NUREG-1668, Vol. 1

NRC Sensitivity and 

Uncertainty Analyses 

for a Proposed HLW 

Repository at Yucca 

Mountain, Nevada, 

Using TPA 3.1 

Conceptual Models and Data

U.S. Nuclear Regulatory Commission 

Office of Nuclear Material Safety and Safeguards 

Washington, DC 20555-0001 

Center for Nuclear Waste Regulatory Analyses 

Southwest Research Institute

p4 r 

fLAug
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NUREG-1668, Vol. 1

NRC Sensitivity and 

Uncertainty Analyses 

for a Proposed HLW 

Repository at Yucca 

Mountain, Nevada, 

Using TPA 3.1 

Conceptual Models and Data 

Manuscript Completed: Janaury 2001 

Date Published: February 2001 

Prepared by 

S. Mohanty, CNWRA 

T.J. McCartin, NRC 

Center for Nuclear Waste Regulatory Analyses 

Southwest Research Institute 

6220 Culebra Road 

San Antonio, TX 78238-5166 

Division of Waste Management 

Office of Nuclear Material Safety and Safeguards 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001
ABSTRACT 

Total system performance assessment (TSPA) is playing an increasingly vital role in regulatory 

decision-making. Within the U.S. Nuclear Regulatory Commission (NRC) high-level waste program, TSPA 

studies are being performed to focus the activities of the NRC key technical issues (KThs); evaluate the 

hypotheses of the U.S. Department of Energy (DOE) Repository Safety Strategy; and prepare for the NRC 

review of the DOE viability assessment (VA) for the Yucca Mountain (YM). 

Conducting a TSPA for the proposed repository involves application of a total-system model that simulates 

the processes affecting repository performance, including propagation of the uncertainties associated with 

model parameters, conceptual models, and future system states. The simulation process, which implements 

a probabilistic framework, integrates a broad spectrum of site-specific data and information and produces 

estimates for a set of performance measures. Building on the previously developed NRC assessment 

methodology, a new Total-system Performance Assessment (TPA) code, designated TPA Version 3.1.4 code, 

was developed by NRC and the Center for Nuclear Waste Regulatory Analyses to evaluate the relative 

significance of the NRC-identified KTIs/subissues and to assess the assumptions and models in forthcoming 

DOE TSPAs, such as the DOE TSPA VA for the YM site. 

The TPA Version 3.1.4 code is designed to estimate total-system performance measures of annual individual 

dose or risk. The TPA Version 3.1.4 code consists of an executive driver, a set of consequence modules, and 

a library of utility modules. The executive driver controls the probabilistic sampling of input parameters, the 

calculational sequence and data transfers among consequence modules, and the generation of output files. 

The various output files are used in parameter sensitivity analyses, post-processing of time-dependent risk 

curves, and synthesis of statistical distributions (e.g., cumulative distribution functions and complementary 

cumulative distribution functions) for appropriate performance measures. Consequence models simulate 

physical processes and events such as unsaturated zone infiltration; evolution of the near-field thermal

hydrologic environment; failure of waste packages; dissolution and release of waste; transport of waste in 

the ground-water system; extraction of ground water; and consumption of ground water which, if 

contaminated, would expose future populations to radiation. In addition to considering climate change, the 

TPA Version 3.1.4 code is designed to calculate the effects of disruptive events such as faulting, seismicity, 

and volcanism. Utility modules ensure the consistency of algorithms and data sets used repeatedly by various 

consequence modules. 

This volume has been prepared to describe the methods and assumptions used in the TPA Version 3.1.4 code. 

It contains descriptions of 

"* Overall TSPA methodology 

"* Consequence modules that simulate physical processes and events that affect the release and transport 

of radionuclides 

It should be noted that the TPA Version 3.1.4 code is only an interim version of the code. An improved 

version will soon supersede it.

°°° NUREG-1668
CONTENTS 

Page 

ABSTRACT .... iii 

FIG U RES .......................................................................... xi 

TA BLES ......................................................................... xli 

FOREW ORD ...................................................................... Xlii 

ACKNOWLEDGMENTS ............................................................ xv 

ACRONYM S ..................................................................... xvii 

TABLE SHOWING ENGLISH/METRIC SYSTEM CONVERSION FACTORS ................ xix 

QUALITY OF DATA AND CODE DEVELOPMENT ..................................... xxi 

INTRODUCTION ........................................................... 1-1 

P, Baca, T McCartin, M Lee 

1.1 TOTAL-SYSTEM PERFORMANCE ASSESSMENT CODE ................... 1-1 

1.2 DESCRIPTION OF THE PROPOSED REPOSITORY SITE ................... 1-3 

1.3 REPORT CONTENT ................................................... 1-6 

2 OVERVIEW OF THE TOTAL-SYSTEM PERFORMANCE ASSESSMENT 

CONCEPTUAL MODELS ..................................................... 2-1 

T. McCartin, S. Mohanty, R Baca 

2.1 CONCEPTUALIZATION OF REPOSITORY AND GEOLOGIC SETTING ....... 2-1 

2.2 CONCEPTUAL MODELS IMPLEMENTED IN THE TOTAL-SYSTEM 

PERFORMANCE ASSESSMENT COMPUTER CODE ....................... 2-3 

2.2.1 Infiltration and Deep Percolation ................................... 2-5 

2.2.2 Near-Field Environment .......................................... 2-6 

2.2.3 Radionuclide Releases from the Engineered Barrier System .............. 2-6 

2.2.4 Treatment of Aqueous-Phase Transport in the Unsaturated 

and Saturated Zones ............................................. 2-8 

2.2.5 Airborne Transport for Direct Releases .............................. 2-9 

2.2.6 Exposure Pathways and Reference Biosphere ......................... 2-9 

2.3 RADIONUCLIDE INVENTORIES ...................................... 2-10 

3 CONCEPTUAL AND MATHEMATICAL MODELS ............................... 3-1 

3.1 UZFLOW MODULE DESCRIPTION ..................................... 3-2 

S. Stothoff, C. Scherer, R. Janetzke 

3.1.1 Information Flow Within TPA ................................... 3-2 

3.1.1.1 Information Supplied to UZFLOW ................................ 3-2 

3.1.1.2 Information Provided by UZFLOW ............................... 3-2 

3.1.2 Conceptual M odel ............................................. 3-3 

3.1.2.1 Climate M odel ................................................ 3-4 

3.1.2.2 Variation in Climate Change Cycle ................................ 3-6 

3.1.2.3 Shallow Infiltration Model ...................................... 3-7 

3.1.2.4 Deep Percolation Model ........................................ 3-9 

3.1.3 Assumptions and Conservatism of the UZFLOW Approach ........... 3-11 

3.2 NFENV MODULE DESCRIPTION ...................................... 3-12 

S. Mohanty, 1. Green, G. Rice, S. Stothoff, B. Leslie 

3.2.1 Information Flow Within TPA .................................. 3-12

V NUREG--1668
CONTENTS (cont'd) 

3.2.1.1 Information Supplied to NFENV ................................ 3-12 

3.2.1.2 Information Provided by NFENV ................................ 3-13 

3.2.2 Conceptual M odel ............................................ 3-13 

3.2.2.1 Heat Transfer, Temperature, and Relative Humidity Calculation ........ 3-13 

3.2.2.2 Near-Field Chemical Composition ............................... 3-23 

3.2.2.3 Near-Field Ground-water Percolation ............................. 3-26 

3.2.3 Assumptions and Conservatism of the NFENV Approach ............. 3-32 

3.3 EBSFAIL MODULE DESCRIPTION ..................................... 3-34 

G. Cragnolino, S. Mohanty, T. Ahn, K. Gruss 

3.3.1 Information Flow Within TPA .................................. 3-35 

3.3.1.1 Information Supplied to EBSFAIL ............................... 3-35 

3.3.1.2 Information Provided by EBSFAIL ............................... 3-35 

3.3.2 Conceptual M odel ............................................ 3-35 

3.3.2.1 Corrosion and Mechanical Failures ............................... 3-35 

3.3.2.2 Dry-Air Oxidation ............................................ 3-36 

3.3.2.3 Humid-Air Corrosion ......................................... 3-37 

3.3.2.4 Aqueous Corrosion ........................................... 3-37 

3.3.2.5 M echanical Failure ........................................... 3-40 

3.3.3 Assumptions and Conservatism of the EBSFAIL Approach ........... 3-41 

3.4 SEISMO MODULE DESCRIPTION ..................................... 3-44 

S. Hsiung, J. Stamatakos, S. Mohanty, A. -B. K. Ibrahim 

3.4.1 Information Flow Within TPA .................................. 3-44 

3.4.1.1 Information Supplied to SEISMO ................................ 3-44 

3.4.1.2 Information Provided by SEISMO ............................... 3-44 

3.4.2 Conceptual M odel ............................................ 3-44 

3.4.2.1 Impact Load and Stress Calculation .............................. 3-44 

3.4.2.2 Joint Spacing and Rock Conditions in the Topopah Spring Welded 

Tuff Unit ................................................... 3-4 8 

3.4.3 Assumptions and Conservatism of the SEISMO Approach ............ 3-49 

3.5 EBSREL MODULE DESCRIPTION ..................................... 3-50 

R. Codell, S. Mohanty, T. Ahn 

3.5.1 Information Flow Within TPA .................................. 3-50 

3.5.1.1 Information Supplied to EBSREL ................................ 3-50 

3.5.1.2 Information Provided by EBSREL ............................... 3-51 

3.5.2 Conceptual M odel ............................................ 3-51 

3.5.2.1 Radionuclide Inventory and Mass Transfer ........................ 3-51 

3.5.2.2 Advective M ass Transfer ....................................... 3-53 

3.5.2.3 Diffusive M ass Transfer ....................................... 3-54 

3.5.2.4 W aste Package Inventory ...................................... 3-56 

3.5.2.5 Spent-Fuel Dissolution Rate .................................... 3-57 

3.5.2.6 Spent-Fuel Surface Area ....................................... 3-58 

3.5.2.7 Cladding .............................................. 3-59 

3.5.2.8 W ater Dripping Abstraction .................................... 3-59 

3.5.2.9 Computational Approach ....................................... 3-61 

3.5.3 Assumptions and Conservatism of the EBSREL Approach ............ 3-62

NUREG-1668 vi
CONTENTS (cont'd) 

3.6 UZFT MODULE DESCRIPTION ........................................ 3-65 

T. McCartin, R. Janetzke, N. Coleman 

3.6.1 Information Flow Within TPA .................................. 3-65 

3.6.1.1 Information Supplied to UZFT .................................. 3-65 

3.6.1.2 Information Provided by UZFIT .................................. 3-65 

3.6.2 Conceptual Model ............................................ 3-65 

3.6.2.1 Unsaturated Zone Flow Model .................................. 3-66 

3.6.2.2 Unsaturated Transport ......................................... 3-68 

3.6.2.3 Efficiency of Simulating Flow and Transport ....................... 3-70 

3.6.3 Assumptions and Conservatism of the UZFT Approach ............... 3-70 

3.7 SZFT MODULE DESCRIPTION ........................................ 3-73 

G. Wittmeyer, R. Rice, R. Janetzke, T. McCartin, J. Winterle 

3.7.1 Information Flow Within TPA .................................. 3-73 

3.7.1.1 Information Supplied to SZFT .................................. 3-73 

3.7.1.2 Information Provided by SZFT .................................. 3-75 

3.7.2 Conceptual Model ............................................ 3-75 

3.7.2.1 Saturated Zone Flow Model .................................... 3-75 

3.7.2.2 Saturated Zone Transport Model ................................. 3-76 

3.7.3 Assumptions and Conservatism of the SZFT Approach ............... 3-77 

3.8 DCAGW MODULE DESCRIPTION-FARMING RECEPTOR GROUP ........ 3-79 

P. LaPlante, G. Wittmeyer, M. Jarzemba, S. Mohanty, R. Rice, R. Fedors, 

C. McKenney, L Deere 

3.8.1 Information Flow Within TPA .................................. 3-79 

3.8.1.1 Information Supplied to DCAGW ................................ 3-79 

3.8.1.2 Information Provided by DCAGW ............................... 3-79 

3.8.2 Conceptual Model .......................... .................. 3-80 

3.8.2.1 Development of Radionuclide Concentrations in 

Water for a Farming Receptor Group ............................. 3-80 

3.8.2.2 Development of Dose Conversion Factors ......................... 3-81 

3.8.3 Assumptions and Conservatism of the DCAGW Approach ............ 3-82 

3.9 DCAGW MODULE DESCRIPTION-RESIDENTIAL RECEPTOR GROUP .... 3-86 

P. LaPlante, G. Wiameyer, M. Jarzemba, S. Mohanty, R. Rice, R. Fedors, 

C. McKenney, L. Deere 

3.9.1 Information Flow Within TPA .................................. 3-86 

3.9.1.1 Information Supplied to DCAGW ................................ 3-86 

3.9.1.2 Information Provided by DCAGW ............................... 3-87 

3.9.2 Conceptual M odel ............................................ 3-87 

3.9.2.1 Development of Radionuclide Concentrations in 

Water for a Residential Receptor Group ........................... 3-87 

3.9.2.2 Development of Dose Conversion Factors ......................... 3-90 

3.9.3 Assumptions and Conservatism of the DCAGW Approach ............ 3-90 

3.10 FAULTO MODULE DESCRIPTION ..................................... 3-91 

J. Stamatakos, A. Ghosh, S. Mohanty, A. -B. K. Ibrahim 

3.10.1 Information Flow Within TPA .................................. 3-91 

3.10.1.1 Information Supplied to FAULTO ............................... 3-91 

vii NUREG-1668
CONTENTS (cont'd) 

3.10.1.2 Information Provided by FAULTO ............................... 3-91 

3.10.2 Conceptual M odel ............................................ 3-92 

3.10.3 Assumptions and Conservatism of the FAULTO Approach ............ 3-93 

3.11 VOLCANO MODULE DESCRIPTION ................................... 3-96 

B. Hill, C. Connor, J. Trapp, S. Mohanty 

3.11.1 Information Flow Within TPA .................................. 3-96 

3.11. 1.1 Information Supplied to VOLCANO ............................. 3-96 

3.11.1.2 Information Provided by VOLCANO ............................. 3-97 

3.11.2 ConceptUal M odel ............................................ 3-97 

3.11.3 Assumptions and Conservatism of the VOLCANO Approach .......... 3-99 

3.12 ASHPLUMO MODULE DESCRIPTION ................................. 3-100 

M. Jarzemba, B. Hill, C. Connor 

3.12.1 Information Flow Within TPA ................................. 3-101 

3.12.1.1 Information Supplied to ASHPLUMO ........................... 3-101 

3.12.1.2 Information Provided by ASHPLUMO ........................... 3-101 

3.12.2 Conceptual M odel ........................................... 3-101 

3.12.3 Assumptions and Conservatism of the ASHPLUMO Approach ........ 3-105 

3.13 ASHRMOVO MODULE DESCRIPTION ................................ 3-107 

M. Jarzemba 

3.13.1 Information Flow Within TPA ................................. 3-107 

3.13.1.1 Information Supplied to ASHRMOVO ........................... 3-107 

3.13.1.2 Information Provided by ASHRMOVO .......................... 3-107 

3.13.2 Conceptual M odel ........................................... 3-107 

3.13.3 Assumptions and Conservatism of the ASHRMOVO Approach ....... 3-111 

3.14 DCAGS MODULE DESCRIPTION-FARMING RECEPTOR GROUP ........ 3-112 

P. LaPlante, M. Jarzemba, C. McKenney, R. Abu-Eid 

3.14.1 Information Flow Within TPA ................................. 3-112 

3.14.1.1 Information Supplied to DCAGS ............................... 3-112 

3.14.1.2 Information Provided by DCAGS ............................... 3-112 

3.14.2 Conceptual M odel ........................................... 3-112 

3.14.3 Assumptions and Conservatism of the DCAGS Approach ............ 3-116 

3.15 DCAGS MODULE DESCRIPTION-RESIDENTIAL RECEPTOR GROUP .... 3-119 

P. LaPlante, M. Jarzemba, C. McKenney, R. Abu-Eid 

3.15.1 Information Flow Within TPA ................................. 3-119 

3.15.1.1 Information Supplied to DCAGS ............................... 3-119 

3.15.1.2 Information Provided by DCAGS ............................... 3-119 

3.15.2 Conceptual M odel ........................................... 3-119 

3.15.3 Assumptions and Conservatism of the DCAGS Approach ............ 3-120 

4 FUTURE IMPROVEMENTS TO THE TPA CODE ................................. 4-1 

5 REFERENCES .............................................................. 5-1

NUREG- 1668 viii
CONTENTS (cont'd)

APPENDICES 

APPENDIX A REFERENCE DATA SET 

M.R. Byrne; J. Weldy; S. Mohanty; T. McCartin; R. Codell; J. Stamatakos; S. Stothoff, 

B. Leslie; R. Pabalan; D. Turner; G. Wittmeyer; S. Hsiung; P. LaPlante; G. Cragnolino; 

B.J. Davis; T. Ain; R. Green; M. Jarzemba; C. McKenney; W. Murphy; H.L. McKague; 

B. Hill; C. Connor; N. Coleman; J. Winterle; J. Bradbury; R. Fedors; and A. Ghosh

APPENDIX B EBSREL FLOW FACTOR DERIVATION 

R. Codell

ix NUREG-1668
FIGURES 

Figures Page 

1-1 Proposed repository site ....................................................... 1-5 

2-1 Repository system ................................................. 2-2 

2-2 Saturated zone conceptual model representation showing the 20-km (12.4-mi) receptor 

location boundary and the center lines (dashed) of the four saturated zone streamtubes ...... 2-4 

2-3 Cm-243, Cm-244, Cm-245, and Cm-246 decay chains .............................. 2-13 

3-1 UZFLOW conceptual model .................................................... 3-3 

3-2 Boundary of proposed Yucca Mountain repository .................................. 3-5 

3-3 Response of MA to changes in MAP during a climate cycle: (a) mean climatic signal only 

and (b) with perturbation ..................................................... 3-10 

3-4 Discretization of repository into nine rectangular regions for the NFENV module ......... 3-15 

3-5 Mountain-scale heat transfer model with heated rectangular regions ................... 3-16 

3-6 Plan view of repository showing emplacement drifts and waste packages ............... 3-17 

3-7 Waste package on an emplacement cart in a drift ................................... 3-18 

3-8 Internals of a large waste package .............................................. 3-21 

3-9 Time snapshots of the vertical distribution of temperature caused by repository heating .... 3-25 

3-10 Conceptualization of drift-scale thermal hydrologic model ........................... 3-27 

3-11 (a) Waste package equivalent beam and (b) waste package vertical supports for SEISMO .. 3-45 

3-12 Schematic of bathtub model with incoming and outgoing water conduits ................ 3-52 

3-13 Schematic drawing for advective and diffusive mass transfer from the waste package to the 

host rock .................................................................. 3-54 

3-14 UZFT contaminant transport legs ............................................... 3-71 

3-15 Saturated zone streamtube model showing four streamtubes extending from just upgradient 

of the repository footprint southward to the major ground-water withdrawal area of 

Am argosa farm s ............................................................. 3-74 

3-16 Farmer exposure pathways .................................................... 3-83 

3-17 Simulated faults within the repository boundary ................................... 3-94 

3-18 Schematic of subsurface disruption area associated with a volcanic conduit using waste 

package dimension [5.68 m (18.6 ft) long, 1.8 m (5.9 ft) wide], waste package spacing [ 19 m 

(62 ft) center-to-center], and emplacement drift spacing [22 m (72 ft)] .................. 3-98 

3-19 ASHPLUMO contaminant release and deposition ................................. 3-102 

3-20 A plot of the areal radionuclide density as a function of time for radionuclides in the 

curium-245 decay chain ..................................................... 3-111 

3-21 Examples where fraction ofresuspended mass that emanated from the contaminated volcanic 

ash layer (f.) (a) is unity and (b) is less than unity ................................. 3-115

Xi NUREG-1668
TABLES 

Tables Page 

2-1 List of 43 Nuclides in the TPA Version 3.1.4 Code Database ......................... 2-11 

2-2 Radionuclide Chains For Ground-Water Release ................................... 2-12 

3-1 Initial Fluid Composition and pH Corresponding to J-13 Well Water ................... 3-24 

3-2 Dripping Parameter Correlation Matrix .......................................... 3-61 

3-3 Stratigraphic Thicknesses (m) for Each of the Seven Repository Subareas ............... 3-66 

3-4 A Listing of the Nonradionuclide-Specific Data Used in Estimating the Surficial 

Radionuclide Areal Density for Members of the Cm-245 Decay Chain ................ 3-110 

3-5 A Listing of the Radionuclide-Specific Data Used in Estimating the Surficial Radionuclide 

Areal Density for Members of the Cm-245 Decay Chain ........................... 3-110

NUREG-1668 xii
FOREWORD 

In accordance with the provisions of the Nuclear Waste Policy Act of 1982, the U.S. Nuclear Regulatory 

Commission (NRC) has the responsibility to evaluate a license application for geological repositories 

constructed for emplacement of high-level nuclear waste (HLW) (i.e., commercial spent fuel and vitrified 

HLW). This act was amended in 1987 to designate one site for detailed characterization in the unsaturated 

region of the tuffaceous rocks of Yucca Mountain (YM), in part located on the Nevada Test Site in southern 

Nevada. To execute its mandated prelicensing and licensing function, the NRC staff will review the critical 

aspects of the U.S. Department of Energy (DOE) Viability Assessment (VA), any future draft environmental 

impact statement for the proposed repository site, and any potential license application. In addition to these 

reviews, NRC will also develop preliminary comments on the sufficiency ofthe at-depth site characterization 

program and waste form proposal. Although the DOE VA is not a licensing document, the NRC staff will 

review the document as a basis for future reviews of the DOE draft license application. A major component 

of the approach the staff will use to review these products is the Total-system Performance Assessment 

(TPA). 

In support of these review activities, the NRC staff is focusing on detailed technical assessments to 

understand and quantify the isolation characteristics and capabilities of the proposed repository system at the 

YM site. Concurrent with these assessment studies, the Center for Nuclear Waste Regulatory Analyses 

(CNWRA), is enhancing the TPA computer code. The TPA code is designed to simulate the behavior of the 

geologic repository, taking into account the essential characteristics of the natural barriers and engineered 

barrier system, as well as the evolutionary changes in the geologic setting (i.e., climate, seismicity, faulting, 

and volcanism). This document presents the latest version of the TPA code, designated as 3.1.4. This is an 

interim version of the code. An improved and personal computer-based version is expected to supersede it 

in the near future. 

It is important to point out that the TPA Version 3.1.4 code was developed to allow the NRC and CNWRA 

staffs to perform interim evaluations of the DOE Total System Performance Assessment approaches and 

parameter values used to estimate the performance of the proposed HLW repository. Because investigations 

at the YM site are ongoing and the analyses are iterative, the TPA Version 3.1.4 code was developed with 

flexibility to analyze a variety of designs and site characterization factors. It is also important to note that the 

particular conceptual models and assignment of initial model parameter values (and distributions) in this 

description of the TPA code do not constitute regulatory acceptance. As DOE's ongoing repository 

investigations develop more information, it can be expected that different conceptual models and parameter 

values and distributions will be used in performance assessments. Thus, estimates of the performance using 

the TPA Version 3.1.4 code are to be considered preliminary and to not represent a regulatory determination 

of total system performance for the YM site.

NUREG-1668 °°.i
ACKNOWLEDGMENTS 

This NUREG was prepared to document work performed by the U.S. Nuclear Regulatory Commission (NRC) 

and the Center for Nuclear Waste Regulatory Analyses (CNWRA), for NRC, under Contract No. NRC-02-97

009. The activities reported were performed on behalf of the NRC Office of Nuclear Material Safety and 

Safeguards, Division of Waste Management. The report is a joint NRC/CNWRA product and does not 

necessarily reflect the NRC's views or regulatory position. 

The authors wish to thank G. Wittineyer; M.P. Miklas, Jr.; E.C. Pearcy; and N. Sridhar, of the CNWRA, for 

their thorough technical reviews and numerous helpful technical comments. The authors also thank W. Patrick, 

B. Sagar, and K. McConnell for the programmatic review and the valuable suggestions for improving the 

quality of the report. The authors appreciate the thorough editorial review conducted by the staff of the 

Publications Services Department at Southwest Research Institute--especially the efforts of B. Ford; C. Gray; 

S. Harley; B. Long; and A. Woods. The authors are grateful for the secretarial support provided by C. Garcia 

and A. Burne. Thanks are also expressed to K. Poor, of Portage Environmental, Inc.; J. Bogan (consultant); 

and C. Scherer (Southwest Research Institute), fortheir assistance in reviewing the document, testing the code, 

and conducting quality assurance-related tests and preparing related documentation. The authors acknowledge 

the tests conducted on the initial version of the code by many CNWRA and NRC staff members. 

In preparing this document and in developing the TPA Version 3.1.4 code, numerous individuals made strong 

technical contributions. Assistance from R. Rice (consultant), L. Deere, and P. LaPlante, in preparation of this 

volume, is gratefully acknowledged. Insofar as was possible, those who contributed to writing the report, 

developing the conceptual models, and programming and testing the code have been recognized by having 

their names listed after the appropriate chapter and section headings in the table of contents. The efforts of C. 

Lui, the Program Element Manager for the Total System Performance Assessment and Integration key 

technical issue, in coordinating the preparation of this document, are also recognized.

xv NUREG-1668
ACRONYMS AND INITIALISMS 

1-D one-dimensional 

2-D two-dimensional 

AML areal mass loading 

BWR boiling-water reactor 

CCDF complementary cumulative distribution function 

CDF cumulative distribution function 

CM consequence module 

CNWRA Center for Nuclear Waste Regulatory Analyses 

CP compliance point 

CSR critical simulation region 

DCF dose conversion factor 

DEM digital elevation model 

DOE Department of Energy 

DVM distributed velocity method 

EBS engineered barrier system 

ENFE evolution of the near-field environment 

EPA U.S. Environmental Protection Agency 

EPRI Electric Power Research Institute 

GPD gallons per day 

GWd gigawatt-day 

GWTT ground-water travel time 

HLW high-level waste 

IA igneous activity 

ICRP International Commission on Radiological Protection 

IPA Iterative Performance Assessment 

JS joint spacing 

KTI key technical issue 

LA license application 

LHS Latin Hypercube Sampling 

MA/ mean annual infiltration 

MAP mean annual precipitation 

MA T mean annual temperature 

MAV mean annual atmospheric vapor density 

MCS Monte Carlo Sampling 

MTU metric tons of uranium 

NFENV Near-field environment 

NRC Nuclear Regulatory Commission 

PA performance assessment 

PDF probability density function 

PNNL Pacific Northwest National Laboratory

xvii NUREG-1668
ACRONYMS AND INITIALISMS (cont'd) 

ppm parts per million 

PWR pressurized-water reactor 

QA quality assurance 

RH relative humidity 

RT radionuclide transport 

SF spent fuel 

SZ saturated zone 

TEDE total effective dose equivalent 

TPA Total-system Performance Assessment 

TSPA total system performance assessment 

UDEC Universal Distinct Element Code 

UZ unsaturated zone 

WP waste package 

YM Yucca Mountain 

YMR Yucca Mountain region

NUREG-1 668 .o.i
TABLE SHOWING ENGLISH/METRIC SYSTEM 

CONVERSION FACTORS 

The preferred of measurement today is the "System Internationale" or the metric system. However, for some 

physical quantities, many scientists and engineers prefer the familiar and continue to use the English system 

(foot-pound units). With few exceptions, all units of measure cited in this report are usually in the metric 

system. The following table provides the appropriate conversion factors to allow the user to switch between 

these two systems of measure. Not all units nor methods of conversion are shown. Unit abbreviations are 

shown in parentheses. All conversion factors are approximate.

Xix NUREG-1668
QUANTITY TO INCH-POUND UNITS FROM METRIC UNITS' CONVERSION FACTOR' 

SPACE AND TIME 

length statute mile (mi) kilometer (km) 0.6214 

foot (ft) meter (m) 3.2808 

inch (in) centimeter (cm) 0.3937 

area square mile (mi2

) square kilometer (kin2

) 0.3861 

acre square kilometer 247.1 

square foot (ft2) square meter (M 2

) 10.7639 

square inch (in2) square centimeter (cm 2) 0.1550 

volume cubic yard (yd3) cubic meter (m3

) 1.3080 

cubic foot (ft3

) cubic meter 35.3147 

liter (I) 0.0353 

cubic inch (m3) centimeter (cm3

) 0.0610 

velocity feet/second (ft/sec) meters/second (m/sec) 3.2808 

acceleration feet/square second (ft/sec2) meters/square second (m/sec2

) 3.2808 

MECHANICS 

mass (weight) pounds (Ib) kilogram (kg) 2.2046 

short ton metric ton (t) 1.1023 

density pounds/cubic foot (lbs/ft3) kilograms/cubic meter (kg/m3 ) 0.0624 

force pound-force (lbf) Newton (N) 0.2248 

dyne (dyn) .2248 1 i0' 

pressure atmosphere (atm) kilopascal (kPa) 0.0099 

pound-force/square foot dyne/square centimeter 0.0021 

(lb/ft2) (dyn/cm2) 

power horsepower (hp) kilowatts (kW) 1.3405 

work foot-pound-force (ft-lbf) joule (J) 0.7376 

HEAT 

temperature degrees Fahrenheit ( F) degrees Celsius (°C) OF = 1.8°C + 32 

Kelvin (K) OF = 1.8 K - 459.67 

IONIZING RADIATION 

activity curie (Ci) megabecquerel (MBq) 2.7027 × 10 

(of a radionuclide) 

absorbed dose rad gray (Gy) 100 

dose equivalent rem sievert (Sv) 100 

'Not all metric units are shown. Most metric units can be arrived at by multiplying the value by 10". 

'Multiply quantity in metric units by the appropriate conversion factor to obtain inch-pound units. For additional unit 

conversions, refer to C.J. Pennycuick, Conversion Factors: SI Units and Many Others, Chicago, The University of Chicago 

Press. 1998.

NUREG-1668 XX
QUALITY OF DATA AND CODE DEVELOPMENT 

DATA: CNWRA-generated data contained in this report meet quality assurance (QA) requirements described 

in the CNWRA QA Manual. Data from other sources, however, are freely used. The respective sources ofnon

CNWRA data should be consulted for determining levels of QA. 

CODE: The TPA Version 3.1.4 code has been developed following the procedures described in the CNWRA 

Technical Operating Procedure (TOP), TOP-018, which implements the QA guidance contained in the 

CNWRA QA Manual.

xxi NUREG-1668
1 INTRODUCTION 

To more effectively fulfill its statutory responsibilities for the prelicensing and licensing actions for the 

proposed high-level waste (HLW) repository at the Yucca Mountain (YM) site, the U.S. Nuclear Regulatory 

Commission (NRC) has focused its repository program on resolving issues most significant to overall system 

performance. This has led to a major restructuring of the NRC HLW program, to encompass ten key technical 

issues (KTIs) (Sagar, 1997). Limiting the program scope to these KTIs was necessary to focus and streamline 

the regulatory review and decision-making process. In addition, the use of the KIl framework provides a 

vehicle for ensuring early, clear, and sound technical feedback to the U.S. Department of Energy (DOE) 

regarding possible licensing concerns in the DOE safety case for the proposed YM repository. 

Some of the core objectives of the restructured NRC HLW repository program are to 

* Evaluate the DOE repository program [including field studies, laboratory experiments, and 

performance assessments (PAs)] to provide early feedback to DOE 

* Evaluate the attributes of the DOE Waste Containment and Isolation Strategy (now called the 

Repository Safety Strategy); Total System Performance Assessment (TSPA) - Viability 

Assessment (VA); and TSPA - License Application (LA) 

0 Provide input for the NRC management decision-making on prioritization of the KTIs and 

related subissues 

0 Provide support for the NRC rulemaking for the HLW repository 

A prerequisite to pursuing the aforementioned objectives is development of a generalized computer code, 

specifically tailored for evaluating the total system performance for the proposed repository at the YM site. 

Although NRC previously developed computer codes (Codell, et al., 1992; Wescott, et al., 1995) to analyze 

repository performance, a more general and versatile TSPA code was necessary to: (i) accommodate the most 

current DOE repository design [e.g., repository layout, waste package (WP), and emplacement design]; 

(ii) quantify total system performance in terms of the new compliance performance measure (i.e., annual 

individual dose or risk) expected in the forthcoming U.S. Environmental Protection Agency (EPA) standard 

and new NRC HLW regulation; and (iii) include the latest site data (e.g., tracer testing at C-well complex), 

knowledge base, and improved models. In addition, the computer code was specifically designed to allow a 

wide spectrum of individuals with diverse technical backgrounds to easily analyze repository performance. 

This report contains descriptions of the conceptual and mathematical models included in the code. 

1.1 TOTAL-SYSTEM PERFORMANCE ASSESSMENT CODE 

One of the basic purposes for applying the Total-system Performance Assessment (TPA) 

Version 3.1.4 code to the proposed repository is to secure a detailed and quantitative understanding of (i) the 

key factors controlling the degradation of the engineered barrier system (EBS); (ii) release of the waste from 

the repository; (iii) subsequent transport of the waste through various environmental pathways; and 

(iv) possible human exposure at the location(s) of the designated receptor group. To achieve this 

understanding, the total repository system is modeled in a probabilistic manner (Thompson and Sagar, 1993) 

that considers significant physical and chemical processes, phenomenological interactions and couplings, and 

potentially disruptive events and processes. This probabilistic approach, although computationally intensive, 

yields a range of potential future evolutions of the repository system. In addition, this approach is widely

1-1 NUREG-1668
favored because it avoids many of the technical shortcomings associated with completely deterministic 

scenario-based assessments such as inability to account for uncertainty in model parameter and site 

characterization data (Thompson, 1988). 

To date, two TSPAs for the proposed YM repository have been conducted by NRC and the CNWRA 

using the probabilistic approach. The first TSPA, referred to as Iterative Performance Assessment (IPA) Phase 

I (Codell, et al., 1992), was conducted to assemble and demonstrate the NRC assessment methodology. The 

second TSPA, designated as IPA Phase 2 (Wescott, et al., 1995), improved on the previous effort by 

developing a more comprehensive and integrated computer code (TPA Version 2.0 code). Those analyses 

yielded many valuable insights into the features, events, and processes influencing isolation performance of 

the proposed YM repository; these insights were put to use in the NRC and CNWRA reviews of early DOE 

TSPAs for YM. In the current effort, the IPA activity emphasizes updating and advancing the NRC TSPA 

capability, in preparation for the forthcoming review of the DOE TSPA-VA (TRW Environmental Safety 

Systems, Inc., 1996) as well as for assisting in focusing the NRC KTI activities on technical issues significant 

to repository performance. 

Consistent with the recommendations of IPA Phase 2 (Wescott, et al., 1995), the new TPA 

Version 3.1.4 code was developed to include 

* Most recent site data and knowledge base for conceptual models 

* Updated abstractions for features, events, and processes 

* Streamlined methodology for data transfers between executive and consequence modules 

* More flexible design to accommodate future code modifications 

* Improved computational algorithms permitting more detailed analyses 

* Improved capabilities for parameter importance analysis and ranking 

Federal regulations for HLW disposal in geologic repositories are being developed by EPA and NRC. Site 

data continue to be acquired-thus repository and WP designs continue to evolve. Therefore, it was deemed 

important to enhance the TPA Version 3.1.4 code by increasing the flexibility to evaluate alternative 

repository and design features; analyze the effect of different areal mass loadings (AMLs); assess the 

significance of various disruptive scenario classes; assess the impact of radionuclide dilution in the saturated 

zone (SZ); and compute the peak dose for a 10,000-yr compliance period and longer time periods that may 

be of interest. 

TPA Version 3.1.4 code is composed of an executive driver program with a set of consequence 

modules and a library of utility modules. The executive driver controls the sampling of stochastic input 

parameters, the calculational sequence and data transfers between modules, and the production of output files. 

Various intermediate output files are generated for later use in representations of repository performance 

including performance of individual components (e.g., WP, SZ), parameter importance analyses, 

post-processing of time-dependent risk curves, and synthesis of statistical distributions [e.g., cumulative 

distribution functions (CDFs) and complementary cumulative distribution functions (CCDFs)] for appropriate 

performance measures. Utility modules ensure that consistent data sets are used by all consequence modules 

and facilitate the discretization of the repository system and surrounding geologic media.

NUREG- 1668 1-2
Technical contributions from the NRC KTI activities resuited in the development of several TPA modules. For example, new conceptual and mathematical models were developed for infiltration, deep percolation, and unsaturated zone (UZ) flow. Other noteworthy KTI contributions include 

* "Unsaturated and Saturated Flow under Isothermal Conditions KTI"-Improved information 

on climate and the distribution of infiltration over the repository area; 

* "Container Lifetime and Source Term KTI"-New WP failure and source term models 

and parameters; 

& "Evolution of the Near-Field Environment KTI"-Estimates of the chemical composition of 

the aqueous environment contacting the WP; 

* "Radionuclide Transport KTr'--Updated estimates of near-field elemental solubilities and 

far-field sorption coefficients; 

"'Thermal Effects on Flow KT'--Improved capability to predict time-dependent temperature and relative humidity for use in the source term module; 

0 "Structural Deformation and Seismicity KITI-Improved hydrostratigraphic model and a 

new faulting consequence module; 

"Igneous Activity KTr'-Improved technical basis for the probability and consequence 

characteristics for volcanic activity; 

"Repository Design and Thermal-Mechanical Effects KTT'•Improved modeling capability for a new seismic consequence module; and 

"Activities Related to Development of the NRC High-Level Waste Regulations KTI"T-Realistic representation of plume dilution induced by receptor group well pumping. 

In addition to contributing to the development of the new TPA Version 3.1.4 code, KTI members participated in the testing and documentation of the code, as well as in preparation of the input data set for the 

reference case. 

1.2 DESCRIPTION OF THE PROPOSED REPOSITORY SITE 

Although the TSPA methodology implemented in the TPA Version 3.1.4 code is somewhat general, the conceptual and mathematical models included in the code were largely selected to be specific to the characteristics and conditions of the YM site. For completeness, a brief description of the YM site geology, hydrology, climatology, and current repository design are presented in this section.

