed a limited number of confirmatory column 

tests on selected radionuclides that DOE had identified as important contributors to mean 

annual dose in previous performance assessment calculations. This is addressed in SAR 

Section 2.3.8.3.1 and SNL Table 4-1 (2007ba). In some cases, DOE indirectly addressed 

uncertainty related to radionuclide solubility by assigning low (conservative) Kd values. In 

selecting experimental data to inform the TSPA Kd distributions, DOE appropriately did not 

include data from experiments where the final radionuclide concentration may have exceeded 

a solubility limit. DOE outlined this approach in SAR Section 2.3.8.3.1, SNL Appendix A 

(2007bj), and SNL (2007ah). 

In SNL Appendix A, Section A6 (2007bj), DOE reported that its modeled uncertainty 

distributions for Kd values tended to underpredict the effectiveness of sorption compared to the 

experimental distributions. In some cases, DOE reduced the upper bounds of the Kd

distributions (specifically, those of cesium, plutonium, and radium) relative to the range 

indicated by available data to account for the possible effects of slow sorption kinetics for 

these elements, as identified in SNL Appendix A, Sections A8.4 and A8.6 (2007bj). By using 

low ranges of Kd values for sorption, DOE’s transport model underpredicts retardation, 

resulting in faster radionuclide travel times through the unsaturated zone. With respect to the 

TSPA model abstraction of radionuclide transport through the unsaturated zone, this 

underprediction means that DOE takes less credit for sorption than the data indicated. The 

NRC staff notes that DOE adequately described how geochemical data were obtained, used, 

and interpreted to derive the Kd parameter distributions used to represent data uncertainty. 

Further, the NRC staff notes that where there is model uncertainty, DOE used assumptions and 

selected parameter values that would likely reduce the credit given to radionuclide sorption in 

its TSPA analysis. 

In terms of model uncertainty and model support, the NRC staff notes that the empirical Kd

modeling approach that DOE implemented is well established (e.g., Freeze and Cherry, 

1979aa; Till and Meyer, 1983aa) and has been broadly used to describe radionuclide transport 

(e.g., Sheppard and Thibault, 1990aa). A potential model uncertainty associated with DOE’s Kd

approach is that individual Kd values are lumped parameters that do not explicitly take into 

account spatial and temporal variabilities or the role of specific surface-related processes that 

may affect radionuclide sorption. DOE addressed model uncertainty in its TSPA calculations by 

sampling Kd values stochastically from uncertainty distributions in which the distribution ranges 

were developed from expected system conditions. Rather than sample the Kd distribution 

independently for each radionuclide, DOE developed a correlation matrix for the 11 sorbing 

radioelements on the basis of their ranked sensitivities to six variables (pH, Eh, water chemistry, 

rock composition, rock surface area, and radionuclide concentration). DOE used this approach 

to approximate similarities in sorption behavior among radioelements and to ensure that 

transport behaviors were represented consistently within a single realization of the model, as 

detailed in SNL Appendix B, Section B1 (2007bj). In addressing model uncertainty, DOE did not 
10-11 

take credit for sorption (i.e., Kd = 0) in fractures (fast flow paths), except for fault zones, and 

DOE implemented Kd uncertainty distributions for matrix sorption that in most cases predicted 

less sorption compared to measured distributions. The NRC staff notes that DOE’s approach 

of taking no credit for sorption in fractures reduces the significance of model uncertainty of 

fractured systems on performance assessment. Excluding this process in fractures, which, 

if present, could contribute to waste isolation, results in radionuclide transport that is not 

underpredicted in fractures. Consequently, this approach is reasonable for addressing 

model uncertainty. 

DOE developed information from natural analogs to provide qualitative comparisons for sorption 

model confidence building at the field scale (SAR Section 2.3.8.4.4). DOE did not apply the 

unsaturated zone transport abstraction to sorption modeling for these analog sites, and DOE did 

not use results from natural analog studies to inform the Kd distributions. Instead, DOE used 

general observations of sorption-related transport behavior to support the conceptual models 

(e.g., SAR Section 2.3.8.4.4.6). DOE also used observations from field sites at Busted Butte 

south of Yucca Mountain and alcove tracer tests with nonradioactive chemical homologues in 

the Exploratory Studies Facility to provide limited quantitative evaluations of sorption in the 

radionuclide transport model abstraction (SAR Section 2.3.8.3.3). The NRC staff notes that 

DOE’s use of natural analogs to support model abstraction and uncertainty is reasonable for 

constraining sorption processes in unsaturated fractured rock. Furthermore, the NRC staff 

notes that it is reasonable to use site-specific sorption parameter values from Yucca Mountain 

to develop distributions instead of values from natural analogs. 

In summary, DOE used consistent and appropriate assumptions to implement a linear sorption 

(Kd) model. DOE adequately described how it obtained, used, and interpreted experimental 

data with site-specific materials, alternative computer models, field tests, and natural analogs to 

provide a technical basis to support the TSPA model abstraction of radionuclide sorption. DOE 

considered appropriate radionuclides in the sorption model, using a linear Kd approach to 

sorption that is consistent with the other transport components of the TSPA model. DOE used 

site-relevant sorption data to address the anticipated effects of pH, Eh, major ion water 

chemistry, rock composition, rock surface area, and radionuclide concentration on radionuclide 

sorption concentration. DOE appropriately defined and documented the limitations of the Kd

approach and used stochastically sampled Kd probability distributions and simplifying 

assumptions about the effectiveness of sorption to address model and data uncertainty. DOE 

considered appropriate geochemical and physical conditions in developing the Kd probability 

distributions, with either conservative or bounding assumptions that reduce the credit given to 

radionuclide sorption in the TSPA model. DOE appropriately considered alternative sorption 

modeling approaches and used them to support the technical basis for the Kd distributions. 

DOE adequately described the method used to assess the sensitivity of radioelement sorption 

behavior to variability in geochemical and physical conditions, and DOE appropriately used that 

method to correlate sorption characteristics among the radioelements, ensuring consistency 

among the sorption parameters for each model realization. 

2.2.1.3.7.3.2.3 Matrix Diffusion 

Diffusion is a physical process in which dissolved species or suspended particles move from a 

region of high concentration to a region of low concentration in accordance with the 

concentration gradient. DOE described matrix diffusion as a fracture–matrix interaction that 

uses diffusion to transfer radionuclides between fractures and the rock matrix. In DOE 

Enclosure 6 (2009an), DOE identified matrix diffusion as an important transport mechanism in 

the unsaturated zone transport abstraction, especially for strongly sorbing radionuclides, 
10-12 

because it is the main process by which radionuclides can move from a fracture-dominated flow 

path into the matrix. 

As DOE described in SNL Section 6.1.2.4 (2007bj), radionuclide transport by matrix diffusion in 

the unsaturated zone depends on (i) the matrix diffusion rate (i.e., the rate that a radionuclide 

can diffuse from water in a fracture into water in the pore spaces of the rock matrix) and (ii) the 

effective fracture–matrix interface, which is the area across which diffusion can occur. In turn, 

the matrix diffusion rate depends on (i) the radionuclide concentration gradient between fracture 

and matrix; (ii) the calculated water saturation of the rock; and (iii) the value of the effective 

matrix diffusion coefficient, which is a measure of how readily a particular radioelement 

diffuses through a tortuous pathway of interconnected pores in the rock matrix. To calculate 

the effective matrix diffusion coefficient, DOE multiplied the tortuosity coefficient by the free 

water diffusion coefficient. The tortuosity coefficient quantifies the reduction in diffusion rates 

resulting from the tortuous diffusion pathways through the rock. DOE determined this 

parameter from empirical data obtained from representative Yucca Mountain tuff samples. DOE 

developed standard normal cumulative probability distributions that were sampled stochastically 

in the TSPA analysis for each radioelement with respect to the individual model units (SAR 

Section 2.3.8.3.2; Reimus, et al., 2007aa). 

DOE stated that not all connected fractures in unsaturated rocks actively conduct water (SAR 

Section 2.3.2.2.2.1), and, instead of uniform flow, individual fractures may have gravity-driven 

fingering flow that wets only a portion of a fracture surface (SAR Section 2.3.8.2.2.1). To adjust 

the size of the effective fracture–matrix interface area to account for the general observations of 

flow in the unsaturated fractures, DOE adopted the active fracture model (Liu, et al., 1998aa) for 

fracture–matrix interactions (SAR Section 2.3.2.2.2.1). In particular, DOE used an active 

fracture model parameter, gamma, and the modeled effective water saturation (i.e., the average 

water saturation of the connected fractures, adjusted by the unsaturated zone flow model for 

residual fracture saturation) to increase the modeled distance between flowing fractures. This 

reduced the size of the effective (i.e., wetted) fracture–matrix interface area for the unsaturated 

zone fracture–matrix interactions, thereby decreasing the amount of matrix diffusion and its 

capacity to retard radionuclide transport through the fractured rock. 

In applying the active fracture model for flow field calculations in the site-scale unsaturated zone 

flow model (SAR Section 2.3.2.2.2.1), DOE used fixed values of the gamma parameter for 

individual model layers, estimated by flow model calibration, as detailed in SNL Section 6.3.2 

(2007ad). In contrast, sensitivity analyses in SNL Section 6.6.4 (2008an) indicated that the 

radionuclide transport model calculations were more sensitive to gamma uncertainty values 

than the fluid flow model calculations. DOE reasoned that it would be inappropriate in the 

radionuclide transport calculations to assume that the gamma parameter was tightly constrained 

by the calibrated values from the fluid flow model. For radionuclide transport calculations, DOE 

instead sampled gamma values independently from an uncertainty distribution that was not 

limited to the calibrated fluid flow model values (SAR Section 2.3.8.5.2.4). 

DOE’s conceptual model of matrix diffusion in SNL Section C5 (2008an) assumes that matrix 

diffusion should be less effective in the unsaturated rocks than in the saturated rocks due to the 

reduced size of the wetted fracture–matrix interface area in unsaturated fractures. DOE cited 

field observations and numerical simulations of tracer migration in several large-scale transport 

experiments in the Exploratory Studies Facility to support its conceptual model of matrix 

diffusion in fractured, unsaturated rocks and use of the active fracture model for matrix diffusion 

calculations in the TSPA model (SAR Sections 2.3.8.3.3.2.1 and 2.3.8.3.3.3). DOE provided 
10-13 

empirical observations and sensitivity analyses from field-scale experiments and process-level 

model analyses to address matrix diffusion model uncertainty. These were addressed in SNL 

Section 6.3.2 (2007ad); BSC Sections 7.4.1 and 7.4.2 (2004ag); BSC Section 6.12.2.4 

(2004av); SNL Section 7.5 (2007bf); and Liu, et al. (2003aa). 

In developing and supporting the matrix diffusion model, DOE acknowledged the impracticality 

of conducting large-scale transport tests to observe and measure the effects of fracture–matrix 

interactions in unsaturated rocks under natural conditions (SAR Section 2.3.8.3), and DOE 

cited uncertainties about the potential significance of scale-dependent transport processes in 

fractured rocks, as described in BSC Section 6.4.1 (2006aa) and Liu, et al. (2004aa). SNL 

Section 6.6.4 (2008an) identified that the size of the effective fracture–matrix interface area was 

the most uncertain term affecting radionuclide diffusion rates in the matrix diffusion model. To 

address model uncertainties about quantifying the effective fracture–matrix interface area for 

unsaturated zone transport calculations, DOE sampled the value of the active fracture model 

gamma parameter from a broad, uniform distribution that covered an intermediate range of 

40 percent of all possible gamma values. The wide range of sampled gamma values produced 

a correspondingly wide range of results for radionuclide transport by matrix diffusion, as 

described in SNL Section 6.6.4 (2008an). 

The NRC staff evaluated the information DOE provided on matrix diffusion in SAR Section 

2.3.8, the hydrogeologic characteristics of the unsaturated zone at the Yucca Mountain site, and 

field and laboratory studies of fracture–matrix interactions in the unsaturated fractured rocks at 

Yucca Mountain and elsewhere, as detailed in NRC Section 5.1.3.7 (2005aa) and McMurry 

(2007aa). The NRC staff notes that DOE adequately described matrix diffusion and its 

integration into the unsaturated zone transport abstraction by coupling two transport-related 

physical phenomena—advection in fractures and diffusion in the rock matrix—in an approach 

that was consistent with DOE’s dual-permeability model framework for unsaturated zone flow 

and radionuclide transport. DOE’s conceptual model for the unsaturated zone matrix diffusion 

appropriately addresses differences between matrix diffusion in the unsaturated zone and the 

saturated zone by assuming that a comparatively smaller effective fracture–matrix interface 

area would be available for fracture–matrix interactions in unsaturated rocks. 

In developing radionuclide-specific effective matrix diffusion coefficients for the unsaturated 

zone transport abstraction, DOE adopted a standard and widely used theoretical approach 

[Freeze and Cherry Section 3.4 (1979aa)] to estimate parameter values from laboratory 

measurements of diffusion properties in Yucca Mountain tuff samples. DOE addressed the 

uncertainty of the effects of fracture coatings on matrix diffusion by conducting diffusion 

experiments with paired core samples (i.e., samples with fracture coatings and without). In 

the tested sample pairs, mineral coatings on fracture surfaces did not impede diffusion rates 

(Reimus, et al., 2007aa). In addition, in BSC Section 5.2.1.1 (2004bi), DOE identified site 

characterization studies that showed secondary mineral coatings (e.g., calcite, oxides, clay 

minerals) are not abundant on fracture surfaces in Yucca Mountain tuffs, limiting their potential 

effects on matrix diffusion. Therefore, the NRC staff notes that DOE included sufficient data 

and addressed data uncertainty by measuring diffusion properties from an appropriate set of 

Yucca Mountain tuff samples, and DOE appropriately adapted the measured values to account 

for unsaturated conditions in the rock matrix. 

The NRC staff compared DOE’s descriptions of large-scale tracer test results with published 

results of other modeling studies and unsaturated zone field studies at Yucca Mountain and 

elsewhere. The comparison reflected DOE’s statement that there is considerable uncertainty 

about the significance of fracture–matrix interactions in unsaturated rocks. Some transport 
10-14 

studies identified fracture–matrix interactions as important (Zhou, et al., 2007aa; Liu, et al., 

2004ab; Salve, et al., 2002aa; Hu, et al., 2001aa; Dahan, et al., 1999aa), and other studies did 

not (e.g., Pearcy, et al., 1995aa; Davidson, et al., 1998aa; Winterle and Murphy, 1999aa). 

Given the wide range of observations from various field studies, the NRC staff notes that DOE’s 

use of a broad uncertainty distribution for the gamma parameter in the active fracture model is a 

reasonable treatment of model uncertainty for matrix diffusion in the unsaturated zone at Yucca 

Mountain. DOE conducted no unsaturated zone field experiments for model support specifically 

to evaluate DOE’s matrix diffusion model under expected repository conditions, but DOE 

observed in several large-scale experiments in the Exploratory Studies Facility that tracer 

transport in fractured rocks took significantly longer than predicted by matrix diffusion in DOE’s 

process-level models (SAR Sections 2.3.8.3.3.2.1, 2.3.8.3.3.2.2, and 2.3.8.3.3.3). DOE’s 

numerical simulations of these tracer tests included numerical analyses based on the same 

model assumptions and the same broad range of gamma parameter values as DOE used for 

matrix diffusion in the unsaturated transport abstraction (SAR Section 2.3.8.4.4.4.3). This 

supports DOE statements in BSC Section 6.4.1 (2006aa) that despite model uncertainties, the 

TSPA transport calculations would not overestimate the potential of matrix diffusion to delay 

radionuclides in the unsaturated zone. DOE also provided the TSPA performance assessment 

results in SAR Section 2.4.2; SNL Section 7.7.1[a] (2008ag); and DOE Enclosure 6 (2009an) to 

demonstrate that the overall effect of matrix diffusion in delaying releases of radionuclides from 

the unsaturated zone was relatively unimportant, even for moderately and strongly sorbing 

radionuclides in the northern (fracture-dominated rapid transport) part of the repository area. 

Therefore, the NRC staff notes that from a risk-informed perspective, DOE’s treatment of matrix 

diffusion model uncertainty and DOE’s analyses to support the matrix diffusion model were 

reasonable because DOE’s approach did not overestimate the effectiveness of matrix diffusion 

in retarding radionuclide transport. 

In summary, DOE has provided an adequate description and technical basis for matrix diffusion 

in the unsaturated zone transport abstraction. DOE calculated effective matrix diffusion 

coefficients using parameters that DOE developed from an appropriate set of data from Yucca 

Mountain rock samples. DOE addressed data uncertainty in calculations of the effective matrix 

diffusion coefficients by including the natural variation of the rock properties in the range of 

parameter values. DOE adequately addressed model uncertainty about matrix diffusion in the 

unsaturated zone transport abstraction by sampling a broad, uniform distribution of values for 

the active fracture model gamma parameter. DOE addressed model uncertainty about the 

extent and importance of fracture–matrix interactions by varying the size and extent of the 

fracture–matrix interface area available for matrix diffusion over a large range of potential 

values, as detailed in SNL Section 6.6.4 (2008an), and by simulating the uncertain effect of 

uneven flow in space and time on transport rates in unsaturated fractures, as provided in SNL 

Section 6.3.9.2 (2008ag). DOE supported the assumptions and uncertainties of the modeling 

approach by demonstrating, through sensitivity analyses and by comparison with large-scale 

transport tests, that radionuclide transport in the unsaturated zone transport abstraction was 

less impeded by matrix diffusion than would otherwise be expected in a natural system. 

2.2.1.3.7.3.2.4 Colloid-Associated Transport 

Colloids are minute solid particles of any origin or composition that are suspended in a liquid. 

Colloids can form by many processes in natural or engineered systems—for example, by 

physical or chemical degradation of preexisting solid materials or by precipitation from a 

solution—or they can be of biological or geological origin (e.g., microbes, clay minerals). 

Colloids influence radionuclide transport in the unsaturated zone because the transport path 

and transport rate of radionuclides associated with a colloid (e.g., radionuclides attached by 
10-15 

sorption to the colloid surface) are determined by the transport behavior of the colloid instead of 

by processes that might otherwise affect the transport rate of the radionuclide as a dissolved 

species (e.g., matrix diffusion, or sorption in the rock matrix). Compared to dissolved 

radionuclides, colloids migrate preferentially in fractures, where travel times tend to be fast, 

because the small size of matrix pore openings inhibits the transfer of colloidal particles from 

fractures to matrix. 

DOE’s conceptual model in SNL Section 6.3.9.1 (2008ag) defines two modes of 

colloid-associated radionuclide transport: reversible colloids, in which radionuclides are 

temporarily (reversibly) attached to colloids by sorption, and irreversible colloids, in which 

radionuclides are assumed to be permanently attached to or embedded in the colloid. 

According to SNL Section 6.5.3 (2007bi), the effectiveness of radionuclide transport by colloids 

depends on the transport characteristics of the colloids themselves, the concentration of 

colloids, and radionuclide sorption coefficients onto colloids and onto the immobile rock matrix, 

but the overall effect of colloid-associated transport of reversible colloids is to facilitate the 

transport of radionuclides through the system 

DOE represented reversible colloid transport by modeling reversible sorption of dissolved 

radionuclides onto naturally occurring colloids in groundwater, using the same empirical Kd

modeling approach that DOE used for reversible sorption in the rock matrix. DOE then applied 

an empirically determined colloid retardation factor, described in BSC Section 6.4.3 (2004bc), to 

account for colloid attachment and detachment processes in fractures that can hinder 

colloid movement in fractures. For simplicity, DOE assumed that all reversible colloids in 

the unsaturated zone are represented by the smectite clay mineral montmorillonite, a 

colloid-forming mineral in Yucca Mountain tuffs that has a high sorption capacity. DOE, in SNL 

Section 6.5.3 (2007bi), described how it modeled colloid-associated reversible sorption for six 

radioelements (americium, cesium, plutonium, protactinium, thorium, and tin) on the basis of 

their strong affinity for sorption onto montmorillonite under expected conditions in the 

unsaturated zone. DOE estimated the concentration of colloids in groundwater from data 

collected in saturated zone field studies from the Yucca Mountain area and from tabulated data 

for groundwater analyses elsewhere, as provided in SNL Table 6-21 (2008an). To address data 

uncertainty, DOE used the same estimated range of variability for groundwater colloid 

concentrations in the EBS, the unsaturated zone, and the saturated zone, but each transport 

abstraction sampled the range of values independently from the others in TSPA code 

simulations to account for the potential variability in groundwater colloid concentrations among 

the different environments, as identified in SNL Section 6.5.12 (2008an). DOE further 

addressed data uncertainty for reversible colloids by selecting ranges of montmorillonite 

sorption coefficients that emphasized large Kd values (i.e., strong sorption onto colloids), so as 

not to underestimate the effectiveness of radionuclide attachment to colloid surfaces, as 

described in DOE Enclosure 14 (2009am). 

For irreversible colloids, DOE’s colloid-associated transport model assumes that all irreversible 

colloids are generated within the EBS by the degradation of metals or wasteform materials, and 

the only radionuclides associated with irreversible colloids are isotopes of plutonium and 

americium (SAR Section 2.3.7.12.3.2). On the basis of field evidence for fast colloid transport in 

groundwater (e.g., Kersting, et al., 1999aa), DOE designated a small fraction (less than 

0.2 percent) of the irreversible colloid flux as a “fast fraction” that is transported from the EBS to 

the accessible environment without any retardation. The rest of the irreversible colloid flux is 

subject to several potential retardation processes, including (i) fracture-related colloid 

attachment and detachment processes, as DOE detailed in SNL Section 6.5.13 (2008an); 

(ii) the direct release of irreversible colloids from the EBS into the low permeability rock matrix 
10-16 

beneath the repository drifts, as described in DOE Enclosure 9 (2009am); and (iii) the advective 

transfer of irreversible colloids laterally from fracture flow paths into the rock matrix, subject to 

flow field conditions (i.e., matrix permeability large enough to accommodate the advective flux) 

and subject to colloid size exclusions at the fracture–matrix interface, as described in SAR 

Section 2.3.8.4.5.4 and SNL Sections 6.3.9.1 and 6.3.9.2 (2008ag). In SNL Section 6.3.9.1 

(2008ag), and in SNL Section 6.5.9 (2008an), DOE also described a fourth retardation process, 

the matrix filtration (straining) of irreversible colloids at the interface between the matrix of one 

rock unit and the matrix of the underlying rock unit, resulting in the permanent immobilization of 

colloids in the unsaturated zone. DOE compared unsaturated zone breakthrough curves for 

irreversible colloids with and without matrix filtration in SNL, ERD 02, Section III (2008an) and 

observed that including matrix filtration as a retardation process diminished the flux of 

irreversible colloids out of the unsaturated zone by as much as 80 percent in the southern half 

of the repository area, as illustrated in SNL, ERD 02, Figure 6.6.2-6[c] (2008an). However, 

DOE’s final TSPA model conservatively did not implement matrix filtration in the TSPA 

simulations, as explained in DOE Enclosure 11 (2009am). 

The NRC staff reviewed DOE’s technical basis for the colloid-associated transport model in the 

context of the NRC staff’s independent understanding of colloid-associated transport modeling, 

colloid stability, and colloid transport properties in natural and engineered systems. As DOE 

noted in SAR Section 2.3.8.3, colloid transport mechanisms in unsaturated, fractured rocks are 

not well characterized by field studies. Accordingly, the NRC staff’s review of DOE’s technical 

basis for colloid-associated transport of radionuclides in the unsaturated zone focuses on how 

DOE addressed data and model uncertainty in developing parameter values and modeling 

colloid-associated transport processes. The NRC staff evaluated information DOE provided in 

SAR Section 2.3.8 and references therein, particularly SNL Section 7.7.1[a] (2008ag) and SNL 

(2008an). The NRC staff also considered additional information that DOE provided to clarify 

details of the colloid-associated transport model in DOE Enclosures 9 through 14 (2009am). 

DOE provided a conceptual model for colloid-associated transport of radionuclides that 

appropriately incorporated observable phenomena to distinguish colloids from solutes, such 

as colloid sizes, colloid sorption properties, and colloid transport behavior in fracture-dominated 

flow systems. In addition, DOE’s conceptual treatment of reversible and irreversible 

colloid-associated transport in the unsaturated zone transport abstraction was consistent with 

DOE’s conceptual model for reversible and irreversible colloids in the EBS and saturated zone 

transport abstractions. Therefore, the NRC staff notes that DOE provided an adequate system 

description of the colloid-associated transport model and DOE integrated the model 

appropriately with other components of the unsaturated zone transport abstraction. 

The NRC staff reviewed the data and methods that DOE used to estimate unsaturated zone 

transport parameters for colloids and notes that DOE compensated for a scarcity of unsaturated 

zone colloid transport data by using data from saturated zone groundwater analyses and Yucca 

Mountain saturated zone colloid transport tests to estimate unsaturated zone colloid properties. 

By incorporating the available site-specific data to set initial and boundary conditions for colloid 

properties, DOE’s colloid-associated transport model adequately accounted for system 

variability and included sufficient data to describe colloids in the natural system. DOE 

addressed data uncertainty by (i) sampling large ranges for colloid-associated parameter values 

to account for data uncertainty about natural colloid properties and (ii) sampling the ranges of 

parameter values separately for the unsaturated zone and saturated zone transport abstractions 

to account for data uncertainty and spatial heterogeneity in the natural system. 
10-17 

DOE addressed model uncertainty for colloid-associated transport in the unsaturated zone by 

applying a number of simplifying assumptions about colloid-associated transport processes. 

The assumptions resulted in fast and relatively unimpeded transport of radionuclides 

associated with colloids compared to slower modeled travel times for the same radionuclides 

transported as solutes. In evaluating DOE’s treatment of model uncertainty for reversible 

colloids, the NRC staff compared DOE’s selection of ranges of sorption coefficients for 

montmorillonite colloids, detailed in SNL Table 6-22 (2008an), with DOE’s selection of ranges of 

sorption coefficients for the unsaturated rock matrix, detailed in SNL Table 6-1[a] (2007bj). The 

NRC staff’s comparison of values confirmed that for the radionuclides of interest, DOE’s 

sorption coefficients for the montmorillonite colloids promoted stronger sorption onto 

colloids than onto the rock matrix. Therefore, the staff notes that including reversible 

colloid-associated transport in DOE’s model does not overestimate radionuclide travel times in 

the unsaturated zone. 

In evaluating DOE’s treatment of model uncertainty for irreversible colloids, the NRC 

staff examined results of DOE unsaturated zone TSPA calculations for plutonium and 

americium radionuclides transported as dissolved species and as irreversible colloids, as 

illustrated by SAR Figure 2.4-108 and DOE Enclosure 10, Figures 3 and 4 (2009am). In 

addition, the NRC staff examined DOE sensitivity analyses in SNL, ERD 02, Section III 

(2008an), where DOE examined how irreversible colloid travel times through the unsaturated 

zone differed if the calculations included the effect of colloid filtration in porous rock matrix. 

From a risk-informed perspective, the NRC staff notes that DOE’s treatment of model 

uncertainty is reasonable because DOE (i) used simplifying assumptions that resulted in faster 

transport of colloids than solutes in the unsaturated zone and (ii) took little or no credit in TSPA 

calculations for retardation processes such as the filtration of colloids at matrix–matrix interfaces 

that may significantly slow the transport of radionuclides associated with irreversible colloids 

compared to radionuclides transported as solutes. 

In summary, DOE provided an adequate technical basis for the unsaturated zone 

colloid-associated transport model. DOE incorporated important processes and features 

of colloid transport that were consistent with the physical setting at Yucca Mountain. DOE 

adequately documented the conceptual and mathematical basis for the associated transport 

processes (e.g., retardation of colloids by attachment processes in fractures, reversible sorption 

of radionuclides onto colloids, colloid size exclusion processes at fracture–matrix interfaces, and 

unretarded colloidal transport), using an approach that was consistent with existing models for 

contaminant transport in fractured rocks in the literature (e.g., Sudicky and Frind, 1982aa). 

Because colloid transport properties in unsaturated, fractured rocks at Yucca Mountain and 

elsewhere were not well quantified by observations or experiments, DOE estimated unsaturated 

zone parameter values from site-specific saturated zone field and laboratory measurements. 

DOE appropriately addressed the large data uncertainty by sampling colloid parameter values 

probabilistically from large distribution ranges. With few empirical observations of unsaturated 

zone colloidal transport in field experiments or natural analogs to support the model abstraction 

output, DOE’s modeling approach appropriately used a number of simplifying assumptions to 

compensate for uncertainty by taking little or no credit for colloid retardation processes in the 

unsaturated zone (SAR Sections 2.3.8.3.4 and 2.3.8.2.2.3). 

2.2.1.3.7.3.2.5 Radionuclide Decay and Ingrowth 

Radioactive decay is a general term for the processes by which unstable radionuclides 

spontaneously disintegrate to form a different nuclide that may or may not also be radioactive. 

DOE’s particle tracking model in the unsaturated zone transport abstraction includes the loss of 
10-18 

radionuclides over time due to radioactive decay and, where applicable, the model calculates 

the corresponding increase (ingrowth) of daughter radionuclides in decay chains, as described 

in SNL Section 6.4.4 (2008an). DOE assumed that upon radioactive decay of plutonium and 

americium in irreversible colloids, the decay chain daughters (e.g., uranium, neptunium) would 

be released from the irreversible colloid to migrate as dissolved species, with the exception of 

Pu-239 produced by radioactive decay of Am-243, which DOE assumed would remain 

irreversibly attached to the colloid (SAR Section 2.3.8.2.2.3). 

The NRC staff examined DOE’s radionuclide transport analyses in SAR Sections 2.3.8 and 

2.4.2 and noted that DOE’s results corresponded with expected changes in the transported 

inventory due to radioactive decay and ingrowth. The NRC staff notes that DOE’s technical 

basis for including radioactive decay and ingrowth in the unsaturated zone transport abstraction 

is reasonable. The NRC staff did not conduct a detailed technical evaluation of DOE’s model 

abstraction, because DOE calculated radionuclide decay and ingrowth using a standard 

mathematic decay equation, with radioactive decay constants that are known with precision 

(International Union of Pure and Applied Chemistry, 1997aa). DOE’s representation of 

radioactive decay and ingrowth is reasonable because (i) DOE used a well-documented 

modeling approach with no significant uncertainties and (ii) DOE’s model assumptions about the 

ingrowth-related transport behavior of decay chain radionuclides in irreversible colloids are 

consistent with DOE’s model assumptions about the sorption behavior of the same 

radionuclides where they are associated with reversible colloids in DOE’s model. 

2.2.1.3.7.4 NRC Staff Conclusions 

NRC staff notes that the DOE description of this model abstraction for radionuclide transport 

in the unsaturated zone is consistent with the guidance in the YMRP. NRC staff also 

notes that the technical approach is reasonable for use in the Total System Performance 

Assessment (TSPA). 

2.2.1.3.7.5 References 

BSC. 2006aa. “Analysis of Alcove 8/Niche 3 Flow and Transport Tests.” 

ANL–NBS–HS–000056. Rev. 00. ACN 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2004ag. “Conceptual Model and Numerical Approaches for Unsaturated Zone Flow and 

Transport.” MDL–NBS–HS–000005. Rev. 01. ACN 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2004av. “In-Situ Field Testing of Processes.” ANL–NBS–HS–000005. Rev. 03. 

ACN 01, ACN 02, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bc. “Saturated Zone Colloid Transport.” ANL–NBS–HS–000031. Rev. 02. 

ACN 01, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004bi. “Yucca Mountain Site Description, Volume I: Sections 1–5.” 

TDR–CRW–GS–000001. Rev. 02 ICN 01. ERD 01, ERD 02. Las Vegas, Nevada: Bechtel 

SAIC Company, LLC. 
10-19 

Dahan, O., R. Nativ, E.M. Adar, B. Berkowitz, and Z. Ronen. 1999aa. “Field Observation of 

Flow in a Fracture Intersecting Unsaturated Chalk.” Water Resources Research. Vol. 35. 

pp. 3,315–3,326. 

Davidson, G.R., R.L. Bassett, E.L. Hardin, and D.L. Thompson. 1998aa. “Geochemical 

Evidence of Preferential Flow of Water Through Fractures in Unsaturated Tuff, Apache Leap, 

Arizona.” Applied Geochemistry. Vol. 13. pp. 185–195. 

Davis, J.A. and G.P. Curtis. 2003aa. NUREG/CR–6820, “Application of Surface Complexation 

Modeling To Describe Uranium (VI) Adsorption and Retardation at the Uranium Mill Tailings Site 

at Naturita, Colorado.” Washington, DC: NRC. 

Davis, J.A. and D.B. Kent. 1990aa. “Surface Complexation Modeling in Aqueous 

Geochemistry. Mineral-Water Interface Geochemistry.” M.F. Hochella, Jr. and A.F. White, eds. 

Mineralogy. Vol. 23. pp. 177–260. 

Davis, J.A., J.A. Coston, D.B. Kent, and C.C. Fuller. 1998aa. “Application of the 

Surface Complexation Concept to Complex Mineral Assemblages.” Environmental Science & 

Technology. Vol. 32. pp. 2,820–2,828. 

DOE. 2009am. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.7, Set 1.” Letter (February 9) J.R. Williams to J.H. Sulima (NRC). 

ML090410352. Enclosures (14). Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

DOE. 2009an. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.1, Set 1.” Letter (February 6) J.R. Williams to J.H. Sulima (NRC). ML090400455. 

Enclosures (7). Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2008ab. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository 

License Application.” Rev. 00. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

Doughty, C. 1999aa. “Investigation of Conceptual and Numerical Approaches for Evaluating 

Moisture, Gas, Chemical, and Heat Transport in Fractured Unsaturated Rock.” Journal of 

Contaminant Hydrology. Vol. 38, No. 1–3. pp. 69–106. 

Freeze, R.A. and J.A. Cherry. 1979aa. Groundwater. Englewood Cliffs, New Jersey: 

Prentice-Hall, Inc. 

Hu, Q., R. Salve, W.T. Stringfellow, and J.S.Y. Wang. 2001aa. “Field Tracer-Transport Tests in 

Unsaturated Fractured Tuff.” Journal of Contaminant Hydrology. Vol. 51. pp. 1–12. 

International Union of Pure and Applied Chemistry. 1997aa. Compendium of Chemical 

Terminology. 2nd Edition. A.D. McNaught and A. Wilkinson, eds. Oxford, United Kingdom: 

Blackwell Science. 
10-20 

Kersting, A.B., D.W. Efurd, D.L. Finnegan, D.J. Rokop, D.K. Smith, and J.L. Thompson. 

1999aa. “Migration of Plutonium in Ground Water at the Nevada Test Site.” Nature. Vol. 397, 

No. 6714. pp. 56–59. 

Langmuir, D. 1997aa. Aqueous Environmental Geochemistry. Upper Saddle River, 

New Jersey: Prentice Hall. 

Leslie, B., C. Grossman, and J. Durham. 2007aa. “Total-system Performance Assessment 

(TPA) Version 5.1 Module Descriptions and User Guide.” Rev. 1. ML072710060. San Antonio, 

Texas: CNWRA. 

Liu, H.H., G.S. Bodvarsson, and G. Zhang. 2004aa. “Scale Dependency of the Effective Matrix 

Diffusion Coefficient.” Vadose Zone Journal. Vol. 3. pp. 312–315. Madison, Wisconsin: Soil 

Science Society of America. 

Liu, H.H., R. Salve, J.S. Wang, G.S. Bodvarsson, and D. Hudson. 2004ab. “Field Investigation 

Into Unsaturated Flow and Transport in a Fault: Model Analyses.” Journal of Contaminant 

Hydrology. Vol. 74. pp. 39–59. New York City, New York: Elsevier. 

Liu, H.H., C.B. Haukwa, C.F. Ahlers, G.S. Bodvarsson, A.L. Flint, and W.B. Guertal. 2003aa. 

“Modeling Flow and Transport in Unsaturated Fractured Rock: An Evaluation of the Continuum 

Approach.” Journal of Contaminant Hydrology. Vol. 62–63. pp. 173–178. 

Liu, H.H., C. Doughty, and G.S. Bodvarsson. 1998aa. “An Active Fracture Model for 

Unsaturated Flow and Transport in Fractured Rocks.” Water Resources Research. Vol. 34. 

pp. 2,633–2,646. 

McMurry, J. 2007aa. “Overview of Field and Laboratory Studies of Unsaturated Zone Matrix 

Diffusion and Related Fracture–Matrix Interactions.” ML072630430 San Antonio, Texas: 

CNWRA. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

Pearcy, E.C., J.D. Prikryl, and B.W. Leslie. 1995aa. “Uranium Transport Through Fractured 

Cilicic Tuff and Relative Retention in Areas With Distinct Fracture Characteristics.” Applied 

Geochemistry. Vol. 10. pp. 685–704. 

Reimus, P.W., T.J. Callahan, S.D. Ware, M.J. Haga, and D.A. Counce. 2007aa. “Matrix 

Diffusion Coefficients in Volcanic Rocks at the Nevada Test Site: Influence of Matrix Porosity, 

Matrix Permeability, and Fracture Coating Minerals.” Journal of Contaminant Hydrology. 

Vol. 93. pp. 85–95. 
10-21 

Robinson, B.A., C. Li, and C.K. Ho. 2003aa. “Performance Assessment Model Development 

and Analysis of Radionuclide Transport in the Unsaturated Zone, Yucca Mountain, Nevada.” 

Journal of Contaminant Hydrology. Vol. 62–63. pp. 249–268. 

Salve, R., J.S.Y. Wang, and C. Doughty. 2002aa. “Liquid-Release Tests in Unsaturated 

Fractured Welded Tuffs: I. Field Investigations.” Journal of Hydrology. Vol. 256. pp. 60–79. 

Sheppard, M.I. and D.H. Thibault. 1990aa. “Default Soil Solid/Liquid Partition Coefficients, Kds, 

Four Major Soil Types: A Compendium.” Health Physics. Vol. 59. pp. 471–482. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008an. “Particle Tracking Model and Abstraction of Transport Processes.” 

MDL–NBS–HS–000020. Rev. 02. ADD 02. Las Vegas Nevada: Sandia National Laboratories. 

SNL. 2007ad. “Calibrated Unsaturated Zone Properties.” ANL–NBS–HS–000058. Rev. 00. 

ACN 01, ERD 01, ERD 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ah. “Dissolved Concentration Limits of Elements with Radioactive Isotopes.” 

ANL–WIS–MD–000010. Rev. 06. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007aj. “EBS Radionuclide Transport Abstraction.” ANL–WIS–PA–000001. Rev. 03. 

ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007au. “Radionuclide Screening.” ANL–WIS–MD–000006. Rev. 02. ACN 01, ERD 01, 

ERD 02. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ba. “Site-Scale Saturated Zone Transport.” MDL–NBS–HS–000010. Rev. 03. 

ACN 01, AD 001. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007bf. “Unsaturated Zone Flow Models and Submodels.” MDL–NBS–HS–000006. 

Rev. 03. ACN 01, ERD 01, ERD 02, ERD 03. Las Vegas, Nevada: Sandia National 

Laboratories. 

SNL. 2007bi. "Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: 

Abstraction and Summary." MDL–EBS–PA–000004. Rev. 03. ERD 01. Las Vegas, Nevada: 

Sandia National Laboratories. 

SNL. 2007bj. “Radionuclide Transport Models Under Ambient Conditions.” (U0060). 

MDL–NBS–HS–000008. Rev. 02. CAN 002, AD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

Sudicky, E.A. and E.O. Frind. 1982aa. “Contaminant Transport in Fractured Porous Media: 

Analytical Solutions for a System of Parallel Fractures.” Water Resources Research. Vol. 18. 

pp. 1,634–1,642. 

Till, J.E. and H.R. Meyer. 1983aa. NUREG/CR–3332, “Radiological Assessment: A Textbook 

on Environmental Dose Analysis.” Washington, DC: NRC. 
10-22 

Turner, D.R., F.P. Bertetti, and R.T. Pabalan. 2002aa. “The Role of Radionuclide Sorption in 

High-Level Waste Performance Assessment: Approaches for the Abstraction of Detailed 

Models.” Geochemistry of Soil Radionuclides. P.-C. Zhang and P.V. Brady, eds. Special 

Publication 59. Madison, Wisconsin: American Society of Agronomy. pp. 211–252. 

Winterle, J.R. and W.H. Murphy. 1999aa. “Time Scales for Dissolution of Calcite Fracture 

Fillings and Implications for Saturated Zone Radionuclide Transport at Yucca Mountain, 

Nevada.” Scientific Basis for Nuclear Waste Management, Boston, Massachusetts, 

November 30–December 4, 1998. Materials Research Society Symposium Proceedings 556. 

Warrendale, Pennsylvania: Materials Research Society. pp. 713–720. 

Zhou, Q., H.H. Liu, F.J. Molz, Y. Zhang, and G.S. Bodvarsson. 2007aa. “Field-Scale Effective 

Matrix Diffusion Coefficient for Fractured Rock: Results From Literature Survey.” Journal of 

Contaminant Hydrology. Vol. 93, Nos. 1–4. pp. 161–187. 
11-1 

CHAPTER 11 

2.2.1.3.8 Flow Paths in the Saturated Zone 

2.2.1.3.8.1 Introduction

This section of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s review of the DOE representation of flow paths in the saturated zone 

within the context of DOE’s performance assessment evaluation. The NRC staff reviewed 

information provided in the U.S. Department of Energy’s (DOE) Safety Analysis Report (SAR) 

submitted on June 3, 2008 (DOE, 2008ab) and information provided in response to requests for 

additional information (RAIs). 

Groundwater flow in the saturated zone is a key process in DOE’s performance assessment 

evaluation for the proposed geologic repository at Yucca Mountain, Nevada. The performance 

assessment analysis summarized in the SAR includes the flow of water from precipitation falling 

on Yucca Mountain, its migration as groundwater through the unsaturated zone above and 

below the repository, and the flow of groundwater in the saturated zone through the controlled 

environment to the accessible environment. This groundwater is the principal means by which 

radionuclides released from the repository could be transported to the accessible environment. 

Exposure to extracted groundwater is one of the risk-significant pathways to the reasonably 

maximally exposed individual (RMEI); therefore, the performance assessment must include 

those components that significantly affect the timing and magnitude of transport for any 

radionuclides released from the repository. 

DOE identified the saturated zone as a feature important to the capability of the lower natural 

barrier (SAR Section 2.1.1.3). Specifically, DOE indicated in Table 2.1-1 Expanded (DOE, 

2009an) that the function of the saturated zone is to substantially reduce the rate of movement 

of radionuclides to the RMEI location by a combination of slow advective flow, long transport 

distance, and geochemical retardation of radionuclides. Hence, groundwater flow in the 

saturated zone is both the principal means for radionuclides to be transported to the location 

of the RMEI as well as an important function of the lower natural barrier to delay the movement 

of radionuclides along the path of travel to the location of the RMEI. Saturated zone 

groundwater flow, as described in SAR Section 2.3.9, includes the features, events, and 

processes (FEPs) that affect the movement of groundwater in the saturated zone to the 

accessible environment and their implementation (or abstraction)1

 in the Total System 

Performance Assessment (TSPA). 

The saturated zone groundwater flow abstraction receives information about the magnitude and 

patterns of groundwater flow downward through the unsaturated zone. In turn, the saturated 

zone flow abstraction provides information about the direction, distance (flow paths), and 

amount (specific discharge) of groundwater flow to the saturated zone transport abstraction. 

TER Section 2.2.1.1 provides the NRC staff’s evaluation of DOE’s identification and description 

of barriers and their capabilities as well as the consistency of these descriptions with the specific 

representations of these barriers in the TSPA and process-level models. The NRC staff’s 

evaluation of those processes and characteristics most specific to radionuclide transport in the 

 

1

As used in the TER, the term “abstraction” refers to the representation of site characterization data, 

process-level models for FEPs, uncertainty and variability, and their overall integration in a (simplified) manner 

in the TSPA evaluation. 
11-2 

saturated zone is provided in TER Section 2.2.1.3.9. DOE’s analysis of the effect of future 

climate change on water flow in the saturated zone is evaluated in this section while the NRC 

staff’s evaluation of the nature of future climate change is presented in TER Section 2.2.1.3.5. 

The other feature of the lower natural barrier DOE identified is the unsaturated zone below the 

proposed repository horizon (SAR Section 2.1.1.3). The NRC staff evaluates those features 

and processes related to the unsaturated zone below the proposed repository horizon in TER 

Sections 2.2.1.3.6 and 2.2.1.3.7. 

2.2.1.3.8.2 Evaluation Criteria

The NRC staff’s review of the model abstractions used in DOE’s postclosure performance 

assessment, including those considered in this chapter for flow paths in the saturated zone, is 

guided by 10 CFR 63.114 (Requirements for Performance Assessment) and 63.342 (Limits on 

Performance Assessments). The resulting DOE Total System Performance Assessment (TSPA) 

is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration 

processes of engineered barriers that would adversely affect performance of the 

natural barriers, consistent with the limits on performance assessment, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4, 6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is given in TER Chapter 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of geologic 

stability, defined in 10 CFR 63.302 as 1 million years following disposal. These sections require 

that through the period of geologic stability, with specific limitations, DOE 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period [10 CFR 63.342] 
11-3 

The NRC staff review of the SAR and supporting information follows the guidance in the Yucca 

Mountain Review Plan (YMRP) (NRC, 2003aa) Section 2.2.1.3.8, Flow Paths in the Saturated 

Zone, as supplemented by additional guidance for the period beyond 10,000 years after 

permanent closure (NRC, 2009ab). The YMRP acceptance criteria for model abstractions that 

provide guidance for the NRC staff’s evaluation of DOE’s abstraction of flow paths in the 

saturated zone are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

The NRC staff review used a risk-informed approach and the guidance provided by the YMRP, 

as supplemented by NRC (2009ab), to the extent reasonable for aspects of flow paths in the 

saturated zone important to repository performance. The NRC staff considered all five YMRP 

criteria in its review of information provided by DOE. In the context of these criteria, only those 

aspects of the model abstraction that substantively affect the performance assessment results, 

as determined by the NRC staff, are discussed in detail in this chapter. The NRC staff’s 

determination is based both on risk information provided by DOE and on NRC staff knowledge 

gained through experience and independent analyses. 

2.2.1.3.8.3 Technical Evaluation

DOE analyzed the groundwater flow system in the vicinity of the proposed repository at 

Yucca Mountain to establish the direction and magnitude of water movement. DOE 

delineated the direction of water flow (flow paths) and computationally estimated the magnitude 

(specific discharge) of water flow using multiple groundwater flow models at different scales and 

degree of simplification. Specific discharge, in turn, is used to determine the timing of 

radionuclide transport. 

The objective of the NRC staff’s technical evaluation in this section is to determine the 

reasonableness of DOE’s delineated flow paths and estimates of specific discharge (for both 

present and future conditions). The information evaluated in this section is from SAR 

Section 2.3.9 and from relevant supporting documents that are cited when referred to in this 

section of the TER. 

TER Section 2.2.1.3.8.4 discusses the NRC staff’s assessment of DOE’s postclosure 

performance assessment for flow paths in the saturated zone. 

2.2.1.3.8.3.1 System Description and Integration of Models Relevant to Flow 

 Paths in the Saturated Zone

DOE used multiple models at different scales to describe and quantify portions of the saturated 

zone groundwater flow system in the vicinity of the Yucca Mountain site. The NRC staff 

evaluates the roles of these models and their interdependencies in this section. The site of the 

proposed repository at Yucca Mountain is within the Death Valley regional groundwater flow 

system located in the southern part of the Great Basin, which in turn constitutes a subprovince 

of the larger Basin and Range physiographic province. At the regional scale, the Death Valley 

groundwater system reflects the arid climatic conditions and the complex geology of Basin and 

Range flow systems (SAR Section 2.3.9.2.1). 
11-4 

Groundwater in the regional system generally flows from recharge areas at high altitudes to the 

regional hydrologic sink in the bottom of Death Valley (SAR Section 2.3.9.2.1). The regional 

groundwater flow pattern is also conceptualized as a series of shallow and localized flow paths 

superposed on deeper regional flow paths (SAR Section 2.3.9.2.1). 

In the Yucca Mountain region, groundwater flows from generally north to south, following these 

regional flow patterns. A relatively small amount of recharge occurs in the immediate vicinity of 

Yucca Mountain migrating downward through the unsaturated zone to the saturated zone. 

Once in the saturated zone, groundwater flows through a volcanic rock aquifer in the northern 

portions of the general flow system, transitioning into an alluvial aquifer system in the southern 

portions of the Yucca Mountain region. Beneath both the volcanic and alluvial aquifers is a 

carbonate rock aquifer (SAR Section 2.3.9.2.1). 

DOE indicated that at the regional scale, a significant amount of groundwater flows through the 

relatively permeable, laterally continuous, and thick carbonate aquifer, below volcanic and 

alluvial aquifers. On the basis of the regional geological framework and observations obtained 

from several drilled boreholes, DOE concluded that an upward hydraulic head gradient 

generally exists between the carbonate aquifer and the overlying volcanic and alluvial aquifers 

(SAR Section 2.3.9.2.2.4), which indicates that vertical groundwater movement, to the extent it 

occurs, is upward rather than downward. 

DOE conceptualizes that the upward hydraulic gradient restricts groundwater flow paths 

originating from the proposed repository location to the shallower volcanic and alluvial 

aquifers, precluding radionuclides from entering the regional carbonate aquifer. DOE also 

believes the upward gradient will be sustained during future climates and water uses (SAR 

Section 2.3.9.2.2.4). 

At the site scale, DOE stated that groundwater flow occurs from the recharge areas in the 

north, through the Tertiary volcanic aquifers into the valley-fill aquifer, and continues south 

toward the RMEI location. DOE used various site-scale saturated zone flow and transport 

models to predict groundwater flow paths and calculate the transport of radionuclides from 

their introduction at the water table below the proposed repository to the accessible 

environment. DOE summarized the interdependencies and information exchanges among 

these models (SAR Figure 2.3.9-1). The nominal case site-scale saturated zone flow model is 

conceptualized, and input parameters determined, on the basis of information derived from 

in-situ field tests, the U.S. Geological Survey Death Valley Regional Groundwater Flow System 

Model (DVRGFSM; which provides recharge and boundary conditions), DOE’s site-scale 

hydrogeologic framework model, DOE’s site-scale unsaturated zone flow model, and expert 

elicitation (SAR Section 2.3.9.1). 

DOE’s site-scale hydrogeologic framework model is a three-dimensional conceptual model of 

the spatial distribution of hydrogeologic units in the Yucca Mountain area. It covers an area of 

1,350 km2

 [521 mi2] and a thickness of about 6 km [3.7 mi] (SNL, 2007an). Direct input to the 

site-scale hydrogeologic framework model consists of hydrogeologic information from the 

DVRGFSM; DOE’s site-scale geologic framework model, which was generated from DOE’s 

investigations; and lithostratigraphic interpretations and coordinates from the Nye County Early 

Warning Drilling Program (NC–EWDP)2

 boreholes (SNL, 2007an). Within the site-scale 

hydrogeologic framework model, DOE divided the Yucca Mountain geologic units into five basic 

 

2

The Nye County Early Warning Drilling Program is a DOE-funded, Nye County-directed and -implemented 

hydrogeologic investigation program. DOE used information from this program to supplement its own investigations. 
11-5 

saturated zone hydrogeologic units on the basis of similar hydrogeologic properties: upper 

volcanic aquifer, upper volcanic confining unit, lower volcanic aquifer, lower volcanic confining 

unit, and lower carbonate aquifer (SNL, 2007an). DOE stated that certain characteristics 

affecting flow, primarily the porosity and permeability3

 of the hydrogeologic units, are highly 

variable. To represent discrete features and regions having distinct hydrological properties 

within the model domain, DOE identified and incorporated 10 hydrogeologic features into the 

flow model to represent such features as fault zones, hydrologic flow barriers, and zones of 

enhanced permeability (SNL, 2007ax). 

DOE’s site-scale saturated zone flow model is a three-dimensional finite-element numerical 

model that simulates groundwater flow in the area defined by the site-scale hydrogeologic 

framework model {i.e., 30 × 45 × 6 km [18.6 × 28.0 × 3.7 mi]}. DOE stated the flow model 

domain is sufficiently large to (i) assess groundwater flow and contaminant transport to the 

accessible environment, (ii) minimize boundary effects on flow magnitude and direction at 

Yucca Mountain, and (iii) include wells in the Amargosa Desert at the southern end of the 

modeled area (SAR Section 2.3.9.2.3.1). The site-scale saturated zone numerical flow model 

requires hydrogeologic information about the saturated zone flow system to predict flow 

magnitude and direction. DOE also provided this information, which includes groundwater flux 

from the system boundaries and physical attributes of the geologic media, in SAR Section 2.3.9. 

DOE stated that the sources of surface recharge in the immediate vicinity of Yucca Mountain 

are precipitation and flood flows from Fortymile Wash and its tributaries (SAR Section 2.3.9.2.1). 

The site-scale saturated zone flow model obtains surface recharge information from DOE’s 

site-scale unsaturated zone flow model over the area that lies directly below the site-scale 

unsaturated zone flow model domain. DOE used the 2004 version of the site-scale unsaturated 

zone flow model. However, DOE stated that an updated site-scale unsaturated zone flow model 

has been developed and is used in other parts of the TSPA (SAR Section 2.3.9.2.2.3). The 

NRC staff evaluates the impact of using an older version of the site-scale unsaturated zone flow 

model to estimate surface recharge over the area directly below the site-scale unsaturated zone 

flow model domain in TER Section 2.2.1.3.8.3.2. 

DOE’s three-dimensional site-scale saturated zone flow and transport abstraction model 

receives flow field information from the site-scale saturated zone flow model to 

generate 200 stochastic realizations of the flow field that reflect uncertainty in key parameters. 

DOE prepared the input for each flow realization by scaling all permeability values in the 

site-scale saturated zone flow model using a scaling factor sampled stochastically from the 

probability distribution of a groundwater-specific discharge multiplier. For permeability values 

within the volcanic aquifer hydrogeologic units, DOE also sampled stochastically the horizontal 

anisotropy ratio (the ratio in the permeability in one horizontal principal direction relative to the 

permeability in a different principal direction, usually vertical). The steady-state groundwater 

flow solution for each realization was established by running the site-scale saturated zone flow 

model (SAR Section 2.3.9.3.4.1). After completing the 200 realizations using the site-scale 

saturated zone flow model, the resulting 200 flow fields were input to the site-scale saturated 

zone flow and transport abstraction model. These flow fields provided the TSPA model with 

200 radionuclide unit mass breakthrough curves at the RMEI location for 4 source subregions 

and 12 radionuclide groups, resulting in 9,600 breakthrough curves (SAR Figure 2.3.9-16). 

These breakthrough curves are evaluated in TER Section 2.2.1.3.9. 

 

3

Permeability is one of the most important characteristics of geologic media that affects the rate at which fluids can 

move through the medium. 
11-6 

The one-dimensional saturated zone transport abstraction model, which provides the transport 

simulation capability for radionuclide daughter products resulting from decay and ingrowth, uses 

a simplistic one-dimensional representation of the three-dimensional saturated zone flows. The 

one-dimensional saturated zone transport abstraction model consists of three pipe segments. 

The first pipe segment is 5 km [3.1 mi] long. The lengths of the second and third pipe segments 

are estimated from particle tracking results of the three-dimensional saturated zone flow and 

transport abstraction model. The variable lengths account for uncertainty in the location of the 

volcanic/alluvial aquifer contact. Average, homogeneous material properties and specific 

discharges are specified within each pipe. The average specific discharge along each pipe 

segment is calculated by dividing the flow path length by the 50th percentile of particle travel 

times in BSC Section 6.5.1 (2005ak). 

The NRC staff evaluated DOE’s description of the saturated zone groundwater flow system in 

the vicinity of the proposed repository and DOE’s approach to integrate the multiple models 

used to quantify groundwater flow paths from the location of the proposed repository to the 

RMEI location. The NRC staff notes the description and approach is reasonable because 

 DOE provided sufficient information to describe the aspects of hydrology, geology, 

physical phenomena, and couplings that are relevant to the regional and site-scale 

saturated zone groundwater flow system 

 The roles of models and the interdependencies between different models were clearly 

identified and illustrated (e.g., DVRGFSM, site-scale hydrogeologic framework model, 

site-scale saturated zone flow model, site-scale saturated zone flow and transport model 

abstraction, and one-dimensional saturated zone transport model abstraction) 

 Conditions and assumptions in models of saturated zone flow were consistently 

identified through the abstraction process (i.e., consistent with other interrelated model 

abstractions) and were consistent with information provided in the SAR 

 Initial and boundary conditions used in the TSPA abstraction (i.e., site-scale saturated 

zone flow and transport model abstraction and one-dimensional saturated zone transport 

model abstraction) were consistent with the nominal case site-scale saturated zone flow 

model, which in turn was consistent with the DVRGFSM 

The NRC staff’s evaluation of the approach DOE used to calibrate the site-scale saturated zone 

flow model is documented in TER Section 2.2.1.3.8.3.2. 

The NRC staff evaluated DOE’s descriptions of relevant included FEPs and the manner in 

which they are included in the saturated zone flow models presented in SAR Section 2.3.9. The 

capability of the saturated zone to function as a barrier to delay radionuclide migration by slow 

advective flow and/or long transport distance depends on a number of processes and 

characteristics. DOE identified a number of general characteristics and processes important to 

the function of the saturated zone barrier including stratigraphy, water-conducting features, 

faults, fractures, properties of host rock and other (alluvial) units, groundwater flow in the 

geosphere (magnitude and direction of groundwater flow), advection and dispersion, climate 

change, matrix diffusion, and sorption (SAR Sections 2.1.1.3 and 2.3.9.1). Additionally, DOE 

stated these types of characteristics and processes of the saturated zone have been included in 

saturated zone flow and transport models presented in SAR Section 2.3.9 (Saturated Zone Flow 

and Transport). 
11-7 

2.2.1.3.8.3.2 Sufficiency of Baseline Data To Justify Models of Flow Paths in 

 the Saturated Zone 

DOE used site-specific data to develop and corroborate the conceptual model of groundwater 

flow in the saturated zone and to calibrate the site-scale saturated zone flow model. The 

site-specific data used include water-level measurements, in-situ hydrologic and tracer testing 

conducted in the vicinity of Yucca Mountain, regional hydrogeologic model predictions, and 

parameters from expert elicitation. The NRC staff evaluated the sufficiency of DOE’s baseline 

data used to develop predictions of flow paths and groundwater flow rates in the saturated zone 

in this section. 

The NRC staff evaluated the sufficiency of geological data that were relevant to the 

hydrogeologic framework used in the site-scale saturated flow model. DOE updated its 

site-scale hydrogeologic framework model to include stratigraphic information inferred from 

recently drilled Nye County wells. While DOE has added more hydrogeologic units to improve 

its hydrogeologic framework model, the contact geometry between volcanic tuff and valley-fill 

alluvial aquifers remains uncertain. Regarding repository performance assessment, the location 

at which groundwater flow moves from fractured volcanic rocks to alluvium is particularly 

significant because of the differences in the hydrologic properties between fractured volcanic 

units and the alluvium (SAR Section 2.3.9.2.1). In the performance assessment model, DOE 

stochastically varied the lateral extent of an alluvium uncertainty zone to propagate uncertainty 

associated with the tuff and alluvium contact. The NRC staff evaluates DOE’s approach to treat 

uncertainty associated with the tuff/alluvium contact in TER Section 2.2.1.3.8.3.3. 

The NRC staff reviewed the test methods and results of the hydraulic and tracer tests DOE 

conducted to corroborate its conceptualization of groundwater flow in the volcanic aquifers. 

These tests included several hydraulic and tracer tests (cross-hole tests) at the C-Wells 

Complex, consisting of boreholes UE-25 c#1, UE-25 c#2, and UE-25 c#3 (SAR Figure 2.3.9-7). 

DOE concluded that flow in the volcanic rock units mainly occurs through a well-connected 

fracture network and that large-scale horizontal anisotropy of aquifer permeability exists in the 

saturated zone, which is preferentially oriented in a north-northeast direction. The open-hole 

surveys done at the C-Wells Complex also yielded information on stratigraphy, lithology, matrix 

porosity, fracture density, and the major flowing intervals. 

On the basis of the NRC staff review of DOE’s hydraulic and tracer tests, the NRC staff notes 

DOE’s hydraulic and tracer tests are appropriate to corroborate its conceptualization of 

groundwater flow in the volcanic aquifers because (i) the C-Wells Complex represents an 

appropriate location for inferring in-situ volcanic aquifer properties, (ii) DOE used appropriate 

techniques in conducting these tests, and (iii) the large-scale and cross-hole hydraulic tests 

yielded sufficient data to substantiate the applicability of the mathematical approach used with 

respect to representing site-scale groundwater flow in volcanic aquifers. DOE used the 

cross-hole well testing results to support its conclusions that (i) well-connected fracture 

networks exist in the volcanic aquifers and (ii) large-scale horizontal anisotropy of aquifer 

permeability exists in the volcanic aquifers, which is preferentially oriented in a north-northeast 

direction, consistent with the dominant fracture network observed at outcrops and in cores. 

The NRC staff notes that DOE’s use of cross-hole testing is an appropriate approach to 

support DOE’s assumptions about well-connected fracture networks and anisotropy of 

aquifer permeability because cross-hole tests tend to sample a larger number of possible 

flow paths and thus are appropriate for interpreting large-scale trends and effective 

permeability values. The results DOE obtained in these tests are consistent with its assumption 

of fracture-dominated flow in the volcanic aquifer system. 
11-8 

The NRC staff reviewed the results of the hydraulic and tracer tests DOE conducted to 

corroborate its conceptualization of groundwater flow in the alluvium. These tests 

included the hydraulic and tracer tests conducted at the Alluvial Testing Complex (centered 

at Nye County well NC–EWDP–19D), which is located along the simulated flow paths (SAR 

Section 2.3.9.2.4.2). DOE indicated that the saturated alluvium significantly reduces the 

movement of radionuclides to the accessible environment. The alluvial aquifer is generally 

conceptualized as a homogeneous hydrogeologic unit in the site-scale saturated zone flow 

model, except near the Fortymile Wash area. Testing results described in SNL Section 7.2.2.3 

(2007ax) from the Alluvial Testing Complex and Nye County well 22S indicated that alluvium 

permeabilities vary over two orders of magnitude. DOE added a high-permeability zone—the 

Lower Fortymile Wash alluvial zone—to take into account “possible channelization” within the 

alluvium, as identified in SNL Section 6.4.3.7 (2007ba) and SAR Table 2.3.9-8. Similarly, DOE 

accounted for the effect of alluvium spatial heterogeneity on radionuclide transport by varying 

the effective porosity parameter in its performance assessment code. The NRC staff notes the 

data derived from alluvial hydraulic tests are reasonable for the intended use because (i) the 

Alluvial Testing Complex is on the simulated flow path and underlying alluvial structure is 

representative of the Lower Fortymile Wash alluvium and (ii) appropriate techniques were 

used to interpret hydraulic and tracer test data including, for example, type curve fitting of 

pump test data. 

The NRC staff evaluated the sufficiency of water-level data DOE used to calibrate its site-scale 

saturated zone flow model. DOE used 161 time-averaged water-level measurements from 

132 wells (multilevel measurements were obtained from some wells) within the model domain to 

(i) provide calibration targets for the site-scale saturated zone flow model, (ii) truncate the top of 

the flow model grid, and (iii) provide the boundary conditions around the perimeter of the model. 

DOE stated that water-level calibration targets represent steady-state values and reflect current 

water uses wherever pumping takes place (SNL, 2007ax). The NRC staff reviewed the 

coverage of water-level measurements that DOE collected and notes that the spatial and 

temporal coverage of water-level data is sufficient to calibrate the site-scale saturated zone 

flow model. 

Water Flow Between the Lower and Upper Aquifers 

DOE used water-level data from 17 wells to determine whether an upward gradient exists 

from the lower volcanic aquifer to the upper volcanic aquifer within the modeled domain (SAR 

Table 2.3.9-6). DOE concluded that (i) a notable upward vertical gradient appears to exist 

between the lower and upper volcanic aquifer at locations nearest Yucca Mountain and (ii) the 

direction of the vertical hydraulic gradient varies from location to location away from Yucca 

Mountain (SNL, 2007ax). 

The NRC staff evaluated the methodology and the sufficiency of data DOE used to establish the 

boundary conditions of the site-scale saturated zone flow model. DOE derived constant-head 

boundary conditions from water-level data. In SNL Section 6.3.1.5 (2007ax), DOE stated that 

coverage of water-level measurements was insufficient to specify depth-dependent head 

boundaries. In SAR Section 2.3.9.2.3.1, DOE indicated that vertical gradients develop internally 

in the model domain in response to geohydrologic conditions and the calibrated model is 

capable of representing the upward vertical gradients observed between the deeper regional 

carbonate aquifer and overlying volcanic aquifers. DOE supplemented the SAR with a contour 

map of vertical hydraulic gradient distributions simulated by the site-scale saturated zone flow 

model, as shown in DOE Figure 1.1 (2009bc). The contour map indicated that the directions of 

the vertical hydraulic gradient developed internally by the site-scale saturated zone flow model 
11-9 

are consistent with the observations in the wells penetrating the lower carbonate aquifer. On 

the basis of the additional information in the supplemental contour map DOE provided, the NRC 

staff notes that the site-scale flow model adequately represents the upward vertical gradient, 

both at the locations of the wells that penetrate the carbonate aquifer (UE-25 p#1 and Nye 

County well NC–EWDP–2DB) and in the rest of the model domain. 

Recharge Data 

The NRC staff reviewed the sufficiency of recharge data used in DOE’s site-scale saturated 

zone flow model. DOE used recharge derived from a now-obsolete version of the site-scale 

unsaturated zone flow model. The NRC staff notes the impact of using an older version of the 

site-scale unsaturated zone flow model is small because the site-scale unsaturated zone 

component of recharge constitutes a small percentage (9 percent) of the total recharge within 

the domain of the site-scale saturated zone flow model. Surface recharge for other portions of 

the upper boundary within the domain of the site-scale saturated zone flow model was derived 

from the DVRGFSM model and measured stream losses along Fortymile Wash. The total 

surface recharge to the site-scale saturated zone flow model represents about 19 percent of the 

total inflow flux to the model domain. As a result, the recharge values from the unsaturated 

zone flow model are less than 2 percent of the total water budget for the model domain; 

therefore, the impact of uncertainty in the surface recharge from the site-scale unsaturated zone 

flow model to the site-scale saturated zone flow model is relatively small. 

Vertical Anisotropy of Permeability 

Vertical anisotropy of permeability is fixed at a ratio of 10:1 on the basis of information the 

expert elicitation panel provided (CRWMS M&O, 1998ac). In an alternative conceptual model, 

DOE considered the effect of vertical anisotropy on simulated flow paths. On the basis of 

previous experience with and knowledge of permeability anisotropy, the NRC staff notes that 

DOE’s permeability vertical anisotropy ratio of 10:1 is in the generally accepted range and is 

consistent with a horizontally layered flow system like that in the Yucca Mountain region (Spitz 

and Moreno, 1996aa). In TER Section 2.2.1.3.8.3.4, the NRC staff evaluates the model 

uncertainty associated with the uncertainty in vertical and horizontal anisotropy ratios. 

Site-Scale Model Calibration 

DOE calibrated the site-scale saturated zone flow model using an industry-standard parameter 

estimation program (PEST) followed by manual adjustments. During calibration, DOE 

appropriately assigned higher weights to observation wells located on potential flow paths to 

the RMEI location. The NRC staff notes this is a reasonable approach. After calibrating the 

site-scale model using the parameter estimation program, manual adjustments were made to 

several zones to improve model match. The calibrated site-scale saturated zone flow model 

has a weighted root-mean-square (RMS) residual of 0.82 m [2.7 ft] (calculated using differences 

between observed and simulated heads). SAR Figure 2.3.9-13 shows locations of all 

water-level measurements and calibration residuals (i.e., differences between simulated and 

observed water levels at the calibration target locations). The magnitude of the weighted 

parameter estimation program calibration residual is reasonable for the scale of the model and 

the nature of the predictions made by the model (flow path direction and groundwater specific 

discharge) because the RMS residual value of 0.82 m [2.7 ft] is small compared to the total 

water-level elevation variation of more than 300 m [986 ft]. 

 
11-10 

The NRC staff notes that the purpose of model calibration is to provide parameter estimates 

for a given conceptual model and is not intended to resolve uncertainties in model 

conceptualization (e.g., uncertainty in stratigraphy). Thus, DOE considered different alternative 

conceptual models to address model uncertainties. The NRC staff evaluates DOE’s treatment 

of model uncertainty and alternative conceptual models in TER Sections 2.2.1.3.8.3.4 and 

2.2.1.3.8.3.5. 

The NRC staff has evaluated the data DOE used to develop and corroborate the conceptual 

model of groundwater flow in the saturated zone and to calibrate and validate the site-scale 

saturated zone flow model and notes the data are reasonable because 

 DOE adequately summarized geological, hydrological, and geochemical data used to 

develop and implement models of saturated zone flow 

 DOE used appropriate techniques correctly to conduct relevant well testing 

 The description and justification of how the data were used, interpreted, and synthesized 

into model parameters were generally sufficient 

 Sufficient data were collected to establish initial and boundary conditions 

 Sufficient information was provided to substantiate that the site-scale saturated zone 

flow model is calibrated and applicable to site conditions 

2.2.1.3.8.3.3 Uncertainty in Data Used in Models of Flow Paths in the 

 Saturated Zone 

Uncertainties in model input parameters may directly affect the advective flow rate of 

groundwater and lengths of groundwater flow paths predicted by DOE’s nominal case site-scale 

saturated zone flow model. In the performance assessment evaluation, DOE incorporated the 

uncertainty in model parameter inputs by stochastically sampling values from probability 

distributions of the groundwater-specific discharge multiplier, horizontal anisotropy in 

permeability, flowing interval spacing and fracture porosity in the volcanic units, effective 

porosity in the alluvium, and longitudinal dispersivity, as identified in SNL Section 6.3 (2007ax). 

This TER section focuses on reviewing DOE’s methodologies for developing probability 

distributions of the specific discharge multiplier and horizontal anisotropy in permeability. 

The NRC staff evaluates the other uncertain model parameter inputs relevant to radionuclide 

transport calculations in TER Section 2.2.1.3.9. 

Specific Discharge Values and Multiplier 

To incorporate uncertainty in specific discharge in model abstractions, DOE generated 

multiple realizations of the three-dimensional saturated zone flow field (refer to 

TER Section 2.2.1.3.8.3.1 for additional discussion on the interdependencies of the different 

saturated zone abstraction models). For each realization, DOE scaled (i) the values of recharge 

and all values of permeability simultaneously using a stochastically sampled specific discharge 

multiplier and (ii) the values of north-south and east-west permeability within the zone of 

volcanic rocks using a stochastically sampled horizontal anisotropy ratio. 
11-11 

DOE established a probability distribution for the groundwater-specific discharge multiplier, 

whose function is to capture the range in variability and uncertainty in the parameters that 

generate the specific discharge calculations. In turn the specific discharge calculations provide 

the basis from which groundwater travel times and radionuclide mass breakthrough curves are 

generated (SAR Section 2.3.9.2.3.3). 

The uncertainty range and probability distribution for the groundwater-specific discharge 

multiplier was originally obtained through an expert elicitation process (CRWMS M&O, 1998ac). 

The expert elicitation panel suggested a truncated log-normal distribution ranging from 

0.01 to 10. The median specific discharge derived from the expert elicitation process is 0.6 m/yr 

[2 ft/yr], as defined in Section 3.2 (CRWMS M&O, 1998ac). On the basis of recent tracer tests 

performed at the Alluvial Testing Complex and Nye County well cluster 22S, DOE reduced the 

range of uncertainty of the specific discharge multiplier using a Bayesian update procedure, 

where the range the expert elicitation panel supplied was assumed as a prior probability 

distribution and the estimated specific discharges from the Alluvial Testing Complex were used 

to estimate a log-normal likelihood function. 

The NRC staff reviewed the Bayesian update procedure used by DOE. Because the Bayesian 

updating method requires that a dataset be composed of independent and identically distributed 

random samples, using this to reduce the specific discharge data set requires additional 

justification to support a meaningful Bayesian statistical analysis. In DOE Figure 1-1 (2009bc), 

DOE provided additional information explaining the rationale for using the Bayesian statistical 

procedure, stating that (i) each combination of interpretation method and effective porosity value 

provides an independent and equally likely outcome and (ii) the 12 data values follow 

approximately a log-normal distribution. After evaluating the additional information that DOE 

provided, the NRC staff notes that (i) DOE did not present a goodness-of-fitting statistical test to 

justify the log-normality of the 12 data, although the small sample size might preclude the 

meaningfulness of such a statistical test and (ii) DOE did not demonstrate the mutual 

independence of the estimation methods. Nevertheless, the NRC staff notes that uncertainty in 

specific discharge is appropriately bounded and propagated in DOE’s performance assessment 

because (i) for each estimation method, DOE obtained three specific discharge estimates by 

assuming the underlying unknown porosity equal to the maximum, median, and minimum 

porosity values of a porosity distribution and, thus, likely bounded the estimation uncertainty 

related to the alluvium heterogeneity; (ii) the four estimation methods may have bounded the 

estimation uncertainty related to using each individual method alone; and most importantly 

(iii) realizations in DOE’s performance assessment produce conservative transport times for 

nonsorbing solutes on the order of 10–100 years for the glacial-transition climate state, which 

the NRC staff notes does not result in an underestimation of the risk estimate (see also TER 

Section 2.2.1.3.8.3.4). 

DOE used specific discharge estimates derived from alluvium testing to update the specific 

discharge multiplier that is subsequently applied to the entire flow model. DOE conceptualized 

fluid flow in volcanic tuff aquifers differently from the alluvium. The former is dominated by 

flow in well-connected fractures, while the latter is a porous medium. The Bayesian prior 

distribution the expert elicitation panel provided was based on tests performed at the C-Wells 

Complex, which were conducted in volcanic aquifers. However, variability of specific discharge 

in volcanic aquifers is likely smaller than that in the alluvium because of the difference in flow 

paths and permeability in the two types of aquifers. Thus, applying the same specific discharge 

multiplier distribution to both volcanic and alluvial aquifers does not result in underestimation of 

the overall uncertainty. 
11-12 

As a final result, DOE obtained a truncated log-normal distribution for the specific discharge 

multiplier that ranges from 1/8.93 to 8.93 (BSC, 2005ak). Because DOE chose to update the 

specific discharge multiplier instead of the specific discharge itself, the NRC staff questioned 

whether the mean specific discharge would change in light of data from Alluvial Testing 

Complex and Nye County well cluster 22S tracer testing. The staff also noticed that a shift in 

the estimated mean specific discharge is not explicitly propagated through Bayesian updating of 

the specific discharge multiplier. Thus, the NRC staff expressed concern that ignoring a 

possible change in the estimated mean specific discharge could lead to underestimation of the 

specific discharge uncertainty in the performance assessment abstraction models. In response 

to a request for additional information (RAI), DOE (2009bc) stated that (i) variations in mean 

specific discharge along the flow paths have been captured in the baseline, three-dimensional 

site-scale flow model and (ii) it is inappropriate to directly use specific discharge data from 

alluvial testing to update the expert elicitation estimates; instead, the update should be 

performed after normalization (i.e., the specific discharge multiplier). After evaluating this RAI 

response, the NRC staff notes that although the normalization process alone would not change 

the fact that specific discharge distribution in the alluvium is different from that in volcanic 

aquifers (also see the evaluation in the next paragraph) and although DOE calibrated the 

three-dimensional site-scale flow model against water levels but not specific discharges, DOE 

has demonstrated that simulated specific discharges are consistent with in-situ estimates (see 

TER Section 2.2.1.3.8.3.5). The NRC staff also notes that DOE’s performance assessment 

model, using the updated specific discharge multiplier to address the glacial-transition climate 

state, produced conservative median transport times from the repository to the 18-km [11.2-mi] 

boundary for nonsorbing solutes on the order of 10–100 years (see TER Section 2.2.1.3.8.3.4). 

Therefore, the predicted radionuclide travel times are not inappropriately reduced, because of 

the approach DOE used to update the specific discharge multiplier. 

Horizontal Anisotropy 

The NRC staff reviewed the probability distribution that DOE established for the horizontal 

anisotropy of permeability. DOE stated in SNL Section 6.2.6 (2007aw) that hydraulic testing at 

the C-Wells Complex indicated significant flow anisotropy at larger scales in the fractured 

volcanic tuffs. In SAR Section 2.3.9.2.2.2.1, DOE indicated the horizontal anisotropy ratio is 

estimated using different methods and the ratio ranges from 3.3 to 17, with directionality 

orienting flow paths more north-south than east-west. The cumulative distribution function for 

the horizontal anisotropy ratio, which has lower and upper bounds of 0.05 and 20, respectively, 

is specified through a tabulated form in the performance assessment model. 

The maximum anisotropy ratio (i.e., 20) is greater than the highest value the NRC staff 

independently estimated on the basis of site-specific data (Ferrill, et al., 1999aa). On the basis 

of the information DOE presented and the staff’s independent estimate, the NRC staff notes the 

horizontal anisotropy ratio probability distribution is reasonable because it represents the level 

of uncertainty associated with the permeability anisotropy at the site scale. 

Potentially Undetected Fast Flow Paths 

The NRC staff reviewed DOE’s conceptual model of the alluvial aquifers as homogeneous 

hydrogeologic units. The drilling Nye County conducted revealed significant spatial 

heterogeneity in the alluvium. In an independent analysis, the NRC staff conceptualized the 

Fortymile Wash alluvium as a gravel-dominated deposit, having lower permeability zones 

interstratified with higher permeability deposits (Sun, et al., 2008aa). The more permeable 

deposits, which have an estimated mean lateral length (i.e., the mean lengths of deposits or 
11-13 

facies in the lateral directions) on the order of kilometers (1 km = 0.6 mi), might constitute fast 

flow pathways for radionuclide migration if well connected (Sun, et al., 2008aa). DOE stated 

that the suite of performance assessment transport simulations encompasses the range of 

behavior that would be obtained with a fault-based flow and transport model and other 

alternative conceptual models that explicitly model fast-flow paths (e.g., channeling in the 

alluvium) (SAR Section 2.3.9.2.3.5). DOE stated that the potential impacts of undetected 

features on groundwater flow are incorporated in the site-scale saturated zone flow and 

transport abstraction model through parameter distributions, which ultimately propagate to 

the TSPA model through variation in radionuclide breakthrough curves (BSC, 2005ak). The 

key parameters DOE used to assess the effect of undetected features on radionuclide 

transport are (i) specific discharge, (ii) porosity, (iii) flowing interval spacing in the volcanic 

rocks, (iv) longitudinal dispersion, (v) horizontal anisotropy of permeability, (vi) alluvial bulk 

density, and (vii) sorption coefficients for the nine classes of radionuclides modeled in both the 

alluvium and volcanic units (BSC, 2005ak). On the basis of the NRC staff review of 

(i) DOE’s characterization and representation of parameter uncertainty in specific discharge and 

effective porosity through corresponding probability distribution functions and (ii) the travel 

times DOE’s performance assessment code predicted, the NRC staff notes DOE’s sampling 

from probability distributions of specific discharge multiplier and effective porosity addressed the 

worst-case scenario resulting from potentially undetected fast-flow paths. 

Volcanic and Alluvial Aquifer Contact Zone 

DOE introduces an alluvium uncertainty zone in the TSPA model to treat uncertainty associated 

with the contact location between volcanic aquifer and the alluvium. On the basis of drilling 

records, DOE conceptualizes the uncertainty zone as a quadrilateral area in which the boundary 

between volcanic units and alluvium is randomly varied among realizations. The boundaries of 

the alluvium uncertainty zone are determined for a particular realization by the parameters 

FPLAW (western boundary) and FPLAN (northern boundary) in DOE’s one-dimensional 

saturated zone transport abstraction model. These parameters have uniform distributions from 

0.0 to 1.0, where a value of 0.0 corresponds to the minimum extent of the uncertainty zone and 

1.0 corresponds to the maximum extent of the uncertainty zone in a westerly direction and 

northerly direction, respectively (SNL, 2007ax). Thus, in the one-dimensional transport 

abstraction model, the flow path length of each pipe segment varies as a function of the 

horizontal anisotropy, the western boundary of the alluvial uncertainty zone, and the region from 

which the radionuclide source originates beneath the repository. The NRC staff has previously 

reviewed well drilling records from various phases of the NC-EWDP (Winterle and Farrell, 

2002aa; Sun, et al., 2008aa). On the basis of these reviews, the NRC staff notes that (i) DOE 

reasonably bounded the extents of the alluvium uncertainty zone and (ii) the uniform 

distributions defined for FPLAW and FPLAN reasonably propagate uncertainties associated with 

the actual geometry of the volcanic and alluvium contact. 

DOE used an expert elicitation process to obtain a probability distribution for the specific 

discharge multiplier (now considered prior distribution) and vertical anisotropy ratio. The NRC 

staff reviewed the information the expert elicitation panel provided. The NRC staff notes that 

the panel’s probability distributions for specific discharge multiplier and vertical anisotropy ratio 

are reasonable. An overall NRC evaluation of DOE’s expert elicitation procedures is provided in 

TER Section 2.5.4. 

The NRC staff evaluates DOE’s consideration of model uncertainty and model support in TER 

Sections 2.2.1.3.8.3.4 and 2.2.1.3.8.3.5. 
11-14 

The NRC staff notes that the data uncertainty characterization and representation in DOE’s 

site-scale saturated zone flow and abstraction models are reasonable for the following reasons: 

 DOE adopted parameter values, assumed ranges, probability distributions, and 

bounding assumptions that reasonably account for uncertainties and variabilities. 

 DOE incorporated the hydrologic effect (e.g., water table rise) of potential climate 

change, on the basis of a reasonably complete search of paleoclimate data, using 

scaling factors for different climate states. 

 The uncertainties associated with included features and events pertaining to 

groundwater flow in the saturated zone were represented in the model abstractions. 

 Results from the expert elicitation panel defining the specific discharge multiplier and 

vertical anisotropy ratio probability distributions are reasonable. 

2.2.1.3.8.3.4 Uncertainty in Flow Paths in the Saturated Zone Models

In this section, the NRC staff evaluates the alternative conceptual models DOE 

used to assess model uncertainties for the saturated zone flow paths, as presented in SAR 

Section 2.3.9.2.3.4. DOE used five alternative conceptual models to assess the significance of 

model uncertainties of certain features and processes in the abstraction, including (i) vertical 

anisotropy, (ii) horizontal anisotropy, (iii) permeability in the northern high-gradient region of 

Yucca Mountain, (iv) increased vertical permeability of the Solitario Canyon Fault, and 

(v) climate-induced water table rise. 

The site-scale saturated zone flow model that DOE used to develop the performance 

assessment abstraction uses a 10:1 anisotropy ratio for horizontal-to-vertical permeability in 

volcanic and valley-fill alluvium units. This vertical anisotropy ratio was originally suggested 

from an expert elicitation panel (CRWMS M&O, 1998ac) because reduced vertical permeability 

is inherent in any layered groundwater flow system. To test whether this assumption leads to 

any systematic bias, DOE considered an alternative model with vertical permeability equal to 

the horizontal permeability. This alternative model resulted in a 28 percent increase in 

calculated specific discharge at a location 5 km [3.1 mi] downgradient from the proposed 

repository boundary and also resulted in a near doubling of the weighted RMS calibration error. 

Because this alternative model results in a degraded calibration and the inclusion of vertical 

anisotropy is considered more representative of the layered system, the model with the 

10:1 vertical anisotropy ratio is the only one DOE used to develop the model abstraction. The 

NRC staff notes that DOE’s analysis reasonably demonstrated that it is not necessary to 

consider alternative models with different vertical anisotropy ratios, because the potential 

effect on specific discharge is not significant compared to the range of uncertainty already 

considered in DOE’s performance assessment model and the deleterious effects on model 

calibration statistics. 

The NRC staff reviewed the alternative model DOE used to demonstrate the sensitivity of the 

nominal case site-scale saturated zone model to horizontal anisotropy. DOE’s analysis 

demonstrated that removal of horizontal anisotropy (i.e., assuming isotropic horizontal 

permeability) results in a 31 percent decrease in modeled specific discharge rates across the 

5-km [3.1-mi] boundary and shifts the flow paths eastward. The effect on model calibration error 

is negligible. This analysis demonstrated that both isotropic and anisotropic cases are relatively 
11-15 

consistent with the observations in calibration wells. On the basis of this analysis, DOE 

included a range of horizontal anisotropy ratios for saturated zone flow and transport model 

abstraction. As discussed in TER Section 2.2.1.3.8.3.3, the NRC staff notes that the range of 

parameter uncertainty considered for horizontal anisotropy of permeability is appropriate. 

The NRC staff reviewed another alternative modeling analysis by DOE in which DOE removes 

the large hydraulic gradient north of the proposed repository area by increasing permeability in 

this region. This alternative model results in a 15-fold increase in calculated specific discharge 

5 km [3.1 mi] downgradient from the proposed repository and an eightfold increase in RMS 

calibration error. On the basis of this result, DOE concluded that, although the cause of the high 

gradient is not entirely certain, it is nevertheless important to represent this feature in the model. 

Similar to DOE’s conclusion, the NRC staff considers that any modeling analysis that does not 

include low-permeability structural features to reproduce the large hydraulic gradients DOE 

measured north and west of the repository area would not be consistent with those measured 

gradients. DOE, therefore, appropriately included these features in the site-scale saturated 

zone flow model used to develop its performance assessment abstraction. 

The NRC staff evaluated DOE’s alternative model used to examine the potential effects of 

vertical anisotropy in the permeability of the Solitario Canyon fault. Such anisotropy could 

support along-fault flow or upward flow at the fault while acting as a barrier to cross-fault flow. 

The result of the analysis shows an insignificant effect on simulated water levels or flow paths; 

therefore, the NRC staff notes that incorporating this alternative model in the abstraction is not 

necessary, because it would not significantly affect the performance assessment results. 

The site-scale saturated zone flow model DOE used to develop the abstracted flow paths for the 

performance assessment does not consider explicitly the effect of an elevated water table under 

future, wetter climate conditions. Instead, DOE incorporates the effect of long-term climate 

change by applying a scaling factor to instantly increase the volumetric flow rate or specific 

discharge. The scaling factors for monsoonal and glacial-transition climatic conditions are 

1.9 and 3.9, respectively. To demonstrate that this simplified approach for including effect of 

climate change does not bias results, DOE provided an alternative evaluation of the potential 

effects of water table rise on abstracted flow paths. On the basis of an estimated increase in 

specific discharge by a factor of 3.9 for the glacial-transition climate state, DOE estimated the 

increased hydraulic gradient necessary to drive this increased groundwater flow would result in 

a water table rise of approximately 20 m [66 ft] at the southern end of the model area that 

gradually increases to about 50 m [160 ft] in the area below the proposed repository location 

and as much as 100 m [328 ft] in areas north of the repository. Projecting this linear increase in 

water table elevation onto the hydrogeologic framework model indicates that elevated flow paths 

could travel a greater proportion of distance through the lower permeability Calico Hills 

formation in the volcanic tuffs, which could result in longer travel times. DOE, therefore, 

concluded that using present-day water table elevations combined with a scaling factor 

approach to increase specific discharge estimates for future climates sufficiently approximates 

the performance-affecting aspects of future, wetter climate conditions. On the basis of the 

staff’s examination of DOE’s alternative evaluation of the potential effects of water table rise on 

abstracted flow paths, the NRC staff notes DOE’s treatment of the effects of future climate on 

saturated zone flow is reasonable. Any bias introduced by excluding climate-induced water 

table rise would only lead to slower groundwater flow and would not significantly alter 

groundwater flow paths. The staff also notes the conclusion of DOE’s evaluation is consistent 

with an independent Center for Nuclear Waste Regulatory Analyses analysis (Winterle, 

2005aa), which indicates an elevated water table would not significantly affect flow paths from 

beneath the proposed repository area. 
11-16 

In SAR Section 2.3.9.2.3.5, DOE provided qualitative consideration of several additional model 

uncertainties that could affect estimates of specific discharge. These considerations and the 

NRC staff’s review are summarized in the following paragraphs. 

The NRC staff evaluated DOE’s treatment of uncertainty in the hydrogeologic contact surfaces 

(as in the hydrogeologic framework model) represented in the model. DOE explained that 

horizontal contact-surface uncertainty would have a lesser effect on specific discharge 

compared to uncertainty of contact surfaces in the vertical direction. DOE concluded that the 

potential effect of this model uncertainty is within the bounds of uncertainty considered for the 

specific discharge uncertainty multiplier parameter used in the performance assessment. The 

NRC staff notes this is reasonable because having a flow path travel a longer horizontal 

distance in a particular unit will not significantly affect the flow rate, but if a permeable layer is 

vertically thicker or thinner than presumed in the model, the specific discharge rate would 

necessarily decrease or increase to accommodate the same volumetric flow. 

The NRC staff evaluated DOE’s consideration of model uncertainty related to the potential for a 

fault-dominated flow system with specific discharge focused in flow paths along major fault 

systems. This type of model conceptualization can produce rapid travel times by focusing high 

discharge rates into narrow zones (the fault systems) within the groundwater flow system. The 

NRC staff notes that this model uncertainty is appropriately addressed in the model abstraction 

by DOE’s use of parameter ranges for effective porosity and specific discharge because the 

combination of very small values for effective porosity in tuff with high specific discharge rates 

introduces numerous realizations for the performance assessment that replicate the behavior of 

a fault-dominated flow and transport system. Numerous realizations in the performance 

assessment produce transport times for nonsorbing solutes on the order of 10–100 years for the 

glacial-transition climate state (SAR Section 2.3.9.3.4.1 and Figure 2.3.9-16). The NRC staff 

notes these realizations with rapid transport times reasonably represent the potential for 

focused high-permeability flow paths. 

The NRC staff has evaluated the methods DOE used to characterize model uncertainty, and 

propagate the effects of this uncertainty, through the performance assessment abstraction and 

determines the methods are reasonable for the following reasons. 

 DOE’s performance assessment considered alternative conceptual models and 

modeling approaches to account for various uncertain FEPs. 

 The modeling approach DOE used in the performance assessment is consistent with 

available data and current scientific understanding, and the results and limitations are 

appropriately considered in the abstraction. 

 Several conceptual model uncertainties were defined and documented, and effects on 

conclusions regarding performance were properly assessed. 