1-3 NUREG-1668
Located in Nye County, the YM site (see Figure 1-1) is approximately 120 km (75 mi) northwest of 

Las Vegas, in southern Nevada. The site is entirely on Federal land controlled by DOE and the Bureau of 

Land Management. The extreme northern portions of the site abut the Nellis Air Force Range. YM itself is 

located in the southern part of the Great Basin, the northernmost subprovince of the Basin and Range 

Physiographic Province. Climate at the site is generally arid to semiarid, with an average precipitation of about 

180 mm/yr (7 in./yr) in the form of winter snow and summer thunderstorms. The YM area exhibits sparse 

vegetation and has a relatively low population density. 

YM is an irregularly shaped cuesta 40-km (25 mi) long by 6- to 10-km (3.7- to 6.2-mi) wide. The crest 

of the mountain ranges between altitudes of 1500 m (4920 ft) and 1930 m (6330 ft) or about 650 m (2132 ft) 

higher than the floor of Crater Flat to the west of the site. The mountain is dominated by a subparallel series 

of north-trending ridges and valleys controlled by steeply dipping faults. YM is bounded by Crater Flat on 

the west, by Jackass Flat-Fortymile Wash on the east and southeast, the Amargosa Desert to the south, and 

by the Timber Mountain Caldera complex to the north. The stratigraphy at YM is composed of a gently 

dipping sequence of Miocene ash-flow tuffs, lavas, and volcanic breccias more than 1800-m (5904-ft) thick. 

The rock unit being considered for the repository facility is a densely welded ash-flow tuff of the Topopah 

Springs Member of the Paintbrush Tuff. There are data for the hydraulic and transport properties of the UZ 

in Flint and Flint (1990); Wittwer, et al. (1995); Rautman, et al. (1995); Schenker, et al. (1995); and Flint, et 

al. (1996). 

The SZ in the YM region (YMR) can be divided into two major aquifer systems, a fractured tuff 

aquifer below the repository location extending more than 10 km (6.2 m) downgradient, and an alluvial 

aquifer extending southward from the terminus of the tuff aquifer to the Amargosa Desert. Patterns of 

ground-water flow in the tuff aquifer appear to be determined by the combined effects of hydraulic boundary 

conditions, dipping layers, and the presence of fault zones and laterally extensive shear fracture zones 

(Geldon, 1993). In contrast, ground-water flow in the alluvial aquifer is likely controlled by the 

interrelationship of recharge and discharge areas, interbasin transfers, and water well pumping in the 

Amargosa Desert area. Modeling studies of regional ground-water flow have been published by the 

U.S. Geological Survey (e.g., Czarnecki and Waddell, 1984). 

Depth to ground water varies from over 350 m (1148 ft) directly below the repository block at YM 

to less than 10 m (33 ft) along a short reach of the Amargosa River in the south central portion of the 

Amargosa Desert. Water quality through the YM and Amargosa Desert regions is generally adequate to drink, 

water stock, and irrigate crops. However, in far southern Amargosa Desert, near Franklin Lake Playa, 

evaporites from lacustrine deposits cause the ground water to have total dissolved solids concentrations in 

excess of 10,000 parts per million (ppm) (Winograd and Thordarson, 1975). Near the communities of 

Amargosa Valley (Lathrop Wells) and Death Valley Junction, there are small clusters of domestic water wells. 

Agricultural uses of ground water are currently confined to the portion of the southern Amargosa Desert that 

lies 30 km north-northwest of Franklin Lake Playa, where depths to water range from 10 to 40 m (33 to 131 

ft) and the topography is suitable for flood and center pivot irrigation. 

The repository proposed for the YM site consists of an underground facility designed to accommodate 

70,000 metric tons of HLW. The specific layout of the underground facility will depend on the thermal 

loading strategy that, as yet, has not been finalized. The waste disposed of in the repository is, at this time, 

expected to consist of both commercial spent fuel (SF), DOE SF, and defense HLW. For simplicity, the TPA 

Version 3.1.4 code assumes the inventory is SF. The current WP design for SF disposal consists of a large 

cylinder [i.e., approximately 1.8-m (5.9-ft) diameter by 5.6-m (18.6-ft) length] made up of a 20-mm (0.8-in.)

NUREG-1668 1-4
Force Base "" ""' ! 

- - 4010000 S"? 

U'. 

C', 4080000 

4055000 

535000 540000 545000 550000 

UTM 

(meters) 

F 

•'Figure l-l, Proposed repository site.

I
wall-thickness inner overpack, and outer overpack with a 100-mm (4-in.) wall thickness. The container would 

be emplaced, in-drift, on v-shaped supports. If licensed, the underground facility would remain open 100 yr 

or more before final closure. Design decisions regarding backfilling or leaving the drifts open have not been 

made at this juncture (TRW Environmental Safety Systems, Inc., 1995). 

1.3 REPORT CONTENT 

This document was prepared to provide a single reference for the TPA Version 3.1.4 code. This report 

provides information on the system parameters, conceptual models, and code outputs essential to 

interpretation of calculational results. Additionally, the documentation communicates the developers' 

perspectives on the degree of conservatism adopted in the modeling approaches, major assumptions, and input 

parameters. 

An overview to the modeling approach is presented, in Chapter 2, that describes the primary 

conceptual models and their interrelationships. Chapter 3 illustrates the conceptual and mathematical models 

implemented in 13 consequence modules; this chapter also delineates the major assumptions and conservatism 

of each module. Chapter 4 outlines future TPA Version 3.1.4 code developments, and Chapter 5 lists the 

technical references for this report. 

Appendix A provides a roadmap to the primary input file (tpa.inp) and is expected to be helpful in 

understanding the TPA Version 3.1.4 code reference data set. The reference data set is subdivided by modules 

and provides a description of the parameters and the basis for values selected for the reference case. The 

parameter description follows the same order as in the primary data file and Chapter 3. Appendix B presents 

the derivation of empirical flow factors used in the source term module.

NUREG-1668 1-6
2 OVERVIEW OF THE TOTAL-SYSTEM PERFORMANCE 

ASSESSMENT CONCEPTUAL MODELS 

Analysis of repository performance is anticipated to be complex, with substantial uncertainties because of the 

first-of-a-kind nature of the repository, extended period of performance (at least 10,000 yrs), and reliance on 

engineered and natural barriers. Detailed simulation models that include all the couplings, heterogeneities, 

and complexities cannot be incorporated into PA models and still maintain reasonable computer execution 

times and meet hardware limitations. Therefore, a key part in developing the TPA computer code is 

determining the level of detail in the processes, design, and attributes of the site necessary to produce a 

credible analysis that provides meaningful insights on performance without an unreasonable computational 

burden. The TPA Version 3.1.4 code includes the repository system description (e.g., representation of the 

site and repository design within the TPA Version 3.1.4 code), conceptual models, and parameter values. A 

discussion of the repository system description and conceptual models is presented in this section to provide 

a general overview of the TPA Version 3.1.4 code. Chapter 3 contains more detailed information on the 

conceptual and mathematical models. The TPA input parameter values and the basis for their selection are 

presented in Appendix A. 

2.1 CONCEPTUALIZATION OF REPOSITORY AND GEOLOGIC SETTING 

For ease of use and computational efficiency, the TPA Version 3.1.4 code replaces the intricate 

repository layout and the complex geologic setting with relatively simple conceptual representations. The 

repository layout, for example, is represented by an idealized planar feature discretized into a set of subareas, 

whereas the geology is replaced by a sequence of layers, each of which is homogenous. Properties and 

conditions for each subarea are assumed to be uniform. Except for the influence of the thermal load, flow and 

transport processes in and below a given subarea are independent of those processes in other subareas. Thus, 

flow is entirely vertical, with no lateral diversion in the UZ. 

As illustrated in Figure 2-1, quadrilateral subareas of uniform thickness are used to represent 

individual subregions of the repository. In the current application, the repository is divided into seven 

subareas; however, the TPA Version 3.1.4 code has the capability to use much finer discretizations of both 

the repository and the geologic setting beneath it. The number of WPs in each subarea is assumed to be 

proportional to the fraction of total repository area represented. Radionuclide releases from the EBS are 

calculated by modeling a single prototypical WP for each subarea and for each failure type. Performance 

characteristics of the WP in each subarea are calculated by considering the evolution of such characteriitics 

as water flux, thermal and chemical conditions, and geologic processes [e.g., seismicity, fault displacement, 

and igneous activity (IA)]. 

The geologic setting is composed of the UZ (i.e., geologic media between the land surface and the 

water table) and the SZ (i.e., ground-water aquifer beneath the repository and extending to the location of the 

receptor group). For simplicity, the stratigraphy is assumed to be laterally continuous and uniform within a 

subarea, to represent the UZ in a separate hydrostratigraphic sequence for each subarea. This simplification 

is consistent with the assumption that, in general, flow in the UZ is primarily vertical, that is, little or no lateral 

diversion of flow along hydrostratigraphic units. The geologic setting also includes features, events, and 

processes, such as seismicity, tectonism, and volcanism, that may adversely affect the performance of 

the repository.

2-1 NUREG-1668
7 Subareas

°.............. 

,.-..... .... . 

. , . . . . . . . . . . . 

. ° " . . . . . . . . . . . . Unsaturated Zone'

(UZFT Module) 

Saturated Zone (SZFT Module)

z 

y•-X

+- Hydrogeologic 

Units ..~ i i .iiI.. . . , . i . ii•. . .

° °. .:. . :° . . ........ .. ° . ° ..... •.. •.. . .. . . .. .. : . . . . . . . . . . . . . . . 

S. . . . . . . . . . . • . : . . . 

Dicag oint

Figure 2-1. Repository system.

00

Lmmýý -ýý\mod,

.............. ........
Direct release of radionuclides to the accessible environment because of an extrusive igneous event 

is also modeled in the TPA Version 3.1.4 code. The physical characteristics of the extrusion and the 

assumption of a uniform distribution of WPs in the repository are used to determine the number of WPs 

affected by the event. Radionuclides are transported to the receptor location, based on characteristics of the 

eruption and meteorological conditions, where the concentration of radionuclides in soil, resulting from the 

deposition of volcanic ash containing SF particles, is calculated. This soil concentration is used in calculating 

the annual dose to the average member of the receptor group. 

2.2 CONCEPTUAL MODELS IMPLEMENTED IN THE TOTAL-SYSTEM 

PERFORMANCE ASSESSMENT COMPUTER CODE 

In developing the TPA computer code, several conceptual models were formulated, integrated, and 

implemented in various abstracted mathematical models. These basic conceptual models, which describe the 

interactions/couplings of physical and chemical processes believed to be present in a proposed geologic 

repository (at YM), can be grouped into the following generic categories: 

* Infiltration and deep percolation 

* Near-field environment 

* .Radionuclide releases from the EBS 

* Aqueous-phase radionuclide transport (RT) through the UZ and SZ 

To model flow and transport in the SZ, the TPA conceptual model consists of four distinct 

streantubes over the width of the repository footprint normal to UZ flow (Figure 2-2). Each of the seven 

streamtubes in the UZ is connected to one of the four streamtubes in the SZ. Radionuclide releases from each 

of the UZ streamtubes provide the source term to the SZ streamtubes. The SZ strearmtubes are treated as 

separate conduits and have flow velocities that vary along the individual flow paths. The mass flow rate of 

radionuclides exiting all SZ streamtubes is used to compute the average concentration at the well head. This, 

in turn, is used in calculating annual dose to the average member of the receptor group. The average 

concentration accounts for all releases, from the ground-water pathway to the location of the receptor group; 

spatial extent of the releases in the SZ at the location of the receptor group; extent of the production zone 

containing the radionuclides (all radionuclides are assumed to be released in one production zone); and the 

influence of the pumping rate attributed to water use by the receptor group. 

* Airborne transport from direct radionuclide releases 

* Exposure scenario and reference biosphere 

These conceptual models are designed to apply to the current DOE repository design and specific site 

characteristics of the YM area and provide flexibility for examining alternative designs and uncertainties in 

site and engineered material performance. In some of these generic categories, alternative conceptual models 

have also been incorporated into the code. 

These conceptual models represent a range of system states, including disruptive events. The 

consequences of disruptive events (e.g., seismicity, fault displacement, and igneous activity) are evaluated 

with the TPA Version 3.1.4 code by assessing the effects on failure (producing releases to ground water) and

2-3 NUREG-1668
403a000WO 

532=3 536=0 540WO 544=00 5480 0 552=0 55 0 56 =( W64 000 

Figure 2-2. Saturated zone conceptual model representation showing the 20-km (12.4-mi) receptor 

location boundary and the center lines (dashed) of the four saturated zone streamtubes.

NUREG-1668 2-4
direct releases of radionuclides (airborne releases to the biosphere). In the TPA computer code, the probability 

of a disruptive event affecting the repository is used to calculate a risk curve. (A risk curve can be determined 

by a calculation, separate from the TPA Version 3.1.4 code execution, that combines the consequences 

calculated with the TPA Version 3.1.4 code and the appropriate probabilities.) 

The following discussion provides a general overview of the key aspects of the major conceptual 

models implemented in the TPA Version 3.1.4 code. More detailed descriptions of these models, including 

the mathematical basis, assumptions, and calculational methodologies, are presented in Chapter 3. 

2.2.1 Inifitration and Deep Percolation 

A one-dimensional (1-D) modeling approach is used in the TPA Version 3.1.4 code 

(Section 3.1 - "UZFLOW Module") to describe how meteoric water at the land surface moves vertically 

downward (i.e., without lateral flow) through the UZ, to the repository horizon, and ultimately to the water 

table. In the 1-D conceptual model, the deep percolation flux (qc) is constrained to be equal to the shallow 

infiltration rate (qi,,). The annual average qia is estimated based on: 

* Present-day qifd 

* Change in climate with time 

* Elevation and soil depth over the repository subarea 

Uncertainty in the present day infiltration rate estimate is accounted for in the TPA Version 3.1.4 code by 

treating it as a statistically sampled input parameter. Temporal variations are incorporated by varying the 

present day q,., over the 100,000-yr period assumed for long-term climatic changes. The effects of 

site-specific soil-cover thickness and elevation are used to reflect the spatial variation over each of 

the subareas. 

The variation of qia because of changes in climate was developed through consideration of 

paleo-climatic information and a process-level auxiliary analysis (Stothoff, et al., 1997). The qifi response 

function depends on two independent variables--present-day mean annual precipitation (MAP) and 

temperature (MAT), as well as the present-day shallow infiltration rate. After computing qim, the water flux 

at the repository horizon is then partitioned into: 

* Water flux diverted around the failed WP 

* Water flux entering the failed WP 

Thus, for the purposes of the TPA computer code, the net water flux carrying dissolved radionuclides is a 

fraction of the total water flux arriving at the repository. It is this net water flux that is used in the TPA 

Version 3.1.4 code (Section 3.5 -- "EBSREL Module") to calculate the radionuclide source term for each 

subarea.

2-5 NUREG-1668
2.2.2 Near-Field Environment

Physical and chemical processes in the near field of the repository, such as heat transfer, water-rock 

geochemical interactions, and refluxing of condensate water, are expected to affect WP performance. In the 

TPA Version 3.1.4 code, a range of near-field characteristics is depicted in the abstracted mathematical 

models for heat and water flow and table look-ups for chemical parameters (Section 3.2 -- "NFENV Module"). 

For the purpose of estimating WP failure times and radionuclide release rates, the near-field environment is 

characterized by: 

* Drift wall rock and WP surface temperatures 

* Relative humidity (RH) (i.e., ratio of vapor pressure at the WP surface to the vapor pressure 

at the drift rock wall) 

* Water chemistry (e.g., pH, chloride concentration, and carbonate ion concentration) 

* Water reflux during the thermal phase 

The average rock temperature in the repository horizon is calculated assuming a conduction-only 

model (i.e., the time history of temperature for each subarea is calculated, accounting for the amount of 

emplaced waste). The WP surface temperature is calculated using a multimode heat transfer (i.e., conduction, 

convection, and radiation) model. Vapor pressure is computed using the standard thermodynamic equation 

relating vapor pressure to temperature. 

Estimates of the pH and chloride concentration histories of water films on the WP surface were 

developed in a separate process-level auxiliary analysis using the multi-component geochemical module of 

the MULTIFLO code (Lichtner and Seth, 1996). MULTIFLO was applied to calculate the pH and chloride 

concentration for water percolating through the matrix of the tuffaceous rock. Because the chloride 

concentration in the water film is likely to be higher than that in the rock mass, the chloride concentration 

history is scaled by a statistically sampled parameter. The TPA Version 3.1.4 code provides the option of 

either using a look-up table that uses the temperature-dependent pH (not currently used) and chloride 

concentration generated with the MULTIFLO code or constant values specified in the input file (Appendix A). 

The amount of water percolating through the drifts varies over time primarily because of the coupled 

processes of heat transfer and fluid flow (e.g., vaporization, condensation, and refluxing). Water refluxing 

produced by these thermohydrologic effects is important over the first few thousand years, after which natural 

percolation determines the rate of water flow into the repository. Two lumped-parameter water reflux models 

are included in the TPA Version 3.1.4 code, both based on bulk flow balances. The first model considers 

episodic reflux associated with time-dependent perching above the repository. The second model assumes that 

refluxing water can be sufficient to depress the boiling isotherm in fractures and reach the WP during times 

when the surface temperature exceeds the boiling point of water. Each reflux model produces estimates of the 

total water flux into the repository during the thermal period. 

2.2.3 Radionuclide Releases from the Engineered Barrier System 

In the TPA Version 3.1.4 code, the performance of a prototypical WP is modeled for each repository 

subarea, considering the failure time and radionuclide release rates for each of the WP failure categories 

(Section 3.3 -- "EBSFAIL Module"). When this prototypical WP fails, all WPs in that subarea under a

NUREG-1668 2-6
specified failure category are assumed to have failed. The estimation of both WP failure times and liquid 

releases depends on the nature and extent of corrosion, near-field environment, percolation flux in the drift, 

and external processes that may impose static loads, dynamic loads, or both. WP failures are grouped into 

three basic categories: (i) corrosion and mechanical failure; (ii) disruptive event; and (iii) initially defective 

WP failures. After determining the WP failure time, the TPA Version 3.1.4 code calculates the aqueous-phase 

radionuclide releases from the WP by considering the dissolution of radionuclides from the SF matrix and 

advective diffusive transport from the WP directly to the UZ beneath the repository. 

Corrosion failure of the WP is defined to occur at the time when the inner overpack is fully penetrated 

by a single pit and the waste form is therefore accessible to water. The abstracted corrosion model uses a 

conceptual framework that assumes the formation of a water film containing a salt solution but does not 

explicitly consider water dripping on the container. The corrosion processes considered in the model 

abstraction consist of: 

* Dry air oxidation 

* Humid air corrosion 

* Aqueous corrosion 

WP surface temperature and the chloride concentration in the water film influence the mode, and hence, the 

rate of corrosion. The predominant mode of corrosion however, depends on the critical RH, as well as the 

container material. Mechanical failure of the WP is considered to be the result of fracture of the outer steel 

overpack, because thermal embrittlement arising from prolonged exposure at temperatures sufficiently 

elevated to cause substantial degradation of mechanical properties (it is assumed conservatively that the inner 

overpack is fractured when the outer overpack is fractured). 

Disruptive event failures are taken into account by modeling the effects of events such as seismicity 

(Section 3.4 - "SEISMO Module"); fault displacement (Section 3.10 - "FAULTO Module"); and igneous 

activity (Section 3.11 - "VOLCANO Module"). In the case of seismicity, WP failures are caused by rock falls 

that mechanically load and deform the WP (the drift is assumed not to be backfilled for rockfall to damage 

the WP). Displacements along yet undetected faults or new faults (because DOE will not emplace the WPs 

within a setback distance from known and well-characterized faults) that exceed a preestablished threshold 

are assumed to fail WPs within the fault zone. For IA, simulated magmatic intrusions intersecting the 

repository are assumed to cause WP failure; WPs within a dike but outside the vent hole are assumed to fail 

and expose the SF to water, whereas those within the vent hole are assumed to be entrained in the magma and 

released directly to the biosphere. For both IA and fault displacement, failures are modeled by superimposing 

the physical dimensions of the perturbation (i.e., length, width, and orientation of the fault and the igneous 

intrusion) on the repository footprint, to determine the total number of WPs potentially affected in each 

repository subarea. Separate failure times are calculated for seismicity, fault displacement, and 

igneous activity. 

In most applications of the TPA Version 3.1.4 code, it is assumed that a small number of WPs are 

failed at the time of repository closure. These initially failed WPs are attributed to fabrication defects or

2-7 NUREG-1668
damage to the WP as a result of improper emplacement. For conservatism, the number of initially defective 

WPs is typically assumed to be 0.1 percent' of the total number of containers. 

Radionuclide releases from the WP are calculated by considering the alteration rate of SF (i.e., rate 

at which radionuclides in fuel become available for release); radionuclide solubility limits; and transport 

mechanisms out of the WP. The TPA Version 3.1.4 code incorporates numerous parameters (e.g., fraction of 

SF that is wet, particle size of the SF, alteration rate of uranium oxides (UO 2÷x), and credit for cladding) that 

control the release of radionuclides from the SF matrix. After radionuclides are leached from the SF waste 

form, the calculated releases are adjusted to ensure consistency with the radioelement solubility limits. 

A parameter value is used to specify the fraction of failed WPs in the subarea that is wetted -- the 

number of failed WPs available to contribute to the source term (auxiliary calculations are typically done to 

justify parameter selection). To compute the time-dependent source term, the TPA provides two alternative 

conceptual models: (i) a bathtub model -- the WP must fill with water before the radionuclides are released; 

and (ii) a flow-through model - radionuclides are released by water dripping on the waste form. For the 

bathtub model, the WP is treated as a stirred tank, with the tank capacity dependent on the statistically 

sampled water outlet height. Water will fill the WP until the capacity (height) is reached and, thereafter, the 

amount of water entering the WP will equal the amount of water flowing out. Water leaving the WP transports 

dissolved radionuclides into the UZ below the repository. Additionally, the TPA Version 3.1.4 code has the 

option to account for diffusive transport of radionuclides remaining in the fluid of the bathtub or WP through 

an assumed backfill. The flow-through model is a variant of the bathtub model except water does not have 

to first fill the bathtub before release and the fraction of fuel wetted is independent of the water level. 

2.2.4 Treatment of Aqueous-Phase Transport in the Unsaturated and Saturated Zones 

Movement of aqueous-phase radionuclides from the repository horizon, through the UZ and SZ and 

ultimately to the receptor group, is modeled in the TPA Version 3.1.4 code (Section 3.6 -- "UZFT Module" 

and Section 3.7 -- "SZFT Module") using the previously described streamtube approach. Each streamtube 

encompasses one or more repository subareas and is composed of a vertical section from the repository to the 

water table and horizontal sections in the SZ. The transport module NEFTRAN II (Olague, et al., 1991) 

simulates the spectrum of processes (e.g., advection, dispersion, matrix diffusion, sorption, and decay) 

occurring within individual streamtubes. For a set of about 20 radionuclides, this module simulates vertical 

transport through the UZ and horizontal transport through the SZ. 

Time-dependent flow velocities in the UZ are calculated using the hydraulic properties of each major 

hydrostratigraphic unit. The transport module simulates the transport of radionuclides through either the 

porous rock matrix or fractures.' Radionuclide retardation by chemical sorption in the rock matrix can 

significantly reduce the transport rates and is therefore included in the model. Retardation on fracture surfaces, 

however, is neglected for conservatism, because the significance of this mechanism has yet to be 

demonstrated. 

'Tschoepe, et al. (1994) suggest fabricated metallic component reliabilities of 99.9 to 99.99 percent. 

2

Transport though rock matrix takes place if the deep percolation (qJr• is less than the hydraulic conductivity of the rock matrix 

(K_,,,) or through fractures when q,_ exceeds K_,,,.

NUREG-1668 2-8
Although ground-water flow in the SZ is assumed to be at steady state, RT within individual 

streamntubes is time-dependent because the source term varies with time. Streamntubes in the SZ exhibit 

variable cross-sections along the flow path; this variable streanitube geometry was determined from a separate 

two-dimensional (2-D) modeling study of the subregional flow (Baca, et al., 1996). The conceptual model of 

the SZ assumes that flow in the tuff aquifer is in localized conductive zones (i.e., permeable fracture zones) 

whereas flow in the alluvium is presumed uniformly distributed in the alluvial aquifer. Although the 

streanitube approach neglects dilution effects arising from lateral dispersion, credit is taken for sorption in 

the alluvium, which is likely to retard aqueous phase transport of many radionuclides. Additionally, matrix 

diffusion from flowing pores and fractures into the more-or-less stagnant matrix pore water within the rock 

is included in the SZ transport model. 

2.2.5 Airborne Transport for Direct Releases 

Radiologic risks associated with the extrusive component of IA are calculated in the TPA 

Version 3.1.4 code by modeling airborne releases of radionuclides for simulated extrusive events. The 

volcanism module assumes that the magma intercepts WPs, moves upward to the land surface, and then ejects 

the ash and SF mixture into the atmosphere. The physical characteristics of each simulated extrusion (e.g., 

vent size, event energetics, and duration) and atmospheric conditions are treated as statistical parameters in 

calculations of ash dispersal and deposition patterns, ash blanket thickness, and radionuclide soil 

concentrations. Three primary factors determining the ash plume geometry and transport rates include: 

* Power and duration of the eruption 

* Wind speed and direction 

* SF particle sizes 

The ash transport model developed by Suzuki (1983) was modified by Jarzemba, et al. (1997) and 

incorporated into the TPA Version 3.1.4 code, to calculate distribution of the released radionuclides. The 

time-dependent radionuclide areal densities are calculated taking into account the thickness of the ash blanket, 

leaching and erosion rates, and radionuclide decay rates. The calculated doses attributed to direct releases are 

strongly influenced by the time of the event (early events result in larger doses in part, because of, the 

contribution to the estimated doses from short-lived fission products present in the SF). 

2.2.6 Exposure Pathways and Reference Biosphere 

Dose calculations are performed in the TPA Version 3.1.4 code for exposure pathways that consider 

an average person of a designated receptor group. These calculations are expressed by the total effective dose 

equivalent (TEDE). Alternative receptor groups are currently included in the exposure scenario. One receptor 

group is a farming community 20 km (12.4 mi) from the repository location, whereas the second is a 

residential community at a specified distance typically less than 20 km (12.4 mi). The average member of the 

designated receptor group is assumed to be exposed to radionuclides transported through the ground-water 

pathway, air pathway, (as a result of direct releases arising from the extrusive component of IA), or both. 

Geographic location and lifestyle characteristics assigned to each receptor group are two primary 

aspects defining the receptor group and are specified in the TPA Version 3.1.4 code by selection of

2-9 NUREG-1668
appropriate input options (Appendix A). In addition, the farming community receptor group is assumed to 

include persons that use the contaminated water for: 

* Drinking [i.e., 2 L/day (0.53 gal./day)] 

* Agriculture, typical of Amargosa Valley area practices (e.g., growing alfalfa and gardening) 

The farming community receptor group is assumed to be exposed to surface contamination through: 

* Consumption of contaminated farm products (i.e., ingestion) 

* Breathing air with ash-SF particles (i.e., inhalation) 

* External exposure 

In contrast, the residential receptor group is assumed to be composed of persons who use contaminated ground 

water only for drinking, but are also exposed to surface contamination (created by ash-SF particle deposition 

from the extrusive component of IA) through inhalation and direct exposure. 

Site-specific dose conversion factors (DCFs) for each radionuclide and pathway are contained in TPA 

data files. These DCFs are used to convert radionuclide concentrations in the ground water and soil to TEDE 

values. The individual DCFs are mean values generated through separate pathway calculations using the 

GENII-S code (Leigh, et al., 1993). In the ground-water pathway, for example, the DCFs are applied to the 

concentrations at the well head. Two separate sets of DCFs are included in the TPA Version 3.1.4 code to 

represent two distinct reference biospheres associated with the present arid climate and the projected future 

pluvial climate. In addition to computing the TEDE history for each stochastic simulation, the TPA 

Version 3.1.4 code scans these dose calculations to identify the magnitude and timing of the peak dose. 

2.3 RADIONUCLIDE INVENTORIES 

In the TPA Version 3.1.4 code, waste inventories and associated thermal output are based on the 

assumptions of an average of 65 percent pressurized water reactor (PWR) waste with a 42 GWd/MTU burnup and 35 percent boiling-water reactor (BWR) waste, with a 32-GWd/MTU burnup. This provides an average 

waste burnup of 38.5 GWd/MTU. The average age of the waste at the time of emplacement is set at 26 yrs. 

After reviewing the literature (Barnard, et al., 1992; Wilson, et al., 1994; TRW Environmental Safety Systems, Inc., 1995). a list of 43 radionuclides (Table 2-1) was compiled for determining HLW inventories. 

All these nuclides are considered for the direct release calculations; however, in ground-water release 

calculations, the computational burden imposed by short-lived nuclides is avoided by specifying only a 

selected list of nuclides. Short lived radionuclides, not expected to arrive at the receptor location because of 

radioactive decay, were not considered in the ground-water pathway. Radionuclide chains considered for 

ground water and direct releases are presented in Table 2-2 and Figure 2-3, respectively. The direct-release 

calculations consider chains with multiple parents, whereas the ground-water release calculations consider 

chains with a single parent.

NUREG-1668 2-10
Table 2-1. List of 43 Nuclides in the TPA Version 3.1.4 Code Database

Nuclide Nuclide Nuclide Nuclide 

Number Name Number Name 

1 U-238 23 Pu-240 

2 Cm-246 24 U-236 

3 Pu-242 25 U-232 

4 Am-242m 26 Sm-151 

5 Pu-238 27 Cs-137 

6 U-234 28 Cs-135 

7 Th-230 29 1-129 

8 Ra-226 30 Sn-126 

9 Pb-210 31 Sn-121m 

10 Cm-243 32 Ag-108m 

11 Am-243 33 Pd-107 

12 Pu-239 34 Tc-99 

13 U-235 35 Mo-93 

14 Pa-231 36 Nb-94 

15 Ac-227 37 Zr-93 

16 Cm-245 38 Sr-90 

17 Pu-241 39 Se-79 

18 Am-241 40 Ni-63 

19 Np-237 41 Ni-59 

20 U-233 42 CI-36 

21 Th-229 43 C-14 

22 Cm-244 

Note: U-uranium; Cm-curium; Am-americium; Pu-plutonium; Th-thorium; Ra-radium; Pb-lead; 

Pa-protactinium; Ac-actinium; Np-neptunium; Th-thorium; Sm-samarium; Cs-cesium; I-iodine; 

Sn-tin; Ag-silver; Pd-palladium; Tc-technetium; Mo-molybdenum; Nb-niobium; Zr-zirconium; 

Sr-strontium; Se-selenium; Ni-nickel; Cl-chlorine; and C-carbon.

2-11 NUREG-1668
Table 2-2. Radionuclide Chains For Ground-Water Release

NUREG-1668

Chain No. Chain Chain No. Chain 

I Cm-246 - U-238 8 Tc-99 

2 Cm-245 - Am-241 - Np-237 9 Ni-59 

3 Am-243 - Pu-239 10 CI-36 

4 Pu-240 11 C-14 

5 U-234 - Th-230 - Ra-226 - Pb-210 12 Se-79 

6 Cs-135 13 Nb-94 

7 1-129

2-12
x 6Y

Cm-244

a

a /3.97 x10' Y

a

432.2 y

105 y

Ix14Y

t 1.9x105 y i.a1 1,00y 

229Th 21 0 2101 

20%i1 

Cm-24S Crn-246 

Figure 2-3. Cm-243, Cm-244, Cm-245, and Cm-246 decay chains.

i - - 2 10 po 

5 d / 

/ 138 d

2-13 NUREG-1 668

Cm-243

2-13
3 CONCEPTUAL AND MATHEMATICAL MODELS 

This chapter describes conceptual and mathematical models used in the TPA Version 3.1.4 code. For each 

module, a description of the conceptual model and its implementation in TPA Version 3.1.4 code is presented 

via the following sections 

(i) Information Flow-general identification of the input supplied by the user and information 

supplied by other modules, and the information that this module provides to other modules; 

(ii) Conceptual Model-description of the technical approach and implementation within 

the code; and 

(iii) Assumptions-listing of some of the important assumptions and, where appropriate, their 

effect on conservatism in the final results. 

The information in this chapter is intended to provide an understanding of the relationship of the input 

parameters to the calculational approach in the code, and to describe the types of results available for 

analyzing performance. 

Numerous types of input parameters are used in the TPA Version 3.1.4 code. A primary input file controls 

the attributes of the simulation (e.g., inclusion of disruptive events, number of realizations to perform when 

conducting probabilistic analyses, and length of simulation) and specification of parameters. It is anticipated 

that the TPA Version 3.1.4 code will typically be used in a probabilistic mode (i.e., performing a large 

number of trials based on Monte Carlo Simulation (MCS) or Latin Hypercube Sampling (LHS) of the input 

parameters) to account for uncertainty and variability in describing the repository system (e.g., porosity, 

retardation, solubility limits). The input parameters included in the primary input file can be sampled. Other 

input data, not considered appropriate for sampling, are used to specify constants of the analysis 

(e.g., elevation of the subarea, radionuclide half-life) or abstractions of detailed models (e.g., definition of 

streamtube characteristics based on a 2-D analysis). A variety of data files is used to supply the nonvariable 

information. For completeness, this section identifies all input relevant to a particular module. 

Two general types of consequence modules are incorporated in the TPA Version 3.1.4 code: (i) base case 

modules primarily associated with the ground-water pathway-UZFLOW, NFENV, EBSFAIL, EBSREL; 

UZFT, SZFT, and DCAGW; and (ii) disruptive event modules associated either with damage to the WP or 

direct release of radionuclides in the air pathway from the extrusive component of LA-SEISMO, FAULTO, 

VOLCANO, ASHPLUMO, ASHRMOVO, and DCAGS. Overall, the consequence modules estimate and 

provide intermediate output information for: 

Water infiltration from the land surface to the subsurface and, subsequently, into the 

emplacement drifts onto WPs; 

Environmental conditions around the WPs (e.g., temperature, humidity, pH, chloride ion 

concentration, and carbonate ion concentration) that affect WP degradation or 

radionuclide release; 

WP failure times caused by corrosion and mechanical processes, seismicity-induced 

rockfall, faulting, IA, and WPs assumed to be initially defective;

3-1 NUREG-1668
Release of radionuclides from failed WPs to the ground-water transport pathway;

• Release of radionuclides from the extrusive component of IA to the air pathway; 

* Transport of radionuclides in ground water through the UZ and SZ; 

• Transport of radionuclides in volcanic ash; 

• Doses to receptors through the radiological contamination in ground water; and 

• Doses to receptors through the radiological contamination of the ground surface (released 

from the repository by an extrusive volcanic event). 

3.1 UZFLOW MODULE DESCRIPTION 

The UZFLOW module calculates the amount of water infiltrating from the ground surface into the 

UZ above the repository. Water that has infiltrated the subsurface affects the repository near-field 

environment and is available for WP corrosion, dissolution of material in the WP, and transport of material 

released from WPs. Specifically, UZFLOW determines the temporal and spatial variation of percolation 

water flux at the repository horizon in the absence of thermal effects from the repository. Although thermal 

effects are not accounted for in UZFLOW, the NFENV module, which simulates the near-field environment, 

explicitly evaluates how repository heating affects percolation through the repository. The calculation of 

percolation flux includes changes in precipitation and temperature that may occur at the ground surface 

throughout the life of the repository. Additional information on modeling of climate change and its effect 

on percolation flux can be found in Stothoff, et al. (1997). 

3.1.1 Information Flow Within TPA 

3.1.1.1 Information Supplied to UZFLOW 

Input provided to the UZFLOW module consists of detailed information on ground surface elevation, 

soil thickness, future climatic conditions, and parameters that specify the present infiltration and the variation 

in climate (provided in the primary input file). Ground surface elevation is supplied as a digital elevation 

model (DEM) in the static file entitled elevdem.dat. A DEM with the same discretization, soildem.dat, 

provides soil thicknesses. The input files climatol.dat and climato2.dat provide data for representing time

dependent future climatic conditions. Data file climatol.dat provides noise data (normally distributed random 

numbers) used in computing time-varying MAP and mean annual temperature (MAT). Data file climato2.dat 

provides a time history of climate change expressed as a fraction of the full glacial maximum in temperature 

and precipitation. All other input parameters to UZFLOW, specifying present infiltration and the magnitude 

of climate variation, are in the UZFLOW section of the primary input file. 

3.1.1.2 Information Provided by UZFLOW 

UZFLOW passes to EXEC the time-varying volumetric flow rate of water infiltrating toward the 

repository, which then provides these values to NFENV module for use in estimating ground-water reflux 

during the repository thermal phase-and the UZFT module in determining transport velocities.

NUREG- 1668 3-2
Evaporation Energy

MAI

Figure 3-1. UZFLOW conceptual model. 

3.1.2 Conceptual Model 

The UZFLOW module is conceptually divided into three components: (i) a climate model for 

specification of the time history of climatic change; (ii) a model for calculation of shallow infiltration rate 

with climate change; and (iii) a deep percolation model for calculation of areal-average percolation flux at 

the repository level. 

The climate model assumes that climate variables change in response to a glacial cycle, with a period 

of roughly 100,000 yrs, with shorter-term perturbations superimposed on the cycle. Two climate variables, 

precipitation and temperature, have the dominant impact on infiltration (Stothoff, et aL, 1997). The time 

history of the dominant variables is calculated in the TPA Version 3.1.4 code using input specifications from 

the climatol.dar and climato2.dat data files and the UZFLOW section of the primary input file. The 

elevation-dependent spatial variability of these climatic variables, as well as vapor density, is superimposed 

on the glacial perturbations using the elevations in the elevdem.dat data file. 

Figure 3-1 illustrates the shallow infiltration conceptual model, including water and energy balances, 

for a system of shallow surficial soil above a fractured impermeable bedrock. Water and heat enter and exit 

the soil at the ground surface. A portion of the water infiltrates after precipitation does not evaporate and 

moves into the deep subsurface (below the root zone) via fracture flow. Thus water escaping evaporation

NUREG--1668

Precipitation

3-3
becomes deep percolation. Offline simulations of shallow infiltration using this conceptual model were 

performed for a simulated time period of at least a decade using hourly time steps (Stothoff, et al., 1997). The 

resulting time-varying infiltration, accounting for short-term variability in climatic parameters, is abstracted 

into relationships between mean annual infiltration (MAI) and long-term-average climate (i.e., MAP and 

MAT). The time history of MAI is obtained from the time history of MAP and MAT, with individual MAI 

values calculated for each grid block lying within the repository footprint in the elevdem.dat data file. 

The gridblock values of MAI are used to determine the total deep percolation for each repository 

subarea (Figure 3-2). The time step for deep percolation is specified in the primary input file but is typically 

on the scale of centuries. The deep-percolation time scale is much longer than is considered in shallow 

infiltration simulations, justified by the assumption of strong temporal smoothing of fluxes as waters pass 

from the ground surface to the repository horizon. The smoothing is conceived of as a local process, so that 

fluxes generally pass vertically to the.repository without systematic lateral diversion. 

3.1.2.1 Climate Model 

Infiltration rates are strongly affected by precipitation and evapotranspiration (which in turn is 

strongly affected by air temperature). Over the period of repository performance, it is anticipated that average 

precipitation and air temperature will change. At the last full glacial maximum, available evidence suggests 

thatMAPat YM may have been 1.5-3 times larger than undercurrent climatic conditions, whereas MATmay 

have been cooler by 5-10 'C (9-18 'F) (Nuclear Regulatory Commission, 1997). The effect of such large 

climatic changes can be examined using the UZFLOW module. 

The shallow infiltration model uses the time history of MAP and MAT to calculate MAI. In arid 

environments such as exist at YM, interannual variability in precipitation can vary by a factor of 10, so that 

the concepts of MAP and MAT implicitly require averaging over several decades or more. The shallow 

infiltration model incorporates climatic variability at time scales shorter than a decade. 

The climate model assumes that the bulk effects of climatic change can be represented by a glacial

change cycle, with shorter time-scale climate changes (century-to-century variability in precipitation and 

temperature) superimposed on the overall change in climate with time (bulk signal). The timing of bulk 

climatic change is felt to be more certain than the magnitude of climatic changes, with a full cycle of climatic 

changes occurring roughly over 100,000 yrs. For purposes of this analysis, MAP and MAT are assumed to 

be known at the start of the simulation (i.e., the present day), whereas changes in MAP and MATunder full 

glacial conditions are considered quite uncertain. 

The UZFLOW model incorporates the effects of climate change by sampling only those parameters 

that have the greatest uncertainty for each TPA realization. For example, climate change can be characterized 

by changes in MAP as a multiplier of current conditions (e.g., 1.5-3 times current values) and changes in 

MAT as an effect of current conditions [e.g., 5-10 'C (9-18 'F) cooler]. Accordingly, the MAP multiplier 

at full glacial maximum and the MAT offset at full glacial maximum are specified in the primary input file. 

A function describing the temporal variation of bulk climatic change is obtained from file climato2.dat. As 

the simulation proceeds from the start through the climate change cycle, MAP and MAT cycle from current 

values through full glacial maximum conditions and back to current conditions. MAP and MAThave the same 

formula for bulk climatic change, so they vary at the same rate over the glacial cycle.

NUREG-1668 3-4
4080 

4079 

UTM 

Northing 

(kin)

4078 t4077 I7 Subareas,/ 

(dashed lines)

4076 I546 547 548 

UTM Easting (km)

549 550

Figure 3-2. Boundary of proposed Yucca Mountain repository.

NUREG-1668

AJ I I I I

I

I I I

I I I I

3-5
The bulk climatic change file (climato2.dat) supplies the code with data generated using the 

following formula, determined by matching the last several Milankovich cycles: 

p = 11 l1l-sin()12 (3)]) 

4 1 4 .3 3 -1. 2 2 (3-1) 

f =- 1 (3-2) -P0 

where T is time (ky) before present (in the Milankovich cycles used to develop the formula),f is the relative 

change between the current climate and the full glacial maximum, p is a periodicity function, and p0 

represents the present-day value of p (i.e., T = 0). The input file has values at each 1000 yrs that are linearly 

interpolated to obtain intermediate values. Note that Eqs. (3-1) and (3-2) predict that conditions are more than 

halfway to full glacial maximum (i.e.,f> 0.5) for roughly two-thirds of the total climatic cycle, although 

conditions may be slightly drier and hotter than present for about 10 kys. 

The climate history generated from the data file is evaluated for uniform time intervals, with length 

specified in the primary input file as TimeStepForClimate[yr]. At the midpoint for each interval, MATand 

MAP are evaluated using: 

MAP(t) = MAPo + fit) (MAPfgm - MAPo) (3-3) 

MAT(t) = MATo + f(t) (MATfgm - MATO) (34) 

where t is time from start of simulation; MATO and MAPo represent MAT and MAP at the start of the 

simulation; and MATfg. and MAPfgm represent MAT and MAP at full glacial maximum. 

3.1.2.2 Variation in Climate Change Cycle 

Relatively short-term climatic changes can significantly change the climate from the bulk predictions 

(e.g., mini-glacial periods), thereby causing periods of higher or lower MAI. As MAI is exponentially 

dependent on climate (discussed in Section 3.1.2.3 -- "Shallow Infiltration Model"), the expected long-term 

average infiltration is larger if these mini-glacial periods are considered, even if the climatic changes are 

evenly distributed above and below the mean cycle. If the variability is small, it may be adequate to use the 

bulk climate formula to predict infiltration. During relatively short simulations, these departures from the 

bulk conditions can provide substantial portions of the total infiltration. 

The UZFLOW module provides a mechanism to examine the importance for repository performance 

of short-term climatic perturbations. A perturbation to the bulk value of both MAT and MAP (developed as 

described previously) is calculated for each of the uniform time intervals. It is assumed that the MAT and 

MAP perturbations are correlated with each other within a time interval, but perturbations are not correlated 

between successive intervals (i.e., MAT and MAP variations in one time interval are not affected by

NUREG- 1668 3-6
variations in previous intervals and do not affect variations in future time intervals). Within a time period, 

k, perturbations are generated using: 

S= p i (3-5) 

where 

i, j = MATor MAP 

4 = a vector of correlated perturbations 

i = a vector of independent standard normal perturbations 

p = the lower triangle of the Cholesky decomposition of a correlation matrix 

The correlation between MATand MAP perturbations is CorrelationBetweenMAPAndMAT in the 

primary input file. The correlation must lie between -1 and 1, and a reasonable value is about -0.8 

(i.e., wetter conditions are generally associated with cooler conditions). 

The actual values for MAT and MAP within time step k are calculated using: 

V - + mj (3-6) 

wherej represents MATor MAP, v, is the calculated value of variablej, a, the standard deviation of variable 

j, and m, the value of variable j, using the bulk climatic interpolation. For MAP, standard deviation values 

are called StandardDeviationOfMAPAboutMeanInOneTimePeriod[mnm/yr] and for MAT, they are termed 

StandardDeviationOfMATAboutMeanInOneTimePeriod[degC] in the primary input file. Both standard 

deviation values are assumed constant over the length of the TPA simulation. 

It is important to note that the time variability in the MAI formula is for time periods of less than a 

decade. Therefore, the standard deviations for MAT and MAP used in Eq. (3-6) should capture variability 

of 10-yr average climate. Reasonable standard deviation values might be 20-30 mm/yr (0.8-1.2 inJyr) 

(MAP) and 0.4-0.5 'C (0.7-0.9 *F) (MAT), based on a 50-yr sequence of daily meteorologic observations 

at Beatty, Nevada. 

3.1.23 Shallow Infiltration Model 

The shallow infiltration model in UZFLOW calculates the mean annual UZ net-infiltration flux 

leaving the root zone for the deeper subsurface. The net-infiltration flux is strongly linked to the MAT and 

MAP climatic parameters, calculated as described earlier. 

Infiltration in arid zones, such as exists at YM, tends to occur during wet years, possibly separated 

by years, to decades of dry years. Simulations of bare-soil infiltration conducted by Stothoff, et al. (1997) 

indicate that MAI is strongly dependent on soil thickness, when the soil thickness is less than 50 cm (20 in.) 

and that different soil thicknesses exhibit different sensitivities to changes in MAP and MAT. Generally, thin 

soil layers allow infiltration to quickly reach fractures and thus percolate below the root zone into the deeper 

subsurface. At YM, most of the repository footprint is overlain by thin soils [i.e., < 50 cm (20 in.)]. 

Accordingly, to calculate MAI over the repository footprint, the shallow infiltration model uses soil thickness 

and infiltration values on a fine scale grid, compared with a typical subarea. Then, the fine-scale calculations 

are averaged to the larger subarea scale.

3-7 NUREG-1668
Simulations conducted external to the TPA Version 3.1.4 code (Stothoff, et al., 1997) looked at 

effects of bedrock fracturing, soil thickness, and meteorologic conditions on shallow infiltration. These 

simulation results indicated thatMAP, MAT, soil thickness, and soil hydraulic properties strongly affect MA!, 

whereas mean annual atmospheric vapor density (MA V) and solar loading have secondary impacts on MAI. 

Both MAT and MAP vary in such a way that MAI increases with elevation, whereas the variation of MA V 

with elevation decreases MAI with elevation. Consideration of solar loading will tend to locally extend MAI 

on north-facing slopes and locally diminish MAI on south-facing slopes, but does not significantly change 

overall MAI estimates. The systematic impact of MAV changes overall estimates of MAI and therefore is 

incorporated in UZFLOW, but the local effect of solar loading is not considered in UZFLOW. 

The relationship among MAI, MAP, MAT, MAV, and soil thickness (b) used in UZFLOW was 

abstracted from bare-soil infiltration simulations (Stothoff, et al., 1997) as: 

In (MAI) =[ao-aBb+ a2 In (P) - a3T - a4V ][1 - a5 In (MAI)] (3-7) 

where: 

B = normalized soil thickness = (b/0.02) [b in m] 

P = normalized MAP = (MAP/162.8) [MAP in mm/yr] 

T = normalized MAT=(MAT- 17.38)/273.15 [MATin 'C] 

V = normalized MAV= (MA V/4.522 x 10-6) [MAVin gm/cm3

] 

ai = regression constant i 

bi = regression exponent i 

The normalization constants for MAP, MAT, and MAV are the corresponding 10-yr average values 

of the hourly meteorologic data for Desert Rock, Nevada, from 1984 through 1993 (National Climatic Data 

Center, 1984-1994). The 10-yr data set was used for the nominal meteorologic boundary condition for 

all 1-D simulations. 

The a constant is used as an adjustable parameter accounting for all uncertainties in characterizing 

MAI. A sampled parameter within the UZFLOW section of the primary input file specifies the areal-average 

MAI within the repository footprint under current climatic conditions. Internally to the code, the a0 constant 

in Eq. (3-7) is adjusted at the start of every realization until the predicted areal-average MAI matches the 

sampled value. 

Simple relationships between elevation and MAP, MAT, and MAV were developed for current 

climatic conditions (Stothoff, et al., 1997). 

MAP(Z) = exp(4.26 + 0.000197 Z) (3-8) 

MAT(Z) = 25.83 - 0.0084 Z (3-9) 

MAV(Z) = exp(-11.96 - 0.000341 Z) (3-10)

NUREG- 1668 3-8
where Z is the ground surface elevation above sea level in meters. MAP has units of mm/yr, MAT is in units 

of 'C, and MAVhas units of gm/cm3. 

To characterize the fine-scale variation in soil thickness, MAP, MAT, and MAV, a digital elevation 

model (DEM) is used. Both elevation (in meters) and soil depth (in meters) are required for every grid cell. 

The DEM provided with the TPA Version 3.1.4 code uses the finest scale available for YM (i.e., 30 x 30-m 

grid cells). 

w The elevation data are used to calculate MA Vfor each grid cell under initial climatic conditions, after 

which the values are assumed to remain constant over the length of the simulation. The calculated values of 

MAP and MAT in each grid cell i are adjusted according to the time history provided by the climate model, 

by adding the climate-induced change to the elevation-dependent component: 

MAP/t) = MAPi(Z) + MAP(t) - MAPo (3-11) 

MAT,(t) = MATi(Z) + MAT(t) - MATO (3-12) 

For example, if MAT is calculated to be I °C (1.8 'F) cooler than at the start of the simulation, the 

elevation-dependent values of MAT are uniformly cooled by 1 'C (1.8 °F). For each climate time step, 

hundreds of estimates of MAI are obtained, one for each grid cell in each subarea. The estimates for all grid 

cells within a subarea are averaged to obtain the subarea value. MAP and the resulting repository-average 

MAI history are shown in Figure 3-3, with and without climatic perturbations imposed (assuming MAP at 

full glacial maximum is double the MAP at the start of the simulation). The extreme sensitivity of MAI to 

climate under bare-soil conditions is evident. 

All else being equal, MAI in areas with vegetation will not exceed MAI in areas without vegetation. 

Under current climatic conditions at YM, potential evaporation is so large that neglecting transpiration from 

vegetation introduces only a relatively small error in infiltration calculations. Under glacial conditions, 

however, the ratio of potential evaporation to precipitation is smaller, desert shrubs become larger and more 

numerous, and the error in MAI predictions introduced by not considering transpiration may become 

significant. By not considering transpiration, the approximation used in the TPA Version 3.1.4 code almost 

certainly results in MAI that is larger than would have been observed in previous glacial maxima. 

The constants and exponents in Eq. (3-7) depend on the fracture-filling material, although the general 

form of the relationship is the same for all filling materials. The effects of soil and fracture hydraulic 

properties on MAI have also been examined, but are not included in Eq. (3-7). 

3.1.2.4 Deep Percolation Model 

Deep percolation at the repository horizon within each subarea is assumed to be the average value 

of MAI within the subarea, with fluxes throughout the repository equilibrating rapidly relative to climatic 

change. The deep percolation model assumes that redistribution of percolating water from the root zone to 

the repository level occurs in each subarea. Because of gravity, percolating water will tend to move vertically

3-9 NUREG-1668
400

350 

300 

S250 E: 5 Mean Annual Precipitation 

E, -- Mean Ana nflrto 

G 200 

o 150 

ILL 

100 

50 

-/ (no plant uptake) 

0 -- "--" -;" * ' t, , . i. I . . I , C=• '--" 

0.0 2.5e+4 5.0e+4 7.5e+4 1.Oe+5 

Time (yr) 

(a) 

400 

350 

300 

" 250 

E 2 Mean Annual Precipitation 

E - - Mean Annual Infiltration 

S 200 

o: 150 

ILL 

100 

/ \N. 

"•- (no plant uptake) 

0.0 2.5e+4 5.0e+4 7.5e+4 1.0e+5 

Time (yr) 

(b) 

Figure 3-3. Response of MAI to changes in MAP during a climate cycle: (a) mean climatic signal only; 

and (b) with perturbation.

NUREG-1668 3-10
in the UZ unless capillary or permeability barriers force lateral redistribution. The resulting model 

conservatively neglects lateral diversion away from the repository and assumes the subarea-average 

shallow-infiltration flux moves vertically down to the repository level. 

3.1.3 Assumptions and Conservatism of the UZFLOW Approach 

The UZFLOW module is an abstraction of the processes controlling MAI and redistribution of 

infiltrating waters above the repository horizon. The main processes that control infiltration are: (i) climatic 

conditions or variation of precipitation and temperature; (ii) near-surface processes such as 

evapotranspiration and runoff; and (iii) lateral diversion of subsurface flow. 

Annual infiltration is expected to vary over the long term (i.e., thousands of years) because of 

variations in temperature and precipitation. Based on historical data and paleoclimatic markers, a full cycle 

of climatic changes is assumed to occur roughly every 100,000 yrs and conditions may be slightly drier and 

hotter than present for about the next 10,000 yrs, after which conditions can be expected to become cooler 

and wetter. This approach is considered a reasonable representation of the long-term evolution of climate. 

Although short-term perturbations of climate will occur (i.e., some years wetter and cooler and some years 

drier and hotter than expected), the net effect on estimated doses at a receptor location will be significantly 

dampened by the anticipated long transport times from the repository to the receptor location (travel times 

may be on the order of hundreds to thousands of years or longer). 

Estimates of infiltration are sensitive to a number of processes affecting near-surface conditions (e.g., 

temperature, precipitation, soil depth, evaporation, incident solar radiation, plant transpiration, and surface 

water runoff). Results of detailed l-D simulations of mass and energy fluxes under a range of conditions 

representative of the surface of YM were used to develop abstracted, predictive equations for MAI as a 

function of MAT, MAP, MAV, and soil depth. The detailed model simulations do not account for plant 

transpiration and runoff that tends to reduce net infiltration. Plant transpiration is not considered to have a 

significant effect on infiltration over the next 10,000 yrs if, as assumed, climate remains similar to present

day conditions. Beyond 10,000 yrs, neglecting plant transpiration will cause infiltration estimates to become 

increasingly overestimated because the climate is expected to become wetter and cooler with resultant 

increases in vegetation and plant transpiration. Also, the abstractions do not account for temporal soil depth 

variation and composition; both influence infiltration that might occur as the climate changes to wetter and 

cooler conditions. 

After penetrating the subsurface, infiltrating water is assumed to pass in pulses vertically through 

the fracture system of the Tiva Canyon welded unit to the Paintbrush Tuff nonwelded unit. Here the pulses 

are assumed to spread and dissipate such that water passes uniformly through to the underlying Topopah 

Springs welded unit and continues vertically to the repository horizon. It is assumed that contacts between 

the welded units and the nonwelded unit do not cause systematic lateral diversion, because of the existence 

of numerous small faults in the nonwelded unit; diversion would tend to reduce fluxes on the western portion 

of the repository and might increase fluxes on the eastern portion of the repository. The current approach, 

which neglects lateral diversion of infiltrating water, is considered conservative because lateral diversion is 

anticipated to reduce the number of containers that encounter dripping water.

3-11 NUREG-1668
There are several specific assumptions related to the UZFLOW module:

It is assumed that the future climate at YM will follow the general form of the paleoclimatic 

record, with shorter variations imposed on the general trend. 

Both MAP and MAT are assumed to vary according to the same formula derived from 

Milankovich cycle data. 

Infiltration is assumed directly related to climatic influences, increasing as precipitation 

increases and temperature decreases. The linkage between climate and infiltration is 

calculated assuming that infiltration occurs under bare-soil conditions. (Bare-soil conditions 

conservatively overpredict infiltration and are most appropriate for present-day climate. As 

conditions become cooler and wetter, predictions become increasingly conservative.) 

For the computation of shallow infiltration, it is assumed that hydrologic properties and soil 

genesis will not change during the simulation. 

It is assumed that MA Tdecreases linearly with increasing elevation, whereas MAP and MA V 

density vary exponentially with elevation, and the rates of change remain the same over 

time. 

3.2 NFENV MODULE DESCRIPTION 

The NFENV module calculates the time-dependent hydrothermal environment of the WP such as 

* Average repository-horizon rock temperature 

* WP surface and SF temperatures 

* RH at the WP surface 

* Flow rate of ground water into the near field 

* pH and chloride concentrations of ground water flowing onto the WP 

The near-field environment includes areas inside the drift and those portions of the geologic setting that the 

repository modifies and that may affect repository performance. Detailed information on calculation of the 

pH and chemistry of ground water flowing onto WPs can be found in Mohanty, et al. (1997). 

3.2.1 Information Flow Within TPA 

3.2.1.1 Information Supplied to NFENV 

The NFENV module uses input data specified in the primary input file and other data files 

(rectedge.dat and multiflo.dat) for computing temperature, ground-water reflux, and ground-water chemistry. 

In addition, for each subarea and realization, EXEC passes to NFENV the time-varying volumetric flow rate 

of water infiltrating toward the repository calculated by UZFLOW for the computation of ground-water 

reflux. The rectedge.dat file provides to NFENV the coordinates for the discretization used exclusively for 

the computation of repository-horizon average rock temperature. Other input parameters to NFENV for 

computing temperature and RH, such as thermal diffusivities and effective thermal conductivities, are 

specified in the NFENV section of the primary input file. Temperature and RH values computed external to

NUREG-1668 3-12
the TPA Version 3.1.4 code can be used via the tefkti.dat file when the appropriate flag is set in the primary 

input file. Chloride concentrations are read from the data file multiflo.dat. 

3.2.1.2 Information Provided by NFENV 

The NFENV module passes to EXEC the values for temperature, RH, pH, and the chloride 

concentration of the representative WP in a subarea for use in EBSFAIL. In addition to the temperature 

history, NFENV also provides the time history of percolation flux at the repository level to EBSREL through 

EXEC. 

3.2.2 Conceptual Model 

The NFENV conceptual model description is divided into three portions: (i) heat transfer, 

temperature, and RH calculation on a mountain and drift scale; (ii) chemical composition of ground-water 

flow in the near field; and (iii) calculation of near-field thermally driven reflux and percolation 

of ground-water. 

3.2.2.1 Heat Transfer, Temperature, and Relative Humidity Calculation 

Mountain-Scale Heat Transfer 

The repository-horizon average rock temperature is computed using an analytic conduction-only 

model for mountain-scale heat transfer. The model is based on a heated rectangular region residing in a 

semi-infinite medium. The modeled repository region was divided into nine rectangular regions (Figure 3-4) 

to cover the repository area corresponding to the seven subareas shown in Figure 3-2. The discretization was 

based on an AML of 80 MTU/acre or greater and thus did not cover the whole area within the repository 

boundaries. Each heated rectangular region is defined by a depth of H below the ground surface width, of 

2B and length of 2L (Figure 3-5). Because more than one rectangular region exists, the temperature increase 

in the semi-infinite medium is the sum of contributions from each heated region. The general solution for 

the temperature increase at any point in space and time is given by [Claesson and Proberts (1996); Carslaw 

and Jaeger (1959)1: 

AT(x,y,z,t)-f rep 1 rf4• (tJ +erf 

B -_t ) +erf +erf B+y rexp 4t- -exp (-(z-2H)'1•tl( 3 -13)

where: 

AT(x,y,z,t) = increase in temperature at any time at any point in space and time in the 

semi-infinite medium from one heated rectangular region [ *C] 

qrep(t) = time-dependent repository heat flux [W/mn2] 

a= thermal diffusivity of the semi-infinite medium [m2

/s]

3-13 NUREG-1668
k = thermal conductivity of the semi-infinite medium [W/(m°C)] 

L = half-length of the heated rectangular region [m] B = half-width of the heated rectangular region [i] 

H = depth of the heated region below the ground surface [m] 

t = actual time after activation of heat flux [s] 

t= time of integration [s] 

x,y,z = location of interest [m] 

The ground surface is assumed to be exposed to atmospheric conditions and has a constant 

temperature (currently not affected by climate change). The analytic equation is valid below the ground 

surface, z < H. The repository heat flux is related to the AML and heat output per MTU of waste: 

qr (t) = AML Qmtu(t) (3-14) 

Likewise, the thermal output for a single WP is related to the WP inventory: 

QwP (t) = MTUwp amtu(t) (3-15) 

where: 

AML = areal mass loading [MTU/m2] 

MTU'P = MTU in a representative WP 

Qpr mu(t) = time-dependent heat output per MTU of waste [W/MTU] 

Waste is assumed to be emplaced uniformly throughout the nine rectangular regions, so there is no 

spatial variation in the waste heat output. Figure 3-6 shows a plan view of the repository with parallel 

emplacement drifts with WPs periodically spaced. The temperature increase at any point is caused by the 

contribution from all subregions. The average rock temperature is computed at an elevation of half the drift 

diameter at the center of each of the subareas as specified in the primary input file and used throughout the 

TPA Version 3.1.4 code as a common basis for transferring and calculating information. The subareas should 

not be confused with the nine rectangular regions in NFENV. The nine rectangular regions are used by the 

NFENV module to predict only the temperatures in the subareas. The analytic mountain-scale conduction 

model predicts the rock-wall temperature (T k) as a function of time. Having computed Tr k for the 

subarea, the WP surface temperature can be calculated. 

Drift-Scale Heat Transfer 

A multimode (i.e., conduction, convection, and radiation) heat transfer model is used for modeling 

drift-scale heat transfer. Figure 3-7 shows a WP resting on an emplacement cart within a drift. A simplified 

thermal network is used to predict the WP surface temperature and the maximum SF temperature given Trk 

(from the mountain-scale model described previously) and Qw (t), and is described in the following sections.

NUREG- 1668 3-14
I I I

4080 I4079 1-

UTM 

Northing 

(km)

4078 1-

4077 

4076

_I I

I

549

i

"- 9 Rectangular 

Regions 

(dashed lines)

i I

I I

546 547

I

548

I

550

UTM Easting (kin) 

Figure 3-4. Discretization of repository into nine rectangular regions for the NFENV module.

NUREG-1668

I

3-15
z 

00

Figure 3-5. Mountain-scale heat transfer model with heated rectangular regions.
Periodic

Elementary tpacing S (60, 35,25 m) 

UnitDSi6 5 

m---- W Dia ' F -

Wat Package Diameter (1.6 ml 

I

Waste Package Spacing (39,26, 18 m) Waste Package Length (-5.6m) Drft Diameter (5.0 m)

0.0 Figure 3-6. Plan view of repository showing emplacement drifts and waste packages.

m --,m •-

.,.,

I
5.6m 

0 0" -0 

__ • - .... . ..... " ___ 

Figure 3-7. Waste package on an emplacement cart in a drift. 

Waste Package Surface Temperature 

Equation (3-16) is used to solve for WP surface temperature, given the rock temperature, thermal 

output of the WP, and thermal conductances. Heat is transferred by both thermal radiation and natural 

convection in the unbackfilled region above a WP, and by conduction through the package support and floor 

material as: 

Qp= (Grad + Gconv + Gcond) (wp.surf - Tock) (3-16) 

where: 

Qp = time-dependent heat output for a WP [W] 

Gad = effective thermal conductance for radiative heat transfer [W/°C] 

Gcon = effective thermal conductance for convective heat transfer [W/°C] 

Gcod = effective thermal conductance for conduction [W/°C] 

Twp,•s = WP surface temperature [°C] 

T k = near-field rock temperature [°C]

NUREG-1668 3-18
The thermal conductance for radiative heat transfer above the WP is based on a linearization of the 

Stefan-Boltzmann law and accounts for the emissivity of the WP and drift rock wall (Incropera and 

DeWitt, 1990):

40(7 3.15 +Trocky 

+ + 

wpnDwpL• Fwp-,.rntDLwpL c-rxDm,

(3-17)

where:

a = Stefan-Boltzmann constant [5.67 x 10' W/(m2K4)] 

e = emissivity of the WP surface [unitless] 

D = diameter of the WP [m] 

LwP = length of the WP [m] 

Fwp_• = radiative view factor from the WP to the rock wall (=1) [unitless] 

r. = emissivity of the drift rock wall surface [unitless] 

Drw = diameter of the rock wall [m] 

Lr = length of drift wall per WP drift [m] (estimated to be - 18 m for 80 MTU/acre) 

The top three-quarters of the WP are available for the radiative/convective heat transfer and the bottom 

quarter of the package participates in conduction through the pedestal/floor. Thermal conductances for 

convective transfer above the WP and conductive transfer below the package are computed from:

27Ekff~a(2Lw) 

In Drw 

Dwp 

In "•_ 

Dw

(3-18) 

(3-19)

= effective thermal conductivity of buoyant air estimated to be 30 times stagnant air 

conductivity [W/(m0 C)] (Manteufel, 1997) 

thermal conductivity of the concrete pedestal/floor material [W/(m°C)]

NUREG-1668

where:

eff.ir

Gcm 

-'=( 4

3-19

Grad =( 3)

Gcow=( ') 

4
The effective axial length for conductive and convective transfer from the WP to the drift wall should 

be larger than the length of the WP and smaller than the package spacing length within a drift. A reasonable 

value for this length is 2 times the WP length. 

For the option of backfilled drifts, heat transfer through the top three-quarters can be predicted, using 

an effective conductivity for the backfill material of:

2 7-k eff~bf (2Lwp)

(3-20)

D wp

where:

Gbf = 

keff.bf =

effective thermal conductance for backfill region [W/°C] 

effective thermal conductivity of backfill material [W/(m°C)]

Waste Package Inner Wall Temperature 

After computing the outer WP surface temperature, the inner surface temperature of the wall is 

calculated. The wall of the WP consists of two cylindrical layers for the inner and outer overpacks. The 

thicknesses and properties of the walls are parameters specified in the primary input file. The inner wall 

temperature is related to the WP heat, according to:

wp Gshell (Tin,surf

Ghel = 

=ilsr

(3-21)

thermal conductance for WP shell [W/°C] 

inner surface temperature of the WP wall [°C]

A schematic of the internals of a WP is shown in Figure 3-8. The shell conductance consists of a 

contribution from the outer carbon steel layer and inner nickel-based alloy layers and is calculated according 

to

L 

Gshell 

wp t ss 

rtD k 

SS SS

Cs + 

7ED k 

CS CS

(3-22)

NUREG-1668

where:

3 -20

G bf 4
(

I PWR

Internal Uner 

PWR Basket Assembly

'-21 PWRs

S-Internal Lid 

*--Outer Lid

Figure 3-8. Internals of a large waste package

le 

z 

00
where:

tss = thickness of the inner stainless steel layer [m] 

Dý = diameter of the inner Ni-based alloy layer [m] 

ks = thermal conductivity of stainless steel [W/(m°C)] 

t• = thickness of the outer carbon steel layer [m] 

Dcs = diameter of the outer carbon steel layer [m] 

ks = thermal conductivity of carbon steel [W/(m°C)] 

Maximum Spent-Fuel Temperature 

Using the WP inner surface temperature, the maximum SF temperature(T.. a) is calculated using 

a conduction shape factor formula that accounts for the volumetric heat generation in the interior region of 

the package, which includes the SF assemblies and the basket assembly (Manteufel and Todreas, 1994): 

Owp = aint(Tma,sf - Tn,surf) (3-23) 

where the conductance of the cylindrical interior region is computed from: 

Gint = ksf S LWP (3-24) 

where: 

k f effective thermal conductivity of the basket and SF in the WP [W/(m°C)] 

S = conduction shape factor for a heated cylindrical region equal to 4a 

The effective thermal conductivity of the SF accounts for the region between the inner wall and the 

basket material, the basket material, and the individual assemblies. There are multiple modes of heat transfer, 

including thermal radiation, buoyant convection primarily in the larger void regions, and conduction in the 

basket material, fuel rods, and regions with primarily stagnant gas. At high temperatures, the heat transfer, 

is dominated by radiative transfer. At lower temperatures, heat transfers can be dominated by conduction. 

Effective thermal conductivity is a function of temperature and is a parameter specified in the primary 

input file. 

Relative Humidity 

The WP temperature and RH are required inputs for the corrosion and release models. The RH is 

defined as the ratio of the actual vapor pressure to the vapor pressure at the WP surface: 

RH = v mi (Tb, TK) (3-25) 

w, (Te) 

where:

NUREG-1668 3-22
P = vapor pressure that is a function of temperature [Pa] 

min ýb., T.) = minimum of Tb and T7 

Tb = boiling point temperature [-370 -K at repository] 

T= drift wall temperature [OK] 

T = WP surface temperature [ * K] 

Below boiling conditions, the definition of RH used in Eq. (3-25) is equivalent to the mole fraction 

definition of RH frequently found in thermodynamic textbooks (e.g., Van Wylen and Sonntag, 1978; Moran 

and Shapiro, 1992). RH is generally defined as the actual mole fraction of water vapor in the air divided by 

the maximum or saturation mole fraction of water vapor in the air at the same temperature and pressure. 

Below boiling conditions, mole fractions are related to the vapor partial pressures so that this definition is 

equivalent to Eq. (3-25). Above boiling conditions, the vapor partial pressure cannot exceed the atmospheric 

pressure within the drift even in the presence of backfill because of its high porosity. When the WP surface 

temperature exceeds the boiling point, it is preferable to define RH as a ratio of the two vapor pressures 

specified previously, which is consistent with the technical literature (Hartman, 1991; Bejan, 1988; 

Fyfe, 1994). 

As an alternative to the temperature and RH models described previously, the use of tabular input 

of temperature and RH into TPA Version 3.1.4 code can be selected in the primary input file, whereupon this 

information is obtained from an external data file, teJkti.inp. Results from detailed 2-D and 3-D modeling 

that can more accurately predict repository edge heat losses can be incorporated in the form of tabular data. 

3±2.2 Near-Field Chemical Composition 

The chemical composition of fluid able to come in contact with the WP is an important consideration 

in the modeling of WP integrity and RT in the TPA Version 3.1.4 code. NFENV is designed to provide the 

chemical composition of the environment as a function of time in the immediate neighborhood of the WP. 

Chemistry parameters, which include solution pH, oxygen fugacity, chloride and bicarbonate concentration, 

dissolved silica, and alkalinity, among other environmental factors, can have important consequences on the 

rate of corrosion of the WP, dissolution of SF, and formation of alteration products. Of special concern in 

the chemical composition of a partially saturated environment are evaporative effects produced by the heat 

released from the WP. Chemistry can also be affected by refluxing of evaporated water and by deep 

percolation at long time periods. 

The maximum silica concentration estimated from equilibrium with quartz, chalcedony, and 

cristobalite gives, respectively, asO2 = 0.000835, 0.00137, and 0.00218 molal. By contrast, J- 13 well water 

has a silica concentration of 0.0011 molal (Table 3-1). Bounds on the pH are more difficult to obtain. 

Likewise, calcium and carbonate have no obvious upper bounds since their concentrations will be 

pH-dependent. Turner (1998) screened available ground-water chemistry data compiled by Perfect, et al. 

(1995), which may provide some limits for current ambient conditions in the vicinity of YM. 

To provide a more detailed calculation of the near-field fluid composition, the computer code 

MULTIFLO (Lichtner and Seth, 1996; Seth and Lichtner, 1996) is used to provide quantitative data in 

tabular form to the TPA Version 3.1.4 code. MULTIFLO simulates the transport of reacting chemical 

constituents coupled to evaporation and condensation processes involving two-phase fluid flow. The code 

sequentially couples two-phase fluid flow of liquid water, water vapor, and air with reactive transport of 

aqueous and gaseous species. Homogeneous reactions in the aqueous phase and heterogeneous reactions

3-23 NUREG-1668
between the aqueous and gaseous phases are assumed to be in local equilibrium. Mineral reactions are treated 

irreversibly through prescribed kinetic rate laws. 

Table 3-1. Initial Fluid Composition and pH Corresponding to J-13 Well Water (Adapted From 

Harrar, et al., 1990) 

Species Molarity x 10W Species _ _Molarit_ ×_104 

Ca+2 2.90 - 3.70 SiO,(aq) 9.50 - 11.40 

Na' 18.30 - 21.70 CI- 1.78-2.37 

K÷ 1.00-1.40 pH 6.8- 8.3 

HC0 3 1.93 - 2.34 -- n 

MULTIFLO computes temperature and the associated moisture redistribution as functions of time, 

using a repository-scale model, represented as a single disk-shaped uniform heat source, in contrast to the 

drift-scale model presented earlier in this chapter. Therefore, the detailed geometry of the WPs is lost. The 

fractured YM tuff host rock is described using the equivalent continuum model (ECM) in which fracture and 

matrix properties are averaged into a single continuum representation. Model predictions indicate the 

formation of zones of enhanced liquid saturation above and below the repository during the dry-out period 

after emplacement of the waste. As capillary suction draws liquid water toward the heat source, evaporation 

occurs, resulting in an increase in salinity and pH as carbon dioxide (CO 2) is degassed from solution. In the 

cooler condensate region, the salinity is expected to decrease as the dilute condensing solution is mixed with 

the ambient ground water. 

The aim of the MULTIFLO calculations is to estimate the composition of the fluid at the boundary 

of the condensate zone that, as a first approximation, may be considered to be similar to the fluid that comes 

in contact with the surface of the WP. Within the confines of the repository-scale model and the ECM 

approximation of fracture-matrix interaction, however it is not possible to fully represent the eventual return 

of liquid water from the condensation front lying above the repository to the WP. This flow most likely 

would take place along open fractures in the form of gravity-driven flow manifested as dripping. 

In the initial phase of work to determine the near-field environment and provide input to the TPA 

Version 3.1.4 code, a 1-D repository-scale calculation using MULTIFLO Version 1.0 code was performed 

with an AML of 80 MTU/acre corresponding to 26-yr-old SF with a mix of 65 percent PWR and 35 percent 

BWR assemblies. With this heat load, the calculations show that a liquid phase is always present and 

complete dryout does not occur after emplacement of the waste. The initial fluid composition corresponds 

to J-13 well water, as given in Table 3-1. The YM host rock was modeled in these preliminary calculations 

as pure quartz with 10 percent porosity. This assumption is considered reasonable for the purpose of 

estimating the change in salinity caused by evaporation and condensation, but may not be valid for the pH, 

if reactions with other silicate minerals prove to be important. Additional work (e.g., increased 

dimensionality) is need to determine the adequacy of this 1 -D approach for representing the local conditions 

of the near field.

NUREG-1668 3-24
repository

I

40 60 80 100 120 140 

"TEMERATURE (IOC

Figure 3-9. Time snapshots of the vertical distribution of temperature caused by repository heating. 

The temperature profile shown in Figure 3-9 was computed at different times. The plateau of 

approximately 100 'C (212 OF) indicates the presence of a heat pipe. Above and below the repository 

horizon, zones of enhanced moisture form as vapor condenses. The pH increases in the vicinity of the 

repository to approximately 10 at 250 yrs after waste emplacement and then begins to decrease slowly as the 

repository cools. The chloride concentration increases by an order of magnitude at 250 yrs after waste 

emplacement, and then declines at much longer times. 