 Uncertainties in data interpretations for several aspects of the model were considered by 

analyzing reasonable alternative conceptual flow models that could not be ruled out by 

site data. 
11-17 

 Alternative modeling approaches DOE considered are consistent with available data and 

current scientific knowledge, and appropriately consider their results and limitations, 

using tests and analyses that are sensitive to the processes modeled. 

2.2.1.3.8.3.5 Model Support Based on Comparison With Alternative Models or 

 Other Information 

In SAR Section 2.3.9.2.4, DOE presented its use of objective comparisons to build confidence in 

the saturated zone flow model abstraction. In this section, the NRC staff review focuses on 

support for the range of flow paths and specific discharge estimates considered for the 

saturated zone flow and transport model abstraction on the basis of relative importance to 

overall system performance. 

The NRC staff reviewed information DOE used to support model-simulated groundwater 

flow paths. This information includes a comparison of simulated water-level elevations to 

those observed in wells not used in the model calibration (e.g., NC–EWDP Phase V data) 

(SAR Table 2.3.9-9). This comparison shows the largest differences between simulated 

and observed water levels generally occur in areas of steep hydraulic gradients near geologic 

features, such as the U.S. Highway 95 fault and the Solitario Canyon fault. Residual errors 

between observed and simulated water levels are generally smaller in the areas of simulated 

flow paths from the repository to the boundary with the accessible environment. The 

highest residual errors are generally in areas where water levels change by tens of meters 

(1 m = 3.28 ft) over short distances, and the overall range of residual errors is shown to be 

similar to the range of errors obtained during the site-scale saturated zone model calibration 

(SAR Figure 2.3.9-13). SNL (2007ax) described the calibration and confidence-building 

process. The NRC staff notes DOE’s comparison of modeled results to water-level 

measurements from wells not used in the model calibration demonstrates the model 

reasonably reproduces present-day water levels in the model areas important to determining 

flow and transport paths from the repository to the boundary with the accessible environment. 

Although an independent analysis by the Center for Nuclear Waste Regulatory Analyses 

(Winterle, et al., 2003aa) showed that such residual error could be significantly reduced by 

adjusting the shapes of modeled geologic features, doing so does not appreciably change the 

modeled groundwater flow paths and specific discharge from the repository to the boundary 

with the accessible environment. 

DOE discussed analyses by Freifeld, et al. (2006aa) of testing done in Nye County well 24PB 

that provides a range of estimates for specific discharge at the top of the Crater Flat tuff unit in 

the transition area from the volcanic aquifer to the valley-fill alluvial aquifer. On the basis of fluid 

electrical conductivity logging and distributed thermal perturbation sensor measurements, and 

assuming a porosity of 0.01, the estimated specific discharge in the flowing intervals ranged 

from 5–310 m/yr [16–1,018 ft/yr], as identified in Freifeld, et al. Section 3.3.3 and Table 4 

(2006aa). The upper end of this range is significantly greater than the specific discharge rates 

considered in the performance assessment. DOE stated the high flow rate was observed in a 

relatively narrow interval of the borehole and that upscaling this estimate using an assumed 

median flow interval spacing of 25.8 m [84.6 ft] (from the parameter uncertainty distribution) 

reduces the estimated specific discharge to a range of 0.07–4.1 m/yr [0.2–13.5 ft/yr]. 

Freifeld, et al. (2006aa) proposed that “... additional data sets at other locations should be 

collected to examine whether the current data set is representative of the regional flow system 

near Yucca Mountain.” The NRC staff notes, however, the observation of high-flow zones 

spaced tens of meters (1 m = 3.28 ft) apart is consistent with the conceptual model of flow in the 
11-18 

fractured tuffs being through a network of relatively widely spaced fracture zones. The identified 

zones of high transmissivity are relatively thin and, when averaged over the entire penetrated 

thickness of the Crater Flat tuffs, which is appropriate for comparison to the model grid scale, 

the specific discharge estimates are reasonably consistent with the upper end of the range of 

uncertainty considered for specific discharge in the abstraction. Additionally, because the high 

groundwater flows entered the well at a lower interval and exited through an interval more than 

40 m [130 ft] higher, the wellbore itself could be the cause of the high flow rates by connecting 

two vertically distinct permeable zones with groundwater flow driven by an upward vertical 

hydraulic gradient. Thus, the calculated high rates of specific discharge for this well could be 

more a reflection of flow driven by a local upward gradient that is short-circuited by the borehole 

and not representative of horizontal flow rates along groundwater flow paths in the aquifer 

system. Thus, the results reported in Freifeld, et al. (2006aa) are not conclusive regarding 

horizontal specific discharge at the scale of interest to the model abstraction, but generally 

support the concept of widely spaced, flowing intervals in the volcanic tuff units. 

DOE also provided model support for estimates of potential future water table rise during future 

climates. This support included observations of mineralogical alteration and analysis of the 

strontium isotope ratio in calcite veins in the saturated and unsaturated rocks beneath the 

proposed repository footprint. This information is interpreted to indicate a past water table rise 

of approximately 85 m [278 ft] above present-day conditions beneath the proposed repository. 

Modeling studies using different assumptions about future recharge (Czarnecki, 1985aa; 

D’Agnese, et al., 1999aa) produce water-table-rise estimates from 130 to 150 m [427 to 492 ft] 

above present conditions beneath the repository. On the basis of this supporting information, 

DOE’s performance assessment included a 120-m [394-ft] water table rise for the unsaturated 

zone flow and transport model abstraction. In the saturated zone flow and transport model 

abstraction, however, future water table water rise is not explicitly considered. Rather, DOE 

used an assumed rise of 85 m [278 ft] beneath the repository in an alternative conceptual model 

analysis to demonstrate the effect on saturated zone groundwater flow paths is not significant 

(see TER Section 2.2.1.3.8.3.4 for NRC staff review of this model uncertainty). South of the 

proposed repository near the RMEI location, the maximum extent of water table rise is 

constrained to approximately 30 m [98 ft] by the ground surface elevation and observed 

evaporite deposits at locations where springs flowed during past occurrences of elevated water 

table (SAR Section 2.3.9.2). 

Observations from well UE-25p#1 cited by DOE as model support show the hydraulic head is 

21 m [69 ft] higher in the carbonate aquifer system compared to the overlying volcanic aquifer 

system. The two aquifer systems are separated by a thick low-permeability aquitard. The 

calibrated site-scale saturated zone flow model reproduces this upward hydraulic gradient at 

well UE-25p#1, but with a lesser magnitude of a 6-m [20-ft] head difference across the aquitard 

unit. The NRC staff notes that, in addition to well UE-25p#1, the presence of this upward 

gradient along the area of model-simulated groundwater flow paths is also supported by 

observations in well NC–EWDP–2DB. DOE stated that these wells, which show an upward 

vertical gradient, are assigned a weight factor of 10 during model calibration to ensure the 

model will match the upward gradient. However, this statement appeared to be inconsistent 

with other information in the SAR where DOE apparently assigned a weight factor of 20 to 

UE-25 p#1 and a weight factor of 1 to Nye County well 2DB. In response to the staff’s RAI, 

DOE confirmed the actual weight factors used in the model calibration were 10 for both well 

locations. DOE agreed to make changes in SAR Section 2.3.9.2.3.2 and the related supporting 

report (SNL, 2007ax) to reflect the actual values used. 
11-19 

The NRC staff has evaluated the approaches DOE used to compare performance assessment 

output to process-level model outputs and/or empirical studies and notes that the approaches 

are reasonable for the following reasons: 

 The models implemented in DOE’s performance assessment abstraction for saturated 

zone flow provided results consistent with output from detailed process-level models and 

empirical observations from field tests. 

 Outputs of flow paths in the saturated zone abstractions reasonably reproduced the 

results of corresponding process-level models and empirical observations. 

 The procedures DOE used to construct and test the mathematical and numerical models 

used to simulate flow paths in the saturated zone were well documented in the SAR and 

in supporting references (SNL, 2007an,aw,ax, 2008ab). 

 The site-scale saturated zone flow model was developed on the basis of an underlying 

geologic framework, calibrated to minimize error compared to observed water levels, 

and compared to results from other models and field testing data not used in the 

model development (procedures that reflect reasonable and generally accepted 

scientific practices). 

 DOE provided several supporting analyses to demonstrate the ranges of flow paths and 

specific discharge estimates used in the abstraction of flow paths in the saturated zone 

are consistent with site data and field tests. 

2.2.1.3.8.4 NRC Staff Conclusions 

The NRC staff notes that the DOE description of this model abstraction for flow paths in the 

saturated zone is consistent with guidance in the YMRP. The NRC staff also notes that the 

technical approach is reasonable for use in the Total System Performance Assessment (TSPA). 

2.2.1.3.8.5 References 

BSC. 2005ak. “Saturated Zone Flow and Transport Model Abstraction.” 

MDL–NBS–HS–000021. Rev. 03. AD 01, AD 02, ERD 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

CRWMS M&O. 1998ac. “Saturated Zone Flow and Transport Expert Elicitation Project.” 

SL5X4AM3. Las Vegas, Nevada: CRWMS M&O. 

Czarnecki, J.B. 1985aa. “Simulated Effects of Increased Recharge on the Groundwater Flow 

System of Yucca Mountain and Vicinity, Nevada-California.” U.S. Geological Survey Water 

Resources Investigations Report 84-4344. 

D’Agnese, F.A., G.M. O’Brien, C.C. Faunt, and C.A San Juan. 1999aa. “Simulated Effects of 

Climate Change on the Death Valley Regional Ground-Water Flow System, Nevada and 

California.” U.S. Geological Survey Water-Resources Investigations Report 98-4041. 
11-20 

DOE. 2009an. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.1, Set 1.” Letter (February 6) J.R. Williams to J.H. Sulima (NRC). ML090400455. 

Enclosures (7). Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 2009bc. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.9.2.2 and 2.3.9.2.3), Safety Evaluation 

Report Volume 3, Chapter 2.2.1.3.8, Set 1.” Letter (January 30) J.R. Williams to J.H. Sulima 

(NRC). Enclosures (3): Numbers 1, 2, and 3. Las Vegas, Nevada: DOE, Office of Civilian 

Radioactive Waste Management. 

DOE. 2008ab. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository 

License Application.” Rev. 00. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

Ferrill, D.A., J. Winterle, G. Wittmeyer, D. Sims, S. Colton, A. Armstrong, and A.P. Morris. 

1999aa. “Stressed Rock Strains Groundwater at Yucca Mountain, Nevada.” GSA Today. 

Vol. 9, No. 5. pp. 1–8. 

Freifeld, B., C. Doughty, and S. Finsterle. 2006aa. “Preliminary Estimates of Specific 

Discharge and Transport Velocities Near Borehole NC-EWDP-24PB.” LBNL–60740. Berkeley, 

California: Lawrence Berkeley National Laboratory. 

NRC. 2009ab. "Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63." Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007an. “Hydrogeologic Framework Model for the Saturated Zone Site-Scale Flow and 

Transport Model.” MDL–NBS–HS–000024. Rev. 01. ERD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007aw. “Saturated Zone In-Situ Testing.” ANL–NBS–HS–000039. Rev. 02. ACN 01, 

ACN 02, ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ax. “Saturated Zone Site-Scale Flow Model.” MDL–NBS–HS–000011. Rev. 03. 

ACN 01, ERD 01, ERD 02. ERD 03. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ba. “Site-Scale Saturated Zone Transport.” MDL–NBS–HS–000010. Rev. 03. 

CAN 01, AD 001. Las Vegas, Nevada: Sandia National Laboratories. 

Spitz, K. and J. Moreno. 1996aa. “A Practical Guide to Groundwater and Solute Transport 

Modeling.” New York City, New York: John Wiley & Sons. 
11-21 

Sun, A., R. Ritzi, and D. Sims. 2008aa. “Characterization and Modeling of Spatial Variability in 

a Complex Alluvial Aquifer: Implication on Solute Transport.” Water Resources Research. 

Vol. 44, W04402, doi:10.1029/2007WR006119. 

Winterle, J.R. 2005aa. “Simulation of Spring Flows South of Yucca Mountain, Nevada, 

Following a Potential Future Water Table Rise.” ML052630242 San Antonio, Texas: CNWRA. 

Winterle, J.R. and D.A. Farrell. 2002aa. “Hydrogeologic Properties of the Alluvial Basin 

Beneath Fortymile Wash and Amargosa Valley, Southern Nevada.” San Antonio, Texas: 

CNWRA. 

Winterle, J.R., A. Claisse, and H.D. Arlt. 2003aa. “An Independent Site-Scale 

Groundwater Flow Model for Yucca Mountain.” Proceedings of the 10th International 

High-Level Radioactive Waste Management Conference (IHLRWM), Las Vegas, Nevada, 

March 30–April 2, 2003. La Grange Park, Illinois: American Nuclear Society. pp. 151–158. 

12-1 

CHAPTER 12 

2.2.1.3.9 Radionuclide Transport in the Saturated Zone 

2.2.1.3.9.1 Introduction 

This Technical Evaluation Report (TER) chapter addresses the U.S. Department of Energy’s 

(DOE’s) model abstraction1

 for transport of radionuclides in the saturated zone. DOE presented 

its description of this abstraction in Safety Analysis Report (SAR) Section 2.3.9 (DOE, 2008ab). 

Radionuclide transport in the saturated zone is a key process in the performance assessment 

for the proposed geologic repository at Yucca Mountain, Nevada. The performance 

assessment includes the flow of water from precipitation falling on Yucca Mountain, its migration 

as groundwater through the unsaturated zone above and below the repository, and the flow of 

groundwater in the saturated zone through the controlled environment to the accessible 

environment. This groundwater flow is the principal means by which radionuclides released 

from the repository are transported to the accessible environment. Because exposure to 

groundwater contaminated with radionuclides from the repository is one of the principal 

contributors to dose to the reasonably maximally exposed individual (RMEI), the performance 

assessment includes those components that affect significantly the timing and magnitude of 

transport for any radionuclides released from the repository. Radionuclide transport in the 

saturated zone, as described in SAR Section 2.3.9, includes the features, events, and 

processes (FEPs) that affect the movement of radionuclides from where they enter the 

saturated zone below the repository to the accessible environment boundary, approximately 

18 km [11.18 mi] south of the repository, and their implementation (or abstraction) in the 

Total System Performance Assessment (TSPA). 

In DOE’s TSPA model, the saturated zone radionuclide transport abstraction receives 

information about the time-dependent flux of radionuclides released from the unsaturated 

zone to the water table below the repository. In turn, the saturated zone radionuclide 

transport abstraction provides information about the mass flux and arrival or transport times 

of radionuclides moving through the saturated zone to the representative volume in the 

accessible environment. 

This chapter provides the U.S. Nuclear Regulatory Commission’s (NRC’s) evaluation of DOE’s 

representation of radionuclide transport in the saturated zone as part of its performance 

assessment. Because DOE represented the barriers to radionuclide migration in the saturated 

zone in terms of radionuclide transport processes and how the processes were influenced by 

the natural features of the saturated zone, this chapter is organized by the major, risk-significant 

processes affecting transport. 

2.2.1.3.9.2 Evaluation Criteria 

The U.S. Nuclear Regulatory Commission (NRC) staff’s review of the model abstractions used 

in DOE’s postclosure performance assessment, including those considered in this chapter for 

the transport of radionuclides in the saturated zone, is guided by 10 CFR 63.114 (Requirements 

for Performance Assessment) and 10 CFR 63.342 (Limits on Performance Assessment). 

 

1

The term “abstraction” is defined as a representation of the essential components of a process model into a suitable 

form for use in a total system performance assessment. Model abstraction is intended to maximize the use of limited 

computational resources while allowing a sufficient range of sensitivity and uncertainty analyses. 
12-2 

The resulting DOE Total System Performance Assessment (TSPA) is reviewed in TER 

Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry of the 

surface and subsurface from the site and the region surrounding Yucca Mountain 

[10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment, and evaluate in sufficient detail 

those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is given in TER Section 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of geologic 

stability, defined in 10 CFR 63.302 as 1 million years following disposal. These sections require 

that through the period of geologic stability, with specific limitations, DOE 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

The NRC staff review of the SAR and supporting information follows the guidance provided in 

the Yucca Mountain Review Plan (YMRP) Section 2.2.1.3.9 (NRC, 2003aa) Radionuclide 

Transport in the Saturated Zone, as supplemented by additional guidance for the period beyond 

10,000 years after permanent closure (NRC, 2009ab). The YMRP acceptance criteria for model 

abstractions that provide guidance for the NRC staff‘s evaluation of DOE’s abstraction of 

radionuclide transport in the saturated zone are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 
12-3 

The NRC staff’s review used a risk-informed approach and the guidance in the YMRP, as 

supplemented by NRC (2009ab), to the extent reasonable for aspects of the transport of 

radionuclides in the saturated zone important to repository performance. The NRC staff 

considered all five YMRP criteria in its review of information provided by DOE. In the context of 

these criteria, only those aspects of the model abstraction that substantively affect the 

performance assessment results, as assessed by the NRC staff, are discussed in detail in this 

chapter. The NRC staff’s assessment is based both on risk information provided by DOE, and 

on NRC staff knowledge gained through experience and independent analyses. 

2.2.1.3.9.3 Technical Evaluation 

DOE provided information in SAR Section 2.3.9 and supporting documents that it uses to 

predict the transport of radionuclides in the saturated zone, from below the repository to the 

accessible environment. The NRC staff’s technical evaluation focuses on (i) how DOE 

represented the geological, hydrological, and geochemical features of the saturated zone in a 

framework for modeling the transport processes; (ii) how DOE integrated the saturated zone 

transport abstraction with other Total System Performance Assessment (TSPA) abstractions 

for performance assessment calculations; and (iii) how DOE included and supported important 

transport processes in process-level transport models and in the saturated zone radionuclide 

transport abstraction. 

From a risk perspective, one of the most significant aspects of transport in the saturated zone 

is the extent to which the movement of contaminants to the accessible environment is limited 

or retarded. This performance aspect is most directly manifest in the calculated travel time for 

each radionuclide, given the transport processes and properties of the saturated zone. The 

calculated travel time results can be expressed as “breakthrough curves” for a given 

radionuclide or set of radionuclides that show the time history of transport along a given path. 

In terms of potential impact on repository performance, saturated zone transport most strongly 

affects those radionuclides which can decay appreciably over the time of transport; longer 

transport means that less of the radionuclide reaches the accessible environment. 

A radionuclide that is long-lived relative to its transport time, however, will reach the 

accessible environment with little decay. Even with retardation during transport, such a 

long-lived radionuclide will eventually “break through” the boundary with the accessible 

environment. Its flux will then approach a steady-state value, comparable to the flux of the 

radionuclide entering the saturated zone. 

This simple transport concept is complicated by the existence of decay chains of radioactive 

daughters from long-lived parents released into the saturated zone (including, for example, 

decay chains from isotopes of uranium, thorium, neptunium, and plutonium). Decay of these 

radionuclides and their immediate daughters produces additional, shorter-lived radionuclides 

during transport. In a closed system, decay chains reach secular equilibrium where all 

component radionuclides have equal activity. Deviations from secular equilibrium, as either 

excess or deficient activity of a daughter relative to a parent, can develop in open or dynamic 

systems, particularly where parents and daughters have different chemical behavior. Given 

sufficient differences in chemical behavior, such disequilibria can manifest over time in 

groundwater transport systems, even where fluxes of long-lived parent radionuclides are in 

steady state. For the Yucca Mountain saturated zone transport system, where modeled 

release of contaminants is slow and limited (see performance assessment results discussed 

in TER Section 2.2.1.3.4), this is most apparent over longer performance periods, on the order 

of hundreds of thousands of years or more. The potential risk impact of disequilibria in decay 

chains on performance is considered in the subsequent sections of this technical evaluation; 
12-4 

specifically, TER Section 2.2.1.3.9.3.1, Section 2.2.1.3.9.3.1.2, Section 2.2.1.3.9.3.2.2, and 

Section 2.2.1.3.9.3.2,5. 

2.2.1.3.9.3.1 Conceptual Model and Model Framework

DOE related Yucca Mountain site characteristics to a conceptual model of the saturated zone 

from beneath the repository to the accessible environment boundary in which the flow of water 

would transport released radionuclides through fractures, major faults, and porous alluvium. 

DOE used the same three-dimensional model grid for saturated zone transport modeling as for 

the site-scale saturated zone flow model (SAR Section 2.3.9.2; TER Section 2.2.1.3.8). There 

are two primary geological units through which water flows in the saturated zone: fractured 

volcanic tuff and alluvium. Unlike the unsaturated zone, where flow in the volcanic tuff occurs in 

fractures, faults, and matrix, DOE models flow in volcanic tuff of the saturated zone to occur 

only through fractures and faults. Water in the volcanic tuff matrix is connected to water in the 

fractures but is considered stagnant (flow velocities in the matrix are extremely low relative to 

the fast-flowing fractures). 

 

DOE simulated the transport of radionuclides as dissolved species and as species sorbed 

to mobile, colloid-sized particles. These two modes of transport are subject to various 

physical and chemical processes that affect their transport in groundwater. DOE identified 

advection, dispersion, sorption and matrix diffusion, colloidal transport, and radionuclide 

decay and ingrowth as important transport-affecting processes, and incorporated these 

processes in the numerical models of radionuclide transport (SAR Table 2.3.9-1). DOE’s 

conceptual model described how each of the transport-affecting processes influences the 

rate at which radionuclides travel through the saturated zone model relative to the rate that 

water travels (SAR Section 2.3.9.2). DOE used sensitivity analyses and single-realization 

analyses of TSPA simulations to demonstrate how the saturated zone transport abstraction 

integrated specific processes with the natural features of the saturated zone to slow the 

migration of radionuclides through the saturated zone, as detailed in SAR Section 2.1.2.3.6 and 

SNL Section 6.3.10 (2008ag). 

The NRC staff notes that DOE included appropriate transport-affecting features, events, and 

processes (FEPs) in the conceptual model for saturated zone radionuclide transport and 

provided a technical basis for their inclusion in the conceptual model because DOE 

demonstrated these FEPs affect transport using appropriately designed laboratory and field 

tests, and analog data. The NRC staff evaluates DOE’s technical bases for excluding other 

transport-affecting FEPs in TER Section 2.2.1.2.1 and notes that DOE’s explanations for 

excluding the identified FEPs are adequate because they were supported by site 

characterization data, field tests, laboratory experiments, and natural analogs. One specific 

excluded FEP—the effect of temporal changes in groundwater composition on radionuclide 

concentrations in the saturated zone—is examined in more detail in TER Section 2.2.1.2.1, 

because information exists that suggests temporal changes occur (Turner and Pabalan, 

1999aa), and these changes could result in releases of certain radionuclides sorbed over time 

to the saturated zone groundwater. 

Turner and Pabalan (1999aa) offer alternative reasons for the observations of apparent 

temporal variability other than by natural causes. These include sampling from different flow 

zones in the same well, differences in sampling, sample preparation techniques, filtration, 

and pumping times. The NRC staff notes that this process, although likely to occur, would 

not affect radionuclide concentrations significantly if the probabilistic analyses include 

temporal uncertainty. 
12-5 

On the basis of field and modeling results, DOE determined groundwater flow and migration of 

radionuclides in the saturated zone would begin in fractured volcanic rock beneath the 

repository and travel southeasterly toward Fortymile Wash before turning in a southerly direction 

beneath the wash to the accessible environment, approximately 18 km [11.18 mi] south of the 

repository. About 10 km [6.21 mi] south of the repository along this path, the water and 

radionuclides are expected to flow from the volcanic rock and enter the alluvium. The location 

of the transition from volcanic rock to alluvium is a key geologic data uncertainty in DOE’s 

transport abstraction (BSC, 2005ak). This uncertainty was incorporated in DOE’s model as an 

alluvium uncertainty zone, which was constrained by geologic data from Nye County Early 

Warning Drilling Program (NC–EWDP) wells. The length of travel of radionuclides in the 

volcanic rock or alluvium is dependent on the source zone (location of release from the 

repository), the value of horizontal anisotropy in permeability, and the size of the alluvium 

uncertainty zone. 

In DOE’s model, the two geologic media encountered in the saturated zone, fractured volcanic 

rock and alluvium, are expected to have very different effects on water flow and radionuclide 

transport. The fractured volcanic rock has low porosity (void space) and sparsely distributed 

fractures that control flow and transport. Consequently, the water and radionuclides move 

relatively quickly through fractures in the volcanic rock. The sparse distribution of fractures 

limits interactions between the radionuclides and the rock that could slow radionuclide transport. 

In contrast, the alluvium was modeled as a porous medium, with significantly higher porosity 

than the volcanic rock. Consequently, water moves more slowly through the alluvium. Because 

of the way in which the alluvium was deposited, some preferential flow paths could exist in 

buried gravel deposits. DOE accounted for these potential higher velocity flow paths by 

including effective porosity as an uncertain parameter, with a range of values to accommodate 

these features in the model abstraction (BSC, 2005ak). Consistent with results from field-based 

transport testing (SNL, 2007aw), DOE did not take credit for one of the retardation processes, 

matrix diffusion, that might result from the dual porosity aspect of the alluvial system. 

DOE modeled radionuclide release from the unsaturated zone into the saturated zone as 

occurring at one to four point sources near the water table. The point source locations were 

randomly sampled from within each of the four corresponding unsaturated zone source regions 

that generally represented preferential flow pathways in the unsaturated zone flow model. 

In DOE’s TSPA model, two model abstractions of saturated zone flow and transport were 

implemented. The primary model of radionuclide transport in the saturated zone was a 

site-scale, three-dimensional, single (effective)-continuum, dual-porosity, particle-tracking 

transport model. The dual-porosity aspect allowed for consideration of matrix diffusion in 

fractured volcanic tuff. The two porosities refer to those of the fractures and of the matrix, while 

the effective continuum aspect of the approach allowed DOE to assign average values to flow 

and transport parameters applied to cells of the numerical model representing the system. 

Radionuclides whose transport was simulated through the saturated zone were subdivided into 

12 groups on the basis of their similar transport characteristics (primarily based on sorption 

behavior, as discussed in detail in TER Sections 2.2.1.3.9.3.2.2 and 2.2.1.3.9.3.2.4). 

Radionuclides with half-lives of less than 29 years were not explicitly transported in DOE’s 

saturated zone flow and transport abstraction. Instead, for shorter-lived radionuclides that are 

part of decay chains with long-lived parents (e.g., U-238 series), DOE determined daughter 

concentrations in groundwater using an assumption of secular equilibrium. Secular equilibrium 

occurs when the activity of a short-lived daughter radionuclide is equal to that of a longer-lived 
12-6 

parent radionuclide, due to radioactive ingrowth. Differences in sorption behavior between 

parent and daughter radionuclides can affect their relative concentrations in groundwater, 

especially at local scales, such as near the boundary of the controlled area where water is 

pumped from wells. The NRC staff notes that the DOE assumption of secular equilibrium may 

not be valid for some parent-daughter pairs and can result in uncertainties not included in the 

conceptual model. Evaluation of the impact of this assumption on calculations of repository 

performance is detailed in TER Section 2.2.1.3.9.3.1.2, Section 2.2.1.3.9.3.2.2, and 

Section 2.2.1.3.9.3.2.5. 

DOE’s three-dimensional model abstraction used flow fields and other hydrologic characteristics 

defined by the three-dimensional saturated zone flow model and calculated unit breakthrough 

curves for each of the 12 radionuclide groups transported in the saturated zone. The 

three-dimensional model was run, and the unit breakthrough curves were developed and 

stored external to the TSPA model, as outlined in SNL Section 6.3.10 (2008ag). 

The saturated zone radionuclide transport abstraction was coupled to input from the 

unsaturated zone and output to the biosphere using the convolution integral method. In this 

method, a unit saturated-zone radionuclide mass breakthrough curve was computed (by the 

three-dimensional model) for a step-function mass flux source. This breakthrough curve was 

then combined with the radionuclide mass flux history from the unsaturated zone to produce a 

radionuclide mass flux history that was output to the biosphere. Within TSPA, the convolution 

integral technique was implemented by a module called SZ_Convolute. SZ_Convolute was also 

used to apply changes in specific discharge due to climate change and to correct radionuclide 

releases from the three-dimensional model for the effects of radioactive decay. The convolution 

integral method rests on the key assumptions of linear behavior and steady-state saturated 

zone flow conditions. The saturated zone output mass that crosses the accessible boundary in 

a year was assumed dissolved in 3.7 × 109

 L [3,000 acre-ft] of water. 

DOE considered three climate states in modeling the initial 10,000 years after repository 

closure: (i) current, (ii) monsoonal, and (iii) glacial transition conditions. For evaluating 

repository performance after 10,000 years following repository closure, DOE used 

“constant-in-time” climate with a prescribed deep percolation rate. The saturated zone transport 

model abstraction assumed steady-state flow conditions under each specified climate state and 

assumed the steady-state conditions were achieved instantaneously for each climate transition. 

Although potential water table elevation changes and flow path changes could occur for different 

climate conditions, DOE did not implement these changes explicitly in the saturated zone 

transport abstraction. Instead, DOE used specific discharge multipliers to increase flux for the 

wetter future climates. Also, the potential change in water table elevation near the repository 

was simulated by shortening the flow path length through the unsaturated zone during wetter 

climates, and releasing instantaneously to the saturated zone any radionuclides that are present 

in the portion of the unsaturated zone that is inundated at the time of climate change. 

The NRC evaluates the use of specific discharge multipliers in TER Section 2.2.1.3.8.3.3. 

The specific discharge multipliers affect radionuclide transport. Increasing the specific 

discharge increases the radionuclide mass that reaches the accessible environment boundary 

within the 10,000 year period. However, DOE demonstrated that the range of specific 

discharges does not affect the retardation processes considered in this chapter. DOE evaluated 

the effects of changes in water table elevations and flow due to climate change by using the 

three-dimensional site-scale transport model to generate particle tracks, as described in SNL 

Appendix E (2007ba). DOE stated that the simulation results showed that the path lengths and 

travel times for radionuclides increased relative to the model formulation used in performance 
12-7 

assessment, and thus, exclusion of these changes in the saturated zone abstraction model did 

not result in an underestimation of dose, as outlined in SNL Appendix E (2007ba). In its 

response to the NRC staff request for additional information concerning geochemical 

assumptions of the saturated zone transport models under different climatic conditions (DOE, 

2009df), DOE described how elevated water-table flow paths would pass through rocks 

containing higher percentages of zeolites. In general, water movement and transport of 

certain radionuclides is slower through rocks containing zeolites compared to non-zeolitic 

rocks. Instead of explicitly considering these different pathways, DOE chose to simplify 

its climate change model by employing only the specific discharge multipliers (see 

TER Section 2.2.1.3.8.3.3). The NRC staff notes that DOE’s climate change abstraction is 

reasonable, because TSPA model results suggest that unretarded radionuclides, which are 

relatively unaffected by changes in chemical conditions, will dominate the radionuclide release 

to the environment in the initial 10,000 years after repository closure, when changes in climate 

are considered. 

The second saturated zone model abstraction was a one-dimensional transport model. The 

main purpose of the one-dimensional model was to calculate the radioactive decay, ingrowth, 

and transport for second-generation daughter radionuclides for four decay chains—the actinium, 

uranium, thorium, and neptunium series. The one-dimensional transport model was 

implemented as four groups of GoldSim (GoldSim Technology Group, 2006aa) pipe elements. 

One group was used for each of the four repository source regions. Each group of pipe 

elements consisted of three segments, representing the volcanic tuffs (the first two segments) 

and the alluvium (the last segment). The lengths of the last two segments were considered 

uncertain and were derived from particle-tracking results of the three-dimensional saturated 

zone model. Groundwater-specific discharge values in each pipe segment were also estimated 

from the three-dimensional model given in SNL Section 6.3.10 (2008ag). Where possible, the 

one-dimensional model used the same transport parameters, such as sorption coefficients, as 

the three-dimensional model. DOE adjusted other features of the one-dimensional model, such 

as dispersivity and flow tube diameters, to improve consistency between the breakthrough curve 

results from the two abstractions. The NRC staff notes that this method of adjusting some 

parameters is reasonable. The parameters that were adjusted were shown to be unimportant to 

performance. Those parameters that were important to performance were held the same for the 

two models. Furthermore, the use of two approaches to simulate transport is consistent with 

YMRP guidance, which describes how alternative modeling approaches consistent with 

available data can be used to support model abstractions. 

The NRC staff notes that DOE provided a technical basis for all features and conditions of the 

saturated zone transport model framework, consistent with the guidance in the YMRP, with the 

exception of the assumption of secular equilibrium for certain decay products to which the 

receptor can be exposed (staff evaluation of this assumption is in TER Section 2.2.1.3.9.3.1.2, 

Section 2.2.1.3.9.3.2.2, and Section 2.2.1.3.9.3.2.5). This is based on the NRC staff’s 

understanding of the Yucca Mountain natural system, obtained from field observations and 

independent analysis of saturated zone transport processes, and DOE’s proposed repository 

design. The NRC staff notes that DOE included important Yucca Mountain site characteristics 

that are expected to affect radionuclide transport, such as flow in the fractures of the volcanic 

tuff, the porous nature and uncertain extent of the alluvium, and uncertainties in groundwater 

flow rates. DOE used Yucca Mountain site characterization data to assign geologic, hydrologic, 

and radionuclide transport parameter values to specific rock units or to define ranges of values 

for these properties to address uncertainty about the natural variability of the system. For 

short-lived decay-chain radionuclides (see discussion of relevant radionuclides in TER 

Chapter 2.2.1.3.14, Biosphere Characteristics), DOE, in its Safety Analysis Report, assumed 
12-8 

that their concentrations in groundwater at the boundary of the controlled area could be 

determined through secular equilibrium with their dissolved long-lived parents. In some cases 

this assumption can lead to an underestimate of the amount of radionuclides present in the 

contaminant plume in the accessible environment. DOE provided further analysis of this topic in 

its response to an NRC staff request for additional information. As previously noted, the NRC 

staff evaluation of these specific analyses is discussed in TER Section 2.2.1.3.9.3.1.2, 

Section 2.2.1.3.9.3.2.2, and Section 2.2.1.3.9.3.2.5. 

2.2.1.3.9.3.1.1 Model Integration for TSPA 

DOE’s saturated zone transport abstraction simulated the transport of dissolved radionuclides 

and colloid-associated radionuclides through the saturated zone from beneath the repository, 

generating breakthrough curves at the accessible environment boundary for 27 radionuclides 

that DOE determined were risk significant. For TSPA calculations, DOE integrated the 

saturated zone radionuclide transport abstraction with three other model components: the 

unsaturated zone transport abstraction model (SAR Section 2.3.8), the site-scale saturated 

zone flow model (SAR Section 2.3.9), and the biosphere model (SAR Section 2.3.10). 

2.2.1.3.9.3.1.2 Unsaturated Zone/Saturated Zone Boundary Condition 

In the unsaturated zone transport abstraction (SAR Section 2.3.8), radionuclides released from 

waste packages migrated through the fractures and rock matrix at rates affected by flow fields 

generated from the site-scale unsaturated zone flow model. The boundary through which 

radionuclides from the unsaturated zone passed to the saturated zone was divided into four 

regions (or subareas). Releases of radionuclides that occurred within any portion of each of the 

subareas were collected for release into the saturated zone at one point (also within the same 

subarea). Each point source mass of radionuclides was transferred to the three-dimensional 

(via SZ_Convolute) and the one-dimensional saturated zone transport model abstractions. The 

three-dimensional model breakthrough curves were generated by randomly sampling different 

locations within each of the four subareas to create a starting point for the saturated zone 

flow path(s). In contrast, the one-dimensional model uses a fixed, centroid location within 

each subarea as a starting point for each of its four flow tubes. As a result of the 

different starting points for the three-dimensional and one-dimensional simulations, the path 

lengths were not necessarily the same for both methods. DOE described the comparison of the 

three-dimensional and one-dimensional simulations as not consistently overestimating or 

underestimating the travel time to the accessible environment. The NRC staff notes that this 

description characterizes the uncertainty associated with the methods. The radionuclide 

species and its type of transport remained consistent in the transfer of mass from the 

unsaturated zone to the saturated zone. For example, radionuclides associated with irreversible 

colloids were passed as irreversible colloids, while those associated with reversible colloid 

transport were repartitioned according to the different colloid concentrations encountered in the 

saturated zone. All radionuclide mass released from the unsaturated zone was transferred to 

flowing fractures in the saturated transport models. 

DOE sensitivity analyses of the effects of releasing radionuclide mass from the unsaturated 

zone as point sources indicated that the point source releases generally produced faster 

breakthroughs, as described in SNL Section 6.8.4 (2007ba). DOE also noted that the release of 

radionuclides as a point source generally produced a plume with less dispersion. 

The NRC staff notes that DOE reasonably transferred radionuclide mass between the 

unsaturated and saturated zone models. The NRC staff also notes that DOE applied consistent 
12-9 

conceptual models and estimates of data and model uncertainty for site conditions relevant to 

the unsaturated and saturated transport abstractions. 

Saturated Zone—Accessible Environment Boundary Condition 

The saturated zone transport model passes to the biosphere model the time-varying mass of 

radionuclides that cross the boundary with the accessible environment. The saturated zone 

transport model explicitly simulates the transport of 27 radionuclides. These radionuclides are 

carried in the groundwater in the dissolved state and as temporarily and/or permanently 

associated with colloids. Parameters affecting the transport in the saturated zone (see TER 

Sections 2.2.1.3.9.3.2.2 and 2.2.1.3.9.3.2.4) were assigned to the 27 radionuclides. In the 

TSPA model four additional radionuclides—Ac-227, Ra-228, Th-228, and Pb-210—were 

assumed to be in secular equilibrium and were released to the biosphere at the same activity 

as the activity in ground water of their long-lived parents, Pa-231, Th-232, U-232, and Ra-226, 

respectively. In response to a request for additional information that asked for justification for 

setting the released activity of daughters equal to that of their released parents, DOE 

(2009de) acknowledged that the activities of these daughters are affected by differences in 

the sorption characteristics of the parents and daughters. DOE (2009de) analyses indicated 

that mean activities for the daughters of Th-232/Ra-228, Pa-231/Ac-227, Ra-226/Rn-222, and 

Ra-226/Pb-210 parent-daughter pairs could range from 1 (e.g., Ra-226/Pb-210) to more than 

1,400 (e.g., Ra-226/Rn-222) times that of the respective activities of the parents. DOE 

defined the sorption enhancement factor (SEF) as the ratio of the daughter activity relative 

to the activity of its parent in groundwater, resulting from the differences in sorption 

coefficients of the parent and daughter. DOE concluded that explicitly including the 

increased daughter activities in the dose calculation of the four parent/daughter pairs would 

increase the calculated maximum total mean annual dose to the RMEI by 20 percent, from 

0.02 to 0.024 mSv [2.0 to 2.4 mrem]. The NRC staff notes that although the method used in 

DOE (2009de) was reasonable to address the effect of the secular equilibrium 

assumption, the Kd distributions used in DOE’s response were biased to lower values, 

potentially decreasing the magnitude of the effect (see the detailed discussion in TER 

Section 2.2.1.3.9.3.2.5). 

The NRC staff performed independent sensitivity analyses (Bradbury, 2010aa) to determine 

the effect of sorption on concentrations of other decay-chain radionuclides to which the 

receptor can be exposed. Whereas DOE assumed all radionuclides with half-lives of less 

than 29 years were in secular equilibrium with their parents, the NRC staff explicitly included 

in its transport analyses radionuclides with half-lives as short as 1 hour. These shorter lived 

radionuclides were included to consider possible effects for direct ingestion of contaminated 

drinking water from wells in the groundwater at the location in the accessible environment that 

includes the highest concentration of radionuclides in the plume of contamination. The 

independent analyses used pipe elements of the GoldSim modeling program to simulate 

one-dimensional transport with sorption and radioactive decay and ingrowth for the four 

radioactive decay series: actinium, neptunium, thorium, and uranium. The NRC staff’s 

analyses showed that some daughter activities were less dependent on the relative Kd of the 

immediate parent than on the relative Kd of the distant parent that acts as the source of 

radioactivity to the pipe. For example, in a simple uranium decay series, a constant source of 

U-234 injected into the pipe will decay as it is transported down the pipe producing Ra-226, 

which subsequently decays to Rn-222. Eventually, a steady state is approached in which the 

fluxes of U-234, Ra-226, and Rn-222 remain relatively constant. The flux of Rn-222 at this 

point depends predominantly on the Kd of U-234 and is largely independent of the Kd of 

Ra-226. Consequently, simulations that involve changes in the Kd of Ra-226 but keep the 
12-10 

U-234 Kd and source flux unchanged result in the same Rn-222 flux. This suggests that Kds 

of radionuclides other than those identified in DOE’s response to the request for additional 

information could affect concentrations of short-lived radionuclides. The NRC staff’s analyses 

showed that, assuming sufficient difference in sorption between U-234 and one of its decay 

products, Po-210 (half-life of 138 days), measurable excess activity of Po-210 can develop in 

the plume at the boundary of the accessible environment. DOE did not analyze transport of 

Po-210 and thus did not provide values for the sorption coefficient of this nuclide. The NRC 

staff evaluation of the specific geochemical conditions necessary to produce significant 

excess Po-210 in groundwater, and the potential for this to occur in the Yucca Mountain 

system, is in TER Section 2.2.1.3.9.3.2.5. 

The saturated zone transport model passes time-dependent fluxes of the 27 radionuclides 

whose transport is explicitly simulated in TSPA, plus the four radionuclides that are assumed 

to be in secular equilibrium with their immediate parents. In turn, the biosphere model 

calculates biosphere dose conversion factors for the groundwater exposure scenario 

equivalent to the annual dose from all potential exposure pathways that the RMEI would 

experience as a result of the release of a unit concentration of 1 Bq/m3

 [0.227 dpm/gal] of the 

primary radionuclide in groundwater at the accessible environment boundary. The biosphere 

model considers 75 radionuclides composed of the 31 primary radionuclides and their 

relatively short-lived daughters, which are assumed to be in secular equilibrium (i.e., have the 

same activity concentrations as those of their parents). 

The NRC staff notes that DOE’s assumption of secular equilibrium adds uncertainty to the 

coupling of the biosphere model to the saturated zone transport model. Although DOE 

(2009de) described the effects of differences in sorption characteristics on four pairs of 

parent-daughter radionuclides, other pairs exist for which the assumption of secular 

equilibrium may not be supported. Independent NRC staff analyses indicate calculated fluxes 

could change for short-lived daughters if they were explicitly considered in the transport 

model. The magnitude of the potential effect of these nuclides on dose depends on their dose 

coefficients as well as their excess activity in the groundwater. The added uncertainty 

resulting from not including these nuclides in the transport model is reasonable given that it 

does not significantly increase the mean of the expected annual dose to the reasonably 

maximally exposed individual. 

2.2.1.3.9.3.2 Saturated Zone Transport Processes 

In DOE’s saturated zone transport abstraction, the migration of radionuclides through the 

saturated zone is influenced by the transport-affecting processes of advection and dispersion, 

sorption, matrix diffusion, and colloid-facilitated transport, as well as radioactive decay and 

ingrowth (SAR Section 2.3.9.1). NRC staff review of these processes focuses on the system 

description and integration, data support, data uncertainty, and model uncertainty and support. 

 

2.2.1.3.9.3.2.1 Advection and Dispersion 

Advection is the process by which radionuclides, both dissolved and associated with colloids, 

are carried in flowing water. Overall, DOE considered advection to be the most important 

transport process in the saturated zone because, as DOE stated, advection is the primary 

mechanism driving the migration of radionuclides in the saturated zone, as outlined in BSC 

Section 6.3.1 (2005ak). Accordingly, the uncertainty of specific discharge, or the measure 

of flow in the saturated zone, has the greatest effect on travel times, as described in SNL 

Section 6.8.4 (2007ba). 
12-11 

Unlike DOE’s model framework for unsaturated zone radionuclide transport, which allows 

radionuclides to travel advectively in both fractures and the matrix, in the saturated zone, 

radionuclides in the fractured volcanic tuff move advectively only in the fractures and fault 

zones. Radionuclides can, however, move into and out of the matrix by diffusion, driven by 

concentration gradients (see TER Section 2.2.1.3.9.3.2.3). Hydrologic testing in boreholes in 

the volcanic aquifer revealed that flow through fractures was generally spaced at intervals 

significantly greater than the spacing of the fractures themselves as determined in drill core 

logging. This site characteristic was included in the model abstraction as the uncertain 

parameter flowing interval spacing in volcanic units. Along with this parameter, DOE coupled an 

uncertain parameter, termed Fracture porosity in volcanic units (SAR p. 2.3.9-132), or flowing 

interval porosity in the fractured tuffs (SAR Section 2.3.9.3.2.1). Values for this parameter were 

estimated using various conservative tracers and reactive tracers in C-Wells Complex testing 

(SNL, 2007aw). DOE used a combination of the spacing and the porosity parameters to 

describe the characteristic of preferential pathways through the fracture volcanic aquifer. 

The NRC staff notes that the methods to constrain the values of these parameters are 

reasonable because these methods are supported by site characteristic data and field testing. 

Although a limited number of locations have been characterized to determine porosity and 

spacing, DOE’s estimation of the distribution range (large spacing and small porosity) 

compensates for the uncertainty in these parameters. 

On the basis of information DOE provided about saturated zone radionuclide transport by 

advection in SAR Section 2.3.9.3 and references therein, NRC staff notes that advective 

radionuclide transport in DOE’s transport abstraction depends on the flow field information 

DOE’s site-scale saturated zone flow model supplies to the transport abstraction. The NRC 

staff notes that DOE’s implementation of advective radionuclide transport is integrated with 

the site-scale saturated zone flow model, using the same three-dimensional model grid, 

hydrologic properties, modeling approach, and flow fields to represent advection fluxes. The 

NRC staff’s technical evaluation of DOE’s site-scale saturated zone flow model is provided in 

TER Section 2.2.1.3.8. 

Advection in alluvium, like advection in the volcanic aquifer, involves preferential pathways. 

In the alluvium, DOE used an effective porosity parameter to compensate for the potentially 

reduced volume of the alluvium through which flow might occur. A smaller effective porosity 

results in higher average linear velocity (i.e., the distance water moves through porous material 

per unit time). DOE modeled the alluvium as a single-continuum medium. Consequently, there 

is no exchange of water or radionuclides between the effective porosity (where flow occurs) and 

the rest of the porosity (where no flow occurs) modeled in the alluvium. Although this 

conceptual model does not explicitly account for possible flow paths within the alluvium, the 

NRC staff considers the approach to be a reasonable method for incorporating preferential 

pathways and minimizing the travel time through the alluvium because it is compatible with site 

characteristics, such as the occurrence of gravel paleochannels and lenses of clay. Field 

evidence of tracer exchange between effective porosity, where water is flowing, and porosity, 

where water is stagnant, was inconclusive (SNL, 2007aw). The NRC staff considers 

DOE’s approach to simulating advection in the alluvium is reasonable because the approach 

includes processes and conditions shown to be present in the alluvium and excludes processes 

that have not been shown to occur. 

 

DOE provided supporting information about advective transport processes from saturated 

zone tracer experiments in densely welded, fractured tuffs (fracture-flow dominated systems) 
12-12 

at the C-Wells Complex and in the porous alluvium (SAR Section 2.3.9.3.2.1; SNL, 2007aw). 

The NRC staff notes that DOE provided a technical basis for implementing advective 

radionuclide transport in the saturated zone radionuclide transport abstraction supported 

by the field testing in the volcanic and alluvial aquifers. 

Dispersion describes the transverse spreading, perpendicular to flow, both horizontal 

and vertical, and longitudinal spreading, parallel to flow, of dissolved radionuclides in 

response to localized differences in flow conditions. At the large scale of the saturated zone 

transport model grid framework, DOE considered the effect of dispersion to be minimal 

(SAR Section 2.3.9.3.2.1). However, to allow transport calculations to provide an analysis 

of radionuclide travel time distributions, DOE included a longitudinal dispersion term in the 

transport model to capture the arrival of a dispersed solute front at the accessible environment 

boundary (SAR Section 2.3.9.3.2.1). Inclusion of longitudinal dispersion is supported by the 

field evidence of preferential pathways in the saturated zone in the vicinity of Yucca Mountain. 

The NRC staff examined DOE’s assumptions and technical approach for including dispersion as 

a transport process in the saturated zone transport abstraction. On the basis of the NRC staff’s 

recognition that dispersion does not significantly affect the results of transport calculations in a 

slowly evolving system where radionuclide concentrations gradually increase with time, the 

NRC staff notes the approach is reasonable. 

2.2.1.3.9.3.2.2 Sorption 

As discussed previously in TER Section 2.2.1.3.7.3.2.2, sorption is a general term for chemical 

and physical processes that transfer a fraction of dissolved species to the surface of a solid 

phase. Depending on specific properties of the dissolved species, the solid, and the liquid, 

some radionuclides will sorb to the solid, some will sorb more onto solids than others, and some 

will not sorb at all. Specific sorption processes DOE considered include ion exchange 

reactions, in which ions of one element replace ions of another element within a mineral 

structure, and surface complexation, which involves reactions that form bound species at the 

mineral–water interface. As modeled in the DOE TSPA, sorption of radionuclides onto the 

fractured volcanic tuff matrix or alluvium results in retardation, or slowing, of transport relative to 

rates of water flow through the saturated zone. In contrast, radionuclide sorption onto mobile 

colloids may enhance the transport rate relative to radionuclides that attach onto a stationary 

solid (see TER Section 2.2.1.3.9.3.2.4). Sorption is an important process contributing to the 

barrier capability of the saturated zone (SAR Section 2.3.9). In particular, sorption within the 

alluvium effectively delays the transport of moderately and strongly sorbing radionuclides for 

thousands of years or longer (SAR Sections 2.3.9 and 2.1.2.3.6). Sorption of dissolved thorium, 

americium, and protactinium is so effective at slowing their movement, that on entering the 

saturated zone, these radionuclides cannot reach the accessible environment within 1 million 

years. To be present at the accessible environment boundary, these radionuclides either are 

transported through the saturated zone as colloids or are ingrown as decay products of mobile 

parents. DOE noted that the primary controls on sorption are (i) characteristics of the minerals 

surfaces onto which sorption occurs, (ii) chemistry of groundwater in the saturated zone, and 

(iii) sorption characteristics of each element (SAR Section 2.3.9.3.2.2). 

DOE represented sorption in the saturated zone with a sorption coefficient (Kd), an empirically 

determined or modeled value that represents the ratio of the sorbed-phase radionuclide 

concentration to the dissolved-phase radionuclide concentration. Low values of Kd indicate that 

little or no sorption (i.e., Kd = 0) occurs; higher values indicate moderate or strong sorption, and 

therefore retardation. Retardation by sorption is expressed in transport calculations by a 

retardation factor (Rf) that depends on the Kd value and the physical properties (porosity and 
12-13 

density) of the solid medium through which the radionuclide is transported. Thus, a retardation 

factor (Rf) equal to one indicates the solute is transported at the velocity of groundwater, while 

an Rf > 1 indicates the solute transport is delayed relative to groundwater. Retardation 

calculations assume that Kd does not vary with changes in radionuclide concentration, sorption 

and desorption reactions are fast relative to the flow rate, and bulk chemical composition of the 

groundwater is constant (Davis and Curtis, 2003aa; Langmuir, 1997aa; Davis and Kent, 

1990aa). DOE provided information, satisfactory to the staff, to support each of these 

assumptions (DOE, 2008ab; BSC, 2005ak; SNL, 2007aw,ba). For example, DOE varied the 

concentration of radionuclides in sorption experiments to determine the effect on Kd. DOE 

varied the duration of sorption/desorption experiments to determine the rate of these reactions. 

By using groundwaters of different compositions, DOE demonstrated the effect of bulk 

chemistry on Kds. 

DOE assumed that sorption of dissolved radionuclides would occur only in the matrix of the 

volcanic tuff or in the alluvium; dissolved radionuclides transported in fractures do not sorb to 

fracture surfaces in the DOE model (SAR Section 2.3.9.3.2.2). DOE excluded sorption onto 

fracture surfaces because of high uncertainties in the nature of fracture coatings (BSC, 2005ak). 

However, solutes transported through designated fault or fracture zones could undergo sorption 

depending on the characteristics of the fault or fracture zone (BSC, 2005ak). In fracture zones, 

a small portion of the rock matrix within the fracture zone was conceptualized as having rapid 

diffusion and a retardation factor was calculated accordingly (BSC, 2005ak). In contrast to 

dissolved radionuclides, mobile colloids were retarded within fractures of the volcanic tuff (SAR 

Section 2.3.9.3.3). Laboratory and field-scale experimental results supported DOE’s conceptual 

model of colloid retardation in fractures (BSC, 2005ak; SNL, 2007aw). 

In the saturated zone transport model and abstraction, DOE assumed that four radioelements 

(carbon, chlorine, iodine, and technetium) were nonsorbing and assigned a fixed value of Kd = 0 

(corresponding to Rf = 1) for each. While results of field-based testing in the alluvium indicated 

that the transport of the important radioelements, technetium and iodine, may be retarded, 

laboratory-based sorption tests indicated a Kd = 0 was warranted, as described in SNL 

Appendix G (2007ba). For the remaining radioelements modeled in saturated zone transport 

calculations (americium, cesium, neptunium, plutonium, protactinium, radium, selenium, 

strontium, thorium, tin, and uranium), DOE developed ranges and statistical distributions of Kd

values for each radioelement and for each modeled rock unit from a combination of empirical 

data, process modeling, statistical analyses, and expert judgment, as shown in SAR Table 

2.3.9-4 and SNL Appendices A, C, G, and J (2007ba). The NRC staff notes that the methods 

applied to determine distributions of Kds are consistent with YMRP guidance because DOE 

followed reasonable practices for determining sorption. 

For sorption modeling, DOE grouped the various stratigraphic units in the saturated zone 

into two geologic media that have different sorption characteristics: fractured volcanic tuff 

and alluvium (SAR Section 2.3.9.3.2.2). DOE measured sorption data from batch and column 

experiments that used site-specific crushed tuff samples and alluvium and used saturated 

zone water samples from wells in the saturated volcanic tuff (UE-25 J-13), carbonate aquifer 

(UE-25 p#1), and alluvium (various EWDP wells). Water from wells UE-25 J-13 and UE-25 p#1 

were used for batch experiments with volcanic tuff samples, while experiments with alluvium 

utilized water from the alluvial aquifer, as described in SNL Appendix G (2007ba). DOE argued 

that these water chemistries bracket the major ion chemistry observed in the saturated zone, as 

outlined in BSC Appendix A (2005ak). For the long-lived actinides (americium, neptunium, 

plutonium, and uranium), DOE further characterized the effects of variability in geochemistry 

and mineral surface area using a non-electrostatic surface complexation modeling approach, 
12-14 

supported by Davis, et al. (1998aa) and BSC Appendix A (2005ak). In some cases, DOE 

supplemented the experimental and modeling sorption data with data from the open literature 

(BSC, 2005ak). In TSPA, DOE sampled Kd values from the specified ranges to account for 

experimental uncertainty and variability in geologic conditions, including water chemistry and 

rock type, as shown in SAR Table 2.3.9-4; BSC (2005ak); SNL Appendices A, C, G, and J 

(2007ba); and DOE Enclosure 3 (2009am). 

The NRC staff is familiar with geochemical controls on radionuclide sorption and with the 

experimental data DOE used to develop the TSPA Kd distributions. The NRC staff notes 

that the major ion chemistry (e.g., calcium, sodium, bicarbonate) of the waters used in 

DOE sorption experiments is comparable to that of saturated zone waters, as described in BSC 

Appendix A (2005ak). The UE-25 J-13 and UE-25 p#1 water chemistries bound the ranges 

reported for saturated zone water chemistries for major ions such as sodium, calcium, and 

bicarbonate, and other parameters such as pH and redox state. These chemical characteristics 

are likely to be the most important for radionuclide sorption (e.g., Turner and Pabalan,1999aa). 

The NRC staff further notes that the chemistries of alluvial aquifer waters used in 

alluvium sorption experiments are representative of conditions in the alluvium. DOE also 

used surface complexation modeling, similar to independent models the NRC staff developed 

(e.g., Turner, et al., 2002aa), to examine the effects of broader chemistry ranges on several 

radionuclides. The NRC staff notes that DOE’s use of surface complexation modeling to extend 

the limited chemical conditions in the batch crushed tuff experiments strengthened the technical 

basis for the upper and lower limits of sorption coefficients for the targeted actinides. 

DOE identified mineral surface area and particle size as potential sources of data uncertainty 

related to the use of crushed tuff and alluvium in experiments. DOE referenced studies both 

from within and outside the DOE program that indicate the effects of particle size on sorption 

are typically small except for the very fine (e.g., clay-sized) fraction (BSC, 2005ak). The 

smallest particle size results in higher Kds. The general DOE approach to addressing this 

uncertainty was to use batch experiments for a range of particle sizes and to bias the minimum 

and maximum limits for the Kd distributions toward lower (weaker sorption) values, as shown in 

DOE Table 1.1.2-1 (2009am). SNL Appendix A (2007ba) gave the range of Kds used in the 

TSPA. In SNL Appendix J (2007ba), however, DOE described how batch sorption experiments 

may underestimate the Kd that should be applied to transport in the field. The experiments 

described in SNL Appendix J (2007ba) involved sorption/desorption of uranium and neptunium. 

The effective Kds for these radionuclides were up to two orders of magnitude greater than those 

used in the TSPA. DOE stated that the higher Kds are likely due to multiple sorption sites of 

different strengths. There are strongly sorbing sites and weakly sorbing sites on the solids in 

the saturated zone. Batch sorption experiments are of short duration and preferentially 

measure the weak sites. Desorption experiments of longer duration measure the reactions with 

the stronger sites. DOE chose not to use the effective Kds from SNL Appendix J (2007ba) for 

uranium and neptunium, because they would be inconsistent with the Kds of the other 

radionuclides determined by the batch sorption method. 

In selecting experimental data to inform the TSPA Kd distributions, DOE excluded data from 

experiments where the final radionuclide concentration indicates that the solubility limit of the 

radionuclide may have been exceeded, as described in SAR Section 2.3.9.3.2, BSC Appendix A 

(2005ak), and SNL (2007ah). However, the data DOE provided appear to indicate that some 

experiments were conducted at initial concentrations above the solubility limits for some 

radionuclides, as described in BSC Appendix A (2005ak). The results from these experiments 

could be biased by precipitation effects, although the range and magnitude of the sorption 

coefficients appear to be consistent with experiments conducted using lower, undersaturated 
12-15 

concentrations, as outlined in BSC Appendix A (2005ak). These particular experiments do not 

significantly influence the Kd distributions. The NRC staff notes that the experimental 

approaches are reasonable. Those experiments involving initial radionuclide concentrations 

higher than their solubility limit are not considered in the evaluation. DOE also addressed data 

uncertainty by obtaining sorption data that assessed Kd variability as a function of time, 

radioelement concentration, atmospheric composition, water composition, particle size, and 

temperature. Although most of the data were gathered from batch sorption experiments, DOE 

also performed a limited number of confirmatory column tests on selected radionuclides that 

DOE had identified as important contributors to mean annual dose in previous performance 

assessments, as outlined in SAR Section 2.3.9.3.2.2 and SNL Table 4-1 (2007ba). 

The NRC staff review of DOE’s sorption data and models indicates most of the Kd uncertainty 

distributions specified by DOE underpredicted the effectiveness of sorption compared to the 

experimental distributions. In some cases, DOE reduced the upper bounds of the Kd

distributions (specifically, those of cesium, plutonium, and radium) relative to the range indicated 

by available data to account for the possible effects of slow sorption kinetics for these elements, 

as shown in BSC Appendix A (2005ak). By using low ranges of Kd values for sorption, DOE’s 

transport model underpredicts retardation, resulting in relatively faster modeled travel times for 

radionuclides through the saturated zone. Following guidance in the YMRP, the NRC staff 

considered whether the use of conservative values to address uncertainty and variability of Kds 

led to conservative estimates of risk and did not cause unintended results (i.e., did not introduce 

undue risk dilution or underestimate overall risk in the system performance). This aspect of 

biasing Kd distributions is described and evaluated later in this section when radioactive decay 

of parents and ingrowth of daughters is considered. 

The empirical Kd modeling approach implemented by DOE is simplistic but well established 

(e.g., Freeze and Cherry, 1979aa; Till and Meyer, 1983aa) and has been broadly used to 

describe radionuclide transport (e.g., Sheppard and Thibault, 1990aa). One assumption 

inherent in the Kd modeling approach for sorption and the convolution integral approach 

used to calculate mass released from the saturated zone is that sorption and system behavior 

are linear (BSC, 2005ak). The ranges of Kd distributions have been adjusted to focus on the 

expected linear range of sorption, but the data DOE provided do not clearly indicate whether the 

adjustments are sufficient for each radioelement, as described in BSC Appendix A (2005ak). 

Another potential model uncertainty associated with the Kd approach is that individual Kd values 

are lumped parameters that do not explicitly take into account spatial and temporal variability or 

the role of specific surface-related processes that may affect radionuclide sorption. DOE 

addressed model uncertainty in TSPA calculations by sampling Kd values stochastically from 

uncertainty distributions in which the distribution ranges were developed from expected system 

conditions and conducted additional analyses to evaluate the effects of model scale and 

heterogeneity, as outlined in SNL Appendices C and D (2007ba). Rather than sample the Kd

distribution independently for each radionuclide, DOE developed a correlation matrix for the 

11 sorbing radioelements on the basis of their ranked sensitivities to six variables (pH, Eh, 

water chemistry, rock composition, rock surface area, and radionuclide concentration). DOE 

used this approach to approximate similarities in sorption behavior among radioelements and to 

ensure that transport behaviors were represented consistently within a single realization of the 

model, as outlined in SNL Appendix A (2007ba). The method of correlation did not appear to 

consider the potential for negative correlations (e.g., the sorption of two radionuclides is affected 

by pH, but in opposite directions). In addressing model uncertainty, DOE neglected sorption 

(i.e., Kd = 0) in fractures (fast flow paths), except for fault zones, and implemented Kd

uncertainty distributions for matrix sorption. In most cases, this underpredicted the 
12-16 

effectiveness of sorption compared to measured distributions so that, as modeled, radionuclide 

transport was less impeded by sorption than would otherwise be expected. 

DOE developed information from natural analogs and field testing to provide qualitative 

comparisons for sorption model confidence building at the field scale (SAR Section 

2.3.9.3.4.1.3). DOE did not apply the saturated zone transport abstraction to sorption modeling 

for the analog sites, nor did DOE use results from natural analog studies to inform the Kd

distributions. However, DOE used general observations of sorption-related transport behavior 

to support the conceptual models (e.g., SAR Section 2.3.9.3.4.1.4). DOE also used 

observations from field tests at Busted Butte south of Yucca Mountain, from the C-Wells 

location, and from two alluvium tracer tests to provide qualitative and limited quantitative 

evaluations of sorption in the radionuclide transport model abstraction. 

The NRC staff recognizes that, in general, underpredicting Kds tends to allow for relatively faster 

transport times in the saturated zone, and thus earlier breakthrough of transported 

contaminants, and potentially greater modeled releases of radionuclides to the accessible 

environment. This approach can be conservative relative to performance, especially for the 

10,000-year period, when DOE models predict that most radionuclides do not reach the 

accessible environment in that timeframe. This approach may not be conservative for a 

1-million-year period, because during the longer period for radionuclides to reach the accessible 

environment, the difference in breakthrough time is less significant. For this longer period, one 

effect of relatively larger Kd values is that sorption can result in accumulation of some 

radionuclides in alluvium along the transport path and in the vicinity of the receptor location 

(albeit later in time). The use of low-biased Kd values increases the uncertainty in predicting the 

timing and magnitude of the peak dose over 1 million years. 

The NRC staff performed independent transport simulations to confirm DOE’s assumption that 

its choice of Kd distributions would not significantly affect its TSPA results for radionuclide 

transport. The NRC staff’s transport simulations (Bradbury, 2010aa) involving radioactive decay 

and ingrowth suggest that biasing Kds of certain radionuclides to lower values may lead to an 

underestimation of total radionuclide concentrations, when considering the sum of the parents 

and their ingrown daughters. In its response to requests for additional information, DOE 

(2009de) acknowledged that Kds affect radionuclide concentrations. The analysis in DOE 

(2009de), however, used the biased distributions of Kds from the TSPA, as described in BSC 

Appendix A (2005ak). The NRC staff used its independent transport simulations to consider the 

sensitivity of TSPA results to the Kd distributions. Using the information from DOE’s 

sorption/desorption experiments, as outlined in SNL Appendix J (2007ba), the NRC staff 

reviewed the potential effects of higher Kds on concentrations of parent-daughter radionuclides 

during saturated zone transport. These effects are most important only over long time periods, 

in transport calculations for one million-years. The NRC staff sensitivity analyses using pipe 

elements of the GoldSim (GoldSim Technology Group, 2006aa) modeling program, the same 

computer code DOE used to model performance of the site, indicate that over such times total 

concentrations of the radionuclides of some decay series increase as a function of Kd of the 

parent. For example, the distribution used by DOE’s TSPA for the Kd of uranium has a mean of 

~5 mL/g. Increasing the Kd of uranium to 200 mL/g, similar to values of the effective Kd in DOE 

experiments, as outlined in BSC Appendix J (2005ak), increases the total flux of parent plus 

daughters up to a factor of 12, compared to using the lower mean Kd for uranium. 

The impact of this excess activity has only a limited effect on performance. In this example, the 

calculated dose from the increase of these radionuclides is on the order of 0.1 mSv [10 mrem], 

as estimated by scaling the calculated dose by the relative increase in fluxes of daughter 
12-17 

radionuclides. To arrive at this estimate, NRC staff followed a method for determining the effect 

of increased flux on dose similar to that used by DOE (2009de). This method begins with the 

DOE TSPA results for the contributions to dose by individual radionuclides, shown in SAR 

Figure 2.4-20b. As shown in this figure, DOE calculated the total mean annual dose, at 1 million 

years after repository closure, to be approximately 0.02 mSv [2 mrem]. The principal 

contributing radionuclides to this dose are Pu-242 {~0.006 mSv/yr [0.6 mrem/yr]}, Np-237 

{~0.004 mSv/yr [0.4 mrem/yr]}, and I-129 {~0.002 mSv/yr [0.2 mrem/yr]}. Daughter 

radionuclides of the uranium decay series account for most of the remaining calculated annual 

dose. Increasing this remaining dose component by the increased flux of uranium daughter 

products (in this example, by a factor of ~12) gives an estimated total mean annual dose on the 

order of 0.1 mSv [10 mrem]. Note that this dose estimate is based on a single value for uranium 

sorption and is not weighted by the probability of occurrence. 

Similar NRC analyses to determine the impact for possibly higher sorption of Np-237 suggest 

that an increased Kd could result in relatively lesser increases in total concentrations of parent 

plus daughters. Considering the relative contribution to total flux of these radionuclides, the 

NRC staff notes that biasing Kds of the parents has only a limited impact on the total calculated 

total annual flux. 

The NRC staff notes that in DOE’s analysis, the pipe elements representing the alluvium were 

between 6.5 and 8.5 km [4.04 and 5.28 mi] long. For each realization, a single Kd

for each radionuclide is assigned to the full extent of the alluvium. The NRC staff analyzed 

the effects of pipe length, and spatial heterogeneity of sorption on radionuclide release of 

parents and daughters to determine the sensitivity of DOE’s approach to these parameters. 

The NRC staff analyses suggest that spatial heterogeneity does affect radionuclide 

concentrations for the post-10,000-year period, but the magnitude of this impact is not 

significant to performance, unless specific local geochemical conditions exist where sorption 

behavior is well outside the nominal ranges. The NRC staff evaluation of potential effects of 

local differences in sorption properties on specific decay chain radionuclides, and the likelihood 

of extreme values, is in TER Section 2.2.1.3.9.2.5. 

In summary, DOE used experimental techniques with site-specific materials, alternative 

computer models, field tests, and natural analogs to provide a technical basis to support the 

TSPA model abstraction of radionuclide sorption. DOE used site-relevant data to address the 

anticipated effects of pH, Eh, major ion water chemistry, rock composition, rock surface area, 

and radionuclide concentration on radionuclide sorption. DOE acknowledged the limitations of 

the Kd approach and used stochastically sampled Kd probability distributions and simplifying 

assumptions about the effectiveness of sorption to address model and data uncertainty. DOE 

considered appropriate geochemical and physical conditions in developing the Kd distributions. 

DOE described the method used to assess the sensitivity of radioelement sorption behavior to 

variability in geochemical and physical conditions. DOE used these rankings to correlate 

sorption characteristics among the radioelements, ensuring consistency among the sorption 

parameters for each model realization. However, DOE described that the batch sorption tests 

provide results that underestimate the sorption of radionuclides expected in the field, compared 

to results from their desorption experiments, as described in BSC Appendix J (2005ak). DOE 

used distributions of Kd values in TSPA that overlap the batch sorption experiments results, but 

biased the distributions to lower values. As discussed previously, this method of biasing Kd

distributions from sorption batch tests can underestimate concentrations of some daughters for 

disequilibrium in certain decay chains (DOE, 2009de). This leads to greater uncertainty in the 

calculated concentrations of the daughter radionuclides in groundwater. As previously 

discussed, the NRC staff analyses confirm DOE’s assumption that the impact of these potential 
12-18 

greater uncertainties is not significant for performance. The NRC staff, therefore, notes that 

while the DOE treatment of sorption might not be conservative for the million-year performance 

period, it is reasonable. 

2.2.1.3.9.3.2.3 Matrix Diffusion 

Diffusion is a physical process in which dissolved radionuclides move from a region of high 

concentration to a region of low concentration without advective flow. DOE described matrix 

diffusion as a fracture–matrix interaction that uses diffusion to transfer radionuclides between 

the water in fractures and the water in the rock matrix. DOE identified matrix diffusion as a 

moderately important transport mechanism in the saturated zone transport abstraction, 

especially for strongly sorbing radionuclides, because it is the main process by which 

radionuclides can move from a fracture-dominated flow path into the matrix. The modeled 

effectiveness of matrix diffusion depends on (i) the matrix diffusion rate (i.e., the rate that a 

radionuclide can diffuse from the water in a fracture into water in the pore spaces of the rock 

matrix) and (ii) the area of the fracture–matrix interface across which diffusion occurs, as 

outlined in SNL Section 6.1.2.4 (2007bj). In turn, the matrix diffusion rate depends on the 

concentration gradient of the radionuclide between fracture and matrix, and the value of the 

effective matrix diffusion coefficient, which is a measure of how readily a particular radioelement 

diffuses through a tortuous pathway of interconnected pores in the rock matrix. DOE estimated 

tortuosities from empirical data for representative Yucca Mountain tuff samples and developed 

standard normal cumulative probability distributions for effective matrix diffusion coefficients that 

were sampled stochastically in TSPA for each radioelement with respect to the individual model 

units (SAR Section 2.3.9.3.2; Reimus, et al., 2007aa). 

In contrast to fractures in the unsaturated zone, where not all connected fractures in 

unsaturated rocks are water-bearing at the same time, all fractures in the saturated zone are, by 

definition, water bearing. However, as described in TER Section 2.2.1.3.9.3.2.1, not all 

fractures of the saturated zone aquifer participate in the flowing system. The flowing interval 

spacing reduces the number of fractures contributing to flow. This is a site characteristic 

measured in the field. Drill core logging yields spacing between fractures. Downhole spinner 

tests at packed locations yield spacing of flowing intervals. In the Yucca Mountain vicinity, the 

flowing interval spacing is greater than that of the fractures. 

 

The NRC staff’s evaluation of the information DOE provided about matrix diffusion in SAR 

Section 2.3.9.3.2.1 and relevant references considers staff’s independent understanding of 

matrix diffusion models, the hydrogeologic characteristics of the saturated zone at the Yucca 

Mountain site, and field and laboratory studies of fracture–matrix interactions in saturated 

fractured rocks at Yucca Mountain and elsewhere. The NRC staff notes that DOE adopted an 

established theoretical approach to estimate radionuclide-specific effective matrix diffusion 

coefficients and, in developing the parameter values for matrix diffusion coefficients, DOE 

appropriately (i) synthesized Yucca Mountain geological and hydrological data, (ii) adapted the 

estimated values for saturated conditions, and (iii) accounted for uncertainty and natural 

variability in diffusion characteristics of different rock units. The NRC staff notes that DOE 

included sufficient data and addressed data uncertainty in developing effective matrix diffusion 

coefficients for the saturated zone transport abstraction. 

DOE accounted for saturated zone transport-related model uncertainties by sampling values for 

the flowing interval spacing, the fracture porosity, and the effective diffusion coefficient in 

volcanic units. DOE’s field experiments at the C-Wells Complex included cross hole tests 

where tracers with distinct diffusion coefficients were simultaneously injected into one well and 
12-19 

the breakthrough curves of the different tracers were measured in a pumped well 30 m [32.8 yd] 

from the first. The differences in the breakthrough curves for the various tracers were used to 

demonstrate matrix diffusion was affecting tracer migration. The NRC staff notes that these 

field experiments provide confirmation that matrix diffusion is a process of attenuation in the 

volcanic rocks of the saturated zone because the experiments were designed to identify the 

process, if present. 

The NRC staff compared DOE’s large-scale tracer tests with published results from other 

saturated zone field experiments, modeling studies, and natural analogs at Yucca Mountain and 

elsewhere. In the context of data and model uncertainty about matrix diffusion as a saturated 

zone transport process, the NRC staff notes that DOE’s performance assessment results 

illustrate that the retardation effect of matrix diffusion in the performance assessment is limited 

primarily to moderately and strongly sorbing radionuclides. The TSPA results with and without 

matrix diffusion in the saturated zone are comparable. Consequently, consistent with a 

risk-informed, performance-based technical evaluation, the NRC staff notes that DOE has 

provided a technical basis for DOE’s representation of matrix diffusion in the saturated zone 

transport abstraction, consistent with YMRP guidance. 

2.2.1.3.9.3.2.4 Colloid-Associated Transport 

As described in TER Section 2.2.1.3.7.3.2.4, colloids are minute solid particles of any origin 

or composition that become suspended in a liquid. Because colloids are mobile in water, 

a radionuclide that is attached to a colloid (e.g., by sorption to the colloid surface) will be 

transported by the processes that move the colloid instead of by processes that would otherwise 

delay transport of the radionuclide as a dissolved species. Moreover, radionuclides attached to 

colloids tend to be transported preferentially in fast flow zones such as fractures or large pore 

throats because the size of colloids, compared to dissolved species, inhibits the transfer of 

colloids into fine-grained matrix, as described in SNL Section 6.8.2 (2008an). 

DOE modeled colloidal transport in the saturated zone consistent with modeling used elsewhere 

in TSPA, with two types of radionuclide attachment: reversible and irreversible (BSC, 2005ak). 

Colloids with irreversibly attached radionuclides were modeled as separate transported entities, 

with a retardation factor applied specifically to the fractured volcanic tuff and alluvial aquifers to 

simulate the effects of nonpermanent filtration (BSC, 2005ak); DOE assumed that the size of 

irreversible colloids could exceed that of the pores of the volcanic matrix. Consequently, 

matrix diffusion of irreversible colloids in the saturated zone was neglected in the transport 

abstraction (BSC, 2005ak). Plutonium and americium were modeled as associated with both 

irreversible colloids and reversible colloids and as dissolved species in the saturated zone 

transport model, consistent with their transport mode in the unsaturated zone model. 

Reversible colloidal transport was modeled using the Kc factor, which represented equilibrium 

sorption of aqueous radionuclides onto natural system colloids (BSC, 2005ak). Radionuclides 

associated with reversible colloid transport comprised 4 of the 12 radionuclide groups modeled 

in the saturated zone flow and transport abstraction (BSC, 2005ak). These four groups included 

(i) plutonium, (ii) cesium, (iii) tin, and (iv) americium, protactinium, and thorium (BSC, 2005ak). 

Application of the Kc factor and inclusion of reversible sorption to colloids lowered the effective 

diffusion coefficient and the sorption coefficient, Kd, for the radionuclides (BSC, 2005ak), 

enhancing advective transport. The NRC staff notes that this approach is reasonable because 

cross hole field tests using microspheres show decreased retardation relative to reactive 

constituents. The NRC staff notes that the radioelements chosen for colloidal-facilitated 

transport are consistent with YMRP guidance because these radioelements are most strongly 
12-20 

sorbed and, given low colloid concentrations, those radioelements that are the most strongly 

sorbed to the colloids contribute the most to release. 

DOE included colloid-associated transport in both the three-dimensional saturated zone flow 

and transport abstraction and the one-dimensional saturated zone transport model (BSC, 

2005ak). In the TSPA model abstraction, radionuclide mass exiting the unsaturated zone was 

partitioned into solution and onto colloids for transport in the saturated zone, as outlined in SNL 

Section 6.1.4.9 (2008ag). Irreversible colloids leaving the unsaturated zone were passed to the 

saturated zone transport abstraction as a single, irreversible colloid flux. For saturated zone 

transport calculations, the irreversible colloid flux was divided into a “slow” irreversible colloid 

fraction that is subject to modeled retardation processes during transport and a much smaller 

“fast” irreversible colloid fraction that was assumed to travel unretarded throughout the 

saturated zone. Colloid-associated transport of radionuclides is affected by filtration, the 

rate of desorption from the colloid, and the colloid concentrations in the groundwater (SAR 

Section 2.3.9.3). Each of these factors was included in the saturated zone colloid-associated 

transport model. 

The DOE colloid-associated transport model treats radioactive decay in irreversible colloids by 

assuming that if a decay product was also one of the two radioelements associated with an 

irreversible colloid in the model (i.e., isotopes of plutonium and americium), then the decay 

product remained irreversibly associated with the colloid (SAR Section 2.3.9.3.2.3). Otherwise, 

the decay product enters the aqueous phase as a dissolved species (SAR Section 2.3.9.3.2.3). 

In the model abstraction there was no permanent filtration of irreversible colloids due to size 

exclusion in the tuff matrix, at the transition from tuff to alluvium, or in the alluvium, so no colloid 

size parameter was required in the saturated zone transport models (SAR Section 2.3.9.3.2.3). 

The nonpermanent filtration of irreversible colloids was implicitly included as part of the basis 

and development of the irreversible colloid retardation factor for both the tuff and the alluvium 

(SAR Section 2.3.9.3.2.3; BSC, 2005ak, 2004bc). These approaches to simulating colloid 

transport used consistent assumptions for parent and daughter. The DOE model that 

does not consider permanent filtration of irreversible colloids allows for larger releases of 

colloid-associated radionuclides at the expense of accumulated masses of colloid-associated 

radionuclides in the saturated zone (see TER Section 2.2.1.3.9.3.2.5 concerning ingrowth). 

 

DOE’s conceptual model assumed that reversible colloids could be represented by particles 

with the composition and characteristics of the clay mineral montmorillonite. Although naturally 

occurring colloids in Yucca Mountain groundwaters consist of montmorillonite, zeolite, and 

silica, the NRC staff notes the use of montmorillonite is reasonable, as the specific mineral is 

less significant than the assigned sorption coefficients. DOE addressed data uncertainty for 

sorption onto reversible colloids by selecting a reasonable range of montmorillonite sorption 

coefficients, which captures that of other colloid minerals. 

DOE developed the uncertainty distributions for the concentration of groundwater colloids from 

data collected in saturated zone field studies from the Yucca Mountain region and from 

groundwater analyses elsewhere (BSC, 2005ak; SNL, 2007aw,bi). The colloid concentrations 

represented in the model covered a broad range of values that account for higher colloid 

concentrations measured in some groundwaters, with these higher concentrations given a low 

probability of occurrence. Colloids were assumed to be stable for all water chemistry conditions 

in the saturated zone. The NRC staff notes that the assumptions for colloid concentrations and 

stability in the saturated zone are consistent with groundwater analyses observations for the 

Yucca Mountain region. 
12-21 

The only radioelements irreversibly associated with colloids in DOE’s model are plutonium and 

americium. After the general corrosion failure of waste containers in TSPA simulations, up to 

30 percent of the Pu-242 flux transported to the accessible environment is by irreversible 

colloids (e.g., SAR Section 2.4.2.2.3.2.2 and Figure 2.4-108). On the basis of data from field 

experiments that some colloids travel with little or no retardation (Kersting, et al., 1999aa; SNL, 

2007aw), DOE designated a small fraction (less than 0.2 percent) of the irreversible colloid flux 

as a completely unretarded “fast fraction.” 

In reviewing DOE’s technical basis for colloid-associated transport in the saturated zone, the 

NRC staff evaluated information DOE provided in SAR Section 2.3.9 and references therein 

(BSC, 2004bc; SNL, 2008ag,an, 2007bi). The NRC staff also considered additional information 

in DOE Enclosures 9–14 (2009am) and the NRC staff’s independent experience with 

colloid-associated transport processes and models in heterogeneous natural systems, such as 

the saturated zone at Yucca Mountain. 

On the basis of the information DOE provided in SAR Section 2.3.9, the NRC staff notes that 

DOE provided a technical basis for the representation of radionuclide transport by reversible 

colloids. DOE accounted for system variability in developing parameter values, where feasible, 

from site-specific data from saturated zone field tests in the Yucca Mountain area and sampling 

colloid-associated parameter values from large uncertainty distributions. The NRC staff notes 

that DOE addressed model uncertainty because the results are consistent with the NRC staff’s 

understanding of colloid-associated transport processes and the uncertainties involved in 

characterizing colloidal transport processes in natural systems. DOE’s approach used 

reasonable distributions of parameter values, simple model abstractions supported by field and 

lab tests, and analyses of natural analogs and underground nuclear tests. 

With respect to DOE’s representation of radionuclide transport by irreversible colloids in the 

model, the NRC staff notes that DOE’s model is reasonable because it includes only processes 

that have been demonstrated to be present in field tests and lab experiments. DOE’s treatment 

of colloid-associated transport is consistent with its model for partitioning of the radioelements 

among the three transport entities (dissolved species, reversibly associated with colloid, and 

irreversibly associated with colloid), which is evaluated in TER Section 2.2.1.3.4. 

In summary, DOE has developed a conceptual and mathematical basis for colloid-associated 

transport processes in the saturated zone (e.g., retardation of colloids by attachment processes 

in fractured volcanic tuff and alluvium, reversible sorption of radionuclides onto colloids, colloid 

exclusion processes, and unretarded colloidal transport) that is consistent with existing models 

for contaminant transport in fractured rocks and porous media in the literature (e.g., Sudicky and 

Frind, 1982aa). DOE’s modeling approach compensated for the high uncertainty in empirical 

observations for saturated zone colloidal transport in field studies or natural analogs by using 

reasonable probability distributions for most colloid-related parameters. 

2.2.1.3.9.3.2.5 Radionuclide Decay and Ingrowth 

Radioactive decay is a general term for the processes by which unstable radionuclides 

spontaneously disintegrate to form a different nuclide that may or may not be radioactive. 

Loss of radionuclides over time due to radioactive decay and, where applicable, the potential 

ingrowth (increase) of radionuclide daughters were included in the DOE saturated zone 

transport abstraction. Several heavy radionuclides are parents to decay chains of multiple 

radioactive daughters. In the absence of chemical fractionation, the radionuclides in the chain 

reach secular equilibrium, where parents and daughters have equal activity. Disequilibrium of 
12-22 

naturally occurring decay chains is observed in many groundwater systems, due to geochemical 

processes in the aquifers (e.g., Faure, 1986aa). 

Unlike its unsaturated zone transport abstraction, DOE did not directly calculate radioactive 

decay and ingrowth processes in its three-dimensional site-scale saturated zone transport 

model. Instead, decay was included in the convolution integral model during the calculation of 

mass breakthrough. Moreover, the three-dimensional saturated zone transport model as 

formulated did not explicitly include the ingrowth of decay progeny. The mass releases of the 

radionuclides C-14, Cs-135, Cs-137, I-129, Sr-90, Tc-99, Am-243, Pu-239, Am-241, Pu-240, 

Pu-242, Pu-238, Cl-36, Se-79, Sn-126, Np-237, U-234, U-232, U-236, and U-238, were 

determined from the results of the three-dimensional transport model. DOE used two 

mechanisms to account for daughter ingrowth. 

First, at the transition from the unsaturated zone to the saturated zone, the masses of 

first-generation daughters, Np-237, U-234, U-236, U-238, and Pu-239, were boosted by the 

amount their parents were expected to decay during the remainder of the simulated 

performance time period, as outlined in SNL Section 6.3.10.3 (2008ag). The parent and 

boosted daughter pairs are Am-241/Np-237, U-238/U-234, Pu-238/U-234, Pu-240/U-236, 

Pu-242/U-238, and Am-243/Pu-239. 

The NRC staff notes that this method can underestimate or overestimate the amount of 

daughter in the saturated zone, depending on the transport characteristics of the parent and 

daughter. At early times in the saturated zone transport model, the daughter concentrations 

could be overestimated, because DOE’s inventory-boosting methodology assumes all of the 

ingrowth occurs when the parent radionuclide enters the saturated zone. At later times, the 

daughter concentrations may be underestimated, because a quickly transported (low Kd) 

daughter may exit the model system before the parent would have actually generated the 

daughter via decay. The NRC staff notes that one-dimensional transport simulations using the 

pipe elements of the GoldSim (GoldSim Technology Group, 2006aa) computer code are 

reasonable because they adequately represent the important processes controlling radionuclide 

transport and concentrations. The NRC staff compared one-dimensional transport simulations 

of the parents and the first-generation daughters with radioactive decay and ingrowth to 

one-dimensional transport simulations with decay and boosted daughters instead of ingrowth 

(Bradbury, 2010ab). The NRC staff notes that the inventory-boosting method is reasonable 

because the results from the two types of simulation were comparable at later times when 

steady state was established. Prior to steady state, the inventory-boosting method 

overestimated the mass of the daughter in the saturated zone. 

DOE used its one-dimensional saturated zone transport model to calculate the ingrowth of 

second-generation daughter radionuclides in selected decay chains (SAR Section 2.3.9.3.4.2; 

BSC, 2005ak). The mass of secondary daughters the one-dimensional model transported 

through the saturated zone was added to the mass of radionuclides the three-dimensional 

model transported to determine the total mass of radionuclides transported through the 

saturated zone. The mass releases of the radionuclides U-235, U-233, Th-230, Pa-231, 

Th-229, Th-232, and Ra-226 were determined from the results of the one-dimensional transport 

model. The DOE model assumed that Ac-227, Ra-228, Th-228, and Pb-210 were in secular 

equilibrium with their dissolved parents. DOE’s assumptions of secular equilibrium in 

groundwater are unsupported for parents and daughters that have substantially different 

sorption properties. 
12-23 

This effect is further analyzed in DOE (2009de). DOE’s analysis showed that the Kd of the 

parent divided by the Kd of the daughter times the activity of the parent in groundwater can be 

used to determine the activity of the daughter in groundwater. In DOE (2009de), DOE 

calculated that the activity of Ra-228 in groundwater is on average 14 times greater than that of 

its parent Th-232, and the activity of Ac-227 in groundwater is on average 6.8 times greater than 

that of its parent Pa-231. The activity of Pb-210 in groundwater is approximately the same as 

its distant parent, Ra-226, due to their comparable Kd distributions. An extreme example 

showing the effect of Kds on activities of parent and daughter is the Ra-226/Rn-222 pair. 

Radon, an inert gas, is assumed not to sorb, whereas radium strongly sorbs, as indicated by its 

uniform Kd distribution from 100 to 1,000 mL/g. DOE calculated the activity of Rn-222 to be, on 

average, 1,400 times that of Ra-226 in groundwater. DOE (2009de) showed that these 

corrections to activity of these daughters were not enough to significantly increase dose {an 

increase from 0.02 to 0.024 mSv [2.0 to 2.4 mrem] for the maximum total mean annual dose}. 

In addition to the pairs DOE (2009de) considered, other parent-daughter pairs could have 

significantly different activities than those assumed in the TSPA model, depending on the 

differences in effective Kds. For example, the NRC staff notes that, given reasonable Kds for 

the two elements, the mean activity ratio of thorium and radium would be on the order of 14 

for all the thorium/radium pairs, not just Th-232/Ra-228 (other pairs include Th-230/Ra-226, 

Th-228/Ra-224, Th-227/Ra-223, and Th-229/Ra-225). The NRC staff notes that the GoldSim 

(GoldSim Technology Group, 2006aa) pipe element correctly simulates sorption and transport 

of radioactive species. Th-230 and Ra-226 are transported in the saturated zone abstraction 

using the pipe element, so their activity ratio should correctly reflect the sorption enhancement 

factor. The other thorium/radium pairs are not explicitly modeled, so the same sorption 

enhancement factor corrections to the activity ratio are appropriate. The NRC staff notes that 

some daughter activities are less than those of the parent and some are more, depending on 

the Kds of the parent and daughter. 

The groundwater exposure scenario in the biosphere model assumes that short-lived daughters 

(half-life less than 180 days) are in secular equilibrium with their parent primary radionuclide. 

The biosphere model calculates biosphere dose conversion factors when the receptor uses 

water containing a nominal activity of the primary radionuclide. That water also is assumed to 

contain the same activity of each of the short-lived daughters. However, on the basis of DOE 

(2009de), when directed to the short-lived daughters, the same enhancement factor should 

apply. For example, given the difference in Kds for thorium and radium, a receptor, on average, 

would be exposed to 14 times more Ra-225 activity than Th-229 (the activities would be the 

same under the assumption of secular equilibrium). Evaluations of biosphere pathways are 

described in TER Section 2.2.1.3.14. 

An important aspect of the contribution of short-lived daughters to dose is that, in general, the 

calculated dose for a given activity decreases down the decay chain. Consequently, increasing 

activities of daughters when considering the sorption enhancement factor are in many cases 

compensated for by the decreased dose coefficient. Evaluations of biosphere dose coefficients 

are described in TER Section 2.2.1.3.14. 

To confirm DOE’s assumption that deviations from secular equilibrium would not significantly 

affect TSPA results, the NRC staff has performed analyses (Bradbury, 2010aa) using the pipe 

elements of the GoldSim (GoldSim Technology Group, 2006aa) code to simulate transport of 

decay chain radionuclides including those short-lived radionuclides not included in DOE’s 

model. The NRC staff notes that although the concentrations and activities of some 

radionuclides may be greater than described in the SAR, the resultant calculated dose for all 

radionuclides is low. 
12-24 

A source of uncertainty is the potential contribution to dose from Po-210, a daughter of Pb-210 

in the U-238 decay chain. There are some observations to indicate that Po-210 may not be 

removed by sorption from natural waters as readily as its immediate parent, Pb-210 (Serne, 

2007aa; Hameed, et al., 1997aa). At two locations in the United States, Po-210 has been 

measured in groundwater at concentrations greater than would be expected based on 

measured Pb-210 concentrations. These locations are an alluvial aquifer in Churchill County, 

Nevada (Seiler, 2011aa; Seiler, et al., 2009aa) and shallow aquifers of west central Florida 

(Harada, et al., 1989aa). One explanation of these Po-210 excesses is that the specific 

geochemical conditions present in these locations leads to differential sorption of parent and 

daughter radionuclides. For this decay chain, NRC staff analyses show that the difference in 

sorption of the much longer-lived parents at the head of the decay chain (U-238, U-234) relative 

to Po-210 is more significant than the relative sorption of the immediate parent (Pb-210) 

(Bradbury, 2010aa,ab). 

For further confirmation of DOE’s assumption that deviations from secular equilibrium would not 

significantly affect performance, the NRC staff reviewed scientific literature to understand the 

sorption behavior of Po-210 in natural systems under different conditions. Bradbury and 

Bayens (2003aa) used selenium as an analog for polonium to estimate a median Kd of 

180 mL/g under reducing conditions (low oxygen) and neutral pH (~7). Experiments by Baston, 

et al. (1999aa) also conducted under reducing conditions, but at higher pH (~9.4), produced 

polonium Kds on the order of 1,500 to 36,500 mL/g. Hameed, et al. (1997aa) derived a 

polonium Kd of 43,000 mL/g for sediment in an organic-rich pond. On the basis of a review of 

literature, Serne (2007aa) suggested a polonium Kd range of 150 to 1,100 mL/g for surface soils 

near the Hanford site. There is a relative lack of recent data for Po-210 sorption under oxidizing 

conditions, but the low concentrations of polonium typically measured in oxidizing groundwaters 

are consistent with high Kds. There are indications that in some settings, polonium in 

groundwater is associated with colloids or other filterable particles (Serne, 2007aa; Vaaramaa, 

et al., 2003aa). 

Although the behavior of polonium in groundwater is not fully understood, there are several 

geochemical parameters that appear common to locations where significant excess Po-210.has 

been recognized. On the basis of the NRC staff review, conditions associated with excess 

polonium in groundwater include microbial-mediated sulfur cycling (Nevada and Florida), very 

low oxygen concentrations (Nevada and Florida), alkaline (pH 9 or above; Nevada) or acidic 

(pH below 6; Florida) conditions, moderate to high organic carbon content (Nevada and Florida), 

detectable sulfide concentrations (Nevada and Florida), and iron or manganese colloids 

(Nevada and Florida) (Harada, et al., 1989aa; Burnett, et al., 1991aa; Upchurch, et al., 1991aa; 

Seiler, et al., 2009aa; Seiler, 2011aa). In the Yucca Mountain saturated zone system, 

groundwater near the boundary with the accessible environment is characterized by oxidizing 

waters of neutral pH (6.8 to 8.5 with a median of 7.85), with low organic content, no measurable 

sulfide, and little or no iron oxide or manganese oxide colloids. Conditions that would appear to 

favor excess Po-210 have not been observed along the potential flow path in the saturated zone 

alluvium south of the controlled boundary. Consequently, the NRC staff notes that excess 

Po-210, beyond the secular equilibrium values assumed by DOE, is unlikely to occur in the 

saturated zone such that it significantly impacts repository performance. To confirm DOE’s 

approach regarding the likelihood of excess Po-210 occurring in the saturated zone, DOE 

should include in its performance confirmation program the following: 

 Data that support the absence of the set of geochemical conditions described previously 

as associated with excess polonium in other locations, by analysis of existing water wells 
12-25 

within Fortymile Wash and the adjacent alluvial basin, considering potential flow paths of 

radionuclide transport into the accessible environment 

 Continued monitoring of those same wells for the set of geochemical conditions 

associated with excess polonium in other locations 

 Experimental determination of polonium sorption on representative alluvial material or 

proxies in order to constrain appropriate polonium Kd values, under a range of relevant 

geochemical conditions 

Given current understanding of the behavior of decay chain radionuclides in the saturated zone 

transport path at Yucca Mountain, the NRC staff notes that the DOE approach to radionuclide 

decay and ingrowth is reasonable because the uncertainties in radionuclide concentrations from 

potential accumulation of decay-chain parent radionuclides are not significant for performance 

of the saturated zone. DOE should confirm its approach for decay chain radionuclide behavior 

by providing through its performance confirmation program information to reduce uncertainty 

related to the likelihood of excess Po-210 occurring in the saturated zone. 

Radioactive decay and ingrowth processes were modeled for dissolved, reversible colloids 

and irreversible colloid radionuclide species all of the types included in the saturated zone 

transport abstraction. The NRC staff notes that the inventory-boosting methodology accounts 

for decay from all parent sources in the saturated zone transport model. The NRC staff notes 

that DOE’s treatment of decay chains in irreversible colloids is consistent with DOE’s model 

assumptions about which radionuclides are associated with reversible and irreversible colloids. 

2.2.1.3.9.4 NRC Staff Conclusions 

NRC staff notes that the DOE description of this model abstraction for radionuclide transport in 

the saturated zone is consistent with the guidance in the YMRP. NRC staff also notes that the 

DOE technical approach discussed in this chapter is reasonable for use in the Total System 

Performance Assessment (TSPA). DOE should confirm its approach for decay chain 

radionuclide behavior by providing through its performance confirmation program information to 

reduce uncertainty related to the likelihood of excess Po-210 occurring in the saturated zone, as 

identified in TER Section 2.2.1.3.9.3. 

2.2.1.3.9.5 References 

Baston, G.M.N., J.A. Berry, M. Brownsword, T.G. Heath, D.J. Ilett, R. McCrohon, C.J. Tweed, 

and M. Yui. 1999aa. “The Sorption of Polonium, Actinium, and Protactinium Onto Geological 

Materials.” Materials Research Society Symposium Proceedings 556. Warrendale, 

Pennsylvania: Materials Research Society. pp. 1,107–1,122. 

Bradbury, J.B. 2010aa. “Secular Equilibrium Assumption Analysis.” Scientific Notebook. 

ML110820653. Washington, DC: U.S. Nuclear Regulatory Commission. 

Bradbury, J.B. 2010ab. “Boosted Inventory Assumption Analysis.” Scientific Notebook. 

ML110820655. Washington, DC: U.S. Nuclear Regulatory Commission. 

Bradbury, M.H. and B. Bayens. 2003aa. “Far Field Sorption Data Bases for Performance 

Assessment of a High-Level Radioactive Waste Repository in an Undisturbed Opalinus Clay 
12-26 

Host Rock.” Bericht Nr. 03-08, Nagra NTB 02-19. Villigen, Switzerland: Paul Scherrer Institut. 

Burnett, W.C., J.B. Cowars, W.-C. Tai, and P.H. Cable. 1991aa. “Investigations of Radon and 

Radon Daughters in Surficial Aquifers of Florida.” No. 05-032-093. Bartow, Florida: Florida 

Institute of Phosphate Research. 

BSC. 2005ak. “Saturated Zone Flow and Transport Model Abstraction.” 

MDL–NBS–HS–000021. Rev. 03. AD 01, AD 02, ERD 01. Las Vegas, Nevada: Bechtel 

SAIC Company, LLC. 

BSC. 2004bc. “Saturated Zone Colloid Transport.” ANL–NBS–HS–000031. Rev. 02. 

ACN 01, ERD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

Davis, J.A. and G.P. Curtis. 2003aa. NUREG/CR–6820, “Application of Surface Complexation 

Modeling To Describe Uranium (VI) Adsorption and Retardation at the Uranium Mill Tailings Site 

at Naturita, Colorado.: Washington, DC: NRC. 

Davis, J.A. and D.B. Kent. 1990aa. “Surface Complexation Modeling in Aqueous 

Geochemistry.” Mineral-Water Interface Geochemistry. M.F. Hochella, Jr. and A.F. White, eds. 

Mineralogy. Vol. 23. pp. 177–260. 

Davis, J.A., J.A. Coston, D.B. Kent, and C.C. Fuller. 1998aa. “Application of the 

Surface Complexation Concept to Complex Mineral Assemblages.” Environmental 

Science & Technology. Vol. 32. pp. 2,820–2,828. 

DOE. 2009am. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.8), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.7, Set 1.” Letter (February 9) J.R. Williams to J.H. Sulima (NRC). 

ML090410352. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009de. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.9), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.9, Set 1.” Letter (November 16) J.R. Williams to J.H. Sulima (NRC). 

ML092820675. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009df. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.9), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.9, Set 1.” Letter (October 9) J.R. Williams to J.H. Sulima (NRC). 

ML093210213. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

Faure, G. 1986aa. Principles of Isotope Geology, 2nd Edition. New York City, New York: 

John C. Wiley & Sons, Inc. 

Freeze, R.A. and J.A. Cherry. 1979aa. Groundwater. Englewood Cliffs, New Jersey: 

Prentice-Hall, Inc. 

GoldSim Technology Group. 2006aa. GoldSim Contaminant Transport Module User’s Guide. 

Version 4.0. Issaquah, Washington: GoldSim Technology Group. 
12-27 

Hameed, P.S., K. Shaheed, and S.S.N. Somasundaram. 1997aa. “A Study on Distribution of 

Natural Radionuclide Polonium-210 in a Pond Ecosystem.” Journal of Biosciences. Vol. 22, 

No. 5. pp. 627–634. 

Harada, K. W.C. Burnett, J.B. Cowart, and P.A. LaRock. 1989aa. “Polonium in Florida 

Groundwater and Its Possible Relationship to the Sulfur Cycle and Bacteria.” Geochimica et 

Cosmochimica Acta. Vol. 53, No. 1. pp. 143–150. 

Kersting, A.B., D.W. Efurd, D.L. Finnegan, D.J. Rokop, D.K. Smith, and J.L. Thompson. 

1999aa. “Migration of Plutonium in Ground Water at the Nevada Test Site.” Nature. Vol. 397, 

No. 6714. pp. 56–59. 

Langmuir, D. 1997aa. Aqueous Environmental Geochemistry. Upper Saddle River, 

New Jersey: Prentice-Hall, Inc. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

Reimus, P.W., T.J. Callahan, S.D. Ware, M.J. Haga, and D.A. Counce. 2007aa. “Matrix 

Diffusion Coefficients in Volcanic Rocks at the Nevada Test Site: Influence of Matrix Porosity, 

Matrix Permeability, and Fracture Coating Minerals.” Journal of Contaminant Hydrology. 

Vol. 93. pp. 85–95. 

Seiler, R.L. 2011aa. “210Po in Nevada Groundwater and Its Relation to Gross Alpha 

Radioactivity.” Ground Water. Vol. 29, No. 2. pp. 160–171. 

Seiler, R.L., L.L. Stillings, and N. Cutler. 2009aa. “Mobilization of Polonium-210 in NaturallyContaminated Groundwater, Churchill County, Nevada.” Presentation at American Geophysical 

Union 2009 Fall Meeting, San Francisco, California, December 14–18, 2009. Washington, DC: 

American Geophysical Union. 

Serne R.J. 2007aa. “Kd Values for Agricultural and Surface Soils for Use in Hanford Site Farm, 

Residential, and River Shorelines Scenarios.” PNNL–16351. Richland, Washington: Pacific 

Northwest National Laboratory. 

Sheppard, M.I. and D.H. Thibault. 1990aa. “Default Soil Solid/Liquid Partition Coefficients, Kds, 

Four Major Soil Types: A Compendium.” Health Physics. Vol. 59. pp. 471–482. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008an. “Particle Tracking Model and Abstraction of Transport Processes.” 

MDL–NBS–HS–000020. Rev. 02. ADD 02. Las Vegas Nevada: Sandia National Laboratories. 
12-28 

SNL. 2007ah. “Dissolved Concentration Limits of Elements with Radioactive Isotopes.” 

ANL–WIS–MD–000010. Rev. 06. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007aw. “Saturated Zone In-Situ Testing.” ANL–NBS–HS–000039. Rev. 02. ACN 01, 

ACN 02, ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ba. “Site-Scale Saturated Zone Transport.” MDL–NBS–HS–000010. Rev. 03. 

ACN 01, AD 001. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007bi. “Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: 

Abstraction and Summary.” MDL–EBS–PA–000004. Rev. 03. ERD 01. Las Vegas, Nevada: 

Sandia National Laboratories. 

SNL. 2007bj. “Radionuclide Transport Models Under Ambient Conditions (U0060).” 

MDL–NBS–HS–000008. Rev. 02. CAN 002, AD 01. Las Vegas, Nevada: Sandia 

National Laboratories. 

Sudicky, E.A. and E.O. Frind. 1982aa. “Contaminant Transport in Fractured Porous Media: 

Analytical Solutions for a System of Parallel Fractures.” Water Resources Research. Vol. 18. 

pp. 1,634–1,642. 

Till, J.E. and H.R. Meyer, eds. 1983aa. NUREG/CR–3332, “Radiological Assessment: 

A Textbook on Environmental Dose Analysis.” Washington, DC: NRC. 

Turner, D.R. and R.T. Pabalan. 1999aa. “Abstraction of Mechanistic Sorption Model Results 

for Performance Assessment Calculations at Yucca Mountain, Nevada.” Waste Management. 

Vol. 19. pp. 375–388. 

Turner, D.R., F.P. Bertetti, and R.T. Pabalan. 2002aa. “The Role of Radionuclide Sorption in 

High-Level Waste Performance Assessment: Approaches for the Abstraction of Detailed 

Models.” Geochemistry of Soil Radionuclides. P.-C. Zhang and P.V. Brady, eds. Special 

Publication 59. Madison, Wisconsin: American Society of Agronomy. pp. 211–252. 

Upchurch, S.B., C.R. Oural, D.W. Foss, and H.R. Brooker. 1991aa. “Radiochemistry of 

Uranium-Series Isotopes in Groundwater.” No. 05-022-092. Bartow, Florida: Florida Institute 

of Phosphate Research. 

Vaaramaa, K., J. Lehto, and H. Ervann. 2003aa. “Soluble and Particle-Bound 234, 238U, 226Ra, 

and 210Po in Ground Waters.” Radiochimica Acta. Vol. 91. pp. 21–27. 
13-1 

CHAPTER 13 

2.2.1.3.10 Igneous Disruption of Waste Packages 

2.2.1.3.10.1 Introduction 

This Technical Evaluation Report (TER) chapter evaluates the U.S. Department of Energy’s 

(DOE) models for the potential consequences of disruptive igneous activity at Yucca Mountain if 

basaltic magma rising through the Earth’s crust intersects and enters a repository drift or drifts 

[DOE’s igneous intrusion modeling case, Safety Analysis Report (SAR) Section 2.3.11.3 (DOE, 

2009av)] or enters a drift and later erupts to the surface through one or more conduits (DOE’s 

volcanic eruption modeling case, SAR Section 2.3.11.4). The proposed Yucca Mountain 

repository site lies in a region that has experienced sporadic volcanic events in the past few 

million years, such that DOE previously determined the probability of future igneous activity at 

the site to exceed 1 × 10!8

 per year (SAR Section 2.2.1.2.2; CRWMS M&O, 1996aa). DOE 

therefore included igneous activity as one of three scenario classes in its performance 

assessment. Because basalt is the only type of magma that has been erupted in the past 

8 million years in the Yucca Mountain region, DOE’s performance assessment considers only 

basaltic igneous activity. As discussed in TER Section 2.2.1.2.2, the probability of more silicic 

(explosive) igneous activity, of the type that produced extensive pyroclastic deposits in the area 

more than 10 million years ago, is well below 1 in 10,000 over 10,000 years; DOE screened this 

out as a potential disruptive event. 

This chapter evaluates the subsurface igneous processes DOE described (i.e., intrusion 

of magma into repository drifts, damage to waste packages and other engineered barriers, 

and formation of conduits to the surface, which involves entrainment of waste into the 

conduit and toward the surface). DOE’s models for volcanic ejection and dispersal of waste 

material into the surface environment are reviewed in TER Section 2.2.1.3.13. Together, TER 

Sections 2.2.1.3.10 and 2.2.1.3.13 evaluate DOE information and output that is used in the 

Total System Performance Assessment (TSPA) under the Igneous Scenario Class (see TER 

Sections 2.2.1.2.1, 2.2.1.2.2, and 2.2.1.4.1). 

DOE examined the consequences of igneous disruption of the repository (Igneous Scenario 

Class) using the results of TSPA calculations through the two linked modeling cases, igneous 

intrusion and volcanic eruption (intrusion always precedes eruption). DOE’s igneous intrusion

modeling case provides TSPA parameter values for the number of waste packages failed (mass 

of waste) during an intrusive event, the temperature in the invaded drifts in the period after 

intrusion, and chemical changes to groundwater that may react with the basalt filling the drifts. 

The igneous disruption of waste packages abstraction integrates with other TSPA model 

components, such as the unsaturated zone radionuclide transport abstraction, and provides 

information about the flux of radionuclides released from the waste form into water entering the 

unsaturated zone after an intrusive event (TER Section 2.2.1.3.7). Exposure to radionuclides in 

groundwater extracted by pumping is one of the principal pathways for radiological exposure to 

the reasonably maximally exposed individual (RMEI). 

In the DOE volcanic eruption modeling case, a key parameter affecting the overall dose 

calculation is the number of directly affected waste packages and thus the amount of waste 

entrained in a volcanic eruption. DOE’s model of the airborne transport and redistribution of 

radionuclides into soil includes the amount of waste erupted into the atmosphere, the amount 

deposited on the ground, and the redistribution of the waste-contaminated volcanic ash. This 
13-2 

airborne transport and redistribution model is evaluated in TER Section 2.2.1.3.13 and provides 

information for the Volcanic Ash Exposure Scenario described in DOE’s Biosphere Model (SAR 

Section 2.3.10). DOE’s estimate of the annual probability of igneous events intersecting the 

repository (1.7 × 10-8 per year; SAR Table 2.3.11-4) is reviewed in TER Section 2.2.1.2.2 and 

briefly discussed later in this chapter. For these abstractions and the TSPA, DOE calculates 

probability-weighted results for both an intrusive-only dose and a total dose (intrusive plus 

volcanic) to the RMEI, which are detailed in TER Section 2.2.1.4.1 and outlined in the Risk 

Perspectives subsection in TER Section 2.2.1.3.10.3.1. 

Igneous disruption models evaluated in this chapter are the first in a sequence of models 

that track radionuclides released from the repository to the RMEI as a result of possible 

future igneous activity. Accordingly, the model abstractions evaluated in this TER chapter 

serve as input to those reviewed in other chapters, including those that examine the effects of 

potential igneous disruption of natural and engineered barriers in the subsurface repository 

(TER Section 2.2.1.3.2). DOE recognized that igneous events potentially have large 

consequences but a low likelihood (probability) of occurring in the future (SAR Section 2.3.11.1). 

Thus, DOE provided only a qualitative description of igneous effects on engineered system 

barrier capabilities in its demonstration of multiple barriers (SAR Section 2.1.1). Nevertheless, 

basaltic igneous activity represents a disruptive event that significantly degrades most of the 

capabilities of the engineered barrier system (SAR Section 2.1.2.2.5). From review of the DOE 

information in SAR Section 2.1 relevant to the effects of igneous events on repository barrier 

capabilities, the U.S. Nuclear Regulatory Commission (NRC) staff notes that this information is 

consistent with the representation of igneous events in the performance assessment. To 

represent igneous events in the performance assessment, DOE removes the barrier capabilities 

of the waste package and drip shield and degrades the waste form, consistent with information 

provided in SAR Section 2.3.11. DOE further concluded in SAR Section 2.1.1 that igneous 

events will have limited effects on the upper and lower natural barrier systems because the 

possible igneous intrusive rock bodies have very small dimensions compared with the large 

volume of rock through which groundwater is flowing and the zone of influence around the 

intrusions is limited (SAR Section 2.1.2.3.5). NRC staff notes this is also consistent with the 

information in SAR Section 2.3.11 and discusses this aspect of DOE’s performance assessment 

in a subsection of TER Section 2.2.1.3.10.3.2. 

2.2.1.3.10.2 Evaluation Criteria

NRC staff’s review of model abstractions used in DOE’s postclosure performance assessment, 

including those considered in this chapter for igneous disruption of waste packages, is guided 

by 10 CFR 63.114 (Requirements for Performance Assessment) and 63.342 (Limits on 

Performance Assessments). The resulting DOE Total System Performance Assessment 

(TSPA) is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 
13-3 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment in 10 CFR 63.342, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of features, events, and processes is 

given in TER Chapter 2.2.1.2.1. 10 CFR 63.114(a) provides requirements for performance 

assessment for the initial 10,000 years following permanent closure. 10 CFR 63.114(b) and 

63.342 provide requirements for the performance assessment methods for the time from 

10,000 years through the period of geologic stability, defined in 10 CFR 63.302 as 1 million 

years following disposal. These sections require that through the period of geologic stability, 

with specific limitations, DOE 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

The model abstraction of igneous disruption of waste packages involves igneous activity. 

10 CFR 63.342(c)(1) provides requirements for assessing the effects of seismic and 

igneous activity on the repository performance, subject to the probability limits given in 

10 CFR 63.342(a) and (b). Specific constraints on the seismic and igneous activity analyses 

are in 10 CFR 63.342(c)(1)(i) and (ii), respectively. 

The NRC staff’s review of the SAR and supporting information follows the guidance in the 

Yucca Mountain Review Plan (YMRP), NUREG–1804, Section 2.2.1.3.10, Volcanic Disruption 

of Waste Packages, (NRC, 2003aa), as supplemented by additional guidance for the period 

beyond 10,000 years after permanent closure (NRC, 2009ab). The YMRP acceptance criteria 

for model abstractions that provide guidance for the NRC staff’s evaluation of DOE’s abstraction 

of igneous disruption of waste packages are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

The NRC staff review used a risk-informed approach and the guidance provided by the 

Yucca Mountain Review Plan (YMRP) (NRC, 2003aa), as supplemented by NRC (2009ab), 

to the extent reasonable for aspects of igneous disruption of waste packages important to 

repository performance. The NRC staff considered all five YMRP criteria in its review 

of information provided by DOE. In the context of these criteria, only those aspects of the 
13-4 

model abstraction that substantively affect the performance assessment results, as determined 

by NRC staff, are discussed in this chapter. The NRC staff’s determination is based both on 

risk information provided by DOE, and on NRC staff knowledge gained through experience and 

independent analyses. 

2.2.1.3.10.3 Technical Evaluation 

DOE’s analysis of potentially disruptive features, events, and processes (FEPs) considered 

ways that igneous activity could affect the proposed repository site. NRC staff evaluation of 

DOE’s FEP screening is given in TER Section 2.2.1.2.1. DOE included the following FEPs 

and defined the igneous scenarios for the performance assessment (SAR Table 2.3.11-1): 

1.2.04.03.0A, Igneous Intrusion into Repository; 1.2.04.04.0A, Igneous Intrusion Interacts 

with Engineered Barrier System Components; 1.2.04.06.0A, Eruptive Conduit to Surface 

Intersects Repository; and 1.2.04.04.0B, Chemical Effects of Magma and Magmatic Volatiles 

(SAR Table 2.2-5). This chapter evaluates repository performance as affected by these FEPs. 

Other included FEPs related to potential igneous activity are reviewed in TER Sections 2.2.1.3.2 

and 2.2.1.3.13. 

The NRC staff’s review is based on information presented in SAR Section 2.3.11 and relevant 

analysis and model reports (AMRs), on material in other publicly available DOE and the NRC 

reports, and on relevant information published in peer-reviewed literature. DOE also described 

and evaluated background information used to assess the likelihood and style of future igneous 

activity in the Yucca Mountain region in SAR Volume 1, General Information, and Volume 2, 

Section 1.1.2. That material is reviewed in TER Section 2.1.1.1.3.6 as part of the NRC staff’s 

evaluation of site characterization. 

2.2.1.3.10.3.1 General Approach by DOE 

Igneous activity can be solely intrusive (i.e., magma intruded into rocks below the Earth’s 

surface) or extrusive (i.e., volcanic, in which, following intrusion, magma breaks through to the 

surface and an eruption ensues). The NRC staff notes that the terms “volcanic” and “intrusive” 

have sometimes been used interchangeably in the DOE SAR and supporting documents. To 

avoid confusion, the NRC staff will refer to igneous activity that occurs beneath the Earth’s 

surface as “intrusive” and activity above surface as “extrusive” or “volcanic.” All subsurface 

igneous processes beneath a possible future active volcano that could disrupt the repository are 

considered intrusive and are reviewed in this chapter, whereas the above-surface volcanic 

processes are discussed and reviewed in TER Section 2.2.1.3.13. 

To evaluate the potential effect of future igneous activity on dose to the RMEI, DOE adopted a 

conceptual model in which rising basalt magma entering a repository drift (or drifts) could cause 

release of radionuclides via two pathways (SAR Section 2.3.11.1). During intrusive igneous 

events, magma rising toward the surface as a dike, or set of dikes, enters drifts but stays 

beneath the surface. DOE also considered the other type of small igneous intrusion, sills 

(relatively small subhorizontal igneous intrusions), but did not treat them separately in its 

analysis. One reason is that dikes must be present to feed magma into sills, and DOE showed 

that the consequences of intruding the repository by a sill would be similar to and more limited 

than by a dike. DOE also pointed to the relatively small size of sills in the Yucca Mountain 

region (Valentine and Krogh, 2006aa; Keating, et al., 2008aa) and thus did not include sills in its 

igneous disruption scenario for the repository (SAR Section 2.3.11.2.1.1). In the igneous 

intrusive scenario, DOE assumed that all drifts in the repository are intersected by the dike(s), 

magma fills all drifts, and all waste packages in the repository are damaged but remain in the 
13-5 

drifts. No waste is released directly into the accessible environment in an intrusive igneous 

event, but radionuclides are released to the accessible environment through subsequent 

groundwater transport. DOE models this transport to occur through the same pathways 

represented in the nominal, seismic and early failure scenario classes, which are evaluated in 

TER chapters on unsaturated zone flow and transport (Sections 2.2.1.3.6, 2.2.1.3.7, 2.2.1.3.8, 

and 2.2.1.3.9). During an extrusive, or volcanic, igneous event, DOE considered that magma 

continues to rise to the surface as a dike after possibly intersecting repository drifts and, on the 

basis of the behavior of basaltic eruptions in general, that surface activity along the resulting 

initial fissure would rapidly localize, or focus, to a single, or few, points of effusion (SNL, 

2007ae; SAR Section 2.3.11.4.1). A wider volcanic conduit would be expected to develop at 

that focus somewhere along the dike by excavation from the surface vent downwards. This 

conduit can potentially intersect a drift(s) or develop in the area between the drifts. Magma flow 

up a drift-intersecting conduit entrains waste from disrupted packages, thereby providing a 

direct pathway for waste material to be released to the accessible surface environment during a 

volcanic eruption. 

DOE explained that the volcanic (extrusive) part of the igneous scenario is an extension of the 

intrusive part (SAR Section 2.3.11.1) and concluded that every intrusive event that might 

intersect the repository is likely to have a conduit develop somewhere along one of the dikes, as 

described in SAR Section 2.3.11.2.1.2 and SNL Table 7-1 (2007ae). The conduit (or conduits) 

may, however, form outside the repository footprint or may not intersect a drift, and in that 

case, no waste material would be entrained into the magma that rises to feed the eruption. 

In effect, this would be equivalent to the intrusive-only case. In addition, DOE determined that 

conduits that might feed surface volcanoes may only develop along specific parts of dikes (SAR 

Section 2.3.11.4.2.1) and thus concluded that the probability of a volcanic event occurring at the 

repository is expected to be lower than the probability of an intrusive event. DOE also 

concluded that if an eruption that entrained waste material and transported it into the surface 

environment did occur at the repository, the potential doses to the RMEI location from 

radionuclides released through the intrusive and extrusive pathways would be additive. Further 

details of conduit development are evaluated in the NRC staff’s review of the volcanic eruption 

modeling case (TER Section 2.2.1.3.10.3.3). 

2.2.1.3.10.3.2 The NRC Staff’s Review of DOE Igneous Intrusion Modeling Case 

The DOE model for igneous intrusion and its effect on repository performance rely on four 

key conclusions:

 Magma rising as a dike beneath repository drifts will intersect and flow into the drifts. 

 Any dike intersection into the repository footprint floods all drifts with magma, causing 

engineered barrier system components, including all waste packages and drip shields, to 

fail while magma and waste remain in the drift. 

 Igneous intrusion does not alter the ambient hydrologic flow and transport regime 

significantly (i.e., the natural barriers above and below the drifts are not affected). 

 Subsurface conduits that develop beneath volcanoes can be represented by cylinders 

and only entrain waste within the part of the cylinder that intersects the drift. 
13-6 

Conclusions 1–3 solely concern the intrusive model case, while Conclusion 4 is also applicable 

to the volcanic model case. NRC staff’s review focuses on the risk-significant aspects of these 

conclusions. NRC staff’s overall risk perspective for these abstractions is given in the next 

subsection, and the specific technical aspects are evaluated under the subsequent subsections. 

NRC Staff Perspective on Risk 

NRC staff has assessed the risks caused by an igneous event at the proposed repository on 

the basis of DOE’s information. As stated in the Introduction section of this chapter, while the 

probability of an igneous event is low, the consequences could be potentially high. The igneous 

intrusion modeling case would constitute most of the calculated dose for the first 1,000 years 

following permanent closure of the repository, as shown in SAR Figure 2.4-18(a), and is 

approximately half the calculated dose for the seismic ground motion modeling case in the 

ensuing 9,000 years. However, for the first 10,000 years, SAR Figure 2.4-18(a) indicates that 

the mean annual dose from igneous intrusion is on the order of 0.001 mSv [0.1 mrem]. 

Moreover, in SAR Section 2.1, DOE indicated that for the post-10,000-year period, the igneous 

intrusion modeling case and seismic ground motion modeling case provide approximately 

equal contributions to the total mean annual dose to the reasonably maximally exposed 

individual (RMEI) for the last 300,000 years of the time period. SAR Figure 2.4-18(b) shows 

that the mean annual dose for each modeling case is on the order of 0.01 mSv [1 mrem] in the 

post-10,000-year period. 

In SAR Section 2.4.2.2.1.2.3, DOE provided the probability-weighted consequences of 

igneous activity (intrusive and extrusive) using the probability distribution from its expert 

elicitation for a Probabilistic Volcanic Hazard Assessment (PVHA). DOE identified that 

the probability-weighted igneous mean intrusive dose is estimated to be less than 0.001 mSv/yr 

[0.1 mrem/yr] for the 10,000-year period and the median dose is less than 0.005 mSv/yr 

[0.5 mrem/yr] for the post-10,000-year time period (SAR Section 2.4.2.2.1.2.3.1). DOE 

estimates for the probability-weighted igneous extrusive (volcanic eruptive) mean dose alone 

are on the order of 10-6 mSv/yr [0.0001 mrem/yr] for the 10,000-year period, and the median 

dose is less than 6 × 10-7 mSv/yr [6 × 10-5 mrem/yr] for the post-10,000-year time period (SAR 

Section 2.4.2.2.1.2.3.2). The NRC staff recognizes that the difference in magnitude for the dose 

consequences between the two igneous scenarios (intrusive and extrusive) predominantly 

results from the different number of waste package failures estimated to occur for each 

scenario, which, from a risk perspective, causes the dose from the extrusive case to be 

relatively low (SAR Section 2.2.1.4.1). 

Effects of Igneous Intrusion on Performance of Natural Barriers 

Because DOE did not rely on an evaluation of igneous events in its demonstration of multiple 

barriers, the NRC staff does not include a discussion of igneous events in TER Section 2.2.1.1. 

DOE screened out of its performance assessment the effect of igneous dikes and sills on 

groundwater flow and transport pathways surrounding drifts in the upper and lower natural 

barriers, as described in SNL (2008ac) (FEP 1.2.04.02.0A). At the drift wall, however, DOE 

included the effect of igneous intrusions, by assuming the drifts become degraded and the 

seepage barrier is eliminated. For this case in the performance assessment, seepage is 

set equal to percolation. Igneous activity near repository drifts may alter hydrologic 

properties of the host rock or cause perching of water in the unsaturated zone. DOE’s 

sensitivity analyses indicate that these effects on unsaturated zone flow in repository 

performance are small (FEP 1.2.04.02.0A; SNL, 2008ac). In particular, the potential effect of 

increased fracturing in and around a dike providing preferred water pathways has relatively little 
13-7 

impact, given the predominance of fracture flow in the existing, undisturbed unsaturated zone 

beneath much of the repository footprint (see the NRC staff review of unsaturated zone flow in 

TER Section 2.2.1.3.6). Farther into the far field, igneous dikes and sills may modify saturated 

zone flow and plume pathways, but again, DOE suggested these effects to be minor for 

performance (FEP 1.2.04.02.0A; SNL, 2008ac). 

The NRC staff reviewed relevant information in the SAR (Section 2.3.11.2.1.1), in SNL 

(2008ac), and in SNL (2007ag) and notes that the DOE treatment of igneous activity in the form 

of dikes and sills is reasonable with respect to groundwater flow in the natural barriers on the 

basis of the following rationale. For dikes and sills that 

 Intersect drifts, the seepage barrier is eliminated from DOE’s performance 

assessment model 

 Occur above the repository, any potential increases in focusing of groundwater flow that 

might be caused by the presence of intrusions (dikes and sills) are not important 

because, as noted in TER Section 2.2.1.3.6.3.2, that uncertainty in spatial variation in 

groundwater percolation (flow) is unimportant to performance 

 Occur below the repository in the unsaturated zone, sensitivity analyses for groundwater 

transport that included potential changes to hydrologic properties of fractures and matrix 

lead to a smaller effect than that already considered for the uncertainty in net infiltration 

(FEP 1.2.04.02.0A; SNL, 2008ac) 

 Occur in the saturated zone, the area the igneous activity and resulting rock bodies 

can potentially modify is small compared with the size of the saturated flow zone, with 

typically only a few decimeters [4–12 in] of disrupted rock around the ~1-m [3 ft]-wide 

dikes (SAR Section 2.3.11.3.2; SNL, 2007ag; Detournay, et al., 2003aa; Keating, et al., 

2008aa); moreover, the probability of an intrusion occurring directly within the 

groundwater plume is also small. 

In the igneous intrusion abstraction, DOE ignored sills for the reasons given in TER 

Section 2.2.1.3.10.3.1 and because DOE’s performance assessment assumes that a 

dike intruding the repository would result in failure of all waste packages and drip shields. 

The NRC staff notes that a potential sill would intersect fewer drifts than a dike swarm, 

and, therefore, DOE’s dike model encompasses the potential consequences of sills. The NRC 

staff notes that DOE’s approach to sills in its igneous intrusive scenario captures the potential 

impacts on repository performance. 

Behavior of Intruding Magma in Drifts and Effects on the Engineered Barrier System 

In developing the model for subsurface igneous processes, DOE concluded that basaltic 

magmas in the Yucca Mountain region would contain appreciable amounts of dissolved 

volatiles, primarily water. This dissolved water would form a gas phase as pressure on the 

magma becomes lower due either to normal ascent toward the surface or by intersection with 

a repository drift (SAR Section 2.3.11.2.1.2). Significant amounts of gas expansion in the upper 

300 m of rise [above ~1,000-ft depth] would cause magma in potential igneous events to flow 

more rapidly, and perhaps more extensively, than would be expected for magmas with little 

gas-driven expansion. In part because of the relatively high dissolved water contents expected 

for Yucca Mountain basaltic magmas, DOE concluded that all repository drifts would be rapidly 
13-8 

filled by magma flow if a future intrusive igneous event occurred within the repository footprint 

(SAR Section 2.3.11.2.1.2). 

The NRC staff reviewed the information DOE presented to support the conclusion that basaltic 

magmas are expected to have relatively high dissolved water contents. The NRC staff notes 

that information DOE cited supports this conclusion (SAR Section 2.3.11.3.2.3), and, in addition, 

the presence of hydrous minerals in some Yucca Mountain region basaltic rocks supports the 

DOE conclusion that dissolved magmatic water contents in these magmas are relatively high 

(Nicholis and Rutherford, 2004aa; SNL, 2007ae). The NRC staff notes that uncertainties in 

estimates of the dissolved volatile content of these basalts do not affect performance 

significantly, given that DOE assumes that magma behavior at repository depths is driven by an 

exsolved gas phase. 

In the DOE intrusive igneous case, DOE assumes that if a single rising dike intersects any part 

of the repository footprint where drifts containing waste packages are located, then all drifts in 

the repository are rapidly filled with magma. DOE developed this approach to account for the 

uncertainties in determining the physical characteristics of dikes at repository depths and for 

uncertainties in magma flow processes in drifts intersected by dikes (SAR Section 2.3.11.3.1). 

For the ascending magma entering the drifts, DOE recognized that there are two end member 

possibilities for flow behavior, considering how rapidly and violently magma could enter a drift. 

The less rapid end-member is termed effusive, as in a lava-like flow, while the other is more 

explosive, resulting in a fragmental, or pyroclastic, flow (SAR Section 2.3.11.2.1.2; SNL, 

2007ag; Woods, et al., 2002aa; Dartevelle and Valentine, 2005aa, 2009aa). The NRC staff also 

conducted independent confirmatory investigations (e.g., Woods, et al., 2002aa; Lejeune, et al., 

2009aa) verifying that potential magma flow into drifts could occur quickly enough so that only 

minor cooling of the magma would occur. On the basis of the results of these independent 

studies and its own evaluation, NRC staff notes that DOE has developed a reasonable 

technical basis to propose that all drifts will be filled with basaltic magma if an intrusive igneous 

event occurs at the repository site. Because this approach involves the disruption of all 

waste packages stored in the proposed repository (SAR Section 2.3.11.3), as explained 

next, the NRC staff further notes that this does not underestimate risk and that there are no 

technical uncertainties in this aspect of DOE’s approach that could reasonably increase the 

DOE risk estimates. 

According to DOE’s calculations, after intersection and intrusion by magma drift temperatures 

are modeled at or near magmatic temperatures of 1,046–1,169 °C [1,915–2,136 °F], at which 

point plastic deformation of the waste packages begins. Additional DOE analysis showed waste 

packages could also be damaged by magmatic pressures as low as 4 MPa [580 lb in-2]. DOE 

concluded waste package failure could result in waste forms that are exposed to high 

temperatures and that undergo chemical reactions with magma and its constituents. DOE 

assumed that the packages would encounter additional mechanical loads from the cooling and 

solidification of enveloping magma. Already weakened by the thermal effects of the magma, the 

mechanical loads associated with the magma would result in deformation of the waste package. 

DOE proposed that similar effects would occur for drip shields exposed to magmatic conditions. 

Thus DOE concluded that uncertainties associated with the potential effects of magma on waste 

package and drip shield performance are sufficient to warrant the assumption that all waste 

package and drip shield barrier capabilities are removed in models for igneous intrusive events 

(SAR Section 2.3.11.3.2.4). DOE also concluded that exposure to magmatic conditions will 

result in unprotected waste forms that are, effectively, instantaneously degraded, such that 

radionuclides are assumed to be immediately available for hydrologic transport, as soon as the 
13-9 

intrusion is cool enough to allow water to contact waste (SAR Section 2.3.11.3.2.4). As 

discussed in the next subsection, the cooling time of the intrusion is short relative to the time 

scale of groundwater percolation and flow, and relative to the period for postclosure repository 

performance. DOE further concluded that although the waste package no longer serves as a 

barrier to water flow after an igneous event, corrosion products from degradation of waste 

package materials will be present and will strongly retard release of certain radionuclides into 

the unsaturated zone, in the same manner as in the nominal scenario. The NRC staff evaluates 

the role of corrosion products in radionuclide release in TER Section 2.2.1.3.4. 

The NRC staff reviewed the information DOE provided in SAR Section 2.3.11.3.2 to support 

the representation of engineered barrier and waste form responses to potential igneous 

intrusive events. The NRC staff notes that the DOE approach for modeling waste package and 

drip shield response to potential intrusive events is reasonable, because this approach is 

consistent with available information on the heat of the magmatic intrusion and possible 

behavior of the proposed type of waste packages, drip shields, and waste form, as discussed in 

the previous paragraphs. Further, given that DOE’s analysis fails the engineered barriers for all 

waste packages, staff has identified no reasonable alternatives to this approach that would 

result in higher calculated doses. The NRC staff notes that the DOE representation of waste 

form response as instantaneously degraded with all radionuclides available for subsequent 

hydrologic transport is reasonable, as this approach is consistent with available information 

listed previously, and there are no reasonable alternatives that would result in higher 

calculated doses. 

Because DOE assumes that all waste package and drip shield capabilities cease during an 

igneous intrusive event, there are few uncertainties that are significant to the evaluation of 

potential intrusive igneous events. Those that DOE proposed to have potential significance 

to dose are discussed in the next two sections. The NRC staff has determined, by reviewing 

DOE-provided information, that the physical conditions following magma intrusion into a drift 

could affect subsequent hydrologic (groundwater) flow and transport processes. Thus, the 

remainder of this section evaluates the DOE basis for calculating the effects of magma cooling 

on the drift environment and subsequent hydrologic flow and transport. 

Magma Cooling and Heat Flow to Host Rock 

The temperature in the drifts after magma intrusion is an output parameter to TSPA (SAR 

Section 2.3.11.6.7). This subsection evaluates the DOE estimates of centerline and wall 

temperature in the invaded drifts in the period after intrusion and the timing of the intrusive 

event with respect to the repository life cycle, reflecting the temperature of the host rocks 

during the period of heating by radionuclide decay. DOE included these for consideration of the 

post-intrusion environment in the damaged drifts and the period of time after which groundwater 

seepage through the drifts could return. 

The temperature in the drifts after magma intrusion provides an estimate of the cooling time 

of the basalt inside the drift. The cooling of the basalt inside the drift and the drift centerline 

temperature, as well as drift-wall temperature, also influence the spent fuel dissolution 

model and the calculation of diffusion coefficients. Diffusion coefficients are used to calculate 

near-field contaminant transport in the unsaturated zone rock. 

DOE concluded that a short-lived (hours to days) intrusive event, as in SNL Figure F–1 

(2007ab), would fill every drift in the proposed repository with basaltic magma at a temperature 

of approximately 1,100 °C [2,012 °F]. Following the intrusive event, the magma in the drifts 
13-10 

begins to cool. DOE performed numerical simulations to model the magma cooling and heat 

flow in the rock between drifts, via non-steady-state heat conduction, with radial flow of heat 

from the magma-filled drifts into the host rock. DOE’s model considers a single basalt-filled 

drift, recognizing that the heat from one 5-m [16-ft]-diameter magma-filled drift will not influence 

the next drift approximately 80 m [262 ft] away (SNL, 2007ag). The calculated temperature 

decreases with time and distance from the centerline of the drift. The thermal diffusivity of the 

rock is calculated using the rock volumetric heat capacity and the thermal conductivity of the 

welded tuff at the repository horizon. The host rock in the heat-flow calculation is assumed to 

be either completely dry or completely wet. Thermal diffusivity of the welded tuff and the 

basaltic magma are assumed to be the same. The drift wall temperature prior to magma 

intrusion in the DOE model runs was between 25 and 200 °C [77 and 392 °F]. DOE concluded 

that this range suitably represents temperatures at different times for the intrusive event 

(reflecting elevated repository temperatures for several thousand years after closure (e.g., see 

SAR Figure 2.3.5-33 for calculated repository drift-temperature decay curves). These 

temperature distributions provided the DOE estimate of the cooling rate and thermal history of 

the repository and the drifts following an intrusive event. 

DOE identified that the model does not include the effects of latent heat of magma 

crystallization or the property contrasts between the magma and the tuff. Without latent heat 

effects, the one-dimensional model results underestimated peak temperatures and time needed 

for cooling. Therefore, DOE considered alternative models, including an analytical solution that 

approximated the effects of latent heat and numerical solutions in two dimensions that included 

both latent and radioactive heat. Noting that latent heat would be liberated during magma 

crystallization and that its effects would be most pronounced at very early times while the 

magma is still partially liquid, DOE accounted for the effect of latent heat by increasing the initial 

temperature of the magma. 

DOE considered the main uncertainty when modeling magma cooling and solidification to be 

the initial magma temperature. For dry magma, 1,150 °C [2,102 °F] was used, but the NRC 

staff notes that magma with a high water content could have a temperature as low as 1,046 °C 

[1,915 °F] (Nicholis and Rutherford, 2004aa). Although the difference is small, the NRC staff 

determined that DOE stated that this effect could slightly reduce the time required for the 

magma to cool to a solidified rock. However, as noted previously, DOE assumed a higher 

initial magma temperature instead of explicitly including latent heat of crystallization. Other 

uncertainties DOE considered included thermal conductivity, grain density, specific heat 

capacity, matrix porosity, saturation, and the lithophysal porosity of the host tuff. Heat loss 

was modeled as purely conductive, as DOE did not expect convection to occur in stagnant 

magma within drifts. For an igneous intrusion event occurring after about 1,000 years into the 

preclosure period, DOE concluded that the repository drift walls would attain a temperature of 

100 °C [212 °F] about 100 years after the intrusive event occurs, as in SNL Figure 2.3.5-33 

(2008ag). 

DOE showed that drift temperatures in the 100-year post-intrusion period abstracted to the 

Total System Performance Assessment (TSPA) have little influence on dose estimated from the 

intrusive scenario. The NRC staff notes this is reasonable, because DOE’s adopted scenario 

involves the disruption of all waste in the repository and because potential changes to dose from 

temperature increases due to magmatic heat are short lived (less than or equal to 100 years) 

compared with the time scale of groundwater percolation and flow, and relative to the period of 

postclosure repository performance. Also, as explained in the next subsection, some 

radionuclides that are important to dose have an inverse solubility relationship with temperature. 

NRC staff also notes that DOE’s conclusion that cooling basaltic magma would not significantly 
13-11 

affect drift characteristics that are relevant to drift degradation is reasonable, again because 

DOE’s adopted scenario involves the disruption of all waste in the repository. DOE used the 

final temperature of the drift and the cooled basalt temperature as an input to calculate the 

spent fuel dissolution model and diffusion coefficients. While the latent heat of crystallization 

would result in a slightly longer cooling time for the basaltic magma while it was still partially 

liquid, NRC staff notes that this would be offset by the DOE assumption of a slightly high initial 

magma temperature. However, DOE concluded that an extended magma cooling time would 

have little influence on the dose estimated from the intrusive scenario, and on the hydrologic 

flow and transport after an igneous event, which the NRC staff also notes is reasonable 

because of the short-lived cooling time for an intrusion compared with the much greater 

timescale for groundwater flow. 

Percolation Flux Through Cooled Basalt 

Chemical changes, expressed as the pH and ionic strength, to groundwater that may 

react with the new basalt rock filling repository drifts after a future intrusive magmatic event 

is an output parameter to TSPA (SAR Section 2.3.11.6.7; SNL, 2005ae). This subsection 

evaluates DOE estimates of possible chemical changes that might occur to groundwater as 

it begins to seep through and possibly react with cooling, and cooled, basaltic material filling 

the drifts. 

In considering percolation of groundwater through the drift after an igneous intrusion into the 

repository, DOE assumed that solidified basalt rock in the drift has the same fracture, porosity, 

and permeability characteristics as the surrounding tuff. DOE also concluded that the newly 

introduced basalt rock could affect the chemistry of water that seeps into the drift; in particular, 

pH and ionic strength. To examine possible changes in these two chemical parameters of the 

seepage water, DOE selected for numerical analysis three groundwater samples from large, 

fractured basalt-hosted reservoirs and conducted an extensive literature review of the chemistry 

of basalt-hosted waters to provide a range of pH and ionic strength values, as described in SAR 

Sections 2.3.7.5.3.1 and 2.3.11.3.2 and SNL Section 4.1.2 (2007ae). Temperature can affect 

the pH of incoming fluids, so to avoid underestimating radionuclide solubilities, DOE calculated 

the parameter values at 25 °C [77 °F], rather than at higher temperatures that would have 

resulted in lower solubility limits (radionuclides of concern show retrograde solubility in this pH 

range) and therefore smaller mass releases. 

As discussed in the previous subsection, for an igneous intrusion event occurring approximately 

1,000 years into the postclosure period, water seepage and flow through the host rock mass is 

estimated to resume about 100 years after an intrusion occurs. This is equivalent to the time 

when the basalt in the drifts would reach ~100 °C [212 °F] along the drift centerline (SAR 

Section 2.3.11.3.3.8; SNL, 2008ag). This time also corresponds to when the repository drifts 

walls are assumed to cool below the local boiling temperature, as shown in SNL Figure 2.3.5-33 

(2008ag). DOE modeled reestablishment of groundwater percolation through the invaded 

repository drifts and failed engineered barriers. DOE did not model release of radionuclides in 

gaseous form from the waste packages, because DOE’s analyses indicate that this does not 

influence the final dose at the receptor (SNL, 2008ag). Hence, groundwater percolation was the 

only pathway DOE considered for release of radionuclides. The NRC staff notes that the 

gaseous releases from a potential intrusive event would have very limited impact on overall 

calculated dose and that the groundwater pathway would be dominant. 

DOE’s review and analysis of relevant information on basalt-hosted groundwater, as a proxy for 

water entering a cooled, intruded drift, showed that pH and ionic strength of water prior to 
13-12 

entering basalt reservoirs, as well as variations within the actual composition of the basalt, are 

likely to have little effect on the pH and ionic strength of the water exiting the invaded drifts. 

DOE’s sensitivity analyses using waters from basaltic aquifers also showed that the liquid influx 

composition has an insignificant effect on the in-package chemistry model estimates. DOE 

analyses using waters equilibrated with the ambient-temperature Columbia River Plateau and 

Iceland basalts also found that the pH and ionic strength of the incoming water would have 

little influence on the resulting pH and the ionic strength after water has passed through the 

basalt-filled repository. 

The NRC staff notes that the Columbia River Plateau and Icelandic basalt compositions 

encompass a wide range of basalt types that include the characteristics of basalts at 

Yucca Mountain and are thus a satisfactory proxy for expected groundwater compositions in 

the case of outgoing (effluent) groundwater flow after passing through basalt-filled drifts from an 

intrusive event at the proposed repository. This is because the basalt rock types in those 

provinces encompass the same compositional ranges as expected for a future basaltic 

igneous event at Yucca Mountain (SNL, 2007ae). Thus, DOE’s analyses of uncertainties 

associated with the expected composition of future repository-filling basalt suggest that the 

uncertainties would not significantly affect the chemical composition of the effluent groundwater 

(SAR Section 2.3.11.3.3.9). In this case, the modeled changes in groundwater chemistry after 

contact with a basalt-filled drift would be negligible. This is consistent with the relatively small 

volume of intruded basalt in comparison with the volume of host rock, and that, even 

following an intrusive event, the effluent groundwater composition would be dominantly 

controlled by reaction with the contents of the failed waste package (i.e., spent fuel or high-level 

waste glass, and corrosion products from internal components) rather than the intruded basalt, 

as described in SNL Section 6.8.10 (2007ae). The NRC staff review of release and transport of 

radionuclides following a possible igneous intrusion is further detailed in TER Section 2.2.1.3.4 

(specifically, Sections 2.2.1.3.4.3.2 on waste form degradation and 2.2.1.3.4.3.4 on colloid 

formation and stability). 

Summary of NRC Staff’s Review of the Igneous Intrusion Modeling Case 

The NRC staff notes that DOE has provided information to support the modeling approach used 

to represent intrusive igneous events in the performance assessment of the proposed 

repository. The NRC staff notes, on the basis of its evaluation of DOE-provided information, 

that DOE has considered how cooling basaltic magma would affect the characteristics of the 

invaded and disrupted repository drifts that are relevant to hydrologic flow and transport and to 

the calculated dose. The DOE modeling approach relies on the assumption that intersection of 

any igneous intrusive feature into the repository footprint fills all of the repository drift with 

basaltic magma and that the magma removes all barrier capabilities from all waste packages 

and drip shields in all drifts. This assumption is reasonable, because it would not underestimate 

the risk from a potential intrusive igneous event. Moreover, the NRC staff notes that DOE has 

evaluated the uncertainties associated with this assumption that could increase the DOE dose 

estimate. DOE has represented the potentially significant effects of igneous intrusive events in 

the performance assessment. The igneous intrusion case provides insight into repository 

performance in the case of failure of engineered barrier components (drip shields and waste 

packages). It shows that the technical basis for the capability of those barrier components is 

based on and consistent with the technical basis for the performance assessments (reviewed in 

TER Section 2.2.1.4.1). 
13-13 

2.2.1.3.10.3.3 The NRC Staff’s Review of DOE Volcanic Eruption 

 Modeling Scenario 

DOE concluded that in all potential igneous intrusive events that intersect the repository 

footprint, a rising dike would reach the surface and develop a conduit at some location along the 

intrusion and magma would be extruded. If a conduit is located wholly or partially in a repository 

drift, waste from disrupted waste packages could be entrained by magma flow up the conduit 

and erupted from a volcano at the surface. Compared with the intrusion scenario, in which the 

contents of all waste packages in the repository are made available for hydrologic transport, 

DOE concluded that, for the volcanic scenario, only a limited amount of high-level waste could 

be entrained directly into a conduit or conduits (SAR Section 2.3.11.4). 

In the type of basaltic volcanic activity considered by DOE for a future eruption through the 

repository, a dike reaches the surface and activity begins along a fissure (an elongated system 

of vents, which is the surface expression of the dike; see SAR Sections 2.3.11.2.1 and 

2.3.11.4.1.1 and SAR Figure 2.3.11.5). In DOE’s model, magma flow to the surface in the dike 

usually localizes to a single, or a few, points over a period of hours to a few days, as observed 

at past basaltic eruptions and previously discussed in TER Section 2.2.1.3.10.3.2. Such 

behavior was seen in analog historic events [e.g., the 1943-1952 eruption of Paricutín, Mexico; 

the 1973 Heimaey eruption in Iceland; and the 1975 Tolbachik eruption in Kamchatka (Pioli, et 

al., 2008aa; Thorarinsson, et al., 1973aa; Doubik and Hill, 1999aa)]. DOE studies of igneous 

products exposed in the rock record also inferred a similar style for some prehistoric basaltic 

eruptions (e.g., SAR Section 2.3.11.4; SNL 2007ae; Valentine, et al., 2006aa; Keating, et al., 

2008aa). At this point in the modeled eruption, a conduit is considered to develop below the 

point of localization, with the main vent at the surface. This conduit feeds an explosive and 

lava-flow-forming Strombolian-style eruption. DOE adopted a violent Strombolian style for the 

entire model eruption considered, on the basis of the characteristics of the young Lathrop 

Wells scoria cone near Yucca Mountain (see TER Section 2.2.1.2.2). DOE recognized that 

conduits grow (widen) downwards from the surface in the plane of the dike, as detailed in SAR 

Section 2.3.11.4.2.1.2 and SNL p. 6-46 (2007ae), and thus, in DOE’s repository-disruption 

scenario, intersect a drift through the roof. 

DOE characterized subsurface volcanic conduits as flaring inward down from the top of 

the surface vent, such that conduit diameters at repository depths will be smaller than 

those observed near the surface. DOE characterized the size and shape of conduits using 

studies at exposed analog volcanoes (e.g., SAR Section 2.3.11.4 and Figure 2.3.11-6; SNL, 

2007ae; Valentine, et al., 2006aa; Keating, et al., 2008aa) and theoretical considerations 

and model studies (e.g., Wilson and Head, 1981aa; Valentine, et al., 2007aa). In the 

performance assessment, DOE represents subvolcanic conduits as simple cylinders (SAR 

Section 2.3.11.4.1). DOE used the area of the conduit that intersects a drift to calculate the 

mass of waste the conduit entrains. DOE concluded that entrained waste is mixed uniformly in 

the volume of magma that is subsequently erupted at the surface. 

From a risk-perspective, the DOE performance assessment calculates that the expected annual 

dose from the igneous volcanic modeling case alone is approximately 0.1 percent of the dose 

calculated for the intrusive scenario (SNL, 2007ag). This difference between the volcanic and 

intrusive scenarios arises, in part, because DOE concluded that the volcanic scenario entrains 

and erupts approximately 0.1 percent of the amount of high-level waste that is disrupted during 

the intrusive case. Thus, the NRC staff’s review of the subsurface processes associated with 

the volcanic case focuses on the DOE basis for concluding that a volcanic conduit, or conduits, 

would entrain a limited amount of waste. 
13-14 

Development of Conduits and Likelihood of Ejecting Waste in a Volcanic Eruption 

In the DOE-developed model, one to three eruptive conduits may occur along the thickest dike. 

DOE treats the predicted location of a single conduit along a dike, the most likely occurrence, as 

random (SAR Section 2.3.11.4.2.1). In SAR Section 2.3.11.2.2, DOE developed a basis to 

determine the likelihood that at least one conduit will form through the repository footprint and, 

more specifically for risk significance, through an emplacement drift containing waste packages 

if a dike intersected the repository. The DOE model for conduit formation is based on 

observations at basaltic volcanoes and supported by calculations constrained by information 

obtained from studies of analog eroded volcanoes (SNL, 2007ae). 

On the basis of observations of Quaternary volcanoes in the Yucca Mountain region, where 

mostly only one volcano develops along a dike (Keating, et al., 2008aa), DOE heavily weighted 

the distribution of the likely number of conduits that might develop along a dike toward one 

conduit per eruption (SAR Section 2.3.11.2.1.2; SNL, 2007ae), and in this way treated 

uncertainty. DOE determined that the presence of repository drifts would not affect the rise 

of a dike, nor subsequent eruptive processes, because the drifts would be negligible in volume 

compared to the volume of rock the dike transects. The NRC staff notes that this assumption is 

reasonable, given the expected small size, on the order of a 1 to 2 m [3 to 6 ft] width below 

repository depths (Keating, et al., 2008aa), and energy of a propagating dike. DOE 

determined that 85 percent of past eruptive events have formed a single conduit, 10 percent 

formed 2 conduits, and 5 percent formed 3 conduits, and this same information also suggests 

that multiple conduits should be spaced between 0.4 and 2 km [0.25 and 1.2 mi] apart. DOE 

also considered five alternative conceptual models to represent the location of a conduit along a 

dike. On the basis of field analogs, models, and studies presented in SNL (2007ae, 2007ag), 

DOE concluded that a model for random location of a conduit along an existing dike is the only 

supportable approach, because conduits do not have any predictable location along surface 

expressions of dikes in analog examples (Doubik and Hill, 1999aa; Hill and Connor, 2000aa; 

Valentine, et al., 2006aa; Valentine and Krogh, 2006aa; Keating, et al., 2008aa). 

To calculate the likelihood that at least one volcanic conduit will form through an emplacement 

drift and entrain waste, DOE used numerical models to simulate the number of dikes that 

could penetrate the repository footprint, using dike characteristics from CRWMS M&O (1996aa). 

For each simulation, DOE calculated the length of the dike, or dikes, located inside and 

outside the repository footprint and found there was a 60 percent chance that more than one 

dike would form in an event. For the widest dike in each simulation, DOE constrained its 

model to form one to three conduits at random locations along that dike and determined 

whether this location coincided with the repository footprint (SAR Section 2.3.11.4.2.1.3; SNL, 

2007ar). Using this approach, DOE estimated that there was a 20 to 35 percent chance, with 

a mean of 28 percent, that at least one conduit would form within the repository footprint. 

This value reflects the relatively small size of the repository footprint in comparison with the 

total area that dikes could impact (SAR Section 2.3.11.2.2). On the basis of alternative 

volcanic event characteristics and behavior, DOE acknowledged that the conditional likelihood 

of at least one eruptive center (conduit) within the repository footprint might range from 43 to 

78 percent (SAR Section 2.3.11.2.2.6). However, DOE concluded that, on the basis of features 

of Yucca Mountain repository volcanoes, a mean conditional eruption probability of 0.28 

(28 percent) times the probability of dike intersection with the repository footprint was 

most consistent with basaltic volcanic events that are expected to include multiple dikes and 

in which conduit(s) form on the widest dike. On this basis, the mean conditional probability of 

a conduit forming within the repository, using the mean intrusive probability from the PVHA 
13-15 

expert elicitation of 1.7 × 10-8 per year (SAR Section 2.3.11.4.2.1; TER Section 2.2.1.1.3), 

is 4.8 × 10-9 per year. 

The 28 percent conditional factor DOE provided is for a conduit that develops within the 

repository footprint, but which may not necessarily eject waste. DOE then developed a 

second conditional probability, given as 0.296 (NRC staff rounded this to 0.3, or 30 percent), 

to represent the fraction of conduits within the repository footprint that may actually intersect 

a drift containing waste packages and eject the waste contents through a volcanic vent (SAR 

Section 2.3.11.4.2.1). This factor accounts for the spatial distribution of waste emplacement 

drifts within the repository footprint area and the likely orientation of dikes. 

The staff reviewed DOE information regarding the likelihood for conduit development at 

repository drifts. Using its knowledge of the characteristics of basaltic volcanism at the 

Yucca Mountain region and DOE and independent confirmatory studies of conduit development 

in basaltic volcanism (BSC, 2003ab; Detournay, et al., 2003aa; Hill and Conner, 2000aa; Pioli, 

et al., 2008aa), the NRC staff determines that DOE has reasonably characterized the number 

and spacing of volcanic conduits. The DOE conclusion that the processes leading to conduit 

development along a dike are reasonably represented as randomized along the widest dike 

segment is reasonable, because available information shows that there is no predictable pattern 

controlling conduit formation at other analogous basaltic volcanoes. The NRC staff reviewed 

the DOE methodology that developed the 28 percent factor for conduit development in the 

repository and the 30 percent factor for conduit intersection with a drift. DOE implemented 

randomized conduit development in developing these factors. NRC staff notes that even if the 

conduit development factor was significantly higher, the implied risk would change by only a 

small amount (e.g., using a factor of 100 percent would increase the amount of waste disrupted 

and ejected to ~0.3 percent of that disrupted in the intrusive case). Given the relatively small 

volume and rapid infilling time of the intersected drifts, NRC staff notes that the presence of 

repository drifts will not significantly affect the localization process for conduit development. 

Thus, NRC staff notes that DOE has evaluated the likelihood of conduit development at 

intersected drifts. 

Eruptive Conduit Growth and Size, and Impact on Waste Packages and Waste 

According to DOE’s scenario presented in SAR Section 2.3.11.4.2.1, one or more conduits may 

intersect repository drifts, all the waste packages within the area of the conduits are assumed to 

be destroyed, and all the waste is assumed to be incorporated into the erupting magma (SNL, 

2007ag). The waste is assumed to mix with magma and be carried up the conduit toward the 

surface, where the magma–waste mixture would be explosively ejected into the atmosphere or 

flow as lava along the ground. 

DOE considered the failed waste packages directly intersected by a conduit to provide no 

protection against waste release, so in the DOE model, the conduit size at repository depth 

directly determines the number of waste packages disrupted. More specifically, DOE calculated 

the number of waste packages intersected by conduits as a cumulative distribution function that 

is based on a distribution for the number of conduits, a distribution for conduit diameters, and 

the likelihood factors for location of the conduits on the dikes, which includes the design 

configuration of the subsurface repository. Accordingly, DOE considered additional parameters 

including waste package size and spacing, drift location and dimensions, and distributions for 

dike length, orientation, thickness, and number of dikes in an intrusive event. DOE concluded 

that rising magma in a dike that enters a drift will slow relative to that in solid rock pillars 

between drifts; thus the dike segment above drifts will lag slightly in breaching the surface. 
13-16 

From that conclusion, DOE proposed that vents and conduits are more likely to form between 

drifts than above them. In most realizations tested by DOE, this led to a condition where the 

volcanic conduit forms along the dike in the rock pillars between drifts and not the drift itself; 

thus the most likely value for the number of disrupted waste packages in the model is zero. 

From zero to seven waste packages were modeled in the Total System Performance 

Assessment (TSPA) as intersected by a conduit during an eruption. 

In DOE’s model, uncertainty in conduit size is bounded by a size distribution based on observed 

host-rock fragments in violent-Strombolian deposits at Lathrop Wells volcano (Doubik and Hill, 

1999aa) and in SNL Section 6.4 and Appendix C (2007ae) and on field studies at analog 

sites, which DOE interpreted as suggesting that the diameter is largest at the surface 

and decreases with depth. DOE gave a distribution for conduit diameters from approximately 

4 m [13 ft] (bounded by dike width) to a mean value of 15 m [50 ft] and a 95th percentile value of 

21 m [69 ft] for an expected conduit diameter at repository depth (SAR Section 2.3.11.4.2.1.2), 

with DOE’s volcanic scenario analysis conduits developed only where the trend of a dike 

intersected a drift (SAR Section 2.3.11.4.1.1.1). DOE concluded that it is highly unlikely that a 

secondary conduit will form at some point along the drift away from the dike intersection. This 

conclusion was based on DOE’s view that magma will solidify quickly and pressures will be 

insufficient to allow the formation (or maintain the opening) of a secondary dike, fed from the 

magma in the drift. In the analysis involving pyroclastic flow of magma inside a drift (an 

alternative conceptual model mentioned previously with respect to the intrusive case), DOE 

assessed one situation where it assumed that a secondary fracture had already formed and a 

secondary opening was created on the drift-top wall (BSC, 2005af). DOE applied a multiphase 

fluid dynamics analysis to this scenario. Simulated results exhibited intermediate behavior with 

a down-drift multiphase flow on the roof and a return flow on the floor. The whole system with 

these two openings formed a clearly defined recirculation pattern in the drift with some materials 

leaving the system and some materials recycling back into the drift along the roof. Simulations 

also showed that this scenario leads to relatively high dynamic pressures compared with a 

single-conduit situation. Other simulations indicated that blockage of the volcanic conduit 

might also create secondary breakouts at a point away from the location of initial dike 

intersection (SAR Section 2.3.11.3.2.2). Although DOE acknowledged that the chance of these 

scenarios occurring was unlikely, it concluded that such scenarios could lead to a one to two 

order-of-magnitude increase in the amount of waste released during a volcanic igneous event 

(essentially equivalent to the waste content of a single drift, ~70–100 waste packages), which 

would cause no more than a one to two order-of-magnitude increase in expected annual dose 

(SAR Section 2.3.11.3.2.2). 

The NRC staff’s review of the DOE approach to modeling the development of subvolcanic 

conduits is guided by the information in SAR Section 2.3.11.3.2.2 regarding dose sensitivity to 

the waste source term and analyses in SAR Section 2.4.2 showing the volcanic case 

contributes approximately 0.1 percent of the total dose for the igneous scenario. Staff 

determined that the DOE mean conduit diameter of 15 m [49 ft] (SAR Section 2.3.11.4.2.1.2) 

appears to be influenced by estimates of the conduit diameter for Lathrop Wells volcano; the 

calculation for the conduit diameter given in SNL Appendix F (2007ae) and adopted in the SAR 

appears to be in error. This diameter was recalculated using the same information by 

Valentine, et al. (2007aa) at ~8 to 9 m [~26 to 30 ft], which NRC staff notes is the correct value. 

In contrast, many of the smaller conduit diameters DOE used in supporting this parameter value 

are from eroded volcanoes in the Yucca Mountain region that DOE concluded are not 

representative of expected basaltic igneous processes (e.g., CRWMS M&O, 1996aa). On the 

basis of this information and other published information about basaltic volcanic conduits 

located several hundred meters [~1,000 ft] below surface (e.g., Doubik and Hill, 1999aa; 
13-17 

Valentine and Groves, 1996aa; Valentine and Krogh, 2006aa; Delaney and Gartner, 1997aa), 

the NRC staff notes that uncertainty in the average and maximum conduit diameter may be up 

to a factor of five greater than DOE considered. The magnitude of this uncertainty, however, 

would increase the expected annual dose for volcanic igneous events by less than an order of 

magnitude. Because DOE calculates that the volcanic case contributes 0.1 percent of the total 

dose to the igneous scenario, NRC staff notes that this increase in uncertainty in conduit 

diameter, and thus dose, would not be significant. Thus, the DOE approach for representing 

subvolcanic conduits is reasonable. 

Evaluation of Magma–Waste Interaction and Mixing in a Drift and Conduit 

In DOE’s TSPA, the amount of waste incorporated into a volcanic conduit is determined by the 

area of a drift intersected by a stylized cylindrical conduit. This model assumes that waste from 

disrupted packages located outside the boundary of the conduit will not be entrained into the 

upward-flowing magma in the conduit. Additional DOE analyses (SAR Section 2.3.11.3.4.4) 

described how circulation of magma and gas might occur between a conduit and other parts of 

the intersected drift. However, DOE did not characterize the extent or magnitude of this 

circulation or evaluate the potential for this circulation to entrain small particles of degraded 

waste from elsewhere in a drift beyond the conduit. Additional degraded waste may be 

available, as DOE assumed that the waste form is instantly degraded when the waste package 

fails during the intrusive event (SAR Section 2.3.11.3.2.4). 

The NRC staff reviewed the DOE information in SAR Section 2.3.11.3.4.4 and associated 

published literature. NRC staff notes that although DOE did not evaluate the effects of potential 

magma circulation, the significance of these potential effects would be less than effects 

associated with secondary conduit development. As Menand, et al. (2008aa) discussed, 

magma circulation in an intersected drift has the potential to transport some sizes of waste 

particles into the erupting conduit, if flow conditions in the drift are appropriate. The NRC staff 

expects that only a relatively small amount of waste particles could potentially be transported by 

magma circulation, because materials on the floor of the drift (e.g., pallets on which the waste 

canisters rest, damaged/degraded engineered barrier system materials, and the invert; SAR 

Section 2.1) would present obstacles to magma flow (SNL, 2007ag,ar; Detournay, et al., 

2003aa; Darteville and Valentine, 2009aa). These obstacles would also present rough surfaces 

that would impede waste particles from entrainment in the circulating magma and thus limit the 

amount of waste that could be released in an eruption. Therefore, the potential increase in 

entrained waste due to magma circulation would be significantly less than an order of 

magnitude (i.e., much less than the amount of waste contained in a potentially intersected drift). 

By providing an analysis showing that the expected annual dose increases linearly with 

increasing source term for the volcanic modeling case, as detailed in SAR Section 2.3.11.3.2.2 

and SNL Appendix P (2008ag), DOE has provided information that shows the potential effects 

of magma circulation are not significant to dose estimates from the igneous scenario. 

Further, the NRC staff reviewed the information DOE provided in SAR Section 2.3.11.3.2.2 to 

evaluate the potential effects of secondary conduits developing away from the location of dike 

intersection with the drift. The NRC staff notes that although the likelihood of secondary 

conduits relies on a series of unusual conditions and thus appears remote, DOE has not 

provided a technical basis to determine this likelihood. However, the NRC staff notes that the 

DOE assumption that development of a secondary conduit could potentially lead to the eruption 

of all waste in an intersected drift is reasonable because the assumption is consistent with 

available information (Woods, et al., 2002aa; Menand, et al., 2008aa; Lejeune, et al., 2009aa; 

Darteville and Valentine, 2009aa). DOE accounted for the uncertainty associated with 
13-18 

secondary conduit formation by providing an analysis in SAR Section 2.3.11.3.2.2 that shows a 

hypothesized two orders-of-magnitude increase in amount of waste entrained in an eruption 

might lead to a two orders-of-magnitude increase in expected annual dose for the volcanic 

modeling case [see also SNL Appendix P (2008ag)]. Thus, DOE has addressed the 

significance of secondary conduit formation, because the likelihood of secondary conduits 

appears remote, and the significance to performance appears to be much smaller than the two 

orders of magnitude presented in SAR Section 2.3.11.3.2.2 (i.e., much less than 10 percent of 

the total igneous scenario). 

In the DOE volcanic eruption modeling scenario, the number of waste packages intersected 

becomes input in TSPA for calculating the amount of waste erupted, along with the probability 

that a conduit will develop in a drift containing waste packages. DOE used a Monte Carlo 

technique to account for parameter uncertainties such as the future time at which an eruption 

might occur and the possibility that more than one eruption could happen in the future of the 

repository. DOE calculates a magma partitioning factor (SAR Section 2.3.11.4.2.2.2; 

SNL, 2007ab) to determine the amount of the waste partitioned into a potential volcanic tephra 

fall deposit, the only volcanic product that is significant to dose (TER Section 2.2.1.3.13.3.1). 

DOE determined that 10 to 50 percent of the total amount of waste entrained in an eruption will 

be in the resulting tephra fall deposit. The magma partitioning factor and the expected style of 

eruption (violent Strombolian) from the volcanic conduit(s) is evaluated as part of the abstraction 

for airborne volcanic transport in TER Section 2.2.1.3.13. 

DOE proposed that the amount of waste particles incorporated into the erupting magma 

would only constitute a minor amount (trace phase) in the magma in all DOE’s scenarios 

and that its presence would not be expected to influence the eruptive behavior of the magma 

(SNL, 2007ab). The NRC staff notes that DOE’s estimate of the amount of the waste that 

could become incorporated into the fallout deposit is documented and supported. Independent 

NRC staff calculations substantiate DOE’s claim that the amount of waste transported in a 

conduit and into the tephra deposit would be on the order of 10-6 of the concentration of tephra 

at any point in the deposit (SAR Section 2.3.11.4.2.2.3). NRC staff notes that the waste 

particles will not affect the eruptive processes occurring in the magma (SNL, 2007ab) 

and that the style of eruption would not be influenced by the presence of the waste. 

Therefore, it is reasonable for DOE to model dispersal and fall for airborne transport of 

radionuclide-contaminated tephra (reviewed in TER Section 2.2.1.3.13) on the basis of past 

and current similar style volcanic activity. 

Summary of the NRC Staff’s Review of the Volcanic Eruption Modeling Case 

DOE has provided information to support the modeling approach used to represent volcanic 

igneous events in the performance assessment. DOE has represented how potential volcanic 

conduits could form randomly along an igneous intrusion and entrain waste. The NRC staff 

notes that DOE assumed that all waste packages located within the footprint of a potential 

drift-intersecting conduit would release all degraded waste into the volcanic eruption. DOE has 

accounted for uncertainties in the amount of waste that potentially could be disrupted and 

erupted during a modeled volcanic event by providing calculations of dose sensitivity to amount 

of waste erupted (see TER Section 2.2.1.4.1.3.3.2). These calculations form the basis for NRC 

staff understanding that uncertainties associated with the potential effects of magma circulation 

in a drift, or the remote chance of secondary conduit development, would not affect dose 

significantly. DOE has provided a basis for the use of the magma partitioning factor and has 

supported that basis with information from suitable volcanic analogs. The NRC staff notes that 
13-19 

DOE has represented the potentially significant effects of subsurface volcanic processes and 

events in the performance assessment. 

The NRC staff notes that the incorporation factor of waste into magma adopted by DOE (the 

entire contents of waste packages are assumed to be mixed into the magma) is conservative as 

it allows for greater amounts of incorporated waste than might realistically occur. There are 

inherent uncertainties in how much waste would be incorporated into erupting magma because 

no suitable natural analogs have been identified for this process. Potential waste incorporation 

is highly dependent on the behavior of the magma during the interaction, particularly on the 

extent of fragmentation, which is driven by degassing of the partially solidified magma and 

depends on many variables. Magma fragmentation may occur as deep as repository depths 

{300 m [~1,000 ft]} in violent Strombolian eruptions (Doubik and Hill, 1999aa). However, in 

most Strombolian eruptions, fragmentation is modeled to occur via explosive gas bubbles 

bursting at less than 100 m [328 ft] below the surface (e.g., Wilson and Head, 1981aa). The 

presence of repository drifts could cause deeper fragmentation than in typical volcanic 

environments (e.g., Woods, et al., 2002aa), and DOE’s model for waste incorporation relies on 

a vigorously degassing, partly fragmenting magmatic environment in the drifts. Despite these 

uncertainties, the NRC staff notes that these factors will not make a significant difference to the 

dose from the volcanic eruption modeling scenario as calculated in the DOE TSPA, because the 

likely increase in the amount of waste incorporated into the erupted magma is within the range 

of uncertainty considered. 

2.2.1.3.10.4 NRC Staff Conclusions

NRC staff notes that the DOE description of this model abstraction for igneous disruption of 

waste packages is consistent with the guidance in the YMRP. NRC staff also notes that the 

DOE technical approach discussed in this chapter is reasonable for use in the Total System 

Performance Assessment (TSPA). 

2.2.1.3.10.5 References 

BSC. 2005af. “Magma Dynamics at Yucca Mountain, Nevada.” ANL–MGR–GS–000005. 

Rev. 00. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2003ab. “Technical Basis Document No. 13: Volcanic Events.” Rev. 02. Las Vegas, 

Nevada: Bechtel SAIC Company, LLC. 

CRWMS M&O. 1996aa. “Probabilistic Volcanic Hazard Analysis for Yucca Mountain, Nevada.” 

BA0000000–01717–2200–00082. Rev. 0. Las Vegas, Nevada: CRWMS M&O. 

Dartevelle, S. and G.A. Valentine. 2009aa. “Multiphase Magmatic Flows at Yucca Mountain, 

Nevada.” Journal of Geophysical Research, Solid Earth. Vol. 113. pp. B12209. 

doi:10.1029/2007JB005367. 

Dartevelle, S. and G.A. Valentine. 2005aa. “Early Time Multiphase Interactions Between 

Basaltic Magma and Underground Repository Openings at the Proposed Yucca Mountain 

Radioactive Waste Repository.” Geophysical Research Letters. Vol. 32. pp. L22311. 

doi:1029/2005GL024172. 
13-20 

Delaney, P.T. and A.E. Gartner. 1997aa. “Physical Processes of Shallow Mafic Dike 

Emplacement Near the San Rafael Swell, Utah.” Geological Society of America Bulletin. 

Vol. 109, No. 9. pp. 1,177–1,192. 

Detournay, E., L.G. Mastin, J.R.A. Pearson, A.M. Rubin, and F.J. Spera. 2003aa. “Final Report 

of the Igneous Consequences Peer Review Panel.” DN2000219072. MOL20031014:0097. 

Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

DOE. 2009av. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository 

License Application.” Rev. 01. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

Doubik, P. and B.E. Hill. 1999aa. “Magmatic and Hydromagmatic Conduit Development During 

the 1975 Tolbachik Eruption, Kamchatka, With Implications for Hazards Assessment at Yucca 

Mountain, NV.” Journal of Volcanology and Geothermal Research. Vol. 91. pp. 43–64. 

Hill, B.E. and C.B. Connor. 2000aa. “Technical Basis for Resolution of the Igneous Activity Key 

Technical Issue.” ML011930254 San Antonio, Texas: CNWRA. 

Keating, D.N., G.A. Valentine, D.J. Krier, and F.V. Perry. 2008aa. “Shallow Plumbing Systems 

for Small-Volume Basaltic Volcanoes.” Bulletin of Volcanology. Vol. 70. pp. 563–582. 

Lejeune, A., B.E. Hill, A.W. Woods, R.S.J. Sparks, and C.B. Connor. 2009aa. “Intrusion 

Dynamics for Volatile-Poor Basaltic Magma Into Subsurface Nuclear Installations.” Volcanic 

and Tectonic Hazard Assessment for Nuclear Facilities. C.B. Connor, N.A. Chapman, and 

L.J. Connor, eds. Cambridge, United Kingdom: Cambridge University Press. 

Menand, T., J.C. Phillips, and R.S.J. Sparks. 2008aa. “Circulation of Bubbly Magma and Gas 

Segregation Within Tunnels of the Potential Yucca Mountain Repository.” Bulletin of 

Volcanology. Vol. 70. pp. 947–960. 

Nicholis, M.G. and M.J. Rutherford. 2004aa. “Experimental Constraints on Magma Ascent 

Rate for the Crater Flat Volcanic Zone Hawaiite.” Geology. Vol. 32. pp. 489–492. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

Pioli, L., E. Erlund, E. Johnson, K. Cashman, P. Wallace, M. Rosi, and H. Delgado Granados. 

2008aa. “Explosive Dynamics of Violent Strombolian Eruptions: The Eruption of Paricutin 

Volcano 1943–1952 (Mexico).” Earth and Planetary Science Letters. Vol. 271. pp. 359–368. 

SNL. 2008ac. “Features, Events, and Processes for the Total System Performance 

Assessment: Methods.” ANL–WIS–MD–000026. Rev. 00. Las Vegas, Nevada: Sandia 

National Laboratories. 
13-21 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ab. “Atmospheric Dispersal and Deposition of Tephra From a Potential Volcanic 

Eruption at Yucca Mountain, Nevada.” MDL–MGR–GS–000002. Rev. 03. ERD 01. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ae. “Characterize Eruptive Processes at Yucca Mountain, Nevada.” 

ANL–MGR–GS–000002. Rev. 03. ERD 01, ERD 02. Las Vegas, Nevada: Sandia 

National Laboratories. 

SNL. 2007ag. “Dike/Drift Interactions.” MDL–MGR–GS–000005. Rev. 02. ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ar. “Number of Waste Packages Hit by Igneous Events.” ANL–MGR–GS–000003. 

Rev. 03. ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2005ae. “In-Package Chemistry Abstraction.” ANL–EBS–MD–000037. Rev. 04. 

ADD 01. Las Vegas, Nevada: Sandia National Laboratories. 

Thorarinsson, S., S. Steinthorsson, T. Einarsson, K. Kristmannsdottir, and N. Oskarsson. 

1973aa. “The Eruption on Heimaey, Iceland.” Nature. Vol. 241. pp. 372–375. 

Valentine, G.A. and K.E.C. Krogh. 2006aa. “Emplacement of Shallowing Dikes and Sills 

Beneath a Small Basaltic Volcanic Center—The Role of Pre-Existing Structure (Paiute 

Ridge, Southern Nevada, USA).” Earth and Planetary Science Letters. Vol. 246, No. 3. 

pp. 217–230. 

Valentine, G.A. and K.R. Groves. 1996aa “Entrainment of Country Rock During Basaltic 

Eruptions of the Lucero Volcanic Field, New Mexico.” Journal of Volcanology and Geothermal 

Research. Vol. 161, No. 1-2. pp. 57–80. 

Valentine, G.A., D.J. Krier, F.V. Perry, and G. Heiken. 2007aa. “Eruptive and Geomorphic 

Processes at the Lathrop Wells Scoria Cone Volcano.” Journal of Volcanology and Geothermal 

Research. Vol. 161, No. 1–2. pp. 57–80. 

Valentine, G.A., F.V. Perry, D.J. Krier, D.N. Keating, R.E. Kelley, and A.H. Cogbill. 2006aa. 

“Small-Volume Basaltic Volcanoes: Eruption Products and Processes, and Posteruptive 

Geomorphic Evolution in Crater Flat (Pleistocene), Southern Nevada.” Geological Society of 

American Bulletin. Vol. 118, No. 5. pp. 1,313–1,330. 

Wilson L. and J.W. Head, III. 1981aa. “Ascent and Eruption of Basaltic Magma on the Earth 

and Moon.” Journal of Geophysical Research. Vol. 86, No. B4. pp. 2,971–3,001. 

Woods, A.W., S. Sparks, O. Bokhove, A. Lejeune, C.B. Connor, and B.E. Hill. 2002aa. 

“Modeling Magma-Drift Interaction at the Proposed High-Level Radioactive Waste Repository at 

Yucca Mountain, Nevada, USA.” Geophysical Research Letters. Vol. 29, No. 13. p. 1,641. 

14-1 

CHAPTER 14 

2.2.1.3.12 Concentration of Radionuclides in Groundwater 

2.2.1.3.12.1 Introduction

This section of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff review of information DOE provided in the Safety Analysis Report 

(SAR) (DOE, 2008ab) on the concentration of radionuclides in groundwater extracted by 

pumping and used in the annual water demand. The NRC staff reviewed the methods and 

assumptions DOE used to estimate groundwater radionuclide concentrations. The NRC staff 

review focused on SAR Sections 2.3.9 and 2.4.4. SAR Section 2.3.9 includes discussions of 

saturated zone radionuclide transport and groundwater. SAR Section 2.4.4 includes the 

analysis of repository performance with respect to the protection of groundwater. 

2.2.1.3.12.2 Evaluation Criteria 

The NRC staff’s review of the DOE’s calculation of the concentration of radionuclides in the 

groundwater is guided by 10 CFR 63.312(c), which states that the reasonably maximally 

exposed individual (RMEI) uses well water with average concentrations of radionuclides 

based on an annual water demand of 3.7 × 109

 L [3,000 acre-ft]. The NRC staff followed a 

risk informed approach and the guidance in the Yucca Mountain Review Plan (YMRP; 

NRC, 2003aa) to evaluate DOE calculation/analysis of the concentration of radionuclides 

in groundwater. 

2.2.1.3.12.3 Assessment of Well Water Concentration Estimates 

In SAR Section 2.4.4, DOE stated that it explicitly assumed that all radionuclides transported by 

groundwater from the Yucca Mountain disposal system in a given year are captured in the 

annual water demand of 3.7 × 109

 L [3,000 acre-ft]. DOE determined the annual mean 

concentrations of transported radionuclides in the saturated-zone groundwater by dividing the 

annual mass flux of radionuclides reaching the accessible environment boundary by the annual 

water demand (SAR Section 2.4.4.1.1.1). As DOE presented in SAR Section 2.3.9, this annual 

mass flux includes both those radionuclides explicitly transported in the TSPA model and those 

calculated assuming secular equilibrium in long-lived decay chains. 

NRC Staff Review 

YMRP Section 2.2.1.3.12 states that if DOE assumes that all radionuclides that reach the 

reasonably maximally exposed individual in a given year are included in the pumping wells with 

annual water demand of 3.7 × 109

 L [3,000 acre-ft], then a simplified review should focus on the 

bounding assumptions. In SAR Section 2.4.4, DOE stated that it explicitly assumed that all 

radionuclides transported by groundwater from the Yucca Mountain disposal system in a given 

year are captured in the annual water demand of 3.7 × 109 L [3,000 acre-ft]. Thus, the NRC 

staff followed the simplified review approach in YMRP Section 2.2.1.3.12. The NRC staff 

verified that DOE determined the annual mean concentrations of transported radionuclides in 

the saturated-zone groundwater by dividing the annual mass fluxes of radionuclides reaching 

the location of the RMEI by the annual water demand (SAR Section 2.4.4.1.1.1). The NRC staff 

evaluation of the radionuclide mass flux is provided in TER Section 2.2.1.3.9. DOE’s saturated 

zone transport abstraction model (SAR Section 2.3.9) explicitly tracks transport of a set of 
14-2 

contaminant radionuclides, and assumes that decay-chain daughter radionuclides are in 

secular equilibrium with their parents at the location of the RMEI. As discussed in TER 

Section 2.2.1.3.9, this assumption may not be reasonable for cases where a long-lived parent 

radionuclide is more strongly sorbed than its decay products. In its response to the NRC staff’s 

request for additional information, DOE evaluated this effect and showed that for the conditions 

expected in the saturated-zone transport path, the magnitude of the predicted excess daughter 

activity is not significant for performance (DOE, 2009de). The NRC staff notes in TER Section 

2.2.1.3.9 that, including the uncertainty from possible excess activity of decay-chain daughter 

radionuclides, DOE’s representation of the annual mass fluxes of radionuclides reaching the 

location of the RMEI is reasonable. The NRC staff notes that the DOE’s calculation is 

consistent with the YMRP guidance because DOE showed that the reasonably maximally 

exposed individual uses well water with an average concentration of radionuclides based on an 

annual water demand of 3.7 × 109

 L [3,000 acre-ft]. 

2.2.1.3.12.4 NRC Staff Conclusions

The NRC staff notes that the DOE’s description of the calculation of the concentration of 

radionuclides in groundwater is consistent with the guidance in the YMRP. The NRC staff notes 

that the technical approach is reasonable for use in the Total System Performance Assessment 

(TSPA). 

2.2.1.3.12.5 References 

DOE. 2009de. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.3.9), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.3.9, Set 1.” Letter (November 16) J.R. Williams to J.H. Sulima (NRC). 

ML092820675. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository 

License Application.” Rev. 0. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 
15-1 

CHAPTER 15 

2.2.1.3.13 Airborne Transport and Redistribution of Radionuclides 

2.2.1.3.13.1 Introduction

This chapter evaluates the U.S. Department of Energy’s (DOE) information on airborne 

transport and deposition of radionuclides expelled by a potential future volcanic eruption 

following igneous disruption of waste packages. It also evaluates DOE information on the 

redistribution of those radionuclides in soil. This evaluation of DOE’s performance assessment 

for the volcanic eruption modeling case is a sequel to the evaluation of possible igneous 

disruption of the proposed repository [DOE’s igneous intrusion modeling case; see Technical 

Evaluation Report (TER) Section 2.2.1.3.10]. This chapter also evaluates redistribution of 

radionuclides in soil in the accessible environment, which in DOE’s model arrives in the 

accessible environment via groundwater transport. The U.S. Nuclear Regulatory Commission 

(NRC) staff’s evaluation is based on information in the DOE Safety Analysis Report (SAR) 

(DOE, 2009av), as supplemented by DOE’s responses (DOE, 2009bk–bm) to the NRC staff’s 

requests for additional information (RAIs). 

This chapter addresses 2 of the 14 model abstraction sections indicated in the Yucca Mountain 

Review Plan (YMRP) (NRC, 2003aa): airborne transport of radionuclides (YMRP Section 

2.2.1.3.11) and redistribution of radionuclides in soil (YMRP Section 2.2.1.3.13). The NRC 

staff’s assessment of information in DOE’s SAR for these two abstraction sections used the 

guidance in the YMRP to conduct a risk-informed review. Together, airborne transport of 

radionuclides during a potential future explosive volcanic eruption that generates tephra (ash) 

and redistribution of radionuclides deposited on the landscape by that eruption constitute DOE’s 

volcanic ash exposure scenario in its biosphere model for the Total System Performance 

Assessment (TSPA) (SAR Section 2.3.10.2.6). As part of the review of redistribution of 

radionuclides, the NRC staff evaluated DOE’s performance assessment for the exposure 

scenario where radionuclide-contaminated groundwater may cause the reasonably 

maximally exposed individual (RMEI) to be exposed to a dose (SAR Section 2.3.10.2.3). SAR 

Figure 2.3.10-1 displayed a separate flow of information for the volcanic ash exposure scenario 

compared to the groundwater exposure scenarios in the DOE performance assessment. This 

chapter reflects this separation of information and presents the NRC staff’s review and 

evaluation, first for the volcanic ash exposure scenario and second for the groundwater 

exposure scenario. 