Within the MULTIFLO code, a simple conceptual model was developed to determine chloride 

concentrations as a function of temperature, evaporation, and precipitation of sodium (Na)-bearing minerals. 

To estimate the expected changes in chloride concentrations caused by evaporation, a simple upper bound 

on the chloride concentration may be derived by assuming equilibrium with halite (NaCl), for example. As 

evaporation occurs, the sodium and chloride concentrations must remain approximately proportional to one 

another. Therefore, assuming that the activity coefficients of Na÷ and chloride (Cl-) are similar at high ionic 

strength 

aNa- = aaCl- (3-26) 

where a is the constant of proportionality and aNao and act- are sodium and chloride activities.

NUREG-1668

;2ý ýr

3-25
Equilibrium with halite implies 

K =aNa ac1 =a -ta (3-27) 

where K is the equilibrium constant. Therefore, 

a acl_ i = (3-28) 

The log for halite at 100 'C (212 'F) is equal to 1.578 (Wolery, 1992). Taking a = 11.11, derived from J-13 

well water (Table 3-1), it follows that aci- = 1.85 molal and aNa. = 20.5 molal. To estimate concentrations, 

activity coefficient corrections must be included. If the sodium concentration is lowered by precipitation of 

other Na-bearing solids, the chloride concentration in equilibrium with halite could be substantially increased 

and the concentration of sodium reduced. 

Although MULTIFLO provides chloride concentration, pH, and oxygen fugacity as time-dependent 

functions, only chloride concentration is used in the TPA Version 3.1.4 code as a function of time. The pH 

value is currently set at a constant value of 9, the highest value obtained from the MULTIFLO simulations. 

Chloride concentrations, as provided by MULTIFLO, are multiplied by a parameter representing 

uncertainties and limitations in the MULTIFLO results (i.e., MULTIFLO results are representative of 

ground-water chemistry in the porous matrix) and chloride concentration on a WP could be higher than 

predicted by MULTIFLO because of evaporation on the WP. 

3.2.2.3 Near-Field Ground-Water Percolation 

NFENV also provides estimates of ground-water reflux and percolation rates. The amount of water 

percolating through the near field is different from the infiltration computed in UZFLOW because of 

thermohydrologic effects resulting from decay heat. WP temperatures may exceed the boiling point of water 

and vaporize water in the rock surrounding the WPs. The vapor will flow away from the WPs and condense 

where temperatures are below boiling. The condensate may then flow back toward the WPs. This return flow 

of condensate is called refluxing. 

The area between the WPs and the condensate zone is called the boiling or dry-out zone. Although 

dry-out and condensate zones would form both above and below the repository, only the zones that form 

above the repository are considered in the reflux models. Water that fully penetrates the dry-out zone would 

be available to contact the WPs, possibly accelerating the corrosion of WP materials and facilitating transport 

of radionuclides released from failed WPs. 

Two reflux models have been developed -- REFLUX1 and REFLUX2. Figure 3-10 is a schematic 

illustrating the movement of water above the repository. Although Figure 3-10 and the following discussion 

refer primarily to REFLUX I, in general, the concepts apply to both models. Differences are noted in the 

relevant sections. 

Figure 3-10 illustrates the vaporization of ambient rock water in the area above the boiling point 

temperature, T,o 1 . The Tboih isotherm is at an elevation Z_,boil, whose location depends on the rate at which 

heat is generated by the waste and the heat loss away from the near-field environment (calculated in the

NUREG- 1668 3-26
ground surface 

qinfl'

condensate zone

Reflux Zone

qliq

Lreflux

-ZdkiftO(ý00 00 0 drifts

(a) no dripping

ground surface 

qinfil 

condensate zone

-ZboiI ..... .. .... 

Reflux Zone 

( -zdnf. 0

4 --

liq qvap

I 'boil -

Lreflux

"0 drifts

(b) dripping 

Figure 3-10. Conceptualization of drift-scale thermal model.

NUREG-1668

.

0 

a, UJi

a 

0 

(D 

M,

f

3-27
mountain-scale heat-transfer model). Above the Tbij isotherm is the condensate zone where water vapor 

condenses to a liquid that is available to move down toward the repository. Below the Tb.11 isotherm is a 

boiling zone through which liquid water from the condensate zone flows until it is heated to the point of 

vaporization and subsequently rises back to the condensate zone. 

Together the condensate and boiling zones constitute a reflux zone containing water driven from the 

rock surrounding the repository by heat generated in the repository. No reflux of ground water into the 

repository can occur when the bottom of the reflux zone is at or above the elevation of the top of the drift 

(which can be the case early in the heating phase) (Figure 3-10a). As heat generated by the WPs diminishes 

over time, the elevation of the Tbo11 isotherm and reflux zone moves closer to the elevation of the repository, 

and dripping in fractures or reflux can occur at the repository. The thickness of the reflux zone, LR, depends 

on qiof and the local heat flux (i.e., the temperature gradient). When KZo1 - LR) is below the elevation of 

the top of the drift, Zdrlft, water begins to drip into the drift (Figure 3-1Ob). Any liquid passing below the level 

of the repository is assumed to continue to the water table. The thermal load reflux is calculated in the 

NFENV module, using one of two models. The source of water in REFLUX 1 is only the water infiltrating 

from the surface, whereas the sources of water for REFLUX2 are infiltrating water and water from the dry

out zone surrounding the repository. 

NFENV uses the time-dependent temperature profiles generated by the heat transfer models 

previously described, along with time-dependent water flux (qinfl) from UZFLOW, to calculate time

dependent flux (qdrip) in fractures entering the near-field environment and available for dripping onto one WP. 

In the development of qdip NFENV considers the time-dependent amount of perching caused by repository 

heating and the time-dependent reflux of liquid and vapor. The REFLUX models are intended to determine qdflp 

for matrix flow and fracture flow. NFENV partitions percolation flux from the reflux zone, qprc, from both 

models on a per WP cross-sectional area basis to obtain qdnp 

REFLUX1 Methodology 

The REFLUX 1 subroutine of NFENV is used to model redistribution of water in the near-field 

environment owing to heat from emplaced WPs. Vaporized water is assumed to move away from the 

repository until conditions are cool enough for condensation (i.e., conditions are cooler than boiling). 

Although a condensate zone would form on either side of the repository, only the zone above the repository 

is considered in REFLUX I. Above the boiling isotherm, a saturated condensate zone forms, bounded below 

by the boiling isotherm. Cool water from the condensate zone is assumed to percolate within fractures toward 

the repository within a reflux zone, with the liquid boiling and returning to the condensate zone as vapor, as 

shown in Figure 3-10. 

A mass balance model is used to model the thickness of the condensate zone above the repository: 

ay dv qinfil - qcerc (3-29) 

where: 

V volume of the condensate zone per unit area [m3

/m2]

NUREG-1668 3-28
t = time [yr] 

qi~fil = infiltration flux entering the condensate zone [m/yr] 

qpc= = percolation flux leaving the reflux zone [m/yr] 

When V, = 0 (no condensate zone exists) and when V, is at a maximum value (set in the primary 

input file with the parameter PerchedBucketVolumePerSAarea[m3/m2]), qp is directly set to q•,,. A 

reasonable value for the maximum perched-water volume might be the fracture porosity times the thickness 

of the Welded Topopah Spring unit above the repository. 

Although the condensate zone is intermediate between the two limiting cases, the position of the 

bottom of the reflux zone determines qp. If the reflux zone is completely above the drifts, all infiltration 

flux is stored in the condensate zone and qp = 0. Within the reflux zone, the liquid flux, qti, is everywhere 

balanced by the vapor flux, q,.. If the reflux zone intercepts the repository horizon, qp,. is determined by 

evaluating q% at the repository horizon, assuming that qlk, decreases linearly from the boiling isotherm to the 

bottom of the reflux zone: 

qp - L briftj (3-30) 

where: 

qrfl = downward liquid flux at the bottom of the condensate zone [m/s] 

Le = thickness of the reflux zone [m] 

Zw = elevation of the boiling isotherm [m] 

Zdft = elevation of the drifts [m] 

The maximum thickness of the reflux zone, L is a parameter in the primary input file called 

LengthOfRefluxZone[m], while the downward liquid flux at the boiling isotherm, qf,, is named 

MaximumFluxInRefluxZone[m/s]. Offline calculations are required to define these parameters. 

The elevation of the boiling isotherm, Z..., which defines the bottom of the condensate zone and 

the top of the reflux zone, is determined by assuming that temperature varies linearly near the repository. 

Alteration of the temperature field, because of moisture redistribution and the effects of salt concentration 

and capillarity on Tba, is neglected. The temperature gradient is calculated by assuming the heat generated 

by the repository is carried by conduction within the boiling isotherm; knowing the thermal conductance of 

the matrix and the thermal flux caused by the repository (obtained from NFENV calculations), the 

temperature gradient can be determined. The temperature at the repository (obtained from NFENV heat 

transfer calculations) is projected according to: 

4MKt(Tn -- = rf + --b" T.0"ql( rp .q ea J (3-31) 

3-29 NUREG-1668
where: 

Zil = elevation of the boiling isotherm [mi] 

Zdft = elevation of the drifts [in] 

Ts = temperature of the repository ['C] 

K he. = thermal conductivity [W Kt' m'] 

qhet = thermal flux due to the repository [W/m2

] 

During much of the boiling phase for the repository, qinfil will exceed qp¢• and liquid (condensate) 

will accumulate in the condensate zone. If the condensate zone grows beyond the user-specified limit 

(L ftlx), the condensate zone overflows and this is added to any fluxes in qpc caused by the reflux zone 

contacting the drifts. 

Once boiling around the repository stops, the reflux zone ceases to exist and any water contained in 

the condensate zone is released over a few time steps. During this post-boiling period, at each time step q~rc 

is set to qinfjl plus one-half of the water remaining in the condensate zone. After a few time steps, qerc 

approaches qinfil" Note that if qrenux is significant, the condensate zone may empty as the boiling isotherm 

returns to the drift level. 

Although dripping in a drift can occur (qperc > 0), the dripping flux, qdi,' is calculated on a per-WP 

basis (i.e., qprc is multiplied by the area occupied by WP) and passed to EBSREL for determination of the 

amount of water that contacts and enters the WP. 

REFLUX2 Methodology 

REFLUX2 was created as an alternate to REFLUX I to calculate the amount of water able to contact 

the WPs based on thermal and gravity driven refluxing. Conceptually, in REFLUX2 the quantity of refluxing 

water can be sufficient to penetrate the boiling isotherm in the fractures and reach the WP during times when 

the surface temperature of the WP exceeds boiling. The primary difference between REFLUXi and 

REFLUX2 is that in REFLUX2 some condensate return flow may penetrate the boiling isotherm no matter 

how far the boiling isotherm is located above the WP. In addition, unlike REFLUXI, REFLUX2 considers 

rock (matrix) water as well as deep percolation of infiltration. As such, REFLUX2 is intended to determine 

flow toward the repository, considering both infiltration from the surface and water originating in the matrix 

of the dry-out zone surrounding the repository in the boiling phase. 

Similar to REFLUXI, REFLUX2 is based on a reflux cycle located above the WPs. A particular 

parcel of water may participate in the reflux cycle many times. With every cycle, however, some portion of 

the refluxing water may escape as either vapor or liquid and flow away from the heat source, possibly toward 

the water table. Alternatively, the refluxing cycle can gain water from two sources: infiltration from ground 

surface (from UZFLOW) and water vaporized from the dry-out zone in rock surrounding the near field. 

The amounts of water contributed to the refluxing cycle by infiltration and from vaporized ambient 

rock pore water are calculated separately. The total amount of refluxing water derived from both infiltration

NUREG-1668 3-30
and rock pore water vaporization in year N is:

RT=RI+RD (3-32) 

where: 

RI = the amount of infiltration-derived water [m3/m2] 

RD = the amount of refluxing ambient rock pore water [m3

/m2 ] 

Reflux Derived from Infiltration 

The amount of R, that refluxes during year N is: 

N-I qf 1 jR0= (1 - I (3-33) 

j=O s 

where: 

qinfil_• = infiltration for a given subarea from UZFLOW for the year (N-j) [m3

/yr] 

sa = area for a given subarea [n2] 

N = years after start of simulation [unitless] 

L = fraction of infiltration that escapes the reflux cycle each year, user input [unitless] 

The variable L1 (Reflux2Lossl) is specified in the primary input file. 

Ref•ux Derived from the Rock Pore Water 

The total amount of water, D [m3 rm2] vaporized from prewaste-emplacement ambient rock pore water 

and available for refluxing is defined by: 

D = (7)(n)(S (3-34) 

where: 

T = thickness of dry-out zone [m] 

n = porosity of rock [unitless] 

S = liquid saturation [unitless] 

Sr = residual saturation [unitless] 

The thickness of the dry-out zone (Reflux2Thickness), porosity (Reflux2Porosity), liquid saturation 

(Reflux2Satlnit), and residual saturation (Reflux2SatResid) are specified in the primary input file. 

A portion of refluxing water may escape each reflux cycle (LD) lowering the amount of refluxing 

ambient rock pore water, RD [m3/m2]. The time required to complete one water reflux cycle may vary from

3-31 NUREG-1668
1 yr to several hundreds to thousands of years. The amount of rock pore water that refluxes in a given year 

is 

RD = D(P-LD D p N (3-35) 

where P is the number of one-year cycles required for water to complete one reflux cycle. The variables LD 

(Reflux2LossD) and P (Reflux2Period) are specified in the primary input file. 

The predominant source of refluxing water may change with time. At early times, refluxing water 

may be dominated by water derived frpm the rock pore water. At later times, water derived from infiltration 

may dominate if rock pore water near the WPs has been vaporized and removed from the refluxing cycles. 

In this case, the amount of refluxing water will converge to the value of R,. If no losses from the refluxing 

cycle are experienced, the amount of refluxing water will grow at a rate equal to infiltration. In REFLUX2 

the temperature and thickness of the dry-out zone do not vary with time. The temperature is assumed to be 

above boiling until all the water in its pores leaves the dry-out zone; however, at long time periods 

(>1,000 yrs) refluxing water will equal infiltration if reasonable values are selected for parameters of 

REFLUX2. 

Water exiting the reflux cycle and entering the repository near-field environment is computed from: 

Rwp = (RIL1 + RDLD)Asa (3-36) 

These values are then converted from a subarea basis to a WP basis by multiplying the flow out of the reflux 

zone by the fraction of the subarea associated with a WP, passed to EBSREL, to determine the amount of 

water that contacts the waste form and enters the WP. 

3.2.3 Assumptions and Conservatism of the NFENV Approach 

The near-field environment will affect the corrosion of the WP and the dissolution of SF and the 

amount of water entering the drift and WP. The NFENV module is an abstraction of the physical and 

chemical processes in the near field of the repository and includes the determination of (i) temperature of 

the WP and waste; (ii) evolution of near-field chemistry; and (iii) refluxing of condensed water and ground

water percolation. 

NFENV has two methods, from which the user must select one, for determining the temperature of 

the WP and the waste form. One method determines the temperatures through an analytic conduction-only 

heat transfer model at the mountain-scale and an analytic, steady-state heat transfer model at the drift-scale 

that accounts for conduction, convection, and radiation in the vicinity of the WP. The analytic solutions 

simplify the problem by using uniform thermal properties and uniform heat loading at the repository level, 

and nine rectangular regions to represent the repository. The other method allows the user to supply WP and 

waste temperatures in tabular form (temperature predictions would presumably be based on the results of a 

more detailed model that would include conductive and advective heat transfer in rocks). Both of these 

methods provide a single temperature for a representative WP in each repository subarea. The use of a single 

temperature and a representative WP for each subarea should provide a conservative result because all WPs 

in a subarea will simultaneously corrode and have the potential for simultaneous release of radionuclides 

(actual release will depend on the number of packages that get wet).

NUREG-1668 3-32
NFENV has two methods, from which the user must select one, for determining the RH. One method 

determines the RH through an analytic expression based on vapot ptessure as a function of temperature, 

whereas the other method allows the user to supply the RH for specific times in tabular form. Both these 

methods provide RH values for a representative WP (at the WP surface) in each repository subarea. This 

approach is considered reasonable and is conservative, based on the reasons discussed previously for 

temperature. 

Water will be redistributed in the near field by evaporation caused by heat from the emplaced waste. 

Estimating the redistribution of water is highly uncertain because of the complicated two-phase flow 

processes, variations in the thermal gradient, and heterogeneities in the fracture and matrix hydrologic 

properties. Refluxing water is assumed to penetrate the boiling isotherm (Phillips, 1996) in the vicinity of 

a WP, allowing liquid water to contact the package even when temperatures exceed the boiling point of 

water. Reflux of water is represented by two different models (one specifies a reflux zone and the other 

specifies a dry-out zone and the fraction of infiltration that escapes the reflux cycle each year). These models 

have the flexibility to produce conditions varying from a long dry-out period to a short dry-out period. 

Detailed numerical analysis and heater experiments are continuing to determine the effect of the 

redistribution of water. The degree of conservatism of any particular approach is uncertain at this time. 

The chemical composition of the water contacting the WP can affect corrosion of the WP and 

dissolution of the waste. The evolution of the chemistry of the water is the result of a variety of complicated 

processes associated with, but not limited to, mineral reactions, evaporation, and condensation. These types 

of complicated processes cannot be accommodated in a system code while maintaining reasonable execution 

times. The chemical composition of water in a partially saturated environment is affected by evaporative 

effects produced by the heat released from the waste. For use in NFENV, an average chemical composition 

of the repository near field is computed using the MULTIFLO numerical model to estimate the composition 

(but not the amount) of the fluid that could contact the WP (e.g., pH, chloride concentration, silica 

concentration, carbonate concentration, and oxygen fugacity). Because of the uncertainty in estimating the 

water chemistry, the parameter values are varied between bounding values, to obtain conservative estimates 

of performance. For example chloride concentration is varied by multiplying the tabular data by a scaling 

factor. 

There are several specific assumptions related to thermohydrologic conditions: 

* The mountain-scale conduction model assumes the ground surface is at a constant 

temperature and is not affected by climate change. This assumption may result in greater 

heat losses, and therefore may be nonconservative. 

0 The thermohydrologic conceptual model implemented in NFENV assumes there are both 

matrix and fracture flow continua. The hydrologic regime in the near field is assumed 

dominated by, and in equilibrium with, the thermal regime, during the thermal pulse. 

* All infiltrating water participates in the reflux cycle. 

* In REFLUXI, matrix waters are assumed to be immobile during the thermal pulse, so that 

refluxing only accounts for fracture waters. This assumption will tend to under-predict 

fluxes at the repository level at early and intermediate times, as thermally driven matrix 

water will contribute to fluxes in the fracture.

3-33 NUREG-1668
REFLUX I tends to return to ambient conditions almost immediately after the repository drops below boiling, with water in the halo above the boiling isotherm flushing through the 

system just before the boiling isotherm reaches the repository. As removal of mobilized 

matrix water would leave a large amount of empty matrix storage and refilling the matrix 

would tend to strongly attenuate fluxes as the boiling isotherm returns to the repository 

horizon, REFLUXI would tend to overpredict fluxes at the repository level. The degree of 

conservatism or non-conservatism of this approach depends on when the WPs fail relative 

to the time of return to ambient conditions. 

REFLUX I and REFLUX2 do not consider elevation of the boiling point caused by salt 

concentration and capillarity. 

In REFLUX2, all matrix water in the dry-out zone is vaporized. 

REFLUX2 water vaporized from the rock matrix above the WP participates in the reflux 

cycle. Vaporized rock water below the package does not participate in the reflux cycle. 

REFLUX2 always assumes that the near-field temperature is above boiling until all the 

water in the pores leaves the dry-out zone. 

In REFLUX2, it is assumed that no resaturation takes place when the pores are dry and 

temperature is below boiling. 

There are also specific assumptions with regard to water chemistry: 

MULTIFLO results are assumed to adequately describe the behavior of the near-field 

chloride concentrations. 

Uncertainties and limitations of MULTIFLO chloride concentration results can be accounted 

for by use of a multiplication factor (Appendix A) representing differences between ground

water chemistry in the porous matrix (calculated by MULTIFLO) and chloride concentration 

on a WP, which could be higher through evaporation from the WP surface. 

For the computation of chloride concentration, the YM rock is modeled as pure quartz with 

10 percent porosity. The assumption is considered reasonable for the purpose of calculations 

present here, which is to estimate the change in salinity and pH caused by evaporation and 

condensation processes. 

In REFLUX2, the dry-out zone thickness does not change as a function of time. 

3.3 EBSFAIL MODULE DESCRIPTION 

The EBS failure (EBSFAIL) module calculates the failure time of the EBS caused by various modes 

of degradation. Modes of WP degradation evaluated in EBSFAIL include dry air oxidation, humid air 

corrosion, uniform and localized (pitting and crevice) aqueous corrosion, and mechanical failure. Other 

degradation modes that may become important under certain conditions, such as stress corrosion cracking and microbially influenced corrosion, are not currently considered in EBSFAIL. Failures by direct disruption 

are calculated in FAULTO, SEISMO, and VOLCANO. Failures caused by seismically induced rockfalls 

calculated in SEISMO are currently considered as a process in the evolution of the repository during the

NUREG-1668 3-34
simulation period. Failures calculated in VOLCANO (Section 3.11) include two categories: (i) failure from 

intrusive IA (contributes only to grund-water releases); and (ii) failure from extrusive IA (contributes only 

to ground surface releases). The disruptive event failures are relevant to TPA calculations only if the events 

occur before the time of failure calculated in EBSFAIL. 

3.3.1 Information Flow Within TPA 

3.3.1.1 Information Supplied to EBSFAIL 

Inputs required by EBSFAIL provided by EXEC include the temperature and RH as a function of 

time and position in the EBS (i.e., subarea) and the chemical composition of the aqueous phase (e.g., pH, or 

chloride concentration) in contact with the WP (calculated by NFENV). All WP properties and corrosion 

input parameters (and other relevant input parameters) supplied to EBSFAIL are specified in the EBSFAIL 

section of the primary input file. 

3.3.1.2 Information Provided by EBSFAIL 

EBSFAIL passes WP failure time because of corrosion and mechanical failure for use in EBSREL 

for release calculation. 

3.3.2 Conceptual Model 

EBSFAIL executes the stand-alone program FAILT as a part of the EBS performance assessment 

code (EBSPAC) (Mohanty, et al., 1997). Three different types of WP failures in the EBS performance 

assessment code are: (i) initial failure (Type 1); (ii) disruptive scenario failure (Type 2); and (iii) corrosion 

and mechanical failures (Type 3). In Type I failure, a portion of the WPs in a subarea is specified to have 

failed at time t = 0 as a result of initial defects produced before repository closure. These WPs are assumed 

to have been defective or damaged before or during emplacement and are specified in the input data as a 

fraction of the total containers in a subarea. The number of WPs undergoing Type 1 failure is read from the 

input file. In Type 2 failure, WPs fail as a result of some disruptive event. The timing and number of WPs 

affected by Type 2 failure are calculated by consequence modules (e.g., SEISMO, FAULTO, VOLCANO). 

Type 2 failure is a distribution of scenario failures over time, unlike Type I and Type 3. To simplify 

radionuclide release calculations, however, the Type 2 failures are coalesced to the earliest failure time for 

any one of the disruptive events. The number of failed WPs attributed to Type 2 failure is the number of WPs 

that failed because of disruptive events before the corrosion failure of WPs. In other words, all WPs in a 

subarea that have not undergone Type 1 and Type 2 failures are potentially subject to Type 3 failure. This 

assumption implies that a Type 3 failure equally affects and fails simultaneously all WPs, in a subarea, that 

have not already failed under Types 1 and 2 modes. 

3.3.2.1 Corrosion and Mechanical Failures 

For simplicity, failure of the WP is defined as the through-wall penetration of the outer and inner 

overpacks by a single pit, by uniform corrosion, or by brittle fracture caused by mechanically dominated 

processes in the presence of residual stresses. Depending on the thermohydrological conditions and the 

evolution of the near-field environment, three different WP degradation processes are assumed to occur as 

determined by the interaction of the changing environment with the container materials. The first process 

is oxidation of the outer carbon steel container by interaction with gaseous oxygen in dry air at relatively 

elevated temperatures [ 100-250 °C (212-482 *F)]. The second process is humid-air corrosion of the outer

3-35 NUREG-1668
container as a result of the air containing water vapor at intermediate RH values, and the third process is 

aqueous corrosion that occurs at even higher RHs. An additional degradation process is mechanical failure 

caused by brittle fracture that may occur as a result of the concurrent effect of thermal embrittlement of the 

steel and residual stresses produced by welding during fabrication or WP closure. No allowance for 

extending WP lifetimes is given to the protection ability of the inner canisters (e.g., pour canister for the 

vitrified defense reprocessed waste) or the fuel cladding against corrosion or mechanical failure. After the 

outer and inner overpacks are penetrated by corrosion or failed by fracture, the SF is considered to be either 

partially or fully exposed to the near-field environment. 

At each time step, a calculation is performed to determine if the RH has reached a critical value. If 

the RH of the environment surrounding the WP is below a lower critical value, the corrosion is treated as dry 

air corrosion (and the penetration of the dry air oxidation front is calculated). In the same time interval, a 

mechanical failure test is conducted for the new thickness resulting from metal oxidation to evaluate if failure 

from mechanical fracture occurs. If the WP does not fail by fracture, then the time is advanced, and the RH 

critical value test is repeated. If RH reaches the lower critical value at any time step, then the calculation of 

dry-air oxidation is interrupted and the calculation of metal penetration by corrosion in humid air begins. If 

the RH reaches a higher threshold, the calculation of metal penetration by aqueous corrosion is initiated. The 

mechanical failure test is performed at all time intervals until failure occurs, regardless of the type of 

corrosion the WP is undergoing (e.g., dry air, humid air, or aqueous corrosion). The initial time of the time 

step at which full penetration occurs is computed as the failure time. 

3.3.2.2 Dry-Air Oxidation 

Oxidation of steel in dry air occurs at low RHs and temperatures ranging from ambient to 250 'C 

(482 'F). The thin oxide layers formed at such temperatures are assumed to protect the container against 

further oxidation. Oxide growth, however, may become localized and lead to deeper penetration of the 

oxidation front, adversely affecting the long-term container integrity. Localized dry-air oxidation includes 

internal oxidation and intergranular oxidation. In the case of internal oxidation, the oxide forms as islands 

in the metal underneath the uniform oxide layer. In intergranular oxidation, the oxide forms preferentially 

along metal grain boundaries. Localized dry-air oxidation takes place by mass transport through short-circuit 

diffusion paths, such as interfaces between metal and oxide, other inclusions and precipitates, or grain 

boundaries. 

In the EBSFAIL module, it is assumed that localized dry-air oxidation occurs intergranularly by 

enhanced diffusion of oxygen anions along grain boundaries. For the calculations of intergranular oxide 

formation, a mathematical model developed by Oishi and Ichimura (1979) is used, in which oxygen diffusion 

in the matrix and along the grain boundary can be calculated simultaneously in an infinite 1-D body (Ahn, 

1995). The main assumptions in the calculations are: (i) the effect of the external oxide layer is negligible; 

(ii) oxygen diffuses into metallic phases (near the interface between grain boundary oxide and metal); and 

(iii) oxygen also diffuses into the metallic matrix from grain boundaries. The distance of oxygen penetration 

in the metal at time t is: 

- 4 D_ D Dn 2 7E2 t = . exp - (3-37) 

Y gr 6 Dg n9i rg

NUREG- 1668 3-36
where:

Y = penetration distance by intergranular oxidation [cm] p 

D = matrix diffusivity [crn-/s] 

D = grain boundary diffusivity [cm2/s] 

5 = the thickness of grain boundary [= 0.7xl0 7-cm, based on Lobnig, et al., 1992] 

r = the grain radius [ Ixl0-3 cm for cast steel, based on Ahn and Soo, 1983, 1984] 

t = time [s] 

Using diffusivities defined by the user in the primary input file, Eq. (3-37) yields the penetration distance 

of oxygen along grain boundaries and is used as a surrogate for oxide formation. 

3.3.2.3 Humid-Air Corrosion 

Corrosion of the steel container in air at moderately high RHs, as expected under certain repository 

conditions after the initial dry period, bears certain similarities to atmospheric corrosion. As discussed 

elsewhere (Cragnolino, et al., 1998), however, significant differences exist. Atmospheric corrosion occurs 

when a metal surface is covered by a water film of sufficient thickness to sustain electrochemical reactions. 

Water can be physically adsorbed to the metal surface in molecular form or it can be chemically bonded in 

a dissociated form that results in the formation of metal-hydroxyl bonds (Leygraf, 1995). The thickness of 

the water film increases with increasing RH. The critical RH above which atmospheric corrosion of most 

metals occurs corresponds to the formation of a water film of multiple monolayers that displays properties 

similar to bulk water. Under these conditions, corrosion is governed by the same electrochemical laws 

applicable to corrosion of metals immersed in an aqueous electrolyte. 

Atmospheric corrosion studies reveal that iron and steel exhibit a primary critical RH of around 

60 percent, similar to most metals (Fyfe, 1994). Above 60 percent RH, corrosion proceeds at a slow rate, but 

at 75-80 percent RH, the corrosion rate sharply increases. This secondary critical RH is attributed to 

capillary condensation of water in the pores of the solid corrosion products. It is assumed in the EBSFAIL 

module that humid-air corrosion occurs above a primary critical value of RH, whereas aqueous corrosion 

takes place above a secondary critical value. If the RH is higher than the primary critical value, humid-air 

corrosion is evaluated using a constant rate set by the user in the primary input file, assumed to be 

independent of temperature and time. 

3.3.24 Aqueous Corrosion 

Aqueous corrosion of the outer carbon steel container could be uniform or localized, depending on 

the composition of the near-field environment. If the aqueous medium contacting the WP is alkaline (pH > 9), 

carbon steel behaves as a passive metal and the presence of C1- in the environment may promote localized 

corrosion. If the corrosion potential is higher than the critical potential for the initiation of localized 

corrosion, the calculation of metal penetration in the form of pit growth begins immediately without an 

initiation or induction time. When the depth of the pit is greater than the initial thickness of the WP outer 

overpack, the potential of the galvanic couple formed by the outer and inner containers is calculated. If the 

corrosion or galvanic potential of the inner container is lower than the critical potential for localized

3-37 NUREG-1668
corrosion, penetration of the inner container is computed as uniform corrosion under passive dissolution 

conditions. Otherwise, pit growth of the inner overpack begins and continues until the depth of the pit 

becomes equal to the inner overpack wall thickness. 

If the RH is higher than the secondary critical value, aqueous corrosion of the steel overpack is 

evaluated. It is assumed that a water layer defined by an arbitrary specified thickness on the order of a few 

millimeters is formed and its thickness is considered to be the same regardless of the presence or absence 

of backfill around the WP. Water films that form on the metal surface usually contain a variety of 

contaminants. Soluble species such as CO, increase the electrical conductivity and decrease the pH of the 

film, leading to increased iron or steel dissolution (Leygraf, 1995). Anionic species such as C1- may escalate 

the rate of dissolution or promote a more localized form of corrosion. 

In the presence of an aqueous phase, corrosion of steel is an electrochemically controlled process. 

This process could be in the form of uniform active dissolution at pH close to or lower than neutral, or in the 

form of localized passive dissolution under the alkaline conditions induced by anions present in ground water 

such as bicarbonate ion (HCO 3 ). Localized corrosion of the carbon steel may be promoted by the 

simultaneous presence of Cl- anions at concentrations higher than a minimum critical value in the range of 

HC03- concentrations and pH within which passivation occurs. At any given time the mode of aqueous 

corrosion depends on the corrosion potential and the appropriate critical potential required to initiate a 

particular localized (pitting or crevice) corrosion process. The corrosion potential is the mixed potential 

established at the metal/solution interface when a metal is immersed in a given environment. Corrosion 

potentials are calculated on the basis of a kinetic expression for the cathodic reduction of oxygen and water 

and the passive current density for the anodic oxidation of the metals. 

Empirically derived equations are used in EBSFAIL for the dependence of critical potentials on 

environmental parameters. Pit initiation and repassivation potentials are assumed to depend only on the 

chloride concentration and temperature. The dependence of the critical potential on chloride concentration 

and temperature (computed as described in sections 3.2.2.1 and 3.2.2.2) is given by: 

Ecrt = E0ri(T) + B(T) log [C1-] (3-38) 

where E',(T) and B(T), which depend on the material, are linear functions of temperature. The equation is 

valid above a minimum Cl- concentration required to promote localized corrosion that also depends on the 

material. It should be noted that E 0t(T) is the value of Ecrit(T) fora CI- concentration equal to 1 M. Both E'l(T) 

and B(T) were evaluated for A516 steel and Alloy 825 from initiation and repassivation potentials for both 

pitting and crevice corrosion (Sridhar, et al., 1993; Cragnolino, et al., 1998). Data for Alloy 625 are available 

only at temperatures close to the boiling point of water [95 'C (203 'F)], because, at temperatures lower than 

60 'C (140 'F) Alloy 625 is resistant to localized corrosion over a wide range of Cl- concentrations (Gruss, 

et al., 1998). Because of the insufficiency of the current database, both E °n,(T) and B(T) are conservatively 

considered to be independent of temperature until more data are available. For Alloy C-22, which exhibits 

even higher resistance to localized corrosion (Gruss, et al., 1998), Ec,,(T) in Eq. (3-35) does not depend on 

C 1- concentration and becomes reduced to E ',,,(T), which represents, in this case, the potential for oxygen 

evolution rather than a critical potential for localized corrosion. Additional data (and eventually the 

application of a critical temperature criterion) will be used for a more rigorous assessment of localized 

corrosion of Alloy C-22.

NUREG-1668 3-38
In the EBSFAIL module, the repassivation potential, EP, is conservatively adopted as the critical 

potential for the initiation of localized corrosion. The same approach is applied to the outer and inner 

containers assuming that steel behaves as a corrosion-resistant alloy in alkaline, passivating environments. 

If the corrosion potential is higher than the repassivation potential, pits are assumed to grow without an 

initiation time. If the corrosion potential falls below the repassivation potential, previously growing pits are 

presumed to cease growing and the material passivates, corroding uniformly at a low rate through a passive 

film. The metal penetration or remaining thickness is calculated at each time step, using rates of uniform and 

localized corrosion, as appropriate. 

The propagation of pits is considered in a simplified manner by introducing an empirical equation 

developed by Marsh and Taylor (1988), for carbon steel in chloride containing bicarbonate/carbonate 

solutions, using an extreme values statistics approach. In this equation, pit penetration is time-dependent and 

given by: 

P = Atn (3-39) 

where: 

P = pit penetration [in] 

t = time [yr] 

A = experimentally determined constant specified in the primary input file 

(CoefForLocCorrOfOuterOverpack) 

The time exponent, n, is specified in the primary input file (ExponetForLocCorrOfOuterOverpack) and is 

approximately equal to 0.5, consistent with a pit growth process controlled by diffusional mass transport and 

a rate that decreases with time. For the inner container, a constant rate of penetration (n = 1) was considered 

for pit growth and specified in the primary input file (LocalizedCorrRateOfInnerOverpack[m/yr]). Recent 

experimental results (Cragnolino, et al., 1998) have shown this is quite conservative since a parabolic rate 

law (n = 1⁄2) was found to apply. 

If the corrosion potential is lower than Ep,, uniform penetration caused by corrosion under passive 

conditions is calculated for the carbon steel container and for any of the alternative inner container materials 

(Alloys 825, 625, and C-22) by using experimentally available values specified in the primary input file as 

materials AA_ll [C/m2/yr] and AA_2l [C/m2Iyr], respectively. 

After penetration of the outer container, electrical contact of the inner and outer container through 

the presence of an electrolyte path (such as that provided by modified ground water) promotes galvanic 

coupling, assuming that metallic contact always exists between both containers. The galvanic coupling model 

evaluates whether penetration of the inner container by localized corrosion is possible. If not, uniform 

corrosion or mechanical fracture becomes the predominant failure mechanism because the inner container 

emerges as protected against localized corrosion. 

The effect of galvanic coupling between the inner and outer overpacks on the failure time of the WP 

is evaluated by a simplified approach (Mohanty, et al., 1997). The corrosion potential of the inner container

3-39 NUREG-1668
in the galvanic couple, E or, formed when the wall of the outer container is penetrated by a pit, is estimated 

using experimentally measured values of the potential bimetallic couple, Ecouple, for a well-defined area ratio 

between both components assuming perfect electrical contact (Dunn and Cragnolino, 1997; 1998). If Ec'o 

is greater than the repassivation potential of the inner overpack material, localized corrosion occurs. 

Otherwise, uniform corrosion takes place. The EcJ is determined through a linear combination of corrosion 

potential, ECo,,, of the inner overpack, in the absence of galvanic coupling at the time of the through-wall 

penetration of the outer overpack, and E according to the empirical expression: 

= = (-I) Ecorr + i Ecoupie (3-40) 

where 0 _• 71 _• 1 is the efficiency of the galvanic coupling. The values adopted for Eo.ple for the different 

electrochemical parameters needed to calculate E~rr and for those establishing the dependence of the critical 

potentials with chloride concentration and temperature for the various materials considered are included in 

Appendix A. 

3.3.2.5 Mechanical Failure 

Mechanical failure of WPs in the EBSFAIL module is considered to be the result of fracture of the 

outer steel overpack. As a first approximation, other mechanical failure processes such as buckling or 

yielding are not regarded plausible for the current design of the WP because of the relatively large thickness 

of the container wall. Active uniform corrosion of the carbon steel overpack is not expected under the 

passivating conditions prevailing in the near-field environment and, therefore, failure modes such as buckling 

or yielding that would require significant generalized thinning of the container wall in the presence of 

external loads are not included. 

The possibility of mechanical failure as a result of thermal embrittlement of the steel promoted by 

long-term exposure to temperatures above 150 'C (302 'F) is evaluated at each time step. Fracture as a result 

of thermal embrittlement of the steel overpack is an important failure mode to be considered for any WP 

design, particularly for high thermal loadings. Thermal embrittlement of low-alloy steels occurs as a 

consequence of prolonged exposure at elevated temperatures and results in a substantial degradation of 

specific mechanical properties. 

One of the important mechanical properties required for a WP material is toughness, which is the 

ability to absorb energy in the form of plastic deformation without fracture. Toughness however, is 

significantly affected by thermal embrittlement, a phenomenon closely related to temper embrittlement. 

Thermal embrittlement is characterized by an upward shift in the ductile-brittle transition temperature, 

measured by the variation of the impact fracture energy for notch specimens as a function of test temperature 

(Vander Voort, 1990). Segregation of impurities (e.g., antimony, phosphorus, tin, and arsenic), along prior 

austenite grain boundaries, is the main cause of temper embrittlement (Briant and Banerji, 1983). The 

segregation of phosphorus, which is the dominant impurity of commercial steels, promotes fracture of 

notched specimens on impact and leads to a change in the low-temperature fracture mode from transgranular 

cleavage to intergranular fracture (Cragnolino, et al., 1996).

NUREG-1668 3-40
A simple fracture model developed on the basis of linear-elastic fracture mechanics is used in 

EBSFAIL. This model is based on a generalized expression for the stress intensity factor (KQ). For the case 

of a cylinder with a surface flaw located on its outer surface, the following equation is applicable: 

KI = Yu (ra) 0

° 5 (3-41) 

where: 

K, = stress intensity factor for the crack opening mode (1) [NMPa m°-?] 

Y = geometry factor to account for the shape of the crack and the load configuration 

[unitless] 

a = applied stress [MPa] 

a = depth of crack [in] 

It is assumed that applied stresses are caused by only residual stresses associated with the 

circumferential weld used for overpack closure. It is also presumed that the maximum value attainable by 

residual stresses produced by welding is the yield strength of the material. The depth of the crack increases 

with time and is conservatively inferred to be equal to that of a pit, as a result of localized corrosion. Values 

of Y are calculated as Y=MKQ- 0

°5 , as presented by Rolfe and Barsom (1977). The geometry factor Y 

corresponds to a part-through thickness thumbnail crack with a length 2c, equal to 2 times its depth, a, for 

a hollow cylinder of wall thickness, t, in which the crack shape parameter, Q, is a function of a/2c. The 

magnification factor, MK, varying from 1.0 to 1.6, is introduced for deep cracks with depths ranging from t/2 

to t .For simplicity, the WP is considered composed of a single shell with the combined thickness of both 

the outer and the inner overpacks, but with the mechanical properties of the outer overpack. 

In addition, a safety factor of 1.4 was applied to calculate the value of K, by assuming that the yield 

strength of the material in the vicinity of the welds is higher than the base material. This value is compared 

at each time step with the critical stress intensity or fracture toughness of the material, K,,, to determine if 

failure by fracture takes place. By definition, fracture occurs instantaneously if K, is greater than Kc. 

Because of the lack of data, no decrease in the value of KIc, with time, is assumed in the present analysis. 

If thermal embrittlement of the steel occurs through prolonged exposure (thousands of years) to temperatures 

above 250 °C (482 *F) however, a substantial decrease in the value of Kc may be expected (Cragnolino, 

et al., 1998). 

3.3.3 Assumptions and Conservatism of the EBSFAIL Approach 

The EBSFAIL model is an abstraction of WP failure that considers: (i) dry-air oxidation; 

(ii) humid-air corrosion; (iii) aqueous corrosion; and (iv) mechanical failure. Other processes and events that 

cause WP failure include: (i) disruptive scenario failure; and (vi) juvenile failure. In addition to the number 

of WPs failed, the time of failure is also estimated. 

Oxidation of steel can occur in dry air at low RH and temperatures ranging from ambient to 250 °C 

(482 *F). It is assumed that localized dry-air oxidation occurs intergranularly by enhanced diffusion of 

oxygen anions along grain boundaries. Because this effect is expected only for a short time at the beginning

3-41 NUREG-1668
of the post-closure period, when RH may be low during the period of high temperatures, it is not anticipated 

to significantly affect the WP lifetime. The inclusion of this process is considered a reasonable approach. 

Corrosion of steel in air at moderately high RH, expected after the initial dry-air period, bears certain 

similarities to atmospheric corrosion. Humid-air corrosion is assumed to occur when the RH is above a 

primary RH value and below a secondary critical RH, above which aqueous corrosion takes place. 

Atmospheric corrosion studies for iron and steel indicate a slow rate of corrosion occurring between a 

primary RH of 60 percent and a secondary RH of 75-80 percent. Humid-air corrosion is not considered to 

have a significant effect on WP lifetime. The inclusion of this process is considered a reasonable approach. 

Among dry-air oxidation, humid-air corrosion, and aqueous corrosion, degradation of the WP will 

be most affected by the aqueous corrosion process. Aqueous corrosion will occur when the secondary RH 

is exceeded, after which determination of whether localized or uniform corrosion occurs is of most 

importance in determining the corrosion rate (localized or pitting corrosion will breach the WP in a much 

shorter time than uniform corrosion). The corrosion potential is a key parameter that represents the driving 

force required to promote the occurrence of various corrosion processes under natural environmental 

conditions. Once the corrosion potential exceeds the repassivation potential, localized corrosion begins and 

pit growth rates are calculated by using experimentally determined expressions and parameters. Failure of 

the WP is defined conservatively as penetration of both overpacks by a single pit or by general dissolution. 

Because the repassivation potential depends on the temperature and chloride concentration, through an 

empirical expression based on mechanistic models, the degree of conservatism of this approach will depend 

on the approach used to estimate temperature and chloride concentration. 

Mechanical failure of the WP is assumed to occur as a result of fracture of the outer steel overpack. 

Fracture of the overpack, as a result of thermal embrittlement, is an important consideration because the 

fracture toughness of the steel can be affected by the repository thermal environment [i.e., long-term 

exposure to temperatures above 150 'C (302 'F)]. Residual stress is compared with the fracture toughness 

of the WP to determine if failure by fracture takes place. This approach is nonconservative because the 

applied stress considered excludes other stresses (e.g., seismicity and faulting); however, the overall 

approach can be conservative depending on the value for fracture toughness specified in the input file. 

Fracture toughness is affected by the prolonged exposure to high temperatures and thus depends on 

repository design (e.g., backfill versus no backfill and thermal load). 

Disruptive scenario failure of the WP is the failure of the WP from disruption caused by seismicity, 

faulting, or igneous activity (further details on the disruptive scenario failures are provided in their respective 

sections). Seismic activity can cause rockfall from the roof of the emplacement drifts, which will cause 

deformation and in some cases failure of the WP. Fault displacement is assumed to fail all WPs intercepted 

by the fault zone when a given fault displacement is exceeded. Intrusive IA is assumed to fail all WPs in an 

effective area intercepted by an intrusive event calculated by an intrusion orientation, length, and width 

(assumption assumes contact between lava and the WP results in WP failure). 

Juvenile failures of the WP may occur because of manufacturing defects and emplacement accidents. 

The TPA Version 3.1.4 code includes a parameter to specify the fraction of WPs assumed damaged at the 

beginning of the simulation. There is little relevant data to determine an appropriate value for 

juvenile failures.

NUREG-1 668 3-42
Unique failure times are assigned to each failure type: (i) juvenile failure (conservatively assumed 

to occur at the beginning of the siiiulation); (ii) disruptive scenario ffiluire (seismicity, faulting, and intrusive 

igneous activity); and (iii) failure caused by degradation (dry-air oxidation, humid-air corrosion, aqueous 

corrosion, and mechanical failure). Because only one failure time is used for each of the three categories, the 

initial failure time for a particular category is used conservatively to represent the failure time of all the WPs 

that fail in that category. The conservative nature of this approach depends on the distribution of failures 

within the simulation period. 

There are several specific assumptions and conservatism of the EBSFAIL module: 

Corrosion failure of all the WPs contained in a subarea occurs simultaneously and is 

determined by the penetration of a single pit. These assumptions do not take into 

consideration the full range of spatial variations in environmental, material, and 

electrochemical conditions that can be expected for different WPs and on different areas of 

a single WP. 

There is no coupling of the EBSFAIL module for mechanical failure (i.e., growth of pits or 

cracks with time that reduces the stresses needed for failure) with WP failure caused by 

disruptive events calculated outside the EBSFAIL module. No inputs from FAULTO and 

SEISMO modules in the form of applied stresses are used in EBSFAIL to calculate 

mechanical failure. Only residual stresses arising from the circumferential weld closure are 

considered. This lack of coupling may result in non-conservative estimates of the number 

of failed WPs and the time of occurrence as computed by the SEISMO module. 

Mechanical failure is calculated assuming an intact WP (i.e., no weakening of the WP 

caused by uniform corrosion), which may yield nonconservatively long times for 

mechanical failure. 

Only mechanical failure from brittle fracture of the outer steel overpack as results of thermal 

embrittlement is considered in EBSFAIL. This may overestimate the WP failure time but 

it may be partially compensated for by the lack of consideration of the contribution to 

mechanical integrity of the inner container, which is a very ductile material. I 

For dry air oxidation of the WP, the rate of oxidation is controlled by oxygen transport into 

grain boundaries and the effect of the external oxide layer is negligible. 

Aqueous corrosion is understood to occur through formation of an alkaline and saline water 

film on the WP surface at RH. Dripping of modified ground water on the WP is not 

considered. This limitation may lead to nonconservative results if there are significant 

spatial and temporal variations in the flow and chemistry of the water. 

Simplified consideration of pit nucleation and growth in EBSFAIL may lead to rapid 

penetration of the outer overpack, despite its thickness. The propagation of deep pits could 

be limited by ohmic drop and mass transport constraints leading to the initiation of new pits 

rather than continuous growth of deeper pits.

3.-43 NUREG-1668
3.4 SEISMO MODULE DESCRIPTION

SEISMO determines the number of WPs ruptured by seismically induced rockfalls. The number of 

ruptured WPs determined from this module is made available to EBSREL for release calculation. 

3.4.1 Information Flow Within TPA 

3.4.1.1 Information Supplied to SEISMO 

The user selects the seismic disruptive process module in the primary input file. EXEC passes to 

SEISMO the TPA time steps and the number, magnitude, and time of the seismic events that are sampled, 

using the seismic hazard curve data set in the primary input file. Rock column height associated with 

different categories of seismic events, and rockfall area determined by joint spacing, are used to compute 

rockfall volume. These data are provided in seismo.dat. Other SEISMO input parameters are specified in the 

SEISMO section of the primary input file. 

3.4.1.2 Information Provided by SEISMO 

SEISMO calculates the cumulative fraction of WPs disturbed by the seismic event, for all subareas. 

These results are passed to EXEC and used in EBSREL for release calculations. 

3.4.2 Conceptual Model 

The SEISMO module evaluates the potential for direct rupture of WPs from rockfall induced by 

seismicity. The SEISMO code uses the weight of rock that is falling from the roof of the emplacement drifts 

to calculate the impact load on a WP. The magnitude of the impact load is assumed to be a function of the 

size of the falling rock block and the distance the rock falls. The size of the falling rock block is, in turn, a 

function of rock conditions and the magnitude of a seismic event. The SEISMO conceptual model is divided 

into: (i) calculation of impact load and stress; (ii) determination of WP rupture; and (iii) determination of 

the number of WPs ruptured. The SEISMO module accounts for variations of rock conditions, the 

relationship between falling rock size and magnitude of seisrmicity, and the timing of the seismic events. 

3.4.2.1 Impact Load and Stress Calculation 

The SEISMO conceptual model assumes that for impact load and stress calculation: 

No energy dissipation takes place at the point of impact because of local inelastic 

deformation of the WP material. 

The deformation of WPs is directly proportional to the magnitude of the dynamically 

applied force. 

The rock body does not fragment on impact.

NUREG-I1668 3-44
0 The inertia of the WP resisting an impact may be neglected.

The WP is treated as an equivalent spring with a spring constant kp [Figure 3-11 (a)]. 

The dynamic or impact loads determined in the SEISMO module for rock falling on a WP are 

approximated based on the principle of conservation of energy, using the weights of the freely falling rocks. 

For a rock hitting the WP, the impact load is calculated as (Popov, 1970):

=,I W 1 + 2 hI

P&yn 

W 

h

(3-42)

impact load [N] 

weight of the rock falling [N] 

falling distance of rocks to WPs [m] 

spring deformation [m]

I.

(a) (b)

Figure 3-11. (a) Waste package equivalent beam and (b) waste package vertical supports for SEISMO.

NUREG-1668

where:

3-45
The supports for WPs are treated as flexible in the SEISMO module. In the current repository 

conceptual design, a WP will be supported by four equally spaced v-shaped thin beams with one vertical 

cylindrical bar on either side of the v-shaped beam. In the SEISMO module, only the supports at both ends 

of a WP are considered [Figure 3-1 1(b)]. In Eq. (3-42), A,, was the static deflection of a WP impacted, 

assuming rigid supports. To account for the deformability of WP support, A,, is made equal to: 

W W 

k__ . 2Nk , (3-43) 

where: 

kwp = stiffness of the WP determined as unit deflection at the center of a simply supported 

beam when subjected to a load at the center [N/m] 

NP = number of the supports at each end of the WP (two, in this case) 

kb = stiffness of the vertical WP support bars [N/m] 

k• can be calculated by: 

k _48EI 

wp 3 (3-44) 

and kb can be calculated by: 

AE 

kb L (345) L 

where: 

A = cross-sectional area of the vertical support bar [M 2

] 

E = Young's modulus of the vertical support bar or WP [N/m 2] 

L = height of the vertical support bar [m] 

I = 7rR avt, where t is the thickness of WP [0.12 m (4.7 in.)] and Ra, is the average of the 

outer and inner wall radii of the WP [in] 

L length of the WP [m] 

No information about the shape and dimension of the bar is currently available. In the SEISMO module, the 

bar is assumed to be a cylinder with a radius of 0.0508 in (2 in.) and a height of 0.3048 m (I ft). 

On obtaining the impact load, the equivalent static stress resulting from the rock impact on a WP can 

be calculated by adopting a simple concept of two spheres in contact and by assuming the pressure is

NUREG-1668 3-46
distributed over a small circle of contact (Timoshenko and Goodier, 1987). The impact stress (p) in N/in 2 

can be obtained by: 

3 [16Pdy 1 

2zc 9n.2 (C, + Crck)2R ( 

where: 

CW = constant for lower sphere or WP [m2

/N] 

Crk = constant for upper sphere or rockfall, and [m2

/N] 

R = radius of lower sphere or WP [m] 

2 

Cro Prock (3-48) 

rErock 

and: 

1- p2 

Cwp P (3-47) 

w EwP 

where: 

pwP = Poisson's ratio of WP [unitless] 

Ep = modulus of elasticity of WP [N/rn2] 

wk = Poisson's ratio of rockfall [unitless] 

Erk = modulus of elasticity of rockfall [N/rn2] 

Failure Criterion 

The impact stress calculated as described earlier is compared with the ultimate tensile strength of 

the WP material to assess whether WP failure has occurred. If the impact stress is determined to be greater 

than the ultimate tensile strength of the WPs, the WPs are assumed ruptured. The ultimate tensile strength, 

with units of N/m2, is specified in the primary input file.

3-47 NUREG-1668
3.4.2.2 Joint Spacing and Rock Conditions in the Topopah Spring Welded Tuff Unit 

It is recognized that not all rocks falling from the roof of the emplacement drifts will rupture the 

WPs. The effective size of the fallen rock that may impact the WPs is envisioned to be controlled by joint 

spacing (JS) (width and length of the rock block) and vertical extent of the rock block falling on the WP. 

Given the wide range distribution ofJS' in the TSw2 thermal-mechanical unit, it is convenient to assume that 

five distinct rock conditions exist in the TSw2 unit. These rock conditions are estimated using available JS 

information (Brechtel, et al., 1995) for the TSw2 unit. Since each rock condition represents a range of JS, 

a normal distribution covering the range of the JS is assumed for the corresponding rock condition (for lack 

of specific information). The size of the rocks used for each rock condition is then based on the JS distribution condition. The volume of a falling rock can be calculated by JS (width) x JS (length) x vertical 

dimension of rock block. At this time, the SEISMO module assumes that the width of a falling rock is equal 

to its length. 

Fractional Coverage of Rock Conditions and Determination of Number of Waste Packages Ruptured 

Based on Brechtel, et al. (1995), rock condition 4 appears to contain a large portion of the TSw2 unit. 

About 62.9 percent of the area can be characterized as rock condition 4, and rock condition 5 occupies 

roughly 35.6 percent of the area. Rock conditions 1, 2, and 3 account for only 1.5 percent of the total area. 

Because of the lack of specific information, the 1.5 percent is equally divided into rock conditions 1,2, and 3. 

In the SEISMO module, the percentage of the total WPs in each of the rock conditions is assumed 

to be equal to the fractional coverage of the rock condition. For a particular seismic event, if the induced 

rockfalls generate an impact stress on the WPs that exceeds the ultimate material strength of the WPs, the 

WPs are assumed ruptured. If a seismic event triggers rockfall for a particular rock condition, rockfall is not 

expected to take place in the entire area under that rock condition. In fact, only a small fraction of the rock 

under that rock condition will fall in responding to a seismic event, because of the inherent variation 

associated with the rocks. A portion of the remaining intact rock may fall at a later time when another seismic 

event, having the same intensity, takes place. The size of the fraction may be related to the event magnitude, 

joint dip angles, and incidence angle of incoming seismic waves. At this time, there is little information 

available to determine such a relationship. In the current SEISMO module, a log-normal probability 

distribution function for the rockfall fraction is implemented so that users can evaluate the potential effect 

of variation of the rockfall fraction for a given rock condition. All WPs under that fraction of area of rockfall 

will be affected. If the impact stress is sufficient, all WPs under that fractional area are computed as having 

failed. The time history of WP failures from seismic event is used in radionuclide releases calculations in 

EXEC. 

Seismic Hazard Parameters 

The SEISMO module requires the generation of a synthetic time history of seismicity over the time 

period of interest. This estimate is made by using the TPA executive SAMPLER utility module. The required 

input for generating event history includes ground acceleration sampling points and the corresponding 

recurrence times, which together form a seismic hazard curve. The horizontal acceleration hazard curve 

provided in DOE (1995) is used to determine the seismic hazard associated with an earthquake with a given 

recurrence interval. This hazard curve is specifically for surface facilities. A common understanding for YM 

is that the seismic acceleration at the repository horizon may be half that at the ground surface 

(Quittmeyer, 1994). In the SEISMO module, half the surface hazard is used as a base case. The SEISMO 

module can be used to assess the effects of different hazard curves; however, in changing the hazard curve,

NUREG- 1668 3-48
the height of rockfall and associated volumes should be modified to reflect the effect of the ground 

acceleration magnitude. 

The seismic recurrence sampling points (accelerations) and the corresponding recurrence time are 

provided in the primary input file. Four discrete sampling accelerations are selected for the SEISMO module. 

These accelerations are 0.1, 0.2, 0.3, and 0.4 g (equivalent to 0.2, 0.4, 0.6, and 0.8 g on the seismic hazard 

curve) (Quittmeyer, 1994). The recurrence times associated with each acceleration are 500, 2400, 8000, and 

20,000 yrs. 

3.4.3 Assumptions and Conservatism of the SEISMO Approach 

The SEISMO module is an abstraction of the effect of seismicity on WP performance. The SEISMO 

module considers: (i) the frequency and magnitude of seismic events; (ii) the quantity of rockfall for each 

seismic event; and (iii) the effect on the WP. 

The frequency and magnitude of the seismic events are based on a seismic hazard curve provided 

as input to the TPA Version 3.1.4 code. The seismic hazard curve provides accelerations and recurrence 

frequencies for seismic events over the time period of interest. The seismic hazard curve is based on 

historical information for surface facilities and is considered a reasonable approach for the underground 

facility. 

The volume of rock that falls for a given event is determined based on properties related to the TSw2 

thermal-mechanical unit. Five distinct rock conditions have been identified to characterize the variability in 

rock properties within the TSw2 unit. The abstraction for rock volume uses JS information (as width and 

length) and height of yield zone (as vertical extent). The height of yield zones was estimated from dynamic 

numerical modeling of coupled thermal-mechanical effects. The height of the yield zone has been 

conservatively set as an upper bound such that the volume of rockfall represents a maximum value. A 

heterogeneity factor is used to account for the function of drifts that experience rockfall for each seismic 

event. Thus, when a seismic event causes sufficient rockfall to rupture the WP, in a particular rock type, all 

WPs within the fraction of that rock type, specified by the heterogeneity factor, are assumed to rupture. 

If a seismic event triggers a rockfall, it is assumed to equally affect the whole area of the repository, 

irrespective of rock conditions. In other words, all five rock conditions experience the same acceleration over 

the entirety of their respective areas and the fraction of each area with rockfall is the same for each rock 

condition (controlled by a single heterogeneity factor). In actuality, a seismic event may trigger more rockfall 

for one rock condition than for another. Intuitively, for a given seismic ground acceleration, a weaker rock 

condition should experience a relatively large area of rockfall compared with a stronger rock condition. In 

the current implementation, although the fractional rockfall area is a function of seismic ground acceleration, 

it is not a function of rock condition. Whether this assumption is conservative cannot be determined at this 

time. 

Rockfall may affect the WP lifetime through rupture of the WP by the impact produced by the falling 

rock and acceleration of corrosion at the location of the impact. Only the first aspect is considered in 

SEISMO. The two key components of the SEISMO module include determination of the impact load and 

failure. In computing the impact load, the WP is treated as a simply supported beam. In converting impact 

load to impact stress, all energy generated through dynamic impact of the rockfall is transferred to the WP. 

A conservative assumption is that the WP will rupture if the impact stress exceeds the ultimate strength of

3--49 NUREG--1668
the WP. The analysis assumes, nonconservatively, that rockfall occurs on an intact WP (i.e., corrosion and 

mechanical fracturing of the WP does not reduce WP strength, and cumulative damage caused by consecutive 

rockfalls is not considered). Overall, the approach is considered conservative over a 10,000-yr compliance 

period. 

There are several specific assumptions and conservatism of the SEISMO module: 

* A WP can be treated as a simply supported beam. 

* No energy dissipation takes place at the point of impact because of local inelastic 

deformation of the WP material. 

0 Deformation of WPs is directly proportional to the magnitude of the impact load. 

0 Inertia of the WP resisting an impact may be neglected. 

0 Estimate of the impact stress assumes a contact area based on a spherical geometry. This 

assumption yields a conservative estimate because the contact area would be larger. 

In converting impact load to impact stress, falling rocks are assumed to have infinite 

strength (i.e., all energy generated through impact is transferred to the WPs). If rock is 

allowed to break, the effective impact stress to the WP should be smaller since some impact 

energy will be absorbed by the rock breaking. 

3.5 EBSREL MODULE DESCRIPTION 

The EBSREL module calculates the time-dependent release of radionuclides from the EBS to the 

UZ. Infiltrating water and refluxed water could reach the drift wall and subsequently the WP. If the container 

is breached, infiltrating water enriched with chloride and other minerals could enter the WP, coming in 

contact with the SF. If the cladding has failed, the infiltrating water could dissolve the SF and then carry the 

radionuclides out of the WP to the geosphere. 

3.5.1 Information Flow Within TPA 

3.5.1.1 Information Supplied to EBSREL 

The values EBSREL obtains from EXEC for the representative WP in a subarea are: (i) WP 

temperature, by reading in a file created in EBSFAIL; (ii) volumetric flow rate of water in the near field 

(calculated in NFENV); and (iii) the cumulative fraction of failed WPs for all failure modes (corrosion 

failure, disruptive event failure, and initial defective failure). Radionuclide decay chains, half-lives, initial 

inventories, and molecular (isotopic) weights are passed to EBSREL by EXEC. Other values such as WP 

dimensions, nuclide-specific solubilities, retardation coefficients, fractions of SF wet, SF dissolution model 

parameters, and factors determining the amount of water entering the representative WP are specified in the 

primary input file.

NUREG-1668 3-50
3.5.1.2 Information Provided by EBSREL

EBSREL calculates the radionuclide releases from the EBS for all subareas and passes these results 

to EXEC for subsequent use in UZFT. EBSREL provides releflv rates as a function of time for all 

radionuclides specified in the primary input file for ground-water dose calculations. 

3.5.2 Conceptual Model 

Using WP failure information from EBSFAJL, SEISMO, FAULTO, and VOLCANO and near-field 

chemistry, temperature, and liquid flow rate information from NFENV, EBSREL [derived from EBSPAC 

(Mohanty, et al., 1996)] calculates release of radionuclides from aWP. Conceptual models used in EBSREL 

are described in the succeeding sections. In calculating releases, EBSREL takes into account radionuclide 

decay, generation of daughter products in the chains, and temporal variation in water flow. Once the 

container is breached, the SF may be exposed to aqueous conditions as determined by the near-field 

calculations. Like EBSFAIL, EBSREL looks at processes affecting an individual WP representative of all 

WPs in the subarea. Releases calculated by EBSREL from this breached WP are then scaled to account for 

the total number of WPs breached from a particular failure type (i.e., initially defective, scenario failure, and 

corrosion failure). Releases from all failure modes are then summed, as presented in Section 3.5.2.9. 

3.5.2.1 Radionuclide Inventory and Mass Transfer 

Two models for aqueous release of nuclides are available for selection by the user: the bathtub model 

and the flow-through model. In the bathtub model, it is assumed that at the time of failure there are at least 

two holes in a horizontally emplaced WP that act as inlet and outlet for water. The holes are located such that 

water enters through one pit and exits through the other. Another presumption is that at least one of the holes 

is located on the side of the WP at a level lower than that of the water entrance hole, situated at the top of 

the horizontally emplaced WP. After the water level in the WP rises to the specified outflow position (a sampled parameter), water begins to flow from the WP, along with the dissolved radionuclides. The 

flow-through model is a variant of the bathtub model, for which the flow out is assumed to be immediately 

equal to the flow in and the fraction of fuel wetted is not a function of water level inside the WP. 

Figure 3-12 presents a schematic representation of a horizontally emplaced WP with holes 

representing the inlet and outlet. In this schematic, the conduit for liquid entry is shown on the upper half 

of the WP and the hole for the liquid exit is shown on the right side. Liquid water will accumulate in the WP 

until its level rises to the level of the exit hole, h. The height of the exit hole is specified in the primary input file via the SFWettedFraction parameter; 0 implies an outlet at the bottom and 1 implies an outlet at the top 

of the WP. In the bathtub model, all SF above this level is assumed to remain dry and does not contribute to 

radionuclide transfer out of the WP. 

When liquid water enters the WP, after its failure, the overall mass balance model for the 

radionuclide inventory in liquid water contacting SF in a failed WP is: 

dm. dt = WliOt - Wci(t) - Wdi(t) - MI - 'i +mi-I k'-1 (3-49)

3-51 NUREG-1668
where:

mI = amount of radionuclide i in the WP water at time t [moll 

wh = rate of transfer from the SF into the resident water in the WP because of or through 

leaching of the SF [mol/yr] 

wc = rate of advective transfer out of the WP [mol/yr] 

Wdi = rate of diffusive transfer out of the WP [mol/yr] 

X. = decay constant of radionuclide i [I/yr] 

mi_ = amount of the parent at time t [moll 

ki = decay constant of the parent [1/yr] 

The product, mi.i, is the amount lost because of decay, and m, 1X,- represents the amount generated by the 

decay of the parent radionuclide. 

q in 

oSF 

Internal surface 0, 1, 79 10 1, /10-z JOýAl x /Iof WP

Figure 3-12. Schematic of bathtub model with incoming and outgoing water conduits.

NUREG- 1668 3-52
3.5.2.2 Advective Mass Transfer

The advective mass transfer out of the WP, we,, can be represented by (Wescott, et al., 1995): 

Wi(t) = C,(t) qot(t) (3-50) 

where: 

Ci = concentration of radionuclide i in the WP water [mol/m3] 

q = water leaving the WP at time t [m3/yr] 

The concentration Ci is determined by dividing the mass of element i, m,, by the volume (V) of water in the 

WP. For the bathtub model, q., is zero if the volume of water in the WP has not exceeded the WP volume 

below the exit hole, Vm., or equal to the input flow rate, qm, if the volume of water exceeds Vm. The 

volume of water, V, in the WP is determined by integrating the flow rate, qm, with respect to time, until it 

reaches Vm•, where: 

V = fo' qin('r)dr (3-51) 

For the flow-through model, the flow out of a WP is equal to the flow in for all times. Calculation of the 

input flow rate, qjn' is described later in this section. 

For stability and efficiency in the numerical integration algorithm, V may not be smaller than V, 

which is defined as: 

Vnun = aqmax (3-52) 

where: 

qmn = maximum flow rate into the WP [m3/yr] 

a = constant currently set at 5 yr 

In the bathtub model, the fraction of fuel wetted is proportional to the water level. There is no release 

from the WP, however, until the actual water level exceeds the maximum water level. The flow-through 

model is implemented by specifying the fraction of the SF wetted and selecting the flow-through option in 

the primary input file. Because water does not have to fill to a certain level inside the WP, release of 

radionuclides occurs as soon as water enters the WP. The exit hole height parameter (SFWettedFraction) of 

the bathtub model is used indirectly to specify the fraction of SF wetted in the flow-through model.

3-53 NUREG-1668
Advective mass transfer is considered to occur from the inside of the WP to the EBS host rock 

interface instantaneously, through the nonmechanistic connection shown in Figure 3-13. 

EBS-Host Rock Interface 

.. ..... : • BackfiUl-EBS Interface 

Interface 

Figure 3-13. Schematic drawing for advective and diffusive mass transfer from the waste package to 

the host rock 

3.5.2.3 Diffusive Mlass Transfer 

Diffusive mass transfer takes place through the medium (crushed tuff backfill properties are 

assumed)' surrounding the WP, as shown schematically in Figure 3-13. After the water leaves the WP, it is 

understood to envelope the whole outer surface of the WP. As a result, the radionuclides are present in 

uniform concentration. Then, assuming the WP can be represented by a sphere of radius r0 , the mass loss 

through molecular diffusion of a radionuclide into the porous medium can be represented by:

Wdit) = 47E r2 pD ci q)~~ crr - (3-53)

IThe presence of backfill, assumed only for diffusive mass transfer calculations, provides continuity in the transport pathway. 

Nominally. the backfill is absent.

NUREG- 1668 3-54
9 

D 

r0

= porosity of the medium surrounding the WP [unitless] 

= diffusion coefficient of the radionuclide in the medium surrounding the WP [m2

/yr] 

= radius of the equivalent WP [in]

The concentration gradient at the outer surface of the WP, I can be obtained by solving the 

following boundary and initial value problem:

Cilt to = C. 

aCi -D 12(r2 aci x-' 

at R, r ar ( ar)

CuIr = = 0

(3-54) 

(3-55)

(3-56)

cq r=ro =- C0) (3-57)

Cq = concentration of radionuclide i [kg m-3] 

t = time [yr] 

R = retardation factor of radionuclide i [unitless] 

.A = decay constant of radionuclide i [yr-I] 

The retardation factor R, is defined as: 

Ri = 1 + 1-qp, K& (P

NUREG-1668

where:

where:

(3-58)

3-55
where:

(P = porosity of the medium [unitless] 

P = grain density [kg m-3] 

Kd, = distribution coefficient of radionuclide i [in 3 kg'] 

This formulation accounts for molecular diffusion, retardation of radionuclides, and radioactive 

decay, but not the generation of radioactive progeny. For simplicity, it is assumed that D is independent of 

the species and includes the effect of tortuosity. The diffusion model is presented for a spherical geometry. 

Thus, the inner-boundary condition is maintained at a distance equivalent to the radius of a sphere with the 

same outer surface area as a single WP.(Appendix A provides WP dimensions). Although the outer boundary 

is defined at infinity, the numerical solution of these sets of equations requires that a finite distance be 

assigned. A distance beyond the drift wall is considered sufficient. The spherical configuration was chosen 

for simplicity and lends itself to simple 1-D analytical or numerical solutions. 

3.5.2.4 Waste Package Inventory 

The inventory in the failed WP is calculated by a material balance, Eq. (3-49), accounting for 

depletion because of decay, generation of radioactive progeny, and mass depletion from diffusive and 

advective releases. For the case in which no mass leaves the WP, Eq. (3-49) reduces to: 

dmi 

dt M 1. . + r in. X.I (3-59) 

milt o = miO (3-60) 

These differential equations are solved to determine the remaining solid mass of the radionuclides in a chain 

at a given time t. The analytical solution (Bateman, 1910) to this initial value problem is given by: 

j- I j -ALt 

N = No 1-[ 0 F X k e 

k=l L:I j H. (•m_ •L)(3-61) 

m•L 

where: 

N = contribution from the i' chain member to thej' chain member [mol] 

NO initial mass of ih member of chain [mol] 

ki = decay coefficient of j member of the chain [I /yr]

NUREG-1668 3-56
The total amount of the j' chain member at any time is:

S° (3-62) 

i=1 

In the previous equations, mi = N1 . The notation N1 is used to preserve the conventional notation for 

representing the solution to the Bateman equation. 

3.5.2.5 Spent-Fuel Dissolution Rate 

The rate of radionuclide transfer from the SF into water in the WP [w1i in Eq. (3-49)] is a function 

of the flow rate of the water, the composition of the water in which the SF is in contact, and the element 

solubility in that water. Fission and activation product radionuclides normally have high solubility limits in 

water that do not limit the release of these radionuclides from a WP. These radionuclides include 

technetium-99 (Tc-99), iodine-129 (1-129), cesium-135 (Cs-135), carbon-14 (C-14), and chlorine-36 (CI-36). 

Neptunium-237 (Np-237) may belong to this category also, depending on the chemistry and the flow rate of 

water in contact with the SF. These radionuclides are understood to be released congruently with the 

dissolving SF matrix for immersed fuel. Gray and Wilson (1995) determined the intrinsic dissolution rate 

of the SF matrix from flow-through tests. The flow-through tests use artificially high flow rates to eliminate 

the precipitation of secondary phases on the SF surface. The secondary phases may modify the intrinsic 

dissolution rate of the SF matrix by altering the area of the reactive surface. 

The dissolution rate in solutions containing carbonate anions, representative of altered ground water 

present in the near field, is expressed by Gray and Wilson (1995) as: 

log r = 9.310 + 0.142 logC03 -16.7 log p 0 ) + 

0.140 log [H÷] -1213 + 6.81 log(T) log (Po) (3-63) 

T 

where: 

r = dissolution rate [mg m-' d'] 

[C02-] = total carbonate concentration [mol/L] 

p0 2 = oxygen partial pressure [atm] 

[H ÷] = concentration of hydrogen ions [mol/L] 

T = temperature [K] 

Normally, the ground water at the YM repository site contains silica (Si) and calcium (Ca) ions. In 

the presence of these species, the dissolution rate decreases about 100 times (Gray and Wilson, 1995). The

3-57 NUREG-1668
dissolution rate in the batch tests (under immersion) is about 10 times lower than that in flow-through tests 

(Gray, 1992). The dissolution rate in J-13 well water containing these ions can be described by: 

r = ro exp - R.1 (3-64) 

where R is given in ki/mol K and 1.4 x 10' < ro _ 5.5 x 10i. This equation was obtained from data at 25 'C 

(77 'F) (Gray, 1992; Gray and Wilson,1995) and at 85 'C (185 'F) (Gray, et al., 1992). The rate magnifies 

by a factor of 10, as temperature increases from 25 to 85 'C (77-185 'F). 

EBSREL allows the user to choose between Eqs. (3-63) and (3-64), or to assign a constant rate of 

dissolution via the primary input file. The wli(t) term in Eq. (3-49) is then calculated by multiplying the SF 

dissolution rates obtained from Eq. (3-63) or Eq. (3-64) (or the constant rate option) by the SF surface area. 

3.5.2.6 Spent-Fuel Surface Area 

Two models for determining the SF surface area are available in the primary input file. The first 

model determines surface area using fragmented pellets (i.e., particles) and the second model uses SF grain 

size criterion. The particle model assumes that SF in a WP is fragmented into small particles [-1 mm 

(0.04 in.) in diameter] and the intergranular porosity does not contribute to the surface area. The total surface 

area for the particle model is then computed as: 

A ( x 4R fwet (3-65) 

where: 

Ap = particle surface area [M 2 ] 

MSF = SF inventory/WP [kg] 

4 3 

M = particle mass equal to 4- trp Pgo [kg] 3 

rpo = particle radius [m] 

Pgo = density of oxidized SF [kg m- 3] 

fe = volume fraction of SF immersed in the WP water [unitless] 

If subgranular fragmentation of the SF takes place through fuel conversion from UO2 to UO2.4 and 

U3O., a smaller particle size (i.e., equivalent) can be considered in the SF particle model to represent

NUREG-1668 3-58
additional exposed surface area. In the case of the second model, mi which SF grains are exposed, the 

following expression is used to compute the total surface area: 

(MF (3 2 1 Ag=4 x 10-12 7E rg - w) 2 + -( 3rgw - 3r w 2 + W3) f 3-6 

% rg 

where: 

As = grain surface area [in2] 

rg = SF grain radius [pm] 

w = width of the oxidized zone [pm] 

The surface area available for leaching is conservatively held constant throughout the leaching 

period, even though the radius of the unoxidized fuel grains or particles would diminish with time. In 

addition, preferential attacks of grain boundaries in the fuel particles were not considered. 

In deriving these equations, the effects of ionizing radiation have not been considered explicitly. 

These effects are complex, potentially leading to higher release rates. Factors such as the age of the waste, 

thickness of water films, cladding protection, and protectiveness of secondary mineral layers on the fuel 

would have to be considered, to take ionizing radiation into account. 

3.5.2.7 Cladding 

SF cladding may protect the -bare SF matrix from exposure to water in the WP and reduce release 

rates significantly. The failure mechanisms of cladding include: (i) mechanical failure by external forces such 

as rockfalls; (ii) localized corrosion; (iii) creep; (iv) hydrogen-induced failure; (v) splitting by matrix volume 

expansion; and (vi) stress corrosion cracking. 