For the volcanic ash exposure scenario, the NRC staff evaluated the following three abstracted 

models addressed in DOE’s SAR: 

1. Airborne transport, dispersion, and deposition of tephra and high-level waste 

2. Redistribution by fluvial (running water or stream) transport of contaminated tephra 

within the Fortymile Wash catchment basin, mixing and dilution with noncontaminated 

sediment, and deposition of the tephra-sediment mixture on the Fortymile Wash alluvial 

fan at the reasonably maximally exposed individual location 

3. The downward migration of radionuclides in the soil at the alluvial fan in the 

accessible environment 
15-2 

The latter two abstracted models comprise DOE’s performance assessment for redistribution 

of radionuclides in soil (YMRP Section 2.2.1.3.13), while the first abstracted model 

constitutes DOE’s performance assessment for airborne transport of radionuclides (YMRP 

Section 2.2.1.3.11). 

For the groundwater exposure scenario, this chapter presents the NRC staff evaluation 

of DOE’s surface soil submodel, which is also part of the performance assessment for 

redistribution of radionuclides in soil. For both exposure scenarios, the final outputs of 

the abstractions evaluated in this chapter are radionuclide concentrations in soil, which 

are direct inputs to the DOE biosphere model for calculating annual doses to the 

reasonably maximally exposed individual (reviewed by the staff in TER Section 2.2.1.3.14). 

Associated with this, TER Section 2.2.1.3.4 presents the NRC staff evaluation of the 

radionuclide inventory, which is an input to the volcanic ash exposure scenario (SAR 

Figure 2.3.10-3). TER Section 2.2.1.4 provides the NRC staff’s evaluation of the overall TSPA. 

2.2.1.3.13.2 Evaluation Criteria

The NRC staff’s review of model abstractions used in DOE’s postclosure performance 

assessment, including those considered in this chapter for airborne transport and redistribution 

of radionuclides, is guided by 10 CFR 63.114 (Requirements for Performance Assessment) and 

63.342 (Limits on Performance Assessments). The DOE Total System Performance 

Assessment model is reviewed in TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment, and evaluate in sufficient detail 

those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of features, events, and processes is 

presented in TER Chapter 2.2.1.2.1. 10 CFR 63.114(a) provides requirements for performance 

assessment for the initial 10,000 years following disposal. 10 CFR 63.114(b) and 63.342 

provide requirements for the performance assessment methods for the time from 10,000 years 

through the period of geologic stability, defined in 10 CFR 63.302 as 1 million years following 

disposal. These sections require that through the period of geologic stability, with specific 

limitations, DOE should 
15-3 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those features, events, and processes used in 

the performance assessment for the initial 10,000-year period (10 CFR 63.342) 

This model abstraction of airborne transport and redistribution of radionuclides involves 

igneous activity. 10 CFR 63.342(c)(1) provides requirements for assessing the effects of 

seismic and igneous activity on the repository performance, subject to the probability limits 

given in 10 CFR 63.342(a) and (b). Specific constraints on seismic and igneous activity 

analyses are in 10 CFR 63.342(c)(1)(i) and (ii), respectively. 

In addition,10 CFR 63.305 states the following requirements for characteristics of the reference 

biosphere, as used in this abstraction for redistribution of radionuclides in soil: 

 Features, events, and processes that describe the reference biosphere must be 

consistent with present knowledge of the conditions in the region surrounding the Yucca 

Mountain site [10 CFR 63.305(a)]. 

 DOE should not project changes in society, the biosphere (other than climate), or human 

biology or increases or decreases of human knowledge and technology; all analyses 

done to calculate dose must assume that all of those factors are constant as they are at 

the present [10 CFR 63.305(b)]. 

 DOE must vary factors related to the geology, hydrology, and climate based upon 

cautious but reasonable assumptions of the changes in these factors that could affect 

the Yucca Mountain disposal system during the period of geologic stability, consistent 

with the requirements for performance assessments specified at 10 CFR 63.342. 

 Biosphere pathways must be consistent with arid or semi-arid conditions 

[10 CFR 63.305(d)]. 

The NRC staff review of the SAR and supporting information follows the guidance laid out in 

YMRP Sections 2.2.1.3.11, Airborne Transport of Radionuclides, and 2.2.1.3.13, Redistribution 

of Radionuclides in Soil, as supplemented by additional guidance for the period beyond 

10,000 years after permanent closure (NRC, 2009ab). The YMRP acceptance criteria that 

provide guidance for the NRC staff’s evaluation of DOE’s model abstraction of airborne 

transport and redistribution of radionuclides are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

NRC staff review used a risk-informed approach and the guidance provided by the YMRP, as 

supplemented by NRC (2009ab), to the extent reasonable for aspects of airborne transport and 

redistribution of radionuclides important to repository performance. The NRC staff considered 
15-4 

all five criteria provided in the Yucca Mountain Review Plan in its review of information provided 

by DOE. In the context of these criteria, only those aspects of the model abstraction that 

substantively affect the performance assessment results, as assessed by the NRC staff, are 

discussed in detail in this chapter. The NRC staff’s assessment is based both on risk 

information provided by DOE, and on NRC staff knowledge gained through experience and 

independent confirmatory analyses. 

2.2.1.3.13.3 Technical Evaluation

In SAR Figure 2.3.11-1, DOE presented the information flow for the volcanic eruption modeling 

case. As stated previously, DOE’s abstracted model on atmospheric dispersal and deposition 

of tephra constitutes its performance assessment of airborne transport of radionuclides. DOE’s 

abstracted models for tephra redistribution and vertical radionuclide migration in soil together 

comprise the performance assessment of redistribution of radionuclides in soil. 

Airborne transport of radionuclides pertains to the volcanic ash exposure scenario, which 

involves a possible disruption of the Yucca Mountain repository by a future volcanic eruption. 

In this scenario, high-level radioactive waste is mixed with magma and ejected into the 

atmosphere incorporated within the volcanic tephra {fragments of cooled magma that are 

transported through the air, including ash particles that have diameters less than 2 mm 

[0.08 in]}. The airborne transport abstracted model accepts the number of waste packages 

intersected by volcanic conduits, provided in SAR Section 2.3.11.4.2.1 and evaluated in TER 

Section 2.2.1.3.10, and estimates the concentration and thickness of radionuclide-contaminated 

tephra that could be deposited on the ground surface of the Yucca Mountain region (SAR 

Figure 2.3.11-1). As depicted in SAR Figure 2.3.11-1, DOE then uses this information as input 

to the volcanic ash exposure scenario (SAR Section 2.3.10) for estimating the dose to the 

reasonably maximally exposed individual via surface redistribution of contaminated tephra and 

by migration of radionuclides from tephra particles into the soil, as described next. 

Redistribution abstracted models together calculate the time-dependent profile of radionuclide 

concentration in the contaminated soil horizon at the reasonably maximally exposed individual 

location. The DOE airborne transport abstracted model (described previously) provides input on 

the tephra deposit for the tephra redistribution calculations of waste concentrations in 

redistributed tephra. Another DOE redistribution-related abstracted model uses this information 

to estimate the downward migration of radionuclides from tephra into soil at the alluvial fan of 

Fortymile Wash and calculate the concentration of waste in redistributed tephra at the 

reasonably maximally exposed individual location (SAR Figure 2.3.11-1). Waste concentration 

information from DOE’s redistribution models is coupled with information on the radionuclide 

inventory (radionuclide activities per unit mass of waste) to yield radionuclide concentration 

profiles in soil. The fraction of tephra that can be resuspended and inhaled by the reasonably 

maximally exposed individual during activities such as tillage is also important; this is the 

dominant exposure pathway for the first 10,000 years after repository closure in DOE’s 

performance assessment analysis. 

In this section, the NRC staff also evaluates the DOE surface soil submodel for the groundwater 

exposure scenario, described in SAR Section 2.3.10. In this model, radionuclides are 

considered to be added to the surface soil from irrigation with contaminated groundwater. 

The surface soil submodel accepts the concentration of radionuclides in groundwater in 

the accessible environment (as provided in SAR Section 2.4.4 and reviewed in TER 

Section 2.2.1.3.12) and calculates loss of radionuclides from the surface soil via mechanisms 

such as radioactive decay, leaching into deeper zones, erosion of soil particles, and gaseous 
15-5 

releases to the atmosphere. As depicted in SAR Figures 2.3.10-1 and 2.3.10-10, the output 

from the surface soil model is used by the rest of DOE’s biosphere model, which is reviewed in 

TER Section 2.2.1.3.14. 

NRC Staff Perspective on Risk 

The volcanic ash exposure scenario and groundwater exposure scenario provide different 

contributions to repository performance in the DOE assessment. The NRC staff has evaluated 

DOE’s assessment that the volcanic ash exposure scenario (volcanic eruption modeling case) 

does not significantly influence repository performance. DOE shows that its mean dose 

contribution is more than a factor of 1,000 smaller than the overall peak dose within the initial 

10,000 years and more than a factor of 10,000 smaller than the overall peak dose after 

10,000 years (SAR Figure 2.4-18). Further, DOE’s dose exposure assessment is consistent 

with the NRC staff’s independent analyses (see TER Section 2.2.1.4.1.3.3.2). The remaining 

DOE modeling cases depicted in SAR Figure 2.4-18 constitute the groundwater exposure 

scenario. The groundwater exposure scenario dominates the overall peak dose within 

10,000 years and after 10,000 years (SAR Figure 2.4-18). Although this risk information 

suggests that the NRC staff should focus on the surface soil submodel in the groundwater 

exposure scenario and only conduct a simplified review of the volcanic ash exposure focusing 

on the bounding assumptions, the NRC staff also used DOE’s multiple barrier information, 

consistent with YMRP Section 2.2.1.3, to inform its review. 

The NRC staff’s review of SAR Section 2.1.3 determined that no aspect of the material reviewed 

in this chapter is identified as a barrier. TER Section 2.2.1.1 describes the NRC staff’s 

evaluation of multiple barriers. However, DOE did identify that a volcanic event could adversely 

affect the engineered barrier system’s ability to prevent the release or reduce the release rate of 

radionuclides from the waste, and to prevent or reduce the movement of radionuclides away 

from the repository (SAR Section 2.3.11.1) by destroying the waste packages and releasing the 

contained radionuclides in the erupting material (SAR Section 2.1.2.2.5). On the basis of the 

NRC staff’s review in TER Section 2.2.1.3.13.3.1, the NRC staff notes that DOE’s technical 

basis for barrier capability was based on, and is consistent with, the technical basis for the 

performance assessment. 

For the volcanic ash exposure scenario, the NRC staff’s evaluation of DOE’s performance 

assessment is presented in TER Section 2.2.1.4.1.3.2. The four input quantities identified 

for that evaluation (fraction entrained in ash, tephra volume, tephra density, and ash areal 

concentration) directly relate to the airborne transport abstracted model reviewed in this 

chapter. All three abstracted models evaluated in this chapter for the volcanic ash 

exposure scenario account for the bulk transport of radionuclides in waste and do not 

include processes that separate radionuclides or transport radionuclides at different rates. 

For this reason, the evaluation of the abstracted models for volcanic ash exposure in this 

chapter does not focus on individual radionuclide contributions to total dose, which are 

considered in TER Section 2.2.1.4.1.3.3.2. 

However, an understanding of the dominant radionuclides, and their exposure pathways, that 

contribute to dose provides risk insights into the important aspects of the performance 

assessment for the DOE volcanic eruption modeling case. SAR Figure 2.4-32 identified the 

contribution of radionuclides to mean annual dose for the volcanic eruption modeling case. 

SAR Table 2.3.10-15 identified the average percentage exposure pathway contributions to the 

annual dose for the volcanic ash exposure scenario. On the basis of its review of DOE’s 

information, NRC staff notes that at early times (i.e., before 500 years), the overall dose is 
15-6 

dominated by six radionuclides: Sr-90, Cs-137, Pu-238, Pu-239, Pu-240, and Am-241. 

At longer times (i.e., after 5,000 years), the dose is dominated by Pu-239 and Pu-240, 

and at very long times (i.e., after 100,000 years), the dose is dominated by Ra-226 (SAR 

Figure 2.4-32). On the basis of its identification of the dominant dose contributors and review 

of information in DOE’s SAR Table 2.3.10-15, NRC staff notes that inhalation of particulates 

from the resuspension of contaminated tephra deposits is the dominant dose pathway for 

10,000 years. After about 100,000 years, for Ra-226, the dominant exposure pathway is 

external exposure (SAR Table 2.3.10-15). The NRC staff’s independent analyses, documented 

in NRC, Volume 2, Appendix D (2005aa), gave similar results about the dominant exposure 

pathway. Thus, the NRC staff has reviewed the performance assessment to (i) focus on those 

processes that most affect the concentration of waste in the resuspendable layer and (ii) focus 

on those processes that most affect the concentration of waste in the soil layers that control 

external exposure. 

For the groundwater exposure scenario, the surface soil submodel reviewed in this chapter is 

one component of the abstracted model for the biosphere that calculates biosphere dose 

conversion factors. For the radionuclides Tc-99, I-129, Np-237, and Pu-242 discussed in 

TER Section 2.2.1.4.1.3 in the table on groundwater biosphere dose conversion factors and 

TER Section 2.2.1.4.1.3.3, the pathways linked to the surface soil submodel account for up to 

50 percent of the radionuclide biosphere dose conversion factor, as identified in SNL 

Tables 6.13-1 and 6.13-2 (2007ac). 

On the basis of these risk considerations, the NRC staff conducted a risk-informed 

review of airborne transport of radionuclides and redistribution of radionuclides in the soil. The 

NRC staff focused on those aspects of these model abstractions that impact calculated dose to 

the reasonable maximally exposed individual. To assess the effect that the combined 

uncertainties could have on calculated dose, the NRC staff also focused on those aspects that 

could cause at least a factor of two effect on intermediate model outputs over the range of an 

individual parameter value. 

As identified previously, SAR Figure 2.3.10-1 displayed a separate flow of information for the 

volcanic ash exposure scenario compared to the groundwater exposure scenarios in the DOE 

performance assessment. The NRC staff’s evaluations of the DOE information on the volcanic 

ash exposure scenario and the surface soil submodel for the groundwater exposure scenario 

are documented in TER Sections 2.2.1.3.13.3.1 and 2.2.1.3.13.2, respectively. 

2.2.1.3.13.3.1 Assessment and Review of the Volcanic Ash 

 Exposure Scenario

The NRC staff’s evaluations of DOE’s abstracted models on (i) airborne transport, dispersion, 

and deposition of tephra and high-level waste, (ii) redistribution of tephra, and (iii) the vertical 

movement of radionuclides in the soil at the alluvial fan in the accessible environment are 

presented separately in three subsections. 

In addition to reviewing the individual abstracted models, the NRC staff also reviewed how DOE 

implemented these models into the Total System Performance Assessment (TSPA). To place 

the individual model abstractions into the framework of the TSPA analysis, the NRC staff 

summarizes DOE’s implementation of the volcanic eruption modeling case next. 

DOE integrated abstracted models of TSPA analysis for the volcanic eruption modeling case 

(volcanic interaction with the repository, atmospheric transport, tephra redistribution, volcanic 
15-7 

ash exposure) in a GoldSim modeling environment. DOE used the initial radionuclide inventory 

from a “blended” waste package to calculate radionuclide transport, as described in the review 

of the previously mentioned submodels. A blended waste package inventory was calculated by 

using a weighted average of commercial spent nuclear fuel and codisposal waste packages and 

inventories. Following a conditional future eruptive event, tephra transport and redistribution are 

abstracted to occur instantaneously (i.e., radionuclide waste transport to the reasonably 

maximally exposed individual is instantaneous) (SAR Section 2.3.11.4.2.3.1). The time 

dependence of radionuclide diffusion (downward migration) into the soil at the reasonably 

maximally exposed individual location was accounted for in the tephra redistribution model. The 

radionuclide concentration in the soil at the reasonably maximally exposed individual location, in 

g/cm2

, was modified by a “decay factor” to account for radionuclide decay and ingrowth. The 

resultant source term was provided to the volcanic ash exposure submodel to calculate dose. 

2.2.1.3.13.3.1.1 Airborne Transport Modeling 

The NRC staff conducted a risk-informed review of airborne transport of radionuclides, 

concentrating on aspects important to the volcanic ash exposure scenario in the DOE 

performance assessment. 

Important Aspects of Airborne Transport 

The abstracted model for atmospheric transport of radionuclides determines the characteristics 

of contaminated tephra deposited on the surrounding landscape. DOE’s analysis results 

indicated that the following parameters for airborne transport were influential to the volcanic ash 

exposure scenario: magma partitioning factor, tephra volume, eruptive power and duration, and 

mean ash particle diameter, wind direction, and wind speed. The magma partitioning factor is a 

fraction between zero and one and acts as a direct multiplier on the eruption source term and 

eruptive dose, similar to the number of waste packages entrained into the erupting magma that 

pertains to the review in TER Section 2.2.1.3.10. DOE’s analysis results [e.g., SNL Figures C–1 

and C–2 (2007ab)] showed that waste concentration in tephra is sensitive to tephra volume, 

eruptive power, and mean ash particle diameter. DOE’s sensitivity analyses concluded that the 

initial tephra thickness at the reasonably maximally exposed individual location (near the 

Fortymile Wash alluvial fan apex) is strongly dependent on wind direction, wind speed, and 

mean ash particle diameter, and moderately dependent on eruptive power and eruptive 

duration, as identified in SNL Appendix C (2007ab). DOE found that other parameters of its 

airborne transport abstracted model were less influential on tephra thickness. 

Summary of Information on Airborne Transport 

The volcanic eruption modeling case was described in SAR Section 2.3.11.4. In SAR 

Table 2.3.11-1, DOE identified the features, events, and processes included in the TSPA. 

For a repository-drift-penetrating basaltic eruption, DOE modeled the contamination of tephra 

with waste and the amount of radionuclides contained within the tephra-fall deposit. On the 

basis of studies of analog volcanoes, DOE apportioned contaminated magma into three 

volcanic products; namely, lava, scoria cone-forming deposits (selectively composed of the 

largest tephra fragments), and more widespread tephra-fall deposits. To account for waste that 

is incorporated in volcanic ejecta that form scoria cones and lava flows, DOE applied a magma 

partitioning factor for the fraction of waste incorporated with tephra to the total waste erupted. In 

the DOE model for the extrusive event, only waste incorporated with tephra contributes 

radiological dose to the reasonably maximally exposed individual; waste apportioned into lava 
15-8 

flows and scoria cones does not contribute to dose. The amount of waste incorporated in 

tephra scales with the magma partitioning factor. 

In its igneous eruption modeling, DOE identified that all the explosive phases of the most 

likely future eruption, on the basis of the interpreted behavior of the youngest volcano 

near the repository site (Lathrop Wells), are considered to be violent Strombolian (SAR 

Section 2.3.11.4.1). The eruption would produce plumes of tephra in the atmosphere that 

could transport particulates, including high-level radioactive waste, downwind from the vent. 

This process could deposit radionuclides at the reasonably maximally exposed individual 

location, either from direct sedimentation of contaminated ash particles from the volcanic plume 

or from the remobilization by wind or surface water of the radionuclide-contaminated volcanic 

ash after initial deposition. DOE’s approach to determining waste concentration in the tephra is 

sensitive to the tephra volume. For example, smaller tephra volumes result in higher waste 

concentration in tephra (i.e., waste mass per unit mass of tephra) for the same number of waste 

packages entrained. In SNL (2008ag), DOE evaluated exposure to airborne concentrations of 

radionuclides that are captured in the tephra during the eruption (direct tephra-fall exposure) 

and found that it did not increase expected annual dose significantly due to the extremely short 

exposure duration. In the following review, “tephra” refers to airborne magmatic fragments of all 

sizes, whereas “ash” refers specifically to particles less than 2 mm [0.08 in] in diameter. 

A violent Strombolian-type eruption is characterized by the development of a sustained, buoyant 

plume of hot air and volcanic tephra that commonly rises several kilometers [a few miles] above 

the volcano. DOE modeled the dispersal processes as turbulent advection diffusion using the 

Suzuki (1983aa) model. The ASHPLUME conceptual model and the ASHPLUME code 

(Jarzemba, et al., 1997aa), as used by DOE, implement the Suzuki approach to model the 

dispersal of tephra on the basis of the diffusion of particles from an eruption column, horizontal 

diffusion of particles by atmospheric turbulence, horizontal advection by atmospheric circulation, 

and settling of particles by gravity. ASHPLUME accounts for incorporation and entrainment of 

waste particles into magma during a potential volcanic eruption through the repository and 

estimates the concentration (expressed as g/cm2

) and thickness of radionuclide-contaminated 

tephra deposited on the ground surface. Following a conditional eruptive event, tephra 

transport is abstracted to occur instantaneously (i.e., radionuclide waste transport to the 

reasonably maximally exposed individual is instantaneous). 

In DOE’s approach, wind direction significantly affects tephra dispersal and deposit location. 

Tephra deposits that might fall at the reasonably maximally exposed individual location are 

strongly dependent on the presence of northerly winds that would transport the tephra plume to 

the south from a volcanic vent within the repository area (SAR Figure 2.3.11-13). The tephra 

deposit at the reasonably maximally exposed individual location becomes negligible for winds 

without a strong northerly component (north, north-northwest, or north-northeast), as identified 

in SNL Figure C–7 and Table D–5 (2007ab). The majority of the wind vectors at the site result 

in tephra being deposited to the east of Yucca Mountain (SAR Figure 2.3.11-15). According to 

SNL Appendix K (2007ab), this wind direction provides a source of material for remobilization 

within the Fortymile Wash catchment basin. DOE’s sensitivity analyses indicated that wind 

direction produced a greater contribution to dose than plume spread and divergence, as 

outlined in SNL Figure K–4c (2007ab). These sensitivity analyses also demonstrated that 

increasing the wind speed causes the tephra deposit center mass to shift downwind. 
15-9 

NRC Staff’s Evaluation of Airborne Transport 

The NRC staff reviewed SAR Section 2.3.11 on the volcanic eruption modeling case, additional 

information provided in response to the NRC staff’s request for additional information (DOE, 

2009bk–bm), the supporting DOE information on atmospheric transport of contaminated tephra 

presented in SNL (2007ab), and information published in peer-reviewed literature (e.g., Suzuki, 

1983aa; Hurst and Turner, 1999aa; Andronico, et al., 2008aa). 

Model Integration 

Potentially relevant features, events, and processes in DOE’s TSPA model were listed in SAR 

Table 2.2-1. Model abstractions comprise features, events, and processes that have been 

screened in from DOE’s scenario analysis. TER Section 2.2.1.2.1 documents the NRC staff’s 

evaluation of the DOE scenario analysis and features, events, and processes screening. As 

part of the review of the volcanic modeling case, the NRC staff examined DOE’s information on 

igneous-related features, events, and processes. TER Section 2.2.1.2.1.3.1 considers DOE’s 

list of features, events, and processes for the volcanic exposure scenario, including airborne 

transport. TER Section 2.2.1.2.1.3.2 considers DOE’s screening of all features, events, and 

processes for the volcanic exposure scenario, including airborne transport. DOE excluded a 

related process from consideration due to low consequence (FEP 1.2.04.07.0B). That process 

is concerned with leaching of radionuclides from tephra on the surface into the subsurface and 

into groundwater, whereby radionuclides could be dispersed via the groundwater transport 

pathway. DOE’s exclusion of this process, identified in DOE Enclosure 1 (2009ab), is 

reasonable because the possible contribution to mean dose via this mechanism is considerably 

lower than contributions from the other modeling cases. The NRC staff’s review of the airborne 

transport abstracted model evaluates DOE’s implementation of the only included process 

(FEP 1.2.04.07.0A) associated with airborne transport modeling. 

FEP 1.2.04.07.0A describes finely divided waste particles that may be erupted from a volcanic 

vent and deposited on the land surface from a waste-particle-contaminated ash (tephra) cloud 

or plume. This process is included in the performance assessment through the modeling of an 

eruption that includes airborne transport and tephra deposition (SNL, 2008ab). 

The NRC staff evaluated modeling assumptions and integration in the DOE airborne transport 

abstracted model. DOE assumed that the tephra in a future eruption would be dispersed by 

a violent Strombolian eruption column, characterized by heating of entrained air. In its 

model, the vertical atmospheric transport of the fragmented magma and gas mixture is 

represented as a thermally buoyant plume. The column rises to an altitude of neutral buoyancy 

compared to the surrounding atmosphere, at which point it spreads laterally and the resulting 

plume (ash or tephra cloud) is transported downwind. DOE modeled the dispersal of ash using 

a peer-reviewed model Suzuki (1983aa) originally developed. Because the scale of horizontal 

atmospheric turbulence is much greater than the scale of vertical turbulence for violent 

Strombolian plumes within tens of kilometers [up to 50 mi] of the vent, horizontal diffusion is the 

dominant factor in the model in determining the width of the plume as it advects downwind. 

Therefore, the ASHPLUME conceptual model DOE used to simulate tephra deposition is based 

on a two-dimensional advection-diffusion model in which turbulent diffusion is considered only in 

the horizontal plane. DOE’s treatment of these processes and assumptions is based on 

well-established modeling techniques (Sparks, et al., 1997aa). 

In its tephra-fall modeling, DOE assumed violent Strombolian activity for the entire duration of 

the tephra-forming activity. Although violent Strombolian eruptions may have interruptions in 
15-10 

activity, modeling such an eruption as a continuous process will not underestimate the amount 

and character of tephra modeled for an eruption and thus will not underestimate dose to the 

reasonably maximally exposed individual. In addition, DOE determined the initial plume rise 

velocity using a relationship among eruptive power, eruption duration, conduit diameter, and 

eruption column height and plume conditions for violent Strombolian eruptions, which would not 

underestimate the amount and character of tephra fall deposit (and incorporated waste) 

modeled for an eruption. 

DOE calculated tephra and waste deposited at the reasonably maximally exposed individual 

location for a point located 18 km [11 mi] south of the volcanic vent, as outlined in SNL 

Section 6.5.2.1.17 and Table 8-2 (2007ab). DOE’s results showed that the point assumption is 

conservative and does not underestimate the concentration of waste deposited in the 33-km2

[13-mi2

] reasonably maximally exposed individual location used in the Fortymile Wash Ash 

Redistribution (FAR) model as part of the Fortymile Wash catchment basin, as identified in 

DOE Enclosure 9 (2009bk). Results of supplemental ASHPLUME simulations, discussed 

in DOE Enclosure 9 (2009bk), demonstrated that single point values for tephra and waste 

measured 18 km [11 mi] south of the potential repository would typically represent the 

maximum concentrations that would be deposited at the reasonably maximally exposed 

individual site. 

For the particle sizes in the atmospheric transport model of tephra, the model Suzuki (1983aa) 

developed is appropriate for particles of a mean diameter greater than tens of micrometers 

[about 6 × 10-4 - 1.2 × 10-3 in]. This cutoff is generally accepted to be the lower limit for the 

importance of simple gravitational settling of particles because the fall of smaller particles is 

governed by different physical laws than the Stokes settling assumption of the Suzuki model 

(Suzuki, 1983aa; Heffter and Stunder, 1993aa). The mass of ash particles smaller than 

15 microns [6 × 10-4 in] is less than 2 percent of the total ash mass in most of DOE’s model 

realizations (DOE, 2009bm) and is consistent with violent Strombolian tephra deposits 

(Andronico, et al., 2008aa). 

Data Sufficiency and Data Uncertainty 

The NRC staff evaluated data sufficiency and uncertainty in the ASHPLUME model. DOE 

values for many input parameters in the ASHPLUME model were developed using analogous 

small-volume basaltic volcanic systems (SNL, 2007ab,ae), which is the commonly used 

approach when modeling volcanic eruptions (e.g., Hill, et al., 1998aa). Analogous historic 

violent Strombolian eruptions cited as sources of parameters for use in the ASHPLUME model 

include Tolbachik, Russia (1975); Parícutin, Mexico (1943–1952); and Cerro Negro, Nicaragua 

(1850–1999). DOE developed eruption parameter distributions on the basis of empirical 

relationships from available field data from the deposits of the aforementioned eruptions. The 

NRC staff reviewed the range of important parameters DOE derived from analog volcanoes 

(SNL, 2007ab,ae). The NRC staff’s evaluation included the parameters for magma partitioning 

factor, magma volume, ash particle diameter, eruptive power and duration, wind direction and 

speed, and an eruption column parameter (the diffusion constant, β). These parameters are 

evaluated individually in the following paragraphs. 

Magma Partitioning Factor 

DOE accounted for a proportion of disrupted and erupted waste that is partitioned into 

erosion-resistant products (scoria cone and lava flows) by using a magma partitioning factor 

with a uniform distribution from 0.1 to 0.5 (also discussed in TER Section 2.2.1.3.10). This 
15-11 

range is based on volumetric proportions of cones and lava flows to total erupted volume 

estimated from field measurements at analog volcanoes, as identified in SNL Section 6.5.2.22 

(2007ab). DOE identified in SAR Section 2.3.11.4.1.1.3 that very little erosional modification of 

lava fields of ~ 350,000-year-old volcanoes (Little Black Peak and Hidden Cone) has occurred. 

DOE also indicated that little if any cone scoria at the 80,000-year-old Lathrop Wells volcano 

has yet been remobilized to the base of the cone where it would be available for fluvial transport 

(SAR Section 2.3.11.4.1.1.3). On the basis of this information provided by DOE, the NRC 

staff notes the assumption is reasonable that the proportion of magma ending up as cones and 

flows would not contribute to dose at the reasonably maximally exposed individual location. 

DOE used data from eight analog volcanic eruptions to determine a range of 0.1 to 0.5 for the 

magma partitioning factor (BSC, 2003ad; SNL, 2007ab); estimated doses from the volcanic ash 

exposure scenario are directly proportional to the magma partitioning factor. However, in the 

NRC staff's view, not all the analog eruptions cited by DOE showed significant violent 

Strombolian behavior, and those eruptions that did tended to have magma partitioning factors 

greater than 0.3. DOE used analog data to support the parameter range for the magma 

partitioning factor, including the lower part of the range with values less than 0.3, as discussed 

in DOE Enclosure 8, Table 1 (2009bk). DOE Enclosure 8 (2009bk) stated the tephra 

component is small in many of these basaltic analog eruptions, which is not typical of violent 

Strombolian eruptions. The NRC staff notes that eruptions of volcanoes such as Cinder Cone in 

California, cited as an analog in DOE Enclosure 8 (2009bk), featured only very minor phases of 

the violent Strombolian activity representative of this style of eruption (Heiken, 1978aa). 

Notwithstanding this limitation, the NRC staff estimates that constraining the magma partitioning 

factor to higher values between 0.3 and 0.5 (mean value of 0.4) would imply an increase in 

calculated doses by a factor of 1.33 compared to results for the full parameter range (between 

0.1 and 0.5, with a mean value of 0.3). The NRC staff notes that this small amount of 

uncertainty on estimated doses due to the magma partitioning factor is offset by the DOE 

conservative assumption for atmospheric transport that the entire eruption is modeled as violent 

Strombolian activity in the DOE TSPA. Therefore, the NRC staff notes that the DOE 

ASHPLUME model reasonably represents the airborne transport of radionuclides and does not 

underestimate estimated doses for the volcanic ash exposure scenario. 

Magma Volume 

The NRC staff reviewed the data synthesis and documentation on likely magma volumes for 

future eruptions provided by DOE in SNL (2007ab) and SNL Section 6.3.4.4 (2007ae). The 

NRC staff compared the DOE range of eruptive volumes to independent estimates (Jarzemba, 

1997aa; Jarzemba, et al., 1997aa; Hill and Connor, 2000aa) and notes that they are in 

reasonable agreement. The upper end of this range was based on doubling of the Lathrop 

Wells tephra volume, which was “intended to capture the upper end of the range of uncertainty,” 

and the lower end of the range was based on a calculated tephra fall volume for the smallest 

cone in the region, Northeast Little Cone, as identified in SNL Section 6.3.4.4 (2007ae) and 

DOE Enclosure 3 (2009bl). In the ASHPLUME model, DOE used the relationship among 

eruption power, eruption volume (rather than tephra volume), and eruption duration to constrain 

the range of total mass of tephra. As discussed in SNL Section 6.5.2.1 (2007ab), DOE 

constrained eruptive power on the basis of a few observed violent Strombolian eruptions. DOE 

showed that the tephra-fall volume in the DOE TSPA ranged from 0.004 to 0.14 km3

 [0.001 to 

0.03 mi3

] with a mean value of 0.038 km3 [0.01 mi3], as identified in DOE Enclosure 3, Figure 1 

(2009bl). When considered using the conservative assumption of an entirely violent 

Strombolian eruption, the NRC staff notes that the resulting range is reasonable because the 

mass fluxes of magma from the vent are within observed limits. 
15-12 

Ash Particle Diameter 

The log-triangular distribution for the mean ash particle diameter {minimum value of 0.001 cm 

[4 × 10-4 in]}, a mode value of 0.01 cm [4 × 10-3 in], and a maximum value of 0.1 cm [0.04 in] 

(SNL, 2007ab) and the presented rationale (SNL, 2007ae) are appropriately derived from data 

obtained at analogous small-volume basaltic volcanoes. The NRC staff considers this 

particle size range to be representative of available information on violent Strombolian eruptions 

(e.g., Andronico, et al., 2008aa; Pioli, et al., 2008aa). For comparison, NRC independently 

assessed basaltic tephra-fall deposits from the 1995 eruption of Cerro Negro (Hill, et al., 

1998aa) and determined an average particle diameter of 0.07 cm [0.03 in]. This diameter is 

within the range of average ash-particle diameters considered by DOE. 

Eruptive Power and Duration 

DOE analyzed the eruptive parameters of analog volcanoes to develop the range and 

parameter distribution for eruptive power. The NRC staff compared the DOE eruptive power 

parameter values to an independent NRC estimate from Leslie, et al. Table 16-1 (2007aa). The 

DOE range was broader than the independently estimated NRC range, but the majority of the 

sampled values in the DOE TSPA fall within the NRC range. The geometric means for the two 

distributions are similar. The DOE parameterization for eruptive power is reasonable because it 

used analog volcano data to develop parameterization for eruptive power. Duration, when 

considered with erupted volume, is one indicator of eruptive power, and hence partly controls 

the height to which the eruption column reaches. The NRC staff notes that DOE’s 

parameterization for eruption duration is reasonable because it is consistent with the range of 

observations from analog eruptions. 

The upper part of DOE’s eruptive power range for possible future basaltic eruptions at Yucca 

Mountain leads to modeled eruption column heights of up to 8.2 km [5.0 mi] (SNL, 2007ab). 

The NRC staff notes that this upper bound for a violent Strombolian eruption is consistent with 

results from studies of historic eruptions (Pioli, et al., 2008aa). The NRC staff notes that SAR 

Sections 2.3.11.1 and 2.3.11.5 mentioned the ability of ASHPLUME to model eruption columns 

up to 13 km [8.1 mi] high. To build confidence in the ASHPLUME model, DOE exercised this 

extended upper range for column height to model tephra dispersal for a volcano in New Zealand 

with a different eruption type, as identified in SNL Appendix J, p. J–23 (2007ab) and compared it 

with published results on tephra dispersal for that volcano. The NRC staff notes that column 

heights above 9 km [5.6 mi] are appropriate for modeling the eruption in New Zealand but 

highlights that such column heights are not appropriate for a violent Strombolian eruption at 

Yucca Mountain, as identified in SNL Appendix E, p. E–3 (2007ab). DOE clarified that column 

heights in the ASHPLUME realizations ranged from lower values of about 2 km [1.2 mi] up to a 

maximum value of 8.2 km [5.0 mi] in the DOE TSPA, as identified in DOE Enclosure 1 (2009bk). 

Consistent with Jarzemba (1997aa), DOE used observations from analog volcanoes to develop 

this range of eruptive power. Because the technical basis provided in DOE Enclosure 1 

(2009bk) to support this range of column heights shows that the heights did not exceed 8.2 km, 

the supporting data and treatment of parameter uncertainty are reasonable for modeling a future 

violent Strombolian-eruption-style event at Yucca Mountain. 

Wind Direction and Speed 

DOE developed distribution functions for wind speed and wind direction from data provided in 

National Oceanic and Atmospheric Administration (NOAA) (2004aa). The full range of wind 

speeds from near zero to the maximum winds observed at the higher altitudes was represented 
15-13 

in the wind-speed distribution used in TSPA analyses (SNL, 2007ab). DOE accounted for 

uncertainty by stochastically sampling wind speed and direction for each eruption realization. 

DOE Enclosure 1 (2009bk) provided a technical basis by demonstrating that wind speed and 

wind direction are not correlated at different altitudes in the ASHPLUME model. DOE assigned 

the same wind speed for the top and lower heights within the column. NOAA (2004aa) showed 

that for the Yucca Mountain region, wind speeds tend to be greater at higher altitudes (a normal 

situation); thus, NRC considers that this assignment will not underestimate the dispersal 

characteristics of a violent Strombolian eruption. This is because NOAA (2004aa) included 

altitudes up to 13 km [8 mi] and DOE sensitivity analyses in SNL Appendix C (2007ab) and 

DOE Enclosure 1 (2009bk) model eruption column heights only up to 8.2 km [5 mi]. The NRC 

staff’s review of the DOE sensitivity analyses (SNL, 2007ab) shows that the concentration of 

tephra at the reasonably maximally exposed individual location after redistribution is relatively 

insensitive to variable wind conditions during an eruption. The NRC staff notes that the 

assumption of constant wind speed and direction during an eruption will not affect performance 

assessment results. For these reasons, the NRC staff notes that DOE provided data and 

documentation (SNL, 2007ab,ae) to support the selection of parameter ranges for wind speed. 

Eruption Column Parameter 

Although DOE determined that the column diffusion constant (β) was not an influential 

parameter, the NRC staff reviewed the DOE use of this parameter in the ASHPLUME model 

because it controls the vertical distribution of the mass of tephra particles within the eruption 

column and helps determine the height at which particles exit the column and enter downwind 

atmospheric transport. The parameter range for β that DOE used is 0.01 to 0.5, and values at 

the lower end of the distribution lead to more of the tephra mass diffusing (falling) from the 

eruption column at relatively low altitudes in the modeled eruption (SNL, 2007ab). DOE 

modeled (DOE, 2009bk) small tephra particle diameters {e.g., 0.005 cm [0.002 in]}, relatively 

high initial rise velocities {e.g., 9,000 cm/s [3,543 in/s]}, and column diffusion coefficient values 

(β) less than 0.3 to support upward-concentration particle distributions at realistic heights for 

violent Strombolian eruption columns. Using the 1995 Cerro Negro eruption in Nicaragua as an 

analog, the NRC staff performed independent sensitivity analyses for β with a different 

theoretical model for tephra dispersal. Results showed variations in deposit thickness up to a 

factor of approximately two over a range of distances on the order of 18 km [11 mi] and beyond 

(Hill, et al., 1998aa; Winfrey, 2005aa; Janetzke, et al., 2008aa). DOE (2009bk) estimated that 

varying β from 0.01 to 0.5 reduces the estimated waste concentration by less than 30 percent. 

On the basis of its review of DOE’s result and general agreement with the confirmatory analyses 

cited previously, the NRC staff notes that DOE’s modeling of tephra dispersal from violent 

Strombolian eruption columns using β values less than 0.3 would not significantly affect 

dose estimates. 

Model Output: Waste Concentration in Tephra 

The NRC staff evaluated the outputs from the DOE abstracted model, described in SNL 

pp. 6–10 (2007ab), on airborne transport for the waste concentration in tephra and its spatial 

variation with distance and direction from the vent. Although DOE did not determine the waste 

incorporation ratio to be a significant parameter, NRC evaluated waste incorporation into 

magma as DOE presented (SNL, 2007ab; SAR Section 2.3.11.4.2) and considered its 

independent analyses (Codell, 2004aa). The NRC staff notes that the DOE tephra and waste 

incorporation analysis combined particles according to the compatibility of their respective 

particle size distributions, consistent with conceptual models of eruptive conduit and 

fragmentation processes. DOE Enclosure 2 (2009bk) and DOE Enclosures 2 and 3 (2009bm) 
15-14 

provided information on the modeled spatial variation of waste concentration in tephra, which 

demonstrates how much calculated concentrations varied within the same realization (i.e., how 

the concentration at downwind distances differed from waste concentrations in tephra closer to 

the vent). On a per-mass basis, waste constitutes a very small fraction of the mass in tephra 

deposits. Specifically, DOE showed the mass of waste per unit mass of tephra was between 

10-5 and 10-8 in deposits for two representative realizations in DOE Enclosure 2, Figure 1 

(2009bk) and DOE Enclosure 3, Supplemental Figure 1 (2009bm). DOE Enclosures 2 and 3 

(2009bm) clarified that these values correspond to a single waste package and do not account 

for the partitioning of waste into scoria cone and lava flows. 

In Center for Nuclear Waste Regulatory Analyses Task P–15, p. P15 B–4 (CNWRA, 2007aa), 

the NRC staff compared these waste concentrations in tephra to independent estimates of 

waste concentration in tephra from a subset of realizations with the largest values for a single 

metric ton of waste erupted and accounted for a typical amount of waste entrained in an 

eruption. NRC staff estimates of waste concentration in tephra deposits are in general 

agreement with the DOE values. On the basis of the variation in waste concentration provided 

by DOE, NRC staff notes that variations in waste concentration over the land area representing 

the location of reasonably maximally exposed individual (i.e., alluvial fan of Fortymile Wash) are 

not expected to be large within individual realizations. These variations in waste concentration 

appear to relate to particle size effects on atmospheric transport. This is because, on the basis 

of the character of tephra deposits in general, smaller sizes of combined tephra and waste 

particles would represent a greater portion of the deposit at farther distances from the vent, as 

described in DOE Enclosure 2, Section 1 (2009bk). Therefore, DOE’s model reasonably 

represents the fraction of waste in the tephra deposit. 

Model Uncertainty 

The NRC staff evaluated model uncertainty in the DOE abstracted model for airborne transport. 

DOE addressed model uncertainty by considering in SNL (2007ab) several different and widely 

accepted alternative models, including a Gaussian plume model (PUFF), a gas-thrust code 

(ASHFALL; Hurst and Turner, 1999aa), and TEPHRA (Winfrey, 2005aa). DOE also evaluated 

an alternative igneous source term model developed by the NRC staff (Codell, 2003aa) to 

investigate the processes of waste fragmentation and incorporation into the tephra, and 

determined that this alternative model was not significantly different from ASHPLUME (SNL, 

2007ab), in accord with Codell (2003aa). DOE specifically chose the ASHPLUME model 

(Jarzemba, et al., 1997aa) because it includes both tephra dispersal and waste incorporation, 

as used for performance assessment analyses. DOE also accounted for model uncertainty by 

considering and evaluating several alternative conceptual models for downwind transport of 

tephra (and waste) from violent Strombolian eruptions. The results of all of these models are in 

general agreement (SNL, 2007ab). 

Model Support 

DOE supported its model results with (i) an independent technical evaluation, SNL Appendix E 

(2007ab); (ii) a comparison to field observations from an analog eruption; and (iii) a comparison 

to another airborne transport code. With regard to the latter, the ASHFALL code (Hurst and 

Turner, 1999aa) represents sophisticated models incorporating the physics of tephra transport 

and deposition but does not include radionuclide transport. ASHFALL uses the same 

advective-diffusive relationships as ASHPLUME, but employs time- and altitude-dependent wind 

conditions for tephra dispersal and more explicitly treats tephra particle settling velocities. DOE 

used the ASHPLUME code in two sets of model runs to reproduce published output from the 
15-15 

ASHFALL code for constant wind conditions and a variable wind field. SNL Appendix J 

(2007ab) compares the ASHPLUME and ASHFALL model computations, showing that 

ASHPLUME calculates tephra thicknesses that are within a factor of two of ASHFALL results. 

DOE also supported its abstracted model with a comparison to field measurements of tephra 

thickness for the 1995 eruption at Cerro Negro, Nicaragua, outlined in SNL Appendix L 

(2007ab). The NRC staff reviewed the information cited in items (i) to (iii) of this paragraph and 

notes that the calculated thicknesses are in reasonable agreement with measured values 

described in SNL Figure L–6 (2007ab). 

Summary of Conclusions for Airborne Transport 

On the basis of this evaluation, the DOE ASHPLUME model provides a reasonable estimate for 

the airborne transport of radionuclides. The documentation provided in SNL (2007ab,ae) 

described parameterization of the abstracted model. 

In TER Section 2.2.1.4.1.3.3.2, the NRC staff evaluates DOE’s performance assessment, 

including the volcanic ash exposure scenario. In that evaluation, four input quantities of the 

airborne transport abstraction (fraction entrained in ash, tephra volume, tephra density, and ash 

areal concentration) relate to the representation of the performance assessment for atmospheric 

transport. These input quantities are considered in the following four paragraphs. 

In the DOE model for an extrusive volcanic event, the amount of waste incorporated in tephra 

scales directly with the magma partitioning factor (SNL, 2007ab). On the basis of the relative 

proportions of eruptive products at analog volcanoes, DOE selected a range between 0.1 and 

0.5 for this parameter, which acts as a direct multiplier on the eruption source term and eruptive 

dose. The NRC staff notes that a fraction entrained in ash of 0.3 is reasonable for use in the 

representation calculation in TER Section 2.2.1.4.1.3.2. 

DOE analyzed tephra-fall volumes for Quaternary Period (approximately last 2 million years) 

volcanoes in the Yucca Mountain region by comparison with fall:cone and cone:lava volume 

ratios for well-preserved young basaltic volcanoes. Violent Strombolian volcanic activity 

usually yields tephra-fall deposit volumes roughly twice those of the volcanic cone (Hill and 

Connor, 2000aa). For Lathrop Wells volcano, an appropriate example of the type of eruptive 

event that could disrupt a repository at Yucca Mountain, the estimated tephra volume is 

0.07 km3

 [0.017 mi3] (SNL, 2007ae). The NRC staff notes that a tephra volume of 0.07 km3

[0.017 mi3

] is reasonable for use in the representation calculation in TER Section 2.2.1.4.1.3.2. 

Bulk in-situ density of tephra-fall deposits typically ranges from 0.3 to 1.5 g/cm3

 [0.01 to 

0.5 lb/in3

] (Sparks, et al., 1997aa), but is rarely directly measured for basaltic volcanoes. Blong 

(1984aa) measured a range of tephra deposits that have a density of approximately 1.0 g/cm3

[0.4 lb/in3

]. DOE (SNL, 2007ae) used 1.0 g/cm3 [0.4 lb/in3] for TSPA calculations on the basis of 

both this value from Blong (1984aa) and a normal distribution of deposit densities ranging from 

0.3 to 1.5 g/cm3

 [0.01 to 0.5 lb/in3] with a mean of 1.0 g/cm3 [0.4 lb/in3]. The NRC staff notes 

that a tephra density of 1 g/cm3 [0.4 lb/in3

] is reasonable for use in the representation calculation 

in TER Section 2.2.1.4.1.3.2. 

The ash areal concentration was derived from an assumed 1-cm [0.54-in] thickness of 

deposited tephra. In SNL Appendix G (2007ac), DOE calculated an arithmetic mean of 0.97 cm 

[0.54 in] for tephra thickness at the reasonably maximally exposed individual location for a wind 

direction fixed to the south. This result indicates that a 1-cm [0.54-in] thickness is 

representative of downwind tephra deposits. The NRC staff notes that an ash areal 
15-16 

concentration of 10,000 g/m2

 [0.14 lb/in2] for an assumed 1-cm [0.54-in]-thick deposit is 

reasonable for use in the representation calculation in TER Section 2.2.1.4.1.3.2. 

2.2.1.3.13.3.1.2 Tephra Redistribution in Fortymile Wash 

The NRC staff conducted a risk-informed review of tephra redistribution, concentrating on those 

aspects important to the volcanic ash exposure scenario in the DOE performance assessment, 

as given next. 

Important Aspects of Tephra Redistribution 

DOE modeling of redistribution of tephra includes fluvial (running water or stream) transport of 

contaminated tephra within the Fortymile Wash catchment basin, mixing and dilution with 

noncontaminated sediment, and deposition of the tephra-sediment mixture on the Fortymile 

Wash alluvial fan at the location of the reasonably maximally exposed individual. These 

processes are modeled to occur instantaneously, thus not allowing for any radioactive decay of 

contaminated tephra before its deposition at the alluvial fan location. In DOE’s model, on the 

alluvial fan, tephra is deposited in distributary channels by redistribution processes and on 

interchannel divides from airborne transport. 

DOE performance assessment results for the volcanic ash exposure scenario are influenced by 

radionuclide concentrations in soil from both distributary channels and interchannel divides, as 

described in DOE Enclosure 5, Figure 1 (2009bk). Radionuclides in distributary channels 

contribute dose to the volcanic ash exposure scenario from the large number of realizations that 

result in an initial tephra deposit in the Fortymile Wash catchment basin. Fluvial sediment in 

distributary channels contributes more (two thirds, on average, in the DOE model) to the 

airborne particle concentration at the reasonably maximally exposed individual location than 

soils on interchannel divides (one third). 

The NRC staff reviewed auxiliary Monte Carlo simulations by Pelletier, et al. (2008aa) that 

indicated the fluvial transport abstracted model reduced the concentration of tephra in sediment 

deposited in distributary channels by a factor of about 100 (arithmetic mean of 20 simulations), 

compared to the tephra concentration in the original tephra deposit. DOE expects any waste 

attached to tephra particles to remain attached during fluvial transport, and thus expects that 

any reduction in tephra concentration from fluvial transport should reduce waste concentration 

by the same amount, as outlined in SNL Section 5.2.5 (2007av). On the basis of its review of 

DOE’s sensitivity analyses described in SNL Section 6.6.1, Figures 6.6.1-1 to 6.6.1-3 (2007av), 

NRC staff notes that realizations with the largest waste concentrations were most sensitive to 

critical slope and scour depth, in that order, and slightly sensitive to drainage density. Because 

DOE included waste dilution during fluvial transport in the FAR model, the NRC staff also 

focused its review on modeling assumptions and model support. 

Summary of Information on Tephra Redistribution 

DOE’s model of radionuclide redistribution in Fortymile Wash for the volcanic ash exposure 

scenario was described in SAR Section 2.3.11. In SAR Table 2.3.11-1, DOE identified the 

features, events, and processes included in the TSPA model. 

Following deposition of contaminated tephra (TER Section 2.2.1.3.13.2) from a potential 

eruption where a volcanic conduit intersects waste packages, DOE’s tephra redistribution model 

accounts for the mobilization of contaminated tephra in the Fortymile Wash catchment basin, 
15-17 

dilution of contaminated tephra with uncontaminated sediments in fluvial (stream) channels, and 

fluvial deposition at the location of the reasonably maximally exposed individual. Fortymile 

Wash lies east of the repository, which DOE showed to be the most likely direction for tephra 

dispersal at typical heights for violent Strombolian eruption columns (SAR Figure 2.3.11-15). 

DOE developed the FAR Version 1.2 code, referred to hereafter as the FAR model, and 

incorporated this code into its TSPA as a dynamically linked library. The tephra redistribution 

is abstracted to occur instantaneously (i.e., radionuclide waste transport to the reasonably 

maximally exposed individual is instantaneous) (SAR Section 2.3.11.4.2.3.1). Eolian 

(wind-induced) processes are not included in the tephra redistribution model. 

In the DOE model described in SNL (2007av), tephra is mobilized and transported downstream 

if it is initially deposited either on slopes steeper than a critical slope angle or in active channels 

with stream power exceeding a threshold value. Critical slope parameter values were 

determined from field measurements at analog sites. DOE determined active channel networks 

from digital elevation model data and drainage density estimates, on the basis of calibrations to 

field observations. Channel geomorphology in the Fortymile Wash catchment basin was based 

on recent observations and is modeled as time invariant. Effects on surface slope, elevation, 

stream power, and drainage density due to the presence of an initial tephra deposit and its 

weathering over time were not modeled. DOE considered these effects within the context of 

existing parametric values and propagated uncertainty, and exclusion of these effects from the 

model was not expected to significantly change the model results, as described in DOE 

Enclosure 10, Section 1 (2009bk). 

DOE used scour depth estimates to determine the mixing and dilution of tephra with 

uncontaminated channel sediments. After the 1995 flood event in Fortymile Wash, DOE 

measured scour depth and estimated a total scour depth to account for the cumulative effect of 

flood events over time for use in the DOE TSPA. Sediment transport time is not explicitly 

accounted for in the model for fluvial remobilization and tephra dilution. Instead, a simplification 

was made such that remobilized tephra would be instantaneously diluted in fluvial sediments 

and directly deposited at the alluvial fan (i.e., fluvial remobilization, dilution, and deposition occur 

at the same simulation timestep as initial tephra-fall deposition). 

The Fortymile Wash alluvial fan is located at the southern end of the drainage system; DOE 

modeled it as active (distributary) stream channels and areas between channels (interchannel 

divides). In the DOE tephra redistribution model, the whole alluvial fan is assumed to be an 

area occupied by the reasonably maximally exposed individual (SAR Figure 2.3.11-13). 

Parameter values for the area of the Fortymile Wash alluvial fan and the fraction of that area 

associated with channels were determined from field measurements and soil geomorphic 

mapping. In the model of a future volcanic eruption, initial radionuclide concentrations on 

interchannel divides arise from original tephra-fall deposits across the fan. Redistributed tephra 

mixed with ambient sediment from the Fortymile Wash drainage system is deposited in 

distributary channels and not on interchannel divides. Radionuclide concentrations in 

distributary channels therefore include a mixture of redistributed tephra from the Fortymile Wash 

drainage system and any original tephra-fall deposits. DOE assumed redistributed tephra is 

transported as bedload material, which neglects the potential for silt-sized material to be 

transported in the suspended (streamflow-borne) load past the reasonably maximally exposed 

individual location and into the Amargosa River Valley. DOE considered alternative modeling 

approaches during the development and validation of the scour-dilution-mixing approach in its 

tephra redistribution abstracted model (SNL, 2007av). DOE also referred to model-confidence 

building, supporting comparisons, and sensitivity analyses documented in SNL (2007av) and a 
15-18 

published application of the scour-dilution-mixing model to the area around the Lathrop Wells 

Volcano (Pelletier, et al., 2008aa). 

Time-dependent radionuclide concentrations with soil depth in stream channels and on 

channel divide surfaces are the ultimate outputs of the tephra redistribution model. TER 

Section 2.2.1.3.13.3.1.3 evaluates the time-dependent vertical migration of radionuclides in 

soil for the volcanic ash exposure scenario. In the biosphere model, reviewed in TER 

Section 2.2.1.3.14.3, the FAR model outputs are combined with biosphere dose conversion 

factors in the DOE TSPA to estimate annual doses to the reasonably maximally exposed 

individual (SAR Figure 2.3.10-10). 

NRC Staff Evaluation of Tephra Redistribution 

The NRC staff reviewed SAR Section 2.3.11 on the volcanic eruption modeling case, additional 

information provided in response to the NRC staff’s request for additional information (DOE, 

2009bk–bm), the supporting DOE information on tephra redistribution presented in SNL 

(2007av), and information published in peer-reviewed literature (e.g., Pelletier et al., 2008aa). 

Model Integration 

Model abstractions comprise features, events, and processes that have been screened in from 

the scenario analysis. In TER Section 2.2.1.2.1.3.1, the NRC staff determines that DOE had 

identified a list of features, events, and processes for the volcanic exposure scenario, including 

tephra redistribution. In TER Section 2.2.1.2.1.3.2, the NRC staff determines that DOE had 

screened all features, events, and processes for the volcanic exposure scenario, including 

tephra redistribution. DOE did not exclude any features, events, and processes associated with 

this abstracted model. The NRC staff’s review of the tephra redistribution abstracted model 

evaluates DOE’s implementation of the included FEP 1.2.04.07.0C, which accounts for the 

surface transport processes that redistribute radionuclides following the initial tephra-fall 

deposition. DOE’s technical basis for this abstracted model is given in SNL (2007av). 

The following addresses the NRC staff’s evaluation of DOE’s modeling assumptions used for 

the fluvial transport in the FAR model. 

DOE determined that wetter future climates would increase vegetation on hillslopes and would 

thus reduce the amount of remobilized tephra from hillslopes into channels, on the basis of the 

influence of vegetation on erosion. DOE also pointed out that additional precipitation from a 

wetter climate in the future could increase the scour depth in channels, which was shown in 

SNL Figure 6.6.1-2 (2007av) to reduce initial radionuclide concentrations in channels. Because 

neglecting tephra redistribution effects from future wetter climates does not underestimate 

radionuclide concentrations in sediment, as identified in SNL Section 5.1.1 (2007av), the NRC 

staff notes that this modeling assumption is reasonable. 

The NRC staff notes that DOE’s rationale for not explicitly modeling long-term changes to active 

channels within the depositional fan of Fortymile Wash is reasonable because it is not possible 

to accurately predict the location and size of future channels in this type of depositional system. 

For the following reasons, the NRC staff notes that DOE’s justification for the area fraction of 

active channels in the Fortymile Wash alluvial fan and associated parameters in their model is 

reasonable. The NRC staff evaluated the implementation of the channel area fraction in the 

FAR model, which also considered its coupling with the biosphere model in the DOE TSPA. 

DOE assessed (i) the relative susceptibility of the two surfaces, interchannel divides and active 
15-19 

fluvial channels, to airborne resuspension and (ii) the assumption that dose contributions from 

these two surfaces are proportional to their respective fractions of the total area of the alluvial 

fan. DOE concluded that differences in these two surfaces were accounted for in the DOE 

TSPA estimates for airborne particle concentration. DOE also showed, in DOE Enclosure 5, 

Figure 1 (2009bk), that dose contributions for the volcanic ash exposure scenario were much 

greater from interchannel divides than from channels, for thousands of years after repository 

closure. Contributions to dose from channels become important after the first 10,000 years as 

the importance of the inhalation pathway decreased relative to the other biosphere pathways. 

On the basis of its review of these results, the NRC staff notes that differences in the relative 

susceptibility of the two surfaces to airborne resuspension, which might have affected their 

relative dose contributions in addition to their area fraction, would not significantly affect 

dose estimates. Therefore, the DOE proportional relationship on dose contribution from 

these two surfaces, on the basis of their respective fractions of the total area of the alluvial fan, 

is reasonable. 

Although DOE acknowledged that a future eruption could alter the channel geomorphology to 

some degree by deposition of fresh tephra, it assumed that eruption-induced changes would 

have little effect on the overall geomorphology of the Fortymile Wash catchment basin, as 

described in SNL Section 5.1.4 (2007av). The NRC staff notes that DOE’s approach, basing 

fluvial redistribution on current channel geomorphology, is reasonable for modeling future fluvial 

redistribution and the associated uncertainties in estimating radiological doses. This approach 

is consistent with information from analog volcanoes such as Parícutin (Segerstrom, 1950aa, 

1966aa; Inbar, et al., 1994aa), where original channels were reestablished in the decades 

following a tephra-fall eruption. SNL Sections 5.1.3 and 6.1.2 (2007av) provide DOE’s 

justification for not modeling overbank deposition (i.e., deposition of fluvial tephra and sediment 

on interchannel divides) at the Fortymile Wash alluvial fan. This approach is reasonable 

because of the absence of fluvial overbank deposits on existing interchannel divides of the 

Fortymile Wash fan as detailed in SNL Appendix A (2007av). 

Rather than accounting for significant rainfall and flooding events individually and tracking the 

movement of redistributed tephra from each event over time, the FAR model applies a 

representative deposit for long-term redistribution and dilution of tephra in the same simulation 

time step. Because FAR model results are integrated with biosphere modeling in the DOE 

TSPA, the NRC staff considered the coupling of the FAR and biosphere models in the 

evaluation of FAR model assumptions concerning time dependency. DOE assessed the 

replenishment of contaminated fluvial deposits over time with respect to time-dependent 

estimates of resuspended airborne particle concentrations in the DOE TSPA, according to 

DOE Enclosure 4 (2009bk). DOE clarified that the active outdoor category for reasonably 

maximally exposed individual activities includes time spent walking outdoors on uncompacted 

soil or tephra. DOE also acknowledged that airborne particle concentrations would be higher in 

its TSPA for walking on uncompacted tephra deposits following an eruption than during 

preeruption conditions. The NRC staff reviewed the DOE parameter ranges for airborne particle 

concentrations from resuspension following a volcanic eruption and notes that they are 

supported by published literature referenced in BSC (2006ad). On the basis of its review of 

information provided [BSC (2006ad); SNL (2007av); DOE Enclosure 4 (2009bk)] and 

independent field measurements that the NRC staff conducted at analog volcanic sites 

[Hill, et al. (2000ab); Benke, et al. (2009aa)], the NRC staff notes that DOE’s estimates of 

inhalation of resuspended particulates from tephra-fall deposits or redistributed tephra in fluvial 

sediments do not underestimate dose. The NRC staff also notes that the model integration in 

the DOE TSPA for the volcanic ash exposure scenario, including the coupling of the assumption 

for instantaneous tephra remobilization in Fortymile Wash with time-dependent values for 
15-20 

airborne mass loading, is reasonable to estimate doses over time. Because DOE did not 

model further mixing and dilution, which could reduce radionuclide concentrations, in the 

instantaneous deposit from subsequent flooding events in the Fortymile Wash drainage basin, 

as identified in SNL Section 5.2.4 (2007av), the NRC staff notes that DOE’s assumption that 

drainage system is open (SAR Section 2.3.11.4.4.3) is reasonable. Therefore, modeling a 

representative deposit for long-term redistribution and dilution of tephra represents the fate of 

material in this ephemeral drainage system and is a reasonable approach for calculating 

radiological consequences. 

In SNL Section 1.2 (2007av), DOE acknowledged that eolian (wind) sediment transport is 

a significant geomorphic process in the Yucca Mountain region. DOE clarified in DOE 

Enclosure 12, Section 1.1 (2009bk) that airborne resuspension of tephra deposits at the 

reasonably maximally exposed individual location is included in the DOE TSPA as a local-scale 

eolian redistribution process. The NRC staff notes that the DOE abstracted model did not 

model long-range eolian redistribution of tephra explicitly. In SNL Section 5.2.2 (2007av), DOE 

also accounted for potential local-scale eolian transport of contamination in channel sediments 

onto interchannel divide surfaces by increasing the range for the channel fraction of land area in 

the Fortymile Wash alluvial fan. As described in SNL Section 5.2.2 (2007av), direct tephra-fall 

deposition and fluvial processes dominate radionuclide concentrations in the soil and air at the 

reasonably maximally exposed individual location. DOE considered the long-range eolian 

transport of freshly deposited tephra south to the location of the reasonably maximally exposed 

individual from the Fortymile Wash catchment basin to be negligible, on the basis of its 

characterization of the prevailing direction for strong southerly winds, as identified in SNL 

Appendix D (2007ab) and Pelletier and Cook (2005aa). DOE also considered relevant wind 

data in CRWMS M&O Site 9 (1997aa) and determined that southerly winds exhibited higher 

wind speeds compared to northeasterly winds, described in DOE Enclosure 12, Section 1.2 

(2009bk). Higher speeds for south-to-north winds would tend to drive the net transport of 

contaminated tephra toward the north and away from the reasonably maximally exposed 

individual location, as DOE Enclosure 12, Section 1.2 (2009bk) identified. DOE therefore 

concluded that eolian transport of radionuclides deposited in the Fortymile Wash catchment 

basin to the reasonably maximally exposed individual would be negligible, as stated in SNL 

Section 5.2.2 (2007av). DOE further concluded that not modeling these eolian effects would 

tend to overestimate tephra and waste concentration at the reasonably maximally exposed 

individual location. The NRC staff similarly notes that predicted future south-to-north surface 

wind directions and associated eolian transport would tend to dilute radionuclide concentrations 

in the soil and the air at the location of the reasonably maximally exposed individual. The NRC 

staff thus notes that DOE’s technical basis for not explicitly modeling long-range eolian transport 

of contaminated tephra to the location of the reasonably maximally exposed individual is 

reasonable. DOE’s approach will not underestimate airborne concentrations of radionuclides at 

that location or dose to the reasonably maximally exposed individual. 

Fluvial transport of sediment and tephra in Fortymile Wash is modeled by DOE as bedload 

transport. From its review of grain-size distributions and textural considerations in SNL 

Section 5.2.3 (2007av), the NRC staff notes that the DOE modeling approach is reasonable, 

because bedload transport would be the primary mode of fluvial transport of tephra and 

sediment in Fortymile Wash. Using grain-size data from analog volcanic eruptions, 

DOE expects a range of tephra particle sizes with an approximate median value of 0.01–1.0 mm 

[0.0004–0.04 in], as identified in SNL Section 5.2.3 (2007av). With diameters between 

0.002–0.05 mm [0.00008–0.002 in], as described in BSC Section 6.5.3.2 (2006ah), silt-sized 

particles represent a small portion of this range. DOE considered that silt-sized material could 

be transported past the reasonably maximally exposed individual location in the suspended 
15-21 

load (rather than in the bedload) and concluded that not modeling suspended load transport 

and deposition is conservative, as stated in SNL Section 5.2.3 (2007av). Because the 

amount of silt-sized material in the fluvial system is small with respect to the total amount 

of fluvial material and the DOE FAR model does not exclude tephra or waste associated with 

silt-sized particles from contributing to dose, the NRC staff notes that DOE’s approach 

for modeling bedload transport and not including other transport mechanisms, such as 

suspended load, is reasonable. 

The NRC staff expects density effects on combined waste-tephra particles (due to the presence 

of waste) would have a minor to negligible impact on bulk transport, mixing with clean sediment, 

and waste concentration in redistributed tephra for the large volumes of tephra. This is due to 

the relatively small ratio of waste concentrations per unit tephra mass, outlined in DOE 

Enclosure 2, Figure 1 (2009bk) and TER Section 2.2.1.3.10.3.2.3, which supports DOE’s 

conclusion in SNL Section 5.1.5 (2007av) that other processes (physical, chemical, or 

biological) for concentrating radionuclides at the reasonably maximally exposed individual 

location are negligible. To enhance confidence in its system description and model integration, 

DOE had critical reviews performed on an earlier version of the technical basis document and 

included the review and resolution of comments in SNL Appendix C, Section 7.3.2 (2007av). 

The NRC staff evaluated the review comments and DOE responses and determined that this 

process provided additional support for the technical basis used in the FAR model. 

Data Sufficiency and Data Uncertainty 

The following subsections consider the data sufficiency and propagation of uncertainty 

presented for the FAR model parameters of critical slope, scour depth, and drainage density, as 

provided in SNL (2007av). 

Critical Slope 

DOE collected field data from several analog volcanic sites near Flagstaff, Arizona 

(i.e., Rattlesnake Crater, Cochrane Hill, Moon Crater, and Cinder Cone), to determine the 

critical slope parameter range and represent the steepest slope for stable tephra deposits on 

hillslopes, as outlined in SNL Section 6.5.2 (2007av). DOE assessed the measurement scale 

of the field observations for critical slope and representativeness of the 30 by 30-m 

[97 by 97-ft]-grid cell size for the digital elevation map of the Fortymile Wash drainage system. 

Slopes were measured at a scale of tens of meters [tens to hundreds of feet] in the field, and 

DOE concluded that the slope angles are representative of hillslopes, in DOE Enclosure 6, 

Section 1.1 (2009bk). The NRC staff acknowledges that estimating slopes at the scale of an 

entire hillslope is appropriate for modeling tephra remobilization in the Fortymile Wash drainage 

system because the potential for tephra mobilization off hillslopes into channels depends on the 

entire hillslope traversed. DOE clarified that the representation of topography in the FAR model 

does not smooth steeper slopes and assessed the appropriateness of field data from analog 

volcanic sites to estimate fluvial erosion of tephra deposits in the Yucca Mountain region. DOE 

assessed sheetwash and rilling at the field sites (where DOE measured critical slope values) 

and provided a justification for why rapid postdepositional erosion was not observed and why it 

is not expected at Yucca Mountain, as described in DOE Enclosure 6, Sections 1.2–1.4 

(2009bk). DOE converted field measurement data at analog volcanic sites to its parameter 

distribution for critical slope, identified in SNL Section 6.5.2 (2007av). The NRC staff notes that 

DOE parameter uncertainty for critical slope reasonably represents fluvial erosion in Fortymile 

Wash for postvolcanic conditions. 
15-22 

Scour Depth 

SNL Section 6.5.6 (2007av) described the site-specific field data used by DOE to establish the 

parameter distribution for scour depth: the depth to which water flow will erode (pick up) and 

move sediment in a stream channel. Parameter values for scour depth were inferred from 

scour chains, installed by the U.S. Geological Survey (USGS) at the Narrows section of 

Fortymile Wash, that were subsequently buried by flood sediment about 10 years later in 1995, 

as shown in SNL Table 6.5.6-1 (2007av). In DOE Enclosure 10, Section 1 (2009bk), DOE 

explained that other stream flow data (e.g., from the Amargosa Valley station) do not represent 

tributary conditions of the upper drainage basin. The NRC staff notes that DOE’s use of scour 

measurements that were taken only at the Narrows, for determining scour depth and discharge 

in Fortymile Wash, is reasonable. Although these scour measurements indicated an 

upper-bound scour depth of 152 cm [5.0 ft], DOE chose to limit the upper bound of the scour 

depth parameter to 122 cm [4.0 ft], as identified in SNL Section 6.5.6 and Table 6.5.10-1 

(2007av), to add conservatism into the calculation. The average measured scour-depth value 

from the scour chain data, 73 cm [2.4 ft], was chosen as the lower bound (SNL, 2007av). In 

SNL Figure 6.6.1-2 (2007av), DOE showed that dilution would be reduced by selecting a 

shallower scour depth because a smaller amount of uncontaminated sediment would be mixed 

with contaminated tephra. The NRC staff notes that DOE’s interpretation of these field data for 

scour depth and the selected range for this parameter is reasonable, because restricting scour 

depth to smaller values overestimates tephra and waste concentrations in fluvial sediment at the 

location of the reasonably maximally exposed individual. 

DOE based its determination of the parameter range for scour depth on site-specific field 

measurements at Fortymile Wash for current conditions without a surplus of tephra. DOE 

assessed the potential effect of fresh tephra in Fortymile Wash on scour depth in channels 

and concluded that scour depth would not be affected by the proportion of tephra 

in channel sediments. According to DOE Enclosure 10, Section 1 (2009bk), the expected 

grain sizes of tephra are similar to the observed grain sizes for channel bed material; therefore, 

different hydraulic conditions were not expected for a fluvial deposit mixing sediment and 

tephra, as outlined in SNL Section 5.2.3 (2007av) and Pelletier, et al., p. 236 (2008aa). 

For distances up to a few kilometers [2–4 mi] from a volcanic vent, the NRC staff 

recognizes that violent Strombolian tephra deposits will consist of particles in the centimeter to 

millimeter [0.4 to 0.04 in] grain-size range, similar to grain-size distributions in bedload 

sediment from Fortymile Wash. The NRC staff confirmed that DOE used values for scour depth 

that tend to underestimate dilution and overestimate dose, described in DOE Enclosure 10, 

Section 1 (2009bk). The NRC staff notes that the DOE technical basis for scour depth is 

reasonable and further notes that the scour depth distribution represents posteruption 

conditions when fresh tephra may be present in Fortymile Wash. 

The NRC staff reviewed DOE’s scaling approach outlined in SNL Section 6.3.3, Step 3 (2007av) 

for computing scour depth at different locations in the Fortymile Wash drainage basin. DOE 

sampled values for the maximum scour depth within the drainage system and computed scour 

depth at other locations in SNL Eq. 6.3-14 (2007av). SNL Figure 6.3.3-6 (2007av) illustrated the 

variability of scour depth within the drainage basin. The NRC staff notes that scour depth is 

dependent on the volumetric water flow (discharge) and acknowledges that contributing area is 

a common approach for representing water flow at specific locations in the drainage system. 

Leopold, et al. (1966aa) studied another ephemeral drainage system in a semiarid area of New 

Mexico and found that most of the erosion occurred in a small percentage of the basin. This 

observation is consistent with the variability in scour depth presented in SNL Figure 6.3.3-6 

(2007av). DOE’s scaling approach for scour depth is reasonable because the variability in 
15-23 

scour depth is based on estimates of slope angle [described in SNL Figure 6.3.3-3 (2007av)], 

contributing area [outlined in SNL Figure 6.3.3-5 (2007av)], and stream order (Leopold, et al., 

1964aa) in the drainage basin. The NRC staff notes that the greatest scour depth is expected 

to occur where the main drainage channel narrows, because scour depth is dependent on 

volumetric water flow and channel width. 

Drainage Density 

The drainage density is the ratio of the total length of streams to the area of the drainage 

system (length per unit area). Compared to critical slope and scour depth, performance 

assessment outputs are less sensitive to drainage density for the volcanic ash exposure 

scenario. DOE estimated drainage density from simulations of 34 channel heads on the eastern 

slope of Yucca Mountain. In SNL Eq. 6.3-7 and p. 6-16 (2007av), DOE used the reciprocal of 

the drainage density as a stream power threshold for determining active channels within the 

Fortymile Wash catchment basin; in SNL Section 6.5.6 and Figure 6.5.6-3 (2007av), DOE 

compared modeled channel head locations to actual locations and selected the drainage 

density that yielded the smallest average distance difference. DOE assigned the parameter 

range for drainage density by considering other values that provided agreement between 

observed and calculated channel heads on Yucca Mountain. This approach is reasonable for 

determining the drainage density because it is based on a site-specific comparison and an 

established scientific relationship for stream power and contributing areas. The NRC staff notes 

that the resulting parameter range for drainage density adequately accounts for uncertainty 

because it is based on a site-specific comparison and a reasonable interpretation of the results 

of the comparison. 

DOE assigned values to the previously mentioned parameters (critical slope, scour depth, and 

drainage density) in the Fortymile Wash Ash Redistribution (FAR) model according to the 30 by 

30-m [97 by 97-ft] grid cells on the digital elevation (topographic) map of the Fortymile Wash 

drainage system. In the DOE TSPA analysis, sampling of these parameters is performed in the 

FAR model independently, without correlation between them [SNL Table 6.5.10-1 (2007av)]. 

The NRC staff notes that representing topography and associated surface processes in the FAR 

model at a 30 by 30-m [100 by 100-ft] scale is a reasonable approach, because a strong 

correlation between critical slope, scour depth, and drainage density is not expected, as these 

parameters characterize different aspects of the system (i.e., material stability on hillslopes, 

intrachannel flow and mixing, and active channel threshold within the network, respectively). 

With this approach, the model will not underpredict the consequences and any alternative 

approach will not significantly affect the results. 

Model Uncertainty 

The NRC staff reviewed the DOE consideration of alternative models in Pelletier, et al. (2008aa) 

and SNL Sections 6.2.2, 6.3.3, and 7.2.4 (2007av). As previously described in TER Section 

2.2.1.3.13.3, the NRC staff determined that the DOE abstracted model for fluvial transport and 

tephra dilution significantly reduces radionuclide concentration and influences the results of the 

volcanic ash exposure scenario. The FAR model is based on a new modeling approach for the 

natural processes of scour, dilution, and mixing developed by DOE specifically for the Fortymile 

Wash drainage system. The classic dilution-mixing model has been generally considered the 

standard approach (Hawkes, 1976aa; Marcus, 1987aa) in the past. In DOE Enclosure 7, 

Section 1 (2009bk), DOE considered the classic dilution-mixing model as appropriate only for 

tributary systems that discharge into the sea or a lake but not well suited for Fortymile Wash, 

because it is a tributary-distributary inland drainage system in a desert region. DOE also 
15-24 

identified other shortcomings with classic dilution-mixing models, such as the inability to model 

the vertical distribution of contamination and dilution of contaminated with uncontaminated 

sediments. Because DOE considered scour-dilution mixing as the predominant mode of 

dilution, it concluded in DOE Enclosure 7, Section 1.1 (2009bk) that the scour-dilution-mixing 

model more accurately represented the processes at Fortymile Wash. DOE further 

concluded in DOE Enclosure 7, Section 1.2 (2009bk) that dilution-mixing models were not 

directly applicable. 

The NRC staff notes that the FAR model reasonably represents the Fortymile Wash drainage 

system and that DOE considered alternative conceptual models to the scour-dilution-mixing 

approach used in the FAR model. The NRC staff notes that the FAR model would not 

underpredict the radiological consequences to the reasonably maximally exposed individual and 

that it is supported by applicable data. 

 

Model Support 

The NRC staff evaluated the model support for the FAR model. DOE supported its model 

results with (i) independent technical evaluations, as outlined in SNL Appendix C (2007av), and 

(ii) a peer-reviewed journal article (Pelletier, et al., 2008aa) that included a site-specific 

comparison for fluvial redistribution and dilution of tephra from the Lathrop Wells volcano. The 

NRC staff reviewed the independent technical evaluators’ comments and notes that DOE 

responded to and resolved comments. The NRC staff also reviewed Pelletier, et al. (2008aa) 

and agrees with the article’s conclusion that a scour-dilution-mixing approach is suitable for 

estimating downstream contamination concentrations when bedload transport dominates and 

overbank sedimentation is not significant. DOE used a modeling assumption to constrain the 

tephra thickness in a channel routed to the Fortymile Wash alluvial fan to not exceed the scour 

depth, as described in SNL p. 6-24 (2007av). In SNL Section 7.3.1.2 (2007av), DOE also 

pointed to the 77,000-year-old analog volcano at Lathrop Wells for observations of long-term 

storage of tephra below the scour depth. Although tephra stored below the scour depth in 

connected channels (i.e., connected to the main Fortymile Wash channel) or in unconnected 

channels cannot contribute to the concentration of radionuclides at the Fortymile Wash alluvial 

fan, total tephra stored below the scour depth was estimated by DOE to be on the order of 3 to 

7 percent of the amount of tephra-fall in the Fortymile Wash drainage system, as identified in 

DOE Enclosure 11 (2009bk). Because increases in scour depth tend to result in greater dilution 

of tephra with uncontaminated sediment in the DOE FAR model, the NRC staff notes that tephra 

storage below the scour depth does not significantly affect the estimates of radionuclide 

concentrations in sediment at the location of the reasonably maximally exposed individual. The 

NRC staff acknowledges that sediment storage in channels is a well-recognized 

geomorphological phenomenon and notes that the DOE model for fluvial transport and dilution 

of tephra in Fortymile Wash has been supported. 

NRC Staff Evaluation of Tephra Redistribution 

On the basis of the evaluation described previously, the DOE FAR model provides a reasonable 

approach for calculating the redistribution of tephra to the reasonably maximally exposed 

individual location. DOE integrated its model of tephra redistribution by incorporating the 

important processes associated with the ash redistribution via soil and sediment transport 

feature, event and process. The NRC staff notes that the parameter determinations for the 

redistribution of tephra and incorporated waste in DOE’s abstracted model for fluvial transport in 

the Fortymile Wash catchment basin are described and justified. The NRC staff notes that the 

data support this abstracted model in the TSPA. The NRC staff notes that data uncertainty was 
15-25 

characterized and propagated through the abstracted model for fluvial transport in Fortymile 

Wash. Parameter ranges were described and justified. The NRC staff notes that DOE’s 

treatment of alternative conceptual models is reasonable because no alternatives that are 

consistent with the presented data would affect the results significantly. Because DOE 

supported its model results with independent technical evaluations and a site-specific 

comparison for fluvial redistribution and dilution of tephra from the Lathrop Wells volcano, the 

NRC staff notes that DOE’s model is supported. 

2.2.1.3.13.3.1.3 Downward Migration of Radionuclides in Soil 

The NRC staff conducted a risk-informed review of the model of downward migration of 

radionuclides in soil, concentrating on those aspects important to the volcanic ash exposure 

scenario in the DOE performance assessment, as given next. 

Important Aspects of Downward Migration of Radionuclides in Soil 

In the DOE TSPA, both long- and short-term inhalation significantly contribute to the total dose 

for the volcanic ash exposure scenario for 100,000 years. Because the short-term inhalation 

contribution is dominated by a much faster rate of reduction in airborne mass loading, the 

vertical migration of radionuclides has greater potential influence on long-term inhalation dose. 

As previously discussed, contributions to total dose from the inhalation of particulates diminish 

after 100,000 years. 

The DOE results indicated that processes contained in this abstracted model for the volcanic 

ash exposure scenario result in a small reduction in radionuclide concentrations over time. The 

NRC staff obtained quantitative insights by investigating intermediate output files from the DOE 

TSPA. For unplowed soil, the reduction of radionuclide concentration due to vertical migration 

out of the resuspendable layer is gradual and slows with increasing time following initial 

deposition. On average, the radionuclide concentration in the resuspendable layer required 

approximately 20, 150, 700, and 4,000 years to decrease by a factor of 2, 4, 8, and 16, 

respectively, from its initial value in the DOE TSPA. For plowed soil, radionuclides are uniformly 

mixed within the tillage depth, and the time-dependent reduction in radionuclide concentration 

due to vertical migration is small (reduction by a factor of about 2 in 10,000 years). For fluvial 

channels, radionuclides are assumed to be well mixed within the fluvial sediment deposit, and 

the time-dependent reduction in concentration due to vertical migration of radionuclides out of 

either the resuspendable layer or tillage depth is minimal (leading to a reduction of less than a 

factor of 2 in 10,000 years). Sensitivity analyses performed by DOE indicated that radionuclide 

concentration is most sensitive to the diffusivity rate in soil on interchannel divides, followed by a 

lesser sensitivity to the diffusivity rate in fluvial channels. There was a negligible sensitivity 

to different values of permeable depth on the interchannel divides, as described in SNL 

Section 6.6 (2007av). 

Summary of Information on Downward Migration of Radionuclides in Soil 

Modeling of radionuclide concentration migration into soil for the volcanic ash exposure scenario 

was described in SAR Sections 2.3.10, Biosphere Transport and Exposure, and 2.3.11, Igneous 

Activity. In SAR Tables 2.3.10-1 and 2.3.11-1, DOE identified the features, events, and 

processes included in the TSPA model. 

Calculation of radionuclide concentrations with soil depth at the reasonably maximally exposed 

individual location is one of the main elements of the DOE FAR model for tephra redistribution. 
15-26 

DOE developed this part of the FAR model specifically for the Fortymile Wash alluvial fan, 

consisting of active channels and interchannel divide surfaces. The exposure of the reasonably 

maximally exposed individual to radionuclides in soil was modeled for two layers: (i) a thin 

upper surface layer from which particles can be suspended into the atmosphere by disturbances 

and (ii) a thicker, lower surface layer that may undergo mixing by agricultural practices such as 

tillage (SAR Section 2.3.10.2.6). 

The FAR model includes the downward migration of radionuclides into soil for the volcanic ash 

exposure scenario. Incorporated into the DOE TSPA as a dynamically linked library, the FAR 

model is connected to the surface soil submodel of the DOE biosphere model, Environmental 

Radiation Model for Yucca Mountain Nevada (ERMYN), which calculates biosphere dose 

conversion factors for the volcanic ash exposure scenario on the basis of unit concentrations of 

radionuclides in volcanic ash deposited on the ground. The surface soil submodel is included in 

the biosphere analysis of all exposure pathways for the volcanic ash exposure scenario (refer to 

SAR Figures 2.3.10-8 and 2.3.10-10). Biosphere dose conversion factors are combined with 

time-dependent radionuclide concentrations in soil from the FAR model to estimate annual 

doses for the volcanic ash exposure scenario. 

DOE modeled time-dependent vertical migration of radionuclides in soil within the FAR tephra 

redistribution model as a diffusive process in one dimension. Values for radionuclide diffusivity 

and permeable depth differed between those areas on interchannel divides and those in fluvial 

channels. Field data on Cs-137 concentration profiles from the upper Fortymile Wash alluvial 

fan were used to determine radionuclide diffusivities and the associated uncertainties. 

Permeable depths in soils were determined from field measurements in pits dug on interchannel 

divides of the Fortymile Wash alluvial fan and from USGS data on scour depth in fluvial 

channels. Although advection is not explicitly modeled, DOE identified that diffusivity data 

accounted for all transport mechanisms, including advection and bioturbation. DOE does not 

include effects of future climate change on the modeled processes and parameters in the tephra 

redistribution model, because DOE concluded that processes associated with future climate 

change would only decrease radionuclide concentrations in soils (SAR Section 2.3.11.4.4.3). 

In the FAR model, radionuclides are restricted from migrating into a deeper horizon by use of a 

reduced permeability. The reduced permeability was assumed to be caused by a greater 

carbonate or clay content than the minor content in surface and near-surface soils. DOE’s 

approach limited possible reduction of radionuclide concentrations in the surface layer due to 

vertical migration over long time periods. 

For the volcanic ash exposure scenario, the surface soil submodel calculates radionuclide 

mass concentrations in the tilled surface soil layer and in the thin resuspendable layer for 

noncultivated soil. In the DOE TSPA, radionuclide concentrations in the resuspendable layer 

and tilled soil are applied to different environmental exposure pathways. Weighting factors for 

land usage (e.g., fractions of land that are tilled and not tilled) are not included in the dose 

calculations. Igneous eruption dose calculations include weighting factors for the fraction of 

land area apportioned into active fluvial channels and interchannel divides. Volcanic material 

(basaltic tephra) is assumed to be mixed uniformly in tilled surface soil. Concentrations of 

radionuclides in tilled surface soil are factored into the pathway analysis for ingestion of 

contaminated crops and animal products. Inhalation and external exposure pathway 

calculations are dependent on radionuclide concentrations in the resuspendable layer. 

Because erosion and other surficial processes are accounted for in the tephra redistribution 

model, DOE’s surface soil submodel does not include these processes for the volcanic ash 

exposure scenario. 
15-27 

NRC Staff Evaluation of Downward Migration of Radionuclides in Soil 

The NRC staff reviewed SAR Sections 2.3.10 and 2.3.11 on the volcanic eruption modeling 

case, additional information provided in response to the NRC staff’s request for additional 

information (DOE, 2009bk), and the supporting DOE information on tephra redistribution 

presented in SNL (2007av). 

Model Integration 

Model abstractions comprise features, events, and processes that have been screened in 

from the scenario analysis. In TER Section 2.2.1.2.1.3.1, the NRC staff determined that DOE 

identified a complete list of features, events, and processes for the volcanic exposure scenario, 

including downward migration in soil. In TER Section 2.2.1.2.1.3.2, the NRC staff notes that 

DOE had screened all features, events, and processes for the volcanic exposure scenario, 

including downward migration in soil. DOE did not exclude any features, events, and 

processes associated with this abstracted model. The NRC staff’s review of the abstracted 

model for downward migration in soil evaluates DOE’s implementation of the included features, 

events, and processes: (i) FEP 1.2.04.07.0C, (ii) FEP 2.3.02.01.0A, (iii) FEP 2.3.02.02.0A, and 

(iv) FEP 2.3.02.03.0A. 

As discussed further in the next sections, the NRC staff notes that the DOE TSPA analysis 

incorporates important features, events, and processes and couplings between different models 

associated with the vertical migration of radionuclides in soil for the volcanic ash exposure 

scenario. TER Section 2.2.1.3.13.1.2 evaluates the treatment of climate in the DOE FAR 

model. The technical basis for this abstracted model was described in SNL (2007av); modeling 

assumptions were described in SNL Section 5 (2007av). DOE neglected effects due to future 

wetter climates in this abstracted model on the basis that wetter climates could increase 

radionuclide diffusivities in soil, increase vertical migration, reduce the radionuclide 

concentrations in surface soil layers, and thus reduce estimated doses. This DOE modeling 

approach is reasonable because wetter climates would likely increase radionuclide migration to 

deeper soil layers, as identified in Till and Moore Eq. 2 (1988aa), and thus result in lower 

estimated doses. Therefore, the NRC staff notes that neglecting the potential effects of a future 

wetter climate on radionuclide migration would not underestimate dose. 

The NRC staff evaluated critical modeling assumptions for this abstracted model. DOE 

assumed that all radionuclides migrate into the soil at the same rate, because in temperate 

climates, weathering of radionuclides from the soil surfaces into deeper soil layers is mainly a 

physical, rather than a chemical, process. This approach is supported by the similar rate of 

radionuclide migration for radionuclides of different chemical characteristics (Anspaugh, et al., 

2002aa). NRC considered the small reduction of radionuclide concentrations over time credited 

in the DOE model and notes that the field data support the reduction presented in SNL 

(2007av). 

The DOE assumption for not explicitly modeling advection (i.e., flow by liquid movement) of 

radionuclides is reasonable because including advective transport would likely increase the 

removal of radionuclides from the soil surface and thus reduce calculated doses. In addition, 

independent critical reviews from non-DOE, academic-based experts were conducted on an 

earlier version of the technical basis document, and DOE included the review comments 

and responses in SNL Appendix C, Section 7.3.2 (2007av). The NRC staff reviewed 

those independent technical evaluations and notes that DOE responded to and 
15-28 

resolved comments by independent reviewers that pertained to the vertical migration of 

radionuclides in soil. 

Data Sufficiency 

The NRC staff evaluated data sufficiency in the DOE abstracted model for the downward 

migration of radionuclides in soil. This abstracted model consists of parameters for permeable 

depth in fluvial channels and on interchannel divides, soil diffusivity of radionuclides in channels 

and on interchannel divides, and land fraction of the Fortymile Wash alluvial fan attributed to 

channels. Parameter values for permeable depth in channels were inferred from USGS data on 

scour depth in channels and the previously mentioned site-specific field data from soil pits, 

as described in SNL Section 6.5.5.2 (2007av). Diffusivity rates for radionuclide migration in 

soils on interchannel divides and in fluvial channels were determined from site-specific field 

data of Cs-137 profiles. These profiles resulted from contaminated fallout deposited 

approximately 50 years earlier from atmospheric nuclear weapons testing. DOE performed 

soil-geomorphic mapping of the Fortymile Wash alluvial fan to determine the fraction of the fan 

area that has been subjected to fluvial erosion and deposition within the past 10,000 years, as 

identified in SNL Appendix A (2007av). Diffusivity rates for fluvial channels were determined 

from measurements from surfaces DOE characterized as active channels. Measured data from 

older terraces were used to calculate the diffusivity rate for interchannel divides. On the basis of 

these two field data sets, DOE specified separate diffusivity parameters for older surfaces of the 

interchannel divides and younger surfaces in fluvial channels. Because the time-dependent 

reduction of radionuclide concentrations in soil is slow in the DOE TSPA, the NRC staff notes 

that modeling of the downward migration of radionuclides tends to overestimate dose to the 

reasonably maximally exposed individual. The NRC staff notes that the data support this 

abstracted model in the TSPA because they are from site-specific field observations. These 

parameter determinations are described and justified. For these reasons, the NRC staff notes 

that the DOE use of measured diffusivity rates for cesium in soil is reasonable for modeling the 

reduction of radionuclide concentrations with time in the DOE TSPA for the volcanic ash 

exposure scenario. 