It is assumed that cladding reduces the fraction of the total SF surface exposed to water entering the 

WP. In the TPA Version 3.1.4 code, the fraction of SF surface area affected by cladding protection is 

controlled by a factor in the primary input file, which specifies the fraction of fuel that is unprotected by 

cladding and thus does not inhibit release (CladdingCorrectionFactor = 1 indicates no cladding protection, 

and 0 indicates complete cladding protection and no release). Currently, cladding protection is assumed to 

remain constant and unaffected by additional failure mechanisms or disruptive events. 

3.5.2.8 Water Dripping Abstraction 

The dripping abstraction determines the quantity of liquid water, qin, eventually entering the WP. 

It is based on the assumption that there is a net downward percolation of meteoric water at the site after 

thermal reflux and water will flow in fractures within the emplacement unit.

3-59 NUREG-1668
The dripping abstraction is represented by sampled distributions rather than a model embedded in 

the code. This approach is computationally efficient and is more easily and transparently factored into 

sensitivity analysis than an embedded model. Furthermore, this approach allows incorporation of alternative 

conceptual flow models such as structural control of wetting along faults or fracture zones, without requiring 

code changes. 

The WP infiltration flow rate is: 

qin = qFow Fmult (3-67) 

where: 

q = ground-water flow rate at the repository horizon after thermal reflux of water from 

deep percolation and reflux [m3/yr] 

Fo, = factor to account for the flow potentially reaching a wetted WP 

Fult" factor to account for near- and in-drift flow diversion 

When the factor Fo, is multiplied by the ground-water flux q and the plan area of the WP, the resulting 

product defines the flow rate that potentially reaches a wetted WP. Fow represents focusing of flow when 

it is assigned a value greater than 1 and diverging of flow when less than 1. F.• is correlated with another 

factor, Fwt, which is the fraction of WP receiving a dripping flux greater than zero. No mechanistic 

treatment of F.. has been included in the TPA Version 3.1.4 code. It is treated as a uniform random variable 

between 0 and 1.0. This approach entails the assumption that the number of WPs wetted is invariant in a 

given run and does not take into consideration that dripping locations may change with time. 

The flow diversion factor, Fruit, which ranges in the model from 0.01 to 0.2 and is lognormally 

distributed, is defined as the fraction of potentially dripping water that will enter the WP and contribute to 

the release and transport of radionuclides. Only a fraction of the water intercepting the drifts is expected to 

come into direct contact with the WPs. Only a portion of the water dripping onto the WPs is expected to get 

inside where it can interact with the waste form. Elements considered in F... include: 

* The reduction in flow to a WP because water is diverted around a drift is based on the 

presence of a capillary barrier. Water flow into the drift will face a capillary barrier if the 

fractures are small enough. Water cannot easily move across the capillary barrier and can 

be diverted in the fracture around the drift opening. 

Water flow crossing the capillary barrier into the drift can drip from the ceiling or from 

protuberances along the drift. Much of the water, however, is likely to be diverted as sheet 

flow along the drift walls rather than drip from the ceiling onto the containers. The closer 

to the crest of the tunnel, the larger would be the propensity to drip. Away from the crest, 

the slope of the tunnel walls would divert water to sheet flow along the walls. 

Water dripping from the ceiling would have to fall onto the WP in such a way that it could 

enter an open, through-going hole. If a drop of water is directly in the path of an open hole,

NUREG-1668 3-60
such as when the hole results from corrosion by dripping water, then this condition would 

be fulfilled. 

A few drops could enter a corrosion hole, but may be unable to enter the canister because 

of the presence of corrosion products from the steel overpack and inner barrier. These 

corrosion products may be flaky, porous, or gel-like, and their density will be considerably 

less than the steel itself. Without a high rate of water flux, there will be no mechanism to 

remove them from the location where they form. If the corrosion products remain in place, 

then water dripping into the corrosion hole would have difficulty entering the canister and 

would simply flow off. Holes at the crest will have a higher probability that water would 

enter because there would be a smaller propensity for water to flow off. Holes on the side 

will have higher probability that the water would flow off rather than enter. 

A more detailed explanation of the derivation of initial Fmu.t (and appropriate values for it) is 

contained in Appendix B. Both Fo, and F,,1 are specified in the primary input file as sampled parameters. 

F. is correlated with areal average MAI at the initial (current) climatic condition, matrix permeability (Kt) 

in the near field, and F.. Table 3-2 presents the correlation among the dripping parameters used in the TPA 

Version 3.1.4 code. It is assumed that these two parameters are independent of location, temperature, and 

water chemistry at the repository level. 

Table 3-2. Dripping Parameter Correlation Matrix 

Parameter <MAI> <K,,> F F0, 

<MAI> 0 0.631 -0.224 

<K,> 1 -0.623 0.13 

F,_ 1 -0.366 

3.52.9 Computational Approach 

The first step in the calculation is to determine when a liquid release starts. The release calculation 

includes the computation of the radionuclide inventory in the solid mass, radionuclide releases from the solid 

mass into the liquid surrounding the waste form, the generation of the new radionuclide inventory in the 

liquid because of radioactive ingrowth, advective release of mass from water leaving the WP, and diffusive 

losses into the medium surrounding the WP. 

At each time step, the inventory of the elements is computed as the sum of the mass of all the 

isotopes of that element. At any given time, the concentration of an element in the WP water is calculated 

by dividing the element inventory in the water by the volume of water in the WP. If the concentration of that

3-61 NUREG-1668
element in the WP water exceeds its solubility limit, then the calculated concentration value is discarded and 

the solubility limit is assigned to the concentration of the element. When release is solubility controlled, 

release of an individual nuclide occurs at a rate proportional to the mass fraction of all the isotopes of the 

element. 

The number of WPs affected by each failure mode (initially defective, scenario failure, or corrosion 

failure) is first determined by EBSFAIL. The fraction of WPs undergoing initially defective failure is 

specified in the primary input file. The number of WPs actually being considered for scenario failure is 

determined based on the corrosion failure time and the onset of wet conditions on the WPs. If the corrosion 

failure occurs before the scenario failure, then the number of WPs undergoing scenario failure is set to zero 

(i.e., WPs can only fail once). If the corrosion failure occurs after the scenario failure and wet conditions 

exist prior to the time of corrosion failure, then calculations of release from scenario failure are performed 

using the specified number of failed WPs. 

Liquid release of contaminants arises only from the wetted fraction of the SF in a WP. Radionuclides 

are released from the waste form at a rate proportional to the dissolution rate of the fuel (congruent release), 

into the volume of water present in the WP. Release of radionuclides from the WP by advection and diffusion 

may be limited subsequently by the elemental solubility of the radionuclides in the WP water. 

At the end of this calculation, the cumulative release is recorded for each nuclide. After advancing 

the time, the calculation is repeated for the next time step. The calculation continues until all radionuclides 

are depleted from the wetted fraction of SF and the water in the WP or the end of the simulation is reached, 

whichever occurs first. Since release calculations focus on release from a single WP for each WP failure 

category, to obtain the total release in the final calculations, the release from one WP is multiplied by the 

total number of wetted WPs for each failure category in a subarea. 

3.5.3 Assumptions and Conservatism of the EBSREL Approach 

EBSREL is an abstraction of the processes that will take place in a failed waste container. The main 

processes that control releases of radionuclides from the SF to the boundary with the geosphere are: 

(i) protection of the SF by cladding; (ii) degradation of the SF by air and water vapor; (iii) contact of the SF 

by liquid water; (iv) mobilization of radionuclides from the SF to the liquid water; and (v) transport of 

dissolved or otherwise mobilized (colloids) radionuclides in the water to the outside of the WP. 

There is no mechanistic model for cladding protection. Instead, cladding protection is specified as 

a factor between 0 and 1, representing the fraction of the surface area of the fuel exposed to dissolution. In 

most cases, it is conservatively assumed that no cladding protection exists. Some runs have considered 

substantial protection by cladding with only 0.5 percent of the available surface area of the fuel exposed. 

These values were derived empirically from laboratory leaching studies with deliberately failed fuel-rod 

samples (Wilson, 1990). 

EBSREL does not explicitly consider degradation of fuel by air and water vapor. There are no 

mechanistic models of degradation by air and water vapor in the module. The SF is assumed in a degraded 

state, on contact with liquid water. It is assumed that there can be no RT from the SF until the time liquid 

water contacts the fuel. This is a conservative yet reasonable approach, because it is expected that most

NUREG-1668 3-62
exposed fuel will degrade in the period between exposure to air and liquid water contact. The model 

conservatively ignores the possibly favorable effects of fuel degradation in terms of how radionuclides 

released from the uranium oxide waste form might be bound into low-solubility secondary phases such as 

uranyl silicates and hydroxides. There is a body of evidence that suggests this mechanism will significantly 

reduce the availability of several important radionuclides such as neptunium. Furthermore, secondary 

minerals may form protective layers, around the fuel, that could lead to reduction in radionuclide releases. 

These effects have been conservatively ignored. 

The contact of exposed fuel by liquid water is presumed to be controlled by either the bathtub or 

flow-through models. In the bathtub model, the fraction of fuel exposed is presumed to be controlled by the 

water height in the container. In the flow-through model, contact is independent of the water level. It seems 

clear that if water were to pool in the container, the fuel that was submerged would be in contact with liquid 

water. Aside from submersion, contact of fuel with liquid water would be controlled by complex and 

ill-defined processes occurring inside the container, such as dripping, water films on the fuel controlled by 

surface tension, evaporation, and thermal reflux. Because the actual processes are not well-understood, the 

choice of coefficients defining the fraction of fuel contacted by liquid water is selected to effect 

conservative results. 

The fraction of fuel contacted by water may or may not be important, depending on whether the 

radionuclides contributing most to the dose are limited by solubility. If solubility controls the release, the 

fraction of fuel contacted is unimportant and the flow rate of water in contact with the fuel is important. 

Conversely, if solubility is not the limiting factor, the fraction of fuel exposed to liquid water is important. 

Released radionuclides are assumed to be transported to the geosphere by water leaving the WP and 

by diffusion through a layer of porous material. There is no accounting for the possibility that radionuclides 

in the water might be captured along the liquid-water pathway by precipitation or sorption on material inside 

the WP. If backfill is considered part of the EBSREL model, sorption of radionuclides from the flowing 

water might offer a significant reduction in releases. Molecular diffusion through backfill or rock is 

considered a minor factor and may be eliminated in future versions. 

There are several specific assumptions related to the EBSREL module: 

Liquid release calculations focus on release from a representative WP in each subarea. It is 

assumed that the release environment within the subarea is not affected by the location or 

the geometric configuration of other WPs. Therefore, to obtain the total release in the final 

calculations, the release from a representative WP is multiplied by the number of WPs for 

each applicable WP failure category in the subarea. 

EBSREL considers only radionuclide releases from SF; no consideration is given to 

radionuclide release from glass waste form or the DOE SF. 

Although the thermal model provides the specifics of backfill emplacement, it is 

conservatively assumed that the backfill is always present in the diffusive release 

calculations. Diffusive releases only occur if backfill is present; they are identically zero if 

backfill is absent. Diffusive releases are generally much smaller than advective releases.

3-63 NUREG-1 668
* Colloidal transport is not explicitly considered, which could be nonconservative. 

The surface area of SF exposed to water during the leaching process is constant even if 

shrinking of particle size takes place with time. Preferential attacks of grain boundaries have 

not been considered. 

For the bathtub model, all fuel below the water level is assumed to come into contact with 

water uniformly. 

No new pits are formed, with time, that would alter the volume of the bathtub. Once the 

water reaches the level of the exit hole, it can leave the WP unimpeded. 

Advective mass transport is considered from the inside to the outside of the WP and into the 

UZ but not into the material surrounding it, as schematically shown in Figure 3-13 for 

diffusive mass transfer. Ignoring transport time through the backfill and invert 

is .conservative. 

There is no radionuclide release from SF above the water level, which could 

be nonconservative. 

Formation of a subsequent release of radionuclides from secondary phases is not considered. 

The change in water chemistry in the WP caused by corrosion of container material is not 

explicitly considered, but can be taken into account by adjusting the parameters of the 

leaching rate equation. 

Gaseous release occurs only from dry SF, but is not used in the dose calculations because 

gaseous contribution to dose is considered negligible. 

SF leaching is based on a congruent release mode. 

For diffusive mass transport, ingrowth of radioactive progeny in the rock is ignored. 

The assumption of spherical geometry for radionuclide diffusion into a porous medium may 

be conservative (Wescott, et al., 1995) because: 

- The boundary condition C1, = r0 implies that the radionuclide concentration at the 

outside wall of the WP is the same as that inside the container. 

- A steeper concentration gradient results from the absence of the generation of 

radioactive progeny in the chain, contributing to faster diffusive release.

NUREG-1668 3--64
The boundary condition C1, = - = Co results in a steeper concentration gradient than 

expected because the contribution of other WPs is conservatively ignored. 

3.6 UZFT MODULE DESCRIPrION 

The UZFT module determines the release rate of radionuclides into the SZ below the repository 

footprint by simulating the transport of radionuclides in the UZ between the repository and the water table, 

taking into account fracture versus matrix flow and retardation, because of adsorption and ion exchange. 

3.6.1 Information Flow Within TPA 

3.6.1.1 Information Supplied to UZFT 

EXEC passes to UZFT the time-varying release rates computed in EBSREL of all ground-water 

pathway nuclides and the time history of the volumetric flow rate from UZFLOW into the subarea. Other 

inputs to UZFT, such as hydrologic properties of stratigraphic units and retardation coefficients, are specified 

in the UZFT section of the primary input file. 

3.6.1.2 Information Provided by UZFT 

UZFT outputs the ground-water release rates of all ground-water pathway nuclides, as a function 

of time from the UZ, along with the ground-water travel time (GWTT) for the UZ. Although GWTT values 

are used only for purposes of reporting intermediate calculational results, the release rate histories are 

provided as input to SZFT. 

3.6.2 Conceptual Model 

The UZFT module simulates the temporal and spatial variation of deep percolation and radionuclide 

transport from the repository horizon to the water table. UZFT uses the stand-alone code NEFTRAN II 

(Olague, et al., 1991) to track contaminant transport through the UZ below the repository. 

UZ flow and RT are complicated by geologic heterogeneities at a variety of spatial scales, in the 

hydrologic and geochemical properties of the fractures and matrix, and in temporal variation in deep 

percolation. Although these complications affect accurate predictions at the small scale (on the order of 

meters), the emphasis is on estimating performance of the overall behavior of the repository. Therefore, an 

approach that considers flow and transport at a larger scale (on the order of tens to hundreds of meters), 

which neglects many of the small-scale spatial and temporal heterogeneities in favor of average properties 

at a larger scale, is assumed to be appropriate. The UZFT module captures a sufficient level of detail in the 

properties of the UZ such that performance calculations will provide insight on how variations in hydrologic 

and geochemical properties of the UZ affect overall performance.

3-65 NUREG-1668
3.6.2-1 Unsaturated Zone Flow Model

UZ flow is the primary mechanism for transporting dissolved radionuclides from the repository to 

the water table below the repository. Fracture versus matrix flow, ground-water velocity, and moisture 

content are the characteristics of the UZ flow used in UZFT. Spatial and temporal variabilities of these 

characteristics are considered, using a simplified approach: (i) spatial variability is accounted for by using 

seven repository subareas where each subarea has a distinct stratigraphy and deep percolation, and each 

stratigraphic unit has its own hydrologic and geochemical properties (Table 3-3 provides stratigraphic unit 

thicknesses for each of the seven repository subareas); and (ii) temporal variation is accounted for by using 

the time-varying deep percolation determined in U`ZFLOW. This simplified approach assumes there is no 

lateral diversion between the repository and the water table (i.e., flow is 1-D and vertical); the UZ flow field 

is in equilibrium (i.e., time variations in the flow field occur rapidly in the UZ); and thermal perturbations 

on UZ flow do not affect the deep percolation below the repository (the waste containers are considered to 

be mostly intact at early times, when this assumption is most in question). 

Table 3-3. Stratigraphic Thicknesses (m) for Each of the Seven Repository Subareas 

Stratigraphic 

Unit Subarea 1 Subarea 2 Subarea 3 Subarea 4 Subarea 5 Subarea 6 Subarea 7 

Topopah 33 116 20 110 20 53 121 

Spring-welded 

Calico 0 0 0 0 113 125 0 

Hills-vitric 

Calico 163 154 122 132 0 0 114 

Hills-zeolitic 

Prow 34 39 40 34 38 26 43 

Pass-welded 

Upper Crater 67 20 158 57 158 136 63 

Flat 

Bullfrog-welded 0 0 0 0 32 0 0 

Total Thickness 297 329 340 333 361 340 341 

Determination of fracture versus matrix flow is considered the most important aspect of the UZ flow 

model because: (i) water velocity in fractures tends to be large relative to velocity in the matrix, because of 

differences in porosity (generally one to two orders of magnitude); and (ii) retardation of radionuclides 

within fractures is generally considered to be much smaller than retardation in the matrix because of the 

much larger surface area available for sorption in the matrix compared with the fracture. For each 

stratigraphic unit in the UZ, a determination is made in the UZFT module whether fracture or matrix flow 

properties will be used, by comparing the deep percolation with the saturated conductivity of the matrix. 

Fracture flow properties are used for transporting radionuclides when the deep percolation exceeds the

NUREG-1668 3-66
saturated conductivity of the matrix. This conceptualization assumes that the flow conditions (fracture versus 

matrix flow) remain the same within a particular stratigraphic unit and any transition between matrix and 

fracture flow occurs only at the interface between stratigraphic units. Thus each stratigraphic sequence has 

its own associated velocity and saturation characteristic of either the fracture or the matrix continuum. 

Calculation of the water velocity and moisture content uses the van Genuchten (1980) characteristic 

curve to represent the relationship between saturation and conductivity: 

Kr = VrS [1 -(1 S 1A/))]2 (3-68) 

where: 

K, = relative hydraulic conductivity [unitless] 

S = saturation [unitless] 

= fitting parameter [unitless] 

The velocity and saturation calculation assumes that deep percolation is occurring in either the matrix or the 

fracture, but not both. This approach represents a simplification of the methodology used in the NRC [PA 

Phase 2 effort (Wescott, et al., 1995), which accounted for the partitioning of fracture and matrix flow. The 

simplification was done to improve efficiency in the UZ flow and transport module to accommodate other 

improvements considered to be more important (e.g., time-varying deep percolation and simulation periods 

beyond 10,000 yrs). Additionally, the partitioning of fracture and matrix flow is not considered as important 

to performance, given the higher estimates for deep percolation (stratigraphic units will be either primarily 

fracture flow or matrix flow). 

As mentioned previously, time-varying deep percolation is determined in UZFLOW. A time-varying 

velocity is calculated for each stratigraphic unit from the time-varying deep percolation, using the formula: 

v) n (3-69) 

AnS 

where: 

v(t) = time-varying velocity for the stratigraphic unit [m/yr] 

Q(t) = time-varying deep percolation rate [m3

/yr] 

n = porosity of the stratigraphic unit corresponding to either the matrix porosity or the 

fracture porosity [unitless] 

S saturation of the stratigraphic unit corresponding to either the matrix saturation or 

the fracture saturation [unitless] 

A = cross-sectional area of subarea [m2]

3-67 NUREG--1668
To aid in the computational efficiency of the NEFTRAN II code, the velocities are artificially limited to an 

upper bound corresponding to a 5-yr residence time for a given stratigraphic unit. This restriction should not 

have a significant effect on the dose calculation. 

From the time-varying velocities, the time-varying residence times are determined for each 

stratigraphic unit, using the formula: 

x 

t, (t) =- (3-70) 

v(t) 

where: 

tt) = time-varying residence time [yr] 

x = thickness of the stratigraphic unit [m] 

v(t) time-varying velocity for the stratigraphic unit [m/yr] 

The mean residence time TT for each stratigraphic unit is determined as a weighted average of the 

time-varying residence times over the time period of interest. A simple trapezoidal integration of the 

residence time is used for the calculation. In addition, a sum of the TT from each stratigraphic unit is 

calculated and represents the total residence time TRr for the entire UZ. The mean residence time of a 

stratigraphic unit is used to select which stratigraphic units satisfy a set of criteria that maintains the 

computational efficiency of the NEFTRAN IH code. That is, stratigraphic units where TT is less than 10 yrs 

or that have a Tr less than T.,/1 0 are removed from consideration for the RT. These criteria should not have 

a significant effect on the dose calculation. 

The time-varying velocities for each contributing stratigraphic unit are output to file nefli.vel for 

input to NEFTRAN II. The UZ residence time TT for each subarea is written to the file gwttuzsz. res, as an 

intermediate result. 

3.6.2.2 Unsaturated Transport 

Transport of radionuclides in geologic media is often retarded relative to the water velocity because 

of reactions (e.g., adsorption and ion exchange) between the dissolved radionuclides and the geologic 

materials. Retardation factors (ratio of the velocity of a dissolved radionuclide to the water velocity) are 

determined for the matrix, using the following distribution coefficient, or Kd approach: 

Rf = 1.0 + p(1-n) Kd (3-71) 

where: 

Rf = retardation factor [unitless]

NUREG-1668 3-68
p = grain density of porous matrix [kg/rn3] 

n = porosity of matrix [unitless] 

e = moisture content of matrix [unitless] 

K = distribution coefficient (radionuclide, and stratigraphic-unit-specific) [m3/kg] 

Retardation within fractures in the UZ is considered constrained by the limited surface area available 

for ion exchange and adsorption. Therefore, the above Kd approach was not considered appropriate for 

fracture flow, and a retardation factor is used to characterize retardation in fractures in the primary input file. 

The use of a retardation factor for fracture transport was included in UZFT for completeness in the event that 

sufficient evidence becomes available that supports retardation of radionuclides within fractures. It is 

expected however, the retardation factor would typically be set for no retardation (value of 1.0). 

NEFTRAN II 

NEFTRAN II (Olague, et al., 1991) uses the following three files: (i) the standard NEFTRAN HI input 

file that specifies the radionuclides, transport path lengths, and transport path properties (nefii.inp); (ii) the 

time history of the transport velocity developed in UZFT, using hydrologic parameters specified in the 

primary input file and time history of deep percolation determined in UZFLOW (nefii.vel); and (iii) the time 

history of radionuclide releases from the repository provided by EBSREL (sonef.dat). NEFTRAN II uses 

a series of I-D transport paths or legs (each representing a particular stratigraphic unit) that are defined by 

their length, velocity, saturation, porosity, dispersion length, and retardation. (Note: matrix diffusion is a 

process contained in the NEFFRAN II program; however, this process was not included in the UZ transport 

because of the uncertainty in assigning parameter values that determine the magnitude of its effect.) As 

described in the previous sections, UZFr calculates velocity, saturation, and retardation factors. NEFTRAN 

H uses this information as well as input parameters (porosity and dispersion length) to simulate advection 

and hydrodynamic dispersion of dissolved radionuclides using a distributed velocity method (DVM). DVM 

is similar to a particular tracking approach where discrete particles are used to simulate contaminant 

movement; however, DVM calculates a distribution of velocities for each radionuclide and each leg, based 

on the mean velocity and the dispersion length, and uses the velocity distribution to transport groups or 

packets of particles. The formulation used in calculating the velocity distribution is 

S2dV Vm 

Vi RfF~d(3-72) =F-it 

where: 

V = J, interval of the velocity distribution (seven intervals are used to represent the 

distribution) [m/yr] 

C = j" abscissa from a standard normal distribution [unitless] 

d = dispersion length [m] 

Vm = mean velocity (velocity calculated in UZFT and used as input to NEFTRAN 11 is 

assumed to be the mean velocity) [m/yr]

3-69 NUREG-1668
R = retardation factor [unitless] 

t = time to exit the leg2 (length of leg I vm) [yr] 

Figure 3-14 illustrates the implementation of the 1-D legs or transport paths in NEFTRAN II to 

represent the UZs and SZs at YM. The UZFT module provides the radionuclide releases to the water table 

at discrete times. This information is then used by the SZFT module for the SZ transport calculation. 

3.6.2.3 Efficiency of Simulating Flow and Transport 

Efficiency in simulating flow and transport in the UZ was considered in development of the UZFT 

module, because of potentially long run times associated with long-term performance periods (much greater 

than 10,000 yrs) together with additional processes such as matrix diffusion. Long simulation times can occur 

when the transport velocity is such that the use of a small time step over the simulation period is required. 

A resolution to this problem was to bypass consideration of stratigraphic units that had short residence times 

(less than 10 yrs or less than 10 percent of the residence time of all legs). When the velocity in a particular 

leg results in a short residence time, the leg is not included in the NEFTRAN II simulation. If all the legs for 

a particular subarea are bypassed, the entire UZ transport calculation is bypassed and releases from the 

repository are provided directly to the SZ transport module, SZFT. 

Additionally, the time intervals over which the velocity varies, which are provided by UZFLOW, 

can affect simulation times (e.g., short intervals resulting in long run times). Therefore, the UZFT module 

varies the velocity over a time interval dictated by a minimum change in the velocity field (an input 

parameter specifies the UZ minimum velocity change factor), where a small fractional change will result in 

shorter time intervals and associated longer simulation time. 

3.6.3 Assumptions and Conservatism of the UZFT Approach 

The UZFT module is an abstraction of the flow of ground water and the transport of radionuclides 

from the repository to the SZ. The main attributes of the UZ that control RT are: (i) the transport velocity 

of radionuclides in ground water; (ii) radionuclide sorption; (iii) matrix diffusion; and (iv) the 

hydrologic stratigraphy. 

The transport velocity of radionuclides in ground water is influenced by the presence or absence of 

fracture flow. Because of differences in porosity between the fracture and matrix, transport velocities can 

be much larger (up to 2 to 3 orders of magnitude) in the fractures than in the matrix, for the same flux. The 

transport velocity within a specific hydrostratigraphic unit (e.g., Calico Hills zeolitic) is determined by 

assuming vertical flow below the repository and comparing the vertical flow with the saturated conductivity 

of the matrix (if the vertical flow exceeds the saturated conductivity any time during the simulation, fracture 

transport velocities are used). This approach is considered a reasonable representation for the bulk behavior 

t

The use of the exit time is a departure from the original NEFTRAN Ii formula that used the value for the time step. When used 

in conjunction with time-varying velocity field, the time step approach led to an unrealistic velocity distribution-the mean velocity 

was small, the time step was small, and a velocity distribution was dependent on the time step of the simulation rather than the 

physical properties of the leg. The revised formula, using the time to exit the leg, corrected these deficiencies and is considered a more 

appropriate approach for determining the velocity distribution.

NUREG-1 668 3-70
Unsaturated Zone 

(above repository)

Unsaturated Zone. . . . .. ! (below repository) .... ....... " ." -i . . . i . . . ..... 

.. .,° . . . . . . . . . . . . . . 

°o -. . . . . . . . . . . . . . . . 

. .-° . . . . . . . . . . . . . . . .

Saturated Zone ... . ..... .

Repository

4-' Hydrogeologic ... Units

. . .I.. "C'. "' -

"'ýz 

y .11ý - x

............. 

................. 

......... .. .. .. ...... 

. . . . . . . ................. ... . . . . . . . . . . . . . .. . . . . . . . . . . . . . 

. . . . . . . . . . . ... . . . . . . . . . ... *1 . . . . . . . . . . .. . . . . . 

. . . . . . . . . . 

Discharge Point

Figure 3-14. UZFT contaminant transport legs.

"-J

0I

.. . . ... . ... ... ... ... ... ..... .... :................. ** ..... • ee=ee=''' ... .... .. ........ .......................................

-- - -- -- - -- -- -- - - ,Q-W NkR0---"-P po ? ?? P.?

L••• ••• •

Land 

Surface•

'11-ý
of a number of the specific hydrologic units at YM because saturated conductivity of the matrix is either 

significantly above or below the net flux (e.g., Calico Hills zeolitic, Calico Hills vitric, and Topopah Springs 

welded). Although this approach does not account for spatial variability of flow caused by heterogeneities 

in the hydrologic properties of the fractures and matrix, or the episodic nature of the infiltration, the approach 

generally yields short travel times to the SZ, using the current hydraulic properties and infiltration estimates. 

Short travel times are considered consistent with the presence of chlorine-36 at multiple locations in the 

Exploratory Studies Facility at YM. The degree of conservatism in these travel times cannot be determined 

until site investigations, including the chlorine-36 data, are complete. 

Transport of radionuclides can be significantly slowed by the sorption of radionuclides on mineral 

and rock surfaces. Retardation of radionuclides is considered limited within fractures relative to the matrix, 

because retardation of radionuclides is assumed largely affected by the surface area available for sorption, 

whereas flow through the matrix encounters significantly more surface area than fracture flow. Although the 

TPA Version 3.1.4 code has the capability to represent retardation of radionuclides within fractures, fracture 

retardation factors have been set to 1.0 (i.e., no retardation) in the data set. This is a conservative approach 

considered to have a limited effect on the results given the assumption that sorption of radionuclides within 

fractures will be limited is true. 

Transport of radionuclides within fractures can be delayed because of diffusion of radionuclides 

within the fractures into the matrix. The rate of diffusion of radionuclides is affected by variations in fracture 

and matrix flow, the concentration gradient between the fracture and matrix water, and the nature of the 

fracture surface (e.g., permeability of fracture coatings).Within the TPA Version 3.1.4 code, it is 

conservatively assumed that matrix diffusion does not occur in the UZ. Overall, this assumption in UZFT 

is consistent with little, if any, delay of radionuclides moving large distances in the unsaturated zone over 

short time scales, as evidenced by the presence of chlorine-36 at multiple locations in the Exploratory Studies 

Facility at YM. Moreover, observed differences in water chemistry between matrix and fracture water would 

not be present if matrix diffusion were significant. The degree of conservatism in this assumption depends 

on the ongoing site investigations. 

It is obvious from the prior discussions that transport in the UZ will be sensitive to the occurrence 

of fracture flow. Fracture flow will dominate in a unit with low matrix conductivity (e.g., Calico Hills 

zeolitic) whereas matrix flow will dominate in a unit with high matrix conductivity (e.g., Calico Hills vitric). 

Thus, the presence or absence of particular units below the repository and the assumption that the flow is 

primarily vertical have a strong influence in determining the occurrence of fracture flow. The abstraction for 

stratigraphy uses seven subareas to account for the spatial variability in the thickness of the 

hydrostratigraphic units (primarily based on the spatial variability of the Calico Hills vitric and zeolitic 

units). This approach was used to provide a general representation of the UZ and does not represent thin 

stratigraphic units [i.e., on the order of a few meters (10 ft)]. Although the inclusion of some of these thin 

units could increase travel times, through lateral diversion or matrix-only flow, they are not included in the 

current approach. 

There are several specific assumptions related to the UZ flow below the repository: 

Flow is downward toward the water table with no lateral diversion.

NUREG-1668 3-72
The UZ flow field is time-varying, but in equilibrium (temporal variation in the near surface 

infiltration, because of climate variation, equilibrates rapidly in the UZ). 

Deep percolation between the repository and the water table is not affected by thermal 

reflux. During the highest thermal period (e.g., first 1,000 yr), when this assumption is most 

uncertain, it is assumed that few containers are failed. 

Fracture flow occurs only when the percolation exceeds the saturated conductivity of the 

matrix; when this occurs, it is assumed the interconnected fractures are capable of 

conducting the remaining flow. 

Transition between matrix and fracture flow occurs only at the interface between 

stratigraphic units (the amount of fracture and matrix flow is uniform within a 

stratigraphic unit). 

There are also specific assumptions related to the UZ transport: 

RT is assumed to occur in either the fractures or matrix and is not partitioned when flow 

occurs in both fractures and matrix (fracture flow is used when the deep percolation exceeds 

the saturated matrix conductivity of the matrix); not accounting for the partitioning of flow 

between the fracture and the matrix will be conservative when the fracture flow is a small 

percentage of the matrix flow. 

Retardation within fractures does not significantly delay RT in the UZ. 

Matrix diffusion does not significantly delay RT in the UZ. 

3.7 SZFT MODULE DESCRIPTION 

The SZFT module describes radionuclide transport in the SZ, from the location at which 

radionuclides enter the water table immediately below the repository, to a receptor location. The SZ transport 

model consists of an array of 1-D streamtubes originating at the water table below the repository and 

terminating at a receptor location. RT in the SZFT module is simulated using the NEFTRAN H code (Plague, 

et al., 1991), which calculates the radionuclide release rate (Ci/yr) at the down-gradient receptor location. 

In the SZFT module, there are a number of SZ streamtubes each connecting to one or more UZ streamtubes. 

Figure 3-15 illustrates a streamtube assuming 1-D flow in the SZ. 

3.7.1 Information Flow Within TPA 

3.7.1.1 Information Supplied to SZFT 

SZFT receives, from EXEC, the radionuclide release rate as a function of time from the UZ, as 

computed by the UZFT module. Other inputs to SZFT are specified in the SZFT section of the primary input 

file or in the strmtube.dat file. Data provided by the primary, input file include hydrologic properties,

3-73 NUREG-1 668
403•0W 544OW 54aO 5mO 

%CAF~_Z

Figure 3-15. Saturated zone streamtubes model showing four streamtubes extending from just 

upgradient of the repository footprint southward to the major ground-water withdrawal area of 

Amargosa farms. The inset shows in detail the connection between the subareas and the streamtubes. 

NUREG-1668 3-74
retardation factors, and matrix diffusion parameters. The distance to the receptor group determines flow unit 

hydrologic properties, streamtube flow rates, widths, and lengths for the tuff and alluvium specified in the 

strmnube.dat file. 

3.7.1.2 Information Provided by SZFT 

SZFT computes estimates of the total activity of the radionuclides transported to the receptor location 

as a function of time in Ci/yr. SZFT results for the time-varying release rates from the SZ are passed to 

EXEC. GWTT for the SZ is reported an an intermediate result for subsequent use in dose calculations. 

3.7.2 Conceptual Model 

The SZFT module describes the temporal and spatial variation of radionuclide mass transport from 

the water table at a location immediately below the repository to the receptor location. SZFT uses the stand

alone code NEFTRAN II (Olague, et al., 1991) to track radionuclide mass transport. 

Transport of radionuclides in the SZ is complicated by: (i) spatial variability in the geochemical 

properties of the fracture surfaces and rock matrix; (ii) heterogeneity of pore-scale to formation-scale 

transport pathways; (iii) temporal variations in the flow field caused by climatic change and pumping for 

water use; and (iv) variability in the rate at which radionuclides transiting the UZ reach the water table. The 

extent to which these features and processes are incorporated into predictive models affect the accuracy of 

point estimates of radionuclide concentrations in the aquifer. The approach adopted in DCAGW (Section 3.8) 

for determining the radionuclide concentration of water that is consumed at the receptor location allows the 

SZFT model to neglect many of the high-resolution spatial and temporal variations in transport processes. 

Radionuclide mass (activity) release rates at the receptor location are converted into radionuclide 

concentrations by multiplying the mass release rate by the fraction of mass captured by a well or well field 

and then dividing this product by the volume of water that is pumped. In DCAGW, simplified well-capture 

zone relationships are used to determine the fraction of mass captured by low-discharge wells or well fields. 

At high-discharge well fields used to supply irrigation water, it is assumed that all radionuclides are captured. 

Therefore, SZFT does not need to accurately describe the 3-D distribution of radionuclide concentrations 

at the receptor location. The SZFT module needs only to predict the mean trajectory and travel times of 

radionuclides in the SZ and take into account the variation in geochemical properties along the transport path. 

Although temporal changes in the flow field caused by climatic change and variations in pumping rates may 

affect transport, in SZFT it is assumed that a steady-state representation of the flow field is sufficient. 

3.7.2.1 Saturated Zone Flow Model 

The steady-state velocity field used in the SZFT module for the SZ region from below the repository 

to the receptor location is abstracted from a detailed 2-D model (Baca, et al., 1996). The abstraction is made 

by constructing four streamtubes that emanate from the vertical projection of the repository lateral boundary 

onto the water table and terminate at the appropriate receptor location. Differences in the mean travel time 

along each of the four streamtubes arise from differences in streaxntube length and the sampled values of 

effective porosity for the tuff and alluvium units.

3-75 NUREG-1668
The boundaries of the detailed 2-D model from which the four streamtubes were abstracted, shown 

in Figure 3-15, are based in part on information from the 2-D subregional flow model developed by 

Czarnecki and Waddell (1984). The eastern and western boundaries of the flow domain shown in Figure 3-15 

are coincident with a pair of streamlines from this subregional model that extend from just upgradient of the 

proposed YM repository southward to the major ground water withdrawal area of Amargosa Farms 

[Czarnecki and Waddell (1984), Plate 1]. The northwestern and southern boundaries, which were selected 

to be roughly coincident with head contours of 800 and 675 m (2600 and 2200 ft), were treated as Dirichlet 

boundaries in the detailed model. Because the western and eastern edges of the flow domain are coincident 

with streamlines, they were treated as no-flow boundaries in the detailed model. 

To match measured head data, the domain of the detailed flow model was divided into seven zones, 

in which the hydraulic conductivity was assumed to be uniform. The boundaries of these seven zones were 

determined from available hydrostratigraphic cross-sections and from inspection of head contours. Initial 

estimates of hydraulic conductivity were obtained by manually calibrating the detailed flow model. These 

estimates were refined, using an automatic calibration routine, to fit heads predicted from the detailed model 

to measured head data (Baca, et al., 1996). Because the detailed model uses two Dirichlet and two no-flow 

boundary conditions, the values of hydraulic conductivity in the seven zones are not uniquely identifiable 

in the absence of prior estimates of either areal flux or hydraulic conductivity (Carrera and Neuman, 1986). 

Inasmuch as areal recharge within this region is minimal, fixing one hydraulic conductivity value is the best 

option. Zone 7, located at the southern end of the detailed model, was assigned a fixed hydraulic conductivity 

value of 1.7 x i0-- m/s (Czarnecki, 1985). 

3.7.2.2 Saturated Zone Transport Model 

The four streamtubes abstracted from the 2-D detailed flow model describe the trajectories of the 

dissolved radionuclides. Mean Darcy velocities along the centerline trajectory of each streamtube are also 

extracted from the detailed flow model. As shown in Figure 3-15, the widths of the streamtubes vary from. 

the repository to the receptor location. Because of the variation in streamtube width, mean centerline Darcy 

velocities change from 0.25 to 1.3 m/yr (0.8 to 4.3 ft/yr). Mean transport velocities along the centerline 

trajectories are determined by dividing the Darcy velocity by the sampled effective or kinematic porosities. 