Data Uncertainty 

In the DOE abstracted model for the vertical migration of radionuclides in soil, parameter 

distributions are applied to account for data uncertainty. As discussed in the preceding 

paragraphs, the NRC staff reviewed these parameter distributions and notes that the range of 

uncertainty in these parameters is consistent with the technical basis used to develop the 

parameters. A permeable depth in fluvial channels of 200 cm [79 in] was derived from field 

measurements and is used as a constant value, as outlined in SNL Section 7.1.3 

and Table 4.1-4 (2007av). DOE supported this constant value with an argument that the 

permeable depth in fluvial channels could be much deeper. The NRC staff notes that neglecting 

deeper permeable layers is conservative because increases in permeable depth reduce 

long-term radionuclide concentrations in soil. Given the minimal effect that vertical migration 

has on radionuclide concentrations in fluvial channels over time, this treatment of uncertainty in 

permeable depth is reasonable. 

Model Uncertainty 

The NRC staff evaluated model uncertainty for the downward migration of radionuclides in soil 

following an eruption. For conditions after a potential future volcanic eruption that intersects 

the repository and entrains waste, radionuclides on the ground surface would originate as 
15-29 

radionuclide contamination in basaltic tephra deposits, as discussed previously. DOE used 

site-specific data from the deposition and migration of fine radionuclide particulates into current 

surface soils of the Fortymile Wash alluvial fan, which are not rich in basaltic material, to support 

its model for the downward migration of radionuclides following deposition in the volcanic ash 

exposure scenario (SAR Section 2.3.11.4.2.3; SNL, 2007av). DOE provided a technical basis 

for neglecting the effects of fresh basaltic tephra on radionuclide diffusion in soil. The technical 

basis for radionuclide migration was provided in two parts: one for channel sediments and the 

other for soils on interchannel divides. For channel sediments, DOE used field observations at 

Lathrop Wells, described in SNL Section 7.3.1.1 (2007av), to show that basaltic and nonbasaltic 

sediments in the drainages exhibited similar grain sizes and transport rates. DOE also found 

basaltic and nonbasaltic sediments to be well mixed. DOE reported a significant amount of 

dilution of fresh basaltic tephra with nonbasaltic sediments during fluvial transport in the 

Fortymile Wash drainage basin. In particular, tephra concentrations in channel sediments were 

less than 20 percent at the reasonably maximally exposed individual location in the DOE TSPA. 

For these reasons, DOE concluded that determining separate diffusion rates of radionuclides in 

basaltic tephra was not necessary for estimating the downward migration of radionuclides in 

mixed channel sediments. 

The NRC staff notes that the DOE diffusion model represents uncertainty for radionuclide 

migration in channel sediments for the volcanic ash exposure scenario. For soils on 

interchannel divides, DOE concluded in DOE Enclosure 3 (2009bk) that differences in diffusivity 

for a basaltic tephra deposit on ambient soils would be negligible because tephra thicknesses at 

the reasonably maximally exposed individual location would be thin {less than 0.33 cm [0.85 in] 

for about 90 percent of TSPA simulations with a primary tephra-fall deposit near the reasonably 

maximally exposed individual location} and grain-size ranges for tephra and ambient soils on 

interchannel divides are similar. The NRC staff notes that the DOE diffusion model does not 

permit radionuclides to migrate below the lower boundary of the surface soils, defined by 

parameters for the permeable depth. For very long times after the volcanic event, radionuclide 

concentration profiles in surface soil layers become uniformly distributed with depth in the DOE 

model. As previously discussed in TER Section 2.2.1.3.13.3.1.3, the NRC staff notes that this 

assumption is reasonable because neglecting deeper permeable depths would reduce the 

long-term radionuclide concentrations in soil. DOE’s parameter ranges and selected 

distributions are reasonable because they represent expected conditions of the Yucca Mountain 

region for the volcanic ash exposure scenario. 

DOE did not identify any alternative conceptual models that would likely affect the timing or 

magnitude of dose. The DOE model slowly distributes radionuclides from surface layers to 

deeper layers and restricts radionuclides from migrating below permeable soil layers. The NRC 

staff notes that the DOE diffusion model will not underestimate radiological dose. The NRC 

staff also notes that the approach DOE used is conservative because there are no alternative 

models consistent with available information that would significantly increase radiological dose. 

Model Support 

The NRC staff evaluated model support for the downward migration of radionuclides in soil. 

DOE used a one-dimensional diffusion model for the downward migration of radionuclides in 

soil with measurements of Cs-137 radioactivity profiles in soils at Fortymile Wash. Pelletier, 

et al. (2005aa) published a peer-reviewed journal article that supported use of a diffusion 

model for radionuclide migration in soil at the Fortymile Wash alluvial fan. The article also 

included the synthesis of field data for determining radionuclide diffusivities in soil at the fan. 

In SNL Section 7.2.6 (2007av), DOE verified the appropriateness of its diffusion model for 
15-30 

characterizing the radionuclide concentration profiles in soil. DOE supported its abstracted 

model with a geomorphic characterization of the Fortymile Wash alluvial fan, as identified in 

SNL Appendix A (2007av), and derivation of its mathematical model for diffusion, described in 

SNL Appendix E (2007av). For the aforementioned reasons, DOE has provided reasonable 

support for its diffusion model for radionuclide migration in soil following an igneous eruption. 

NRC Staff Evaluation of Downward Migration of Radionuclides in Soil 

On the basis of the evaluation described previously, the NRC staff notes that the DOE FAR 

model provides a reasonable approach for calculating the downward radionuclide migration in 

the soil at the location of the reasonably maximally exposed individual. DOE integrated its 

model of downward radionuclide migration in soil by incorporating the important processes 

associated with the four included features, events, and processes. The parameter 

determinations and their uncertainties for the downward radionuclide migration are reasonably 

described and justified. The NRC staff notes that the data support this abstracted model in the 

TSPA because they are from site-specific field observations. The NRC staff notes that data 

uncertainty was characterized and propagated through the abstracted model for downward 

radionuclide migration in soil. DOE’s treatment of alternative conceptual models is reasonable 

because no alternatives that are consistent with the presented data would affect the results 

significantly. The NRC staff also notes that DOE supported its abstracted model with a 

geomorphic characterization of the Fortymile Wash alluvial fan and derivation of its 

mathematical model for diffusion. 

Summary of NRC Staff Evaluation of the Volcanic Ash Exposure Scenario 

The NRC staff has reviewed the information in the SAR and supporting information. 

DOE provided information for the airborne transport of radionuclides, tephra redistribution in 

Fortymile Wash, and downward migration of radionuclides abstracted models, and also for their 

implementation in the GoldSim modeling environment of the TSPA analysis. The SAR 

considered data from the site and surrounding region, considered uncertainties and variability in 

parameter values, and used alternative conceptual models in the analyses. Specific features, 

events, and processes have been included in the SAR, and technical bases have been provided 

for inclusion or exclusion of features, events, and processes. The SAR included specific 

degradation, deterioration, and alteration processes, and the effects of these processes were 

considered in evaluating annual dose. The SAR included technical bases for models used in 

the performance assessment for time periods after 10,000 years and through the period of 

geologic stability. 

2.2.1.3.13.3.2 Assessment and Review of Groundwater Exposure Scenarios 

For the groundwater exposure scenario, the surface soil submodel addresses the vertical 

movement of radionuclides in the soil that follows from irrigation with contaminated 

groundwater (SAR Figure 2.3.10-1) and calculates a time-dependent profile of radionuclide 

concentration in the contaminated soil horizon at the reasonably maximally exposed 

individual location. Radionuclide contamination in groundwater can result from waste package 

failure due to corrosion, mechanical disruption, or potential disruption by intruding magma. 

Radionuclide contamination in groundwater serves as input to the surface soil submodel. TER 

Section 2.2.1.3.12 documents the NRC staff’s review of the DOE approach to estimating 

radionuclide contamination in groundwater. This section addresses the vertical movement of 

radionuclides in the soil from contaminated groundwater irrigation together with background 
15-31 

precipitation. As described next, DOE’s results indicated the influence of the surface soil 

submodel on the DOE calculated repository performance. 

The NRC staff conducted a risk-informed review of DOE’s surface soil submodel using YMRP 

Section 2.2.1.3.13. The NRC staff reviewed the important aspects of the groundwater exposure 

scenario in the DOE performance assessment. 

Important Aspects of the DOE Surface Soil Submodel 

The NRC staff reviewed the SAR and assessed the extent to which the DOE surface soil 

submodel influences the DOE calculation of repository performance. The surface soil submodel 

is used to estimate radionuclide doses for the groundwater exposure scenarios, including the 

seismic ground motion and igneous intrusion scenarios. SAR Figure 2.4-18 (a and b) showed 

that the seismic ground motion and igneous intrusion scenarios dominate the estimated total 

mean annual dose for 10,000 and 1 million years after repository closure. Total doses from the 

groundwater exposure scenarios are controlled by multiple radionuclides and dose pathways. 

Because the surface soil submodel is a component of the DOE biosphere model ERMYN (SAR 

Figure 2.3.10-9; BSC, 2006ah; SNL, 2007ac), its importance within the DOE TSPA depends on 

specific exposure pathways and radionuclides. 

SAR Figures 2.4-20 and 2.4-30 indicated that a combined set of radionuclides—C-14, Tc-99, 

I-129, Ra-226, Np-237, Pu-238, Pu-240, and Pu-242—can contribute significantly to total dose 

over time. The NRC staff used this DOE information and radionuclide set to assess the 

influence of the surface soil submodel on TSPA results. Pathways linked to the surface soil 

submodel (radon inhalation and external exposure) account for more than 80 percent of the 

Ra-226 biosphere dose conversion factor, as described in SNL Tables 6.13-1 and 6.13-2 

(2007ac). Pathways linked to the surface soil submodel account for approximately 50 percent 

of the Tc-99, Pu-239, Pu-240, and Pu-242 biosphere dose conversion factors, as identified in 

SNL Tables 6.13-1 and 6.13-2 (2007ac). In SNL Tables 6.13-1 and 6.13-2 (2007ac), pathways 

linked to the surface soil submodel accounted for less than 35 percent of the Np-237, I-129, and 

C-14 biosphere dose conversion factors. On an individual radionuclide basis, the DOE surface 

soil submodel can have a large influence on estimated doses from Ra-226, a moderate 

influence on doses from Tc-99 and various plutonium isotopes, and a small influence on 

other radionuclide doses. Because no single, dominant radionuclide exists and Ra-226 

contributes only a fraction to the total dose [SAR Figure 2.4-20(b)], NRC staff notes that these 

DOE results indicate the limited influence of the DOE surface submodel on the DOE-calculated 

repository performance. 

Summary of Information on the Surface Soil Submodel 

The surface soil submodel is one component of the DOE biosphere model, which is described in 

SAR Section 2.3.10, Biosphere Transport and Exposure. In SAR Table 2.3.10-1, DOE identified 

the features, events, and processes included in the TPSA model. 

The surface soil submodel calculates the radionuclide concentrations in both cultivated field and 

garden surface soils following radionuclide release in the groundwater pathway. The output 

from the surface soil submodel serves as input for various biosphere submodels (animal, 

ingestion, external, plant, and air). The outputs of the biosphere model are biosphere dose 

conversion factors, which are factors that provide the dose per unit concentration in a medium 

such as water, for groundwater exposure (SAR Figure 2.3.10-9). Biosphere dose conversion 

factors are combined with the time-dependent radionuclide concentrations in groundwater from 
15-32 

the saturated zone transport models to calculate annual dose to the reasonably maximally 

exposed individual from groundwater exposure (SAR Figure 2.3.10-9). Results from the surface 

soil model are used to determine potential doses from inhalation of suspended soil particles, 

consumption of radionuclide-containing crops, soil ingestion by humans and animals, exposure 

to radioactive gases from the surface soil, and external exposure to radionuclide-containing 

soils. TER Section 2.2.1.3.14 documents the NRC staff’s evaluation of the biosphere dose 

conversion factors and biosphere submodels other than the surface soil submodel. 

In the surface soil submodel, radionuclides are considered to be added to the soil from irrigation 

using contaminated groundwater. They may decrease through the mechanisms of radioactive 

decay, leaching into deeper zones, erosion of soil particles, and gaseous release to the 

atmosphere (i.e., radon and carbon dioxide). Two soil layers are considered: a thin upper 

surface layer from which particles can be suspended into the atmosphere by disturbances and 

a thicker lower surface layer that may undergo mixing by agricultural practices such as tilling 

the land. 

NRC Staff Evaluation of the Surface Soil Submodel 

The NRC staff reviewed SAR Sections 2.3.10 on the surface soil submodel and the supporting 

DOE information on the surface soil submodel presented in SNL (2007ac) and BSC (2006ah). 

Model Integration 

Model abstractions comprise features, events, and processes that have been screened in from 

the scenario analysis. In TER Section 2.2.1.2.1.3.1, the NRC staff determined that DOE had 

identified a complete list of features, events, and processes for the groundwater exposure 

scenario. DOE screened out the transport of radionuclides past these soil layers to greater 

depths in its analysis of features, events, and processes. In TER Section 2.2.1.2.1.3.2, the 

NRC staff determined that DOE had screened all features, events, and processes for the 

groundwater exposure scenario, including those associated with the surface soil submodel. The 

NRC staff’s review of the downward migration modeling in soil evaluates DOE’s implementation 

of the included features, events, and processes: (i) FEP 2.3.02.01.0A, (ii) FEP 2.3.02.02.0A, 

and (iii) FEP 2.3.02.03.0A. 

DOE considered two soil layers in the surface soil submodel: (i) a thin surface layer that is 

susceptible to particles being suspended in the atmosphere from disturbances such as 

agricultural activities and wind and (ii) a lower, thicker layer that is approximately the thickness 

of the plow or till zone. Radionuclide concentrations for primary radionuclides and two 

long-lived decay products are calculated for varying climate conditions. Radionuclide 

concentrations are assumed to be uniform in the thin resuspendable layer and uniform in the 

thicker surface layer, if tilling is practiced. 

The NRC staff evaluated the modeling assumptions and integration for the surface soil 

submodel. Radionuclides in the surface soil submodel originate from contaminated 

groundwater used for irrigation. DOE used a unit concentration for each radionuclide of 1 Bq/m3

in the irrigation water to determine normalized biosphere dose conversion factors. TSPA 

computes the radionuclide doses by multiplying these normalized factors by the radionuclide 

concentrations in the groundwater. DOE allowed the radionuclide concentration absorbed on 

soils to build up toward equilibrium conditions before estimating the potential dose to the 

reasonably maximally exposed individual. NRC staff notes that once equilibrium conditions are 

obtained, longer irrigation periods would not affect radionuclide concentrations in soil. So that 
15-33 

the potential dose to the reasonably maximally exposed individual at earlier times would not be 

underestimated, DOE determined radionuclide concentrations in soil by assuming irrigation with 

contaminated well water for periods up to 1,000 years before estimating the potential dose to 

the reasonably maximally exposed individual. This approach to the prior irrigation period and 

buildup of radionuclides is reasonable because the use of higher radionuclide concentrations in 

the surface soil would tend to overestimate dose. 

The mathematical model DOE used, outlined in SNL Eq. 6.4.1-1, p. 6-73 (2007ac), to represent 

addition and removal of radionuclides in the surface soil is a differential equation that considers 

the dominant governing processes. The differential equation relates the rate of radionuclide 

accumulation to the difference between the rate of radionuclide addition and the rate of 

radionuclide loss in a volume of soil. This type of differential equation is known as an equation 

of continuity and is commonly used to track mass movement through systems (Bird, et al., 

1960aa). Inherent in the equation is mass balance that accounts for the difference between 

radionuclide addition and removal per unit time. The differential equation accounts for changes 

in storage or radionuclide concentration in the soil’s surface. Although the mathematical model 

and associated parameters DOE used do not account for all phenomena at a small (pore-level) 

scale, the NRC staff notes that the overall behavior at larger scales, for which the parameters 

are justified, is represented because small-scale phenomena are captured in the parameter 

determination. This mathematical model describes radionuclide movement at a scale where 

parameter values do not vary significantly with relatively small changes in spatial scales. For 

analyses evaluating potential doses to the receptor (reasonably maximally exposed individual), 

the NRC staff notes that this is a reasonable modeling approach. For the reasons described 

previously, the conceptual and mathematical surface soil submodels are reasonable for 

determining average radionuclide concentrations in the surface soil resulting from irrigation with 

radionuclide-containing groundwater. 

Radioactive decay, transport (i.e., leaching) to deeper soil, erosion of soil particles, and gaseous 

release to the atmosphere of radon and carbon dioxide are the dominant mechanisms for 

removal of radionuclides from the surface soil layers. Short-lived radioactive decay products 

(i.e., having half-lives shorter than 180 days) are assumed by DOE to be in equilibrium with the 

long-lived primary radionuclides. The NRC staff acknowledges this assumption as a common 

approach in environmental modeling and notes that it will not underestimate the effects of 

short-lived radionuclides on dose because any nonequilibrium in radionuclides having half-lives 

shorter than 180 days, produced by decay from long-lived parent radionuclides, will not affect 

the average annual dose to the reasonably maximally exposed individual. The potential 

removal of radionuclides that are incorporated into crops, which could then be harvested from 

the fields and gardens, is not considered by DOE. Radionuclides incorporated in these plants 

and animal waste are assumed to be returned to the soil as fertilizer. Because crops grown in 

Amargosa Valley are assumed by DOE to be consumed by the reasonably maximally exposed 

individual or local livestock, the NRC staff notes that the modeling assumption to neglect 

radionuclide removal in crops and include radionuclide return to soil does not underestimate the 

dose. For the reasons described previously, the NRC staff notes that DOE’s assumptions for 

radionuclide return to soil and removal in the surface soil submodel are reasonable. 

Data Sufficiency and Data Uncertainty 

The NRC staff evaluated data sufficiency and uncertainty for the irrigation rate source term. 

The irrigation rates were determined separately for field and garden crops. Irrigation rates 

directly affect radionuclide concentrations in the soil, because more radionuclide mass is 

added to the soil when the irrigation rate is higher. An average irrigation rate was calculated 
15-34 

from irrigation rates from several crops on the basis of current practices at Amargosa Valley, 

Nevada. Vegetables and fruit were assumed to be grown in gardens, whereas grains and 

forage were assumed to be grown in fields. An average irrigation rate was used due to crop 

rotation in fields and gardens in Amargosa Valley. DOE accounted for crop overwatering to 

prevent the buildup of soluble salts in the rooting zone. Overwatering introduces more 

contaminated groundwater than is needed to grow the crops. The NRC staff views 

overwatering to be a standard approach for determining the irrigation rate for crops, because 

limiting salt buildup in soils is desired and practiced worldwide [Hillel, p. 229 (1971aa)]. The 

NRC staff notes that the DOE assumptions for irrigation source term in the surface soil 

submodel do not underestimate the potential dose and are consistent with present knowledge of 

the Yucca Mountain region and consistent with semiarid conditions. 

The NRC staff evaluated data sufficiency and uncertainty for surface soil submodel parameters. 

DOE developed parameters for the surface soil submodel from surveys of land use in Amargosa 

Valley (e.g., type of crops grown, crop rotation, and crop acreage). DOE applied documented 

physical and chemical properties (e.g., soil properties, radionuclide properties/characteristics). 

DOE generally field checked or verified the data obtained from the surveys against independent 

records. The data were also typically collected over several years to account for variability. 

Documented physical and chemical parameters were obtained from measurements and 

analyses by independent groups, such as the U.S. Department of Agriculture’s soil surveys and 

established literature sources. DOE determined parameter values using relationships between 

parameters and measured quantities, which have been published in the scientific literature 

(e.g., Food and Agriculture Organization and the U.S. Department of Agriculture Natural 

Resources Conservation Service), and documented its analyses in the Biosphere Model Report 

(SNL, 2007ac). DOE used parameter distributions to account for parameter uncertainty. The 

NRC staff reviewed the parameter distributions and notes that these distributions are 

representative of the range of conditions in Amargosa Valley. Parameters were also adjusted 

by DOE to reflect differing climate conditions, where appropriate. For example, the average 

irrigation rate was adjusted to represent projected future climates. The NRC staff notes that 

adjusting the surface soil submodel parameters to account for climate changes, on the basis of 

cautious and reasonable assumptions, is consistent with the characteristics of the reference 

biosphere. Because DOE addressed parameter uncertainty for differing climate conditions in a 

similar manner to the representation of the present-day climate (discussed previously), the NRC 

staff notes that the resulting parameter distributions address uncertainty. 

Model Uncertainty and Model Support 

The NRC staff evaluated DOE’s model support and treatment of model uncertainty for the 

surface soil submodel. In SNL Section 6.3.3 (2007ac), DOE found that there are no alternative 

conceptual models for the biosphere evaluation. For its review of the redistribution of 

radionuclides in soil, the NRC staff evaluated this conclusion in terms of the surface soil 

submodel. Because the DOE surface soil model relies on first principles of mass balance to 

represent radionuclide redistribution in soil, the NRC staff notes that this DOE result is 

reasonable. The NRC staff is not aware of an alternative approach to representing radionuclide 

redistribution in soil that uses a conceptual model that is significantly different from the 

first-principles approach used in the DOE surface soil model. DOE compared ERMYN with two 

other established models that assess radionuclide concentrations in soil, GENII (Napier, et al., 

2006aa) and BIOMASS ERB2A (International Atomic Energy Agency, 2003aa), to evaluate the 

technique used to solve the mathematical model. The mathematical development all these 

models used, including the surface soil submodel used in ERMYN, is similar after the terms are 

combined or redefined, as identified in SNL Section 7.3.1.1 (2007ac). DOE explained 
15-35 

differences in the models and concluded that the calculations were equivalent. The NRC staff 

notes that the differences were explained, and differences in these models are not expected to 

significantly affect performance assessment results. An external review conducted by 

independent experts [SNL Section 7.6 (2007ac)] provides additional confidence in the ERMYN 

model. This external review covered a broader scope including surface soil components; it did 

not explicitly address the surface soil submodel. Nonetheless, the external review concluded in 

SNL Section 7.6 (2007ac) that the overall ERMYN model was a well-constructed, transparent, 

and complete biosphere modeling tool. On the basis of its review of the comparisons to 

established models, the NRC staff notes that DOE’s assessment of model uncertainty and 

model support for the surface soil submodel is reasonable. 

NRC Staff Evaluation of the Groundwater Exposure Scenario 

On the basis of the previously described evaluation, the NRC staff notes that the surface soil 

submodel provides a reasonable approach for calculating the radionuclide concentrations in 

both cultivated field and garden surface soils following radionuclide release in the groundwater 

pathway. DOE integrated its surface soil submodel by incorporating the important processes 

associated with the three included features, events, and processes. DOE described the 

governing processes of radionuclide buildup, retention, and removal in the surface soil. The 

NRC staff notes that the parameter determinations and their uncertainties for the surface soil 

submodel are described and justified. The NRC staff notes that the data support the abstracted 

model for radionuclide transport in the soil because they are based on documented soil 

properties of the Yucca Mountain region. The NRC staff notes that data uncertainty was 

characterized and propagated through the abstracted model by stochastic sampling of 

parameter ranges. The NRC staff notes that DOE addressed uncertainty in the conceptual 

model through comparisons to soil submodels in two other established biosphere models. 

Because DOE supported the surface soil model results by comparison to results from other 

published biosphere models, DOE’s model support is reasonable. 

The NRC staff has reviewed information in the SAR and other supporting information. DOE has 

provided information that addresses the criteria described in YMRP Section 2.2.1.3.13.3 for the 

surface soil submodel and its implementation in the GoldSim modeling environment of the 

TSPA analysis. 

2.2.1.3.13.4 NRC Staff Conclusions 

NRC staff notes that the DOE description of this model abstraction for airborne transport and 

redistribution of radionuclides is consistent with the guidance in the YMRP. NRC staff also 

notes that the DOE technical approach discussed in this chapter is reasonable for use in the 

Total System Performance Assessment (TSPA). 

2.2.1.3.13.5 References 

Andronico, D., S. Scollo, S. Caruso, and A. Cristaldi. 2008aa. “The 2002–03 Etna Explosive 

Activity: Tephra Dispersal and Features of the Deposits. Journal of Geophysical Research. 

Vol. 113, No. B04209. doi:10.1029/2007JB005126. 

Anspaugh, L.R., S.L. Simon, K.I. Gordeev, I.A. Likhtarev, R.M. Maxwell, and S.M. Shinkarev. 

2002aa. “Movement of Radionuclides in Terrestrial Ecosystems by Physical Processes.” 

Health Physics. Vol. 82, No. 5. pp. 669–679. 
15-36 

Benke, R.R., D.M. Hooper, J.S. Durham, D.R. Bannon, K.L. Compton, M. Necsoiu, and 

R.N. McGinnis, Jr. 2009aa. “Measurement of Airborne Particle Concentrations Near the 

Sunset Crater Volcano, Arizona.” Health Physics. Vol. 96, No. 2. pp. 97–117. 

Bird, R.B., W.E. Stewart, and E.N. Lightfoot. 1960aa. Transport Phenomena. New York City, 

New York: John Wiley & Sons. pp. 74-76. 

Blong, R.J. 1984aa. Volcanic Hazards—A Sourcebook on the Effects of Eruptions. Orlando, 

Florida: Academic Press. P. 427. 

BSC. 2006ad. “Inhalation Exposure Input Parameters for the Biosphere Model.” 

ANL–MGR–MD–000001. Rev. 04. ACN 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2006ah. “Soil-Related Input Parameters for the Biosphere Model.” 

ANL–NBS–MD–000009. Rev. 03. AD 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2003ad. “Characterize Eruptive Processes at Yucca Mountain, Nevada.” 

ANL–MGR–GS–000002. Rev. 01. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

CNWRA. 2007aa. “Software Validation Report for Total-System Performance Assessment 

(TPA) Code Version 5.1.” ML072840502. San Antonio, Texas: CNWRA. 

Codell, R.B. 2004aa. “Alternate Igneous Source Term Model for Tephra Dispersal at the Yucca 

Mountain Repository.” Nuclear Technology. Vol. 148. pp. 205–212. 

Codell, R.B. 2003aa. “Alternative Igneous Source Term Model for the Yucca Mountain 

Repository.” Proceedings of the 10th International High-Level Radioactive Waste Management 

Conference (IHLRWM), Las Vegas, Nevada, March 30–April 2, 2003. LaGrange Park, Illinois: 

American Nuclear Society. 

CRWMS M&O. 1997aa. “Regional and Local Wind Patterns Near Yucca Mountain.” 

B00000000–01717–5705–00081. Rev. 00. MOL.19980204.0319. Las Vegas, Nevada: 

CRWMS M&O. 

DOE. 2009ab. “Yucca Mountain—Response to Request for Additional Information 

Regarding License Application (Safety Analysis Report Sections 2.2, Table 2.2-5), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.2.1, Set 2.” Letter (February 23) J.R. Williams to 

J.H. Sulima (NRC). ML090550101. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

DOE. 2009av. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository 

License Application.” Rev. 01. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

DOE. 2009bk. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.10 and 2.3.11), Safety Evaluation 

Report Vol. 3, Chapter 2.2.1.13, Set 1.” Letter (July 27) J.R. Williams to J.H. Sulima (NRC). 

ML092090273. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 
15-37 

DOE. 2009bl. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.1, 2.3.11, 2.4.3, and 2.4.4), Safety 

Evaluation Report Vol. 3, Chapters 2.2.1.4.1, 2.2.1.4.2, and 2.2.1.4.3, Set 1.” Letter (July 29) 

J.R. Williams to J.H. Sulima (NRC). ML092110472. Las Vegas, Nevada: DOE, Office of 

Civilian Radioactive Waste Management. 

DOE. 2009bm. “Yucca Mountain—Response to Request for Additional Information 

Regarding License Application (Safety Analysis Report Sections 2.3.10 and 2.3.11), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.13, Set 1.” Letter (September 17) J.R. Williams to 

J.H. Sulima (NRC). ML092610250. Las Vegas Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

Hawkes, H.E. 1976aa. “The Downstream Dilution of Stream Sediment Anomalies.” Journal of 

Geochemical Exploration. Vol. 6. pp. 345−358. 

Heffter, J.L. and B.J.B. Stunder. 1993aa. “Volcanic Ash Forecast Transport and Dispersion 

(VAFTAD) Model.” Weather and Forecasting. Vol. 8. pp. 533–541. 

Heiken, G. 1978aa. “Characteristics of Tephra from Cinder Cone, Lassen Volcanic National 

Park, California.” Bulletin Volcanologique. Vol. 41, No. 2. pp. 119-130. 

Hill, B.E. and C.B. Connor. 2000aa. “Technical Basis for Resolution of the Igneous Activity Key 

Technical Issue.” ML011930254 San Antonio, Texas: CNWRA. 

Hill, B.E., C.B. Connor, J. Weldy, and N. Franklin. 2000ab. “Methods for Quantifying Hazards 

from Basaltic Tephra-Fall Eruptions.” Proceedings of the Cities on Volcanoes 2 Conference, 

Auckland, New Zealand, February 12–14, 2001. C. Stewart, ed. Lower Hutt, New Zealand: 

Institute of Geological and Nuclear Sciences Limited. Institute of Geological and Nuclear 

Sciences Information Series. Vol. 49. p. 158. 

Hill, B.E., C.B. Connor, M.S. Jarzemba, P.C. La Femina, M. Navarro, and W. Strauch. 1998aa. 

“1995 Eruptions of Cerro Negro Volcano, Nicaragua and Risk Assessment for Future 

Eruptions.” Geological Society of America Bulletin. Vol. 110, No. 10. pp. 1,231–1,241. 

Hillel, D. 1971aa. Soil and Water: Physical Principles and Processes. New York City, 

New York: Academic Press. 

Hurst, A.W. and R. Turner. 1999aa. “Performance of the Program ASHFALL for Forecasting 

Ashfall During the 1995 and 1996 Eruptions of Ruapehu Volcano.” New Zealand Journal of 

Geology and Geophysics. Vol. 42. pp. 615–622. 

Inbar, M., J. Lugo, and L. Villers. 1994aa. “The Geomorphological Evolution of the Paricutin 

Cone and Lava Flows, Mexico, 1943–1990.” Geomorphology. Vol. 9. pp. 57–76. 

International Atomic Energy Agency. 2003aa. “Reference Biospheres” for Solid Radioactive 

Waste Disposal, Report of BIOMASS Theme 1 of the BIOsphere Modelling and ASSessment 

(BIOMASS) Programme, Part of the IAEA Co-ordinated Research Project on Biosphere 

Modelling and Assessment (BIOMASS).” IAEA–BIOMASS–6. Vienna, Austria: International 

Atomic Energy Agency, Waste Safety Section. 
15-38 

Janetzke, R., R. Benke, and D. Hooper. 2008aa. “Software Validation Test Plan and Report 

for the Volcanic Ash Dispersal and Deposition Code TEPHRA, Version 1.0.” ML081210677 

San Antonio, Texas: CNWRA. 

Jarzemba, M.S. 1997aa. “Stochastic Radionuclide Distributions After a Basaltic Eruption for 

Performance Assessments at Yucca Mountain.” Nuclear Technology. Vol. 118. pp. 132–141. 

Jarzemba, M.S., P.A. LaPlante, and K.J. Poor. 1997aa. “ASHPLUME Version 1.0—A Code for 

Contaminated Ash Dispersal and Deposition.” CNWRA 97-004. San Antonio, Texas: CNWRA. 

Leopold, L.B., W.W. Emmett, and R.M. Myrick. 1966aa. “Channel and Hillslope Processes in a 

Semiarid Area, New Mexico.” U.S. Geological Survey Professional Paper 352–G. pp. 193–253. 

Leopold, L.B., M.G. Wolman, and J.P. Miller. 1964aa. “Fluvial Processes in Geomorphology.” 

San Francisco, California: W.H. Freeman and Co. 1964. Reprinted in 1995. New York City, 

New York: Dover Publications. 

Leslie, B., C. Grossman, and J. Durham. 2007aa. “Total-System Performance Assessment 

(TPA) Version 5.1 Module Descriptions and User Guide.” Rev. 1. ML080510329. San Antonio, 

Texas: CNWRA. 

Marcus, W.A. 1987aa. “Copper Dispersion in Ephemeral Stream Sediments.” Earth Surface 

Processes and Landforms. Vol. 12. pp. 217−228. 

Napier, B.A., D.L. Strenge, J.V. Ramsdell, Jr., P.W. Eslinger, and C. Fosmire. 2006aa. “GENII 

Version 2 Software Design Document.” PNNL–14584. Rev. 1. Richland, Washington: Pacific 

Northwest National Laboratory. 

NOAA. 2004aa. “Upper Air Data for Desert Rock, Nevada, Years 1978–2003.” National 

Climatic Data Center Digital Upper Air Files TD 6201 and 6301. Asheville, North Carolina: 

National Oceanic and Atmospheric Administration. 

NRC. 2009ab. "Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63." Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

Pelletier, J.D. and J.P. Cook. 2005aa. “Deposition of Playa Windblown Dust Over Geologic 

Time Scales.” Geology. Vol. 33, No. 11. pp. 909–912. 

Pelletier, J.D., S.B. DeLong, M.L. Cline, C.D. Harrington, and G.N. Keating. 2008aa. 

“Dispersion of Channel-Sediment Contaminants in Distributary Fluvial Systems: Application 

to Fluvial Tephra and Radionuclide Redistribution Following a Potential Volcanic Eruption at 

Yucca Mountain.” Geomorphology. Vol. 94. pp. 226–246. 
15-39 

Pelletier, J.D., C.D. Harrington, J.W. Whitney, M. Cline, S.B. DeLong, G. Keating, and 

K.T. Ebert. 2005aa. “Geomorphic Control of Radionuclide Diffusion in Desert Soils.” 

Geophysical Research Letters. Vol. 32. p. L23401. 

Pioli, L., E. Erlund, E. Johnson, K. Cashman, P. Wallace, M. Rosi, and H. Delgado Granados. 

2008aa. “Explosive Dynamics of Violent Strombolian Eruptions: The Eruption of Parícutin 

Volcano 1943–1952 (Mexico).” Earth and Planetary Science Letters. Vol. 271. pp. 359–368. 

Segerstrom, K. 1966aa. “Parícutin, 1965—Aftermath of Eruption.” U.S. Geological Survey 

Professional Paper 550–C. pp. C93–C101. 

Segerstrom, K. 1950aa. “Erosion Studies at Parícutin, State of Michoacán, Mexico.” 

U.S. Geological Survey Bulletin 965–A. pp. 1–164. 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01. Las Vegas, 

Nevada: Sandia National Laboratories. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ab. “Atmospheric Dispersal and Deposition of Tephra From a Potential Volcanic 

Eruption at Yucca Mountain, Nevada.” MDL–MGR–GS–000002. Rev. 03. ERD 01. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ac. “Biosphere Model Report.” MDL–MGR–MD–000001. Rev. 02. ERD 01. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2007ae. “Characterize Eruptive Processes at Yucca Mountain, Nevada.” 

ANL–MGR–GS–000002. Rev. 03. ERD 01. Las Vegas, Nevada: 

Sandia National Laboratories. 

SNL. 2007av. “Redistribution of Tephra and Waste by Geomorphic Processes Following a 

Potential Volcanic Eruption at Yucca Mountain, Nevada.” MDL–MGR–GS–000006. Rev. 00. 

ERD 01. Las Vegas, Nevada: Sandia National Laboratories. 

Sparks, R.S.J., M.I. Bursik, S.N. Carey, J.S. Gilbert, L.S. Glaze, H. Sigurdsson, and 

A.W. Woods. 1997aa. Volcanic Plumes. New York City, New York: John Wiley & Sons. 

p. 574. 

Suzuki, T. 1983aa. “A Theoretical Model for the Dispersion of Tephra.” Arc Volcanism: 

Physics and Tectonics. D. Shimozuru and I. Yokoyama, eds. Tokyo, Japan: Terra Scientific 

Publishing Company. pp. 95–113. 

Till, J.E. and R.E. Moore. 1988aa. “A Pathway Analysis Approach for Determining 

Acceptable Levels of Contamination of Radionuclides in Soil.” Health Physics. Vol. 55, No. 3. 

pp. 541–548. 

Winfrey, B. 2005aa. “Software Validation Test Plan and Report, TEPHRA, Version 1.0.” 

ML063050217 San Antonio, Texas: CNWRA. 

16-1 

CHAPTER 16 

2.2.1.3.14 Biosphere Characteristics 

2.2.1.3.14.1 Introduction 

This chapter of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s evaluation of the U.S. Department of Energy’s (DOE’s) postclosure 

performance assessment model used to calculate biosphere transport and the annual dose to 

the reasonably maximally exposed individual, as presented in DOE’s Safety Analysis Report 

(SAR) (DOE, 2008ab). The sources of radionuclides used in DOE’s biosphere model 

calculations are calculated by other models in its performance assessment analysis. Those 

models calculate repository releases from postclosure engineered-barrier-system failures and 

then model the transport of the released radionuclides from the repository location to the 

biosphere. Results from these other transport models provide the sources of radionuclides from 

two primary biosphere media: groundwater and soil contaminated with tephra deposits. In the 

model, tephra (hereafter, volcanic ash) is deposited on the ground from postulated volcanic 

events. DOE’s biosphere model then calculates the subsequent transport of these 

radionuclides within the biosphere through a variety of exposure pathways (e.g., soil, food, 

water, air) and applies dosimetry modeling to convert the reasonably maximally exposed 

individual exposures into annual dose. 

10 CFR 63.2 defines the reference biosphere as “the description of the environment inhabited 

by the [reasonably maximally exposed individual].” The reasonably maximally exposed 

individual is defined at 10 CFR 63.312 as a hypothetical adult who (i) lives in the accessible 

environment above the highest concentration of radionuclides in the plume of contamination; 

(ii) has a diet and living style representative of current Amargosa Valley, Nevada, residents; 

(iii) uses well water with average concentrations of radionuclides based on an annual water 

demand of 3.7 × 109

 L [3,000 acre-ft]; (iv) drinks 2 L [0.528 gal] of water per day from 

groundwater extracted from wells drilled at the location specified in (i) of this paragraph; and 

(v) is an adult with metabolic and physiological considerations consistent with present 

knowledge of adults. 

DOE estimated the dose to the reasonably maximally exposed individual on the basis of the 

concentrations of radionuclides in groundwater and in contaminated ash. These concentrations 

were calculated by DOE’s model abstractions for saturated zone transport [Safety Analysis 

Report (SAR) Revision 1 Section 2.3.9], extrusive (volcanic eruption) atmospheric dispersal 

(SAR Section 2.3.11.4.5.2), and volcanic ash redistribution (SAR Section 2.3.11.4.5.3). These 

model abstractions, which provide inputs for the biosphere calculations within the Total System 

Performance Assessment (TSPA) model, are reviewed in Technical Evaluation Report (TER) 

Sections 2.2.1.3.9 and 2.2.1.3.13, respectively. This chapter of the TER focuses on the NRC 

staff’s review of the performance assessment calculations of biosphere transport and dose to 

the reasonably maximally exposed individual described in SAR Section 2.3.10. The NRC staff’s 

evaluation of biosphere modeling of radionuclide concentrations in soil can be found in 

TER Section 2.2.1.3.13. 

In SAR Section 2.3.10, DOE analyzed the characteristics of the Yucca Mountain region and 

Amargosa Valley, Nevada, for its biosphere transport and reasonably maximally exposed 

individual dose calculations. DOE identified features, events, and processes (FEPs) and 

developed biosphere conceptual and mathematical models for use in its TSPA computer model. 
16-2 

DOE described environmental conditions, resident lifestyle, exposure media, environmental 

transport pathways, and human exposure pathways it used for evaluating the impacts of 

repository performance on dose to the reasonably maximally exposed individual. 

Exposure pathways in the DOE biosphere model are based on assumptions about residential 

and agricultural uses of the water and indoor and outdoor activities. These pathways 

include ingestion, inhalation, and direct exposure to radionuclides deposited to soil from 

irrigation (SAR Section 2.3.10.1). Ingestion pathways include drinking contaminated water, 

eating crops irrigated with contaminated water, eating food products produced from livestock 

raised on contaminated feed and water, eating farmed fish raised in contaminated water, and 

inadvertently ingesting soil. Inhalation pathways include breathing resuspended soil, aerosols 

from evaporative coolers, and radon gas and its decay products. 

DOE’s approach to biosphere modeling was twofold. DOE used a standalone computer code 

entitled Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) to calculate 

biosphere dose conversion factors, which were used as inputs in DOE’s TSPA code. The TSPA 

multiplied the appropriate biosphere dose conversion factor by either a soil concentration or a 

water concentration to obtain the dose to the reasonably maximally exposed individual for each 

exposure scenario (i.e., volcanic ash, groundwater). The substance of DOE’s biosphere 

modeling approach is contained primarily in the ERMYN code. 

This chapter evaluates the technical bases for DOE’s conceptual and mathematical biosphere 

models, input parameter selections, parameter uncertainty propagation, model support, and 

model implementation and integration within DOE’s performance assessment evaluation. 

These evaluations are organized by subsections that address specific components of DOE’s 

biosphere model (or model development process), including system description and model 

integration, biosphere transport pathways, human exposure, dosimetry, and integrated 

biosphere modeling results. The NRC staff’s review evaluates both the biosphere modeling in 

the ERMYN code and how DOE used the ERMYN output (the biosphere dose conversion 

factors) to calculate reasonably maximally exposed individual dose in the TSPA model. 

2.2.1.3.14.2 Evaluation Criteria

NRC staff’s review of model abstractions used in the DOE postclosure performance 

assessment, including those considered in this chapter for climate and infiltration, is guided by 

10 CFR 63.114 (Requirements for Performance Assessment) and 63.342 (Limits on 

Performance Assessments). The DOE Total System Performance Assessment is reviewed in 

TER Section 2.2.1.4.1. 

The regulations in 10 CFR 63.114 require that a performance assessment 

 Include appropriate data related to the geology, hydrology, and geochemistry (including 

disruptive processes and events) of the surface and subsurface from the site and the 

region surrounding Yucca Mountain [10 CFR 63.114(a)(1)] 

 Account for uncertainty and variability in the parameter values [10 CFR 63.114(a)(2)] 

 Consider and evaluate alternative conceptual models [10 CFR 63.114(a)(3)] 

 Provide technical bases for either the inclusion or exclusion of features, events, and 

processes (FEPs), including effects of degradation, deterioration, or alteration processes 
16-3 

of engineered barriers that would adversely affect performance of the natural barriers, 

consistent with the limits on performance assessment in 10 CFR 63.342, and evaluate in 

sufficient detail those processes that would significantly affect repository performance 

[10 CFR 63.114(a)(4–6)] 

 Provide technical basis for the models used in the performance assessment to represent 

the 10,000 years after disposal [10 CFR 63.114(a)(7)] 

The NRC staff’s evaluation of inclusion or exclusion of FEPs is in TER Chapter 2.2.1.2.1. 

10 CFR 63.114(a) provides requirements for performance assessment for the initial 

10,000 years following disposal. 10 CFR 63.114(b) and 63.342 provide requirements for the 

performance assessment methods for the time from 10,000 years through the period of 

geologic stability, defined in 10 CFR 63.302 as 1 million years following disposal. These 

sections require that through the period of geologic stability, with specific limitations, the DOE 

dose calculation should 

 Use performance assessment methods consistent with the performance assessment 

methods used to calculate dose for the initial 10,000 years following permanent closure 

[10 CFR 63.114(b)] 

 Include in the performance assessment those FEPs used in the performance 

assessment for the initial 10,000-year period (10 CFR 63.342) 

10 CFR 63.305 states the following requirements for characteristics of the reference biosphere: 

 Features, events, and processes that describe the reference biosphere are consistent 

with present knowledge of the conditions in the region surrounding the Yucca Mountain 

site. 

 Changes in society, the biosphere (other than climate), or human biology or increases or 

decreases of human knowledge and technology should not be projected; in all analyses, 

all of those factors should be assumed to be constant as they are at the present time. 

 Factors related to the geology, hydrology, and climate should be varied based upon 

cautious but reasonable assumptions of the changes in these factors that could affect 

the Yucca Mountain disposal system during the period of geologic stability, consistent 

with the criteria for performance assessments specified at 10 CFR 63.342. 

 Biosphere pathways are consistent with arid or semiarid conditions. 

10 CFR 63.312 states the following requirements for characteristics of the reasonably maximally 

exposed individual (RMEI): 

 Lives in the accessible environment above the highest concentration of radionuclides in 

the plume of contamination 

 Has a diet and living style representative of the people who now reside in the Town of 

Amargosa Valley, Nevada (projections are based upon surveys of the people residing in 

the Town of Amargosa Valley, Nevada, to determine their current diets and living styles); 

mean values of these factors are to be used in performance assessment 
16-4 

 Uses well water with average concentrations of radionuclides based on an annual water 

demand of 3000 acre-ft 

 Drinks 2 L [0.07 ft3

] of water per day from wells drilled into the groundwater in the 

accessible environment where the RMEI resides 

 Is an adult with metabolic and physiological considerations consistent with present 

knowledge of adults 

 Has biosphere pathways consistent with arid or semi-arid conditions 

The approach to be taken for implementation of the total effective dose equivalent (TEDE) is 

specified at 10 CFR 63.102 (Concepts). 

The NRC staff review of the SAR and supporting information follows the guidance laid out in 

YMRP Section 2.2.1.3.14, Biosphere Characteristics, as supplemented by additional guidance 

for the period beyond 10,000 years after permanent closure (NRC, 2009ab). The YMRP 

acceptance criteria for model abstractions that provide guidance for the NRC staff evaluation of 

DOE’s abstraction of biosphere characteristics are 

1. System description and model integration are adequate 

2. Data are sufficient for model justification 

3. Data uncertainty is characterized and propagated through the abstraction 

4. Model uncertainty is characterized and propagated through the abstraction 

5. Model abstraction output is supported by objective comparisons 

NRC staff review used a risk-informed approach and the guidance provided by the YMRP, as 

supplemented by NRC (2009ab), to the extent reasonable for aspects of biosphere 

characteristics important to repository performance. The NRC staff considered all five criteria 

provided in the YMRP in its review of information provided by DOE. In the context of these 

criteria, only those aspects of the model abstraction that substantively affect the performance 

assessment results, as determined by the NRC staff, are discussed in detail in this chapter. 

The NRC staff’s determination was based both on risk information provided by DOE and on 

NRC staff knowledge gained through experience and independent confirmatory analyses. 

2.2.1.3.14.3 Technical Evaluation

The NRC staff’s technical review of DOE’s biosphere characteristics model abstraction 

evaluated both the biosphere model and the model development process. The review 

focused on five topics: (i) system description and model integration, (ii) biosphere transport 

pathways, (iii) human exposure, (iv) dosimetry, and (v) the integrated biosphere modeling 

results. These reviews are documented in subsections of this TER chapter. The system 

description and model integration review evaluated DOE’s overall conceptualization of the 

biosphere including features, events, and processes that were selected and included in DOE’s 

biosphere conceptual models. 

The NRC staff’s detailed review focused on the most risk-significant parts of DOE’s biosphere 

model. Risk insights that apply to both DOE’s TSPA results and to DOE’s detailed abstraction 

modeling of the biosphere (i.e., using the ERMYN code to generate the biosphere dose 
16-5 

conversion factors) informed the NRC staff’s review. These risk insights focused the NRC 

staff’s detailed review on those aspects of DOE’s biosphere modeling that contributed most to 

the calculated reasonably maximally exposed individual dose results in the TSPA. 

 

The NRC staff analyzed the risk-significance aspects of the TSPA biosphere model abstraction 

in the TSPA code by evaluating DOE’s sensitivity analysis results using the TSPA code. These 

results indicated that biosphere dose conversion factors (ERMYN code outputs) significantly 

affected the TSPA results, as identified in SNL, Appendix K, pp. FK–63 to FK–65 (2008ag). The 

NRC staff therefore performed a detailed technical evaluation of DOE’s biosphere model. 

The NRC staff’s risk-informed review evaluated a subset of DOE’s biosphere model abstraction 

to determine whether DOE’s overall methodology was reasonable. This detailed review focused 

on the subset of radionuclides and exposure pathways that were the most risk significant in 

DOE’s performance assessment analysis. These radionuclides and exposure pathways are 

summarized in TER Tables 16-1 and 16-2. 

On the basis of DOE’s documentation of its performance assessment results, the 

NRC staff developed TER Tables 16-1 and 16-2 using the following two-step approach. 

First, the NRC staff identified those radionuclides that individually account for the largest 

fraction of DOE’s peak total mean annual dose results from its TSPA analysis, as shown in SAR 

Figure 2.4-20. Next, the primary pathways that account for the largest fraction of DOE’s 

calculated biosphere dose conversion factors for each identified radionuclide were included. 

DOE’s pathway contributions to each radionuclide-specific biosphere dose conversion factor 

were documented in SAR Table 2.3.10-11. On the basis of this analysis of DOE’s results, the 

NRC staff notes that the radionuclides and pathways identified in TER Tables 16-1 and 16-2 are 

the most risk-significant contributors to DOE’s TSPA results. 

The NRC staff focused its detailed review on the subset of radionuclides and pathways that are 

the most risk-significant contributors to DOE’s performance assessment results. An example is 

provided here to clarify how this approach identifies the most risk-significant contributors to 

DOE’s results. For DOE’s 1-million-year results presented in SAR Figure 2.4-20(b), the peak 

Table 16-1. Exposure Pathways and Radionuclides Determined the Most Risk 

Significant in the DOE Performance Assessment for the 10,000-Year Simulation Period 

Radionuclide* 

Source of 

Radionuclides†

Route of 

Exposure‡ Primary Pathways‡ 

Tc-99 

Estimated 

Releases to 

Groundwater 

Ingestion 42% drinking water 

37% animal product 

C-14 Ingestion 59% fish 

22% drinking water 

Pu-239 Inhalation 50% particulates 

24% evaporative cooler aerosols 

Ingestion 19% drinking water 

I-129 Ingestion 60% drinking water 

28% animal products 

*Radionuclides presented in order of their contribution to the DOE peak total mean annual dose results in SAR 

Figure 2.4-20. 

†Modeling cases that contribute most to the DOE total mean annual dose are based on release to groundwater as 

shown in SAR Figure 2.4-18 and SAR Section 2.4.2.2.1.2. 

‡Routes of exposure and primary pathways from SAR Table 2.3.10-11. Various pathways not listed contribute the 

remaining percentage of each radionuclide dose. 
16-6 

Table 16-2. Exposure Pathways and Radionuclides Determined the Most Risk 

Significant in the DOE Performance Assessment for the 1-Million-Year 

Simulation Period 

Radionuclide* 

Source of 

Radionuclides†

Route of 

Exposure‡ Primary Pathways‡ 

Pu-242 

Estimated 

Releases to 

Groundwater 

Inhalation 51% particulates 

24% evaporative cooler aerosols 

Ingestion 19% drinking water 

Np-237 Inhalation 35% evaporative cooler aerosols 

21% particulates 

Ingestion 29% drinking water 

Ra-226 Inhalation 74% radon 

I-129 Ingestion 60% drinking water 

28% animal products 

*Radionuclides presented in order of their contribution to the DOE peak total mean annual dose results in SAR 

Figure 2.4-20. 

†Modeling cases that contribute most to the DOE total mean annual dose are based on release to groundwater as 

shown in SAR Figure 2.4-18 and SAR Section 2.4.2.2.1.2. 

‡Routes of exposure and primary pathways from SAR Table 2.3.10-11. Various pathways not listed contribute the 

remaining percentage of each radionuclide dose. 

total mean annual dose is approximately 0.02 mSv/yr [2 mrem/yr]; four radionuclides contribute 

approximately 0.015 mSv/yr [1.5 mrem/yr] (75 percent) to that value. The NRC staff identified 

these four radionuclides as the most risk-significant contributors because they represent the 

smallest number of radionuclides that comprise the largest fraction of the peak mean dose. The 

remaining 17 radionuclides in DOE’s analysis each contributed a small fraction to the peak 

mean dose. The pathways for these four radionuclides were then individually evaluated to 

identify the subset of pathways that contributed the largest fraction to the dose contribution from 

the radionuclide using information provided in SAR Table 2.3.10-11. For example, Pu-242 is 

responsible for 30 percent of DOE’s peak mean dose. The NRC staff evaluated the pathways 

through which Pu-242 contributed to the dose and noted that the inhalation of particulates 

pathway was responsible for 51 percent of the Pu-242 dose, the inhalation of evaporative cooler 

aerosols pathway was responsible for 24 percent of the Pu-242 dose, and the drinking 

groundwater pathway was responsible for 19 percent of the Pu-242 dose. Twelve other 

pathways are responsible for the remaining 6 percent of the Pu-242 dose; therefore, three 

pathways were identified as being the most risk significant for Pu-242. This example illustrates 

the NRC staff’s approach to identifying the radionuclides and their pathways that are the most 

risk significant to DOE’s performance assessment calculation. 

TER Table 16-1 contains the radionuclides and their pathways that are the most risk 

-significant contributors to DOE’s performance assessment dose results for the time period of 

10,000 years following disposal. TER Table 16-2 contains the radionuclides and their pathways 

that are the most risk-significant contributors to DOE’s performance assessment dose results for 

the time period after the initial 10,000 years up to 1 million years. The radionuclides listed in 

TER Tables 16-1 and 16-2 include radionuclides determined to be important contributors to 

dose results in prior independent NRC performance assessment results, as identified in NRC 

Volume 2, Appendix D (2005aa). 
16-7 

While the NRC staff’s technical review evaluated all of DOE’s biosphere modeling 

documentation at a general level of detail, the NRC staff’s focused review evaluated DOE’s 

biosphere submodels and input parameters that are risk significant in the biosphere dose 

conversion factor calculations on the basis of the NRC staff’s identification of risk-significant 

pathways in TER Tables 16-1 and 16-2. In particular, the NRC staff’s review of the data 

supporting the biosphere transport pathway input parameters (TER Section 2.2.1.3.14.3.2) 

focused on parameters in DOE’s transport submodels. These transport submodels include 

plant uptake, animal uptake, fish uptake, and air modeling. Similarly, the NRC staff’s review of 

DOE’s data supporting input parameters for the human exposure submodels evaluated in TER 

Section 2.2.1.3.14.3.3 focused on the inhalation and ingestion exposure submodels because 

those are the routes of exposure identified to be most risk significant in Tables 16-1 and 16-2. 

2.2.1.3.14.3.1 System Description and Model Integration

In SAR Section 2.3.10.2 and in supporting references, DOE described the biosphere 

characteristics of the Yucca Mountain region; of Amargosa Valley, Nevada, that impact its 

residents; of included features, events, and processes; and of the biosphere conceptual models 

in the ERMYN code that were used to calculate biosphere dose conversion factors. This 

section documents the NRC staff’s review of these descriptions. An additional part of the NRC 

staff’s review evaluated integration (i.e., couplings, consistency, and assumptions) of the TSPA 

biosphere model abstraction with other TSPA model abstractions. 

Features, Events, and Processes 

DOE described the Yucca Mountain region characteristics in SAR Section 2.3.10.2.1. This 

information addressed topics including climate, topography and soils, native flora and fauna 

(i.e., plants and animals), local communities, infrastructure (including water source), and 

agricultural conditions. Information on the characteristics of Amargosa Valley, Nevada, 

residents (summarized in SAR Section 2.3.10.2.2) originated predominantly from local and 

national surveys. The SAR addressed topics such as diet and lifestyle factors, including the 

use of evaporative coolers, gardening, employment, commuting, housing, and metabolic 

considerations. DOE documented the screening approach for the features, events, and 

processes in SAR Section 2.2.1.2 and listed all the features, events, and processes that were 

evaluated for the TSPA model in SAR Table 2.2-1. Features, events, and processes that were 

included in the biosphere model were tabulated in SAR Table 2.3.10-1 and are reviewed in this 

TER section. The NRC staff’s review of excluded features, events, and processes is 

documented in TER Section 2.2.1.2.1.3.2. 

The NRC staff’s review evaluated the technical bases DOE used to support its disposition 

of included features, events, and processes in the performance assessment with respect to the 

following: (i) whether DOE provided reasonable technical bases for including biosphere 

features, events, and processes; (ii) whether the included features, events, and processes were 

consistent with present knowledge of the conditions in the region surrounding the Yucca 

Mountain site; and (iii) whether DOE included all biosphere-related features, events, and 

processes that could significantly change the magnitude or timing of the radiological exposures 

to the reasonably maximally exposed individual in its performance assessment. 

The NRC staff evaluated DOE’s technical bases for included features, events, and processes 

and reviewed DOE’s descriptions of how each included biosphere feature, event, and process 

was incorporated into the performance assessment calculation. In this review, the NRC staff 

verified that the features, events, and processes which could significantly contribute to the 
16-8 

reasonably maximally exposed individual dose were included in the performance assessment 

calculation and that information supporting the features, events, and processes was based on 

present knowledge of the Yucca Mountain region conditions. Because many features, events, 

and processes are general in nature (e.g., climate change, biosphere characteristics, plant 

uptake), the NRC staff evaluated whether the performance assessment evaluation included the 

specific aspects of a feature, event, and process that would be expected to contribute to 

reasonably maximally exposed individual dose and therefore should be included in the model. 

As part of its review of DOE’s analyses, the NRC staff also incorporated its understanding of 

Yucca Mountain region characteristics obtained from prior experience and independent 

analyses of the biosphere characteristics at Yucca Mountain (e.g., LaPlante and Poor, 1997aa). 

The NRC staff notes that DOE’s list of included features, events, and processes is consistent 

with the NRC staff’s independent assessment of the characteristics of the Yucca Mountain 

region and Amargosa Valley. The NRC staff’s review noted that features included in DOE’s 

performance assessment (i.e., wells, soil type, agricultural land use, irrigation, animal farms, 

fisheries, and human lifestyle and characteristics) were supported by the technical bases in the 

SAR and supporting references and are representative of the present knowledge of the Yucca 

Mountain region. The NRC staff’s review noted that processes included in DOE’s performance 

assessment (i.e., radionuclide accumulation in soils, atmospheric transport, plant and animal 

uptake, radioactive decay, ingestion, inhalation, external exposure, and radiation dose) were 

supported by the technical bases in the SAR and supporting references that represented 

present knowledge of the Yucca Mountain region. The NRC staff notes that DOE’s list of 

included features, events, and processes is suitable for use in its biosphere model. In addition, 

the NRC staff has not identified any additional features, events, and processes that are 

excluded from DOE’s biosphere model that would be expected to significantly increase the dose 

or affect the timing of dose to the reasonably maximally exposed individual. 

Conceptual Models 

In SAR Section 2.3.10.2.3, DOE considered features, events, and processes for the reference 

biosphere model and dose calculation of the reasonably maximally exposed individual in 

identifying applicable exposure pathways and developing exposure scenarios for modeling dose 

to the reasonably maximally exposed individual. An exposure scenario, in general, describes a 

set of facts, assumptions, and inferences about how exposure occurs. In DOE’s Yucca 

Mountain biosphere model, an exposure scenario is a conceptual model that describes the 

biosphere characteristics which lead to the reasonably maximally exposed individual’s exposure 

to radionuclides that enter the biosphere from different transport routes (i.e., groundwater or 

volcanic ash). DOE’s conceptual representations of the exposure pathways for groundwater 

and volcanic ash exposure scenarios were provided in SAR Figures 2.3.10-6 and 2.3.10-8. 

DOE incorporated these conceptual representations into mathematical submodels in the 

ERMYN code. The mathematical submodels in the ERMYN code were depicted in SAR 

Figures 2.3.10-9 and 2.3.10-10 and described in SAR Sections 2.3.10.2.5 and 2.3.10.2.6. 

The NRC staff evaluated DOE’s conceptual representations (i.e., conceptual models) 

and associated mathematical submodels in the ERMYN code. The NRC staff reviewed both 

DOE’s groundwater exposure scenario (the modeling of biosphere characteristics that lead to 

the reasonably maximally exposed individual exposure to radionuclides from contaminated 

groundwater) and the volcanic ash exposure scenario. These reviews, documented in the 

subsections that follow, evaluated the potential pathways of radionuclide transport and exposure 

in DOE’s conceptual representations of the biosphere model. NRC staff review of the portions 

of the biosphere and reasonably maximally exposed individual criteria that are applicable to the 
16-9 

input parameters and data is discussed in TER Sections 2.2.1.3.14.3.2, 2.2.1.3.14.3.3, 

and 2.2.1.3.14.3.4. 

Groundwater Exposure Scenario Conceptual Model 

The NRC staff’s review of DOE’s conceptual model of the groundwater exposure scenario 

included biosphere features, events, and processes, their functional relationships, and the 

resulting exposure pathways for modeling biosphere transport and dose to the reasonably 

maximally exposed individual. The NRC staff’s evaluation of functional relationships among 

features, events, and processes considered how DOE accounted for interactions among related 

features, events, and processes in the biosphere conceptual model so that all potential 

pathways could be identified. For example, farming practices, such as soil irrigation, can lead to 

soil accumulation of radionuclides, which can contribute to plant uptake of radionuclides from 

that soil. 

DOE’s groundwater exposure scenario includes a reasonably maximally exposed individual who 

is assumed to be an adult who lives in the accessible environment above the highest 

concentration of radionuclides in the plume of contamination. The reasonably maximally 

exposed individual is assumed to use wells to draw groundwater from the contamination plume 

for domestic and agricultural purposes. The NRC staff notes that the general reasonably 

maximally exposed individual and biosphere characteristics in DOE’s model address the use of 

groundwater for drinking, irrigating crops, watering livestock, raising fish, and operating 

evaporative coolers and provide a reasonable approach. 

DOE’s conceptualization of dose to the reasonably maximally exposed individual involves three 

routes of exposure: external exposure, inhalation, and ingestion. The inhalation dose portion of 

DOE’s conceptual model includes reasonably maximally exposed individual inhalation of 

radionuclides in (i) resuspended soil particles, (ii) gaseous emissions from the soil and their 

radioactive decay products, and (iii) aerosols generated by evaporative coolers. The ingestion 

dose portion of DOE’s conceptual model includes (i) drinking water; (ii) crops, including leafy 

vegetables, other vegetables, fruits, and grains; (iii) animal products, including meat, poultry, 

milk, and eggs; (iv) freshwater fish; and (v) soil. The meat category is a combination of all 

edible portions of beef, pork, and wild game (BSC, 2005ab). 

On the basis of the preceding NRC staff’s review of the biosphere characteristics that are 

evaluated in DOE’s groundwater exposure scenario conceptual model (described in SAR 

Section 2.3.10.2) and the associated mathematical models described in SAR Section 2.3.10.3, 

the NRC staff notes that these models include all potential pathways of radionuclide transport 

and exposure. This is based on DOE’s inclusion of pathways that (i) the NRC staff considers 

characteristic of the Yucca Mountain region, (ii) are commonly included in dose models and 

assessments, and (iii) are based on unique site-specific considerations (e.g., evaporative 

coolers, fish farming, wild game, and radon). The NRC staff notes that DOE’s groundwater 

conceptual model and associated mathematical models are reasonable. 

Volcanic Ash Exposure Scenario Conceptual Model 

The NRC staff’s review of the volcanic ash exposure scenario conceptual model evaluated 

the included biosphere system features, events, and processes; their functional relationships; 

and the included exposure pathways for modeling biosphere transport and dose to the 

reasonably maximally exposed individual. The review noted that DOE’s volcanic ash exposure 

scenario includes reasonably maximally exposed individual exposure to radioactive waste 
16-10 

entrained in (i) volcanic ash that is deposited from the initial plume of released radionuclides 

directly to the ground at the location of the reasonably maximally exposed individual and 

(ii) regional volcanic ash deposits that are redistributed via eolian (carried by wind) and fluvial 

(carried by water) processes. Other models in DOE’s performance assessment address the 

transport of contaminated volcanic ash to the ground in the biosphere. Those models are 

reviewed in TER Section 2.2.1.3.13. Because DOE calculates the reasonably maximally 

exposed individual dose from volcanic ash in the soil on the basis of the potential routes of 

exposure to the reasonably maximally exposed individual that are similar to those already 

reviewed for the groundwater scenario (including external, inhalation, and ingestion exposures), 

this review emphasizes the aspects of the volcanic exposure scenario conceptual model that 

are not duplicated in the groundwater exposure scenario conceptual model. DOE’s sharing of 

submodels with the groundwater scenario is technically reasonable because the 

methods for calculating dose to the reasonably maximally exposed individual from 

radionuclide-contaminated soil are the same once the radionuclide concentration of the soil, 

whether from irrigation or volcanic ash, is calculated. 

DOE’s conceptualization of inhalation dose in the volcanic ash exposure scenario includes 

resuspension of radionuclides in soil particles and release of Rn-222 (radon) gas. The NRC 

staff notes that DOE’s inclusion of resuspension modeling is consistent with DOE’s description 

of an arid climate characterized by low rainfall (SAR Sections 2.3.10.2.1 and 2.3.1). DOE’s 

inclusion of resuspension in its conceptual approach also addressed a variety of 

dust-generating activities and reasonably maximally exposed individual exposure environments 

(SAR Sections 2.3.10.2.6 and 2.3.10.3.2.2). Therefore, DOE has reasonably addressed the 

potential dust inhalation exposure pathways. DOE’s ingestion dose calculations applied the 

same soil-contamination-based exposure pathways that were used in DOE’s scenario for 

groundwater exposure. DOE considered that groundwater pathways which did not include a 

soil component (e.g., evaporative coolers, ingestion of groundwater, and ingestion of fish) did 

not apply to the volcanic ash exposure scenario, based upon DOE’s exclusion of features, 

events, and processes for ash in groundwater (FEP 1.2.04.07.0B; SNL, 2008ab). The NRC 

staff’s review of DOE’s exclusion of this FEP is documented in TER Section 2.2.1.2.1.3.2. 

On the basis of characteristics of DOE’s volcanic ash exposure scenario conceptual model, the 

NRC staff notes that the conceptual model includes all potential pathways of radionuclide 

transport and exposure. This is based on DOE’s inclusion of pathways that (i) the NRC staff 

considers characteristic of the Yucca Mountain region, (ii) are commonly included in dose 

models and assessments, and (iii) are based on unique site-specific considerations 

(e.g., various particulate resuspension exposure environments and radon). 

Integration of Biosphere Model in the TSPA 

The NRC staff reviewed the integration between the biosphere model abstraction and other 

TSPA abstractions for couplings among models that share or utilize similar information and 

consistency of assumptions among models. This review was conducted because the 

abstraction models are designed and implemented to function as intended within the larger 

TSPA model (i.e., appropriately receive and pass data), and the abstraction models that share 

features, events, and processes (e.g., climate change can affect both Yucca Mountain 

infiltration and biosphere conditions) are expected to have consistent representations of 

features, events, and processes (e.g., assumptions) to avoid bias in TSPA calculations. Based 

upon the NRC’s review of DOE’s integration of the TSPA biosphere model abstraction with 

other TSPA model abstractions, the NRC staff notes that the integration of the biosphere 

abstraction is reasonable for calculating the biosphere transport and dose to the reasonably 
16-11 

maximally exposed individual in the TSPA code. This is based on the evaluation of direct 

couplings between the TSPA biosphere abstraction and other TSPA model abstractions and 

evaluation of shared assumptions in the biosphere and other abstractions. 

The NRC staff’s evaluation of direct couplings between the TSPA biosphere abstraction and 

other TSPA model abstractions considered the flow of information from other abstractions to the 

biosphere abstraction within selected TSPA model files. Specifically, DOE’s model file for 

seismic ground motion for the 1-million-year modeling case passes radionuclide-specific 

saturated zone model results (radionuclide groundwater concentrations) to the biosphere model 

where they are multiplied by the groundwater exposure scenario biosphere dose conversion 

factors. DOE’s model file for the volcanic eruption modeling case passes the ash redistribution 

model results (radionuclide and pathway-specific soil concentrations) for multiplication by the 

volcanic ash exposure scenario radionuclide and pathway-specific biosphere dose conversion 

factors. These evaluations show that couplings between the biosphere model abstraction and 

other model abstractions are in agreement with DOE’s documentation of the calculations (SAR 

Sections 2.3.10.5.1.2 and 2.3.10.5.2.2). The NRC staff’s technical expertise gained from 

developing and using performance assessment models confirms that these couplings are 

appropriate for use in DOE’s performance assessment. 

The NRC staff also evaluated other, less direct, points of model integration addressing 

consistency between the TSPA biosphere abstraction and other abstractions, including 

assumptions. The NRC staff noted that the biosphere model abstraction includes biosphere 

dose conversion factors applicable to the radionuclides identified in the inventory analysis that 

was developed for the postclosure performance analyses (SAR Section 2.3.7.4.1.2). 

Consistency in the radionuclides evaluated in the TSPA abstractions is important to ensure that 

the biosphere model includes biosphere dose conversion factors for the radionuclides that are 

included in the other TSPA abstraction models. The NRC staff also evaluated how DOE 

integrated climate evolution in its biosphere modeling with the climate evolution considered in 

the infiltration and unsaturated zone flow process models. The NRC staff evaluated DOE’s 

technical bases for this climate implementation approach for the biosphere and noted that DOE 

reasonably explained that the use of current climate biosphere dose conversion factors (i.e., not 

explicitly modeling separate climate states in the biosphere model) is conservative and 

adequate for use in the reasonably maximally exposed individual dose calculations throughout 

the period of geologic stability in the biosphere model. DOE quantitatively evaluated the effects 

of climate change as follows. DOE’s analysis (i) evaluated biosphere model parameters on the 

basis of the expected parameters impacted by climate change, (ii) derived values for these 

parameters on the basis of its analysis of potential future climate states, and (iii) executed 

biosphere calculations for three separate climate states (present-day interglacial, monsoon, 

glacial transition). The results showed that future climate evolution in the biosphere lowers dose 

to the reasonably maximally exposed individual (SAR Section 2.3.10.5.1.1). The NRC staff 

notes that DOE’s methods, including use of the same biosphere model with different sets of 

climate-dependent input parameters to evaluate the effect of climate change on biosphere dose 

conversion factor results, are reasonable for evaluating whether TSPA calculations should 

include separate sets of biosphere dose conversion factors for each climate state. DOE’s result 

was also consistent with the results from prior NRC-sponsored biosphere analyses (LaPlante 

and Poor, 1997aa). Both the NRC’s and DOE’s analyses suggested that future climate states, 

which are expected to be cooler and wetter than the current climate, would result in the 

reasonably maximally exposed individual using less water (e.g., irrigation) and therefore lower 

the amount of radionuclides deposited to soils and lower the calculated reasonably maximally 

exposed individual dose. The NRC staff evaluated whether DOE’s technical basis reasonably 

supported its conclusion that the biosphere dose conversion factors calculated for the current 
16-12 

climate state (modern interglacial climate) are conservative for calculating the dose to the 

reasonably maximally exposed individual in the TSPA biosphere abstraction throughout the 

period of geologic stability (SAR Section 2.3.10.5.1.1). The NRC staff notes that DOE’s findings 

in this regard are reasonably supported. 

In summary, after reviewing DOE’s system description and model integration, the NRC staff 

notes that DOE’s performance assessment is reasonable. This is based on the NRC staff’s 

review of information discussed in this subsection, including DOE’s description of the 

characteristics of the Yucca Mountain region; the documentation of features, events, and 

processes DOE has included in the biosphere model; and the integration of included features, 

events, and processes into the conceptual models of the biosphere system. 

2.2.1.3.14.3.2 Assessment of Biosphere Transport Pathways 

A series of integrated submodels in the DOE ERMYN biosphere model calculates radionuclide 

transport through pathways within the biosphere. Five transport submodels (surface soil, 

plant uptake, animal uptake, fish uptake, and air) calculate environmental media concentrations 

used in the ERMYN calculations of biosphere dose conversion factor input parameters for 

the TSPA model. The NRC staff’s review of the surface soil submodel is documented in TER 

Section 2.2.1.3.13.3.2. This section documents the NRC staff’s review of DOE’s technical 

bases for input parameters, treatment of parameter uncertainty, and, as appropriate, evaluation 

of alternative conceptual models applicable to the biosphere transport submodels in ERMYN. 

The NRC staff’s risk-informed review focused on transport submodels and applicable input 

parameters for exposure pathways that contribute most to the TSPA results, as discussed in 

TER Section 2.2.1.3.14.3. 

These submodels address plant uptake, animal uptake, fish uptake, and air transport. 

Air transport includes localized resuspension of particulates from soil or ash, generation 

of indoor evaporative cooler aerosols, and the release of radon gas from soil or ash. 

While groundwater-release-related modeling cases are the primary contributors to the total 

TSPA dose results (as summarized in TER Section 2.2.1.3.14.3), the NRC’s review of 

DOE’s biosphere transport models also considered the risk-significant aspects of the 

volcanic-ash-related biosphere transport modeling. DOE’s TSPA dose results documented in 

SAR Figure 2.4-32 list Pu-239 and Pu-240 as the largest contributors to its calculated peak 

mean annual dose for the volcanic ash modeling case. DOE further documented that 

reasonably maximally exposed individual inhalation of resuspended particulates was the 

predominant pathway for the volcanic ash exposure scenario biosphere dose conversion factors 

for Pu-239 and Pu-240 (SAR Table 2.3.10-15). DOE’s information is consistent with the NRC 

staff results that show inhalation of resuspended particulates is a predominant contributor to 

volcanic exposure scenario dose results (NRC, 2005aa; LaPlante and Poor, 1997aa). 

Transport submodels for plant uptake, animal uptake, and radon are also used in volcanic 

ash biosphere dose conversion factor calculations, but contribute less to DOE’s calculated 

peak mean annual dose for the volcanic ash modeling case (SAR Figure 2.4-32; SAR 

Table 2.3.10-15). 

Plant Uptake Submodel 

DOE’s plant uptake submodel in the ERMYN code (SAR Section 2.3.10.3.1.3) calculates plant 

radionuclide concentrations on the basis of direct deposition of irrigation water and dust on 

plant surfaces and root uptake from estimated soil radionuclide concentrations computed by 

the surface soil model (or provided as direct model input for volcanic ash biosphere dose 
16-13 

conversion factor calculations as discussed in SAR Section 2.3.10.3.2.1). For root 

uptake processes, soil-to-plant transfer factors are used as input parameters to calculate 

plant radionuclide concentration from the radionuclide concentration in the soil. DOE selected 

soil-to-plant transfer factors from laboratory and field study results obtained from available 

literature using the methods discussed in BSC (2004ap). 

DOE evaluated soil-to-plant transfer factors from data obtained through a variety of references, 

including original data from literature reviews and biosphere analyses that selected and reported 

values from available sources. For its analysis, DOE identified five unique crop groups: leafy 

vegetables, other vegetables, fruit, grain, and forage. For each crop group and radionuclide, 

DOE selected soil-to-plant transfer factor values that it considered most applicable to the Yucca 

Mountain biosphere conditions, based upon area soil characteristics and crop types (SAR 

Section 2.3.10.3.1.3). DOE then calculated geometric means and standard deviations from the 

values selected from each reference. DOE assumed that soil-to-plant transfer factors followed 

truncated lognormal distributions with a 99 percent confidence interval around the geometric 

mean of the selected point values (BSC, 2004ap). 

The NRC’s review of DOE’s soil-to-plant transfer factor input parameters focused on the 

adequacy of supporting data sources, the selection of values, the applicability of selected values 

to Yucca Mountain biosphere calculations, and DOE’s approach to propagating uncertainty and 

variability in selected values. The NRC staff also reviewed the magnitude of DOE’s selected 

geometric mean values and geometric standard deviations for key contributing radionuclides in 

relation to the values provided by the supporting references from which the DOE values were 

derived. Data sources DOE used to derive input parameters included (i) commonly referenced 

original data compilations that evaluated a variety of peer-reviewed field and laboratory studies 

and (ii) other technical analyses that reported soil-to-plant transfer factors selected or derived 

from available source data or compilations. These references provide a technical basis for 

selecting composite transfer factor values because they include the most extensive international 

literature compilation of scientific data on the topic (International Atomic Energy Agency, 

1994aa), as well as a variety of technical reports authored by various radiological assessment 

practitioners, including NRC. Therefore, the NRC staff notes that the referenced documents are 

representative of available scientific data on soil-to-plant transfer factors, because experts in the 

field have reviewed and compiled the available scientific data. 

A number of approaches can be used to select soil-to-plant transfer factors from crop-specific 

values to derive representative values for plant groups from available data sources. DOE 

averaged applicable point estimates from a combination of original data sources and other 

documented analyses; this approach results in selecting geometric mean values that are 

representative of values presented in the source documents. For example, as shown in BSC 

Table 6-2 (2004ap), DOE evaluated the data for soil-to-plant transfer of technetium in leafy 

vegetables. This data included eight point values ranging from 9.5 to 180. The DOE-derived 

truncated lognormal distribution ranged from 3.8 to 550 with a geometric mean of 46. 

Therefore, the DOE approach resulted in a derived distribution that includes the range of 

values presented in the source documents. 

In addition to reviewing DOE’s approach, the NRC staff evaluated a subset of transfer factor 

values for technetium, iodine, neptunium, americium, and plutonium, as provided in BSC 

Section 6.2.1.2 (2004ap). This subset includes radionuclides the NRC staff identified as risk 

significant to DOE’s TSPA results (TER Section 2.2.1.3.14.3). The NRC staff compared DOE’s 

values with values independently selected from the available literature and reported in prior 

NRC-sponsored documents and analyses (NRC, 1992ae; LaPlante and Poor, 1997aa). The 
16-14 

NRC staff noted that DOE-derived geometric mean values were within reasonable ranges of the 

NRC reported values. For example, considering the prior example of DOE’s transfer factor for 

technetium in leafy vegetables where the source data ranged from 9.5 to 180, the values from 

NRC (1992ae) and LaPlante and Poor (1997aa) were 44 and 76, respectively. These results 

compare favorably with the geometric mean of 46 that DOE chose. On the basis of similar 

evaluations conducted for the remaining radionuclides in the evaluated subset, the NRC staff 

notes the DOE-derived geometric mean values are within reasonable ranges of the NRC 

reported values. The NRC staff notes that DOE has provided a reasonable technical basis for 

the soil-to-plant transfer factors used in the biosphere model. This is based on the results of 

NRC staff’s review of (i) DOE’s approach to deriving geometric mean soil-to-plant transfer 

factors and (ii) the resulting geometric mean values for a subset of the factors. 

DOE’s approach to propagating uncertainty in the assumed lognormal distributions of 

soil-to-plant transfer factors generated ranges that, as represented in the technetium-derived 

lognormal distribution example, encompass the values reported in the source documents. The 

NRC staff therefore notes that DOE’s TSPA biosphere calculations have a reasonable technical 

basis. By deriving a distribution that encompasses the values reported in the source 

documents, DOE ensured that parameter sampling in the ERMYN code selects input parameter 

values from a distribution that encompasses the range of values which are reported in the 

source documents. Because the source documents are representative of the available 

scientific data on soil-to-plant transfer factors, this approach results in biosphere dose 

conversion factor calculations that use soil-to-plant transfer factors based on available 

scientific data. 

For direct deposition of radionuclides on plant surfaces, the plant uptake model in the ERMYN 

code calculates the radionuclide concentrations in crops from leaf uptake and retention of 

intercepted irrigation water and dust (SAR Section 2.3.10.3.1.3). These calculations are based 

on the irrigation deposition rate or dust deposition rate, the fraction of irrigation that originated 

from above-plant spraying, the crop interception fraction, the translocation factor (fraction of 

deposited radionuclides that are absorbed and move to other parts of the plant), the weathering 

half-life (removal rate of contaminants from leaves), the crop growing time, and the crop yield, 

as identified in SAR Section 2.3.10.3.1.3 and SNL Sections 6.4.3.2 and 6.4.3.3 (2007ac). 

The NRC staff’s review of DOE’s sensitivity and uncertainty analysis of the groundwater 

biosphere dose conversion factors, as provided in SNL Section 6.13 and Table 6.13-3 (2007ac), 

showed that these direct deposition inputs had relatively low, or no, effect on biosphere dose 

conversion factor distributions. Therefore, the NRC staff conducted a general review of these 

inputs to verify that DOE provided reasonable technical bases. This review noted that DOE has 

provided reasonable technical bases for the direct deposition input parameters in the ERMYN 

code. The NRC staff noted that DOE’s sensitivity analysis methods involved statistical 

correlation analyses of individual input parameter distributions with radionuclide-specific 

biosphere dose conversion factor distributions. These methods are reasonable because they 

are statistical analysis methods that are commonly used to quantify the relationship between 

individual model input parameter variability and the variability in model output. 

Thus, on the basis of the information discussed in this subsection, the NRC staff notes that DOE 

has accounted for uncertainty and variability in parameter values, and has provided reasonable 

technical bases for parameter ranges, probability distributions, and bounding values used in the 

plant uptake submodel in its performance assessment. 
16-15 

Animal Uptake Submodel 

SAR Section 2.3.10.3.1.4 described DOE’s ERMYN code animal uptake submodel. This 

submodel calculates radionuclide concentrations in human food products that are derived from 

livestock that ingest contaminated food and water. For the purpose of modeling, DOE identified 

four distinct animal product groups: meat, milk, eggs, and poultry. The animal product 

radionuclide concentrations were calculated on the basis of estimated animal intakes 

of radionuclides from contaminated feed, water, and soil, as applicable to the groundwater or 

volcanic eruption modeling cases. Animal feed radionuclide concentrations are computed 

by the plant uptake submodel (e.g., DOE assumes cows eat locally grown forage and chickens 

eat local grain). As discussed in BSC (2004ap), DOE used animal product transfer coefficients 

as input parameters for the fraction of an animal’s daily intake of a radionuclide that is 

transferred to a unit mass or volume of produced food product. DOE’s animal product transfer 

coefficients were selected using the same methods (BSC, 2004ap) described in the previous 

subsection for soil-to-plant transfer factors. 

The NRC staff evaluated DOE’s technical bases and supporting data for the selected values 

and uncertainty ranges for the animal product transfer coefficients used in ERMYN code to 

calculate biosphere dose conversion factor input parameters for the TSPA model. Data 

sources DOE used to derive the animal product transfer coefficient input parameters included 

(i) commonly referenced original data compilations that evaluated a variety of peer-reviewed 

studies and other technical reports and (ii) other technical analyses that reported soil-to-plant 

transfer factors selected or derived from available source data or compilations. These 

references provide a technical basis for selecting generally applicable animal product transfer 

coefficient values because they include the most extensive international literature compilation of 

scientific data on the topic (International Atomic Energy Agency, 1994aa) as well as a variety of 

technical reports authored by various radiological assessment practitioners, including the NRC. 

Therefore, the referenced documents are representative of the available scientific data on 

soil-to-plant transfer factors. The documents were reviewed and compiled by experts in the field 

and provide a reasonable body of technical information to support DOE’s derivation of input 

parameters for the biosphere model. 

On the basis of DOE’s derivation of animal product transfer coefficients for the biosphere model, 

DOE’s approach of averaging applicable point estimates from a combination of original data 

sources and other documented analyses is a reasonable one, as it results in selecting 

geometric mean values that are representative of values presented in the source documents. 

For example, the data DOE evaluated [provided in BSC Table 6-39 (2004ap)] for the transfer of 

technetium from feed to meat include 14 point values ranging from 1.0 × 10−4

 to 8.7 × 10−3. 

DOE derived truncated lognormal distribution ranges from 6.9 × 10−6

 to 1.8 × 10−1 with a 

geometric mean of 1.1 × 10−3

. Therefore, the DOE approach resulted in a derived distribution 

that included the range of values presented in the source documents. 

In addition to the review of DOE’s approach, the NRC staff evaluated a subset of DOE’s transfer 

factor values for technetium, iodine, neptunium, americium, and plutonium, as identified in BSC 

Section 6.3.3 (2004ap). This subset included radionuclides the NRC staff identified as risk 

significant to DOE’s TSPA results (TER Section 2.2.1.3.14.3). The NRC staff compared the 

DOE geometric mean values with values independently selected from the available literature 

and reported in prior NRC documents and analyses (NRC, 1992ae; LaPlante and Poor, 

1997aa). For example, the values for technetium from NRC (1992ae) and LaPlante and Poor 

(1997aa) are 8.5 × 10−3

 and 1.0 × 10−4, respectively. The NRC staff notes that these data are 

reasonably comparable to the geometric mean of 1.1 × 10−3

 that DOE derived. When used in 
16-16 

DOE’s dose model, DOE’s higher value in this comparison is more conservative than the NRC 

reported values because it would transfer more radionuclides to meat and therefore increase 

the dose to the reasonably maximally exposed individual relative to the NRC-reported values. 

On the basis of similar evaluations conducted for the remaining radionuclides in the evaluated 

subset, the DOE-derived geometric mean values are within reasonable ranges of the 

NRC-reported values. After reviewing DOE’s approach to deriving geometric mean animal 

product transfer coefficients and the resulting geometric mean values for a subset of the 

coefficients reviewed, the NRC staff notes that DOE has provided a reasonable technical basis 

for the animal product transfer coefficients used in the biosphere model. 

In reviewing DOE’s approach to propagating uncertainty in the assumed lognormal distributions 

of animal product transfer coefficients, the NRC staff noted that the approach generated ranges 

that encompass the values reported in the source documents and therefore has a reasonable 

technical basis for use in TSPA biosphere calculations. DOE assumed a truncated lognormal 

distribution using the geometric standard deviation computed from the source data and applied 

a 99 percent confidence interval approach similar to that used for deriving parameter 

distributions for soil-to-plant transfer factors. By deriving a distribution that encompassed the 

values reported in the source documents, DOE ensured that parameter sampling in the ERMYN 

code selects input parameter values from a distribution that encompasses the range of values 

that are reported in the source documents. Because the source documents are representative 

of the available scientific data on animal product transfer coefficients, this approach resulted in 

biosphere dose conversion factor calculations that use animal product transfer coefficients 

based on available scientific data. 

In summary, on the basis of the information discussed in this subsection, DOE has accounted 

for uncertainty and variability in parameter values and has provided the technical bases for 

parameter ranges, probability distributions, and bounding values used in the animal uptake 

submodel in its performance assessment. 

Fish Uptake Submodel 

The ERMYN code fish uptake submodel (SAR Section 2.3.10.3.1.5) calculates radionuclide 

concentrations in fish raised in local fish farms that are assumed to use contaminated 

groundwater. The input parameter that most influences the results of this model is the 

bioaccumulation factor. This element-specific factor relates the concentration of radionuclides 

in the edible portion of the fish to the concentration of radionuclides in the contaminated water in 

which the fish is submerged. DOE selected bioaccumulation factors on the basis of a review of 

the applicable literature. DOE’s review included fish in all portions of the food chain as well as 

bottom-feeding fish. DOE assumed a lognormal distribution. For the fish farms noted in 

Amargosa Valley during DOE’s consumption survey, the fish were fed commercial feed that is 

not locally derived. Feed that is not locally derived would therefore not be expected to become 

contaminated with radionuclides from a Yucca Mountain release scenario. Therefore, DOE 

applied a bioaccumulation factor (that accounts for fish ingesting contaminated food and water) 

to a Yucca Mountain biosphere calculation. Actual conditions suggest that only the water would 

be contaminated and the analysis will overestimate the fish intake and thereby overestimate the 

radionuclide concentration in fish. The resulting dose to the reasonably maximally exposed 

individual from fish consumption is therefore expected to be overestimated.

The NRC staff evaluated the applicability of DOE’s technical bases and supporting data for 

the selected point values and uncertainty ranges for the bioaccumulation factors used in the 

fish uptake submodel. Data sources DOE used to derive input parameters included 
16-17 

(i) a commonly referenced original data compilation that evaluated a variety of technical reports 

and some peer-reviewed studies and (ii) other technical analyses that reported bioaccumulation 

factors selected or derived from available source data or compilations. The NRC staff notes 

that the referenced documents are representative of available scientific data on fish 

bioaccumulation factors. The references were reviewed and compiled by experts in the field 

and include the most extensive international literature compilation of scientific data on the topic 

(International Atomic Energy Agency, 1994aa) as well as a variety of technical reports authored 

by various radiological assessment practitioners including the NRC. Therefore, these 

references provide a technical basis for the selection of generally applicable fish 

bioaccumulation values. 

In reviewing bioaccumulation factor values, the NRC staff notes that DOE’s 

approach of averaging applicable point estimates from a combination of original data 

sources and other documented analyses resulted in selection of geometric mean values 

that are representative of values presented in the source documents. For example, the 

values for fish uptake of carbon DOE evaluated included eight point values ranging from 

4.6 × 103

 to 5.0 × 104 L/kg [5.5 × 102 to 6 × 103 gal/lb], with a DOE-derived geometric mean 

of 1.6 × 104

 L/kg [1.9 × 103 gal/lb], as identified in BSC Table 6-64 (2004ap). On the basis of 

the importance of carbon and the fish pathway in DOE’s TSPA results relative to other 

radionuclides and pathways (TER Section 2.2.1.3.14.3), the NRC staff evaluated DOE’s 

supporting information for the fish bioaccumulation factor for carbon. The NRC staff compared 

the derived value with values independently selected from the available literature (NRC, 

1992ae; International Atomic Energy Agency, 1994aa). The NRC staff noted that the 

DOE-derived geometric mean value was within a reasonable range of the NRC- and the 

International Atomic Energy Agency-reported values. The values from NRC (1992ae) 

and the International Atomic Energy Agency (1994aa) are 4.6 × 103

 and 5.0 × 104 L/kg 

[5.5 × 102

 to 6 × 103 gal/lb], respectively, and compare favorably with the geometric mean of 

1.6 × 104

 L/kg [1.9 × 103 gal/lb] DOE derived. Because the data sources DOE used included 

studies of fish in natural ecosystems that are contaminated with radionuclides, bioaccumulation 

factors evaluated in those documents would include contributions to fish uptake from 

contaminated food. Consideration of nonlocally derived feed, which would be uncontaminated, 

would reduce bioaccumulation factors. The NRC staff notes that DOE’s use of these factors 

leads to an overestimation of fish uptake, thus generating a conservative result. 

The NRC staff reviewed DOE’s approach to propagating uncertainty in the assumed 

lognormal distributions of fish bioaccumulation factors and notes that the approach generated 

ranges that encompass the values reported in the source documents. This is considered 

reasonable for use in TSPA biosphere calculations. DOE assumed a truncated lognormal 

distribution using the geometric standard deviation computed from the source data and 

applying a 99 percent confidence interval approach. As an example, the DOE-derived 

lognormal distribution of fish bioaccumulation factors for technetium ranged from 3.3 to 120 L/kg 

[0.4 to 14 gal/lb], which encompasses the range of point values {15 to 78 L/kg [1.8 to 9.4 gal/lb]} 

in DOE’s source documents. 