As specified in the primary input file, the effective porosity varies from 0.001 to 0.01 for the portion of the 

streamtube that transits fractured tuff, and from 0.10 to 0.15 for the portion transiting the alluvium. Based 

on these ranges for effective porosity, linear transport velocities in the fractured tuff and alluvium range from 

40 to 1300 and from 1.7 to 4 m/yr (130 to 4300 and from 5.6 to 13 ft/yr). 

Other sampled transport parameters whose probability distribution or sampling range is defined in 

the primary input file include: (i) longitudinal dispersivity (aD); (ii) retardation coefficients for each 

radionuclide; and (iii) parameters used to represent matrix diffusion in the fractured tuff unit. Inasmuch as 

the longitudinal macrodispersivity is generally assumed to increase with the scale of the contaminant plume 

until an asymptotic upper bound is attained (Gelhar, 1993), it is incorrect to assign a separate dispersivity 

value to each NEFTRAN II transport leg. Consequently, a single value is sampled that is dependent on the 

distance to the critical group location. The retardation coefficients set in the primary input file depend on the 

dominant mineralogy of the medium in the transport leg as well as the particular radionuclide 

being transported. 

NEFTRAN II has the capability to account for the migration of dissolved contaminants from flowing 

pores and fractures (dynamic) into the more-or-less stagnant water within the rock matrix pores. The

NUREG-1668 3-76
governing equation used in NEFTRAN H to account for the rate of change of concentration in the immobile 

phase is (Olague, et al., 1991): 

OsR aCs(x't) = I4Cd(x,t) - C,(xt)] (3-73) 

at 

where: 

0 = immobile porosity or stagnant volume fraction occupied [unitless] 

R = retardation factor [unitless] 

C = stagnant concentration [mass per unit volume] 

x - distance in direction of flow [length] 

t = time [yr] 

= mass transfer rate coefficient [ l/yr] 

Cd = dynamic concentration [mass per unit volume] 

This process is commonly referred to as matrix diffusion. Parameters defined in the primary input file for 

matrix diffusion are immobile porosity and the mass transfer rate coefficient (l/yr). 

3.7.3 Assumptions and Conservatism of the SZFT Approach 

The abstracted model implemented in the SZFT module is assumed to adequately capture the range 

of processes and features that control and affect the transport of dissolved radionuclides from the SZ below 

the repository to the receptor location. Processes simulated by the SZFT module include: (i) advective 

transport through the tuff and alluvial aquifers; (ii) longitudinal dispersion during transport; (iii) chemical 

sorptive processes that retard the RT in the alluvial aquifer and in the matrix of the tuff aquifer; and 

(iv) diffusion of radionuclides from the fractures to the matrix in the tuff aquifer. 

Because the estimated magnitude of water well pumping for irrigation at the receptor location is 

sufficient to capture most radionuclides emanating from the repository (Fedors and Wittmeyer, 1998), a 

relatively simple 1-D model can be used to convey radionuclides directly from the repository to the receptor 

location. The 1-D flow tube model used in SZFT is based, in part, on the 2-D ground-water flow model 

developed by Czarnecki and Waddell (1984) and, in part, on more recently obtained hydraulic conductivity 

and head measurements. By definition, the volumetric discharge per unit saturated thickness is constant along 

the flow tube; however, the Darcy velocity varies inversely with the width of the flow tube. Darcy velocities 

in the four flow tubes range from approximately 0.25 to 1.3 m/yr (0.8 to 4.3 ftlyr). Hydraulic conductivities 

obtained from pump tests performed in numerous wells in the tuff aquifer range from 1 to 10 m/day 

(3.3 to 33 ft/day). Using this range for hydraulic conductivity and a measured mean hydraulic gradient in the 

tuff aquifer of 0.00125, ambient Darcy velocities range from 0.46 to 4.6 m/yr (1.5 to 15 ft/yr), which agree 

reasonably well with those used in the SZFT abstraction. Transport velocities along the flow tubes are equal 

to the ratio of the Darcy velocity to the effective porosity, assumed to vary from 0.001 to 0.01 in the tuff 

aquifer, and from 0.10 to 0.15 in the alluvial aquifer.

3-77 NUREG-1668
The effects of lateral dispersion on vertical spreading of the plume are not explicitly accounted for 

in constructing the 2-D flow tubes. Since vertical dispersion would likely reduce in situ radionuclide 

concentrations or reduce the fraction of mass captured by the pumping well, neglecting its effects is 

conservative. The effects of longitudinal dispersion are included in the model; however, the value of 

longitudinal dispersivity used in the SZFT abstraction is conservatively assumed to be between 0.01 to 0.1 

of the total transport distance. 

Transport of radionuclides can be significantly retarded by sorption of radionuclides on mineral 

surfaces. Retardation of radionuclides is considered much more limited in fractures than in the matrix 

because sorption is assumed to be primarily controlled by the mineral surface area, which is much greater 

in the rock matrix. Although the TPA Version 3.1.4 code has the capability to represent retardation of 

radionuclides within fractures, fracture retardation factors have been set to 1.0 (i.e., no retardation) in the 

data set. This approach for fracture retardation, although conservative, will not have a significant impact on 

transport if indeed fracture retardation effects are small. Retardation caused by sorption of radionuclides on 

the abundant clays and iron minerals in the alluvium is assumed to be significant and is included in the SZFT 

model abstraction. 

Transport of radionuclides in the tuff aquifer can be delayed by diffusion of radionuclides within the 

fractures into the matrix. The rate of diffusion of radionuclides is affected by variations in fracture and matrix 

flow, the concentration gradient between the fracture and matrix water, and the permeability of mineral 

coatings on the fracture surface. Within the TPA Version 3.1.4 code, it is conservatively assumed that matrix 

diffusion generally does not occur in the saturated tuff aquifer. Matrix diffusion can be accounted for in the 

tuff aquifer; however, it is conservatively limited to a small fraction of the total matrix porosity. The degree 

of conservatism in this assumption cannot be assessed until ongoing site investigations are complete. 

There are several key assumptions regarding the SZ flow field: 

The ground-water model of Czarnecki and Waddell (1984) adequately describes the 

vertically averaged 2-D velocity field in the YM to Amargosa Farms area. 

Flow in the fractured tuff aquifer(s) can be adequately modeled using a vertically averaged 

2-D flow model. 

Recharge is so small that it has little effect on the saturated flow regime; vertical velocity 

components are negligible. 

Water table exits the tuff aquifer and enters the alluvium approximately 15 km (9.3 mi) 

south of the repository. 

Climate change during the period of repository performance has no effect on the regional 

ground-water flow. 

There are also key assumptions regarding transport in the SZ:

NUREG-1668 3-78
* The velocity field can be adequately modeled by dividing the region into four flow tubes 

whose lateral boundaries are defined by streamlines emanating from either edge of 

the repository. 

* Within the tuff aquifer, advective transport takes place primarily through 

interconnected fractures. 

* Radionuclides entering a flow tube are uniformly mixed across the width of the flow tube. 

0 The longitudinal dispersivity is related to the length of the transport path. 

There is conservatism adopted in SZFT: 

Use of relatively small longitudinal dispersivities reduces mixing during transport. 

Assumption of steady-state flow precludes dispersion of the radionuclide plume caused by 

changes in the magnitude and direction of the mean velocity field through pumping and 

climatic change. 

3.8 DCAGW MODULE DESCRIPTION-FARMING RECEPTOR GROUP 

The DCAGW module calculates annual doses (TEDE) from exposure to radionuclide concentrations 

in ground water to an average member of a farming receptor group. 

3.8.1 Information Flow Within TPA 

3.8.1.1 Information Supplied to DCAGW 

Time-varying release rates computed in SZFT for each radionuclide released via ground water are 

passed to DCAGW in the EXEC, from which radionuclide ground-water concentrations are calculated using 

the dilution volume. Time evolution of average annual precipitation and temperature from UZFLOW are 

passed by the EXEC to DCAGW, to determine the time when the current condition will switch to pluvial 

conditions. Ground-water pathway DCFs used in determining doses from radionuclide concentrations for 

a farming receptor located 20 km (12 mi) from YM, coincident with the location of the ground-water 

releases, are contained in two data files: gwcb_.ad.dat and gw_pb_&ad.dat. Each file pertains to different 

climatic conditions (either current biosphere, designated by cb, or pluvial biosphere, designated by pb in file 

names). Parameters that specify the pumping rates of the receptor group, location of the receptor group, 

location of the conductive zone being pumped, and plume thickness are specified in the primary input file. 

Other input parameters are read from the DCAGW section of the primary input file. 

3.8.1.2 Information Provided by DCAGW 

The ground-water pathway dose for each radionuclide, as a function of time, is passed to EXEC.

3-79 NUREG-1668
3.8.2 Conceptual Model

The DCAGW module calculates dilution volume at the pumping well, using the water-use 

characteristics of the farming receptor group, a community at distances at least 20 km (12 mi) from YM and 

chosen based on location from the repository and assumptions made on the depth to the water table. Once 

the dilution volume is determined, the module converts the activity per unit time values calculated by SZFT 

to activity per unit volume of water, by dividing by the pumping volume. DCFs are then used to determine 

dose to the average member of the receptor group. The streamtube information used in SZFT is tied to the 

location of the receptor group. Therefore, any change to the location of the receptor group must be consistent 

with the streamtube data files. 

For each time step, the product of each radionuclide concentration and DCF are summed within and 

among ground-water pathways and radionuclides, to calculate total doses. In addition to total doses, selected 

output is stratified by realization, time step, and radionuclide. The EXEC uses results from all realizations 

to identify and report peak doses. 

3.8.2.1 Development of Radionuclide Concentrations in Water for a Farming Receptor Group 

For the farming receptor group, the volume of water into which the released radionuclides are diluted 

is the greater of the flow rate of water within the uppermost producing horizon in the pumped aquifer and 

the volumetric flow rate of water pumped for household and agricultural needs of the receptor group. The 

flow rate of water within the uppermost horizon is set to the greater of the UZ flow rate and SZ flow rate. 

This condition sets a reasonable lower limit for the dilution volume that is consistent with the assumption 

that the combined well discharge rates at the farming location are large enough to capture all released 

radionuclides. Borehole concentrations at the farming location are computed by dividing the radionuclide 

release rate by the appropriate dilution flow rate. 

For the farming receptor group location, there are no readily available data that can be used to assess 

the variation in hydraulic conductivity or borehole inflow with depth. The nearest wells completed in 

alluvium from which there is detailed lithologic information are located in the northwestern Amargosa Valley 

near the Beatty low-level waste facility. The soil texture varies from clay to gravel in this area; however, 

drillers' logs indicate that the predominant soil texture classes are sands and gravels. Data compiled from 

drillers' logs by Oatfield and Czarnecki (1991) show that the fraction of coarse-grained sediments in the 

Amargosa Farms area ranges from 0.50-0.70. Because sands and gravels are the predominant alluvial facies, 

it is reasonable to assume wells are screened over their entire depth. In fact, statistics on water well screen 

depths and lengths suggest that the typical well in the Amargosa Farms region is continuously screened over 

the lower 75 percent of the saturated section penetrated by the borehole. The mean and standard deviation 

of the thickness of the uppermost producing horizon, which are defined as extending from the water table 

to the bottom of the well screen, are 55 and 32 m (180 and 105 ft). The minimum and maximum values of 

the producing horizon thickness are 8.5 and 158 m (28 and 520 ft), respectively. 

The distribution of the producing horizon thickness for the farming receptor group is positively 

skewed and may be suitably represented by a log-normal or gamma distribution; however, it is assumed to 

be uniform. For producing horizon thicknesses of 20, 80, and 140 m (66, 260, and 460 ft), the flow rates in 

the uppermost producing horizon in the alluvial aquifer are 40,940; 163,760; and 286,580 m3

/yr (1.4, 5.8, 

and 10 million ft3

/yr), respectively. Expected flow rates for farming are much greater than those for

NUREG-1668 3-80
residential use. Assuming that the primary water use is for center-pivot irrigation of quarter-section alfalfa 

fields, a reasonable range of consumptive water use is 6,222,230-17,973,330 m3

/yr (220-630 million ft?/yr) 

[13 and 27 quarter-section alfalfa fields; 126 irrigated acres (0.5 kIn 2

) per quarter-section; and 0.9-1.3 m 

(3.1-4.38 ft) of water applied each growing season]. 3 4 Clearly, the volumetric flow rate of water pumped 

exceeds the flow rate of water within the uppermost producing horizon. 

3.8.2.2 Development of Dose Conversion Factors 

The DCAGW module contains DCFs designed to convert ground-water concentrations to TEDEs 

for the average member of the receptor group. A farming receptor group is used because site-specific 

information indicates farms exist down-gradient of YM. Two climate regimes are assumed for the biosphere

one based on the present climatic conditions in the Amargosa Valley area and a second based on a future 

pluvial climate (i.e., a cooler and wetter climate). DCAGW uses DCFs for present climate conditions until 

the MAP and MAT (used as indicators for climate change) exceed a threshold value based on the Koppen

Geiger system of climate classification, at which point pluvial DCFs are to be used for the dose conversion. 

In the K6ppen-Geiger system of climate classification, the annual precipitation boundary between 

humid and arid/semiarid precipitation/climate regimes is determined for places where winter is the wet 

season (such as YM) by: 

Rhumid/subhumid boundary = 0.44(T-32) (3-74) 

where R is the average annual boundary precipitation in inches and Tis the MATin 'F. For YM, which has 

a MAT of about 16 'C or 61 °F, the boundary between a humid and subhumid climate would be 32 cm 

(12.8 in.). 

The boundary in the K6ppen-Geiger climate classification between arid and semiarid climates is 

one-half the precipitation value obtained for the humid/subhumid boundary: 

Rsemiard/arid boundary -- 0.22(T-32) (3-75) 

Thus, the precipitation boundary for an arid climate classification at YM would be 16 cm (6.4 in.). In 

DCAGW, R, is calculated using MAT from UZFLOW. If MAP from UZFLOW is greater than 

R..wd, pluvial DCFs are used. 

Pluvial DCFs reflect estimated changes to the biosphere as described in LaPlante and Poor (1997). 

Estimated pluvial conditions did not change receptor groups or exposure pathways considered in DCF 

calculations (i.e., similar types of fanning activities are expected under both estimated climate states). 

10.9 rn/yr (3.1 ft/yr) near Los Angeles, California (Table 14-2, page 377, Linsley and Franzini, 1979). 

1.3 rn/yr (4.38 ft/yr) in Mesa, Arizona (Table 2-50, page 99, van der Leeden, et al., 1990).

3-81 NUREG-1 668
The exposure pathways considered in the farming scenario are depicted in Figure 3-16 and include: 

ingestion (of contaminated water, crops, and animal products); inhalation (from resuspension of 

contaminated soil); and direct exposure. The following assumptions apply to the farming receptor group. The 

farmer grows alfalfa (for beef and milk cow feed); vegetables, fruits, and grains for personal consumption; 

and feed for egg-laying hens. Drinking and irrigation water is pumped from a ground water well at the farm. 

Water is consumed at a rate of 2L/day (0.5 galJday) pumped from a ground water well. Additional details 

of methods and assumptions used in the exposure scenarios for DCF calculations are provided in the 

succeeding sections and in LaPlante and Poor (1997). 

3.8.3 Assumptions and Conservatism of DCAGW Approach 

DCAGW is an abstraction of complex and uncertain processes occurring in the biosphere. The 

conversion of ground-water concentrations to receptor dose involves assumptions about: (i) the location of 

the receptor group; (ii) the lifestyle characteristics, of the receptor group, that form the basis for exposure 

pathways; (iii) processes that determine fate and transport of contaminants in the biosphere; (iv) calculation 

of human doses from factors that convert exposure to contaminated media to effective dose equivalents; and 

(v) well-pumping rates. 

Receptor location is based on the assumption that present physical constraints (topography, depth 

to water table, and soil conditions) that limit present farming to the Amargosa Valley area will continue to 

limit farming south of YM to within approximately 20 km (12 mi) from the proposed repository site. Because 

the receptor location and lifestyle assumption relates to potential human behavior, it is speculative. The use 

of present knowledge to define the receptor group location helps to limit speculation and is consistent with 

recommendations of the National Academy of Sciences (1995). 

The conversion of ground-water concentrations to receptor dose requires conceptualization of 

potential exposure pathways based on the site-specific characteristics of the release pathway, YM biosphere, 

and receptor group. When ground water is the source of contamination, the potential exposure pathways are 

assumed to be those resulting from pumping (including dilution) and agricultural use of water consistent with 

present farming conditions south of YM. These pathways include ingestion (of contaminated water, crops, 

and animal products); inhalation (from resuspension of soil contaminated by irrigation); and direct exposure 

to contaminated soil. 

The assumed diet of locally produced food is important to estimating the dose for a farming receptor 

group. The farmer is assumed to grow alfalfa (for beef and milk cow feed); vegetables, fruits, and grains for 

personal consumption; and feed for egg-laying hens. The practices the farmer is presumed to engage in have 

been confirmed to exist in areas south of YM; however, it appears unlikely that all activities identified would 

be practiced by a single individual. Therefore, the doses calculated from such assumptions are expected to 

be greater than what might occur under current conditions. Because bartering is known to exist among 

community members (Eisenberg, 1996)', it is reasonable to project that (for the most highly exposed group) 

a significant portion of diet could be obtained from local sources. Thus, the assumption is considered 

conservative, but not excessively so. These assumptions may change when additional information on local 

consumption patterns is available. 

5

Eisenberg, N.A. 1996. Staff Visit to Amargosa Valley: Trip Report, Memorandum (May 14) to M. Federline, Nuclear Regulatory 

Commission, Washington DC: Nuclear Regulatory Commission.

NUREG-1668 3-82
Y U 00 0 

N 

C 

E 

N 

T 

R 

A 

T Sol Concentration 

0 

N 

External 

Radiatlon 

00°' Figure 3-16. Farmer exposure pathways.
Modeling the fate and transport of radionuclides in the biosphere involves consideration of 

radionuclide deposition (from ground water) to soil, using local irrigation rates; determination of crop 

concentrations, using soil/plant uptake factors; determination of air concentrations from resuspended 

contamination, using a mass load model; and accounting for feed intake and transfer of contamination 

to livestock. 

Radionuclide deposition from irrigation of crops is calculated from the irrigation rate, irrigation 

duration, and radionuclide concentration in ground water. The irrigation rate and duration are relevant to 

local conditions in the Amargosa Farms area and Nye County. Loss of contaminants to deeper soil layers 

from leaching is accounted for by a Kdbased model that includes factors which contribute to infiltration, such 

as rainfall and evapotranspiration. Rainfall and evapotranspiration rates are obtained from local or regional 

information sources and KAs are relevant to the general soil texture classification reported for Amargosa 

farming soils. Full documentation of parameters is provided in LaPlante and Poor (1997). 

Crop concentrations are based on transfer factors that represent the ratio of plant and soil 

concentrations for a class of crops grown in contaminated soil. Food transfer factors are generic values based 

primarily on published information (International Atomic Energy Agency, 1994; Baes, et al., 1982).The 

selected factors are expected values and have not been arbitrarily increased for conservatism. 

Air concentrations from resuspended contamination are calculated by applying a mass loading model. 

This model uses a mass loading factor that is the ratio of airborne and surface soil radionuclide 

concentrations. Mass loading factors are highly uncertain and influenced by a number of processes including 

wind speed, surface roughness, resuspended particle size and density, and the moisture content of soil. 

Because the GENII-S code does not provide a mass loading model for crop deposition, a resuspension model 

was used with an adjusted mass loading factor. 

Determination of radionuclide concentrations in livestock products is done by quantifying the intake 

of contaminated feed and applying a transfer coefficient. The animal transfer coefficients are based on 

studies that measure radionuclide concentrations in livestock, based on measured intake. The coefficients 

are element- and livestock-specific and based on published information (International Atomic Energy 

Agency, 1994; Baes, et al., 1982). 

Calculation of human dose is accomplished through the use of dose factors that convert the estimated 

concentrations in water, food, air, and soil, together with the corresponding intakes and residence times, to 

TEDEs. The dosimetry models that form the basis for the internal dose factors (U.S. Environmental 

Protection Agency, 1988) are consistent with those described in the International Commission on 

Radiological Protection (ICRP) Publication 30. The dosimetry models represent state-of-the-art techniques 

and methodologies in existence at the time the publication was released. Refinements to dosimetry models 

have since been published by the ICRP, but have not yet been adopted by the U.S. Government and are 

therefore not used in DCAGW. 

For the farming receptor group, sampled cumulative well discharge rates are based on assumed 

ranges of irrigated acreage (13-27 quarter-sections) and consumptive water use [0.9-1.3 m (3.1-4.38 ft) of 

water per year for alfalfa]. It is assumed that the radionuclide plume emanating from the repository would 

be completely captured by any configuration of wells whose total discharge equals the sample discharge rate.

NUREG-1668 3-84
Borehole concentrations at the farming receptor locations are calculated by dividing the radionuclide mass 

release rate by the cumulative well discharge rate. 

More specific assumptions for DCAGW are provided in the following list and in LaPlante and 

Poor (1997). 

The dose calculation from consumption of crops does not account for washing fruits and 

vegetables before consumption and is, therefore, conservative. 

Food transfer factors are generic values based primarily on a large survey of the literature 

(International Atomic Energy Agency, 1994). Because of the lack of site-specific 

information, it is reasonable to use generic values; however, they may not be representative 

of arid conditions that exist in southern Nevada. The selected factors are expected values 

and have not been arbitrarily increased for conservatism. 

Consumption rates for food and water are based on national average values owing to lack 

of site-specific information. These values may not be representative of local consumption 

practices. Use of average values is not expected to be overly conservative. DOE has 

conducted a survey of local consumption practices and, when results are available, DCFs 

may be recalculated if the new consumption rates are found to differ significantly from the 

national averages used for current DCFs. 

For human dosimetry modeling, the average member of the receptor group is an adult, 

consistent with the EPA-proposed "Federal Radiation Protection Guidance for Exposure of 

the General Public" (U.S. Environmental Protection Agency, 1994). The individual is 

presumed to have physiology consistent with the assumptions of the reference man model 

(International Commission on Radiological Protection, 1975). 

Irrigation rates are assumed to be at the maximum permitted amount allowed by local water 

authorities in water permits (Nevada Division of Water Resources, 1995). When compared 

with State averages for alfalfa irrigation (U.S. Department of Commerce, 1994), the 

maximum permitted values are about 2 times greater. Thus, it is likely the irrigation rates 

used for DCF calculations are higher than amounts used locally, leading to some 

conservatism. These may not be unrealistically high, however, because southern Nevada is 

hotter and drier than the rest of the State, and water use is expected to be higher than the 

State-wide average. 

Inhalation and external exposure times are based on generic information provided in 

Kennedy and Strenge (1992). For the farming group, 55 percent of total time is assumed 

inside the residence, 20 percent outside of residence, 24 percent at offsite work, and 

1 percent gardening and farming alfalfa, which is not a labor-intensive crop to farm. These 

activity times are considered to be conservative for a common residential setting (Kennedy 

and Strenge, 1992), but may not be for a more active farming resident.

3-85 NUREG-1668
Resuspension of contaminated soil applies to crop deposition, inhalation, and external dose 

modeling. A mass loading model is applicable to a homogeneously mixed layer of 

contamination in the soil; therefore, a reasonable mass loading factor from the literature is 

used (Kennedy and Strenge, 1992). For crop deposition, the GENII-S code includes a 

resuspension model; therefore, the selected mass loading factor is converted to a 

resuspension factor, using an equation in the GENII-S user manual (Napier, et al., 1988). 

The mass loading and resuspension models are abstractions of complex and varying 

processes. Thus, considerable uncertainty exists in applying them to YM conditions. 

For pluvial climate conditions, the biosphere is assumed to be similar to that of northern 

Nevada and southeastern Idaho. These areas have average annual rainfall and monthly 

temperature profiles similar to predictions of future YM climate made from analysis of local 

paleoclimatic studies 'summarized by NRC (1997). 

Use of a concentration of radionuclides uniformly mixed in water for household and 

agricultural needs of the critical group is assumed appropriate for estimating doses to the 

average member of the critical group. Although some variation in concentrations would be 

expected between specific locations and well characteristics, assuming all crops within the 

farming community are irrigated with water containing radionuclides, a concentration 

uniformly mixed in the volume pumped by the fanning community is considered a 

reasonable approach for estimating average exposure conditions. 

3.9 DCAGW MODULE DESCRIPTION-RESIDENTIAL RECEPTOR GROUP 

The DCAGW module calculates annual doses (TEDEs) from exposure to radionuclide concentrations 

in ground water to an average member of a residential receptor group. A residential receptor group located 

between 5 and 20 km (3.1 and 12 mi) has been included in the TPA Version 3.1.4 code, to assist timely 

completion of the sensitivity analyses by allowing shorter simulation periods appropriate to the shorter 

distances. Having the ability to evaluate a residential receptor group also provides the staff with the 

flexibility to address alternative assumptions for the receptor group should that be necessary. 

3.9.1 Information Flow Within TPA 

3.9.1.1 Information Supplied to DCAGW 

Time-varying release rates computed in SZFT for each radionuclide released via ground water are 

passed to DCAGW in the EXEC from which radionuclide ground-water concentrations are calculated using 

the dilution volume and the mass fraction of the ground-water contaminant plume captured by the residential 

receptor group. Time evolution of average annual precipitation and temperature from UZFLOW are passed 

by the EXEC to DCAGW, to determine the time when the current condition will switch to pluvial conditions. 

Ground-water pathway DCFs used in determining doses from radionuclide concentrations for a resident 

receptor group, located between 5 and 20 km (3.1 and 12 mi) from YM, coincident with the location of the 

ground-water releases, are contained in two data files: gw.cbci.dat and gw_.pb._ci.dat. Each file pertains 

to different climatic conditions (either current biosphere, designated by cb or pluvial biosphere, designated 

by pb, in file names). A detailed calculation is used to determine the portion of the plume that enters the

NUREG-1668 3-86
receptor group water supply. Parameters that specify the pumping rates of the residential receptor group, 

location of the residential receptor group, location of the conductive zone being pumped, and plume 

thickness, are specified in the primary input file. Other input parameters are read from the DCAGW section 

of the primary input file. 

3.9.1.2 Information Provided by DCAGW 

The ground-water pathway dose for each radionuclide, as a function of time, is passed to EXEC. 

3.9.2 Conceptual Model 

The DCAGW module calculates dilution volume at the pumping well using the water use 

characteristics of the residential receptor group. The receptor group is represented by a residential community 

downgradient at a distance between 5 and 20 km (3.1 and 12 mi) from YM. Once the pumping volume is 

determined, the module converts the activity per unit time values calculated by SZFT to activity per unit 

volume of water, using a site-specific mass fraction of radionuclides captured by the receptor group. DCFs 

are then used to determine dose to the average member of the residential receptor group. The streamtube 

information used in SZFT is tied to the location of the receptor group. Therefore, any change to the location 

of the receptor group must be consistent with the streamtube data files. 

For each time step, the products of each radionuclide concentration and DCF are summed within and 

among ground-water pathways and radionuclides, to calculate total doses. In addition to total doses, selected 

output is stratified by realization, time step, and radionuclide. The EXEC uses results from all realizations 

to identify and report peak doses. 

3.9.2.1 Development of Radionucide Concentrations in Water for a Residential Receptor Group 

At the residential receptor group location [between 5 and 20 km (3.1 and 12 mi) from YM], a 

calculation is performed to determine the fraction of the radionuclide plume captured in the well withdrawal. 

Using sampled values of well discharge, aquifer thickness, and plume thickness, steady-state well hydraulics 

equations are solved to determine the fraction of the radionuclide plume captured. Borehole concentrations 

at the residential location are computed by multiplying the radionuclide release rate by the capture fraction 

and dividing the product by the volumetric flow rate for the well. 

It is assumed the tuff aquifer from below the repository to the residential receptor location is 

homogenous and isotropic with a hydraulic conductivity of 1 m (3.3 ft)/day and a regional gradient of 

0.00125. The radionuclide plume in the SZ below the repository is assumed to have a width equal to the 

width of the four streamntubes described in Section 3.7. It is also assumed that all wells have an effective 

radius of 0.254 m (0.8 ft). 

After sampled values are obtained for the volumetric flow rate at the well and the thickness of the 

aquifer, an estimate of the appropriate well screen length is interpolated from a table of values determined 

using Thiem's equations for steady state, confined radial flow (Lohman, 1972), and Muskat's adjustment for 

partially penetrating wells (McWhorter and Sunada, 1977). Using the analytic element model GFLOW 

(Haitjema, 1995), tables of capture zone thickness and capture zone width as functions of pumping rate and

3-87 NUREG-1668
aquifer thickness were constructed. After interpolated values of capture zone thickness and width are 

obtained from the look-up tables for the sampled pumping rate and aquifer thickness, the fraction of mass 

captured is determined using one of three procedures listed. 

If the screen length is greater than the sampled plume thickness or if the capture zone 

thickness is greater than 90 percent of the aquifer thickness, the fraction of mass captured 

is equal to the ratio of the capture zone width to the plume width. 

If the screen length is less than the plume thickness, but the capture zone thickness is greater 

than the plume thickness, the fraction of mass captured is equal to the ratio of the capture 

area within the plume to the cross-sectional area of the plume. The capture area within the 

plume is composed of two parts: (i) that portion of the plume lying above the bottom of the 

well screen; and (ii) that portion of the half-elliptical section of the capture zone that lies 

below the bottom of the well screen and above the lower vertical extent of the plume. 

If the screen length and capture zone thickness are less than the plume thickness, the fraction 

of the mass captured is equal to the ratio of the cross-sectional area of the capture zone to 

the cross-sectional area of the plume. The capture area within the plume is composed of two 

parts: (i) that portion of the plume lying above the bottom of the well screen; and (ii) the 

area of the half-elliptical section of the capture zone that lies below the bottom of the 

well screen. 

The radionuclide concentration at the wellhead is computed by multiplying the radionuclide activity 

release rate by the fraction of mass captured and dividing this product by the volumetric pumping rate. 

It is assumed that all water at the residential receptor group location is supplied by a single, partially 

penetrating well in an aquifer of specified thickness. Pumping rates are sampled from a uniform distribution 

with a range of 57 to 1000 m3/day [1.5 x l0 to 2.64 x 10' gallday (gpd)]. The minimum value for the 

distribution is based on a community with a population of 100 persons with a per capita daily water use of 

0.6 mn3 (150 gal.), whereas the maximum value is based on a community of 880 persons with a daily per 

capita water use of 1.1 in3 (300 gal). 6

'

7 The range of 0.6-1.1 ma/day (150-300 gpd) encompasses local 

estimates of per capita water use: (i) 0.8 m3

/day (210 gpd) for Nye County (Basse, 1990); (ii) 0.7 m3

/day 

(182 gpd), from 1995 metering for Beatty, Nevada (Buqo, 1996); and (iii) 1.2 m3/day (319 gpd) by assuming 

1200 m3/yr (1 ac-ftlyr) and 2.8 people per household for Amargosa Valley, Nevada (Buqo, 1996). 

Data from flowmeter surveys conducted at boreholes penetrating the saturated tuffs at YM indicate 

that flow is confined to a few, relatively thin, interconnected fracture zones. In conformance with the 

assumptions underlying the vertically averaged, 2-D flow model used to define the flow tubes in SZFT, it 

is assumed these highly conductive fracture zones can be represented by an equivalent aquifer. At YM, the 

saturated tuffs are divided into two fractured tuff aquifers separated by an aquitard composed of bedded 

ash-fall tuffs. The upper volcanic aquifer is composed of the Topopah Spring member of the Paintbrush 

6 Per capita daily water use for Tucson, Arizona, is 0.6 m3 (150 gal.). (Table 5-16, page 319, van der Leeden, et al., 1990). 

" Per capita daily water use for Las Vegas, Nevada, is 1.1 m3 (300 gal.). (Table 5-16, page 319, van der Leeden, et al., 1990).

NUREG-1668 3-88
Group and is over the upper volcanic confining unit composed of the Calico Hills Formation, which is in turn 

over the lower volcanic aquifer composed of the Prow Pass, Bullfrog, and Tram members of the Crater Flat 

Group (Luckey, et al., 1996). These hydrogeologic units are separated from the underlying Paleozoic 

carbonate aquifer by the lower volcanic confining unit composed of the Lithic Ridge tuff and older tuffs 

(Luckey, et al., 1996). 

As noted in Luckey, et al. (1996), the upper volcanic aquifer is present east of YM at water supply 

wells J-12 and J-13, where 70 percent of the aquifer is saturated; south of YM near boreholes USW WT-1 1 

and UE-25 WT#12, where the lower 8-15 percent of the aquifer is saturated, and in Crater Flat, at VH-I, 

where the aquifer is fully saturated. In the region immediately below the proposed repository, only the lower 

volcanic aquifer is saturated. Therefore, radionuclides released from the repository will most likely be 

transported within the lower volcanic aquifer until they encounter major north-trending faults across which 

there is significant offset of hydrostratigraphic units. Although site-scale transport modeling studies 

(Baca, et al., 1996; Cohen, et al., 1997) indicate that faults may have a significant effect on the vertical and 

horizontal spreading of the radionuclide plume, these effects were not incorporated into the SZFT or 

DCAGW modules. It is assumed, however, that the upper and lower volcanic aquifers function as a single 

composite aquifer. It is also presumed that vertical mixing in this composite tuff aquifer is greater than would 

be expected in a relatively homogeneous sand and gravel aquifer owing to heterogeneities at a variety of 

scales. 

Data from Luckey, et al. (1996) indicate that the composite saturated thickness of the upper and 

lower volcanic aquifers ranges from 337 to 719 m (1100 to 2400 ft.). It is therefore assumed that the aquifer 

thickness is uniformly distributed with a minimum of 300 m (980 ft) and a maximum of 700 m (2300 ft). 

Compilations of dispersivity values estimated from laboratory and field data suggest that longitudinal 

dispersivity is approximately equal to one-tenth of the distance traveled by the contaminant plume 

(de Marsily, 1986). Data compiled by Gelhar, et al. (1992) suggest that horizontal transverse dispersivities 

are an order of magnitude smaller than longitudinal dispersivities and vertical transverse dispersivities are 

one to two orders of magnitude smaller than horizontal transverse dispersivities. The length of the mean 

particle trajectory from the repository to a 5-km (3.1-mi) boundary is approximately 7 km (4.3 mi). Using 

the empirical method suggested previously, the estimated vertical transverse dispersivity ranges from 7 to 

70 m (23 to 230 ft). In the final report of the "Saturated Zone Flow and Transport Expert Elicitation Project" 

(Geomatrix Consultants, Inc., 1998), one member of the expert panel (Lynn Gelhar) estimated a mean 

(geometric) longitudinal dispersivity of 50 m (160 ft), but estimated that the mean (geometric) horizontal 

transverse dispersivity is 0.5 m (1.6 ft) and the vertical transverse dispersivity is 0.005 m (0.2 in.). Gelhar's 

estimates of transverse dispersivity were primarily based on data from the Cape Cod and Borden test site 

experiments, which were both conducted in relatively homogeneous sand and gravel aquifers (Geomatrix 

Consultants, Inc., 1998), and may be too small to apply to the complex fractured and faulted tuff units at YM. 

Assuming a mean Darcy velocity of 0.46 m/yr (1.5 ft/yr), kinematic porosities ranging from 0.001 

to 0.01, a plume trajectory 7 km (4.3 mi) in length, and vertical transverse dispersivities ranging from 7 to 

70 m (23 to 230 ft), the one standard deviation vertical spreading thickness (a) of a point source Gaussian 

plume at a 5-km (3.1-mi) receptor location varies from 104 m (340 ft) to 330 m (1100 ft).' Using the same 

calculational procedure, but substituting the vertical transverse dispersivity estimate of 0.005 m (0.2 in.) 

'3a =(2Czv7)V2, where az is the vertical dispersivity, v is the linear ground-water velocity, and T is the mean travel time.

3-89 NUREG-1668
provided by Gelhar, the one standard deviation vertical spreading thickness is 8.4 m (28 ft). Because there 

are no site-specific data for vertical transverse dispersivities, it is extremely difficult to select an appropriate 

distribution for the thickness of the plume at the receptor location. Although it may appear to be conservative 

to select a distribution of plume thicknesses that maximizes the likelihood that a low-discharge well captures 

the entire plume, even the smallest pump discharge rate considered 57 m3 (1.5 x 10 gal.)/day creates a 

capture zone more than 150 m (490 ft) thick. Based partly on the bounding calculations described previously 

and on a need to account for variability in this important parameter, plume thickness was assumed uniformly 

distributed between 10 and 100 m (33 and 330 ft). Although this analysis at 5 km (3.1 mi) was used in 

determining the primary input file parameters, the same methodology applies to all distances of the 

residential group. 

3.9.2.2 Development of Dose Conversion Factors 

The DCAGW module contains DCFs designed to convert ground-water concentrations to TEDEs 

for the average member of the receptor group. Site-specific information indicates residential dwellings exist 

down-gradient of YM. Two climate regimes are assumed for the biosphere in the TPA Version 3.1.4 code; 

however, for the residential exposure group the exposure pathway (drinking water) is not expected to be 

affected by the predicted biosphere changes. Thus, no biosphere variations are included in the exposure 

pathway calculations for this receptor group. The following assumptions apply to the residential receptor 

group. The residential exposure pathways are limited to ground-water consumption. Water pumped from a 

water well is consumed at a rate of 2 L (0.5 gal.)/day. Additional details of methods and assumptions used 

in the exposure scenarios for DCF calculations are provided in the succeeding sections and in LaPlante and 

Poor (1997). 

3.9.3 Assumptions and Conservatism of DCAGW Approach 

The DCAGW module for the residential receptor group converts ground-water radionuclide 

concentrations to residential receptor dose. Therefore, the module involves assumptions about: (i) the 

location of the receptor group; (ii) the lifestyle characteristics of the receptor group; (iii) the conversion of 

intake to dose; and (iv) well pumping rates. 