The NRC staff’s review of the fish bioaccumulation factors identified an apparent transcription 

error in the DOE report (BSC, 2004ap). The geometric mean of the fish bioaccumulation 

factor for carbon was reported differently in two separate tables that should have contained 

the same values. Specifically, BSC Table 6-64 (2004ap), which is the table that initially 

derives the value from source data, showed a geometric mean fish bioaccumulation factor for 

carbon of 1.6 × 104

 L/kg [1.9 × 103 gal/lb]. BSC Table 6-65 (2004ap) listed a different value of 

4.6 × 103

 L/kg [5.5 × 102 gal/lb] for this same parameter (a factor of 3.5 lower than the original 
16-18 

value computed in Table 6-64). DOE used the lower value reported in Table 6-65 in the 

ERMYN calculations, as indicated by SAR Table 2.3.10-10. To evaluate the significance of this 

discrepancy, the NRC staff considered whether using the lower value would significantly affect 

DOE’s dose results. This evaluation considered that DOE’s fish consumption dose scales 

linearly with the bioaccumulation factor. The NRC staff also evaluated the TSPA results for the 

10,000-year simulation period in SAR Figure 2.4-20, which shows that the carbon dose 

contributes approximately 20 percent of the peak mean total dose. SAR Table 2.3.10-11 

indicated that the fish pathway contributed 59 percent to the carbon dose. 

The NRC staff’s evaluation of these results of DOE’s TSPA shows that correcting the 

geometric mean bioaccumulation factor would not significantly change the all-radionuclide total 

TSPA results. 

In summary, on the basis of the information discussed in this subsection, the NRC staff notes 

that DOE has accounted for uncertainty and variability in parameter values and has provided 

reasonable technical bases for parameter ranges, probability distributions, and bounding values 

used in the fish uptake submodel in its performance assessment. 

Air Submodel 

The ERMYN air submodel (SAR Sections 2.3.10.3.1.2 and 2.3.10.3.2.2) models radionuclide 

concentrations in air from (i) resuspension of contaminated soil or ash, (ii) evaporative cooler 

aerosols from the use of contaminated groundwater, and (iii) radon gas emanation from 

contaminated soil or ash. Inhalation of resuspended particulates is the predominant 

exposure pathway for Pu-239, Pu-240, and Pu-242 in DOE’s performance assessment. 

Resuspended particulate exposure is modeled in both the groundwater and volcanic ash 

exposure scenarios. Particulate inhalation also contributes approximately 21 percent to the 

groundwater dose from Np-237 in the DOE model (SAR Table 2.3.10-11). The other air 

pathways in DOE’s model contribute less to the performance assessment results 

than particulate inhalation, but are represented in the radionuclides that contribute most to 

DOE’s results as summarized in TER Tables 16-1 and 16-2. Therefore, the NRC staff’s detailed 

review of the technical bases for input parameters and ranges in the air submodel discussed in 

this TER section evaluated those parameters in DOE’s biosphere transport calculations 

involving particulates, evaporative cooler aerosols, and radon gas. 

An important input parameter in the DOE calculation of air concentration of particulates is the 

mass loading factor {g/m3 [lb/ft3

]} based on the DOE biosphere dose conversion factor sensitivity 

analysis results documented in SNL, p. 6-150 (2007ac) and SAR Table 2.3.10-17. The NRC 

staff reviewed the DOE sensitivity analysis methods and notes that the DOE approach, which 

includes a statistical correlation analysis of sampled ERMYN model input and biosphere dose 

conversion factor distribution output, reasonably quantifies the correlation between model input 

variation and output variation as a means of identifying the sampled input parameters that most 

influence model results. These methods are reasonable because they are statistical analysis 

methods that are commonly used to quantify the relationship between individual model input 

parameter variability and the variability in model output. The mass loading factor computes the 

concentration of radionuclides in air {Bq/m3

 [Ci/m3]} from the estimated concentration of 

radionuclides deposited on the soil surface {Bq/g [Ci/g]}. 

In SAR Sections 2.3.10.3.1.2 and 2.3.10.3.2.2 and supporting references, DOE described its 

derivation of separate mass loading factors for each exposure scenario (i.e., irrigated soil, 

volcanic ash) on the basis of available literature. DOE derived individual mass loading input 
16-19 

parameters for reasonably maximally exposed individual activity level (active and inactive) and 

environment (outdoor, indoor, or away from potentially contaminated areas) in its inhalation 

exposure model. 

The NRC staff evaluated DOE’s technical bases and supporting data for the mass loading input 

parameters used in biosphere exposure scenarios involving groundwater and volcanic ash. 

DOE detailed these in BSC (2006ad). 

In reviewing DOE’s technical bases for groundwater exposure scenario mass loading input 

parameter values, the NRC staff evaluated the supporting data DOE used to derive mass 

loading input parameters against independent NRC estimates derived from available technical 

information. On the basis of its review of the supporting data, the NRC staff notes that DOE 

evaluated a reasonable range of studies in published, peer-reviewed literature that measured 

airborne concentrations of total suspended particulate and PM10 {small suspended particles 

that are less than 10 micrometers [3.9 × 10−4

 in] in diameter} for a variety of environments and 

surface-disturbing activities. The NRC staff notes that DOE based its soil mass loading values 

on site-specific studies that included measurements of airborne dust in Amargosa Valley 

applicable to various surface-disturbing activities, including walking, pitching hay, driving, 

working near construction equipment, and dog walking. DOE also considered a variety of other 

studies from sites that the NRC staff notes are representative or analogous to Yucca Mountain 

regional conditions. These include arid or semiarid environments and rural agricultural 

dust-generating activities and exposure conditions. The NRC staff notes that DOE’s supporting 

information for the mass loading values provides a broad base of technical support that 

addresses the effects of a range of dust-generating activities and site-specific conditions. 

As part of the NRC staff’s review of DOE’s groundwater exposure scenario mass loading 

values, the NRC staff evaluated the magnitude of DOE’s selected values. This evaluation 

involved comparing the results of two of the NRC staff’s calculations of the mass of soil 

the reasonably maximally exposed individual inhaled (LaPlante, 2010aa). In these calculations, 

the mass of soil inhaled is the product of constant values for input parameters for mass loading, 

exposure time, and breathing rate. One of these calculations was based on DOE’s mass 

loading values, and the other calculation was based on the input parameters the NRC staff 

derived for the Total-System Performance Assessment 5.1 code by evaluating the available 

peer-reviewed and other scientific literature (Leslie, et al., 2007aa). Exposure time and 

breathing rate input parameters in both calculations were set to the same values (Leslie, et al., 

2007aa) to isolate the effect of the differences in mass loading inputs on the mass of soil 

inhaled. Due to the large number of individual mass loading input parameters DOE used, this 

calculation efficiently evaluated the combined effect of DOE’s mass loading parameter choices 

on an intermediate result in the inhalation dose calculation (i.e., mass of soil inhaled). This 

comparison showed the calculated daily mass of soil resuspended and inhaled based on the 

DOE mass loading values was 2.5 times larger than the same result computed using the NRC 

staff’s derived mass loading values. This indicates DOE’s selected mass loading values are 

more conservative than the values that the NRC staff independently derived from available 

scientific data. For example, the magnitude of DOE’s derived values for mass loading, when 

evaluated in the context of their effect on dose to the reasonably maximally exposed individual 

(i.e., using the calculation of soil mass inhaled), produce dust inhalation results that are greater 

than results based on independently derived mass loading and other applicable input 

parameters, as identified in Leslie, et al., Table 17-1 (2007aa). Therefore, DOE’s methods and 

technical bases for these input parameters are reasonable. 
16-20 

For the volcanic ash scenario, the NRC staff recognizes that limited data are applicable to 

mass loading for a volcanic eruption in the Yucca Mountain region or for analogous conditions 

elsewhere. DOE reviewed literature that included measured dust levels of volcanic ash 

resuspended in air for ambient and surface-disturbing conditions at various sites where 

volcanoes had recently erupted (within 5 years) and also compared the relevance of each 

analog site (including the Soufrière Hills Volcano in Montserrat, British West Indies, and the 

Mt. Spurr Volcano in Alaska) to expected conditions in the Yucca Mountain region. 

The NRC staff’s review of DOE’s technical bases for volcanic ash exposure scenario mass 

loading input parameter values evaluated the applicability of the supporting data DOE used 

and the methodology used to derive mass loading input parameters. The NRC staff also 

evaluated the magnitude of DOE’s values against independent NRC estimates derived from 

available technical information. The NRC staff notes that DOE’s consideration of a range of 

studies that included dust-level measurements taken during a variety of surface-disturbing 

conditions at volcanic eruption sites provides reasonable technical support for its derived mass 

loading values. 

The NRC staff’s review of the magnitude of DOE’s volcanic ash exposure scenario mass 

loading values involved comparing two NRC staff calculations of the mass of resuspended ash 

the reasonably maximally exposed individual inhaled (LaPlante, 2010aa). In these calculations, 

the mass of ash inhaled is the product of constant values for input parameters for mass loading, 

exposure time, and breathing rate. One of these calculations was based on DOE’s mass 

loading values, and the other calculation was based on the input parameters NRC staff 

derived for the Total-System Performance Assessment 5.1 code by evaluating the available 

peer- reviewed and other scientific literature, as documented in NRC Table 17-1 (Leslie, et al., 

2007aa). Exposure time and breathing rate input parameters in both calculations were set to 

the same values (Leslie, et al., 2007aa) to isolate the effect of the differences in mass loading 

inputs on the mass of ash inhaled. Due to the large number of individual mass loading input 

parameters DOE used, this calculation efficiently evaluated the combined effect of DOE’s mass 

loading parameter choices on an intermediate result in the inhalation dose calculation 

(i.e., mass of ash inhaled). This comparison showed the calculated daily mass of resuspended, 

inhaled ash based on the DOE mass loading values was consistent with the same result 

computed using the mass loading values the NRC staff independently derived from available 

scientific data. Therefore, the magnitude of DOE’s derived values for mass loading, when 

evaluated in the context of their effect on dose to the reasonably maximally exposed individual 

(i.e., using the calculation of the mass of ash inhaled), produces dust inhalation results that are 

within a reasonable range of results based on independently derived mass loading values and 

other applicable input parameters, as documented in NRC Table 17-1 (Leslie, et al., 2007aa). 

This independent verification of DOE’s derived mass loading input parameters further supports 

DOE’s methods and technical bases for these input parameters. 

The NRC staff reviewed DOE’s treatment of uncertainty and variability in mass loading values 

for both exposure scenarios and notes that DOE has accounted for uncertainty and variability in 

parameter values and has provided the technical bases for parameter ranges and probability 

distributions used in the air submodel. The NRC staff notes that DOE’s approach for developing 

parameter distributions is reasonable, because its values are supported by applicable scientific 

studies. Although supporting data are limited, DOE reviewed enough information to derive input 

parameter ranges and a mode1

 to characterize simple distributions for use in the TSPA code. 

 

1

A mode is a statistic of central tendency for a set of values that represents the value that occurs most frequently in 

that set. 
16-21 

DOE accomplished this by considering the range of values from the available literature and the 

applicability of each study to the Yucca Mountain exposure scenarios in terms of similar 

surface-disturbing activities, an arid or semiarid environment, and measurements of total 

suspended particulates. DOE then addressed uncertainty and variability in the mass loading 

parameters [provided in BSC Sections 6.2 and 6.3 (2006ad)] by deriving triangular parameter 

distributions. These distributions were based on its assessment of the range of applicable 

literature values and the central tendencies in the data that support selection of a value for the 

mode of each distribution. DOE conducted similar literature-based evaluation and selection of 

mass loading ranges and modes to characterize triangular input distributions for each activity 

environment and for groundwater and ash exposure scenarios. The resulting input distributions 

were provided in BSC Table 7-1 (2006ad). 

DOE evaluated personal exposure measurements of total suspended particulates collected 

during farming activities at 10 farms near Davis and Sacramento, California, that supported a 

range of 0.30 to 7.93 mg/m3 [8.1 × 10−6

 to 2.1 × 10−4 oz/yd3]. After evaluating additional data 

from 7 other studies involving mostly farming activities and 22 sets of measurements taken in 

Amargosa Valley for various types of activities, a range of 1 to 10 mg/m3

 [2.7 × 10−5 to 

2.73.6 × 10−4

 oz/yd3] {with a mode of 3 mg/m3 [8.1 × 10−5 oz/yd3]} was derived for the ERMYN 

input for the TSPA analyses for mass loading in the active outdoor environment for 

groundwater-based biosphere dose calculations. The mode of 3 mg/m3

 [8.1 × 10−5 oz/yd3] was 

the mean of the maximum mass loading values measured for 22 surface-disturbing activities in 

Amargosa Valley, as identified in BSC Section 6.2.1.3 (2006ad). 

The NRC staff considers these mass loading data reasonable for inclusion in the biosphere 

model of Yucca Mountain for three reasons: (i) the data describe activities that are consistent 

with the characteristics of the Yucca Mountain region (e.g., farming, arid conditions), (ii) the data 

are based on a subset of the measurements are taken from the Yucca Mountain region, and 

(iii) the data include breathing zone sampling measurements. The breathing zone sampling 

data are particularly relevant for supporting an inhalation exposure scenario because the 

measurements were taken in air within the breathing zone. On the basis of the NRC staff’s 

review of DOE’s methods for deriving uncertainty distributions in the mass loading values, the 

NRC staff notes that DOE has addressed uncertainty and variability in the mass loading 

parameter values, and has thus provided reasonable technical bases for parameter ranges and 

probability distributions used in the air submodel in the ERMYN code. 

As discussed in SAR Section 2.3.10.3.1.2, the air submodel in the ERMYN code also calculates 

indoor air radionuclide concentrations from aerosols released from evaporative coolers. This 

calculation is based on the concentration of radionuclides in groundwater, the rate of water 

evaporation from coolers, the indoor air exchange rate, and the fraction of radionuclides in water 

that transfer to air (the water-to-air transfer fraction). DOE identified the water-to-air transfer 

fraction in the evaporative cooler model as an important input parameter in the evaporative 

cooler calculation. The NRC staff evaluated the derivation of the water-to-air transfer fraction 

and notes that values of the water-to-air fraction were selected conservatively to bound possible 

values. DOE assumed a uniform concentration ratio distribution from 0 to 1 for dissolved solids 

and 1 for gases on the basis of a lack of available studies on contaminant aerosols from 

evaporative coolers. The NRC staff notes that this value is conservative because dissolved 

solids do not evaporate when water evaporates (the same process is used to purify water by 

distillation). The assumed distribution causes the model to release an average of 50 percent of 

the dissolved solids (including dissolved radionuclides) that are in the groundwater directly to 

indoor air. This increases the inhalation dose from aerosols beyond what would be expected 

under actual conditions. On the basis of the information reviewed, the NRC staff notes that 
16-22 

DOE has addressed uncertainty and variability in parameter values and has provided the 

technical bases for parameter ranges, probability distributions, and bounding values used in the 

aerosol release from evaporative cooler calculation within the ERMYN air submodel. 

 

Airborne concentrations of radon gas released from soils irrigated with contaminated 

water or from contaminated volcanic ash were also calculated in the air submodel (SAR 

Sections 2.3.10.3.1.2 and 2.3.10.3.2.2; SNL, 2007ac; BSC, 2004ap). Both exposure scenarios 

consider indoor and outdoor radon concentrations. In the volcanic ash scenario, the outdoor air 

concentration is also used for the indoor air concentration because DOE expected the outdoor 

concentration to be higher than the indoor concentration as a result of the small radon 

contribution from ash below the reasonably maximally exposed individual’s house. DOE’s 

groundwater scenario calculates separate radon concentrations for indoor and outdoor 

environments. DOE’s indoor radon concentration calculations evaluated radon released from 

soil beneath a hypothetical house built on land that was previously irrigated by contaminated 

water. In this model, the rate of radon released into the house is a proportion of the outdoor 

radon flux that accounts for diffusion of radon from underlying soil through the foundation. 

Indoor radon concentrations in the model were calculated based on (i) the radon flux into the 

house from soil beneath the house and from outdoors, (ii) the interior air exchange rate, and 

(iii) the interior volume of the house. The interior air exchange rates account for periods of 

evaporative cooler use and nonuse based on increased ventilation during cooler operation, 

which decreases radon concentration in air. The indoor radon diffusion methods are 

consistent with those used in the RESRAD dose assessment code (Yu, et al., 2001aa) that 

the U.S. Environmental Protection Agency (EPA) developed. Outdoor radon concentrations 

are based on factors that relate the airborne concentration of Rn-222 to either (i) the Ra-226 

concentration in the soil for the groundwater scenario or (ii) the Rn-222 flux density for the 

volcanic ash scenario. 

The NRC staff evaluated DOE’s technical bases and supporting data for input parameters 

used in the indoor and outdoor radon concentration modeling in the ERMYN code. Input 

parameters that were reviewed included the fraction of radon flux entering the foundation 

from soil, and home ventilation rates. DOE chose the concentration fraction of the radon 

flux from soil underneath the house that would diffuse into the house to be uniformly distributed 

from 0.1 to 0.25 on the basis of measurements in homes with concrete foundations (SAR 

Section 2.3.10.3.1.2). The home ventilation rates (for evaporative cooler nonuse periods) were 

based on minimum ventilation recommendations for manufactured homes, data from a survey of 

approximately 3,000 U.S. homes, and information from a trade organization representing home 

ventilation equipment manufacturers (BSC, 2004ap). The home ventilation rates for evaporative 

cooler use were estimated on the basis of cooler flow rates and the average home interior 

volume. Uncertainty and variability in the ventilation rates were propagated by deriving a 

truncated lognormal distribution on the basis of the survey data (for no cooler use) and a 

uniform distribution for cooler use ventilation rates that spans the estimated range (BSC, 

2004ap). Regarding DOE’s radon concentration calculation, the NRC staff notes that DOE 

used diverse information sources (including information applicable to manufactured homes, 

national survey data of home ventilation rates, and ventilation equipment information) and 

further notes that DOE has provided a reasonable technical basis for deriving input parameters 

for that calculation. 

The NRC staff notes that the approaches used to derive these distributions use common 

methods that result in distributions that reasonably represent the values reported in the 

referenced information. On the basis of the review of the supporting documentation, the NRC 

staff notes that DOE has accounted for uncertainty and variability in parameter values and has 
16-23 

provided the technical bases for parameter ranges and probability distributions used in the air 

submodel radon concentration calculations. The radon concentrations are used for calculating 

biosphere dose conversion factor input parameters for the TSPA code. 

On the basis of information discussed in this subsection, including the NRC staff’s review 

of the data supporting selected input parameters and distributions that DOE used to 

model the inhalation of resuspended soil and ash, aerosols from evaporative coolers, and 

radon released from irrigated soils, the NRC staff notes that DOE has accounted for 

uncertainty and variability in parameter values and has provided reasonable technical bases for 

parameter ranges, probability distributions, or bounding values used in the air submodel in 

its performance assessment. 

2.2.1.3.14.3.3 Assessment of Human Exposure 

DOE calculated human exposures from estimated concentrations of radionuclides in 

groundwater and soil in three exposure submodels of the ERMYN code (SNL, 2007ac). These 

submodels are the primary exposure pathways addressed in the DOE exposure scenarios and 

include the external exposure submodel, the inhalation exposure submodel, and the ingestion 

exposure submodel (SAR Sections 2.3.10.3.1.7, 2.3.10.3.1.8, and 2.3.10.3.1.9 for the 

groundwater exposure scenario and SAR Sections 2.3.10.3.2.5, 2.3.10.3.2.6, and 2.3.10.3.2.7 

for the volcanic ash exposure scenario). Considering the biosphere pathways that are the 

primary contributors to dose to the reasonably maximally exposed individual (TER Tables 16-1 

and 16-2), the NRC staff’s risk-informed review focused on the inhalation and ingestion 

exposure submodels. These exposure submodels compute the reasonably maximally exposed 

individual’s annual intake of radionuclides {e.g., Bq/yr [Ci/yr]} on the basis of the environmental 

media concentrations (e.g., air, water, livestock products, fish) calculated by the biosphere 

environmental transport submodels that are depicted in SAR Figures 2.3.10-9 and 2.3.10-10, 

described in SAR Section 2.3.10.3, and evaluated in TER Sections 2.2.1.3.14.3.2 and 

2.2.1.3.12.3. DOE’s exposure models also convert the calculated reasonably maximally 

exposed individual radionuclide intakes to dose. The NRC staff’s review of DOE’s conversion 

of intakes into dose is evaluated in TER Section 2.2.1.3.14.3.4. 

Inhalation Exposure Model 

DOE’s ERMYN inhalation exposure submodel calculates reasonably maximally exposed 

individual radionuclide intakes by modeling the inhalation of contaminated air. In the model, 

airborne contaminants include resuspended soil or ash particulates, aerosols from evaporative 

coolers, or radon gas emanating from contaminants in soil or ash. DOE calculations showed 

that the inhalation exposure pathways that are the most risk significant contributors to DOE’s 

performance assessment results (TER Tables 16-1 and 16-2) are resuspended particulates 

from soil and aerosols from evaporative coolers. Inhalation of gaseous emissions of radon from 

contaminants in soil also contributes to DOE’s long-term dose calculation results, but that 

contribution is less than the contributions from particulates and aerosols. 

DOE’s exposure calculations for these three inhalation exposure pathways involve exposure 

time and breathing rate input parameters that are the focus of the NRC staff’s review. 

The NRC staff focused on these parameters because (i) they directly influence the calculated 

dose; (ii) they are influenced by a number of other complex variables including the types of 

human activities, activity durations, and intensity of physical activity; (iii) they are used in all 

three of the inhalation exposure pathways; and (iv) DOE’s documented technical bases for 

these inputs are particularly complex relative to the other inhalation exposure submodel inputs 
16-24 

such as the fraction of houses with coolers, the evaporative cooler use factor, and the 

equilibrium factor for radon decay products. 

DOE’s exposure time input parameters in the ERMYN inhalation exposure submodel apportion 

the amount of time the reasonably maximally exposed individual spends in various 

environments where exposure could occur into three categories: outdoor, indoor, and away 

from areas potentially contaminated by Yucca Mountain activities. The DOE inhalation 

exposure submodel also apportions the amount of time spent conducting surface-disturbing 

activities, which DOE identified as active, to account for increased exposure to radionuclides 

resuspended from the ground surface by the activity. DOE identified the amount of time that the 

reasonably maximally exposed individual was not conducting surface-disturbing activities as 

inactive. DOE grouped the Amargosa Valley population into four population categories: 

nonworkers, commuters, local outdoor workers, and local indoor workers. DOE apportioned 

time spent into five activity–environment categories (by combining the three environment 

categories with the two activity-level categories): active outdoors, inactive outdoors, active 

indoors, inactive indoors (sleep), and away from areas potentially contaminated by Yucca 

Mountain activities. Exposure times were derived from census information on age distribution; 

employment; commuting characteristics of Amargosa Valley, Nevada, residents; and national 

survey data on activity times (BSC, 2005ab). 

The NRC staff evaluated DOE’s technical bases and supporting data for the exposure times that 

were used to derive activity–environment categories (BSC, 2005ab). DOE provided a derivation 

of exposure times on the basis of data from surveys of the Amargosa Valley and national 

populations. Year 2000 census data from the Amargosa Valley census county division (Bureau 

of the Census, 2002aa) provided the population distribution by age, work status and hours 

worked, commute time, and industry of employment. DOE also used detailed national survey 

data on activity time budgets from Klepeis, et al. (1996aa) and EPA (1997aa) to assign the 

fraction of time spent inside a residence; outdoors; in a vehicle; and at stores, restaurants, and 

other indoor locations. 

The NRC staff notes that DOE’s data reasonably support DOE’s derivation of exposure times. 

This is based on DOE’s use of local and national data obtained from sources including the 

Bureau of the Census and the EPA to derive exposure times using a data synthesis approach. 

DOE’s data synthesis approach used percentages of time spent conducting activities at various 

locations by age group with Amargosa Valley population information to generate percentages 

applicable to the Amargosa Valley population. DOE then used additional national survey 

results (EPA, 1997aa) on time spent outdoors to derive active and inactive outdoor exposure 

times and apportioned the resulting times spent at various locations to derive its exposure time 

input parameter values. DOE’s estimates for the fraction of outdoor activity that includes 

surface-disturbing activities (20 percent of public outdoor time and 50 percent of construction 

worker outdoor time) were based on EPA survey data and information on local practices. The 

NRC staff evaluated a sample of values used in the derivation against source documents to 

verify that the values were incorporated accurately. The NRC staff also conducted simplified 

calculations of the documented results to verify the data synthesis and found no discrepancies 

or errors. DOE lognormal distributions of exposure time estimates to propagate variation in the 

ERMYN code were based on the standard errors provided with the survey data and the 

application of standard lognormal distribution statistical methods. For some parameters, DOE 

intentionally assigned standard errors that were larger than those associated with the national 

survey data to account for uncertainties in applying national data to local conditions. These 

uncertainties result from potential differences in local human activity practices compared to 

national patterns and the effect of differences in survey sample sizes on standard errors. 
16-25 

Because propagating variation from survey data directly quantifies empirical data variability, the 

NRC staff notes that DOE has accounted for uncertainty and variability in exposure time 

parameter values and has provided reasonable technical bases for parameter values, ranges, 

and probability distributions used in the ERMYN inhalation exposure submodel. The NRC staff 

also notes that the exposure time input parameters used in the inhalation exposure calculations 

are consistent with the living styles of current Amargosa Valley residents, because the 

parameters were accurately derived from survey information applicable to the local population. 

DOE derived breathing rates for each population group and for each level of activity within the 

four potentially contaminated exposure environments (active outdoors, inactive outdoors, active 

indoors, and inactive indoors). DOE combined breathing rate information for adults by gender 

and level of physical activity from International Commission on Radiological Protection (1994aa) 

with census demographic information for Amargosa Valley to derive population gender-weighted 

breathing rates. DOE then used information from International Commission on Radiological 

Protection (1994aa) on the fraction of daily time devoted to different levels of activity to derive 

breathing rates for each exposure environment category. In this manner, the exposure 

environment categories applied to all population groups considered in the model and were 

derived, in part, on the basis of surveys of Amargosa Valley, Nevada, residents. 

The NRC staff evaluated DOE’s technical bases and supporting data for the breathing rates 

detailed in BSC (2005ab). The gender-weighted breathing rate values DOE calculated from 

International Commission on Radiological Protection breathing rates and census data for 

Amargosa Valley were 0.39, 0.47, 1.38, and 2.86 m3

/hr [0.51, 0.61, 1.81, and 3.74 yd3/hr] 

for sleep, sitting, light exercise, and heavy exercise, respectively. These values compare 

favorably with EPA-recommended adult breathing rate values for use in short-term exposure 

calculations (EPA, 1997aa). The EPA values are 0.4, 0.5, 1.0, 1.6, and 3.2 m3

/hr [0.5, 0.7, 1.3, 

2.1, and 4.2 yd3

/hr] for a similar progression of increasing activity level including rest, sedentary, 

light, moderate, and heavy activities, respectively. The EPA value for heavy activity {3.2 m3

/hr 

[4.2 yd3

/hr]} is somewhat higher than the DOE value. However, the EPA values apply to 

short-term exposures that would have higher breathing rates than values used for the long-term 

exposure calculations (i.e., annual dose). Additionally, the EPA-recommended value for 

outdoor workers for heavy activity {2.5 m3

/hr [3.3 yd3/hr]} is lower than the DOE value. 

The NRC staff also reviewed DOE’s methods for deriving the final set of breathing rates for 

each of the four exposure environments. These methods involved use of a weighted sum 

approach to compute a single breathing rate value applicable to all population groups for each 

exposure environment. The NRC staff notes that DOE’s methods [BSC Table 6-15 (2005ab)] 

are reasonable for use in long-term exposure calculations in the ERMYN biosphere inhalation 

exposure model. DOE used these breathing rates in ERMYN calculations as individual fixed 

input parameters for each exposure environment, and the model propagates breathing rate 

input parameter uncertainty or variability due to differences in activity level by using different 

exposure environments. Because the DOE approach accounts for variability and uncertainty in 

breathing rates based on human activity level and the magnitude of the breathing rates compare 

favorably with other data sources NRC staff identified as discussed previously, the NRC staff 

notes that DOE has accounted for uncertainty and variability in parameter values and has 

provided the technical bases for the breathing rates used in the performance assessment. 

In summary, on the basis of the information discussed in this subsection, the NRC staff notes 

that DOE has accounted for uncertainty and variability in parameter values and has provided 

the technical bases for parameter ranges, probability distributions, or bounding values used in 

the inhalation exposure submodel in its performance assessment. 
16-26 

Ingestion Exposure Model 

DOE’s ingestion exposure submodel in ERMYN calculates radionuclide intakes by modeling 

reasonably maximally exposed individual consumption of contaminated water, crops, animal 

products (milk, meat, poultry, eggs, fish), and soil. Ingestion pathways have a more pronounced 

effect on DOE’s performance assessment results during the initial 10,000-year period than on 

the results for the 1-million-year period because of the radionuclides that dominate the dose 

calculations. Exposure pathways that contribute most to DOE’s performance assessment 

results (TER Section 2.2.1.3.14.3) include drinking water, fish consumption, and animal product 

consumption (milk, meat, eggs). The exposure calculations for these ingestion exposure 

pathways involve consumption rate input parameters for modeled food products the reasonably 

maximally exposed individual consumes (i.e., water, fish, and animal food products including 

milk, meat, and eggs). 

DOE derived food consumption rates used in the ERMYN ingestion exposure submodel from a 

DOE-sponsored survey of Amargosa Valley residents (DOE, 1997ab). The exception is DOE’s 

modeling of drinking water consumption, which DOE assumed to be 2 L/day [0.53 gal]. The 

Amargosa Valley survey measured how often residents consumed various locally produced 

food products. The calculated arithmetic mean annual consumption rates for various food types 

and corresponding standard deviations were then used as input parameters for sampling 

parameter values in ERMYN assuming a lognormal distribution, as detailed in BSC Section 

6.4.2, Table 6-21 (2005ab). 

The NRC staff evaluated DOE’s technical bases and supporting data for the food and water 

consumption rates as detailed in BSC (2005ab). The supporting data included a survey of 

195 of the reported 872 adult Amargosa residents (22 percent of the population) to obtain 

information on local food consumption frequency. The survey yielded useful responses from 

187 of these contacts and provided explanations for the 8 responses that were eliminated from 

further analysis. The NRC staff’s review of the survey notes that DOE reasonably generated 

data that provide a technical basis for deriving consumption rate input parameters for the 

biosphere calculations. 

The NRC staff reviewed DOE’s methods for calculating the annual consumption rates for 

locally produced food on the basis of the survey data, census information (to incorporate more 

recent Amargosa Valley population information), and national average daily intakes from the 

U.S. Department of Agriculture. The resulting values for annual food consumption rates appear 

to be low compared to values based on the more commonly used national food consumption 

surveys (e.g., NRC, 1992ae). This is because DOE’s values are population averages of 

individual consumption rates that apply specifically to the consumption of locally produced 

foods [detailed in BSC Figures 6-3 through 6-12 (2005ab)] rather than all food consumed. 

Additionally, a large number of Amargosa Valley residents who do not consume locally 

produced food weight the average consumption in the population to lower values than are found 

in national average food consumption rates. The low local food consumption rates also reflect 

the limited capacity of agricultural food production in the Amargosa Valley (LaPlante and Poor, 

1997aa). The NRC staff notes that the methods used by DOE are reasonable for supporting 

the dose calculations. The NRC staff notes that DOE has accounted for uncertainty and 

variability in parameter values and has provided technical bases for consumption rate 

parameter values, ranges, and probability distributions used in the ingestion exposure submodel 

in its performance assessment. 
16-27 

In summary, on the basis of the information discussed in this subsection, the NRC staff notes 

that DOE has accounted for uncertainty and variability in parameter values and has provided 

reasonable technical bases for parameter ranges, probability distributions, or bounding values 

used in the ingestion exposure submodel in its performance assessment. 

2.2.1.3.14.3.4 Assessment of Dosimetry 

The DOE biosphere model uses dose coefficients from the Federal Guidance Report 13 

(EPA, 1999aa), which uses tissue-weighting factors recommended in International Commission 

on Radiological Protection, Publication 60 (1991aa) to calculate effective dose from both 

internal and external radiation sources. In its TSPA modeling, DOE identified 28 primary 

radionuclides that were the primary contributors to dose to the reasonably maximally exposed 

individual using a radionuclide screening analysis (SNL, 2007ac; SAR Section 2.3.7.4.1.2). 

DOE then converted radionuclide intake or external exposure to dose using the dose 

coefficients for the 28 primary radionuclides. DOE used dose coefficients for external exposure 

that are defined as the effective dose rate per unit radionuclide concentration in the soil. DOE 

also used dose coefficients for inhalation and ingestion as the committed effective dose per unit 

radionuclide intake by inhalation or ingestion. 

DOE chose dose coefficients for intake of radionuclides used in the biosphere model for 

adults and a total effective dose equivalent commitment period of 50 years. The biokinetic and 

dosimetric models used to develop these dose coefficients are based on a hypothetical average 

adult person with the anatomical and physiological characteristics the International Commission 

on Radiological Protection (1975aa) defined with further modifications as described in Federal 

Guidance Report 13 (EPA, 1999aa). DOE used breathing rates in its biosphere model that are 

based on the more recent biometric results for adults from the respiratory tract model the 

International Commission on Radiological Protection (1994aa) developed, as discussed in TER 

Section 2.2.1.3.14.3.3. The NRC staff notes that DOE used reasonable scientific models and 

methodologies (recommended by the International Commission on Radiological Protection) to 

calculate the total effective dose equivalent. 

For ingestion and inhalation, DOE typically chose dose coefficients for the chemical form of 

the radionuclide that resulted in the highest dose to avoid underestimating dose. In a few 

cases, such as C-14, DOE chose dose coefficients that were consistent with the form that 

was being transported [i.e., the gaseous (carbon dioxide) and solid (particulate) forms have 

different dose coefficients]. The NRC staff notes that this approach is reasonable because 

using these dose coefficients would not result in underestimating dose to the reasonably 

maximally exposed individual. 

DOE calculated the total effective dose equivalent to the reasonably maximally exposed 

individual as the sum of the effective dose equivalent from external sources plus the committed 

dose equivalent from internal sources (i.e., sources either inhaled or ingested). This approach 

is reasonable, as it is consistent with International Commission on Radiological Protection 

recommendations for calculating the total effective dose equivalent. 

The description that DOE provided in the SAR is adequate to fully assess the dosimetry 

models used in the TSPA. The NRC staff performed a detailed review of the dosimetry data 

for a selection of radionuclides, including Tc-99, C-14, I-129, Ra-226, Pu-239, Pu-240, Pu-242, 

and Np-237. The review also included comparing a sample of dose coefficients for other 

radionuclides that were used in the TSPA. No discrepancies were found between the dose 

coefficients included in the biosphere model report and those tabulated in Federal Guidance 
16-28 

Report 13 (EPA, 1999aa). The dose coefficients DOE used are reasonable because they 

incorporate the most current and appropriate scientific models and methodologies for an 

adult receptor. 

In summary, on the basis of the information discussed in this subsection, the NRC staff notes 

that DOE has provided reasonable technical bases for values used in the dosimetry model in 

its performance assessment. DOE’s dosimetry method uses the current and appropriate 

scientific models and methodologies (recommended by the International Commission on 

Radiological Protection). DOE’s dose coefficients were developed on the basis of an adult 

reasonably maximally exposed individual metabolic and physiologic data that is consistent with 

present knowledge. 

2.2.1.3.14.3.5 Assessment of Integrated Biosphere Modeling Results 

DOE biosphere modeling results were provided in SAR Section 2.3.10 and analyzed in greater 

detail in the Biosphere Model Report (SNL, 2007ac). The exposure pathways determined to be 

the most risk significant in the DOE performance assessment varied depending on the particular 

radionuclide. The radionuclides and pathways that were most risk significant in the DOE TSPA 

calculations are summarized in TER Table 16-1 for the 10,000-year simulation period and TER 

Table 16-2 for the 1-million-year simulation period. 

To validate the integrated biosphere model, DOE compared the calculation results for each 

environmental transport and exposure submodel of the ERMYN code with comparable 

calculation results from five other biosphere transport and exposure process-level models 

(SAR Section 2.3.10.5). DOE stated that the results of the process-level calculations used in 

those other models were the same, or similar, to the results obtained using the biosphere model 

(SNL, 2007ac). To verify implementation, DOE compared the results of the biosphere model for 

representative radionuclides (Pu-239, Ra-226, Th-232, and C-14) with the results of 

spreadsheet calculations—on the basis of equations used in the biosphere mathematical 

model—and the results were identical (SNL, 2007ac). On the basis of the information reviewed, 

DOE’s model support calculations provide reasonable technical bases for the biosphere models 

used in the performance assessment. 

The NRC staff also performed confirmatory calculations to further assess the ERMYN model 

for a subset of the radionuclides that DOE’s performance assessment identified as the most 

risk significant (TER Tables 16-1 and 16-2) (LaPlante, 2010aa). These confirmatory 

calculations were performed for the drinking water ingestion pathway of the groundwater 

exposure scenario. Drinking water ingestion was chosen because (i) it was identified as one of 

the most risk-significant pathways in the DOE performance assessment, (ii) the model is not 

complex and therefore could be executed efficiently, and (iii) the results of the confirmatory 

calculations could be directly compared to DOE results. The results of the confirmatory 

groundwater calculations were within 2 percent of the mean biosphere dose conversion factor 

results DOE reported in SAR Table 2.3.10-2, weighted by the DOE drinking water ingestion 

pathway fractions in SAR Table 2.3.10-11. Given that the DOE results are summary statistics of 

the model output, a difference of only 2 percent confirms that both calculations were consistent. 

The NRC staff notes that DOE’s groundwater calculations provide reasonable technical bases 

for the biosphere models used in the performance assessment. 

In SAR Section 2.3.10.4.1, DOE described how its performance assessment considered 

and evaluated alternative conceptual models for seven processes in the biosphere model 

abstraction. These processes are (i) radon release from soil, (ii) transfer of radionuclides to 
16-29 

air from evaporative cooler operation, (iii) transfer of radionuclides to plants from direct 

deposition of irrigation water, (iv) transfer of airborne particulates to plants from direct 

deposition, (v) transfer of radionuclides to animal products from livestock inhalation, (vi) transfer 

of C-14 to crops, and (vii) environment-specific inhalation exposure. DOE’s consideration of 

alternative conceptual models provides reasonable technical bases for the biosphere models 

used in the performance assessment. 

2.2.1.3.14.4 NRC Staff Conclusions 

The NRC staff notes that DOE’s description of this model abstraction for the 

biosphere characteristics is consistent with the guidance in the YMRP. NRC staff also notes 

that the technical approach for the biosphere characteristics discussed in this chapter is 

reasonable for use in the Total System Performance Assessment (TSPA). 

2.2.1.3.14.5 References 

BSC. 2006ad. “Inhalation Exposure Input Parameters for the Biosphere Model.” 

ANL–MGR–MD–000001. Rev. 04. ACN 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

BSC. 2005ab. “Characteristics of the Receptor for the Biosphere Model.” 

ANL–MGR–MD–000005. Rev. 04. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

BSC. 2004ap. “Environmental Transport Input Parameters for the Biosphere Model.” 

ANL–MGR–MD–000007. Rev. 02. ERD 01. Las Vegas, Nevada: Bechtel SAIC 

Company, LLC. 

Bureau of the Census. 2002aa. “2000 Summary File 3 (SF 3) Sample Data, Amargosa Valley 

CCD, Nye County, Nevada.” ANL–MGR–MD–000005, Rev. 4, DIRS 159728. Washington, DC: 

U.S. Department of Commerce, Bureau of the Census. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

DOE. 1997ab. “The 1997 Biosphere Food Consumption Survey Summary Findings and 

Technical Documentation.” ANL–MGR–MD–000005. Rev. 4. DIRS 100332. Las Vegas, 

Nevada: DOE, Office of Civilian Radioactive Waste Management. 

EPA. 1999aa. “Federal Guidance Report 13, Cancer Risk Coefficients for Environmental 

Exposure to Radionuclides.” EPA 402–R–99–001. Washington, DC: U.S. Environmental 

Protection Agency. 

EPA. 1997aa. “Exposure Factors Handbook, Volume III, Activity Factors.” 

EPA/600/P–95/002Fc. Washington, DC: U.S. Environmental Protection Agency. 

International Atomic Energy Agency. 1994aa. “Handbook of Parameter Values for the 

Prediction of Coefficients: Workers and Members Radionuclide Transfer in Temperate 

Environments.” Technical Report Series No. 364. Vienna, Austria: International Atomic 

Energy Agency. 
16-30 

International Commission on Radiological Protection. 1994aa. “Human Respiratory Tract 

Model for Radiological Protection.” ANL–MGR–MD–000005. Rev. 4. DIRS 153705. Annals of 

the ICRP. Publication 66. Vol. 24, No. 1-3. New York City, New York: Pergamon Press. 

International Commission on Radiological Protection. 1991aa. “1990 Recommendations of the 

International Radiological of Commission Protection.” ANL–MGR–MD–000005. Rev. 4. DIRS 

153705. Annals of the ICRP. Publication 60. Vol. 21, No. 1-3. New York City, New York: 

Pergamon Press. 

International Commission of Radiological Protection. 1975aa. “Report of the Task Group on 

Reference Man.” Annals of the ICRP. Publication 23. Vol. 23. New York City, New York: 

Pergamon Press. 

Klepeis, N.E., A.M. Tsang, and J.V. Behar. 1996aa. “Analysis of the National Human Activity 

Patter Survey (NHAPS) Respondents From a Standpoint of Exposure Assessment, Percentage 

of Time Spent, Duration, and Frequency of Occurrence for Selected Microenvironments by 

Gender, Age, Time-of-Day, Day-of-Week, Season, and U.S. Census Region—Final Report.” 

EPA/600/R–96/074. ANL–MGR–MD–000005. Rev. 4. DIRS 159299. Washington, DC: 

U.S. Environmental Protection Agency, Office of Research and Development. 

LaPlante, P. 2010aa. "Calculations Supporting the Biosphere Characteristics Section, 

Volume 3, Technical Evaluation Report for the NRC Review of the DOE Yucca Mountain 

Repository License Application." Electronic Scientific Notebook 1056E. San Antonio, Texas: 

CNWRA. 

LaPlante, P.A. and K. Poor. 1997aa. “Information and Analyses to Support Selection of Critical 

Groups and Reference Biospheres for Yucca Mountain Exposure Scenarios.” CNWRA 97-009. 

San Antonio, Texas: CNWRA. 

Leslie, B., C. Grossman, and J. Durham. 2007aa. “Total-system Performance Assessment 

(TPA) Version 5.1 Module Descriptions and User Guide.” Rev. 1. ML072710060. 

San Antonio, Texas: CNWRA. 

NRC. 2009ab. “Division of High-Level Waste Repository Safety Director's Policy and 

Procedure Letter 14: Application of YMRP for Review Under Revised Part 63.” Published 

March 13, 2009. ML090850014. Washington, DC: NRC. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

Washington, DC: NRC. 

NRC. 1992ae. NUREG–5512, “Residual Radioactive Contamination From Decommissioning: 

Technical Basis for Translating Contamination Levels To Annual Total Effective Dose 

Equivalent—Final Report.” Vol. 1. Washington, DC: NRC. 

SNL. 2008ab. “Features, Events, and Processes for the Total System Performance 

Assessment: Analyses.” ANL–WIS–MD–000027. Rev. 00. ACN 01, ERD 01, ERD 02. 

Las Vegas, Nevada: Sandia National Laboratories. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories. 
16-31 

SNL. 2007ac. “Biosphere Model Report.” MDL–MGR–MD–000001. Rev. 02. ERD 01. 

Las Vegas, Nevada: Sandia National Laboratories. 

Yu, C., A.J. Zielen, J.-J. Cheng, D.J. LePoire, E. Gnanapragasam, S. Kamboj, J. Arnish, 

A. Wallo, W.A. Williams, and H. Peterson. 2001aa. “User's Manual for RESRAD Version 6.” 

ANL/EAD–4. Argonne, Illinois: Argonne National Laboratory. 

17-1

CHAPTER 17 

2.2.1.4.1 Postclosure Individual Protection Calculation 

2.2.1.4.1.1 Introduction

This chapter of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s evaluation of the U.S. Department of Energy’s (DOE’s) Total System 

Performance Assessment (TSPA) for the individual protection calculation, as presented in 

DOE’s Safety Analysis Report (SAR) Section 2.4.2 (DOE, 2008ab). DOE conducted an 

analysis, through its TSPA computer model, that evaluates the behavior of the high-level waste 

repository in terms of an annual dose due to potential releases from the repository. The 

performance assessment provides a method to evaluate the range of features (e.g., geologic 

rock types, waste package materials), events (e.g., earthquakes, igneous activity), and 

processes (e.g., corrosion of metal waste packages, sorption of radionuclides onto rock 

surfaces) that are relevant to the behavior of a repository at Yucca Mountain. In particular, the 

NRC staff’s review evaluates whether (i) the performance assessment analysis includes the 

appropriate scenario classes [a set or combination of features, events, and processes (FEPs) 

that the performance assessment model uses to represent a class or type of scenario such 

seismic activity], (ii) the representation of the scenario classes within the performance 

assessment is credible (e.g., the performance assessment results are consistent with the 

models, parameters, and assumptions that make up the performance assessment), and (iii) the 

annual dose the performance assessment estimates is statistically stable. 

2.2.1.4.1.2 Evaluation Criteria

The regulations specify that a performance assessment must be used to evaluate the individual 

protection calculation. According to 10 CFR 63.2, performance assessment is defined as an 

analysis that 

 Identifies the FEPs (except human intrusion) and sequences of events and processes 

(except human intrusion) that might affect the Yucca Mountain disposal system and their 

probabilities of occurring during 10,000 years after disposal 

 Examines the effects of those FEPs and sequences of events and processes upon the 

performance of the Yucca Mountain disposal system 

 Estimates the dose incurred by the reasonably maximally exposed individual (RMEI), 

including the associated uncertainties, as a result of releases caused by all 

significant FEPs, and sequences of events and processes, weighted by their 

probability of occurrence 

The requirements for developing performance assessment analyses (e.g., consideration of 

features, events, and processes included in the performance assessment; determination of 

event probabilities; and consideration of uncertainties) are relevant to previous TER Sections 

2.2.1.2 and 2.2.1.3. These previous sections evaluate DOE’s development of the analytic 

models used in the performance assessment analysis. The requirements also specify how the 

performance assessment model is used to estimate the annual dose to the reasonably 

maximally exposed individual (RMEI). In general, the performance assessment requirements 

are to 
17-2

 Estimate the annual dose as a result of releases caused by all significant features, 

events, and processes weighted by their probability of occurrence (10 CFR 63.2) 

 Calculate the arithmetic mean (i.e., average) of the annual dose for the period within 

1 million years after disposal (10 CFR 63.303) 

The NRC staff’s review followed the guidance in the Yucca Mountain Review Plan (YMRP) 

(NRC, 2003aa). The acceptance criteria address the following: 

 Scenarios used in the calculation of the annual dose as a function of time are adequate. 

 Total System Performance Assessment provides a credible representation of 

repository performance. 

 The annual dose to the reasonably maximally exposed individual (RMEI) is 

adequately demonstrated. 

2.2.1.4.1.3 Technical Evaluation

2.2.1.4.1.3.1 Introduction 

DOE’s performance assessment is implemented through its TSPA code. The TSPA code is 

used to represent the range of behavior of a Yucca Mountain repository, accounting for 

uncertainty in the features, events, and processes (FEPs) that could affect the repository 

evolution. DOE developed its analysis of repository performance using distinct groupings of 

FEPs—referred to as “scenario” or “event” classes. In very general terms, there are two broad 

categories of scenario classes: nominal and disruptive. The nominal scenario class comprises 

those FEPs that are present under “normal” conditions (e.g., infiltration of water, corrosion of the 

waste package, release of radionuclides, transport of radionuclides in groundwater). The 

disruptive scenario class includes additional FEPs that account for the effects of specific events 

(i.e., seismic events, volcanic activity, fault movement) that disrupt or alter the repository 

performance differently from what the nominal scenario class portrays. In DOE’s TSPA model, 

the nominal scenario class is considered part of the seismic ground motion modeling case so 

that the combined effects of waste package corrosion, which degrades the mechanical strength 

of the waste package, and mechanical damage of the waste package due to seismic ground 

motion are appropriately considered in the post-10,000-year period (SAR p. 2.4-36). (Note: For 

the initial 10,000 years, the nominal scenario class does not result in any dose, as detailed in 

SAR p. 2.4-62 and Figure 2.4-22a.) A key aspect of the disruptive scenario classes is the 

consideration of the probability or likelihood that the disruptive event will occur. The annual 

dose is weighted by the probability of its occurrence. 

The NRC staff reviewed SAR Sections 2.4.1 and 2.4.2 and the Total System Performance 

Assessment (TSPA) model files including intermediate results provided as part of the SAR. 

Additionally, the NRC staff’s review in this chapter relies on the NRC staff’s reviews, presented 

in the previous 16 chapters, on the multiple barriers, scenarios, event probabilities, and model 

abstractions implemented in DOE’s TSPA model (TER Sections 2.2.1.1–2.2.1.3.14). Specific 

TER chapters, as applicable, are referenced in this chapter. 
17-3

The NRC staff’s review entails 

 Determining that the probabilities and consequences of each of the scenario classes are 

appropriately included in the average annual dose (TER Section 2.2.1.4.1.3.2)

 Determining that the results of the performance assessment provide a credible 

representation of repository performance [e.g., the intermediate results, such as waste 

package failure, and release rates from the engineered barrier system (EBS), 

unsaturated, and saturated zones, are consistent with the model abstractions and the 

average annual dose; confirmatory calculations are consistent with the performance 

assessment results] (TER Section 2.2.1.4.1.3.3)

 Determining that the calculated average annual dose is statistically stable 

[e.g., increasing the number of simulations (statistical sample size) performed with 

DOE’s TSPA is not expected to significantly change the average annual dose] (TER 

Section 2.2.1.4.1.3.4)

2.2.1.4.1.3.2 Scenarios Used in Calculation of Annual Dose 

2.2.1.4.1.3.2.1 Summary of DOE Approach 

DOE has identified three distinct event scenario classes (sometimes referred as event classes) 

that are included in its TSPA model for the individual protection calculation: (i) early failures, 

(ii) seismic events, and (iii) igneous events. DOE has used two modeling cases within each 

scenario class to represent specific aspects of the scenario. The early failure scenario class is 

composed of an early waste package failure modeling case and an early drip shield failure 

modeling case. The seismic scenario class is composed of a seismic ground motion modeling 

case and a seismic fault displacement modeling case. The igneous scenario class is composed 

of an igneous intrusion modeling case and a volcanic eruption modeling case. 

DOE’s average annual dose curve for individual protection (SAR Figure 2.4-18) is determined 

by summing the effects of all the scenario classes (i.e., early failure, seismic, and igneous). 

The annual doses attributed to each of the scenario classes are a direct result of the features, 

events, and processes used to represent the scenario class and its probability of occurrence. 

Features, events, and processes included in the scenario classes are reviewed in the NRC staff 

model abstraction review (TER Sections 2.2.1.3.1–2.2.1.3.14). The NRC staff also reviewed 

and evaluated the features, events, and processes that DOE considered and excluded from the 

performance assessment (see TER Section 2.2.1.2.1). As previously stated in the TER, DOE’s 

approach for identifying the appropriate FEPs used to represent the scenario classes is 

reasonable. The NRC staff’s reviews are presented in the previously identified TER Sections 

2.2.1.2.1 and 2.2.1.3.1–2.2.1.3.14.

Scenario Class Probabilities

DOE’s TSPA assessment incorporates the following three distinct event scenario classes: 

(i) the igneous activity scenario class, which has a very low annual probability [on the order of a 

1 in 100 million chance of occurring per year, as outlined in CRWMS M&O (1996aa)]; (ii) the 

seismic scenario class, which typically results in numerous events occurring over 1 million 

years (according to SAR Section 2.4.2.1.6, p. 2.4-50, seismic events are expected to occur 

frequently; however, it is important to evaluate the timing and magnitude of seismic events); 
17-4

and (iii) the early failure scenario class, for which there is a low probability of occurrence for an 

individual waste package (SAR Section 2.4.2.1.6, p. 2.4-49). These three event scenario 

classes include the occurrence of nominal processes, whereas the nominal scenario class 

represents repository behavior in which no events occur (i.e., no seismic events, no igneous 

events, and no early failure events; see SAR Section 2.4.2.1.3, pp. 2.4-30–31). DOE has 

described how its approach to combine the scenarios to derive aggregated annual dose 

estimates is appropriate in that it tends to slightly overestimate dose by double counting waste 

packages potentially affected by different failure modes from the different scenarios (e.g., waste 

packages failed by a seismic event and an igneous event would be double counted; see 

SAR Section 2.4.2.1.7). 

Igneous Scenario Class 

Probability 

The igneous scenario class is composed of an igneous intrusion modeling case and a volcanic 

eruption modeling case. The probability for the igneous intrusion modeling case is described in 

the DOE model as a Poisson process (a random process in which the events occur 

independently of one another), and intrusive events are distributed in time with a mean 

recurrence frequency of 1.7 × 10!8

 per year, with a 5th and 95th percentile uncertainty spanning 

nearly two orders of magnitude, 7.4 × 10!10 to 5.5 × 10!8

 per year. DOE describes the 

probability of the volcanic eruption modeling case as a subset of the probability used for the 

igneous intrusion modeling case by using a conditional probability that an igneous intrusive 

event will also have an eruptive component that ejects waste into the atmosphere. The 

conditional probability is composed of (i) a conditional probability of 0.28 that an igneous 

intrusive event could have an eruptive component and (ii) a conditional probability of 0.2968 

that the eruptive component of an igneous intrusive event could intersect the waste packages. 

The combination of these two probabilities results in a net conditional probability of 0.083 that 

an igneous intrusive event would also manifest a volcanic eruption that intersects waste 

packages in the repository footprint. Thus, the mean recurrence frequency for the volcanic 

eruption modeling case is 1.4 × 10!9

 per year based on the mean recurrence frequency 

provided previously for the igneous intrusion modeling case (i.e., 1.7 × 10!8

 per year). 

Igneous Event Contribution to Average Annual Dose Curve 

DOE evaluated the igneous intrusion modeling case and the volcanic eruption modeling case 

separately assuming the entire intact or degraded repository inventory is available for release 

for each modeling case. A decrease in the repository inventory due to the occurrence of other 

scenarios is not considered. DOE stated this is a conservative assumption in that it does not 

underestimate the annual dose to the reasonably maximally exposed individual (RMEI) [SAR 

Section 2.4.2.1.7.4, p. 2.4-53]. 

The average annual dose from the igneous scenario class is the sum of the contributions 

from the igneous intrusion modeling case and the volcanic eruption modeling case. The 

average annual dose from the igneous intrusion modeling case is more than 99 percent of the 

average annual dose from the igneous scenario class (intrusion plus volcanic eruption) 

(SAR Figure 2.4-18). 

The igneous intrusion modeling case is the second largest contributor to the overall average 

annual dose (i.e., summation of average annual dose from all scenario classes) in the 

10,000-year period and the largest contributor to the overall average annual dose after 
17-5

10,000 years (SAR Figure 2.4-18). In the 10,000-year period, Tc-99, Pu-239, Pu-240, and I-129 

radioactive elements or radionuclides are the dominant contributors to the average annual dose. 

After 10,000 years, the dominant radionuclide contributors to the average annual dose are 

Pu-239, Pu-242, Np-237, and Ra-226 (SAR Figure 2.4-30). 

Early Failure Scenario Class 

Probability 

The early failure scenario class includes two modeling cases: early drip shield failure and early 

waste package failure. Failure of the drip shield allows seepage water to contact a waste 

package. Failure of the waste package allows release of radionuclides out of the waste 

package by diffusion and/or advection. Early failure of either the drip shield or waste package is 

associated with undetected defects accounting for manufacturing processes such as improper 

heat treatment, base metal selection flaws, improper weld filler material, and emplacement 

errors. DOE assumed that all of the waste packages under early failed drip shields would also 

be considered failed if contacted by seepage water, as described in SNL Section 6.4.1, p. 6.4-4 

(2008ag). The probability of having a large number of drip shields and waste packages fail 

early due to undetected defects is very small. On average, the number of waste packages 

affected by early failure of drip shields and waste packages is less than 0.02 percent of the total 

number of waste packages (SAR Section 2.4.2.1.7.2, p. 2.4-52). 

Early Failure Contribution to Average Annual Dose Curve 

The early failure scenario class contributes on the order of 1 percent or less to the overall 

average annual dose curve (SAR Figure 2.4-18). In particular, the average annual dose for the 

early drip shield failure modeling case, which includes contributions from waste packages under 

drip conditions and unprotected by the drip shields against contact with seepage water, is below 

1 × 10!5

 mSv [0.001 mrem] for all times. The average annual dose for the early waste package 

failure modeling case is generally 10 times or more greater than the average annual dose for 

the early drip shield failure modeling case but still on the order of 100 times less than the overall 

average annual dose curve (SAR Figure 2.4-18). 

Seismic Scenario Class 

Probability 

The seismic scenario class is composed of a seismic ground motion modeling case and a 

seismic fault displacement modeling case. The DOE model describes seismic ground motion 

events as a Poisson process, with events distributed in time with a maximum mean recurrence 

frequency of 4.287 × 104

 per year {corresponding to the frequency of events with a peak ground 

velocity exceeding 0.219 m/s [0.72 ft/s] (SAR Section 2.4.2.1.6, p. 2.4-50)}. This is the 

maximum recurrence frequency of seismic ground motion events that could result in repository 

damage. Given a recurrence frequency of 4.287 × 10!4

 per year, it is expected that seismic 

ground motion events of that magnitude could occur, on average, every 2,200 years. Thus, 

because it is expected that multiple seismic events will occur during the 1-million-year period, 

DOE considered cumulative effects of seismic ground motion from multiple events. 

The DOE model describes seismic fault displacement events as a Poisson process with events 

distributed in time, with a maximum mean recurrence frequency of 2 × 10!7

 per year. Because 

multiple seismic fault displacement events that could affect the repository are expected to be 
17-6

sufficiently rare and therefore inconsequential to repository performance, DOE evaluated only 

the effects of single seismic fault displacements. 

In both modeling cases for the seismic scenario class, the consequence of events that have a 

recurrence frequency between the maximum mean recurrence frequency (i.e., those which 

could cause repository damage) down to the probability cutoff of 1 × 10!8 per year are 

evaluated. The magnitude of an individual seismic event is determined through the use of a 

probabilistic seismic hazard analysis (PSHA) curve. The PSHA was developed primarily through 

the use of an expert elicitation process and is documented in CRWMS M&O (1998aa) (see also 

TER Sections 2.2.1.2.2.3.2 and 2.2.1.3.2 for further details on the NRC staff evaluation). 

Seismic Event Contribution to Average Annual Dose Curve 

The average annual dose from the seismic scenario class is the sum of the contributions from 

the two seismic modeling cases: (i) the seismic ground motion modeling case, which addresses 

the potential for seismic events to damage waste packages and drip shields due to vibratory 

ground motion and (ii) the seismic fault displacement modeling case, which addresses the 

effects of fault displacement on waste packages and drip shields. 

Nominal corrosion processes have the potential to alter the susceptibility of the waste package 

to damage during seismic ground motion events as the corrosion processes gradually weaken 

the mechanical strength of the waste package. Therefore, the seismic ground motion modeling 

case also includes both waste package degradation from the nominal processes (e.g., general 

corrosion) and seismic ground motion. 

The average annual dose from the seismic ground motion modeling case is at least 10 times 

larger than the average annual dose from the seismic fault displacement modeling case over 

the entire 1-million-year period (see SAR Figure 2.4-18). Although the average annual dose 

curve from the seismic ground motion modeling case also includes the effects from the nominal 

scenario class, the nominal scenario class contributes no more than 50 percent to the seismic 

ground motion modeling case average annual dose curve (compare SAR Figures 2.4-18 

and 2.4-22). 

The seismic ground motion modeling case is second only to the igneous intrusion modeling 

case in overall significance to the overall average annual dose curve. The seismic ground 

motion modeling case is the largest contributor to the overall average annual dose curve for the 

period after 1,500 years through 20,000 years. The overall average annual dose curve in either 

the initial 10,000 years or after 10,000 years is dominated by contributions from the seismic 

ground motion modeling case and the igneous intrusion modeling case. 

2.2.1.4.1.3.2.2 NRC Staff Evaluation of Scenarios Used in Calculation of Annual Dose 

The NRC staff has reviewed DOE’s analytic models and assumptions used in its TSPA 

analyses, as documented in the SAR and supporting documents, and notes that DOE’s 

inclusion of the annual dose from each of the scenario classes into the overall average annual 

dose curve is reasonable for the following reasons. 

 The two modeling cases (i.e., igneous intrusion modeling case and seismic ground 

motion modeling case) that result in the greatest number of failed waste packages 

are the largest contributors to the overall average annual dose curve (see TER 

Section 2.2.1.4.1.3.3.1.1.2. 
17-7

 DOE’s overall average annual dose curve appropriately includes the probabilities of 

occurrence for the scenario classes (the values for probability of igneous and seismic 

activity are appropriate and reflect the uncertainty for the occurrence of these events; 

see TER Sections 2.2.1.2.2.3.1 and 2.2.1.2.2.3.2). 

 The value for the probability of early failures reflects the uncertainty in potential defects 

regarding the drip shield and waste package (see TER Section 2.2.1.2.2.4). 

 Model assumptions of the early failure scenario class tend to overestimate release 

consequences in the sense that no credit is given to early failed waste packages to 

impede contact of water or moisture with waste forms. Additionally, no credit is assigned 

to an early failed drip shield as a barrier against seepage, and the corresponding 

exposed waste packages are also given no credit to contain or impede contact of 

waste forms with seepage if located under seepage conditions (see TER 

Section 2.2.1.3.1.3.1.2 for the drip shield and TER Section 2.2.1.3.1.3.2.4 for the 

waste package). 

 The consequences for an eruptive igneous event are appropriate and reflect 

conservative assumptions in certain key areas of uncertainty such as the amount of 

waste entrained in the tephra (volcanic ash) (see TER Section 2.2.1.3.13). 

 The consequences for an intrusive igneous event are appropriate and reflect 

conservative assumptions in certain key areas of uncertainty such as all waste packages 

and drip shields being rendered ineffective (see TER Section 2.2.1.3.10). 

 The magnitude of seismic events leading to waste package damage is appropriate 

and reflects the material properties of the engineered barriers and design (see TER 

Section 2.2.1.3.2). 

 The number of waste packages that can be potentially affected by fault displacement is 

appropriate and reflects the geologic setting of Yucca Mountain and the layout of the 

repository footprint (see TER Section 2.2.1.3.2.4). 

2.2.1.4.1.3.3 Credible Representation of Repository Performance 

This section of the TER documents the NRC staff review to determine the credibility of the 

representation of repository performance in DOE’s Total System Performance Assessment 

(TSPA). In particular, the NRC staff evaluates the consistency of the characteristics of 

repository performance in the TSPA (e.g., number of waste packages failed, transport of 

radionuclides in the geosphere, and scenario probabilities) with the overall dose estimated by 

the TSPA. The focus of the NRC staff review is on those aspects of repository performance that 

have the most significance to risk (i.e., the probability-weighted dose estimate). The NRC staff 

review of individual components of DOE’s TSPA (i.e., model abstractions; features, events, and 

processes included in the TSPA; scenario probabilities; and barrier capabilities) is documented 

in the previous 16 chapters of this Postclosure Volume of the TER. 

The NRC staff reviewed the TSPA documentation in SAR Volume 2 and in the TSPA GoldSim 

computer model and associated computer files (including intermediate results saved in the 

GoldSim output files). The NRC staff’s review of the TSPA analyses considered how the 
17-8

collection of features, events, and processes that are included in the TSPA model represents a 

credible characterization of the repository. The NRC staff’s review approach entails a 

quantitative evaluation of the attributes of DOE’s TSPA calculation that most significantly impact 

estimating the annual dose to the reasonably maximally exposed individual (RMEI). 

Identification of the important attributes for performance are based on the NRC staff review of 

the capabilities of the barriers important to waste isolation (TER Section 2.2.1.1), the model 

abstractions in the TSPA code (TER Sections 2.2.1.3.1–2.2.1.3.14), and the NRC staff 

independent analysis with its performance assessment model, as outlined in CNWRA and NRC 

(2008aa) and NRC Appendix D (2005aa). 

DOE has used two modeling cases for each of the three scenario classes [for igneous (intrusive 

and eruption), for seismic (ground motion and fault displacement), and for early failure (drip 

shield and waste package)] to estimate overall performance of the Yucca Mountain repository 

(see Table 17-1). Only one modeling case (volcanic eruptive) releases radionuclides directly to 

the atmosphere via volcanic ash. The other five modeling cases (seismic ground motion, 

seismic fault displacement, igneous intrusion, early waste package failure, and early drip shield 

failure) release radionuclides through groundwater movement. The NRC staff review of DOE’s 

TSPA calculation related to groundwater releases is provided in Section 2.2.1.4.1.3.3.1 and the 

NRC staff review of DOE’s TSPA calculation for the volcanic eruption modeling case is provided 

in Section 2.2.1.4.1.3.3.2. 

2.2.1.4.1.3.3.1 DOE’s TSPA Calculation Related to Groundwater Releases 

2.2.1.4.1.3.3.1.1 Summary of DOE Approach in TSPA 

The modeling cases associated with groundwater releases are described by tracking the water 

through the system. For example, water could infiltrate the top of the mountain and move 

downward to the repository; after waste packages are breached and radionuclide releases 

occur, water could transport radionuclides through the unsaturated zone then through the 

saturated zone to the location of the reasonably maximally exposed individual (RMEI). In 

general, the description of the groundwater releases is based on the following repository 

performance characteristics: 

 Seepage of water entering the drifts (tunnels containing the waste packages) 

 Damage to engineered barriers (drip shield and waste package) 

 Seepage of water into the waste packages 

 Release of radionuclides from the waste package 

 Transport of radionuclides in the unsaturated zone 

 Transport of radionuclides in the saturated zone 

 Annual dose to the reasonably maximally exposed individual (RMEI) 

The volcanic eruption modeling case evaluates the release of radionuclides via volcanic ash 

deposited on the ground. The volcanic eruption modeling case is evaluated separately (see 

“Description and Understanding of TSPA Calculation Related to Releases from a Volcanic 

Eruption Event” later in this chapter) from the modeling cases that involve radionuclide release 

through the groundwater pathway. 
17-9

Table 17-1. Scenario Classes and Modeling Cases Included in DOE’s TSPA 

Scenario Class Modeling Case Transport Pathway* 

Early failure 

Drip shield Groundwater 

Waste package Groundwater 

Seismic 

Ground motion Groundwater 

Fault displacement Groundwater 

Igneous 

Intrusive Groundwater 

Eruption Atmospheric (volcanic ash) 

*Transport pathway indicates the primary pathway for radionuclides to be transported away from the repository 

to the accessible environment. 

2.2.1.4.1.3.3.1.1.1 Summary of DOE’s TSPA for Seepage of Water Into Drifts 

The flux of water reaching the drifts (i.e., drift seepage) is originally derived from rainfall over the 

mountain. Two important metrics for performance for the upper natural barrier are seepage flux 

(the amount of liquid water entering the repository drifts) and seepage fraction (number of waste 

package locations with dripping water). The latter is the fraction of the repository area where 

seepage occurs (the seeping environment); the remainder of the area would not receive 

seepage. Seepage, or dripping water, has the potential to fall onto the drip shields and later 

contact the waste packages after the drip shields degrade sufficiently to allow water to pass 

through the drip shield. 

Precipitation to Deep Percolation 

DOE divided the first 10,000 years into three periods: present day (0–600 years), 

monsoonal (600–2,000 years), and glacial transition (2,000–10,000 years) climates (SAR 

Tables 2.3-1–2.3-4; DOE, 2008ab). The glacial transition climate spans 80 percent of the first 

10,000 years and has the most significant impact on the performance of the repository over this 

initial period. For the glacial period, DOE calculated an average precipitation of 296.7 mm/yr 

[11.7 in/yr] in the repository footprint, as described in DOE Enclosure 1, Table 1 (2010ai). 

Processes including runoff of water from hillsides, evaporation, and lateral diversion of water 

caused by the Paintbrush Tuff nonwelded rock layer alter the amount of rainfall that eventually 

ends up as deep percolation (the amount of water reaching the repository level). DOE 

estimated an average deep percolation of 21.74 mm/yr [0.86 in/yr] at the repository horizon, 

as detailed in DOE Enclosure 1, Table 1 (2010ai) at the repository footprint for the initial 

10,000 years. Thus, approximately 7 percent of the rainfall ends up as deep percolation at the 

repository footprint. (TER Sections 2.2.1.3.5 and 2.2.1.3.6 provide further details on climate 

and infiltration.) 

For the post-10,000-year period, the specified time-independent flux for deep percolation, 

representing average climate conditions over the long term is a range of 10 to 100 mm/yr 

[0.39 to 3.9 in/yr] using a truncated lognormal distribution, which results in an arithmetic mean 

of 37 mm/yr [1.5 in/yr] for the deep percolation. In the SAR, DOE used a log-uniform distribution 
17-10

with an arithmetic mean of 32 mm/yr [1.3 in/yr] and a range between 13 and 64 mm/yr 

[0.51 and 2.5 in/yr] (SAR Section 2.3.2.3.5.1) on the basis of the proposed regulation. 

The specified deep percolation range represents a 16 percent higher average deep percolation 

than the value DOE used in the SAR (see TER Section 2.2.1.3.6 for further discussion on the 

reason for the different values used for deep percolation). Using this higher deep percolation for 

a bounding calculation, DOE estimated a corresponding increase in the 1-million-year 

reasonably maximally exposed individual (RMEI) average annual dose from 0.02 mSv/yr to 

0.023 mSv/yr [2.0 mrem/yr to 2.3 mrem/yr], as detailed in DOE Enclosure 6 (2009cb). 

Deep Percolation to Seepage 

The TSPA model includes a number of factors such as focusing or diverging of flow at the 

repository footprint, vapor barrier surrounding a drift when the drift temperature is above the 

boiling point of water, different waste package types, capillary diversion, drift degradation 

(prevalent in the seismic cases) in estimating seepage fraction, and drift seepage. Seepage is 

set to zero if the drift wall temperature exceeds 100 °C [212 °F] (SAR Section 2.3.3.4.1.1). The 

period of time when drift wall temperatures exceed 100 °C [212 °F] is generally limited to the 

first 2,000 years, as the heat generated by radionuclide decay decreases. 

The longevity of the drip shield and waste package, however, limits the significance of the 

seepage at early times, especially during the initial 10,000 years. The uncertainties of the 

effects on the longevity of the drip shield and waste package are evaluated in 

TER Sections 2.2.1.3.1 and 2.2.1.3.2. 

Over the repository footprint, seepage flux is approximately 10 percent of the deep percolation 

for intact drifts and can increase up to 49 percent for degraded drifts as DOE predicted through 

the seismic ground motion modeling case, outlined in DOE Enclosure 5 (2010ai). For the 

igneous intrusive scenario, all of the percolating flux enters the drift at all locations, as described 

in DOE Enclosure 7 (2009ct). Table 17-2 provides the DOE values for the seepage fraction and 

the average seepage rate over the repository footprint (i.e., averaged over both seeping and 

nonseeping environments). As the seepage rate increases, the number of locations where 

dripping occurs (seepage fraction) also increases. 

2.2.1.4.1.3.3.1.1.2 Summary of DOE’s TSPA for Damage to Engineered Barriers (Drip 

Shield and Waste Package) 

The drip shield and the waste package are two important components of the engineered barrier 

system (EBS) (TER Sections 2.2.1.3.1 and 2.2.1.3.2 evaluate behavior of the drip shield and 

waste package). The drip shield degrades gradually over time (from general corrosion) or at 

specific times from large seismic events or igneous events. From the distributions considered 

in the TSPA model to represent corrosion rates of Titanium Grade 7, time to failure by 

general corrosion of the drip shield was computed to range from 260,000 to 340,000 years 

(SAR Figures 2.1-8 and 2.4-24). Seismic ground motion can collapse the drip shield by 

mechanical failure due to static and dynamic loads caused by rockfall and ground motion. 

As illustrated in SAR Figure 2.1-11, drip shield collapse due to seismic ground motion occurs 

primarily between 25,000 and 350,000 years with the vast majority of the failures occurring 

between 200,000 and 300,000 years (see SAR Figure 2.4.24). Drip shields are assumed to fail 

whenever an intrusive igneous event occurs, when a fault displacement event breaches the 

waste package, or when significant general corrosion occurs. Once the drip shield is failed, 

seepage water that enters the drifts can contact the surface of the waste package. 
17-11

Table 17-2. DOE’s Mean Values for the Seepage Rate Into Drifts* 

Time Period 

Nominal/Early 

Failure 

Seismic 

Ground Motion 

Igneous 

Intrusive 

Seepage from 2,000 to 10,000 years 2.0 (mm/yr) 2.3 (mm/yr) 21.7 

(mm/yr) 

Seepage fraction from 2,000 to 10,000 

years

31% 31% 100% 

Seepage after 10,000 years 3.4 (mm/yr) 15.5 (mm/yr) 31.7 

(mm/yr) 

Seepage fraction after 10,000 years 40% 69% 100% 

*See TER Section 2.2.1.3.6 for further information. 

The damage mechanisms leading to waste package failure are “crack” and “patch” (holes) 

failure. Within DOE’s TSPA model, crack and patch failures of the waste package are treated 

separately because of differences in how water may enter a breached waste package. DOE 

assumed that seepage water cannot freely flow through cracks on the waste package because 

of the small size of the cracks. Because of processes such as general corrosion, or ruptures 

and punctures of the waste package, patch failures represent significantly larger openings and 

seepage water is assumed to enter the waste package through these holes or openings. The 

waste packages with large or patch openings could allow release of radionuclides carried by 

flowing water (i.e., advective release) and diffusion, while those with crack openings could only 

allow release by diffusion. 

The five modeling cases associated with groundwater releases have very distinct characteristics 

for the timing and extent of waste package failure. Assumed to occur at the time of closure, the 

early failure scenario class consists of two modeling cases: drip shield early failure and waste 

package early failure. The early failure scenario has, in probabilistic terms, on average, less 

than one waste package and one drip shield failing (see TER Section 2.2.1.3.1.3.2.4 for early 

waste package failure and TER Section 2.2.1.3.1.3.1.2 for drip shield failure). The igneous 

intrusion modeling case assumes all waste packages fail at the time of the event and lead to 

release to the water pathway (see TER Section 2.2.1.3.10 for further details). The seismic 

scenario class consists of a ground motion modeling case, where the waste packages are 

damaged by seismic ground motion, and a fault displacement modeling case, where 

displacement along a fault may damage the waste packages that lie along the fault. The 

seismic fault displacement modeling case has, on average, tens of waste packages failing (see 

TER Section 2.2.1.3.2 for further details). The seismic ground motion modeling case contains a 

range of waste package failures—typically initial damage is primarily due to cracks in the waste 

package from ground motion, and, later in time (e.g., after 100,000 years), general corrosion, 

ruptures and punctures, and further cracking damages the waste packages. 

Table 17-3 provides the cumulative number of waste package failures accounted for in the 

seismic ground motion modeling case for selected times (i.e., 10,000; 100,000; 400,000; 

and 800,000 years) to provide some perspective on the time-dependent nature of waste 

package failure. The values in Table 17-3 for the seismic ground motion modeling case were 
17-12

Table 17-3. Cumulative Number of CSNF* and CDSP† Waste Packages Breached for 

the Seismic Ground Motion and Igneous Intrusion Modeling Cases in DOE’s TSPA 

Process 

10,000 

Years 

100,000 

Years 

400,000 

Years 

800,000 

Years 

Seismic 

Ground 

Motion 

All failure types 

(cracks and 

patches) 

CSNF 1.6 

CDSP 34.2

CSNF 20.5 

CDSP 

1,024.8 

CSNF 739.2 

CDSP 1,366.4 

CSNF 

3,531.6 

CDSP 

2,049.6 

Ruptures and 

punctures 

(patches) 

CSNF 0 

CDSP 0.3 

CSNF 0 

CDSP 0.7 

CSNF 24.6 

CDSP 13.7 

CSNF 82.1 

CDSP 34.2 

General 

corrosion 

(patches) 

CSNF 0 

CDSP 0 

CSNF 0 

CDSP 0 

CSNF 0.3 

CDSP 4.8 

CSNF 328.5 

CDSP 239.1

Igneous 

IntrusionI

All failures are 

patch failures 

CSNF 1.4 

CDSP 0.6 

CSNF 14 

CDSP 5.8 

CSNF 55.9 

CDSP 23.2 

CSNF 111.7 

CDSP 46.5 

*Repository contains 8,213 commercial spent nuclear fuel (CSNF) waste packages. 

†Repository contains 3,416 codisposal (CDSP) waste packages. 

IIgneous intrusion values calculated assuming all waste packages failed weighted by the probability of 

occurrence of an igneous event on or before the given time (i.e., annual probability for the event of 1.7 × 10−8 per 

year multiplied by the time period). 

taken from DOE Enclosure 1, Figures 9, 10, 13, and 14 (DOE 2009bj) and SAR Figures 2.1-12 

a and c. These values are weighted by the probability for the seismic events to occur. In the 

first 10,000 years, a small number of waste packages fail in the seismic ground motion 

modeling case due to rare but potentially damaging earthquakes. The majority of the 

failed waste packages are codisposal packages (CDSP). These packages are not equivalent 

to the more robustly designed transportation, aging, and disposal canisters that contain 

the commercial spent nuclear fuel (CSNF) waste packages. Most of the initial damage is 

attributed to cracks that are small enough to prevent seepage water from entering the waste 

package. At 100,000 years, the number of failed codisposal waste packages due to cracks is 

1,025 (30 percent of the 3,416 CDSP waste packages in the repository) and the number of 

failed commercial spent nuclear fuel waste packages is 20 (less than 1 percent of the 8,213 

CSNF waste packages in the repository). At 800,000 years, approximately 40 percent of the 

commercial spent nuclear fuel and 50 percent of the codisposal packages waste packages 

fail due to cracks. Some of these cracks are from seismically induced stress corrosion 

cracks on the waste package surface. Others are due to stress corrosion cracks in the closure 

welds—considered a general corrosion process. Waste packages start to fail by general 

corrosion patches at around 500,000 years, and 239 (approximately 7 percent) of the 

codisposal waste packages and 328 (approximately 4 percent) of the commercial spent nuclear 

fuel waste packages have at least one failed patch due to general corrosion by 800,000 years, 

as described in DOE Enclosure 1, Response Number 1, Figures 9 and 10 (2009bj). Waste 

package failure due to ruptures and punctures is limited: approximately 1 percent of the 

commercial spent nuclear fuel and codisposal waste packages failed after 800,000 years, as 

shown in DOE Enclosure 1, Response Number 1, Figures 13 and 14 (2009bj). 
17-13

The majority of the waste package failures are associated with the seismic ground motion 

modeling case, which includes the nominal processes such as general corrosion, and the 

igneous intrusion modeling case. In DOE’s TSPA model, the seismic ground motion and 

igneous intrusion modeling cases contribute most to the overall average annual dose curve 

and are generally more than a factor of 10 greater than the other modeling cases 

(SAR Figure 2.4-18). The NRC staff review in TER Sections 2.2.1.3.1 and 2.2.1.3.2 notes that 

DOE’s representation for the timing and extent of waste package failures is reasonable. 

Therefore, the NRC staff detailed review of DOE’s groundwater releases focuses on these 

two modeling cases, which are the dominant contributors to the overall average annual dose 

curve (SAR Figure 2.4-18). 

2.2.1.4.1.3.3.1.1.3 Summary of DOE’s TSPA for Seepage of Water Into Waste Packages 

In the TPSA model, two conditions are required for seepage water to enter a waste package: 

(i) drip shield failure must occur to allow water to contact the waste package outer barrier and 

(ii) the waste package outer barrier must be breached by patches (it is assumed that seepage or 

dripping water cannot flow into waste package cracks due to the small opening of cracks; 

therefore, seepage water enters the waste package only through patch failures). When these 

required conditions are met, water flow through the waste package is modeled as quasi-steady 

state, where water flux into the waste package is equal to water flux out. 

The waste package surface is divided into a large number of patches with each patch having 

distinct properties that can affect the corrosion rate of the patch. The extent of waste package 

degradation (i.e., number of patch failures on the waste package) determines the quantity of 

water entering the waste package. The waste package outer barrier is considered unable to 

divert water when a mean value of approximately 62 patches fail (62 patches comprises 

approximately 4 percent of the total surface area of the waste package), at which point 

water flow through the waste package equals the incoming seepage rate. When fewer than 

62 patches fail, water flux through the waste package is linearly related to the number of 

patches that fail. Generally, a single patch failure allows 1/62 of the seepage flux to pass 

through the waste package. Because of uncertainty incorporated into the submodel, this value 

can range from 0 to 2.4 times the 1/62 value. 

The waste package patch failures in the seismic ground motion modeling case are of 

limited extent (i.e., the waste package surface area has a limited number of failed patches). 

Approximately 1 percent of the waste package surface area is breached by general 

corrosion after 1 million years, as described in DOE Enclosure 1, Response Number 1, 

Figures 11 and 12 (2009bj). Patch failure by ruptures and punctures compromises 

approximately 0.2 percent of the waste package surface by 1 million years, as outlined in 

DOE Enclosure 1, Response Number 1, Figures 15 and 16 (2009bj). Thus, the compromised 

area remains limited for the 1-million-year period after patch failure occurs due to general 

corrosion, ruptures, or punctures. Additionally, the number of waste package patch failures is 

limited over the 1-million-year time period: 10 percent of waste package failures are from 

general corrosion and approximately 3 percent of waste package failures from ruptures and 

punctures after 1 million years. These failures occur primarily at long times [e.g., after 400,000 

years, as shown in DOE Enclosure 1, Response Number 1, Figures 9, 10, 13, and 14 (2009bj)]. 

For the igneous intrusion modeling case, the drip shield and waste package are assumed to be 

ineffective barriers against seepage in the TSPA model (i.e., no credit to decrease seepage is 

given to the drip shield or waste package after the time at which the event occurs). Thus, DOE 
17-14

assumes all drip shields and waste packages are failed after the igneous intrusion event occurs 

[SAR Volume 2, p. 2.4-42; DOE Enclosure 8, p. 13 (2009ct)]. 

2.2.1.4.1.3.3.1.1.4 Summary of DOE’s TSPA for Release of Radionuclides From the 

Waste Package 

Waste form degradation and subsequent radionuclide release cannot occur prior to waste 

package breach and/or failure. Assuming waste package failure, radionuclide release may be 

advective if seepage water enters the waste package; otherwise, the release will be a 

diffusive release. 

Because seepage water does not flow through crack failures, the radionuclide release from 

cracks in the waste package is controlled by radionuclide diffusion in an assumed continuum of 

aqueous pathways through the cracks. DOE described patch failure as a more extensive 

damage mechanism of the waste package surface from general corrosion, or waste package 

ruptures and punctures driven by seismic events and mechanical interactions with drip shields 

or other waste packages. Radionuclide release through damaged patches is assumed to be 

diffusive if seepage does not contact the waste package, which could occur in the DOE model if 

the drip shield is not breached, or if the waste package is under nondrip conditions. If, on the 

other hand, the waste package is under drip conditions, the drip shield is failed, and the waste 

package is breached by patches (by corrosion or processes driven by seismic events) then 

radionuclides are released from the waste package by flowing water (i.e., advective release) 

and diffusion. Advective release is effective also in the igneous intrusion case, in which all 

waste packages fail completely and seepage is assumed to contact all waste packages. 

In general, diffusive and advective release of radionuclides from a waste package will be 

affected by the size of the openings, degradation rate of the waste, solubility limits, sorption onto 

corrosion products, and the presence of colloids. The significance of these features and 

processes can vary for specific radionuclides, as described next. 

Size of the Openings 

The overall surface area of the crack and patch openings directly affects radionuclide diffusion 

out of the waste package (more surface area results in more release). Additionally, the overall 

surface area of the patch openings can affect the amount of water entering the waste package 

and thus the amount of dissolved radionuclides released from the waste package in the 

advective or flowing water. (Note: Once an average of 4 percent of the waste package surface 

is failed, due to patches, DOE assumes that the waste package no longer limits the amount of 

seepage water that enters the waste package. Thus, all seepage water is assumed to enter the 

waste package when 4 percent or more of the waste package surface area is failed.) 

Degradation Rate of Waste 

Radionuclides cannot leave the waste package faster than the waste degrades. Generally, the 

degradation rates used in TSPA for CSNF result in somewhat short times (e.g., hundreds to 

thousands of years) for the waste form to significantly degrade, as outlined in DOE Enclosure 5, 

Table 1.1-1 (2009an); therefore, the degradation rate only affects those radionuclides that are 

not limited by other release constraints inside the waste package (e.g., Tc-99 and I-129 are not 

solubility limited and are not sorbed or attached onto corrosion products). For CDSP waste 

packages, the glass waste form can degrade much slower than CSNF (e.g., thousands to 

millions of years for the glass waste form to significantly degrade versus hundreds to thousands 
17-15

of years for CSNF); however, the defense spent nuclear fuel waste form is assumed to instantly 

degrade, as described in DOE Enclosure 5, p. 6 (2009an). 

Solubility Limit 

Some radionuclides have a solubility limit—a function of the properties of the radionuclide and 

the water chemistry inside the waste package—that controls the amount that can be dissolved 

in water. Radionuclides such as plutonium (e.g., Pu-242) and neptunium (e.g., Np-237) have 

the potential for low release rates due to solubility limits (see TER Section 2.2.1.3.4 ). 

Corrosion Products 

The TSPA includes a process by which certain radionuclides attach onto corrosion products 

within the waste package, and thus release from the waste package is delayed. This is 

especially effective for a radionuclide such as Np-237 that is somewhat soluble and attaches 

onto corrosion products, as described in DOE Enclosure 5, p. 22 (2009an). 

Colloids 

Colloids can facilitate release of radionuclides out of the waste package; radionuclides sorbed 

or attached onto irreversible colloids are not affected by solubility limits and stationary 

corrosion products. Colloids can also sequester radionuclides by becoming unstable (see TER 

Section 2.2.1.3.4). DOE stated that the contribution to annual dose from irreversible colloids is 

small [i.e., contribution from irreversible colloids never exceeds 30 percent, as shown in DOE 

Enclosure 5, pp. 24–25 (2009an)]. The contribution to annual dose from a radionuclide such as 

Pu-242 will be mainly from aqueous releases, which are composed of both dissolved 

radionuclides and reversible colloids (e.g., SAR p. 2.4-93 and SAR Figure 2.4-73). 

Release rates of radionuclides from an individual waste package are dependent on the type of 

radionuclide. High mobility characterizes the soluble, nonsorbing radionuclides (e.g., Tc-99, 

I-129, Cl-36, Se-79), which may result in nearly complete release of the inventory of the 

high-mobility radionuclides from the engineered barrier system (EBS) over the 1-million-year 

period (SAR Figure 2.1-24). Much lower mobility characterizes the relatively insoluble, sorbing 

nuclides (e.g., Np-237, Pu-242). For the concentration-limited radionuclides (e.g., Pu-242 and 

Np-237), DOE explained that releases will be significantly lower than the release rates for 

soluble radionuclides (e.g., Tc-99) and will increase as water flow into the waste packages 

increases. For example, as corrosion patch area increases in size over time, more water may 

enter the waste package (SAR p. 2.4-63). At the end of the 1-million-year period, approximately 

0.1 percent of the Np-237 inventory has been released from the engineered barrier system 

(EBS) with the majority of the release occurring over the later portion of the 1-million-year period 

(SAR Figure 2.1-25). 

2.2.1.4.1.3.3.1.1.5 Summary of DOE’s TSPA for Transport of Radionuclides in the 

Unsaturated and Saturated Zones 

Transport of radionuclides through the unsaturated zone is affected by the following processes: 

(i) relatively fast fracture flow versus slow flow in the porous rock matrix, (ii) radionuclides that 

sorb onto mineral surfaces, and (iii) colloid-facilitated transport of radionuclides. 

Transport of radionuclides can depend significantly on whether water flow occurs principally 

in fractures or in porous media. Flow in fractures is conceptualized as being relatively 

fast because the effective porosity is relatively small [average estimated value of 0.001 
17-16

(SAR Table 2.3.9-4)]. Conversely, flow in porous media is conceptualized as relatively 

slow because the effective flow porosity is relatively high [average estimated value of 0.18 

(SAR Table 2.3.9-4)]. Additionally, given the limited surface area for fracture surfaces 

as compared to rock pores, radionuclides can be significantly delayed by sorption to 

mineral surfaces. 

Colloids are tiny particles that remain suspended in water and are thus able to move with the 

water and facilitate the transport of certain radionuclides. Transport times of strongly sorbing 

radionuclides, such as plutonium and americium, can decrease (e.g., increase transport 

velocity) by permanently attaching onto colloids. This colloid attachment can also occur 

reversibly when radionuclides temporarily attach to or detach from colloids as they move 

through the system. Generally, most of the release of Pu-242 from the unsaturated and 

saturated zone is via dissolved plutonium and plutonium reversibly associated with colloids 

(referred to as “aqueous” release in SAR Figure 2.4-108). Limited release of Pu-242 is 

associated with irreversible colloids, whereby Pu-242 permanently attaches onto the colloid. 

Transport of Radionuclides in the Unsaturated Zone 

Transport of radionuclides in the unsaturated zone depends to some extent on the location from 

which they are released. In the northern area of the repository, water is expected to move 

principally within fractures. Average travel times for nonsorbing solutes from the repository to 

the saturated zone from the northern area of the repository are on the order of 5 to 100 years for 

an infiltration rate of 12 mm/yr [0.47 in/yr] (SAR Figure 2.3.8-36). Conversely, in the southern 

repository area, the Calico Hills nonwelded tuff unit has higher matrix permeability that can 

accommodate flow almost entirely within the rock matrix (porous flow). Average travel times 

from the southern repository area to the saturated zone are on the order of 500 to 5,000 years 

for an average infiltration rate of 12 mm/yr [0.47 in/yr] (SAR Figure 2.3.8-36). 

For sorbing radionuclides, travel times depend on the radionuclide-specific sorption coefficient. 

More strongly sorbing aqueous species, such as Pu-242, have transport times on the order of 

hundreds of thousands of years and longer in the southern area. Radionuclide species with 

half-lives that are short relative to their unsaturated zone transport times can be significantly or 

completely decayed before reaching the water table (e.g., Cs-137, Sr-90, Ra-226, Th-229, 

Th-230, Th-232, Pu-238, Am-241, Am-243). 

Table 17-4 shows how the combined processes affect flow of different radionuclides through the 

unsaturated zone by providing representative transport times for nonsorbing Tc-99, moderately 

sorbing Np-237, strongly sorbing Pu-242, and Pu-242 attached to colloids. 

Transport of Radionuclides in the Saturated Zone 

Radionuclides released from a Yucca Mountain repository would eventually enter the 

saturated zone within the fractured volcanic tuffs of the Crater Flat group. Transport away from 

the repository area would occur through permeable flowing fracture networks in the volcanic 

aquifer system for more than 10 km [6.2 mi] and transition to a valley fill alluvial flow system for 

the last few kilometers before reaching the accessible environment approximately 18 km 

[11.2 mi] from the southern boundary of the repository footprint. The exact location of the 

volcanic rock–alluvium contact is uncertain and is treated stochastically in the saturated zone 

transport abstraction model using an alluvium uncertainty zone. The fracture flow path for the 
17-17

Table 17-4. Radionuclide Transport Times in the Unsaturated Zone for the Northern 

and Southern Repository Areas From DOE Breakthrough Curves 

Transport Time* for 

Release in Northern 

Repository Area 

Transport Time* for 

Release in Southern 

Repository Area 

Tc-99 10 years 1,000 years 

Np-237 10 years 10,000 years 

Pu-242 30 years >1 million years 

Pu-242 irreversible colloids 100 years 1,000 years 

*Transport times reflect approximate arrival for 50 percent of peak concentration for a model case with point 

releases at representative locations in northern and southern model areas, representative parameter values, and 

Glacial Transition 10th percentile infiltration map. See SAR Figures 2.3.8-43 for Tc-99, 2.3.8-44(b) for Np-237, 

2.3.8-47(a) for Pu-242, and 2.3.8-48 for Pu-242 irreversible colloids for complete breakthrough curves for 

all radionuclides. 

volcanic tuff is conceptualized as being relatively fast because the effective porosity is 

relatively small [average estimated value of 0.001 (SAR Table 2.3.9-4)]. Flow in the alluvial 

portion of the flow system is conceptualized as relatively slow because the effective flow 

porosity is relatively high [average estimated value of 0.18 (SAR Table 2.3.9-4)]. Overall, the 

transport time for nonsorbing radionuclides ranges from about 10 years to several thousand 

years (SAR p. 2.3.9-9). Sorbing radionuclides can be significantly delayed by sorption to 

alluvium mineral grains in which case transport times for strongly sorbing radionuclides 

generally exceed 10,000 years (SAR p. 2.3.9-9). Table 17-5 provides transport times for select 

radionuclides representing a range of sorption behavior taken from BSC Table 6-10a (2005ak). 

Table 17-5. Summary of DOE Simulated Transport Times in the 

Saturated Zone Under Glacial-Transition Climate State 

Species 

Range of Median 

Transport Times 

(years) 

Median Transport 

Time Among All 

Realizations 

C-14, Tc-99, I-129 (aqueous, nonsorbing) 10 to 22,190 230 

Reversible colloids: americium, thorium 1,000 to >1 million >1 million 

Reversible colloids: plutonium 3,000 to >1 million 95,000 

Neptunium 100 to 455,300 3,700 

Irreversible colloids: plutonium, americium 100 to 501,900 4,500 
17-18

2.2.1.4.1.3.3.1.1.6 Summary of DOE’s TSPA for Annual Dose to the Reasonably Maximally 

Exposed Individual (RMEI) 

Following postclosure engineered barrier system (EBS) release and groundwater radionuclide 

transport, the DOE TSPA model executes the biosphere model abstraction to calculate 

biosphere radionuclide transport and the annual dose to the reasonably maximally exposed 

individual (RMEI). The exposure scenarios implemented in the DOE TSPA model 

(i.e., groundwater, volcanic ash) calculate annual dose to an individual adult member of a 

hypothetical farming community located 18 km [11.2 mi] south of the potential repository along 

the path of groundwater flow. Exposure pathways in the DOE biosphere model are based on 

assumptions about residential and agricultural uses of the water and indoor and outdoor 

activities. These pathways include ingestion, inhalation, and direct exposure to radionuclides 

deposited to soil from irrigation (SAR Section 2.3.10.1). Ingestion pathways include drinking 

contaminated water, eating crops irrigated with contaminated water, eating food products 

produced from livestock raised on contaminated feed and water, eating farmed fish raised in 

contaminated water, and inadvertently ingesting soil. Inhalation pathways include breathing 

resuspended soil, aerosols from evaporative coolers, and radon gas and its decay products 

resulting from the high-level radioactive waste. 

DOE biosphere model results are quantified by the Biosphere Dose Conversion Factors 

(BDCFs). A biosphere dose conversion factor is the calculated annual dose to the reasonably 

maximally exposed individual (RMEI) from all potential exposure pathways as a result of a unit 

concentration of a radionuclide in groundwater or surface soil mixed with volcanic ash (SAR 

Section 2.3.10.1). Mean groundwater exposure scenario biosphere dose conversion factors 

and primary exposure pathways (from SAR Tables 2.3.10-11 and 2.3.10-12) for radionuclides 

that are important contributors to DOE’s TSPA annual dose results (SAR Figure 2.4-26 a and b) 

are provided in Table 17-6. (Note: The volcanic ash exposure scenario for the igneous eruptive 

modeling case is discussed in the next section.) 

The average annual doses are largest for the seismic ground motion and igneous intrusive 

modeling cases (generally a factor of 10 or more larger than the other modeling cases; see 

SAR Figure 2.4-18). Tc-99 (a nonsorbing radionuclide) is the largest contributor to the average 

Table 17-6. DOE Groundwater BDCFs* 

Radionuclide 

Mean BDCF 

Sv/yr per Bq/m3

[mrem/yr per pCi/L] Primary Pathways 

Tc-99 1.1 × 10!9

[4.1 × 10!3

] 

42% drinking water 

37% animal product 

17% crop 

Np-237 2.7 × 10!7

[1.0] 

56% inhalation 

29% drinking water 

Pu-242 9.1 × 10!7

[3.4] 

75% inhalation 

19% drinking water 

*Biosphere dose conversion factors 
17-19

annual dose in the initial 10,000 years. Tc-99 accounts for approximately 0.001 mSv/yr 

[0.1 mrem/yr] of the peak of the overall average annual dose of approximately 0.003 mSv/yr 

[0.3 mrem/yr] (SAR Figure 2.4-20a). After 10,000 years and up to 1 million years, the peak of 

the overall average annual dose occurs at 1 million years, with Pu-242 and Np-237 being the 

largest contributors to the peak of the overall average annual dose. Pu-242 and Np-237 

account for approximately 0.01 mSv/yr [1.0 mrem/yr] of the peak of the overall average annual 

dose of approximately 0.02 mSv/yr [2.0 mrem/yr] at 1 million years (SAR Figure 2.4-20b). 

2.2.1.4.1.3.3.1.2 NRC Staff Evaluation of DOE’s TSPA Calculation Related to 

Groundwater Releases 

The NRC staff conducted confirmatory calculations to assist its review of DOE’s TSPA results. 

The confirmatory calculations provide both a quantitative understanding of the attributes of the 

performance assessment and an understanding of whether there is a general consistency 

between submodels of the performance assessment and the overall results, including 

uncertainty (e.g., whether the timing and extent of breaching of the waste package are 

consistent with the timing and magnitude of the average annual dose). The confirmatory 

calculations were performed for selected time periods (i.e., 10,000; 100,000; 400,000; and 

800,000 years) to provide perspective on the time-dependent nature of waste package failure, 

associated radioactive decay, and release of specific radionuclides. Detailed documentation of 

the NRC staff’s confirmatory calculation is provided in NRC and CNWRA (2011aa). 

The confirmatory calculations are based on the NRC staff’s understanding of the TSPA 

calculation obtained from its SAR review, including the TSPA models and the code’s 

intermediate outputs. Thus the confirmatory calculations address key quantitative attributes of 

the repository system to help evaluate overall performance. This approach provides a 

straightforward method for determining whether the TSPA results provide a credible 

representation of the repository performance (i.e., the average annual dose curve is consistent 

with the model abstractions, probabilities, and treatment of uncertainties, which have been 

reviewed in TER Sections 2.2.1.2–2.2.1.3.14). 

In assessing the credibility of DOE’s TSPA average annual dose curve resulting from the 

groundwater pathway, separate confirmatory calculations were conducted for (i) the amount of 

water entering failed waste packages, (ii) the release of radionuclides from the waste packages, 

(iii) transport of radionuclides through the saturated and unsaturated zones, and (iv) annual 

dose to the reasonably maximally exposed individual (RMEI). 

Amount of Water Entering Failed Waste Packages 

The NRC staff determined a representative quantity of seepage water entering the waste 

package using average values from the SAR for the seepage rates, seepage fraction. 

Specifically, the NRC staff divided the seepage rates by the seepage fraction (Table 17-2) 

to obtain the seepage rate at dripping locations in the drifts and multiplied this rate by the 

cross-sectional area of an emplacement drift {27.5 m2

 [296 ft2]} to determine the seepage 

volume entering the drift. The amount of seepage water entering the drift that then enters the 

waste package is determined by the extent of the damage to the surface of the waste package 

(i.e., the size of the opening in the waste package from patch openings). DOE’s TSPA assumes 

when 4 percent or more of the surface is failed, all seepage enters the waste package; 

otherwise, the amount of seepage entering the waste package linearly decreases below 

4 percent to 0 percent. NRC’s approach closely approximates the value DOE used (see TER 
17-20

Section 2.2.1.3.3.3.3 for the NRC staff evaluation of the quantity of water entering the 

waste package). The average damage to the surface of the waste package was obtained from 

DOE Enclosure 1, Response Number 1, Figures 11, 12, 15, and 16 (2009bj). Table 17-7 

presents the average volume of seepage that might enter a waste package that has patch 

failure (assuming seepage is present and the drip shield has failed) at specific times over the 

1-million-year period (i.e., 10,000; 100,000; 400,000; and 800,000 years). One particularly 

important aspect of the values in Table 17-7 is that the average amount of seepage water 

entering patches, due to ruptures and punctures, decreases between 400,000 years and 

800,000 years. This counterintuitive result is due to (i) ruptures, which can damage a larger 

surface area than punctures, occurring at early times and dominating the early average value 

for damage and (ii) punctures, occurring primarily at later times (when drifts are degraded and 

drip shields and waste packages are weakened by corrosion) and dominating the later average 

values. General corrosion of the waste package after very long times results in the largest 

amount of water contacting waste for the seismic ground motion modeling case because there 

are approximately 5 times more waste packages breached by general corrosion than by 

ruptures and punctures after 800,000 years (see Table 17-3). The igneous intrusion modeling 

case, which assumes the waste package and drip shield no longer prevent water from 

contacting waste, represents the maximum amount, on average, of water that can enter a failed 

waste package and contact waste. 

Release of Radionuclides From Waste Packages 

Release rates of radionuclides from an individual waste package are dependent primarily on 

the type of radionuclide. High mobility characterizes the soluble, nonsorbing radionuclides 

(e.g., Tc-99, I-129, Cl-36, Se-79) that result in relatively rapid waste package depletion after 

failure, whereby repository release is controlled by the amount present in the waste package or 

inventory and the degradation rate of the waste form. The overall release rate from the 

repository is dependent on the releases from the individual waste packages and how the 

releases from all the failed waste packages add together (or overlap at a particular time) to 

produce an overall release rate for the repository. For example, if all the packages failed at the 

same time, then the releases from all the waste packages would be occurring at the same time 

Table 17-7. NRC Staff Confirmatory Calculation Results for the Volume 

of Seepage Water Entering Patch Failures in a Single Waste Package for 

Seismic Ground Motion (Ruptures, Punctures, and General Corrosion) 

and Igneous Intrusion Modeling Cases for CSNF* and CDSP† Waste Packages 

Process 

Volume (in liters/yr) 

10,000 Years 100,000 Years 400,000 Years 800,000 Years 

Ruptures and 

punctures 

CSNF 0 

CDSP 208 

CSNF 0 

CDSP 630 

CSNF 63 

CDSP 94 

CSNF 47 

CDSP 47 

General corrosion CSNF 0 

CDSP 0 

CSNF 0 

CDSP 0 

CSNF 11 

CDSP 13 

CSNF 47 

CDSP 63 

Igneous intrusion CSNF 609 

CDSP 609 

CSNF 889 

CDSP 889 

CSNF 889 

CDSP 889 

CSNF 889 

CDSP 889 

*Commercial spent nuclear fuel 

†Codisposal 

Note: Values of zero indicate no failed packages for the indicated failure type and time. 
17-21

and would combine to produce the repository release rate. If, however, waste packages fail at 

different times, the potential for the releases to overlap in time will depend on the length of time 

between the failed packages and the time it takes a waste package to release the inventory of a 

particular radionuclide. When releases from a waste package are somewhat rapid, occurring 

over hundreds to thousands of years as is the case for the high-mobility radionuclides, the 

potential for releases from all the waste packages to overlap in time is reduced unless all the 

waste packages fail within the same time period over which the rapid release occurs. High 

release rates will persist for short periods of time (e.g., hundreds to thousands of years); thus, 

the overlap period for high waste package release rates will be short (a smaller number of waste 

package releases could potentially overlap in time). In contrast, low release rates may persist 

for hundreds of thousands of years and longer, and the overlap time period would be much 

longer and include the potential for a larger number of failed packages to contribute to the 

overall repository release rate. 

As part of the NRC staff confirmatory calculation (NRC and CNWRA, 2011aa), a simplified 

approach was used to estimate releases from the waste package for three radionuclides 

important to the annual dose: Tc-99 (a soluble, nonsorbing radionuclide) and Np-237 and 

Pu-242 (relatively insoluble, sorbing radionuclides). The release rate from the repository 

engineered barriers is determined by multiplying the release rate from a single waste package 

times the number of waste package failures. This simple approach has the potential to 

calculate releases from the repository that would exceed, over the time period of the waste 

package failures, the amount of material that was present in the failed waste packages when 

releases rates are high. A bounding value for the release from a single waste package was 

calculated to ensure the releases from the repository would not exceed the amount of material 

present in the failed waste packages [bounding value is calculated as the inventory of a single 

waste package times the waste package failure rate; see NRC and CNWRA (2011aa) for further 

details]. The release rate from the engineered barriers for the repository were determined for 

selected time periods (i.e., 10,000; 100,000; 400,000; and 800,000 years) to provide some 

perspective on the time-dependent nature of waste package failure, the effect of radioactive 

decay, and release rate for a specific radionuclide. 

For soluble, nonsorbing radionuclides (e.g., Tc-99), the release rate from the waste packages is 

assumed to be relatively rapid including release by diffusion through small cracks. Because 

diffusional release of soluble, nonsorbing radionuclides can be significant, all waste package 

breach types (e.g., rupture and puncture, general corrosion patches, stress corrosion cracks) 

are significant to estimating releases from the repository for the soluble nonsorbing 

radionuclides. Consistent with a rapid release time for soluble, nonsorbing radionuclides, DOE 

has stated that the release of Tc-99 tracks the waste package failure (SAR p. 2.4-63). The NRC 

staff used the bounding value for the release rate of Tc-99 in its confirmatory calculation 

because a relatively rapid release rate for a soluble, nonsorbing radionuclide can result in most, 

if not all, of the inventory being released over time periods that are short (e.g., thousands of 

years) relative to the time periods of the confirmatory calculation. The results of this simple, 

bounding calculation for Tc-99 showing the peak values at various times for the seismic ground 

motion modeling case and the igneous intrusion modeling case appear in Table 17-8. 

(Note: The waste package failure rate for the intrusion modeling case also includes a 

multiplicative factor to account for the annual probability for the intrusive event to occur.) 

Given that the majority of waste package failures in the seismic ground motion modeling case 

are due to crack failures (see Table 17-3) where the only release will be from the diffusion 

process, the diffusional releases are significant for the release of soluble, nonsorbing 

radionuclides (e.g., Tc-99) in the ground motion modeling case. 
17-22

Table 17-8. NRC Staff Confirmatory Calculation Results for the 

Average Release Rates for Tc-99 (Seismic Ground Motion Modeling Case) 

for CSNF* and CDSP† Waste Packages 

 10,000 Years 100,000 Years 400,000 Years 800,000 Years 

Waste package 

failure rate (waste 

packages/yr)‡ 

CSNF 1.6 × 10−4

CDSP 3.4 × 10−3

CSNF 2.1 × 10−4

CDSP 0.011 

CSNF 2.4 × 10−3

CDSP 1.1 × 10−3

CSNF 7.0 × 10−3

CDSP 1.7 × 10−3

Inventory§ (grams) CSNF 7,405 

CDSP 1,131 

CSNF 5,526 

CDSP 844 

CSNF 2,082 

CDSP 318 

CSNF 567 

CDSP 86 

Average release rate 

(grams/yr) 

CSNF 1.2 

CDSP 3.8 

CSNF 1.2 

CDSP 9.3 

CSNF 5.0 

CDSP 0.35 

CSNF 4.0 

CDSP 0.15 

*Commercial spent nuclear fuel 

†Codisposal 

‡Determined using all waste package failures types (i.e., cracks and patches) from Table 17-3 for the time periods 

(i.e., 0 to 10,000 years; 10,000 to 100,000 years; 100,000 to 400,000 years; and 400,000 to 800,000 years). 

§Inventory is for one waste package at the specific time (Tc-99 half-life is 213,000 years); initial inventory from SAR 

Volume 2, Table 2.3.7-5. 

Much lower mobility characterizes the relatively insoluble, sorbing nuclides (e.g., Np-237, 

Pu-242). Advective releases of these radionuclides are limited by maximum limits on their 

concentrations in water (from either precipitation or sorption to stationary corrosion products) 

and water flow through the waste package. Because releases of the lower mobility nuclides can 

take tens of thousands of years and longer, the potential for releases from individual waste 

packages to overlap in time is greater. For the concentration-limited radionuclides (e.g., Pu-242 

and Np-237), DOE has explained that releases will be significantly lower than the release rates 

for soluble radionuclides (e.g., Tc-99) and will increase as water flow into the waste packages 

increases (e.g., as corrosion patch area increases in size over time more water may enter the 

waste package; see SAR p. 2.4-63). 

For the relatively insoluble, sorbing radionuclides (e.g., Np-237, Pu-242), the approximation for 

the repositorywide engineered barrier system (EBS) release rate accounts for key aspects of 

repository performance that affect the release rate from the waste package (i.e., solubility limits, 

volume of water flux moving through a breached waste package, corrosion products, and 

radioactive decay and production). The release rate from the repository engineered barriers is 

determined by multiplying the release rate from a single waste package times the number of 

waste package failures, subject to the previously described constraint that the releases from the 

repository cannot exceed the amount of material present in the failed waste packages. 

Generally, the constraint on the release rate is necessary when the inventory is small and/or 

when the solubility limits and water flow rates are high. As the length of time for the inventory of 

a specific radionuclide to be completely released from a waste package increases, the potential 

for releases from waste packages, which failed at different times, to overlap or combine and 

result in larger releases from the repository increases. For example, if the time to release the 

entire inventory of a given radionuclide was 100,000 years, then the releases from all waste 

packages that failed within 100,000 years would all overlap by the end of the 100,000-year 

period. The NRC staff confirmatory calculation accounted for the overlap of waste package 

releases, which can vary with time due to radioactive decay and increasing water flow through 

the waste package, attributed to an increased damaged area of waste package patch breaches, 
17-23

which generally occurs at later times [e.g., increased general corrosion patch area at longer 

times, as shown in DOE Enclosure 1, Response Number 1, Figures 11 and 12 (2009bj)]. 

Generally, for the insoluble radionuclides, the larger repository release rates occur at later times 

(e.g., hundreds of thousands of years) when a larger number of waste packages are breached 

by patch failures, which allow seepage water to enter the waste package and release 

radionuclides by the advective flow of water out of the waste package. The advective releases, 

when present, tend to be significantly larger than the diffusive releases [see NRC staff analysis 

in NRC and CNWRA (2011aa)]. Therefore, the NRC staff’s confirmatory calculation considers 

only advective releases out of the waste package for the insoluble radionuclides, which occurs 

only for failed packages in the dripping environment after the drip shield fails. The NRC staff’s 

calculation assumes that the drip shield is always failed when the waste package is breached by 

a patch failure (i.e., an opening sufficiently large that dripping water can enter the waste 

package and the waste form degradation rate does not limit the release rate). Advective 

releases are calculated by estimating (i) the failure rate for waste packages breached by 

patches, such as by general corrosion and ruptures and punctures from the seismic ground 

motion modeling case; (ii) the amount of seepage that enters and flows through the waste 

package, which is dependent on the size of the waste package holes or patches, the seepage 

fraction and seepage rates, and for the NRC staff’s calculation, an assumption that the drip 

shield is not functioning; and (iii) the effects of solubility limits and corrosion products on the 

concentration of radionuclides in the water flowing through the waste package. The results of 

the NRC staff’s simple calculations for Np-237 and Pu-242 for the ground motion modeling case 

are shown Table 17-9. To account for uncertainty in the release of these radionuclides, 

solubility limits were based on the upper and lower values representative of the solubility limit 

Table 17-9. NRC Staff Confirmatory Calculation Results for the 

Average Release Rates for Np-237 and Pu-242 in the Seismic Ground Motion 

Modeling Case for CSNF* and CDSP† Waste Packages 

 10,000 

Years 

100,000 

Years 

400,000 

Years 

800,000 

Years 

General corrosion 

failure rate‡ 

(waste packages/yr) 

CSNF 0 

CDSP 0 

CSNF 0 

CDSP 0 

CSNF 1.0 × 10!6

CDSP 1.6 × 10!5

CSNF 8.2 × 10!4

CDSP 5.9 × 10!4

Water flux through 

general corrosion§ 

(liters/yr/waste 

package) 

CSNF 0 

CDSP 0 

CSNF 0 

CDSP 0 

CSNF 11 

CDSP 13 

CSNF 47 

CDSP 63 

Rupture and puncture 

failure rateI 

(waste packages/yr) 

CSNF 0 

CDSP 3.0 × 10!5

CSNF 0 

CDSP 4.4 × 10!6

CSNF 8.2 × 10!5

CDSP 4.3 × 10!5

CSNF 1.4 × 10!4 

CDSP 5.0 × 10!5

Water flux through 

ruptures and 

punctures 

(liters/yr/waste 

package) 

CSNF 0 

CDSP 208 

CSNF 0 

CDSP 630 

CSNF 63 

CDSP 94 

CSNF 47 

CDSP 47 

Np-237 inventory2

(grams/waste 

package) 

CSNF 15,530 

CDSP 504 

CSNF 15,084 

CDSP 490 

CSNF 13,687 

CDSP 444 

CSNF 12,024 

CDSP 390 
17-24

Table 17-9. NRC Staff Confirmatory Calculation Results for the 

Average Release Rates for Np-237 and Pu-242 in the Seismic Ground Motion 

Modeling Case for CSNF* and CDSP† Waste Packages (continued) 

Np-237 release rate&

(grams/yr)

10,000 

Years 

100,000 

Years 

400,000 

Years 

800,000 

Years 

CSNF 0 

CDSP 

2.9 × 10−4

 to 

4.7 × 10−3

CSNF 0 

CDSP 

1.5 × 10!3

CSNF 

0.016 to 0.32 

CDSP 

0.014 to 0.018 

CSNF 

0.20 to 4.0 

CDSP 

0.17 

Pu-242 inventory2

(grams/waste 

package) 

CSNF 5,360 

CDSP 39 

CSNF 4,542 

CDSP 33 

CSNF 2,614 

CDSP 19 

CSNF 1,251 

CDSP 9 

Pu-242 release rate&

(grams/yr) 

CSNF 0 

CDSP 

8.1 × 10−5

 to 

3.6 × 10−4

CSNF 0 

CDSP 

 1.0 × 10−4

 

CSNF 

4.5 × 10−3

 to 0.15 

CDSP 

7.4 × 10−4

 to 

7.6 × 10−4

CSNF 

0.056 to 0.83 

CDSP 

4.0 × 10−3

*Commercial spent nuclear fuel 

†Codisposal 

‡Determined from Table 17-3 patch failures (i.e., ruptures, punctures, and general corrosion) for the time periods 

(i.e., 0 to 10,000 years; 10,000 to 100,000 years; 100,000 to 400,000 years; and 400,000 to 800,000 years). 

§Water flux taken from Table 17-7. 

2Inventory is for one waste package at the specific time (Np-237 half-life is 2.14 million years, Pu-242 half-life is 

376,300 years); initial inventory from SAR Volume 2, Table 2.3.7-5 (Np-237 inventory includes complete decay of 

Am-241 into Np-237). 

&Release rate determined with solubility limits of 0.3 to 6 mg/L for Np-237 and 0.006 to 0.5 mg/L for Pu-242 and 

corrosion product factor of 0.05 for Np-237 and 0.7 for Pu-242.

range DOE used in its TSPA analysis (see TER Section 2.2.1.3.4). The release rates from the 

commercial spent nuclear fuel waste packages overall are larger than the releases from the 

codisposal fuel packages due to the larger inventory for these radionuclides present in 

commercial spent nuclear fuel and the larger number of commercial spent nuclear fuel waste 

packages in the repository. The NRC staff’s calculation does not account for releases of 

radionuclides from the waste packages that are associated with colloids. This is because 

releases of radionuclides that are not associated with colloids (i.e., radionuclides that are 

dissolved in water) are larger than releases of radionuclides associated with colloids (see NRC 

staff’s review in TER Section 2.2.1.3.4.3.4). 

The NRC staff performed confirmatory calculations for the igneous intrusive modeling case 

similar to those performed for the seismic ground motion modeling case. The igneous intrusive 

modeling case is somewhat simpler in that it is assumed that all waste packages are completely 

failed (i.e., no diversion of seepage water) once the event occurs and the seepage fraction is 

100 percent (i.e., all packages experience dripping water). The NRC staff accounted for the 

probability of the event occurring (i.e., mean annual frequency of an igneous event intersecting 

the repository is 1.7 × 10!8

 per year; SAR p. 2.3.11-9) by incorporating the event probability into 

the determination of the number of waste package failures (see Table 17-3). The number of 
17-25

waste package failures in the seismic ground motion modeling case reported in Table 17-3 also 

represents values that incorporate the probability of the seismic events occurring. The releases 

for the soluble radionuclide Tc-99 and for the insoluble radionuclides Np-237 and Pu-242 from 

the igneous intrusive modeling case, as determined through an NRC staff confirmatory 

calculation, are presented in Tables 17-10 and 17-11, respectively. 

Transport of Radionuclides through the Unsaturated and Saturated Zones 

As part of the NRC staff’s confirmatory calculation, the NRC staff developed multiplicative 

factors to account for the effect of transport in DOE’s TSPA evaluation for the unsaturated and 

saturated zones on the releases of radionuclides to the reasonably maximally exposed 

individual (RMEI) location. The effectiveness of the travel times in the geosphere is related to 

the time at which the annual dose occurs. For example, a delay of tens of thousands years is 

not expected to significantly affect annual dose at hundreds of thousands of years but would 

affect annual dose at 10,000 years. On the basis of these considerations, the NRC staff 

developed factors for representing the effects of the unsaturated and saturated zones for 

reducing radionuclide releases (see Table 17-12). The nonsorbing radionuclide Tc-99 is typified 

by short delay time, and thus the releases are unaffected (i.e., no reduction in the release rates) 

by either the unsaturated or saturated zones. As noted in Table 17-4, DOE’s representation for 

the unsaturated zone is split with slower releases for sorbing radionuclides for half of the 

repository, which would be in the southern portion. For the NRC staff’s confirmatory calculation, 

the unsaturated zone reduces the releases for the sorbed radionuclides (Np-237 and Pu-242) 

up to 50 percent due to delay times on the order of thousands of years and longer in the 

southern portion of the unsaturated zone. Sorption of radionuclides causing the delay or 

slowing of radionuclide travel times for thousands of years will be less noticeable at longer times 

(e.g., 100,000 years). After 10,000 years, only the more strongly sorbed radionuclide Pu-242 

continues to be reduced by the effectiveness of the unsaturated zone. (Note: a small 

quantity of Pu-242 is associated with irreversible colloids that would not be reduced by 

Table 17-10. NRC Staff Confirmatory Calculation Results for the 

Average Release Rates for Tc-99 in the Igneous Intrusive Modeling Case for 

CSNF* and CDSP† Waste Packages 

 10,000 Years 100,000 Years 400,000 Years 800,000 Years 

Waste package 

failure rate‡ 

(waste packages/yr) 

CSNF 1.4 × 10-4

CDSP 6.0 × 10-5

CSNF 1.4 × 10-4

CDSP 5.8 × 10-5

CSNF 1.4 × 10-4

CDSP 5.8 × 10-5

CSNF 1.4 × 10-4

CDSP 5.8 × 10-5

Inventory§ (grams) CSNF 7,405 

CDSP 1,131 

CSNF 5,526 

CDSP 844 

CSNF 2,082 

CDSP 318 

CSNF 567 

CDSP 86 

Average release 

rate (grams/yr) 

CSNF 1.0 

CDSP 0.068 

CSNF 0.77 

CDSP 0.049 

CSNF 0.29 

CDSP 0.018 

CSNF 0.079 

CDSP 5.0 × 10-3

*Commercial Spent Nuclear Fuel 

†Codisposal 

‡Determined using values in Table 17-3 for the time periods (i.e., 0 to 10,000 years; 10,000 to 100,000 years; 

100,000 to 400,000 years; and 400,000 to 800,000 years). 

§Inventory is for one waste package at the specific time (Tc-99 half-life is 213,000 years); initial inventory from SAR 

Volume 2, Table 2.3.7-5. 
17-26

Table 17-11. NRC Staff Confirmatory Calculation Results for the 

Average Release Rates for Np-237 and Pu-242 in the Igneous Intrusive 

Ground Motion Case for CSNF* and CDSP† Waste Packages 

 10,000 Years 100,000 Years 400,000 Years 800,000 Years 

Waste package 

failure rate‡ 

(packages/yr) 

CSNF 1.4 × 10-4

CDSP 6.0 × 10-5

CSNF 1.4 × 10-4

CDSP 5.8 × 10-5

CSNF 1.4 × 10-4

CDSP 5.8 × 10-5

CSNF 1.4 × 10-4

CDSP 5.8 × 10-5

Water flux§ 

(liters/yr/waste 

package) 

CSNF 609 

CDSP 609 

CSNF 889 

CDSP 889 

CSNF 889 

CDSP 889 

CSNF 889 

CDSP 889 

Np-237 

inventory2

(grams/waste 

package) 

CSNF 15,530 

CDSP 504 

CSNF 15,084 

CDSP 490 

CSNF 13,687 

CDSP 444 

CSNF 12,024 

CDSP 390 

Np-237 release 

rate& (grams/yr) 

CSNF 0.013 to 

0.26 

CDSP 5.5 × 10!3

to 0.030 

CSNF 0.19 to 

2.1 

CDSP 0.028 

CSNF 0.75 to 1.9 

CDSP 0.026 

CSNF 1.5 to 1.7 

CDSP 0.023 

Pu-242 

inventory2

(grams/waste 

package) 

CSNF 5,360 

CDSP 39 

CSNF 4,542 

CDSP 33 

CSNF 2,614 

CDSP 19 

CSNF 1,251 

CDSP 9 

Pu-242 release 

rate&

(grams/yr) 

CSNF 3.6 × 10!3

to 0.30 

CDSP 1.5 × 10!3

to 2.3 × 10!3

CSNF 0.052 

to 0.64 

CDSP 1.9 × 10!3

CSNF 0.21 to 

0.37 

CDSP 1.1 × 10!3

CSNF 0.18 

CDSP 5.2 × 10!4

*Commercial Spent Nuclear Fuel 

†Codisposal 

‡Determined from Table 17-3 for the time periods (i.e., 0 to 10,000 years; 10,000 to 100,000 years; 100,000 to 

400,000 years; and 400,000 to 800,000 years). 

§Water flux taken from Table 17-7. 

2Inventory is for one waste package at the specific time (Np-237 half-life is 2.14 million years, Pu-242 half-life is 

376,300 years); initial inventory from SAR Volume 2, Table 2.3.7-5 (Np-237 inventory includes complete decay of 

Am-241 into Np-237). 

&Release rate determined with solubility limits of 0.3 to 6 mg/L for Np-237 and 0.006 to 0.5 mg/L for Pu-242 and 

corrosion product factor of 0.05 for Np-237 and 0.7 for Pu-242. 

sorption; however, this amount is small in DOE’s TSPA analysis, as explained in 

TER Section 2.2.1.4.1.3.3.1.1.4, and is not considered in the NRC staff’s confirmatory 

calculation.) The saturated zone provides somewhat more effectiveness for Pu-242 

(reversible colloids and dissolved Pu-242) at longer times and is assumed to significantly reduce 

releases (i.e., 97 percent reduction) over the 10,000-year time period due to the median 
17-27

Table 17-12. NRC Staff Confirmatory Calculation Values for the Effectiveness 

(Expressed as a Percentage Reduction in Release) of the Unsaturated and Saturated 

Zones for Reducing Release Rates for Specific Radionuclides 

 0 to 

10,000 Years 

10,000 to 

200,000 Years 

200,000 to 

600,000 Years 

600,000 to 

1 Million Years 

Tc-99 

(nonsorbing) 

UZ 0% 

SZ 0% 

UZ 0% 

SZ 0% 

UZ 0% 

SZ 0%

UZ 0% 

SZ 0%

Np-237 

(moderately sorbing) 

UZ 45% 

SZ 40% 

UZ 5% 

SZ 10% 

UZ 0 % 

SZ 5%

UZ 0% 

SZ 0%

Pu-242 (aqueous) 

(strongly sorbing) 

UZ 50% 

SZ 97% 

UZ 50% 

SZ 60% 

UZ 50% 

SZ 35%

UZ 50% 

SZ 25%

transport time of 95,000 years (see Table 17-5). The saturated zone is less effective for Np-237 

because median transport time is 3,700 years (see Table 17-5). The confirmatory calculation 

(i) considered radionuclides that are released to the reasonably maximally exposed individual 

(RMEI) location representing the more significant contributors to the TSPA calculated average 

annual dose and (ii) did not consider releases of a variety of other radionuclides that were 

reduced to much lower levels due to radioactive decay when the waste package is intact and 

during transport in the unsaturated and saturated zones (e.g., reversible colloids for americium 

and thorium are delayed more than 1 million years in the saturated zone; see Table 17-5), 

because the contributions to the average annual dose would have been so small. 

Annual Dose to the Reasonably Maximally Exposed Individual (RMEI) 

The NRC staff’s confirmatory calculation for the annual dose to the reasonably maximally 

exposed individual (RMEI) is completed by multiplying the biosphere dose conversion factors 

(BDCFs) in Table 17-6 with the NRC staff’s estimated saturated zone releases to estimate an 

annual dose for comparison with DOE’s TSPA results. Tables 17-13 through 17-15 compare 

the confirmatory calculation and the TSPA results. Overall, the annual doses from the 

confirmatory calculation are in general agreement with the TSPA results (e.g., a majority of 

either single value comparisons are within a factor of two or single values for the TSPA results 

fall between the upper and lower values when the confirmatory calculation provides both an 

upper and lower value). The igneous intrusive modeling case, which is already somewhat 

simplified in the TSPA model by assuming all waste packages fail when the event occurs, tends 

to exhibit the best fit between the confirmatory calculation and the TSPA results. The fit for 

Tc-99 also exhibits a better fit regardless of the modeling case because the representation of 

Tc-99 in the repository is less complex: high solubility and mobility for Tc-99 limits the factors 

affecting release and transport of Tc-99. Although the ground motion modeling case is a bit 

more complicated due to the variety and timing of waste package breaches (e.g., cracks, 

ruptures, and patches), the results of the NRC staff’s confirmatory calculation are in general 

agreement with the TSPA results. There is no precise agreement between the NRC staff’s 

confirmatory calculation and the results of DOE’s Total System Performance Assessment 

(TSPA) results due to the simplifying assumptions made in the confirmatory calculation [see 

NRC and CNWRA (2011aa) for further details on assumptions for the NRC staff confirmatory 
17-28

Table 17-13. NRC Staff Confirmatory Calculation Results for the Average Dose 

Estimates for Tc-99 for the Seismic Ground Motion Modeling Case and Igneous 

Intrusive Modeling Case 

 10,000 Years 100,000 Years 400,000 Years 800,000 Years 

Annual release from 

repository engineered 

barrier system 

(grams/year) 

(from Tables 17-8 and 

17-10) 

Seismic 5.0 

Igneous 1.1 

Seismic 10 

Igneous 0.82 

Seismic 5.4 

Igneous 0.31 

Seismic 4.2 

Igneous 0.084 

Reduction factor for 

unsaturated and 

saturated zone 

transport 

(from Table 17-12) 

1 1 1 1 

BDCF* (mrem/yr per 

pCi/l) 

(from Table 17-6) 

4.1 × 10-3 4.1 × 10-3 4.1 × 10-3 4.1 × 10-3 

NRC confirmatory 

calculation of annual 

doseH (mrem/yr) 

Seismic 0.094 

Igneous 0.021 

Seismic 0.19 

Igneous 0.015 

Seismic 0.10 

Igneous 

5.8×10-3

Seismic 0.079 

Igneous 

1.6×10-3

DOE’s TSPA average 

annual dose‡ 

(mrem/year) 

Seismic 0.10 

Igneous 0.017 

Seismic 0.16 

Igneous 0.013 

Seismic 0.13 

Igneous 

7.0×10-3

Seismic 0.090 

Igneous 

1.6×10-3

*Biosphere dose conversion factor 

HAnnual dose calculation based on an annual water demand of 3,000 acre-ft per 10 CFR 63.112(c). 

‡TSPA results are approximate based on SAR Figures 2.4-26 and 2.4-30. 

calculation]. The NRC staff’s confirmatory calculation was used to confirm NRC’s 

understanding of the key attributes of the repository performance in DOE’s TSPA analyses and 

to assess that those attributes are consistent with DOE’s dose results. The confirmatory 

calculation considered the effect on dose by (i) the number of waste packages and the extent 

of waste package damage, (ii) the drift seepage, (iii) solubility limits for individual radionuclides 

(including the effect of corrosion products), (iv) the inventory of specific radionuclides, 

(v) sorption in the geosphere, (vi) and the probability of disruptive events. Consistency between 

the confirmatory calculation and DOE’s TSPA results provides further confidence that DOE’s 

TSPA analysis is consistent with the model abstractions described in the SAR and reviewed in 

TER Sections 2.2.1.3.1 through 2.2.1.3.14. 
17-29

Table 17-14. NRC Staff Confirmatory Calculation Results for the Annual Dose 

for Np-237 for the Seismic Ground Motion Modeling Case and Igneous 

Intrusive Modeling Case 

 10,000 

Years 

100,000 

Years 

400,000 

Years 

800,000 

Years 

Annual release from 

repository engineered 

barrier system 

(grams/yr) 

(from Tables 17-9 and 

17-11) 

Seismic 

2.9 × 10−4

 to 

4.7 × 10−3 

Igneous 

0.018 to 0.29 

Seismic 

1.5 × 10−3

Igneous 

0.22 to 2.1 

Seismic 

0.030 to 0.34 

Igneous 

0.78 to 1.9

Seismic 

0.37 to 4.2 

Igneous 

1.5 to 1.7

Reduction factor for 

unsaturated and 

saturated zone 

transport 

(from Table 17-12) 

0.33 0.855 0.95 1 

BDCF* (mrem/yr per 

pCi/l) 

(from Table 17-6) 

1 1 1 1

NRC confirmatory 

calculation of annual 

dose† (mrem/yr) 

Seismic 

1.8 × 10−5

 to 

3.0 × 10−4 

Igneous 

1.0 × 10−3

 to 

0.018 

Seismic 

2.5 × 10−4 

Igneous 

0.036 to 0.34 

Seismic 

5.3 × 10−3

 to 

0.061 

Igneous 

0.14 to 0.34 

Seismic 

0.070 to 0.80 

Igneous 

0.29 to 0.32 

DOE’s TSPA average 

annual dose‡ (mrem/yr) 

Seismic 

1.5 × 10−6

 

Igneous 

3.0 × 10−3

 

Seismic 

2 × 10−4

Igneous 

0.05 

Seismic 

2 × 10−3

 

Igneous 

0.13 

Seismic 

0.04 

Igneous 

0.22 

*Biosphere dose conversion factor 

†Annual dose calculation based on an annual water demand of 3,000 acre-ft per 10 CFR 63.112(c). 

‡TSPA results are approximate based on SAR Figures 2.4-26 and 2.4-30. 
17-30

Table 17-15. NRC Staff Confirmatory Calculation Results for the Annual Dose 

for Pu-242 for the Seismic Ground Motion Modeling Case and Igneous 

Intrusive Modeling Case 

 10,000 

Years 

100,000 

Years 

400,000 

Years 

800,000 

Years 

Annual release from 

repository engineered 

barrier system 

(grams/yr) 

(from Tables 17-9 and 

17-11) 

Seismic 

8.1 × 10−5

 to 

3.6 × 10−4

Igneous 

5.1 × 10−3

 to 

0.3 

Seismic 

1.0 × 10−4

Igneous 

0.054 to 0.64 

Seismic 

5.2 × 10−3

 to 

0.15 

Igneous 

0.21 to 0.37

Seismic 

0.060 to 0.83 

Igneous 

0.18 

Reduction factor for 

unsaturated and 

saturated zone 

transport 

(from Table 17-12) 

0.015 0.20 0.325 0.375 

BDCF* (mrem/yr per 

pCi/l) 

(from Table 17-6) 

3.4 3.4 3.4 3.4

NRC confirmatory 

calculation of annual 

dose† (mrem/yr) 

Seismic 

4.3 × 10−6

 to 

2.0 × 10−5

Igneous 

2.8 × 10−4

 to 

0.016 

Seismic 

7.2 × 10−5

Igneous 

0.040 to 0.47 

Seismic 

6.2 × 10−3

 to 

0.18 

Igneous 

0.25 to 0.44 

Seismic 

0.080 to 1.1 

Igneous 

0.25 

DOE’s TSPA average 

annual dose‡ 

(mrem/year) 

Seismic 

0 

Igneous 

0 

Seismic 

4.0 × 10−4

Igneous 

0.05 

Seismic 

0.013 

Igneous 

0.23 

Seismic 

0.15 

Igneous 

0.23 

*Biosphere dose conversation factor 

†Annual dose calculation based on an annual water demand of 3,000 acre-ft per 10 CFR 63.112(c). 

‡TSPA results are approximate based on SAR Figures 2.4-26 and 2.4-30.

The NRC staff conducted its confirmatory calculation to assist in its review of DOE’s TSPA 

models and calculations. The confirmatory calculation provides both a quantitative 

understanding of the attributes of the performance assessment and an understanding of 

whether there is a general consistency between submodels of the performance assessment 

and the overall results, including uncertainty (e.g., whether the timing and extent of breaching 
17-31

of the waste package is consistent with the timing and magnitude of the average annual dose). 

On the basis of its confirmatory calculation, the NRC staff makes the following observations: 

 The NRC staff’s confirmatory calculation follows the water and key radionuclides through 

the repository system (seepage to release to transport to annual dose) for the dominant 

modeling cases, whereby seismic ground motion and igneous intrusion events have the 

largest number of waste package failures and are the largest contributors to annual 

dose. The NRC staff notes that DOE’s TSPA calculated average annual dose curve is 

reasonable in that the average annual doses are consistent with the intermediate model 

results [e.g., seepage, waste package failures, release of radionuclides from the 

engineered barrier system (EBS), and transport in the geosphere]. The NRC staff’s 

review of the intermediate models of DOE’s TSPA is described under the NRC staff’s 

model abstraction review in TER Sections 2.2.1.3.1 through 2.2.1.3.14. 

 DOE’s description of the barriers important to waste isolation is fully consistent with the 

key attributes of the repository in the NRC staff’s confirmatory calculation. In particular, 

(i) The waste package prevents significant releases for long periods of time 

(e.g., less than 1 percent of commercial spent nuclear fuel waste packages are 

breached after 100,000 years; see Table 17-3) 

(ii) Once breached, the releases from the waste package are limited due to the 

manner in which the waste package fails (i.e., majority of waste package 

breaches are due to cracks that do not allow seepage water to enter the waste 

packages), which limits the amount of water that enters the waste package; 

solubility limits and corrosion products within the waste package that reduce the 

release of many radionuclides from the waste package; and the limited amount of 

seepage that is present due to the upper natural barrier (i.e., rock layers above 

the repository) 

(iii) After release from the waste package, a variety of radionuclides sorb onto rock 

surfaces and are delayed for thousands of years in portions of both the 

unsaturated zone (see Table 17-9) and the saturated zone (see Table 17-10) 

 The confirmatory calculation focused on certain long-lived radionuclides, on the order of 

100,000 years or longer, that could eventually be released at the RMEI location in 

sufficient quantities to be important for dose calculations. Both a nonsorbing 

radionuclide (Tc-99) and sorbing radionuclides (Np-237 and Pu-242) were considered in 

the NRC staff’s calculations. The initial inventory of high-level waste is composed of a 

large quantity (in terms of curies) of radionuclides (e.g., Cs-137, Sr-90, Am-241) with 

shorter half-lives, on the order of 1,000 years and less. For the short-lived radionuclides, 

DOE described in DOE Enclosure 7, Response Number 7, Table 1-3 (2009an) that delay 

in the geosphere has the capability to reduce releases nearly 100 percent. 

 The effect of uncertainties on DOE’s average annual dose curve is limited primarily 

because a number of important aspects of repository performance are near maximum 

values. For example, after 10,000 years nearly all the waste packages are dripped on 

(seepage fraction of 69 percent for seismic ground motion modeling case and 

100 percent for igneous intrusion; see Table 17-2) and, given the 1-million-year period, 

a variety of long-lived radionuclides can eventually make it to the reasonably maximally 
17-32

exposed individual (RMEI) location (see Tables 17-4 and 17-5). Releases from the 

waste package will be affected by the failure rate for the waste package, including the 

areal extent of the waste package breaches, solubility limits, and the effect of corrosion 

products. The confirmatory calculation considered the low and high values of the 

solubility limits to provide some insight on how uncertainty in release from the waste 

package might impact the annual dose. Use of the highest solubility limit, as 

expected, increases the annual dose in the NRC staff confirmatory calculation. 

The estimated peak dose in the NRC staff’s confirmatory calculation at 10,000 years 

is 0.0015 mSv/yr [0.15 mrem/yr] and at 800,000 years is 0.026 mSv/yr [2.6 mrem/yr]. 

On the basis of DOE’s SAR description of the TSPA models and its results, the NRC staff’s 

confirmatory calculation provides further confidence that DOE’s average annual dose curve is 

consistent with the model abstractions, scenario probabilities, and the capabilities of the barriers 

important to waste isolation. 

2.2.1.4.1.3.3.2 DOE’s TSPA Calculation for the Volcanic Eruption Modeling Case 

2.2.1.4.1.3.3.2.1 Summary of DOE Approach 

An eruptive volcanic event at the repository involves the intersection of ascending magma and a 

drift and eruption at the surface (see TER Section 2.2.1.3.10). Radioactive material entrained in 

tephra can be transported downwind and deposited on the ground surface where potential 

exposures can occur from (i) inhalation of radionuclides due to high-level waste entrained in ash 

particles, which are suspended in the air, including the breathing of radon gas and its daughter 

products from high-level waste entrained in the ash deposited on the ground surface and 

(ii) ingestion of radionuclides from locally produced crops and animal products that are assumed 

to be contaminated from direct (e.g., crops grown in soil containing contaminated tephra) and 

indirect (e.g., animals raised on feed that has been grown in soil containing contaminated 

tephra) contact with contaminated tephra. Estimating the consequences of such an event is 

dependent on the concentration of radionuclides in tephra and the amount of ash persisting at 

the reasonably maximally exposed individual (RMEI) location (from both the direct deposition of 

tephra during the event and redistribution of tephra after the event due to water and wind action 

over time). 

On average, the volcanic eruption modeling case impacts four (a range of one to seven) waste 

packages and entrains all of the waste into magma. Of this magma and waste, 30 percent is 

considered to form tephra; thus 30 percent of the waste in the waste packages hit by the event 

is, on average, contained in the tephra (range of 10 to 50 percent). Once radioactively 

contaminated volcanic tephra is present at the reasonably maximally exposed individual (RMEI) 

location, potential exposures are estimated for three specific pathways (external exposure; 

ingestion; and inhalation, which includes radon exposure) using Biosphere Dose Conversion 

Factors (BDCFs). DOE’s BDCFs are provided in Tables 17-16 and 17-17 for some of the 

radionuclides included in DOE’s Total System Performance Assessment (TSPA) relevant to the 

volcanic eruption modeling case. 

The volcanic eruption modeling case average annual dose curve is one of the lowest dose 

curves of all the modeling cases resulting in average doses that are less than 10-5 mSv/yr 

[0.001 mrem/yr] at all times (SAR Figure 2.4-18). During the initial 10,000 years, the annual 

dose is dominated by Pu-239, Pu-240, and Am-241 (SAR Figure 2.4-32). For these three 

radionuclides, the inhalation exposure accounts for more than 98 percent of the average annual 
17-33

Table 17-16. DOE Volcanic Eruption Modeling Case Short-Term 

and Long-Term Inhalation BDCFs* 

Radionuclide 

BDCF 

Sv/yr per Bq/kg 

[mrem/yr per pCi/g] Primary Pathways 

Pu-239 4.0 × 10!7 [1.5]

short term† 

6.1 × 10!7

 [2.3] 

long term 

98% of Pu-239 eruptive dose is 

inhalation: 

39% short term 

60% long term 

Am-241 3.2 × 10!7

 [1.2] 

short term† 

5.0 × 10!7

 [1.8] 

long term 

94% of Am-241 eruptive dose is 

inhalation: 

37% short term 

57% long term 

*Biosphere dose conversion factors 

†The short-term inhalation exposure is applicable only for the initial year of the eruption. 

Table 17-17. DOE Volcanic Eruption Modeling Case Combined 

Ingestion, Radon, and External BDCFs* 

Radionuclide 

BDCF 

Sv/yr per Bq/m2

[mrem/yr per pCi/m2

] Primary Pathways 

Sr-90 1.8 × 10!9 

[6.7 × 10!6] 

79% external exposure 

Cs-137 7.2 × 10!9

[2.7 × 10!5] 

99% external exposure 

Ra-226 3.3 × 10!8

[1.2 × 10!4

] 

65% external exposure 

33% radon decay products 

*Biosphere dose conversion factors 

dose for the volcanic eruption modeling case, as shown in SAR Table 2.3.10-15. At very early 

times (i.e., the initial 500 years), there is some contribution from Sr-90 and Cs-137 (primarily 

from external exposure). At very long times (i.e., after 100,000 years), the annual dose is 

dominated by Ra-226 (SAR Figure 2.4-32). These results are partially due to the half-lives for 

these radionuclides. Sr-90 and Cs-137 have half-lives less than 100 years, and Am-241 has a 

half-life of 432 years; thus the hazard is somewhat short lived. The longer term hazard is with 

Pu-239 (half-life of 24,000 years), Pu-240 (half-life of 24,000 years), and Ra-226, which is a 

daughter product in the long-lived U-234 chain (the half-life of U-234 is 240,000 years, whereas 

the half-life of Ra-226 is 1,600 years). 
17-34

2.2.1.4.1.3.3.2.2 NRC Staff Evaluation of DOE’s TSPA Calculation for the Volcanic 

Eruption Modeling Case 

The NRC staff reviewed the TSPA and SAR with respect to representing an extrusive igneous 

event, including the treatment of uncertainty, and notes that DOE’s approach is reasonable. 

In particular, the following is reasonable: (i) the probability for an extrusive event to intersect the 

repository and hit waste packages (TER Section 2.2.1.2.2), (ii) the model abstractions for 

disruption of the waste package by an extrusive igneous event (TER Section 2.2.1.3.10), (iii) the 

model abstractions for airborne transport and deposition of radionuclides expelled by a potential 

future volcanic eruption following igneous disruption of waste packages and the redistribution of 

those radionuclides in soil (TER Section 2.2.1.3.13), and (iv) the volcanic exposure scenario for 

the reasonably maximally exposed individual (RMEI) (TER Section 2.2.1.3.14). 

Generally, the largest contribution to annual dose is from the inhalation and external exposure 

pathways (see SAR Table 2.3.10-15). Annual dose from each of these pathways is dependent 

on the amount of radionuclides that are transported with the tephra (ash), and the inhalation 

pathway is also dependent on the amount of this material that is potentially available for 

inhalation by the reasonably maximally exposed individual (RMEI) (i.e., the mass loading of 

tephra and radioactive material in the air). In particular, the NRC staff notes that 

 The amount of radionuclides entrained in tephra (ash) is reasonably conservative 

relative to entrainment of rock fragments in other volcanic conduits within similar and 

different geologic environments to Yucca Mountain (TER Section 2.2.1.3.10) 

 Values for mass loading are appropriate and consistent with available information and 

are generally consistent with the NRC staff studies and analyses conducted over the 

past 5 years (TER Section 2.2.1.3.14) 

The NRC staff also performed a confirmatory calculation to help evaluate the reasonableness of 

DOE’s TSPA average annual dose curve for this modeling case [detailed documentation of the 

NRC staff’s confirmatory calculation is provided in NRC and CNWRA (2011aa)]. Because the 

average annual dose for the volcanic eruption modeling case is so small {i.e., less than 10-5

mSv/yr [0.001 mrem/yr]}, a simplified calculation was performed for a few key radionuclides that 

would tend to bound some of the assumptions for this modeling case. The confirmatory 

calculation looked at radionuclides that include both the inhalation and direct exposure 

pathways and assumed that waste entrained in tephra (ash) was deposited directly at the 

reasonably maximally exposed individual (RMEI) location and persists without erosion removal 

at that location over the time period of the estimated dose (i.e., only radioactive decay reduces 

the level of radionuclides at the exposure location after the time of the initial deposition of the 

tephra). Additionally, the confirmatory calculation assumes tephra from the eruptive event is 

always deposited directly to the RMEI location (i.e., during the volcanic eruption the wind always 

blows in the direction of the RMEI such that ashfall occurs at the RMEI location). To provide a 

perspective on how radioactive decay and probability of the event affects the annual dose, two 

time periods were selected for the calculation: a time equal to the half-life of the radionuclide 

and three times the half-life. Radioactive decay will tend to decrease the annual dose to an 

individual over time. Tables 17-18 through 17-20 present the calculations for a few of the 

radionuclides that are most significant to performance or radiation exposure to the reasonably 

maximally exposed individual (RMEI) in the volcanic eruption modeling case. 

As anticipated, the resulting annual doses from the NRC staff’s confirmatory calculation are 

larger than the average annual doses calculated by DOE’s detailed TSPA model. This is not 
17-35

Table 17-18. NRC Staff Confirmatory Calculation Results for Pu-239 and Am-241 

Annual Doses for the Volcanic Eruption Modeling Case (Inhalation Pathway) 

Performance Aspect 

Pu-239 

(Half-Life 24,065 Years) 

Am-241 

(Half-Life 432 Years) 

24,065 Yrs 72,195 Yrs 432 Yrs 1,296 Yrs 

Number of waste packages 

(SAR Figure 2.3.11-12) 

4 4 4 4 

Fraction of waste entrained in ash 

(SAR p. 2.3.11-51) 

0.3 0.3 0.3 0.3 

Tephra volume (km3) 0.038 0.038 0.038 0.038

Inventory per CSNF* waste package 

(with mixed oxide added) [Ci] 

(SAR Table 2.3.7-5) 

1,370 343 17,554 4,390 

Concentration in ash (pCi/g)† 43 11 550 140 

Weighted annual dose‡ (mrem/yr) 3.3 × 10−3 2.6 × 10−3 6.0 × 10−4 4.6 × 10−4

TSPA weighted average annual dose 

(mrem/yr) 

(SAR Figure 2.4-32) 

8.0 × 10−5

 6.0 × 10−5 3.0 × 10−5 2.0 × 10−5

*Commercial spent nuclear fuel waste package 

†Concentration in ash calculated by dividing quantity of radionuclide released in tephra by tephra volume times 

the tephra density (tephra density is 1 gram/cc; SAR p. 2.3.11-61). 

‡Weighted annual dose calculated by multiplying concentration with BDCF (Table 17-16), annual probability 

(1.4 × 10!9

), and the time. 

unexpected because (i) the NRC staff’s confirmatory calculation, which assumes direct deposits 

persist without erosion and the wind always blows in the direction of the RMEI, is more 

conservative than DOE’s TSPA, which accounts for erosion of the tephra deposit, and (ii) the 

wind direction can vary (see TER Section 2.2.1.3.13). For the volcanic eruption modeling case 

(Tables 17-18 through 17-20), the annual dose from the confirmatory calculation represents the 

smallest dose for the modeling cases estimated in the NRC’s confirmatory calculation {i.e., less 

than 0.0001 mSv [0.01 mrem]}. Consistency between the NRC staff’s confirmatory calculation 

for the volcanic eruption modeling case (Tables 17-18 through 17-20) and the DOE’s 

representation of the volcanic eruption modeling case provides further confidence that DOE’s 

TSPA analysis is consistent with the assumptions and model abstractions described in the SAR 

and reviewed in TER Sections 2.2.1.2.2, 2.2.1.3.10, 2.2.1.3.13, and 2.2.1.3.14. 

2.2.1.4.1.3.4 Statistical Stability of Average Annual Dose Estimates 

2.2.1.4.1.3.4.1 Summary of DOE Approach 

DOE addressed the question of the stability in SAR Section 2.4.2.2.2. The term stability refers 

to the numerical reproducibility of statistics (e.g., average annual dose) or their level of 

convergence as a function of model features such as size of the statistical sample and 
17-36

Table 17-19. NRC Staff Confirmatory Calculation of Sr-90 and Cs-137 Annual Doses 

for the Volcanic Eruption Modeling Case (External Pathway) 

Performance Aspect 

Sr-90 

(Half-Life 29 Years) 

Cs-137 

(Half-Life 30 Years) 

29 Yrs 87 Yrs 30 Yrs 90 Yrs 

Number of waste packages 

(SAR Figure 2.3.11-12) 

4 4 4 4 

Fraction of waste entrained in ash 

(SAR p. 2.3.11-51) 

0.3 0.3 0.3 0.3 

Tephra volume (km3) 0.038 0.038 0.038 0.038

Inventory per CSNF* waste package with 

mixed oxide added) [Ci] 

(SAR Table 2.3.7-5) 

52,011 13,009 81,518 20,388 

Ash areal concentration† (pCi/m2) 1.6 × 107 4.1 × 106 2.6 × 107 6.4 × 106

Weighted annual dose‡ (mrem/yr) 4.4 × 10−6 3.4 × 10−6 2.9 × 10−5 2.2 × 10−5

TSPA weighted average annual dose 

(mrem/yr) 

(SAR Figure 2.4-32) 

2.0 × 10−6

 1.8 × 10−6 2.0 × 10−5 1.0 × 10−5

*Commercial spent nuclear fuel waste package 

†Ash areal concentration calculated by dividing quantity of radionuclide released in tephra by tephra volume times 

the tephra density and assuming a 1-cm [0.39-in]-thick deposit (tephra density is 1 gram/cc; SAR p. 2.3.11-61). 

‡Weighted annual dose calculated by multiplying concentration with BDCF (Table 17-17), annual probability 

(1.4 × 10−9

), and the time. 

numerical approximations. Variation in the TSPA results is a function of a particular 

combination of uncertain and variable parameters (DOE described its treatment of epistemic 

and aleatory uncertainty in SAR Section 2.4.2.1.1). DOE identified aleatory parameters as 

those parameters with uncertainty irreducible by additional experiments or site characterization. 

Examples of aleatory parameters are the time of seismic and igneous events, the extent of 

waste package damage during a seismic or faulting event, the location of the compromised or 

breached waste package in the repository, and the type of waste package [e.g., commercial 

spent nuclear fuel (CSNF) or codisposal (CDSP) waste packages] compromised after a 

disruptive event. The stability of the average annual dose will, in part, be a function of the size 

of the discrete sample of aleatory parameter values. DOE analyzed the effect of the size of 

these discrete samples by increasing the number of aleatory realizations from 30 to 90, 

considering more waste package damage fractions for the seismic and faulting modeling case, 

nd accounting for more event times (e.g., doubling the number of event times for the seismic 

ground motion modeling case, increasing the number of event times from 10 to 50 for the 

igneous intrusion modeling case) and determined that these types of changes would have a 
17-37

Table 17-20. NRC Staff Confirmatory Calculation of Ra-226 Annual Dose for the 

Volcanic Eruption Modeling Case (External Pathway) 

Performance Aspect 

Ra-226 (Half-Life 1,600 Years) 

[U-238 (Half-Life 4.5 H 109

 Years)] 

1 Million Years* 

Number of waste packages (SAR Figure 2.3.11-12) 4 

Fraction of waste entrained in ash (SAR p. 2.3.11-51) 0.3 

Tephra volume (km3) 0.038

Inventory per CSNF† (waste package with mixed oxide 

added) [Ci] 

(SAR Table 2.3.7-5) 

2.65 

Ash areal concentration‡ (pCi/m2) 840 

Weighted annual dose§ (mrem/yr) 1.4 × 10−4

TSPA weighted average annual dose (mrem/yr) 

(SAR Figure 2.4-32) 

5.0 × 10−5

*1-million-year period selected for this calculation due to persistence of Ra-226 based on decay of 

long-lived U-238. Ra-226 inventory assumes Ra-226 activity equals that of parent radionuclide U-238; SAR 

Table 2.3.7-5. 

†Commercial spent nuclear fuel 

‡Ash areal concentration calculated by dividing quantity of radionuclide released in tephra by tephra volume times 

the tephra density and assuming a 1-cm [0.39-in]-thick deposit (tephra density is 1 gram/cc; SAR p. 2.3.11-61). 

§Weighted annual dose calculated by multiplying concentration with BDCF (Table 17-17), annual probability 

(1.4 ×10!9), and the time. 

a minor effect on the magnitude of the overall average annual dose curve. DOE compared 

annual dose curves for a set of five realizations for all modeling cases in SAR Figures 2.4-55 

to 61 and concluded, in qualitative terms, that the annual dose curve for the analyzed 

realizations was stable with respect to aleatory uncertainty. 

DOE also examined the stability of the average annual dose curve to the treatment of the 

epistemic uncertainty. The epistemic parameters are generally those parameters that describe 

the repository components and behavior under nominal conditions (e.g., variability in hydraulic 

conductivity of a particular rock unit; variability in material properties over the surface of the 

waste package). DOE used a statistical sample size of 300 realizations for each modeling 

case in the SAR. To examine the stability of the annual dose curve with respect to the 

treatment of the epistemic uncertainty, DOE estimated dose statistics (mean, median, 5th and 

95th percentiles) for the nominal modeling case considering 1,000 realizations and compared 

those statistics to corresponding 300-realization statistics (SAR Figure 2.4-38). DOE showed 

the 300-realization and 1,000-realization annual dose statistics (mean, median, 0.05 and 

0.95 percentiles) were comparable (SAR Figure 2-4-38). 
17-38

For all of the modeling cases, DOE considered three replicates with 300 realizations and 

compared statistics (mean, median, 0.05 and 0.95 percentiles) among the replicates (SAR 

Figures 2.4-37 to 2.4-52). Each replicate sample had the same number of realizations; 

however, the combination of sampled parameter values was different for each replicate. DOE 

qualitatively concluded that the statistics were similar for the three replicates. Also, DOE 

estimated 95 percent confidence bounds for the average annual dose using information from 

the replicates and a t-distribution with 2 degrees of freedom, as described in SNL Section J4.10 

(2008ag). In all model cases, the 0.95 percentile in the average annual dose was relatively 

close {e.g., largest difference of 0.01 mSv/yr [1 mrem/yr] between the three replicates and 

generally much less for the vast majority of the 1-million-year period, as shown in SNL 

Figure J5-5(a) (2008ag)} to the overall average annual dose. DOE concluded the overall 

average annual dose, computed using 300 realizations, to be statistically stable, as described 

in SAR Section 2.4.2.2.2 and SNL Section 7.3.2 (2008ag). 

DOE updated its model from TSPA Model v5.000 to v5.005, with most validation and model 

stability analyses performed with TSPA Model v5.000, but the annual doses reported in the 

SAR are based on TSPA Model v5.005 (SAR p. 2.4-76 to 78). DOE compared the effect 

of the change from version v5.000 to v5.005 and documented those analyses in SNL 

Figures 7.3.1-17[a] to 7.3.3-13[a] (2008ag). Although the stability analyses were not 

repeated, the comparisons indicate a similar numerical behavior of versions v5.000 to v5.005, 

and, thus, DOE stated that the same conclusions, with regard to stability, apply to 

version v5.005. DOE computed a range for the overall average annual dose using bootstrap 

analyses, compared the results of these analyses in SAR Figures 2.4-53 and 54, and concluded 

that 300 epistemic realizations were sufficient to estimate the average annual dose and that the 

results of TSPA Model v5.005 are statistically stable (SAR Section 2.4.2.2.2.2, p. 2.4-82). 

2.2.1.4.1.3.4.2 NRC Staff Evaluation of Statistical Stability of Average Annual Dose 

The NRC staff reviewed DOE’s TSPA model and analytic results, as well as the SAR, and notes 

the calculated overall average annual dose curve is statistically stable because 

 The overall average annual dose curve is reasonably stable with respect to the different 

approaches for representing epistemic and aleatory uncertainties (i.e., the average 

annual dose does not significantly change under the different approaches; for example 

see SAR Figures 2.4-38, 2.4-37 to 52, 2.4-55 to 61) 

 Model updates from TSPA Model v5.000 to v5.005 caused only a moderate change in 

the magnitude of the overall average annual dose; the same results with respect to 

average annual dose stability are expected to apply to both versions v5.000 and v5.005 

{i.e., the model updates do not cause different numerical model behavior in regard to 

statistical stability; for example, see SNL Figures 7.3.1-17[a] to 7.3.3-13[a] (2008ag)} 

Finally, the NRC staff’s confirmatory calculation provided further confidence that DOE’s TSPA 

results were consistent with the model abstractions and capabilities of the barriers important to 

waste isolation described in the SAR. The NRC staff reviewed and noted the model 

abstractions, including uncertainties; scenario probabilities; the technical basis for excluding 

features, events, and processes (FEPs); and the description of the capabilities of the barriers 

important to waste isolation (see TER Sections 2.2.1.1–2.2.1.3) are reasonable. The NRC 

staff’s confirmatory calculations identified that only a limited number of performance attributes 

(e.g., failure rate of the waste package, seepage flux into the waste package, solubility limits, 

and retardation in the saturated and unsaturated zones) had the potential to significantly alter 
17-39

the resulting average annual dose. As described in TER Section 2.2.1.4.3.3.1.2, the effect of 

uncertainties on DOE’s average annual dose curve is limited. As addressed previously, DOE 

incorporated the model uncertainties into the analyses and the analyses were shown to 

converge to a stable solution. The NRC staff determined the analytic models reviewed in the 

previous 16 TER chapters are reasonable in that they were technically sound and provide a 

suitable representation of the performance of repository performance (i.e., the radiological 

consequences for the Yucca Mountain facility would not be significantly underestimated). 

2.2.1.4.1.4 NRC Staff Conclusions

The NRC staff notes that DOE’s representation of repository performance in its Total System 

Performance Assessment (TSPA) for the individual protection calculation is consistent with 

the guidance in the YMRP. The NRC staff also notes that the DOE technical approach for its 

TSPA and the TSPA results, including the average annual dose values, discussed in this 

chapter are reasonable. 

2.2.1.4.1.5 References

BSC. 2005ak. “Saturated Zone Flow and Transport Model Abstraction.” 

MDL–NBS–HS–000021. Rev. 03. AD 01, AD 02, ERD 01. Las Vegas, Nevada: Bechtel 

SAIC Company, LLC. 

CNWRA and NRC. 2008aa. “Risk Insights Derived From Analyses of Model Updates in the 

Total-system Performance Assessment Version 5.1 Code.” ML082240343. San Antonio, 

Texas: CNWRA. 

CRWMS M&O. 1998aa. “Probabilistic Seismic Hazard Analyses for Fault Displacement and 

Vibratory Ground Motion at Yucca Mountain, Nevada.” WBS 1.2.3.2.8.3.6. Las Vegas, 

Nevada: CRWMS M&O. 

CRWMS M&O. 1996aa. “Probabilistic Volcanic Hazard Analysis for Yucca Mountain, Nevada.” 

BA0000000–01717–2200–00082. Rev. 0. Las Vegas, Nevada: CRWMS M&O. 

DOE. 2010ai. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.3.1), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.3.5, Set 1 and (Safety Analysis Report Sections 2.3.2 

and 2.3.3), Safety Evaluation Report Vol. 3, Chapter 2.2.1.3.6, Set 1.” Letter (February 2) 

J.R. Williams to J.H. Sulima (NRC). ML100340034. Washington, DC: DOE, Office of 

Technical Management. 

DOE. 2009an. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.1), Safety Evaluation Report Vol. 3, 

Chapter 2.2.1.1, Set 1.” Letter (February 6) J.R. Williams to J.H. Sulima (NRC). ML090400455. 

Washington, DC: DOE, Office of Technical Management. 

DOE. 2009bj. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.4.4) Safety Evaluation Report Vol. 3, 

Chapters 2.2.1.4.1, 2.2.1.4.2, and 2.2.1.4.3, Set 1.” Letter (July 29) J.R. Williams to J.H. Sulima 

(NRC). ML092110472. Washington, DC: DOE, Office of Technical Management. 
17-40

DOE. 2009cb. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.2, Table 2.2-5), Safety Evaluation Report 

Vol. 3, Chapter 2.2.1.2.1, Set 5.” Letter (June 5) J.R. Williams to J.H. Sulima (NRC). 

ML091590581. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009ct. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Sections 2.3.2, 2.3.3, and 2.3.5), Safety Evaluation 

Report Vol. 3, Chapter 2.2.1.3.6, Set 2.” Letter (July 20) J.R. Williams to J.H. Sulima (NRC). 

ML0920204130 and ML092020410. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

ML051360241. Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

NRC and CNWRA. 2011aa. “Documentation of Analyses in Support of the NRC Staff’s 

Technical Review of DOE's Total System Performance Assessment (TSPA) Calculations.” 

ML110831032. Washington, DC: NRC. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

Las Vegas, Nevada: Sandia National Laboratories.
18-1 

CHAPTER 18 

2.2.1.4.2 Human Intrusion Calculation 

2.2.1.4.2.1 Introduction

This section of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s evaluation of the U.S. Department of Energy’s (DOE’s) Total System 

Performance Assessment (TSPA) for the human intrusion calculation, as presented in DOE’s 

Safety Analysis Report (SAR) Section 2.4.3 (DOE, 2008ab). The geologic record provides a 

basis for evaluating the likelihood of geologic processes and events, but there is no similar 

record of extended duration that can be used to constrain either the probability that human 

intrusion could occur or the characteristics of such intrusion. Regulations specify that the 

potential effects of human intrusion on waste isolation be considered when evaluating repository 

performance. The NRC staff’s review evaluates repository performance if its barriers are 

breached by a human intrusion. 

2.2.1.4.2.2 Evaluation Criteria

Requirements for the human intrusion calculation are specified at 10 CFR 63.321(a) and 

63.322. Other aspects of the regulations applicable to the performance assessments for 

the individual protection, human intrusion, and separate groundwater protection calculations 

are described in TER Section 2.2.1.4.1.2 (Evaluation Criteria) under the individual 

protection calculation. Accordingly, the human intrusion calculation considers when a 

human intrusion might occur and the consequences of the human intrusion. In particular, 

the human intrusion calculation 

 Considers the earliest time after disposal that the waste package would degrade 

sufficiently that a human intrusion could occur without the drillers recognizing it 

[10 CFR 63.321(a)] 

 Assumes (i) there is a single human intrusion as a result of exploratory drilling for 

groundwater, (ii) the intruders drill a borehole directly through a degraded waste 

package into the uppermost aquifer underlying the Yucca Mountain repository, 

(iii) the drillers use the common techniques and practices that are currently employed 

in exploratory drilling for groundwater in the region surrounding Yucca Mountain, 

(iv) careful sealing of the borehole does not occur—instead, natural degradation 

processes gradually modify the borehole, (v) no particulate waste material falls into the 

borehole; (vi) the exposure scenario includes only those radionuclides transported to the 

saturated zone by water (e.g., water enters the waste package, releases radionuclides, 

and transports radionuclides by way of the borehole to the saturated zone), and (vii) no 

releases are included which are caused by unlikely natural processes [10 CFR 63.322] 

 Calculates the dose that the reasonably maximally exposed individual (RMEI) receives, 

as a result of the human intrusion [10 CFR 63.321(b)] 

The NRC staff’s review followed the guidance in the Yucca Mountain Review Plan (YMRP) 

Section 2.2.1.4.2c (NRC, 2003aa) for human intrusion. The relevant acceptance criteria in the 

YMRP address the timing of an intrusion event, the representation of the human intrusion event 
18-2 

in the Total System Performance Assessment, and the approach for estimating the annual dose 

to the reasonably maximally exposed individual. 

In addition, the NRC staff reviewed DOE’s description of the human intrusion event as part of its 

review of events that were included in the performance assessment in TER Section 2.2.1.2.2.3. 

2.2.1.4.2.3 Technical Evaluation

The performance assessment used to estimate the dose for the human intrusion calculation is 

subject to specific criteria regarding the determination of the timing of the human intrusion and 

assumptions with respect to the nature and extent of the intrusion scenario. Accordingly, the 

performance assessment for the human intrusion calculation is somewhat different than the 

performance assessment used for the individual protection calculation. The two performance 

assessments are expected to differ because the performance assessment used to evaluate the 

human intrusion scenario includes disruption of the repository due to a postulated human 

intrusion event. However, those portions of the performance assessment not affected by the 

specifications for the human intrusion scenario would be expected to be the same as the 

performance assessment used for individual protection (e.g., transport of radionuclides in the 

saturated alluvium, characteristics of the biosphere are not affected by the postulated human 

intrusion event). 

The NRC staff review includes a determination that the Total System Performance Assessment 

for human intrusion is identical to the Total System Performance Assessment for individual 

protection, except that it assumes the occurrence of the postulated human intrusion scenario. 

As a result, the NRC staff review of DOE’s performance assessment for the human intrusion 

scenario evaluates (i) whether or not the performance assessment used for the human intrusion 

scenario is the same as the performance assessment used for individual protection (i.e., except 

for the representation of the human intrusion scenario, there are no differences between the 

performance assessment used for individual protection and the performance assessment used 

for human intrusion that would result in a significant underestimation of the peak dose for the 

human intrusion scenario) and (ii) whether or not those portions of the performance assessment 

for the human intrusion scenario are adequately represented in the performance assessment. 

Those portions of the Total System Performance Assessment for human intrusion that are 

identical to the Total System Performance Assessment for individual protection (e.g., biosphere) 

are evaluated as part of the review of the Total System Performance Assessment for the 

individual protection calculation (TER Section 2.2.1.4.1) and are not duplicated in this section. 

The NRC staff’s evaluation involves reviewing DOE’s Safety Analysis Report (SAR), 

Total System Performance Assessment (TSPA) Analysis Model Report, and the TSPA 

model files including intermediate results. 

The NRC staff review entails determining whether 

 DOE’s selection of the earliest time for the human intrusion to occur is adequately 

supported (TER Section 2.2.1.4.2.3.1) 

 The performance assessment for the human intrusion calculation provides a credible 

representation of the human intrusion scenario (TER Section 2.2.1.4.2.3.2)
18-3 

 Dose limits are statistically stable [e.g., increasing the number of simulations (statistical 

sample size) performed with DOE’s TSPA model is not expected to significantly change 

the calculated average dose] (TER Section 2.2.1.4.2.3.3)

2.2.1.4.2.3.1 Timing of Human Intrusion Event

Description of DOE Approach 

DOE made a determination of the earliest time at which a driller would penetrate a waste 

package without recognition (e.g., that a metal object had been contacted rather than rock), 

which is referred to as the human intrusion event. In SAR Section 2.4.3.2, DOE identified 

general corrosion as the process that, given sufficient time, could cause significant degradation 

of the drip shield and waste package such that drilling performance would most likely not be 

affected by the presence of the drip shield and waste package. DOE determined that there is 

only a 0.0001 percent chance that the drip shield will fail by corrosion before approximately 

230,000 years under nominal conditions. DOE also determined that the waste package has 

only a 5 percent chance of failure (i.e., significant degradation or thinning of the walls of the 

waste package) from general corrosion prior to 600,000 years. On the basis of these results, 

DOE selected 200,000 years as the earliest time the waste package would degrade sufficiently 

that a human intrusion could occur without the drillers recognizing it. DOE considered this a 

conservative approach because the waste package is estimated to have experienced limited 

degradation due to corrosion (i.e., waste package to be substantially intact) by that time. 

DOE also evaluated other events that might affect the timing of the human intrusion event. 

DOE did not consider unlikely natural processes and events (i.e., those events with less than 

1 chance in 100,000 per year of occurring) in the evaluation of human intrusion. DOE evaluated 

the likelihood of early undetected defects, igneous events, and seismic events. For early 

undetected defects and igneous disruptive events, DOE determined that the likelihood was less 

than the limit for likely events. For seismic events, DOE determined that damage to either the 

drip shield or waste package that might compromise the structural integrity of drip shield or 

waste package (e.g., rupture or framework buckling of the drip shield, punctures and ruptures of 

the waste package) is also less than the limit for likely events (SAR Section 2.4.3.2.2, p. 2.4-303 

and 304). For seismic damage that is considered likely to occur [i.e., stress corrosion cracking 

(SCC) of the drip shield or waste package], DOE asserted such damage would not be sufficient 

to prevent the driller from recognizing it (SAR Section 2.4.3.2.2, p. 2.4-303 and 304). In 

summary, DOE concluded that events such as early failures and igneous and seismic events 

are unlikely or would not cause enough damage to the drip shield and waste package to prevent 

the driller from recognizing the damage. 

NRC Staff Evaluation of Timing of Human Intrusion Event 

The NRC staff evaluated DOE’s technical basis supporting the selection of the time of 

occurrence of human intrusion and notes that it is reasonable for the following reasons.

 General corrosion, which uniformly thins the entire surface of a material, represents a 

degradation process that could eventually thin or reduce the thickness of the outer 

barrier to the extent that a driller may not recognize the presence of a waste package. 

DOE has shown that (i) after 1 million years, approximately 10 percent of the waste 

packages, on average, are failed due to general corrosion of the waste package and 

(ii) prior to 400,000 years, less than 0.01 percent of commercial spent nuclear fuel 

waste packages and approximately 0.1 percent of codisposal waste packages are 
18-4 

breached, as described in DOE Enclosure 1, Figures 9 and 10 (2009bj). Additionally, 

after 1 million years, approximately 1 percent of the surface area of the waste package is 

breached. The NRC staff’s review of the DOE model for general corrosion of the waste 

package outer barrier that was implemented in the Total System Performance 

Assessment noted that the DOE model for general corrosion of the waste package outer 

barrier is reasonable and that DOE provided technical support for its calculations of the 

timing and magnitude of waste package breach by general corrosion (see TER Section 

2.2.1.3.1.3.2.1). 

 Stress corrosion cracking generally refers to a process whereby cracks form in metals or 

alloys in a corrosive environment and under sustained tensile stresses. Using the TSPA 

code, DOE calculated that even if there is sufficient stress to initiate and propagate 

cracks, the breached area of the waste package will be limited by the small crack size 

and density. The seismic ground motion modeling case, which included unlikely seismic 

events, resulted in less than 0.1 percent of the surface area damaged due to SCC over 

the 1-million-year period, as identified in DOE Enclosure 1, Figures 3, 4, 7, and 8 

(2009bj). The NRC staff’s review of the DOE models for stress corrosion cracking of the 

waste package outer barrier that were implemented in the TSPA code noted that DOE 

accounts for stress corrosion cracking of the waste package outer barrier in the TSPA 

code (see TER Section 2.2.1.3.1.3.2.3). Given the limited surface area of the waste 

package affected by stress corrosion cracking of the waste package, the NRC staff 

notes that the assumption that such degradation would not prevent the driller from 

recognizing the presence of the waste package is reasonable (i.e., general corrosion of 

the waste package rather than stress corrosion cracking could prevent the driller from 

doing so). 

 Although the igneous intrusion modeling case assumes that the waste package and drip 

shield are failed when the event occurs and it might be assumed that the extent of the 

damage to the waste package would prevent the driller from recognizing it, the 

probability for the igneous intrusion is considered an unlikely event (i.e., those that are 

estimated to have less than 1 chance in 100,000 per year of occurring and at least 

1 chance in 100 million per year of occurring) and, therefore, is excluded from the 

analysis of human intrusion. The NRC staff noted that (i) the preponderance of 

information indicates that the mean annual probability for igneous disruption of the 

proposed repository by a basaltic dike (intrusive case) is on the order of 1 in 100 million 

per year to 1 in 10 million per year and (ii) mean probability values significantly higher 

(i.e., 1 in 1 million per year) or lower (i.e., 1 in 1 billion per year) than this range are not 

consistent with past patterns of activity in the Yucca Mountain region and therefore are 

not considered credible (see TER Section 2.2.1.2.2.3.1). 

 Seismic damage to the waste package due to tensile tearing (i.e., rupture and puncture 

of the waste package that represent relatively large openings in the waste package) 

could damage significantly more surface area of the waste package than seismically 

induced stress corrosion cracking, which represents relatively small openings in the 

waste package (see TER Sections 2.2.1.4.1.3.3.1.1.2 and 2.2.1.4.1.3.3.1.1.3). 

However, DOE estimated that the probability of seismic events of sufficient magnitude to 

cause such damage is less than 1 chance in 100,000 per year and therefore designated 

seismic damage as an unlikely event that can be excluded from the human intrusion 

scenario. The NRC staff noted that DOE’s exclusion of waste package rupture was 

appropriately supported by kinematic analyses that considered the mechanical 
18-5 

properties of the waste package, impact velocities of the waste package during seismic 

events, and degradation of drifts and drip shields (see TER Section 2.2.1.3.2.7). 

Early failure of the drip shield and waste package could be a factor affecting the timing of an 

intrusion event. DOE estimated a probability for early failure of a drip shield that has less than 

1 chance in 100,000 and therefore designated it as an unlikely event that can be excluded from 

the human intrusion scenario. The NRC staff noted that the probability for drip shield failure 

(i.e., mean value of approximately 2 chances in 1 million) based on the methodology used to 

estimate the probability of damage to the drip shield and data from industrial analogues for 

fabrication and handling of the drip shield is reasonable (see TER Section 2.2.1.2.2.4). DOE 

assumed the time of the human intrusion event occurs after the drip shield fails and did not take 

credit for any delay in the time for the human intrusion event due to the waste package being 

intact. This approach would not underestimate the dose. The NRC staff noted that this 

approach for consideration of early waste package failure is reasonable because the human 

intrusion is assumed to penetrate the waste package at the same time the drip shield fails. 

2.2.1.4.2.3.2 Representation of Intrusion Event

Description of DOE Approach 

DOE developed a separate performance assessment to evaluate the consequences of a 

postulated human intrusion event assumed to occur 200,000 years after permanent closure. 

The key elements of the postulated human intrusion event are the effects of the borehole on 

seepage into the waste package, release of radionuclides from the waste package, and 

transport of radionuclides through the unsaturated zone to the saturated zone. 

The performance assessment for individual protection is used for the human intrusion analysis, 

including the identical sampling approach for treating uncertainty (i.e., Latin hypercube 

sampling). DOE modified its performance assessment for individual protection to represent 

human intrusion. Specifically, the performance assessment for human intrusion 

 Does not include unlikely events (e.g., igneous activity or faulting) 

 Assumes that damage to a single waste package occurs at 200,000 years and is the 

result of drill bit penetration with a cross-sectional area of 0.0324 m2

 [0.349 ft2]]; the area 

is based on the cross section of a borehole with a diameter of 20.3 cm [8 in] 

 Assumes that seepage water enters the waste package through the borehole 

 Assumes the borehole is degraded and filled with rock debris 

 Assumes that releases from the waste package are passed into a fracture pathway that 

is assumed to exist in the borehole all the way to the saturated zone (SAR p. 2.4-296) 

 Assumes radionuclides move with the flowing water down the borehole fracture to the 

saturated zone and are slowed only due to matrix diffusion of dissolved radionuclides 

from the fracture into the rock matrix 

 Considers only radionuclides transported by water from the waste package to the 

saturated zone by way of the borehole in the exposure scenario 
18-6 

The quantity of water that enters the waste package and matrix diffusion in the borehole are key 

aspects of the representation of the human intrusion event that affect the estimated doses 

(e.g., infiltration was identified as an important parameter affecting the expected dose in 

SAR Figure 2.4-173). The quantity of water that enters the waste package through the 

borehole affects the release of radionuclides that are solubility limited (e.g., the release of 

solubility-limited radionuclides such as Np-237 will commonly be proportional to the amount of 

water leaving the waste package). 

DOE described how the amount of water that enters the waste package through the borehole is 

limited to the seepage entering the borehole (deep percolation is assumed to pass directly into 

the borehole opening). Other processes (e.g., drift seepage water splashing on the waste 

package surface and entering the waste package through the hole created by the borehole) 

were evaluated and determined to not significantly add to the quantity of water entering the 

borehole, as described in DOE Enclosure 4, Section 1.1 (2009bj). DOE also described the 

basis for the process of matrix diffusion in the borehole, which can potentially delay both sorbing 

and nonsorbing radionuclide transport by providing a means for radionuclides to move from the 

relatively fast-flowing water in the borehole fracture into the slower moving water in the porous 

matrix of the rubble in the borehole. Although water in the borehole is estimated to take 

approximately 3 years to move through the unsaturated zone to the saturated zone, nonsorbing 

(I-129) and sorbing (Np-237) radionuclides are estimated to be delayed approximately 1,250 

and 64,000 years, respectively, as outlined in DOE Enclosure 5, p. 8 (2009bj). DOE described 

in DOE p. 2 (2009cp) that this effect is due to the large effective surface area for communication 

between the fracture and the matrix in the degraded borehole and along the borehole. 

DOE also evaluated the potential effect on repository performance if the borehole penetrated a 

perched water zone (i.e., groundwater separated from an underlying body of groundwater by an 

unsaturated zone) below the repository. If radionuclides were present in a perched water zone, 

the borehole penetration of a perched water zone could potentially affect the transport of 

radionuclides from within the perched zone to the saturated zone. DOE described that the 

effect of the borehole would be limited because (i) perched water zones below the repository 

are isolated and have limited volume, as described in DOE Enclosure 6, p. 2 (2009bj); (ii) the 

significance of an equivalent 20.3-cm [8-in]-diameter borehole to capture and divert any lateral 

flow associated with the perched water is expected to be small because the area associated 

with fractures in the rock is more than 10,000 times greater than the area associated with the 

borehole, as identified in DOE Enclosure 6, p. 5 (2009bj); and (iii) the performance assessment 

already includes fast transport times in fault zones, which would not be significantly influenced 

by another fast pathway—namely, the borehole—as outlined in DOE Enclosure 6, p. 6 (2009bj). 

NRC Staff Evaluation of Representation of Intrusion Event 

The NRC staff has evaluated DOE’s technical basis supporting its separate performance 

assessment for the postulated human intrusion event. The NRC staff notes that DOE’s 

separate performance assessment for the human intrusion event is reasonable for the following 

reasons.

 Assumptions for the method of transport from the waste package are reasonable. 

DOE’s approach described on SAR p. 2.4-295 provided for radionuclide release from the 

waste package directly following the assumed time of the human intrusion event. The 

releases from the waste package pass directly into the borehole and travel down the 
18-7 

borehole via water in an assumed continuous fracture all the way to the saturated zone 

{i.e., via the flow of water in a single small fracture with an approximate fracture aperture 

of 3 mm [0.12 in]}, which was estimated to have an average water travel time through 

the unsaturated zone to the saturated zone of a few years, as described in DOE 

Enclosure 5, p. 6 (2009bj). DOE’s approach for the transport of radionuclides from a 

breached waste package is reasonable because the assumed continuous fracture path 

provides (i) a consistent method of transport among the models of the Total System 

Performance Assessment that connects the releases from the waste package to the 

saturated zone and (ii) water travel times in this continuous fracture path on the order of 

a few years are considered to be a conservative estimate for travel from the waste 

package through the unsaturated zone to the saturated zone compared to the 100-year 

travel times DOE has presented for fault zones, which are also characterized as a 

continuous “fracturelike” path through the unsaturated zone to the saturated zone [DOE 

Enclosure 6, p. 6 (2009bj)]. Further, travel times on the order of years represent no 

significant delay for water down the borehole; thus, this value would not result in the 

dose being underestimated. 

 Additionally, DOE determined the amount of seepage water that enters the waste 

package from the borehole, which provides the water flux for advective transport of 

radionuclides out of the waste package using the same deep percolation values from the 

performance assessment for individual protection and assuming that this seepage enters 

the cross-sectional area of the borehole, as outlined in SAR p. 2.4-317. This approach, 

which assumes all water flowing downward in the borehole enters the waste package, is 

reasonable because it is consistent with the movement of deep percolation vertically 

downward toward the repository horizon. Additionally, as DOE evaluated in DOE 

Enclosure 4, pp. 2–3 (2009bj), the potential for other adjacent seepage water entering 

the drift to enter the waste package is limited due, in part, to the limited distance that 

such seepage water “splashing” on the corroded waste package surface could travel and 

enter the borehole opening into the waste package. 

 Physical processes associated with the postulated human intrusion have 

been verified. 

Matrix diffusion in the borehole is the primary means for delay of radionuclides 

transported from the waste package through the unsaturated zone to the saturated zone. 

In DOE Enclosure 5, pp. 8–11 (2009bj), DOE provided support to verify its approach for 

matrix diffusion within the borehole by providing a comparison with an analytical solution, 

which had essentially an identical match between the human intrusion approach in the 

performance assessment and the analytical solution. DOE also explained differences 

between matrix diffusion within the borehole and its approach for representing matrix 

diffusion within the unsaturated zone in the performance assessment for individual 

protection using the Active Fracture Model. DOE explained in DOE Enclosure 1, p. 1 

(2009cp) that the Active Fracture Model was developed for fracture networks rather than 

a single fracture (as in the borehole) and, therefore, is not appropriate for the human 

intrusion borehole pathway. DOE’s approach of representing the borehole as a single 

fracture is reasonable because of the limited diameter of the borehole and, as described 

under the previous bullet of this evaluation, the water travel time in the single fracture 

is on the order of a few years, representing no significant delay for water moving down 

the borehole. 
18-8 

 The uncertainty in the results is consistent with the postulated intrusion event. 

The results of the treatment of uncertainty, as displayed in the spread of dose curves in 

SAR Figures 2.4-11 and 2.4-159, are consistent with (i) the radionuclide inventory of the 

intruded waste package and (ii) the release and transport characteristics of the soluble, 

nonsorbing radionuclides (e.g., Tc-99 and I-129 are the main contributors to the peak 

dose shortly after the human intrusion) and less soluble, sorbing radionuclides 

(i.e., Pu-242 and Np-237, which are main contributors to dose long after the human 

intrusion occurs). 

 Additionally, the NRC staff performed a confirmatory calculation to understand the 

magnitude of releases to the location of the RMEI due to the human intrusion event. 

Using the average dose curve and the average biosphere dose conversion factors from 

DOE’s performance assessment, the NRC staff has calculated the magnitude of the 

releases for radionuclides most relevant to the dose calculation. For those radionuclides 

that do not sorb onto rock surfaces or corrosion products, especially Tc-99 and I-129, a 

very large fraction of the inventory for these radionuclides (on the order of 1 percent over 

a 100-year period) must be released to the RMEI location to produce the peak dose. 

Conversely, releases of radionuclides that do sorb onto rock surfaces, especially Pu-242 

and Np-237, release a much smaller fraction than 0.01 percent over a 100-year period to 

sustain the peak dose for these radionuclides. The results of this confirmatory 

calculation are consistent with the postulated human intrusion event and the relevant 

aspects of the performance assessment, including uncertainties, DOE used in the 

individual protection calculation (see TER Section 2.2.1.4.1 for further details on the 

performance assessment used for individual protection).

 The sampling method ensures the range for uncertain parameters are sampled. 

DOE used the stratified Monte Carlo technique (Latin hypercube sampling) for sampling 

uncertain parameters, which also was used for the performance assessment for 

individual protection. DOE’s approach to sampling ensures the sampled parameters 

were sampled across their range of uncertainty because (i) stratified Monte Carlo 

sampling is a common sampling approach used in analyses involving uncertain 

parameters such as waste disposal, (ii) DOE considered alternative sampling 

combinations (called “replicates” in SAR Section 2.4.3.3.3) that all resulted in nearly 

identical dose curves, and (iii) scatter plots (SAR Figures 2.4-174 and 2.4-176) showed 

that DOE’s sampling approach produced sampled values with a range of uncertainty 

[the scatter plots presented sampled values for an uncertain parameter (x-axis) 

versus the resulting dose for the TSPA simulation that used the specific value for the 

parameter (y-axis)]. 

 DOE also evaluated the potential effects on performance from a borehole penetrating 

a perched water zone. The NRC staff evaluated DOE-provided information to 

understand how a borehole might affect the transport of radionuclides from a 

perched water zone. DOE explained that the potential for a borehole penetrating a 

perched water zone to significantly impact repository performance is limited; this is 

reasonable because (i) a single, 20.3-cm [8-in]-diameter borehole (cross-sectional area 

less than 1/10 of a square meter or approximately 1/3 square foot) filled with rubble does 

not represent a significant feature that could divert significant water flow relative to the 

unsaturated flow already occurring in faults and fractures beneath the repository footprint 

of approximately 5.7 million m2

 [61 million ft2] (SAR p. 2.3.1-85) [e.g., DOE estimated in 
18-9 

DOE Enclosure 6, p. 5 (2009bj) that 30 percent of the total water flux below the 

repository reaches the water table via faults]; (ii) given the borehole would primarily 

affect water flow in the matrix (e.g., water flow in fractures and faults would not be that 

dissimilar from the continuous fracture path assumed for the borehole), the impact is 

limited due to the small amount of water that reaches the water table below Yucca 

Mountain via matrix flow [i.e., less than 20 percent of the flux at the water table is from 

matrix flow with the remaining flux coming from fractures and faults, as identified in DOE 

Enclosure 6, Table 1 (2009bj)]; and (iii) perched water is of somewhat limited areal 

extent {i.e., DOE estimated an equivalent area of 900 m2

 [9,700 ft2] for the radius of a 

perched zone, as described in DOE Enclosure 6, p. 4 (2009bj), which occurs mostly in 

the northern part of the repository where fracture flow is more prevalent, as outlined in 

DOE Enclosure 6, p. 2 (2009bj)}. 

2.2.1.4.2.3.3 Annual Dose to RMEI 

Description of DOE’s Approach 

DOE presented the dose curve for the human intrusion scenario in SAR Figure 2.4-11. The 

peak of the mean dose curve is approximately 0.0001 mSv/yr [0.01 mrem/yr] shortly after the 

time of the intrusion (i.e., 200,000 years). DOE performed tests to determine the computational 

stability of the average dose curve for the human intrusion calculation (SAR Section 2.4.3.3.3). 

The tests (i) computed three replicates to allow for different combinations of sampled values 

over their parameter ranges (SAR Figure 2.4-160); (ii) increased the number of aleatory 

samples from 30 to 90 (SAR Figure 2.4-161), and (iii) refined the timestep scheme, as shown in 

SNL Figures 7.3.3-10[a] and 7.3.3-11[a] (2008ag). DOE determined that expected doses were 

relatively unaffected (i.e., stable) by changes in values of sampled parameters, sample size, 

and timestepping. 

NRC Staff Evaluation of Annual Dose to RMEI 

The NRC staff notes that DOE’s estimated peak dose of 0.0001 mSv/yr [0.01 mrem/yr] for the 

human intrusion calculation is reasonable for the following reasons.

 The TSPA for the human intrusion calculation is performed separately from the 

TSPA for individual protection and is consistent with the YMRP and other 

guidance criteria. 

DOE developed a separate dose curve (SAR Figure 2.4-11) for the human intrusion 

scenario using the separate TSPA model described in SAR Section 2.4.3. The TSPA for 

the human intrusion calculation is reasonable because DOE (i) provided the technical 

basis for the relevant attributes of the human intrusion scenario (i.e., water entering the 

waste package through the borehole and transport with the borehole), and these were 

different from the performance assessment for individual protection; (ii) accounted for 

uncertainties in the representation of the human intrusion (see TER Section 

2.2.1.4.2.3.2, NRC Evaluation, Item 2); (iii) excluded unlikely features, events, and 

processes (see TER Section 2.2.1.4.2.3.1, NRC Evaluation, Items 3, 4, and 5); and 

(iv) provided a comparison with an alternative model [i.e., the analytical solution of 

Sudicky and Frind described in DOE Enclosure 5, Figure 2 (2009bj)] to support the 

approach for matrix diffusion in the borehole. 
18-10 

 The TSPA model for the human intrusion calculation assumes the specified 

characteristics. 

Those portions of the performance assessment that DOE used to represent 

the human intrusion event included that (i) there is a single exploratory borehole 

that intersects the waste package providing a conduit to the saturated zone, and as a 

result, water enters the waste package and transports radionuclides from the intersected 

waste package to the saturated zone (SAR pp. 2.4-293 to 2.4-298); (ii) the borehole is 

depicted as a 20.3-cm [8-in]-diameter borehole, which results in a cross-sectional area 

of 0.0324 m2

 [0.349 ft2], based on current drilling practices, that is assumed to be filled 

with rubble of collapsed host rock and not carefully sealed, and particulate waste 

material does not fall into the borehole (SAR pp. 2.4-293 to 2.4-298); and (iii) unlikely 

features, events, and processes were excluded (see TER Section 2.2.1.4.2.3.1 for the 

review of the excluded events). 

 Those portions of the performance assessment for human intrusion that have not been 

“stylized” to represent the human intrusion event are consistent with the performance 

assessment used for individual protection. The TSPA model for the human intrusion 

calculation and the performance assessment for individual protection are the same 

(i.e., any differences would not result in a significant underestimation of the peak dose 

for the human intrusion calculation) regarding the following relevant portions of the 

performance assessment used for individual protection: releases from the waste 

package, where releases are affected by solubility limits; degradation rates for the waste 

forms and sorption onto corrosion products; radionuclide transport in the saturated zone; 

and the representation of the characteristics of the biosphere and RMEI, which includes 

biosphere dose conversion factors. 

 The estimate of the mean dose is statistically stable. 

The NRC staff reviewed SAR Section 2.4.3 as well as the relevant information in SNL 

Section 7.3 (2008ag). DOE provided reasonable support for the statistical stability of the 

expected dose because DOE considered a range of tests (i.e., different combinations of 

sampled values, increased aleatory sample size, and reduced timesteps), all of which 

resulted in dose curves that do not change the overall result that the peak dose is on the 

order of 0.0001 mSv/yr [0.01 mrem/yr] [see SAR Figures 2.4-160(a) and 2.4-161 and 

SNL Figures 7.3.3-10(a) and 7.3.3-11(a) (2008ag)]. 

 The dose estimate is consistent with the overall repository performance and the 

assumed characteristics of the human intrusion scenario. 

The human intrusion scenario occurs at 200,000 years and intercepts a single waste 

package. Thus, it is expected that any radionuclide with a radioactive half-life on the 

order of 20,000 years and less, without a long-lived parent radionuclide, would decay 

sufficiently prior to the intrusion event to significantly limit the contribution to dose. 

The DOE’s TSPA results for the human intrusion calculation are consistent with the 

concept that radionuclides with long half-lives are expected to be the largest 

contributors to dose [i.e., significant contributors to peak dose are Tc-99 (half-life of 

213,000 years), I-129 (half-life of 15.7 million years), Pu-242 (half-life of 376,300 years), 

Se-79 (half-life of 1.13 million years), Cs-135 (half-life of 2.3 million years), and Np-237 

(half-life 2.14 million years), as shown in SAR Figure 2.4-159]. Additionally, both the 

human intrusion scenario (SAR Figure 2.4-159) and the waste package early failure 
18-11 

modeling case [SAR Figure 2.4-18(b)] result in peak doses over the million-year period 

on the order of .0001 mSv/yr [0.01 mrem/yr], consistent with the characteristic that the 

human intrusion scenario considers one waste package and the waste package early 

failure modeling case considers approximately one failed waste package (see TER 

Section 2.2.1.4.1.3.2). On the basis of these reasons, the NRC staff notes that DOE’s 

estimated dose for the human intrusion calculation is reasonable. 

2.2.1.4.2.4 NRC Staff Conclusions 

NRC staff notes that DOE’S representation of repository performance in its Total System 

Performance Assessment (TSPA) for the human intrusion calculation is consistent with the 

guidance in the YMRP. NRC staff also notes that the DOE technical approach for its TSPA and 

the TSPA results, including the timing of the intrusion event and average annual dose values, 

discussed in this chapter are reasonable. 

2.2.1.4.2.5 References 

DOE. 2009bj. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.4.4) Safety Evaluation Report Vol. 3, 

Chapters 2.2.1.4.1, 2.2.1.4.2, and 2.2.1.4.3, Set 1.” Letter (July 29) J.R. Williams to J.H. Sulima 

(NRC). ML092110472. Washington, DC: DOE, Office of Technical Management. 

DOE. 2009cp. “Yucca Mountain—Supplemental Response to Request for Additional 

Information Regarding License Application (Safety Analysis Report Section 2.4.3), Safety 

Evaluation Report Vol. 3, Chapter 2.2.1.4.2, Set 1.” Letter (October 20) J.R. Williams to 

J.H. Sulima (NRC). ML092940188. Washington, DC: DOE, Office of Technical Management. 

DOE. 2008ab. DOE/RW–0573, “Yucca Mountain Repository License Application.” Rev. 0. 

ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

ML032030389. Washington, DC: NRC. 

SNL. 2008ag. “Total System Performance Assessment Model/Analysis for the License 

Application.” MDL–WIS–PA–000005. Rev. 00. AD 01, ERD 01, ERD 02, ERD 03, ERD 04. 

ML090790353. Las Vegas, Nevada: Sandia National Laboratories. 

19-1 

CHAPTER 19 

2.2.1.4.3 Separate Groundwater Protection Calculation 

2.2.1.4.3.1 Introduction

This section of the Technical Evaluation Report (TER) provides the U.S. Nuclear Regulatory 

Commission (NRC) staff’s evaluation of the U.S. Department of Energy’s (DOE’s) Total 

System Performance Assessment (TSPA) for the separate groundwater protection calculation, 

as presented in DOE’s Safety Analysis Report (SAR) Section 2.4.4 (DOE, 2008ab). 

10 CFR Part 63 provides separate standards to protect the groundwater resources in the 

vicinity of Yucca Mountain and the approach to be taken to estimate the concentration of 

radionuclides in groundwater. This approach (10 CFR 63.331) is similar to that used in 

estimating dose to the reasonably maximally exposed individual (RMEI). There are three 

distinct groups of radionuclides evaluated under groundwater protection: (i) radionuclides that 

are characterized as alpha emitters (e.g., Np-237) (this group explicitly excludes radon and 

uranium); (ii) radionuclides that are characterized as beta- and photon-emitting radionuclides 

(e.g., I-129, Tc-99); and (iii) the combined concentration of Ra-226 and Ra-228 released from 

the repository and the natural background levels of Ra-226 and Ra-228 in the groundwater. 

There are a number of similarities in the performance assessment used for the individual 

protection calculation and the performance assessment used for the separate groundwater 

protection calculation. 

2.2.1.4.3.2 Evaluation Criteria

Separate groundwater protection standards for the initial 10,000 years after closure of the 

repository are in 10 CFR 63.331. Provisions in 10 CFR 63.331, 63.332, and 63.342 specify 

constraints for the performance assessment used for the groundwater protection calculation and 

the determination of the “representative volume” of groundwater (i.e., the volume of water used 

to estimate the concentration of radionuclides). Other criteria applicable to the performance 

assessments for the individual protection, human intrusion, and separate groundwater 

protection calculations are described in TER Section 2.2.1.4.1.2 (Evaluation Criteria) under the 

individual protection calculation. The criteria specific to the separate groundwater protection 

calculation are summarized next. 

Performance Assessment for Groundwater Protection Calculation 

 The performance assessment excludes unlikely features, events, and processes (FEPs) 

that are estimated to have less than 1 chance in 100,000 per year of occurring 

[10 CFR 63.342(b)]. 

 The performance assessment excludes the consideration of human intrusion 

(i.e., representing undisturbed performance) (10 CFR 63.331). 

Representative Volume 

 The representative volume is the volume of groundwater that would be withdrawn 

annually from an aquifer containing less than 10,000 mg of total dissolved solids per liter 

of water [10 CFR 63.332(a)]. 
19-2 

 The concentration of radionuclides that will be released from the Yucca Mountain 

repository is determined for the representative volume of groundwater 

[10 CFR 63.332(a)]. 

 The position and dimensions of the representative volume use average hydrologic 

characteristics that include the highest concentration level in the plume of contamination 

in the accessible environment [10 CFR 63.332(a)(1) and (2)]. 

 The representative volume contains 3,000 acre-ft {about 3,714,450,000 L 

[977,486,000 gal]} of water [10 CFR 63.332(a)(3)]. 

Concentrations To Be Estimated for the Separate Groundwater Protection Calculation 

The following concentrations are to be estimated for the separate groundwater 

protection calculation (10 CFR 63.331): 

 The combined concentration of Ra-226 and Ra-228 from repository releases (including 

natural background radiation presently in groundwater at Yucca Mountain) 

 The concentration for gross alpha activity (including Ra-226 but excluding radon and 

uranium), including natural background radiation presently in groundwater at Yucca 

Mountain (Np-237 is an example of an alpha-emitting radionuclide) 

 The combined concentration of beta- and photon-emitting radionuclides from repository 

releases and the associated dose per year to the whole body or any organ {on the basis 

of drinking 2 L [0.53 gal] of water per day from the representative volume} (Tc-99 and 

I-129 are examples of beta- and photon-emitting radionuclides) 

The performance assessment for the individual protection calculation is the same performance 

assessment used for the groundwater protection calculation (i.e., except for differences due to 

the regulatory requirement that unlikely events are not to be included in the performance 

assessment used for groundwater protection, there are no differences between the performance 

assessment used for estimating individual dose and the performance assessment used for 

estimating concentrations for the separate groundwater protection calculation that would result 

in a significant underestimation of the concentration of radionuclides in groundwater). As a 

result, the NRC staff review of DOE’s groundwater protection analysis focused on DOE’s 

determination of the representative volume and the approach for estimating the radionuclide 

concentrations. The performance assessment for individual protection is reviewed in TER 

Section 2.2.1.4.1. 

The NRC staff’s review followed the guidance in the Yucca Mountain Review Plan (YMRP) 

(NRC, 2003aa). Because DOE assumed that all radionuclides which reach the accessible 

environment in a given year are included in the annual water demand of 3,000 acre-ft, YMRP 

Section 2.2.1.4.3.1 states that the NRC staff may conduct a simplified review. 

2.2.1.4.3.3 Technical Evaluation 

The NRC staff review of DOE’s separate groundwater protection calculation focused on those 

portions of the analysis that are distinct to the groundwater protection analysis. Specifically, the 

NRC staff review focused on DOE’s approach for including the highest concentration level of the 
19-3 

plume in the representative volume, the dimensions of the representative volume, and the 

concentration of radionuclides in groundwater. 

2.2.1.4.3.3.1 Representative Volume Location

DOE used the same performance assessment model for evaluating the separate groundwater 

protection calculation as it used for evaluating the individual protection calculation in the sense 

that the model abstractions for flow paths in the saturated zone and radionuclide transport in the 

saturated zone are the same. However, DOE excluded the consideration of unlikely FEPs from 

the performance assessment used for groundwater protection (i.e., igneous activity and low 

probability seismic events are excluded). The location of the representative volume of 

groundwater was consistent with the approach used for the determining the pathway for 

radionuclide transport to the location of the reasonably maximally exposed individual, which is 

approximately 18 km [11 mi] south of the repository, as identified in the Safety Analysis Report 

(SAR) Volume 2, p. 2.1-1 (DOE, 2008ab). Additionally, DOE used the same approach for 

determining the concentration of radionuclides in groundwater for both the separate 

groundwater protection and individual protection calculations {i.e., the annual average 

radionuclide concentration, due to releases from the repository, was determined by assuming 

that all radionuclides that reach the RMEI location in a given year are included in 3,000 acre-ft, 

which is the annual water demand for the individual protection calculation and the 

representative volume for groundwater (SAR Section 2.4.4)}. 

NRC Staff Evaluation of the Representative Volume Location 

The NRC staff has reviewed SAR Section 2.4.4 and noted that DOE’s approach for identifying 

the location of the representative volume and including the highest concentration within the 

plume in the accessible environment is reasonable for the following reasons:

 The location of the representative volume is approximately 18 km [11 mi] south of the 

repository. This location is based on DOE’s specification for the postclosure controlled 

area, which extends the southern boundary of the controlled area to 3640’13.6661” 

north latitude (SAR Volume 2, p. 2.4-7). Thus the location of the representative volume 

is in the accessible environment immediately outside the controlled area. 

 The location of the representative volume has been determined consistent with the 

radionuclide transport paths in the performance assessment used for the individual 

protection calculation because the same radionuclide transport paths were used in the 

performance assessment for individual protection and the performance assessment for 

groundwater protection (SAR Volume 2, p. 2.4-337). 

 The location of the representative volume ensures that all radionuclides released to the 

accessible environment are considered in the assessment because DOE’s Total System 

Performance Assessment (TSPA) assumes all radionuclides are captured in the 

representative volume (i.e., “total radionuclide capture,” SAR Volume 2, p. 2.4-337). 

 The highest concentration level of radionuclides in the plume of contamination in the 

accessible environment is included in the representative volume because all 

radionuclides (i.e., the entire plume of contamination, which includes the highest 
19-4 

concentration level of radionuclides) released into the accessible environment are 

included in the representative volume that is annually withdrawn (SAR Volume 2, 

p. 2.4-337). 

2.2.1.4.3.3.2 Representative Volume Dimensions

DOE used the models and assumptions to estimate flow and transport paths in the saturated 

zone for the individual protection calculation and for estimating the dimensions of the 

representative volume. DOE estimated, using the slice of the plume method, that dimensions of 

a width of 3,000 m [9,842 ft], a depth of 200 m [656 ft] and a length of 30 m [98 ft] in the 

direction of groundwater flow would include all the simulated flow paths of radionuclides 

crossing into the accessible environment (SAR Volume 2, p. 2.4-337). DOE estimated these 

dimensions using average properties for hydrologic parameters such as groundwater flow rate 

and alluvium flow porosity (SAR Volume 2, p. 2.4-337). DOE calculated the representative 

volume with these dimensions that yielded a volume of approximately 3,000 acre-ft. 

DOE also presented a more detailed depiction of the cross section of the plume in the 

accessible environment to further support the dimensions of the representative volume. The 

more detailed analysis was based on numerous particle tracks, provided in DOE Enclosure 7, 

Figure 1 (2009bj), representing potential release points for repository releases to the saturated 

zone using the saturated zone site-scale flow model. Although the cross section of the plume, 

based on the particle traces, is not a rectangular shape, DOE estimated that a rectangular 

shape of approximately 3,300 m [10,827 ft] in width (horizontally) by 220 m [722 ft] in depth 

(vertically) would enclose the horizontal and vertical extent of the plume cross section {an area 

of 726,000 m2

 [7.8 million ft2]}. DOE estimated that approximately 40 percent of this rectangular 

shape did not contain any significant portion of the plume; thus, DOE estimated a crosssectional area of 435,000 m2

 [4.7 million ft2] given the irregularities of the shape produced by the 

particle traces depicted in DOE Enclosure 7, Figure 1 (2009bj). DOE’s simple rectangular 

approximation {i.e., 3,000 by 200 m [9,842 by 656 ft]} results in a cross-sectional area of 

600,000 m2

 [6.4 million ft2], which provides a value between the two values calculated from the 

particle tracks {one a rectangular shape of 726,000 m2

 [7.8 million ft2] and the other an irregular 

shape of 435,000 m2

 [4.7 million ft2]}. The third dimension of the representative volume was 

selected to obtain the volume of 3,000 acre-ft. DOE calculated the third dimension, or the 

length parallel to the flow direction (i.e., perpendicular to the cross section), to be approximately 

34.4 m [113 ft] on the basis of the cross-sectional area of 600,000 m2

 [6.4 million ft2] and an 

average effective porosity of 0.18, as identified in DOE Enclosure 7, p. 4 (2009bj). 

DOE used water quality data from the Alluvial Testing Complex (SAR Volume 2, p. 2.4-334) 

to determine that there were fewer than 500 mg/L [500 ppm] of total dissolved solids in the 

aquifer in the accessible environment. 

NRC Staff Evaluation of the Representative Volume Dimensions 

The dimensions of the representative volume are to include the highest concentration level in 

the plume of contamination. DOE determined (i) the dimensions of the representative volume 

using the slice of the plume approach and (ii) these dimensions are sufficient to capture all the 

releases into the accessible environment. The NRC staff reviewed SAR Section 2.4.4 and 

determined that the dimensions of the representative volume are reasonable because (i) the 

dimensions are sufficient to capture the entire radionuclide plume and thus include the highest 

concentration levels in the plume and (ii) the dimensions are supported by particle tracks that 

used the hydrologic characteristics of the site and releases from the engineered barrier system.
19-5 

Specifically, the NRC staff notes that 

 The representative volume of groundwater analyzed by DOE is within an aquifer containing 

fewer than 10,000 mg/L of total dissolved solids and no more than 3,000 acre-ft 

 DOE estimated the dimensions of the representative volume on the basis of the slice of the 

plume method 

 DOE used (i) average hydrologic characteristics representative of the aquifers along the flow 

paths in the saturated zone and (ii) the flow paths predicted by the saturated zone site-scale 

flow model used for the performance assessment (SAR Volume 2, p. 2.4-337) 

 The representative volume of groundwater of 3,000 acre-ft is consistent with the water 

usage of the reasonably maximally exposed individual (i.e., annual water demand of 

3,000 acre-ft) 

 The dimensions of the representative volume (i) do not exclude any radionuclides from the 

estimate of the concentration of radionuclides in the representative volume (i.e., all 

radionuclides are assumed to lie within the dimensions of the representative volume that is 

annually withdrawn) and (ii) are reasonably consistent with the estimated shape of the 

contaminant plume 

Additionally, DOE used a particle tracking approach to support the dimensions of the 

representative volume. The NRC staff evaluated DOE’s particle tracking approach and notes 

the following: 

 DOE’s particle tracking approach released particles over the entire repository footprint, 

which provided the initial areal extent of the potential plume. This initial release area is 

consistent with the performance of the repository regarding the potential for damaged 

packages over the entire footprint. The performance assessment for the groundwater 

protection calculation is primarily influenced by the seismic ground motion modeling 

case (see SAR Figure 2.4-181). DOE presented information in the SAR that shows 

the seismic ground motion modeling case results in a significant number of codisposal 

waste packages (e.g., hundreds of waste packages) being breached due to stress 

corrosion cracks prior to 10,000 years (see SAR Figures 2.4-19 and 2.4-77). TER 

Sections 2.2.1.3.2 and 2.2.1.4.1 further detail the extent of damage to codisposal waste 

packages in the seismic ground motion modeling case. 

 DOE’s particle tracking approach used the saturated zone site-scale model consistent 

with the performance assessment abstraction for flow paths in the saturated zone (see 

TER Section 2.2.1.3.8 regarding NRC staff review of the saturated zone site-scale flow 

model). Thus, the spreading of the plume during transport to the accessible 

environment uses the same hydrologic characteristics reviewed for the performance 

assessment used for individual protection (see TER Sections 2.2.1.3.8 and 2.2.1.3.9). 

 The two values DOE estimated from the detailed analysis for the cross-sectional area of 

the representative volume {one a rectangular shape of 726,000 m2

 [7.8 million ft2] and 

the other an irregular shape of 435,000 m2

 [4.7 million ft2]} bound the value of 

600,000 m2

 [6.4 million ft2] DOE specified for the representative volume. Given 
19-6 

dimensions of this magnitude {i.e., hundreds of thousands of square meters [millions of 

square feet]}, it is reasonable to assume a significant portion of the releases of 

radionuclides into the accessible environment would be captured in the representative 

volume. DOE assumed all the radionuclides are released into the representative 

volume; thus the concentration of radionuclides in the representative volume did not 

change based on changes to the dimensions of the representative volume for the 

range of values estimated from the detailed analysis. 

2.2.1.4.3.3.3 Concentration of Radionuclides in the Representative Volume

DOE determined the average concentration of radionuclides, due to repository releases, by 

assuming the annual releases of radionuclides were all included in the representative volume of 

3,000 acre-ft and determined the dose to the whole body and individual organs for the beta- and 

photon-emitting radionuclides on the basis of drinking 2 L [0.53 gal] per day of water at the 

concentration level estimated for the representative volume (SAR Section 2.4.4.1.1.4). DOE 

also estimated the natural background level of radioactivity presently in the groundwater at 

Yucca Mountain for Ra-226, Ra-228, and the alpha-emitting radionuclides, excluding radon and 

uranium (SAR Section 2.4.4.1.1.3). 

DOE estimated the combined concentrations for Ra-226 and Ra-228, due to releases 

from the repository and the natural background radiation presently in the groundwater at 

Yucca Mountain, was 0.5 pCi/L with the largest contribution coming from natural background 

radiation (i.e., the largest annual release of Ra-226 and Ra-228 into the representative 

volume from the repository was estimated to be almost 1 million times less than the natural 

background level). 

DOE estimated the concentration for the gross alpha activity, due to releases from the 

repository and the natural background radiation presently in the groundwater at Yucca Mountain 

(excluding radon and uranium), was 0.5 pCi/L with the largest contribution coming from 

natural background radiation (i.e., the largest annual release of the relevant alpha-emitting 

radionuclides into the representative volume from the repository was estimated to be more than 

1,000 times less than the natural background levels). 

DOE estimated the dose from beta- and photon-emitting radionuclides, due to releases from the 

repository, to be 0.0006 mSv/yr [0.06 mrem/yr] for the whole body and the largest dose to any 

organ to be 0.0026 mSv/yr [0.26 mrem/yr] (e.g., dose to the thyroid from I-129) as result of 

drinking 2 L [0.53 gal] of water per day assumed to be at a concentration level of radionuclides 

in the representative volume. (Natural background radiation is not considered for beta- and 

photon-emitting radionuclides in the separate groundwater protection calculation.) 

NRC Staff Evaluation of the Concentration of Radionuclides in the 

Representative Volume 

The NRC staff has reviewed SAR Section 2.4.4 and determines that DOE’s analysis for the 

level of radioactivity in the representative volume is reasonable. The NRC staff notes that

 The same performance assessment used for the individual protection calculation 

was used for the separate groundwater protection calculation (see TER 

Section 2.2.1.4.1 for details regarding the review of the performance assessment 

used for individual protection)
19-7 

 Unlikely natural FEPs are excluded from the performance assessment for the separate 

groundwater protection calculation (SAR Volume 2, p. 2.4-328) 

 The effects of human intrusion are not included in the performance assessment for the 

separate groundwater protection calculation (i.e., undisturbed performance was 

evaluated) (SAR Volume 2, p. 2.4-329) 

 The average concentrations from repository releases are consistent with the 

performance assessment used for the individual protection calculation for the initial 

10,000 years (e.g., number and types of waste package failures) and the specific 

constraints on the performance assessment used for the separate groundwater 

protection calculation (e.g., exclusion of unlikely FEPs) (see TER Section 2.2.1.4.1 for 

further details) 

 The average concentrations from repository releases are determined by dividing the 

annual flux of radionuclides crossing into the accessible environment by the 

representative volume of 3,000 acre-ft that is withdrawn annually (SAR Volume 2, 

p. 2.4-329) 

 DOE estimated the mean natural background activity concentration for the combined 

Ra-226 and Ra 228 and for the relevant alpha-emitting radionuclides (i.e., excluding 

radon and uranium) using samples collected in the vicinity of the RMEI location (SAR 

Section 2.4.4.1.1.3) and other locations 

 Dose estimates for beta- and photon-emitting radionuclides consider the highest dose 

among the whole body or any organ on the basis of drinking 2 L [0.53 gal] per day from 

the representative volume 

2.2.1.4.3.4 NRC Staff Conclusions

NRC staff notes that DOE’s representation of repository performance in its Total System 

Performance Assessment (TSPA) for the separate groundwater protection calculation is 

consistent with the guidance in the YMRP. NRC staff also notes that the DOE technical 

approach for its TSPA and the TSPA results, including the concentrations of radionuclides 

and the location and dimensions of the representative volume, discussed in this chapter 

are reasonable. 

2.2.1.4.3.5 References

DOE. 2009bj. “Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 2.4.4) Safety Evaluation Report Vol. 3, 

Chapters 2.2.1.4.1, 2.2.1.4.2, and 2.2.1.4.3, Set 1.” Letter (July 29) J.R. Williams to J.H. Sulima 

(NRC). Enclosures (8). ML092110472. Las Vegas, Nevada: DOE, Office of Civilian 

Radioactive Waste Management. 

DOE. 2008ab. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository License 

Application.” Rev. 00. ML081560400. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

ML032030389. Washington, DC: NRC. 

20-1 

CHAPTER 20 

2.5.4 Expert Elicitation 

2.5.4.1 Introduction 

This chapter of the Technical Evaluation Report (TER) evaluates the information provided in the 

U.S. Department of Energy (DOE) Safety Analysis Report (SAR) for uses of expert elicitation. 

DOE’s uses are described in SAR Section 5.4 (DOE, 2009av). 

Expert elicitation is a formal, structured, and well-documented process for obtaining the 

judgments of multiple experts. The U.S. Nuclear Regulatory Commission (NRC) routinely 

accepts, for review, expert judgments used to evaluate and interpret the factual bases of SARs. 

In previous interactions between NRC and DOE, NRC staff acknowledged that DOE could elect 

to use the subjective judgments of experts, or groups of experts, to interpret data and address 

technical issues and inherent uncertainties when assessing the long-term performance of a 

geologic repository. In its SAR, DOE used the results of three formal expert elicitations to 

complement and supplement other sources of scientific and technical information such as data 

collection, analyses, and experimentation. In this context, the NRC staff reviewed DOE’s use of 

expert elicitation regarding the proposed geologic repository at Yucca Mountain. 

In supporting its SAR, DOE presented the results of three expert elicitations in the areas of 

seismic hazard (SAR Section 2.2.2.1), igneous activity (SAR Sections 1.1.6.2, 2.2.2.2, and 

2.3.11), and saturated zone flow and transport (SAR Section 2.3.9.2). SAR Section 5.4 

summarized DOE’s bases for its assertion that these elicitations were conducted in a manner 

that is generally consistent with NRC guidance. In conducting its review of DOE’s use of expert 

elicitation, NRC staff sought to verify that DOE followed the process suggested in NUREG–1563 

(NRC, 1996aa), or some other equivalent stepwise process, such as that outlined in 

NUREG/CR–6372 (NRC, 1997aa). 

2.5.4.2 Evaluation Criteria 

10 CFR 63.21(c)(19) requires that the SAR include an explanation of how expert elicitation 

was used. In 1996, the NRC staff published guidance for the use of expert elicitation in 

NUREG–1563 (NRC, 1996aa). NUREG–1563 provided general guidelines for deciding whether 

a formal expert elicitation would be useful, and suggested a nine-step procedure that could 

serve as one acceptable process to conduct an elicitation. The guidance explicitly states that 

the suggested procedure was not provided with the intent that it be rigidly applied. Rather, the 

guidance in NUREG–1563, p. 22 (NRC, 1996aa) provides that the suggested procedure 

“...should be viewed as a general framework for a formal elicitation that would be acceptable to 

the NRC staff.” 

Subsequent to the release of NUREG–1563, NRC staff published NUREG/CR–6372 

(NRC, 1997aa). This document, referred to informally as the Senior Seismic Hazard Analysis 

Committee (SSHAC) report, or the SSHAC guidelines, provided a process for obtaining, 

communicating, and quantifying the uncertainties associated with elicitation received from 

seismic experts in the course of conducting Probabilistic Seismic Hazard Assessments (PSHAs) 

for commercial nuclear power plants and other critical facilities. The stepwise processes for 

eliciting experts described in the SSHAC guidelines for the most formal (Level 4) analysis and 

that which is recommended in NUREG–1563 are very similar. While presented in a slightly 
20-2 

different order and structure (in seven steps as opposed to nine, respectively), the two 

documents recommend essentially the same approach for formally eliciting and documenting 

expert opinion. For example, the important content identified in NUREG–1563 as Step 4, 

“Assembly and Dissemination of Basic Information”; Step 6, “Elicitation of Judgments”; and 

Step 7, “Post-Elicitation Feedback” is not treated as discrete steps in the SSHAC guidelines. 

Instead, the SSHAC guidelines encompass the substance of all three in a single Step 5, 

referred to as “Group Interaction and Individual Elicitation.” 

NRC staff’s review of DOE’s use of expert elicitation was guided by the Yucca Mountain Review 

Plan (YMRP) Section 2.5.4 (NRC, 2003aa). YMRP Section 2.5.4.3 identifies two acceptance 

criteria: that DOE use NUREG–1563 or equivalent procedures and that any updated elicitations 

follow appropriate methods and are adequately documented. These are the only two 

acceptance criteria applicable to NRC staff’s review of DOE’s use of expert elicitation. NRC 

staff evaluated the techniques DOE used to conduct three expert elicitations to verify whether 

the elicitations either followed procedures suggested by NRC staff guidance or used equivalent 

procedures. DOE updated only one of the three elicitations. NRC staff evaluated the methods 

DOE used to update that elicitation to verify whether it was updated appropriately and 

adequately documented. 

2.5.4.3 Technical Evaluation 

This section briefly summarizes the information provided in SAR Section 5.4 for each of the 

three expert elicitations DOE used. The discussion at the end of this section provides NRC 

staff’s evaluation on the basis of the guidance in the YMRP. 

Probabilistic Volcanic Hazard Assessment (PVHA) Expert Elicitation 

SAR Section 2.2.2.2 described the DOE approach to developing a volcanic hazard assessment 

for Yucca Mountain. This overall approach included an expert elicitation to develop a PVHA for 

Yucca Mountain. DOE conducted the expert elicitation in 1995 and published the final report in 

1996 (CRWMS M&O, 1996aa). SAR Section 5.4.1 summarized DOE’s bases for how its PVHA 

was conducted in a manner generally consistent with the nine-step procedure suggested in 

NUREG–1563 (NRC, 1996aa). 

For PVHA, DOE empanelled 10 subject matter experts to assess the relevant technical issues, 

including a range of conceptual and probability models, associated uncertainties in model 

parameters, and model sensitivity to these uncertainties. The elicitation consisted of four 

workshops and two field trips to the Yucca Mountain area. Each panel member made an 

individual assessment or model of the igneous hazard on the basis of his or her interpretations 

of various probabilistic models. A logic tree approach was used to combine alternatives and to 

incorporate uncertainty. Each of the 10 experts’ probability estimates was then combined with 

equal weight to produce a probability distribution of the annual frequency of intersection of a 

basaltic dike within the proposed repository footprint. 

PSHA Expert Elicitation 

SAR Section 2.2.2.1 described DOE’s overall approach to developing a seismic hazard 

assessment for Yucca Mountain, including fault displacement hazards. This approach included 

an expert elicitation to develop a PSHA for Yucca Mountain (CRWMS M&O, 1998aa; 

BSC, 2004bj). DOE conducted its PSHA in the late 1990s using a methodology that DOE 

claims is consistent with a Level 4 expert elicitation as described in NUREG/CR–6372 
20-3 

(NRC, 1997aa). SAR Section 5.4.2 summarized DOE’s bases and concluded that this 

methodology was also generally consistent with the nine-step procedure suggested in 

NUREG-1563 (NRC, 1996aa). 

DOE’s PSHA also followed the standard framework for PSHAs in using the recurrence curve 

approach (e.g., Cornell, 1968aa; McGuire, 1976aa). The basic elements of this framework are 

(i) identification and spatial distribution of seismic sources; (ii) characterization of each source in 

terms of its activity, recurrence rates for various earthquake magnitudes, and maximum 

magnitude; (iii) description of ground motion attenuation relationships to model the distribution 

of the ground motions expected when a given magnitude earthquake occurs on a particular 

source; and (iv) incorporation of the inputs into a logic tree to integrate the seismic source 

characterization and ground motion attenuation relationships, along with their associated 

uncertainties. Each logic tree pathway is intended to represent one expert’s weighted 

interpretations of the seismic hazard at the site. The computation of the hazard for all possible 

pathways results in a distribution of hazard curves that DOE considers representative of the 

seismic hazard at a site, including variability and uncertainty. 

To accomplish the PSHA, DOE hired two panels of experts. The first expert panel consisted 

of six three-member teams of geologists and geophysicists (seismic source teams) who 

developed probabilistic distributions to characterize relevant potential seismic sources in the 

Yucca Mountain region. These distributions included location and activity rates for fault 

sources, spatial distributions and activity rates for background sources, distributions of moment 

magnitude and maximum magnitude, and site-to-source distances. The second panel consisted 

of seven seismology experts (ground motion experts) who developed probabilistic point 

estimates of ground motion for a suite of earthquake magnitudes, distances, fault geometries, 

and faulting styles. These point estimates, expressed along with estimates of their 

uncertainties, were specific to the regional crustal conditions of the western Basin and Range 

Province. The ground motion attenuation point estimates were then fitted to yield the ground 

motion attenuation equations used in the PSHA. 

Inputs from the expert teams were combined into a logic tree and the hazard computed using a 

modified version of the FRISK88 computer code (Risk Engineering, Inc., 1998aa). In the 

integration, DOE gave equal weight to all six source teams and seven ground motion experts. 

The resulting ground motion hazard curves express increasing levels of ground motion as a 

function of the annual probability that the ground motion will be exceeded. These curves 

include estimates of uncertainty. 

 

The seismic source teams also developed a Probabilistic Fault Displacement Hazard 

Assessment as part of the PSHA. In that aspect of the PSHA elicitation, the experts derived 

probabilistic fault displacement hazard curves for nine demonstration points at or near 

Yucca Mountain. These demonstration points represent a range of faulting and related fault 

deformation conditions in the subsurface and near the sites of proposed surface facilities. 

NRC staff’s technical evaluation of the geological, geophysical, and seismological 

information used to support the expert elicitation are provided in TER Sections 2.2.1.2.2.3.2 

and 2.1.1.1.3.5.2. 

Saturated Zone Flow and Transport Expert Elicitation (SZEE) 

SAR Section 2.3.9.2.2.6 discussed DOE’s use of expert elicitation to address key issues 

associated with groundwater flow and transport in the saturated zone. SAR Section 5.4.3 
20-4 

summarized DOE’s bases for asserting that SZEE was conducted in a manner generally 

consistent with the nine-step procedure suggested in NUREG–1563 (NRC, 1996aa). 

In 1997, DOE carried out an expert elicitation to evaluate saturated zone flow and transport at 

the Yucca Mountain site (CRWMS M&O, 1998ab). The objective of SZEE was to quantify 

uncertainties associated with models and parameters key to modeling flow and transport in the 

saturated zone. A second objective was to reveal needed data collection and modeling that 

could reduce some of the more significant uncertainties. In this way, the expert elicitation was 

used to complement and guide data collection already underway, as well as to provide input to 

iterative performance assessment modeling by DOE. 

Over 6 months, a panel of 5 experts in saturated zone hydrology was asked to address 

16 technical issues related to the study of saturated zone groundwater flow and 

radionuclide transport at Yucca Mountain. DOE implemented many of the panel members’ 

recommendations in subsequent site characterization activities. In particular, the panel 

recommended a range of values for vertical anisotropy, dispersivity, and specific discharge that 

DOE later used, along with other sources of information, to characterize the uncertainty of flow 

and transport of radionuclides beneath and downgradient of Yucca Mountain. Written elicitation 

summaries, prepared by each expert, were included in an appendix to the final elicitation report 

(CRWMS M&O, 1998ab). 

NRC staff’s technical evaluation of the geological, geophysical, and hydrological information 

used to support the expert elicitation, as well as of that information developed as a result of it, is 

provided in TER Volume 3, Section 2.2.1.3.8. 

Staff Evaluation 

The YMRP guidance for review of expert elicitation considers the nine-step procedure outlined 

in NUREG–1563 (NRC, 1996aa). These steps will be discussed in turn, with specific examples 

cited from the three elicitations, where appropriate. 

Definition of Objectives (Step 1) 

NRC staff notes that, in general, DOE defined specific objectives for each elicitation, on the 

basis of the descriptions of these objectives in the SAR (pp. 5.4-4, 5.4-7, and 5.4-10), 

discussion of the rationale for the elicitations in the respective elicitation reports, and direct 

observation by NRC staff members of the elicitation workshops and meetings. 

In the PVHA elicitation, however, staff recognizes some shortcomings in the specific definition of 

objectives. Staff has previously documented, in NUREG–1762, Section 5.1.2.2.4.1, Igneous 

Activity (NRC, 2005aa), that a common definition of an igneous event or event class was not 

adequately specified at the beginning of the elicitation, and that these terms were not used 

consistently in the experts’ probability models. Probability estimates for intrusive and extrusive 

events were not calculated separately, but were initially considered as a single probability by the 

experts. Because separate probability estimates needed to be developed for the DOE Total 

System Performance Assessment, DOE developed extrusive and intrusive probability estimates 

subsequent to the 1996 PVHA without re-engaging the experts to seek their opinions. 
20-5 

Selection of Experts (Step 2) 

NRC staff notes that DOE generally followed published guidance in selecting experts. This is 

based on review of the criteria DOE used to select experts, as described in SAR Section 5.4; 

professional information provided about each expert in the elicitation reports; and NRC staff 

members’ direct observations of the open, frank, and detailed technical discussion among the 

experts at the elicitation workshops. 

For the PVHA, NRC staff notes that the 10 experts possessed the necessary knowledge and 

expertise and showed their ability to apply their knowledge and expertise. All 10 experts were 

identified in the 1996 PVHA report, and each expert’s judgments were clearly documented. 

As was identified in its 1999 Issue Resolution Status Report (NRC, 1999aa), NRC staff believes 

that a greater balance of panel experts would have encompassed a wider range of viewpoints. 

NRC staff attributes DOE’s failure to achieve this balance, in part, to the fact that some of the 

experts invited by DOE declined to participate as panel members. Also, subsequent to PVHA, 

NRC staff suggested that DOE strive for more thorough documentation of the expert selection 

processes and identify sources of potential bias and conflicts of interest (Austin, 1997aa, 

1996aa). 

For the PSHA and the SZEE, the NRC staff notes that the experts possessed the necessary 

knowledge and expertise. NRC staff also notes that the assembled experts for the two 

elicitations collectively represent an appropriately broad spectrum of the larger seismology and 

hydrology communities. 

All of the final elicitation reports identified the participating subject matter experts, included 

summaries of their input to the elicitations, and provided rationales for their respective opinions. 

As DOE stated in SAR Section 5.4, the experts were not asked directly to disclose potential 

conflicts of interest, but each expert provided sufficient information about his or her past and 

current affiliations to satisfy the intent of the guidance in NUREG–1563. 

Refinement of Issues (Step 3) and Assembly and Dissemination of Basic 

Information (Step 4) 

On the basis of its direct observation and review, NRC noted that the geological, geophysical, 

hydrological, and seismological information DOE made available to each panel provided an 

adequate technical basis to support the three elicitations. During the early workshops and field 

trips, the experts developed lists of the most important subissues. This helped organize and 

focus the discussions in later elicitation workshops. Among the numerous subissues that the 

PVHA experts identified were structural control of igneous activity in the vicinity of Yucca 

Mountain, the quality and reliability of available age dating, and the selection of relevant 

natural analogues. DOE divided the PSHA into two panels of experts, each with its own set of 

experts. One panel focused on description and characterization of seismic sources, while the 

other panel focused on ground motion attenuation and modeling. Within the seismic source 

panel, DOE further developed three-person elicitation teams, composed of experts with varied 

expertise in geology, seismology, geophysics, and Basin and Range tectonics. Among the key 

subissues the experts identified in the SZEE were the causes and implications of the large 

hydraulic gradient, spatial distribution of flow, and the range of uncertainty in groundwater 

specific discharge. 
20-6 

Pre-Elicitation Training (Step 5) 

On the basis of documentation provided in the elicitation reports as well as direct observations 

by NRC staff at the elicitation workshops, NRC staff notes that the subject matter experts 

received appropriate pre-elicitation training, consistent with NRC guidance/NUREG–1563. 

Experts received training on the elicitation process during the first workshop of each elicitation, 

as well as during subsequent workshops, including presentations on topics such as probability 

encoding, quantifying uncertainty, and identifying sources of bias.

Most of the workshops were held with sufficient advanced notice so that members of the public, 

affected parties, and NRC staff could directly observe the discussions among the experts and 

supporting technical teams. Many of the workshops included presentations by subject matter 

experts, both from within the teams or external to the elicitation. At later workshops, the experts 

presented their preliminary interpretations in a discussion format that allowed them to receive 

direct feedback from other experts or expert teams. Each of the elicitation projects included at 

least one field trip that allowed the experts to directly observe many of the important geologic 

features in the Yucca Mountain region. These field trips included discussions with subject 

matter experts and generalists on specific field investigations carried out on behalf of DOE in 

support of the site characterization. DOE provided meeting summaries of all the workshops in 

the elicitation reports. 

Elicitation of Judgments (Step 6) 

Upon completion of the workshops and field trips, facilitation teams, comprising generalists and 

normative experts, conducted comprehensive interviews of the experts to elicit their inputs that 

included discussion of how the information would be represented in the logic tree format used to 

calculate the results. These interviews were conducted expert by expert or, where applicable, 

team by team, and followed up with written documentation of the inputs. 

Post-Elicitation Feedback (Step 7) 

As documented in the elicitation reports and SAR Section 5.4, the experts were provided with 

both informal and formal feedback at many of the workshops. At least one workshop in each 

elicitation was dedicated to feedback and included initial sensitivity studies provided by the 

facilitation team to quantify the initial expert interpretations and, through sensitivity studies, to 

show which inputs had the greatest impact on the overall results. NRC staff noted that this 

aspect of DOE’s elicitation process met the criteria for timely feedback. However, DOE did not 

require the experts or expert teams to document the rationale for any changes made to their 

assessments after the feedback session. As stated in SAR Section 5.4 and in DOE (2009gn), 

DOE stated that this requirement could anchor the experts to their initial interpretations. DOE 

asserted that the experts would thus be reluctant to revise their interpretations after receiving 

feedback because doing so would also require them to provide full justification for the change. 

DOE also stated that its approach, in this regard, is consistent with the guidance contained in 

NUREG/CR–6372 (NRC, 1997aa). The NRC staff considers that, in this one respect, DOE’s 

approach is more comparable to that described in NUREG/CR–6372, in that the guidance in 

NUREG/CR–6372 does not specify that experts document the rationale for changes made to 

their assessment during or after feedback sessions. 
20-7 

Treatment of Disparate Views and Aggregation of Judgments (Step 8) 

For all three elicitations, equal weighting was used to aggregate the elicited results. In the case 

of PSHA, results were aggregated giving equal weights to the inputs from the source teams, 

and equal weights to the ground motion models from the individual ground motion experts. In 

the other cases, equal weight was assigned to the results from each expert. The elicitation 

reports provided summaries of each expert’s (or source team’s) input, including sensitivity 

information to demonstrate the impact each expert or each source team’s interpretations had on 

the final result. 

Documentation (Step 9) 

NRC staff notes that DOE properly documented all three elicitations. The elicitation reports 

provided comprehensive records of each elicitation, with the noted exception being formal 

documentation of individual experts’ reasons for revising their interpretations during the 

elicitation process. DOE explained its rationale for this deviation in SAR Section 5.4. As stated 

previously, the NRC staff notes that DOE selected an approach that in this regard is similar to 

that of NUREG/CR–6372 (NRC, 1997aa). 

The YMRP guidance for review of expert elicitation also considers the documentation and 

methodology used in updates to an elicitation. DOE chose not to update the PSHA or the 

SZEE. DOE did, however, reconvene the PVHA elicitation in 2004 to consider new information 

and to rely on a consistent set of event definitions and extrusive scenarios. Members of the 

NRC staff attended the public PVHA-Update (PVHA-U) workshops as observers. DOE 

published the results from the updated PVHA, or PVHA-U, after it submitted the SAR (SNL, 

2008ah). 

DOE did not directly use the PVHA-U results in its SAR or in direct support of models or 

parameters in the Total System Performance Assessment (TSPA). Estimates of the probability 

of igneous activity in the TSPA are based solely on the original 1996 PVHA. In a letter providing 

the PVHA-U report to NRC (Boyle, 2008aa), DOE characterized the PVHA-U results as 

information that supports the results of the 1996 PVHA. While the NRC staff notes that the 

results are similar, the underlying technical bases for the two elicitations are not the same. 

Because DOE referred to the PVHA-U results as confirming the 1996 PVHA results, the staff 

reviewed the PVHA-U report. On the basis of staff’s subsequent review and direct observation 

of the PVHA-U workshops, the NRC staff noted that the PVHA-U was conducted in a manner 

generally consistent with the procedure suggested in NUREG–1563 (NRC, 1996aa). For these 

reasons, NRC staff notes that the PVHA-U was adequately documented and used appropriate 

elicitation methods, consistent with NUREG–1563. 

NRC staff’s technical evaluation of DOE’s estimates of igneous event probability as they relate 

to the PVHA-U is given in TER Section 2.2.1.2.2. 

2.5.4.4 NRC Staff Conclusions 

The NRC staff notes that DOE adequately explained how expert elicitation was used consistent 

with the applicable guidance in NUREG–1563 and the YMRP. 
20-8 

2.5.4.5 References

Austin, J.H. 1997aa. “Documenting and Disclosing Potential Conflict of Interest in Expert 

Elicitations for the Geologic Repository Program.” Letter (January 7) to R.A. Milner, DOE. 

Washington, DC: NRC. 

Austin, J.H. 1996aa. “Implementation of NUREG–1563 in Expert Elicitations in Yucca 

Mountain, Nevada, Site Characterization Programs.” Letter (December 31) to R.A. Milner, 

DOE. Washington, DC: NRC. 

Boyle, W.J. 2008aa. “Transmittal of Report: Probabilistic Volcanic Hazard Analysis Update 

(PVHA-U) for Yucca Mountain, Nevada.” Letter (October 17) to Director, DHLWRS, NRC. 

Las Vegas, Nevada: DOE, Office of Civilian Radioactive Waste Management. 

BSC. 2004bj. “Technical Basis Document No. 14: Low Probability Seismic Events.” Rev. 1. 

MOL 20000510.0175. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 

Cornell, C.A. 1968aa. “Engineering Seismic Risk Analysis.” Bulletin of the Seismological 

Society of America. Vol. 58. pp. 1,583–1,606. 

CRWMS M&O. 1998aa. “Probabilistic Seismic Hazard Analyses for Fault Displacement and 

Vibratory Ground Motion at Yucca Mountain, Nevada.” WBS 1.2.3.2.8.3.6. Las Vegas, 

Nevada: CRWMS M&O. 

CRWMS M&O. 1998ab. “Synthesis of Volcanism Studies for the Yucca Mountain Site 

Characterization Project.” 3781MR1. MOL 19981207.0393. Las Vegas, Nevada: 

CRWMS M&O. 

CRWMS M&O. 1996aa. “Probabilistic Volcanic Hazard Analysis for Yucca Mountain, Nevada.” 

BA0000000–01717–2200–00082. Rev. 0. Las Vegas, Nevada: CRWMS M&O. 

DOE. 2009av. DOE/RW–0573, “Safety Analysis Report Yucca Mountain Repository 

License Application.” Rev. 01. Las Vegas, Nevada: DOE, Office of Civilian Radioactive 

Waste Management. 

DOE. 2009gn. "Yucca Mountain—Response to Request for Additional Information Regarding 

License Application (Safety Analysis Report Section 5.1), Safety Evaluation Report Vol. 4, 

Chapter 2.5.1, Set 1." Letter (February 6) J.R. Williams to B.J. Benney (NRC). ML090371019. 

Washington, DC: DOE, Office of Technical Management. 

McGuire, R.K. 1976aa. “FORTRAN Computer Program for Seismic Risk Analysis.” 

USGS Open-File Report 76-67. Reston, Virginia: U.S. Geological Survey. 

NRC. 2005aa. NUREG–1762, “Integrated Issue Resolution Status Report.” Rev. 1. 

Washington, DC: NRC. 

NRC. 2003aa. NUREG–1804, “Yucca Mountain Review Plan—Final Report.” Rev. 2. 

Washington, DC: NRC. 

NRC. 1999aa. “Issue Resolution Status Report, Key Technical Issue: Structural Deformation 

and Seismicity.” Rev. 2. Washington, DC: NRC. 
20-9 

NRC. 1997aa. NUREG/CR–6372, “Recommendations for Probabilistic Seismic Hazard 

Analysis: Guidance on Uncertainty and Use of Experts.” Washington, DC: NRC. 

NRC. 1996aa. NUREG–1563, “Branch Technical Position on the Use of Expert Elicitation in 

the High-Level Radioactive Waste Program.” Washington, DC: NRC. 

Risk Engineering, Inc. 1998aa. FRISK88 User Manual, Version 2.0. Boulder, Colorado: 

Risk Engineering, Inc. 

SNL. 2008ah. “Probabilistic Volcanic Hazard Analysis Update (PVHA U) for Yucca Mountain, 

Nevada.” Rev. 01. Las Vegas, Nevada: Sandia National Laboratories. 

21-1 

CHAPTER 21 

Conclusions 

The U.S. Nuclear Regulatory Commission staff has reviewed the Safety Analysis Report 

and the other information the U.S. Department of Energy (DOE) submitted. NRC staff notes 

that (i) the repository design is composed of multiple barriers and (ii) the results of the 

performance assessments for individual protection, human intrusion, and separate groundwater 

protection calculations are consistent with applicable YMRP guidance. In particular, the staff 

notes the following. 

Multiple Barriers 

 The repository includes multiple barriers, consisting of both natural barriers and 

engineered barrier systems. 

 The proposed engineered barrier systems can be expected to work in combination with 

the natural barriers, consistent with the criteria for a system of multiple barriers. 

 DOE identified the design features of the engineered barrier system and natural 

barrier features of the geologic setting that are considered barriers important to 

waste isolation, described the capability of the barriers identified as important to waste 

isolation (to isolate waste), and provided the technical basis for the description of the 

capability of the barriers that is consistent with the technical basis for the performance 

assessment evaluations. 

Performance Assessments 

 The performance assessment evaluations used to estimate repository performance 

include the appropriate features, events, and processes. DOE has provided an 

appropriate basis for the features, events, and processes that have been excluded from 

the performance assessment analysis. 

 DOE appropriately considered events that have at least 1 chance in 100 million per year 

of occurring (i.e., igneous events, seismic events, and early failure of waste packages 

and drip shield events) in its performance assessment analyses. 

 The model abstractions used in the performance assessment evaluations 

included applicable data related to the natural systems and the engineered 

barrier systems, appropriate consideration of uncertainty and variability in 

parameters and models, consideration of alternative models, and technical bases 

in support of the model abstractions. DOE should confirm its approach for decay chain 

radionuclide behavior by providing, through its performance confirmation program, 

information to reduce uncertainty related to the likelihood of excess Po-210 occurring in 

the saturated zone, as identified in TER Section 2.2.1.3.9.3. 

 DOE has provided a reasonable basis for the statistical stability of the quantitative 

results of the Total System Performance Assessment (TSPA). 
21-2 

DOE submitted information consistent with the guidance in the Yucca Mountain Review Plan 

(YMRP). The technical approach and results in DOE’s TSPA, including the average annual 

dose values and the performance of the repository barriers, as discussed in this volume are 

reasonable. DOE should confirm its approach for decay chain radionuclide behavior by 

providing, through its performance confirmation program, information to reduce uncertainty 

related to the likelihood of excess Po-210 occurring in the saturated zone, as identified in TER 

Section 2.2.1.3.9.3. 
22-1 

CHAPTER 22 

Glossary 

This glossary is provided for information and is not exhaustive. Terms shown in italics are 

included in this glossary. 

absorption: The process of taking up by capillary, osmotic, solvent, or chemical action of 

molecules (e.g., absorption of gas by water) as distinguished from adsorption. 

abstracted model: A model that reproduces, or bounds, the essential elements of a more 

detailed process model and captures uncertainty and variability in what is often, but not always, 

a simplified or idealized form. See abstraction. 

abstraction: Representation of the essential components of a process model into a form 

suitable for use in a total system performance assessment. A model abstraction is intended to 

maximize the use of limited computational resources while allowing a sufficient range of 

sensitivity and uncertainty analyses. 

adsorb: To collect a gas, liquid, or dissolved substance on a surface as a condensed layer. 

adsorption: The adhesion by chemical or physical forces of molecules or ions (as of gases or 

liquids) to the surface of solid bodies. For example, the transfer of solute mass, such as 

radionuclides, in groundwater to the solid geologic surfaces with which it comes in contact. 

The term sorption is sometimes used interchangeably with this term. 

advection: The process in which solutes, particles, or molecules are transported by the motion 

of flowing fluid. 

aging: The retention of commercial spent nuclear fuel on the surface in dry storage to reduce 

its thermal output as necessary to meet proposed repository thermal management goals. 

airborne mass loading: The amount of fine particulates resuspending above a surface 

deposit, generally expressed as mass per unit volume of air. 

aleatory uncertainty: An uncertainty associated with the chance of occurrence of a feature, 

event, or process of a physical system or the environment such as the timing of a volcanic 

event. Also referred to as irreducible uncertainty because no amount of knowledge will 

determine whether or not a chance event will or will not occur. See also epistemic uncertainty.

Alloy 22: A nickel-based, corrosion-resistant alloy containing approximately 22 weight percent 

chromium, 13 weight percent molybdenum, and 3 weight percent tungsten as major alloying 

elements. This alloy is used as the outer container material in U.S. Department of Energy’s 

waste package design. 

alluvium: Detrital (sedimentary) deposits made by flowing surface water on river beds, flood 

plains, and alluvial fans. It does not include subaqueous sediments of seas and lakes. 
22-2 

alternative: In the context of system analysis, plausible interpretations or designs that use 

assumptions other than those used in the base case, which could also be applicable or 

reasonable given the available scientific information. When propagated through a 

quantitative tool such as performance assessment, alternative interpretations can illustrate the 

significance of the uncertainty in the base case interpretation chosen to represent the system’s 

probable behavior. 

ambient: Undisturbed, natural conditions, such as ambient temperature caused by climate or 

natural subsurface thermal gradients, and other surrounding conditions. 

anisotropy: Variation in physical properties when measured in different directions. For 

example, in layered rock, permeability is often greater within the horizontal layers than across 

the horizontal layers. 

annual frequency: The number of occurrences of an event in 1 year. 

aqueous: Pertaining to water, such as aqueous phase, aqueous species, or aqueous transport. 

aquifer: A saturated underground geologic formation of sufficient permeability to transmit 

groundwater and yield water of sufficient quality and quantity to a well or spring for an intended 

beneficial use. 

ash: Fragments of volcanic rock that are broken during an explosive volcanic eruption to less 

than 2 mm [0.08 in] in diameter. See also tephra and pyroclastic.

ash flow tuff: A type of volcanic rock formed by the deposition and accumulation of dominantly 

ash-size particles during an explosive eruption. Ash flows (also called pyroclastic flows) 

commonly result from eruptions of more viscous, silica-rich magma such as rhyolite. This rock 

type forms the host horizons for the proposed geologic repository at Yucca Mountain. See also 

tuff and welded tuff. 

basalt: A common type of igneous rock that forms black, rubbly-to-smooth-surfaced lavas and 

black-to-red tephra deposits (frequently used as “lava rock” for barbecues). 

borosilicate glass: A predominantly noncrystalline, relatively homogenous glass formed by 

melting silica and boric oxide together with other constituents such as alkali oxides. Borosilicate 

glass is a high-level radioactive waste material in which boron takes the place of the lime used 

in ordinary glass mixtures. 

boundary condition: For a model, the establishment of a set condition for a given variable,

often at the geometric edge of the model. An example is using a specified groundwater flux for 

net infiltration as a boundary condition for an unsaturated zone flow model. 

bound: An analysis or selection of parameter values that yields limiting results, such that any 

actual result is certain to exceed these limits only with an extremely small likelihood. 

breach: A penetration in the waste package caused by failure of the outer and inner containers 

or barriers that allows the spent nuclear fuel or the high-level radioactive waste to be exposed to 

the external environment and may eventually permit radionuclide release. 
22-3 

burnup: A measure of nuclear reactor fuel consumption expressed either as the percentage 

of fuel atoms that have undergone fission, or as the amount of energy produced per unit weight 

of fuel. 

burnup credit: The concept of taking credit for the reduction in reactivity (ability to undergo 

fission) due to fuel irradiation. The reduction in reactivity is due to the net reduction of fissile 

nuclides and the production of parasitic neutron-absorbing nuclides. 

caldera: A volcanic depression in the Earth’s surface more than 1 km [0.7 mi] wide, formed by 

the collapse of the upper crust into an evacuated magma chamber during or after a large 

volcanic eruption. Many calderas resulting from the explosive eruption of large amounts of 

rhyolite magma are several tens of kilometers [up to 20 mi] wide. 

calibration: (1) Comparison of model results with actual data or observations, and adjusting 

model parameters to increase the precision and/or accuracy of model results compared to 

actual data or observations. (2) For tools used for field or lab measurements, the process of 

taking instrument readings on standards known to produce a certain response, to check the 

accuracy and precision of the instrument. 

canister: An unshielded cylindrical metal receptacle that facilitates handling, transportation, 

storage, and/or disposal of high-level radioactive waste. It may serve as (i) a pour mold and 

container for vitrified high-level radioactive waste; (ii) a container for loose or damaged fuel 

rods, nonfuel components and assemblies, and other debris containing radionuclides; or 

(iii) a container that provides radionuclide confinement. Canisters are used in combination 

with specialized overpacks that provide structural support, shielding, or confinement for 

storage, transportation, and emplacement. Overpacks used for transportation are usually 

referred to as transportation casks; those used for emplacement in a proposed repository are 

referred to as waste packages. 

carbon steel: A steel made with carbon up to about 2 weight percent and only residual 

quantities of other elements. Carbon steel is a tough but ductile and malleable material 

that is used in some components in U.S. Department of Energy’s design of the engineered 

barrier system. 

cask: (1) A heavily shielded container used for the dry storage or shipment (or both) of 

radioactive materials such as spent nuclear fuel or other high-level radioactive waste. Casks 

are often made from lead, concrete, or steel. Casks must meet regulatory requirements and are 

not intended for long-term disposal in a proposed repository. (2) A heavily shielded container 

that the U.S. Department of Energy would use to transfer canisters between waste handling 

facilities at the proposed repository. 

cinder cone: A steep, conical hill formed by the accumulation of ash and coarser erupted 

material around a volcanic vent. Synonymous with scoria cone.

cladding: The metal outer sheath of a fuel rod generally made of a zirconium alloy, and in the 

early nuclear power reactors of stainless steel, intended to protect the uranium dioxide pellets, 

which are the nuclear fuel, from dissolution by exposure to high temperature water under 

operating conditions in a reactor. Often referred to as “clad.” 

climate: Weather conditions, including temperature, wind velocity, precipitation, and other 

factors, that prevail in a region. 
22-4 

climate states: Representations of climate conditions. 

colloid: As applied to radionuclide migration, colloids are large molecules or very small 

particles, having at least one dimension with the size range of 10-6 to 10-3 mm [10-8 to 10-5 in] 

that are suspended in a solvent. Colloids in groundwater arise from clay minerals, 

organic materials, or (in the context of a proposed geologic repository) from corrosion of 

engineered materials. 

commercial spent nuclear fuel: Nuclear fuel rods, forming a fuel assembly, that have been 

removed from a nuclear power plant after reaching the specified burnup. 

conceptual model: A set of qualitative assumptions used to describe a system or subsystem 

for a given purpose. Assumptions for the model are compatible with one another and fit the 

existing data within the context of the given purpose of the model. 

conduit: A pathway along which magma rises to the surface during a volcanic eruption. 

Conduits are usually cylindrical and flare upwards toward the surface vent. Conduits are 

near-surface features and develop along dikes, focusing magma flow from the longer and 

possibly narrower dike to the vent. 

consequence: A measurable or calculated outcome of an event or process that, when 

combined with the probability of occurrence, gives a measurement of risk. 

conservative: A condition of an analysis or a parameter value such that its use provides a 

pessimistic result, which is worse than the actual result expected. 

corrosion: The deterioration of a material, usually a metal, as a result of a chemical or 

electrochemical reaction with its environment. 

coupled processes: A representation of the interrelationships between processes

such that the effects of variation in one process are accurately propagated among all 

interrelated processes. 

crevice corrosion: Localized corrosion of a metal surface at, or immediately adjacent to, an 

area that is shielded from full exposure to the environment because of close proximity between 

the metal and the surface of another material. 

criticality: The condition in which a fissile material sustains a chain reaction. It occurs 

when the number of neutrons present in one generation cycle equals the number generated 

in the previous cycle. The state is considered critical when a self-sustaining nuclear chain 

reaction is ongoing. 

diffusion: (1) The spreading or dissemination of a substance caused by concentration 

gradients. (2) The gradual mixing of the molecules of two or more substances because of 

random thermal motion. 

diffusive transport: Movement of solutes because of their concentration gradient. Diffusive 

transport is the process in which substances carried in groundwater move through the 

subsurface by means of diffusion because of a concentration gradient. 
22-5 

dike: A tabular, generally vertical body of igneous rock that cuts across the structure of 

adjacent rocks. Dikes transport molten rock from depth to an erupting volcano. 

dimensionality: Modeling in one, two, or three dimensions. 

direct exposure: The manner in which an individual receives dose from being in close 

proximity to a source of radiation. Direct exposures present an external dose pathway. 

dispersion (hydrodynamic dispersion): (1) The tendency of a solute (substance dissolved in 

groundwater) to spread out from the path it is expected to follow if only the bulk motion of the 

flowing fluid were to move it. The tortuous path the solute follows through openings (pores and 

fractures) causes part of the dispersion effect in the rock. (2) The macroscopic outcome of the 

actual movement of individual solute particles through a porous medium. Dispersion dilutes 

solutes, including radionuclides, in groundwater.

disposal canister: A cylindrical metal receptacle designed to contain spent nuclear fuel and 

high-level radioactive waste as an integral part of the waste package. 

disruptive event: An unlikely, off-normal event that, in the case of the proposed repository, 

could include volcanic activity, seismic activity, and nuclear criticality. Disruptive events alter 

the normal or likely behavior of the system. 

dissolution: Dissolving a substance in a solvent. 

distribution: In a total system performance assessment, the overall scatter of values for 

a specific set of numbers (e.g., corrosion rates, values used for a particular parameter, 

dose results). A term used synonymously with frequency distribution or probability 

distribution function. Distributions have structures that are the probability that a given value 

occurs in the set. 

drift: From mining terminology, a horizontal underground passage. In the proposed Yucca 

Mountain repository design, drifts include excavations for emplacement (emplacement drifts) 

and access (access mains). 

drift degradation: The progressive accumulation of rock rubble in a drift created by weakening 

and collapse of drift walls in response to stress from heating or earthquakes. 

drip shield: A metallic structure placed along the extension of the emplacement drifts and 

above the waste packages to prevent seepage water from directly dripping onto the waste 

package outer surface. The drip shield may also prevent the drift ceiling rocks (e.g., due to drift 

spallation) from falling on the waste package. 

dry storage: Storage of spent nuclear fuel without immersion of the fuel in water for cooling or 

shielding; it involves the encapsulation of spent fuel in a steel cylinder that might be in a 

concrete or massive steel cask or structure. 

effective porosity: The fraction of a porous medium volume available for fluid flow and/or 

solute storage, as in the saturated zone. Effective porosity is less than or equal to the total void 

space (porosity). 
22-6 

empirical: Reliance on observation or experimentation rather than on a theoretical 

understanding of fundamental processes. 

emplacement drift: See drift. 

enrichment: The act of increasing the concentration of fissile isotopes from their value in 

natural uranium. The enrichment (typically reported in atom percent) is a characteristic of 

nuclear fuel. 

eolian: Relating to processes caused by near-surface winds. 

epistemic uncertainty: A variability that is due to a lack of knowledge of quantities or 

processes of the system or the environment. Also referred to as reducible uncertainty, because 

the state of knowledge about the exact value of a quantity or process can increase through 

testing and data collection. See also aleatory uncertainty.

equilibrium: The state of a chemical system in which the phases do not undergo any 

spontaneous change in properties or proportions with time; a dynamic balance. 

events: In a total system performance assessment, (1) occurrences of phenomena that have a 

specific starting time and, usually, a duration shorter than the time being simulated in a model. 

(2) Uncertain occurrences of phenomena that take place within a short time relative to the time 

frame of the model. 

event tree: A modeling tool that illustrates the logical sequence of events that follow an 

initiating event. 

expected annual dose: The average annual radiological dose calculated for the reasonably 

maximally exposed individual, which includes the likelihood of the individual receiving a dose 

from all relevant exposure scenarios.

expert elicitation: A formal, highly structured, and well-documented process whereby expert 

judgments, usually of multiple experts, are obtained. 

Exploratory Studies Facility: An underground laboratory at Yucca Mountain that includes a 

7.9-km [4.9-mi] main loop (tunnel); a 2.8-km [1.75-mi] cross drift; and a research alcove system 

constructed for performing underground studies during site characterization. 

extrusive (extrusion): In relation to igneous activity, an event where magma erupts at the 

surface. An extrusion is the deposit formed by an extrusive event. See also intrusive. 

fault (geologic): A planar or gently curved fracture across which there has been displacement 

parallel to the fracture surface. 

features: Physical, chemical, thermal, or temporal characteristics of the site or proposed 

repository system. For the purposes of screening features, events, and processes for the total 

system performance assessment, a feature is defined to be an object, structure, or condition 

that has a potential to affect disposal system performance. 
22-7 

finite element analysis: A commonly used numerical method for solving mathematical 

equations in a variety of areas (e.g., hydrology, mechanical deformation). A technique in which 

algebraic equations are used to approximate the partial differential equations that comprise 

mathematical models to produce a form of the problem that can be solved on a computer. 

For this type of approximation, the area being modeled is formed into a grid with irregularly 

shaped blocks. This method provides an advantage in handling irregularly shaped boundaries 

(e.g., internal features such as faults) and surfaces of engineered materials. Values for 

parameters are frequently calculated at nodes for convenience, but are defined everywhere in 

the blocks by means of interpolation functions. 

fissure: In relation to igneous activity, a fissure is an elongate vent or line of vents, formed 

when a dike breaks to the surface to start a volcanic eruption. 

flow: The movement of a fluid such as air, water, or magma. Flow and transport are processes 

that can move radionuclides from the proposed repository to the receptor group location. 

flow pathway: The subsurface course that water or a solute (and dissolved material) would 

follow in a given groundwater velocity field, governed principally by the hydraulic gradient. 

fluvial: Processes related to the downslope movement of water on the Earth’s surface. 

fracture: A planar discontinuity in rock along which loss of cohesion has occurred. It is often 

caused by the stresses that cause folding and faulting. A fracture along which there has been 

displacement of the sides relative to one another is called a fault. A fracture along which no 

appreciable movement has occurred is called a joint. Fractures may act as fast paths for 

groundwater movement. 

fragility: Fragility of a structure, system, or component is defined as the conditional probability 

of its failure, given a value of the ground motion, or response parameter, such as stress, 

bending moment, and spectral acceleration. 

frequency: The number of occurrences of an observed or predicted event during a specific 

time period. 

galvanic: Pertains to an electrochemical process in which two dissimilar electronic conductors 

are in contact with each other and with an electrolyte, or in which two similar electronic 

conductors are in contact with each other and with dissimilar electrolytes. 

galvanic corrosion: Accelerated corrosion of a metal resulting from electrical contact with a 

more noble metal or nonmetallic conductor in a corrosive electrolyte. 

geochemical: The distribution and amounts of the chemical elements in minerals, ores, rocks, 

soils, water, and the atmosphere; the movement of the elements in nature on the basis of their 

properties. 

geophysics (geophysical survey; geophysical magnetic survey): The study of the 

physical properties of rocks and sediment and interpretation of data derived from 

measurements made. Properties commonly measured are the velocity of sound (seismic 

waves) in rocks, density, and magnetic character. A program of measurements made on a 

series of rocks is usually termed a survey. 
22-8 

groundwater: Water contained in pores or fractures in either the unsaturated or saturated 

zones below ground level. 

half-life: The time required for a radioactive substance to lose half of its activity due to 

radioactive decay. At the end of one half-life, 50 percent of the original radioactive material 

has decayed. 

heterogeneity: The condition of being composed of parts or elements of different kinds. 

A condition in which the value of a parameter varies over the space an entity occupies, such as 

the area around the proposed repository, or with the passage of time. 

hydrologic: Pertaining to the properties, distribution, and circulation of water on the surface of 

the land, in the soil and underlying rocks, and in the atmosphere. 

igneous: (1) A type of rock that has formed from a molten, or partially molten, material. (2) A 

type of activity related to the formation and movement of molten rock, either in the subsurface 

(intrusive) or on the surface (extrusive). 

infiltration: The process of water entering the soil at the ground surface. Infiltration becomes 

percolation when water has moved below the depth at which evaporation or transpiration can 

return it to the atmosphere. See also net infiltration. 

intrusive (intrusion): In relation to igneous activity, an event where magma approaches the 

surface but does not break through in an eruption. An intrusion is the solidified rock formed 

below the surface by an intrusive event. See also extrusive. 

invert: A constructed surface that would provide a level drift floor and enable emplacement and 

support of the waste packages. 

lithophysal: Containing lithophysae, which are holes in tuff and other volcanic rocks. One way 

lithophysae are created is by the accumulation of volcanic gases during the formation of the tuff. 

localized corrosion: Corrosion at discrete sites (e.g., pitting and crevice corrosion). 

magma: Molten or partially molten rock that is naturally occurring and is generated within the 

Earth. Magma may contain crystals along with dissolved gasses. 

mathematical model: A mathematical description of a conceptual model. 

matrix: Rock material and its pore space exclusive of fractures. 

matrix diffusion: The process by which molecular or ionic solutes, such as radionuclides

in groundwater, move from areas of higher concentration to areas of lower concentration. For 

the proposed Yucca Mountain repository, this process refers to the movement of radionuclides 

by diffusion between the fracture and matrix continua. 

matrix permeability: The capability of the matrix to transmit fluid. 

mean (arithmetic): For a statistical data set, the sum of the values divided by the number of 

items in the set. The arithmetic average, sometimes referred to as expected value. 
22-9 

mechanical disruption: Damage to the drip shield or waste package because of 

external forces. 

median: A value such that one-half of the observations are less than that value and one-half 

are greater than the value. 

meteorology: The study of climatic conditions such as precipitation, wind, temperature, and 

relative humidity. 

microbe: An organism too small to be viewed with the unaided eye. Examples of microbes are 

bacteria, protozoa, and some fungi and algae. 

microbially influenced corrosion: Deterioration of metals as a result of the metabolic activity 

of microorganisms. 

migration: Radionuclide movement from one location to another within the engineered barrier 

system or the environment. 

mineralogical: Of or relating to the chemical and physical properties of minerals, their 

occurrence, and their classification. 

model: A depiction of a system, phenomenon, or process, including any hypotheses required 

to describe the system or explain the phenomenon or process. 

model support: A process used to gain confidence in the reasonableness of model results 

through comparison with outputs from detailed process-level models and/or empirical 

observations such as laboratory tests, field investigations, and natural analogues.

natural analogues: Observable features, events, or processes, which provide insights 

on similar features, events, or processes that are difficult to observe in the proposed 

repository system. 

near-field: The area and conditions within the proposed repository including the drifts and 

waste packages and the rock immediately surrounding the drifts. The near-field is the region in 

and around the proposed repository where the excavation of the proposed repository drifts and 

the emplacement of waste have significantly impacted the natural hydrologic system. 

net infiltration: The downward flux of infiltrating water that escapes below the zone of 

evapotranspiration. The bottom of the zone of evapotranspiration generally coincides with the 

lowermost extent of plant roots. 

nominal scenario class: The scenario, or set of related scenarios, that describes the expected 

or nominal behavior of the system as perturbed only by the presence of the proposed 

repository. The nominal scenarios contain all likely features, events, and processes that have 

been retained for analysis. 

numerical model: An approximate representation of a mathematical model that is constructed 

using a numerical description method such as finite volumes, finite differences, or finite 

elements. A numerical model is typically represented by a series of program statements that 

are executed on a computer. 
22-10 

occupational dose: The dose received by an individual in the course of employment in which 

the individual’s assigned duties involve exposure to radiation or to radioactive material from 

licensed and unlicensed sources of radiation, whether in the possession of the licensee or other 

person. Occupational dose does not include doses received from background radiation, from 

any medical administration the individual has received, from exposure to individuals who were 

administered radioactive material and released under 10 CFR 35.75, from voluntary 

participation in medical research programs, or as a member of the public (10 CFR 20.1003, 

“Occupational dose”). 

oxidation: A corrosion reaction in which the corroded metal forms an oxide, usually applied to 

reaction with a gas containing elemental oxygen, such as air. 

parameter: Data, or values, such as those that are input to computer codes for a total system 

performance assessment calculation. 

patch: In the U.S. Department of Energy modeling of waste package corrosion, a patch is the 

minimal surface area of the waste package over which uniform corrosion occurs, as opposed to 

localized corrosion in pits. 

pathway: A potential route by which radionuclides might reach the accessible environment and 

pose a threat to humans. For example, direct exposure is a human external pathway, and 

inhalation and ingestion are human internal pathways. 

permeability: A measure of the ease with which a fluid such as water or air moves through a 

rock, soil, or sediment. 

phase: A physically homogeneous and distinct portion of a material system, such as 

the gaseous, liquid, and solid phases of a substance. In liquids and solids, single phases 

may coexist. 

phase stability: A measure of the ability of a particular phase to remain without transformation. 

pit: A small cavity formed in a solid as a result of localized corrosion. 

pitting corrosion: Localized corrosion of a metal surface, confined to a small area, that takes 

the form of cavities (pits). 

porosity: The ratio of the volume occupied by openings, or voids, in a soil or rock, to the total 

volume of the soil or rock. Porosity is expressed as a decimal fraction or as a percentage. 

probabilistic: Based on or subject to probability.

probability: The chance that an outcome will occur from the full set of possible outcomes. 

Knowledge of the exact probability of an event is usually limited by the inability to know, or 

compile, the complete set of possible outcomes over time or space. 

probability distribution: The set of outcomes (values) and their corresponding probabilities for 

a random variable. See distribution. 

processes: Phenomena and activities that have gradual, continuous interactions with the 

system being modeled. 
22-11 

process model: A depiction or representation of a process, along with any hypotheses 

required to describe or to explain the process. 

pyroclastic: In relation to igneous activity, this describes fragments or fragmental rocks and 

deposits produced by explosive volcanic activity, where the magma is ripped apart during the 

release of gas and/or by interaction with surface and near-surface water. 

quaternary: The period of geologic time from about 2.6 million years ago to the present day.

radioactive decay: The process in which one radionuclide spontaneously transforms into one 

or more different radionuclides, which are called daughter radionuclides. 

radioactivity: The property possessed by some elements (such as uranium) of spontaneously 

emitting energy in the form of radiation as a result of the decay (or disintegration) of an unstable 

atom. Radioactivity is also the term used to describe the rate at which radioactive material 

emits radiation. 

radiolysis: Chemical decomposition by the action of radiation. 

radionuclide: An unstable isotope of an element that decays or disintegrates spontaneously, 

thereby emitting radiation. Approximately 5,000 natural and artificial radioisotopes have 

been identified. 

range (statistics): The numerical difference between the highest and lowest value in any set. 

receptor: An individual for whom radiological doses are calculated or measured. 

redistribution: Mobilization and transport of surface deposits by wind and water. 

reliability: The probability that the item will perform its intended function(s) under specified 

operating conditions for a specified period of time. 

repository footprint: The outline of the outermost locations of where the waste is proposed to 

be emplaced in the proposed Yucca Mountain repository. 

retardation: Slowing or stopping radionuclide movement in groundwater by mechanisms that 

include sorption of radionuclides, diffusion into rock matrix pores and microfractures, and 

trapping of particles in small pore spaces or dead ends of microfractures. 

rhyolite: A common type of igneous rock that forms light-colored, rough blocky-surfaced 

lavas and white-grayish-yellow tephra deposits. A common fragment type is pumice. Rhyolitic 

magma has a high viscosity, and the resulting lava flows are usually quite short and thick. 

It more frequently erupts explosively from the volcano and forms ash-flow tuffs. 

risk: The probability that an undesirable event will occur, multiplied by the consequences of the 

undesirable event. 

risk assessment: An evaluation of potential consequences or hazards that might be the 

outcome of an action, including the likelihood that the action might occur. This assessment 

focuses on potential negative impacts on human health or the environment. 
22-12 

risk informed, performance based: A regulatory approach in which risk insights, engineering 

analysis and judgments, and performance history are used to (i) focus attention on the most 

important activities; (ii) establish objective criteria on the basis of risk insights for evaluating 

performance; (iii) develop measurable or calculable parameters for monitoring system and 

licensee performance; and (iv) focus on the results as the primary basis for regulatory 

decision making. 

rockfall: The release of fracture-bounded blocks of rock from the drift wall, usually in response 

to an earthquake. 

rock matrix: See matrix. 

runoff: Lateral movement of water at the ground surface, such as down steep hillslopes or 

along channels, that is not able to infiltrate at a specified location. 

scenario: A well-defined, connected sequence of features, events, and processes that can be 

thought of as an outline of a possible future condition of the proposed repository system. 

Scenarios can be undisturbed, in which case the performance would be the expected, or 

nominal, behavior for the system. Scenarios can also be disturbed, if altered by disruptive 

events such as human intrusion or natural phenomena such as volcanism or nuclear criticality. 

scenario class: A set of related scenarios sharing sufficient similarities that they can usefully 

be aggregated for screening or analysis. The number and breadth of scenario classes depend 

on the resolution at which scenarios have been defined. 

scoria cone: See cinder cone. 

seepage: The inflow of groundwater moving in fractures or matrix pores of permeable rock to 

an open space in the rock. For the proposed Yucca Mountain repository, seepage refers to 

water dripping into a drift. 

seismic: Pertaining to, characteristic of, or produced by earthquakes or Earth vibrations. 

seismic hazard curve: A graph showing the ground motion parameter of interest, such as 

peak ground acceleration, peak ground velocity, or spectral acceleration at a given frequency, 

plotted as a function of its annual probability of exceedance. 

seismic performance: Seismic performance of structures, systems, and components

refers to their ability to perform intended safety functions during a seismic event, expressed 

as the annual probability of exceeding a specified limit condition (stress, displacement, or 

collapse). This is also referred to as the probability of failure, or probability of unacceptable 

performance, PF. 

sill: A tabular, generally flat-lying body of intrusive igneous rock that lies along (is concordant 

with) the structure of adjacent rocks. Sills are part of the transport system for molten rock 

(magma) rising from depth to the surface. See also dike. 

sorb: To undergo a process of sorption.
22-13 

sorption: The binding, on a microscopic scale, of one substance to another. Sorption is a term 

that includes both adsorption and absorption and refers to the binding of dissolved 

radionuclides onto geologic solids or waste package materials by means of close-range 

chemical or physical forces. Sorption is a function of the chemistry of the radioisotopes, the 

fluid in which they are carried, and the material they encounter along the flow path. 

sorption coefficient (Kd): A numerical means to represent how strongly one substance sorbs

to another. 

source term: Types and amounts of radionuclides that are the source of a potential release. 

spatial variability: A measure of how a property, such as rock permeability, varies at different 

locations in an object such as a rock formation. 

speciation: The existence of the elements, such as radionuclides, in different molecular forms 

in the aqueous phase. 

spent nuclear fuel: Nuclear reactor fuel that has been used to the extent that it can no longer 

effectively sustain a chain reaction and that has been withdrawn from a nuclear reactor following 

irradiation, the constituent elements of which have not been separated by reprocessing. This 

fuel is more radioactive than it was before irradiation and releases significant amounts of heat 

from the decay of its fission product radionuclides. 

stainless steel: A class of iron-base alloys containing a minimum of approximately 10 percent 

chromium to provide corrosion resistance in a wide variety of environments. 

stratigraphy: The branch of geology that deals with the definition and interpretation of rock 

strata; the conditions of their formation, character, arrangement, sequence, age, and 

distribution; and especially their correlation by the use of fossils and other means of 

identification. See stratum. 

stratum: A layer of rock or soil with geologic characteristics that differ from the layers above or 

below it. 

stress corrosion cracking: A cracking process that requires the simultaneous action of a 

corrosive substance and sustained (residual or applied) tensile stress. Stress corrosion 

cracking excludes both the fracture of already corroded sections and the localized corrosion 

processes that can disintegrate an alloy without the action of residual or applied stress. 

structure: In geology, the arrangement of the parts of geologic features or areas of 

interest such as folds or faults. This includes features such as fractures created by faulting, 

and joints caused by the heating of rock. For engineering usage, see structures, systems, 

and components. 

structures, systems, and components: A structure is an element, or a collection of elements, 

that provides support or enclosure, such as a building, aging pad, or drip shield. A system is a 

collection of components, such as piping; cable trays; conduits; or heating, ventilation, and air 

conditioning equipment, that are assembled to perform a function. A component is an item of 

mechanical or electrical equipment, such as a canister transfer machine, transport and 

emplacement vehicle, pump, valve, or relay. 

tectonic: Pertaining to geologic features or events created by deformation of the Earth’s crust. 
22-14 

tephra: A collective term for all clastic (fragmental) materials ejected from a volcano during an 

eruption and transported through the air. 

thermal chemical: Of or pertaining to the effect of heat on chemical conditions and reactions. 

thermohydrologic: Of or pertaining to changes in groundwater movement due to the effects of 

changes in temperature. 

thermal mechanical: Of or pertaining to changes in mechanical properties from effects of 

changes in temperature. 

total system performance assessment: A risk assessment that quantitatively estimates how 

the proposed Yucca Mountain repository system will perform in the future under the influence of 

specific features, events, and processes, incorporating uncertainty in the models and 

uncertainty and variability of the data. 

transparency: The ease of understanding the process by which a study was carried out, which 

assumptions are driving the results, how they were arrived at, and the rigor of the analyses 

leading to the results. A logical structure ensures completeness and facilitates in-depth review 

of the relevant issues. Transparency is achieved when a reader or reviewer has a clear picture 

of what was done in the analysis, why it was done, and the outcome. 

transpiration: The removal of water from the ground by vegetation (roots). 

transport: A process that allows substances such as contaminants, radionuclides, or colloids, 

to be carried in a fluid from one location to another. Transport processes include the physical 

mechanisms of advection, convection, diffusion, and dispersion and are influenced by the 

chemical mechanisms of sorption, leaching, precipitation, dissolution, and complexation. 

tuff: A general term for volcanic rocks that formed from rock fragments and magma that 

erupted from a volcanic vent, flowed away from the vent as a suspension of solids and hot 

gases, or fell from the eruption cloud, and consolidated at the location of deposition. Tuff is the 

most abundant type of rock at the Yucca Mountain site. 

uncertainty: How much a calculated or measured value varies from the unknown true value. 

See also aleatory uncertainty and epistemic uncertainty.

unsaturated zone flow: The movement of water in the unsaturated zone, as driven by 

capillary, viscous, gravitational, inertial, and evaporative forces. 

variable: A nonunique property or attribute. 

variability (statistical): A measure of how a quantity varies over time or space. 

volcanism: Pertaining to extrusive igneous activity. 

wash: In a relation to landforms, a streambed, dry or running, usually in an arid environment. 

watershed: The area drained by a river system including the adjacent ridges and hillslopes. 

welded tuff: A tuff deposited under conditions where the particles that make up the rock 

remained sufficiently hot to cohere. In contrast to nonwelded tuff, welded tuff is denser, less 

porous, and more likely to be fractured (which increases permeability). 



UNITED STATES 

NUCLEAR REGULATORY COMMISSION

WASHINGTON, DC 20555-0001

--------------------

OFFICIAL BUSINESS
NUREG-2107

 TECHNICAL EVALUATION REPORT ON THE CONTENT OF THE U.S. 

DEPARTMENT OF ENERGY’S YUCCA MOUNTAIN REPOSITORY

LICENSE APPLICATION