The residential receptor is based on an assumption that local topography and the economics of 

ground-water extraction allow for a potential nonfarming residential receptor group to exist closer to the 

potential repository site than a farming receptor group. Because this assumption relates to potential human 

behavior, it is speculative. The use of present knowledge to define the receptor group location helps to limit 

speculation and is consistent with the recommendations of the National Academy of Sciences (1995). 

Dose calculation involves consideration of only the drinking water ingestion dose pathway because 

this pathway is the primary route of exposure for residential uses. Other household uses are not included in 

the dose calculation because it is assumed the ingestion dose will far exceed that caused by bathing and other 

potential uses. 

The drinking water dose is calculated from the well head concentration, drinking water consumption 

rate, and the ingestion DCF. The consumption rate is assumed conservatively to be 2 L (0.5 gal.)/day, and 

the DCF is from Federal Guidance 11 (U.S. Environmental Protection Agency, 1988). For human dosimetry

NUREG-1668 3-90
modeling, the average member of the receptor group is an adult, consistent with the EPA proposed "Federal 

Radiation Protection Guidance foi Exposure of the General Public" (Ut.S. Environmental Protection Agency, 

1994). The individual is presumed to have physiology consistent with the assumptions of the reference man 

model (International Commission on Radiological Protection, 1975). 

The range of pumping rates for the residential community is based on a population assumed to vary 

from 100 to 880 persons having a per capita daily water use that varies from 0.6 to 1. 1 n3 (150 to 300 gal.). 

Corresponding daily well discharge rates for the residential receptor location vary from 57 to 1000 m3 

(15,000 to 260,000 gal.)/day. Analytical methods are used to calculate the fraction of the radionuclide plume 

of a given thickness that would be captured by a single well having the sampled daily discharge rate. 

Borehole radionuclide concentrations are calculated by dividing the product of the radionuclide mass release 

rate and the fraction captured by the well discharge rate. Use of a concentration of radionuclides uniformly mixed in water for household drinking water use by the critical group is assumed to be appropriate for 

estimating doses to the average member of the critical group. Although some variation in concentrations 

would be expected between specific locations and well characteristics, a concentration uniformly mixed in 

the volume pumped by the residential community is considered a reasonable approach for estimating average 

exposure conditions. 

3.10 FAULTO MODULE DESCRIPTION 

The FAULTO module determines the number of WP failures from direct fault disruption of the 

proposed repository block. The number, location, and time of WP failures are made available to EBSREL. 

Additional information about FAULTO and the stand-alone FAULTING code can be found in Ghosh, et al. 

(1997, 1998). As described in Chapter 2, a faulting event is assumed to not disturb the thermohydrological 

characteristics of the proposed repository. 

3.10.1 Information Flow Within TPA 

3.10.1.1 Information Supplied to FAULTO 

The user selects the faulting disruptive event module in the primary input file. Other FAULTO input 

parameters, including time of the event and fault length and width, are specified in the FAULTO section of 

the primary input file. 

3.10.1.2 Information Provided by FAULTO 

FAULTO computes a time history of WP failure from faulting events for all subareas for subsequent 

use in radionuclide release calculations in EBSREL.

3-91 NUREG-1668
3.10.2 Conceptual Model

FAULTO evaluates the potential for direct WP rupture from fault displacement along planar 

decoupled faults. In the code, faults are considered as zones or bands of deformation with a finite width. WPs 

within these zones are assumed damaged, provided the fault slip exceeds a threshold displacement value. It 

is assumed that WP emplacement in the proposed repository will be appropriately set back from those faults 

known to present a potential hazard and that a minimum amount of fault displacement is needed to disrupt 

a WP. Thus, FAULTO is designed to evaluate hazards related to faults not accounted for in the proposed 

repository design such as: (i) new faults that may form during the period of concern; (ii) hidden faults within 

the proposed repository that are presently unknown and unmapped; or (iii) underestimated faults that are 

mapped and not considered significant during design or construction, but that pose significant risk over the 

lifetime of the proposed repository. 

The code initiates a new fault zone within the repository based on geometric and recurrence 

parameters sampled from a series of probability distribution functions (PDFs). Geometric parameters include 

fault-zone location, orientation, strike, length, width, and displacement. Recurrence parameters include 

recurrence rate, time of faulting event, and cumulative displacement rate. Although in nature these geometric 

and recurrence properties may be related (i.e., longer faults seem to correlate with wider deformation zones 

or bigger faults tend to be more active), for simplicity they are specified as independent parameters in the 

TPA Version 3.1.4 code. 

In FAULTO, parameters describing future faulting events in the proposed repository are sampled 

independently and the effective recurrence rate is sampled from a single exponential distribution. Calculating 

the effective recurrence interval within the repository boundary is not straightforward because, unlike point

source events, fault zones are tabular volumes with planar boundaries that have orientation, length, and 

width. Moreover, fault orientation, length, and width have their own PDFs. Recurrence rate, therefore, does 

not scale directly with change in simulation area. There is a critical simulation region (CSR) below which 

the recurrence interval does not increase in proportion to the reduction in the size of the simulation area. If 

the simulation region is smaller than the critical area, a fault could still initiate outside the area and have the 

necessary length and orientation to intersect the proposed repository. To overcome this problem, an empirical 

solution uses an area of interest larger than the repository footprint. This approach is described in the 

following paragraphs. 

The CSR for faulting was developed to include the range of fault orientations and lengths (e.g., fault 

map in Simonds, et al., 1995). Approximately 90 percent of the faults have lengths of 30 km (19 mi) or 

shorter, or half lengths of 15 km (9.7 mi) or shorter. In addition, fault orientations range between N55' W 

(azimuth 3050) and N25' E (azimuth 250). Given these constraints, the size of the CSR is 15.2 km x 32.8 km 

(9.4 x 21 mi). The ratio of the CSR area to the area used to estimate faulting recurrence from paleoseismic 

studies is 2.2 (498.6 divided by 225.0). Thus, the scaled recurrence of the CSR is 5909 yrs (13,000 divided 

by 2.2) yielding a conditional probability for faulting of 1.69 x 10-4/yr. 

Recurrence value was then scaled to only the repository footprint using the FAULTING stand-alone 

code (not a part of the TPA Version 3.1.4 code) to empirically estimate what percentage of faults generated 

in the CSR would actually intersect the proposed repository, given a 5909-yr recurrence interval for the CSR. 

Modeling results show that an average of 3 percent of all simulated faults intersect the repository, based on

NUREG-1668 3-92
a number of different realizations between 500 and 1,000,000. Thus, the recurrence for faults within the 

repository itself is 197,000 yrs (5909 divided by 0.03), or an annual probability (referred to as absolute 

probability) of 5.0 x 10-6. 

Some parameters normally used to fully describe faulting events were not used in FAULTO, 

including fault-dip angle, number of slip surfaces, partition of fault slip among different slip surfaces, 

cumulative displacement rate, and cumulative displacement rate partitioned among slip surfaces. These 

parameters were excluded for the following reasons: 

Fault-dip angle is not important because the WPs are assumed to be emplaced within a 

single almost horizontal geological stratum. Fault dips at the surface at YM are rarely less 

than 45 *. Therefore, the problem essentially reduces to two dimensions. Variations in fault

dip angle over the vertical thickness of the emplacement drifts will not affect the 

consequence. 

Number of slip surfaces of a fault does not affect performance because the area affected is 

constant and the entire fault has the same maximum displacement. The fault slip is either 

divided among the slip surfaces or allowed on one surface. Because the WPs are assumed 

to be uniformly distributed, the area affected (fault width times the trace length of the fault 

within the footprint of the proposed repository)--not the exact location of the slip 

surface(s)-is important. 

Cumulative displacement rates for faults at YM (see Table 2-4 of Ghosh, et al., 1997) are 

too small to significantly affect the performance of the proposed repository, given a 

10,000-yr or even 20,000-yr period of concern. 

Using the effective recurrence interval for the repository, FAULTO samples midpoint location, 

orientation, length, width, and displacement slip of a fault in the repository. The displacement slip is sampled 

from the user-supplied distribution for maximum slip and is the same for the entire fault. With the orientation 

and width known, FAULTO calculates the intersection area of the fault with the repository. If the sampled 

displacement slip exceeds the threshold displacement specified by the user, all WPs in the intersection area 

are reported to EBSREL as having failed. Figure 3-17 illustrates a simulated fault within the proposed 

repository boundary. Time for WP failure is the instant the fault is generated. 

3.10.3 Assumptions and Conservatism of the FAULTO Approach 

The FAULTO module is an abstracted model of the failure of WPs from instantaneous fault 

displacement along steeply dipping, new, or inadequately characterized faults. The FAULTO module 

considers: (i) distribution of WPs within the emplacement drifts; (ii) fault recurrence parameters; (iii) fault 

geometric parameters; and (iv) fault-slip and threshold displacements. 

WPs are assumed to be uniformly distributed throughout the repository as a first-order 

approximation. Repository design could influence the extent to which this assumption is acceptable, but 

using a uniform density of emplaced WPs is acceptable to estimate the significance of fault displacement 

without speculating on repository design.

3-93 NUREG-1668
SkmhWa~l Fauft Z~me 

Figure 3-17. Simulated faults within the repository boundary. 

The time for the next faulting event is calculated from the effective recurrence rate. The effective 

recurrence rate has been estimated by taking into consideration the length and orientation of the mapped 

faults in the YM region. The critical simulation region concept, described in Section 3.10.2, was used to 

estimate the annual probability of faulting in the proposed repository region at 5.0 x 10'. Fifty percent of 

faulting occurs on new faults or faults where WPs are not set back in the repository design. This assumption 

is conservative because most geological observations infer that large faulting events in YM will occur on 

known and mapped faults. For example, paleoseismic studies show that nearly all the large faults at YM 

produce evidence for repeated earthquake ruptures (e.g., Chapter 4, U.S. Geological Survey, 1996). 

Wemicke, et al. (1998) have suggested an increase in seismicity by an order of magnitude (recurrence 

rate - 104 yr). However, geologic evidence does not support this proposed increase in seismicity.9 

The fault geometric parameters include fault midpoint location, orientation, length, and width. These 

parameters are generated from PDFs developed from field-measured data (Scott and Bonk, 1984; 

U.S. Geological Survey, 1996). Most of the faults mapped in the YMR predominantly strike in the north

northeast direction (about 95 percent) with dip angle rarely less than 45°. As a result, variations in fault-dip 

angle over the vertical thickness of the emplacement drifts will not significantly affect the performance. 

Therefore, fault-dip angle was not considered in the FAULTO module. Similarly, other parameters, such as 

number of slip surfaces of a fault and cumulative displacement, were ignored. It is considered a reasonable 

9Connor, C.B., J.A. Stamatakos, D.A. Fenmill, and B.E. Hill. 1998. Technical Comment: Anomalous Strain Accumulation in the 

Yucca Mountain Area, Nevada. Science. In Press.

NUREG-1668 3-94
approach to drop these parameters from the module as their effects on the consequence were found 

insignificant. Mapped faults in the YMR tend to have widths clustered around small values, although a fault 

may have a large width at least on the surface. A beta distribution with small median value is considered 

reasonable to approximate the fault width distribution. 

The fault-slip displacement is sampled from the log-normal distribution of maximum slip and is 

considered the same for the entire fault plane. A log-normal distribution is considered a reasonable 

assumption to describe the distribution of fault slips observed in the YMR. In the absence of both a model 

transferring forces from fault displacement to emplaced WPs and a well-developed WP design, assumptions 

are made about the amount of fault displacement that leads to WP failure. The threshold fault displacement 

directly affects the WP disruption. If the fault slip exceeds the threshold value, all WPs within the repository 

block along the fault zone (fault length within the repository boundary multiplied by the fault width) are 

considered failed. The threshold displacement is an input parameter supplied by the user that reflects lack 

of mechanistic understanding of the WP disruption process. Although the TPA Version 3.1.4 code does not 

couple the degradation of WP strength caused by corrosion and other phenomena with the threshold 

displacement, the approach is considered conservative because a small threshold of displacement leading to 

WP failure (specified in the primary input file) is believed to provide a conservative estimate of WP failure. 

Although WP disruption is assumed to occur only within the fault zone boundary, the assumption that all 

WPs within the boundary fail-if the fault displacement threshold is exceeded-is believed to provide a 

conservative estimate of WP failure in the absence of more sophisticated modeling. 

Several specific assumptions of faulting disruption effects in the FAULTO module of the TPA 

Version 3.1.4 code are: 

Fifty percent of faulting is assumed to occur on new faults or faults where WPs are not set 

back in proposed repository design. This assumption is conservative. 

In FAULTO there is assumed to be no link between faulting, seismicity, and volcanism. In 

nature, volcanic eruptions are always accompanied by numerous pre- and syn-eruption 

earthquakes (Luhr and Simkin, 1993; Fedotov and Markhinin, 1983). Likewise, all faulting 

events that would affect the proposed repository would be accompanied by significant 

seismicity. Therefore, the current assumption of uncorrelated faulting, seismic, and volcanic 

phenomena is not conservative. 

The emplacement drifts are assumed to be randomly oriented. Most YM faults are oriented 

roughly north-south. If the actual emplacement drifts are subparallel to the fault trend, much 

greater number of WPs will be affected by each faulting event than currently estimated in 

the code. The current plan ("Civilian Radioactive Waste Management System Management 

and Operating Contractor," 1997) suggests that the emplacement drifts will be oriented 

approximately in the N 600 W direction. Therefore, the assumption of random orientation 

for the emplacement drifts may be nonconservative. 

The TPA Version 3.1.4 code is limited to one faulting event per realization, regardless of 

the selected recurrence interval. This simplification is acceptable as long as the estimate of 

recurrence intervals longer than or equal to IlW yr is correct, and the time period of interest

3-95 NUREG-1668
is 10,000-20,000 yr. Otherwise, this limitation reduces the potential consequence of faulting 

and is, therefore, not conservative if the WP has not already failed by other mechanisms. 

There is no correlation among fault length, fault zone width, fault orientation, and probable 

displacement along the fault plane. In nature, longer faults have undergone larger 

displacements. Observations of fault zone widths in the YMR show that fault zone widths 

seem to be large for incipient faults and narrow as the fault zone matures. In the FAULTO 

module, fault zone widths are sampled from a broad range, including a few wider than 

100 m (330 ft). 

Faulting could weaken WPs and make them more susceptible to corrosion over time. To 

compensate for not accounting for this effect, a small threshold displacement for WP failure 

is specified in the primary input file. 

WP failure in FAULTO is not linked to a WP mechanical failure approach, as used in 

EBSFAIL. This simplification is conservative because the WP is always assumed to fail 

when the fault displacement exceeds a threshold value. 

All WPs intersected by the fault zone are considered failed. This conservative assumption 

is necessary at present because the forces WPs could encounter in fault zones are 

poorly understood. 

The module assumes the damage zone caused by slip along a fault is entirely restricted 

within the fault zone boundaries (i.e., no diffuse deformation outside the fault zone). This 

assumption is not conservative. 

0 The module does not take into account the effects of slip on a new or an uncharacterized 

fault caused by rupture on existing faults in the repository region. 

3.11 VOLCANO MODULE DESCRIPTION 

The VOLCANO module provides determination of key IA processes such as: (i) timing of future 

igneous events; (ii) subsurface area affected by a volcanic event; (iii) type of event (i.e., intrusive only or 

intrusive and extrusive); and (iv) number of WPs affected by intrusions extending laterally from the 

volcanic conduit. 

3.11.1 Information Flow Within TPA 

3.11.1.1 Information Supplied to VOLCANO 

The VOLCANO disruptive event module is selected by the user in the primary input file. Other 

VOLCANO input parameters, including time of the event, conduit diameter, and dike length, are specified 

in the VOLCANO section of the primary input file.

NUREG-1668 3-96
3.11.1.2 Information Provided by VOLCANO

VOLCANO computes WP failure time from a volcanic event for all subareas. VOLCANO also 

computes the mass of waste (in MTUs) ejected by the volcanic event for ground-surface radionuclide release 

calculations in ASHPLUMO, and the fraction of WPs disturbed by the volcanic dike for all subareas, for 

ground-water source term calculations in EBSREL. 

3.11.2 Conceptual Model 

Igneous activity is a disruptive scenario with a relatively low annual probability of occurrence. 

Event consequences can be broadly categorized as volcanic processes that disrupt WPs and directly transport HLW into the accessible environment, via the airborne pathway, and intrusive processes that enhance WP 

degradation, but do not directly transport HLW from the repository. 

A volcanic center is allowed to form randomly within a specified area. The volcanic center is 

commonly restricted to the center of the proposed repository block to ensure igneous disruption is evaluated. 

For the currently proposed 4.8-km2 (1.8 mi2) occupied repository block at YM, NRC (1998) concluded that 

an annual probability of 10'• provides a reasonably conservative value for volcanic disruption of this site. 

The annual probability of igneous intrusion at the proposed repository site currently is assumed to be 2 to 

5 times the probability of volcanic disruption (Nuclear Regulatory Commission, 1998). Timing of the igneous 

event is determined by sampling a finite exponential distribution between 100-yr and 10,000-yr postclosure, 

using a recurrence probability of 10-'. 

In the TPA Version 3.1.4 code, two types of events may be modeled to occur. These are either an 

extrusive vent, which intersects the repository and ejects SF into the air (WP is assumed to have no effect 

on release) and affects other WPs through lateral intrusion, or a basaltic intrusion that disrupts WPs but does 

not directly exhume SF to the accessible environment. An extrusive volcanic center is always associated with 

a lateral intrusion, but a subsurface intrusion does not necessarily coincide with the occurrence of an 

extrusive event. Information required to be input into the data set by the user includes distributions for event 

geometries (length and width of intrusions and diameters of volcanic conduits). When an event occurs, the 

location for the event is identified within the repository footprint. Using parameter values sampled from the 

user-supplied distributions, areas for the circular conduits and linear intrusion are calculated, with the conduit 

area being subtracted from the total intrusion area (length times width). 

Volcanic centers disrupt a finite volume of subsurface material during the emplacement and 

eruption of ascending basaltic magma. Work conducted at an analog volcano site in Kamchatka, Russia 

(Hill, 1996), demonstrated that the roughly circular conduits associated with YMR-type volcanoes may widen to a 50 ± 7 m (160 ± 20 ft)diameter during late-stage disruptive events. Several of the youngest YMR 

volcanoes have near-vent evidence of similar, late-stage disruptive events (Hill, 1996). Although additional 

work is being conducted to develop an independent technical basis for this parameter, the diameter provides 

a basis to calculate the number of WPs potentially disrupted by a volcanic conduit. The number of WPs 

disrupted, however, depends on the WP dimensions, WP spacing, and centerline spacing between 

emplacement drifts (Figure 3-18). Average repository waste loading values may underestimate the number 

of WPs disrupted by a volcanic conduit. For example, using an average waste loading of 83 MTU/acre and 

a 9.76-MTU WP loading, a 50 m (160 ft) diameter conduit will only disrupt 4.1 WPs. Inspection of Figure 

3-18 reveals more than four WPs are disrupted by most 50 m (160 ft) diameter volcanic conduit locations.

3-97 NUREG-1 668
Figure 3-18. Schematic of subsurface disruption area associated with a volcanic conduit (Hill, 1996) 

using waste package dimension [5.68 m (18.6 ft) long, 1.8 m (5.9 ft) wide], waste package spacing [19 m 

(62.3 ft) center-to-center], and emplacement drift spacing [22 m (72.2 ft)] (U.S. Department of Energy, 

1996). 

Geometric relationships suggest that disruption of I to 10 WrPs presents a reasonable range for volcanic 

conduits that may vary between I to 50 m (3-160 ft) in diameter. 

Subsurface igneous intrusions may extend for many kilometers from the central volcanic conduit. 

These intrusions may penetrate farther into the repository than the area disrupted by the conduit. The effects 

of these intrusions are poorly constrained (e.g., Barr, et al., 1993) but likely will degrade WP performance 

significantly because of adverse thermal, chemical, and mechanical loading. The VOLCANO module 

currently calculates the number of WPs directly intersected by igneous intrusions extending from the central 

conduit, but does not account for additional WP failures owing to thermo-chemical effects. Area affected also 

does not currently account for the likelihood of lava-flow formation in open drifts. The number of WPs 

intersected by an intrusion is calculated by sampling uniform distributions of intrusion orientation, length, 

and width. This area is converted to a number of WPs using an average HLW loading for the repository.

NUREG-1668 3-98
The annual probability of volcanic disruption of the proposed repository site is bounded by 10', 

which represents a reasonably conservative value based on independent analyses (Nuclear Regulatory 

Commission, 1998). Probabilities at least one order of magnitude higher and lower than this value can be 

derived from the reviewed literature (Nuclear Regulatory Commission, 1998). This version of the TPA 

Version 3.1.4 code assumes that the probability of igneous intrusion is equal to the probability of volcanic 

disruption (i.e., a single recurrence probability is specified in the primary input file). The probability of 

igneous intrusion, however, is likely larger by a factor of 2-5 than the probability of volcanic disruption 

alone (Nuclear Regulatory Commission, 1998). 

3.11.3 Assumptions and Conservatism of the VOLCANO Approach 

The VOLCANO module defines the source term for HLW transport by a basaltic volcanic 

eruption. The VOLCANO module determines: (i) timing of the volcanic event; (ii) amount of HLW available 

for transport by the eruption column; and (iii) area affected by igneous intrusions. 

Timing of the volcanic event follows a Poisson distribution with a 10- annual probability of 

volcano formation for the proposed repository site. Although igneous intrusions are assumed to occur with 

about five times the frequency of a volcano [with a 5 x 107' annual probability (Nuclear Regulatory 

Commission, 1998)], current TPA methodology assumes it to occur with the same frequency as the volcanic 

event, which could be nonconservative. 

The volcanic conduit ranges in size sufficient to disrupt 1 to 10 WPs, based on the shallow 

subsurface areas commonly disrupted by basaltic volcanic eruptions. All waste in these disrupted packages 

is available for transport, as staff have assumed that a WP will fail during a basaltic volcanic event. 

Additional WPs are disrupted by a planar intrusion extending from the conduit. These additional 

WPs, however, are not available for extrusive volcanic transport, as it is unlikely that material can be 

transported laterally in a shallow intrusion for more than tens of meters and the SF in these WPs is available 

for ground-water releases. 

There are several specific assumptions and conservatism in the VOLCANO module: 

"* Volcanic conduit is assumed to form in the center of the repository block and the stated 

probabilities are for volcano formation at the repository site. 

"* Timing of the volcanic event follows a Poisson distribution, with a l0' annual probability 

of volcano formation for the proposed repository site, which is a conservative value 

(Nuclear Regulatory Commission, 1998). 

"* Intrusions away from the volcanic conduit are assumed linear and have dimensions 

characteristic of YMR intrusions (Nuclear Regulatory Commission, 1998). The number of 

WPs intersected by an intrusion may not be conservative because this model does not 

account for magma flow into open or partially backfilled drifts. This assumption does not 

currently affect dose from direct releases associated with igneous events because affected

3-99 NUREG-1 668
WPs do not contribute to direct release of HLW (i.e., HLW inside WPs affected by intrusion 

is made available for release via the ground water). 

"* Consequences of intrusive processes are assumed limited to the area directly occupied by 

an igneous intrusion. The extent of processes that will likely enhance WP degradation or 

affect nondisturbed repository performance, such as increasing temperatures and dry-out 

zones around intrusions (e.g., Connor, et al., 1997), are not considered in the current 

module. 

"* In the case of an intrusive volcanic event, the drifts are assumed to be unbackfilled. All WPs 

intersected by the magma dike are assumed to fail simultaneously and contribute to ground

water releases. 

"* The diameter of a volcanic conduit in an undisturbed geologic setting is assumed to 

constrain the number of WPs disrupted for direct transport of HLW via the air pathway. This 

assumption may not be valid because it does not evaluate the response of overpressured 

magma encountering the ambient pressure conditions of the 300-m (980-ft) deep repository 

drifts. 

"* The number of WPs intersected by the volcanic conduit is derived from the number of WPs 

intersected by a conduit ranging from 2 to 50 m (6 to 160 ft) in diameter, using WP 

dimensions, emplacement spacings, and interdrift spacings from the DOE advanced 

conceptual design. These calculations show 1 to 10 WPs can be intersected by a 2- to 50-m 

conduit. 

"* Areas affected by volcanic conduits and igneous intrusions are assumed to have simple 

geometries. Redistribution of rock stress because of drift emplacement will likely result in 

complex, irregular feature geometry. Conduits may become more elliptical and affect larger 

areas or may bypass drifts entirely because of drift-induced changes in local rock stress. 

"* Based on conservative interpretations of data and analyses presented by NRC (1998), all 

WPs are assumed to fail when intersected by the volcanic conduit. 

3.12 ASHPLUMO MODULE DESCRIPTION 

The purpose of the ASHPLUMO code module is to estimate the deposition of both tephra 

(i.e., ash) and incorporated SF in mass per unit area, at the point of compliance, after an extrusive volcanic 

event has penetrated the proposed repository. The ASHPLUMO module invokes execution of the 

ASHPLUME stand-alone code (Jarzemba, et al., 1997) in deterministic mode, using input from EXEC or 

other modules (e.g., VOLCANO) that describes the characteristics of the eruption, such as volcanic power, 

durations, and size distribution of the ash particle, to accomplish the aforementioned purpose.

NUREG--1668 3-100
3.12.1 Information Flow Within TPA

3.12.1.1 Information Supplied to ASHPLUMO 

The mass of SF ejected in the volcanic event (i.e., ground-surface radionuclide source term), as 

estimated by the VOLCANO code module, is provided by EXEC. Additionally, the ASHPLUMO module 

uses input values from the ASHPLUMO section of the primary input file, including height of the eruption 

column, wind speed, and ash particulate characteristics, to determine the transport and deposition of 

radionuclides in volcanic ash. 

3.12.1.2 Information Provided by ASHPLUMO 

ASHPLUMO estimates the areal deposition of both ash and SF, at the receptor group location, that 

is passed to EXEC for subsequent use by the ASHRMOVO module. 

3.12.2 Conceptual Model 

The ASHPLUME code calculates the areal density of SF (in grams of SF per square centimeter) 

at points on the surface of the earth after an extrusive volcanic event penetrates the repository and exhumes 

SF. Using published data for wind velocity at the YM site and the estimate of pertinent volcanic parameters 

of events similar to those that may have occurred at YM in the past, the ASHPLUME code simulates the 

transport of contaminated particles (composed of SF and ash) to surface points downwind. The SF 

concentration from deposition is provided to the ASHRMOVO code module and subsequently to the DCAGS 

code module for the dose calculation. 

Basaltic volcanism can encompass a variety of eruption styles, depending on the eruption energy. The energy of basaltic eruptions varies from effusive activity, where the predominant product is lava flows, 

to explosive activity, resulting in fragmentation of the magma into scoria fragments and transport of scoria 

in the atmosphere as pyroclasts. This latter style of activity generally results in the formation of cinder cones 

such as those found in the YMR. Explosive volcanic activity of this kind has the potential to cause dispersal 

of radionuclides through the biosphere. This dispersion of radionuclides resulting from volcanic activity is 

modeled in the ASHPLUME code, using approaches originally developed by Suzuki (1983) to model the 

dispersal of ash after volcanic eruptions. 

Figure 3-19 shows the exposure scenario investigated with the ASHPLUME code. The exposure 

scenario analyzed can be divided into four subprocesses: 

(i) Magma enters the repository and becomes contaminated with SF particles. 

(ii) Tephra forms from the magma and SF is incorporated into the tephra (Jarzemba and 

LaPlante, 1996). 

(iii) Eruption column and contaminant plume form and produce contaminated ash fallout at 

various distances downwind from the volcano (Suzuki, 1983; Jarzemba, 1997).

3-101 NUREG-1 668
S\ ......................... /. ....... ....... 

00~ ....................... 

Figure 3-19 O............................tion ASHPLUM 
(iv) Radionuclide contamination causes doses to be incurred at receptor locations (LaPlante, 

et al., 1995; Jarzemba and LaPlante, 1996). 

Specifically, ASHPLUME addresses (ii) and (iii) from the previous list. 

To assess the hypothetical radiation doses that would occur after a basaltic eruption, the 

distribution of SF (and hence radionuclides) in the biosphere after such an event needs to be estimated. It is 

assumed that the ash particles from the eruption are the carriers of the SF particles. Methods used previously 

to estimate radionuclide dispersal by volcanism (Wescott, et al., 1995) theorize that the ash cloud travels in 

a Gaussian plume, released at a stack height of one-half of the volcanic column height. Application of the 

Gaussian plume model presumes that a plume of contaminants travels in the same direction as the prevailing 

wind (x-direction), but may be somewhat depressed toward the earth surface owing to gravitational settling. 

Contaminants in the plume follow a Gaussian distribution in the dimensions perpendicular to the direction 

of travel (y- and z-directions). 

The Gaussian plume model is suitable for modeling airborne and ground concentrations of 

contaminants for a point source release of contaminants above the surface of the earth (i.e., the stack height). 

A point source approximation may not be appropriate for a volcanic eruption because a volcanic eruption colurrmn is a line source of contaminants in the upward direction. Also, the Gaussian plume model does not 

accurately account for the effects of gravitational settling of volcanic particles with large diameters 

[i.e., centimeters]. This shortcoming may lead to the Gaussian plume model predicting much greater particle 

ranges than would be the case in reality and, hence, wider radionuclide distributions than would normally 

be expected after a basaltic eruption. This wider distribution of radionuclides may tend to underestimate the 

radiation exposure of persons in a small receptor group. 

Models to predict the distribution of ash, after an eruption, have been developed with the intention 

of relating eruption magnitude to ash dispersion (Suzuki, 1983; Hopkins and Bridgeman, 1985; Glaze and 

Self, 1991). The ASHPLUME code uses the model described in Suzuki (1983) that relates eruption magnitude to ash distribution, which is modified to relate eruption magnitude to SF distribution for YM, 

based on a few simple assumptions. The model uses the power and duration of the eruption, along with other 

properties of the ash particulates, and develops an SF distribution from those sampled parameters. The SF 

distribution can be translated into the radionuclide distribution that can be used to model dose-to-man. 

The model described in Suzuki (1983) can be summarized by the equation that describes the areal 

density of accumulated ash on the Earth's surface after an eruption: 

P m H 5QP(z) f(p)e 5[((x - ut2 + Y21 dpdz 

X(xIy) = f f -exp (3-76) 

P=~w z=0 M-1C. + [ 8C" + t' 

where: 

X(x~y) = mass of ash per unit area accumulated at location (x~y) [g/crm2

] 

p,, = maximum value of p 

p = common logarithm of particle diameter d [cm]

3-103 NUREG-1668
Pu = minimum value of p 

H = height of the eruption column above the vent [km] 

z = vertical distance from the ground surface [km] 

Q = total quantity of erupted material [g] 

P(z) = function for particle diffusion at height within dz about z [unitless] 

ftp) = distribution function for particles with a log-diameter within dp about p normalized 

per unit mass [unitless] 

C = constant relating the eddy diffusivity and the particle fall time [cm 2/s'] 

t = particle fall time [s] 

t, = particle diffusion time in the eruption column [s] 

x = x-coordinate on the surface of the earth [cm]; coordinate oriented in the same 

direction as the prevailing wind 

u = wind speed [cm/s] 

y = y-coordinate on the surface of the earth [cm]; coordinate oriented perpendicular to 

the direction of the prevailing wind 

For calculation of dose, the necessary quantity to track is the mass of SF per unit area at the 

compliance point after ash released from the eruption that penetrates the proposed repository settles on the 

surface of the earth. To calculate this quantity, a model for SF incorporation into ash was created. This model 

requires the introduction of a function to determine the mass of fuel per unit mass of volcanic ash as a 

function of the log-diameter of the ash after the ash has been contaminated with SF [FF(p0)]. The volcanic 

ash mass is assumed to be distributed log-normally: 

(a 1a 2 

fpa) 1 exp (3-77) 

where: 

ftpa) = normalized (per unit mass) probability distribution of ash mass as a function of pa 

d = standard deviation of the log particle size 

pa log-diameter of ash particle size [cm] 

a 

pn = mean of the log-diameter of ash particle size [cm] 

To determine FF(pa), the fuel fraction (ratio of fuel mass to ash mass) as a function of p, one must 

consider that all fuel particles of a size smaller than (pa - pc) have the ability to simultaneously be 

incorporated into volcanic ash particles of size pa or larger, where p, is the incorporation ratio. The fuel 

fraction as a function of pa is determined by summing all the incremental contributions of fuel mass to the

NUREG-1668 3-104
volcanic ash mass from fuel sizes smaller than (g' - p,). An expression for the fuel fraction is given as: 

Flpa)= f dp (3-78) 

where: 

FF(pa) = fuel fraction as a function of particle size [unidess] 

Q = total mass of ash ejected in the event [g] 

U = total mass of fuel ejected in the event [g] 

F(p) = cumulative distribution of ash mass with p 

m(p - p fi normalized probability distribution of fuel mass with (p - p,) 

This equation assumes the resulting contaminated particles follow the same size distribution as the 

original volcanic ash particles. This assumption seems reasonable since, for most events sampled in these 

analyses, the total mass of volcanic ash is on the order of 10'-101' g (2.2 x 101•--2.2 x 1012 lb) and for 

most events, only several WPs are disrupted [106 g (2.2 x IW0 ib) of fuel each]. For each simulation, 

Eq. (3-78) is numerically integrated to calculate the distribution of the SF and volcanic ash on the earth's 

surface resulting from a basaltic eruption assumed to disrupt the repository. The integrand of Eq. (3-76) is 

multiplied by FF(p0 ) and then evaluated to find the SF density at location (xy). 

The model developed by Suzuki (1983) is appropriate for particles of mean diameter greater than 

about 15-30 gim (6 x 10--1.2 x 10-3 in.). This cutoff is generally accepted to be the lower limit for the 

importance of gravitational settling of particles (Cember, 1983; Heffter and Stunder, 1993). For particle sizes 

less than about 15 prm (6 x 10'- in.), atmospheric turbulence is great enough to keep the particle aloft for a 

longer time than would be predicted by the model. Since the typical mean diameter of ash particles after an 

eruption is generally much larger than 15 pm (6 x 10-' in.) (Suzuki, 1983), this model is useful for 

calculating the distribution for the vast majority of ash and, hence, radionuclides released. Jarzemba and 

LaPlante (1996), Jarzemba (1997), and Jarzemba, et al. (1997) contain more complete descriptions of the 

derivation, structure, and use of the ASHPLUME code and its algorithms. 

3.12.3 Assumptions and Conservatism of the ASHPLUMO Approach 

The ASHPLUMO module is an abstraction of the transport of radioactively contaminated ash 

(i.e., tephra) from the proposed repository to points located downwind from the event. The main processes 

that control the transport of SF in the ash are: (i) SF particulate incorporation in the ash; (ii) dispersion in 

the eruption column and plume; and (iii) transport of ash downwind from the event. 

The model in ASHPLUME for SF incorporation in ash assumes that all the SF particles are 

eventually incorporated into larger (by a pre-set factor) ash particles. Actually, it is likely that only a 

percentage of the SF particles may be incorporated in this fashion, and some of the SF may be transported 

as bare particles. The conservatism of this approach is not yet known.

3-105 NUREG-1668
The transport model in ASHPLUME uses a dispersion-advection transfer function type solution 

that is empirical (e.g., eddy diffusivity constant and column shape parameter). Since the solution uses 

empirical constants based on observations at analogous volcanoes, it requires that future eruptions that may 

occur at the repository site be similar to those observed at the present time at other locations. 

The transport model in ASHPLUME as implemented in the TPA Version 3.1.4 code assumes the 

wind speed and direction do not vary during the course of the event. This is clearly a conservative 

assumption, since variances in wind velocity over the event will lead to a wider distribution of SF than those 

predicted with a single velocity. The level of conservatism is unknown at this time, and may be averaged out 

when the average dose over numerous realizations is used as the performance measure. 

In addition to those assumptions described before, the derivation of Eq. (4-76) assumes the 

following, which are reiterated here from Suzuki (1983): 

"* The distribution of the diameter of the released particles is well-described by a log-normal 

distribution. 

"* All particles fall at the terminal velocity and finally accumulate on the ground. 

"* The particles have a probability of diffusing out of the column during upward travel such 

that the probability density distribution, P(z), has the following form. 

fiWo Yexp(-Y) 

P VoH[I_(I +Yo)exp(-_o)](3-79) 

where: 

f8 = constant controlling the diffusion of particles out of the eruption column 

WO= volcanic eruption velocity at the vent exit [cm/s] 

- 8iW(z) 

Vo 

Vo = particle terminal velocity at sea level [cm/s] 

-O =W 

Vo 

W(z) particle velocity as a function of height equal to Wo(1-z/H) 

The terms Y and Y. have been modified from Suzuki (1983) where they were equal to 

fl(W(z) -Vo)/Vo and fl(Wo-Vo)IVo because with the former definition of Y, P(z) has a negative 

value at heights approaching the top of the column. The definition of Y. has been altered to 

maintain identity as the value of Y(z) at z = 0. The modified model has been benchmarked 

against actual eruptions with good agreement (Hill, et al., 1998).

NUREG-1 668 3-106
* The distribution of SF particle diameter is well described by a log-triangular distribution. 

The size distribution of contaminated ash particles is unaffected by the incorporation of 

SF particles. 

3.13 ASHRMOVO MODULE DESCRIPrION 

The purpose of the ASHRMOVO module of TPA is to calculate areal radionuclide densities at the 

compliance point for times after a volcanic event has deposited a contaminated ash blanket. The radionuclide 

areal densities are greatest at the time of the event and decrease thereafter through surface erosion, leaching 

of radionuclides from the blanket, and radioactive decay. Ingrowth of radionuclides is also accounted for but, 

in general, ingrowth is more than compensated by the removal processes. 

3.13.1 Information Flow Within TPA 

3.13.1.1 Information Supplied to ASHRMOVO 

The areal deposition of both SF and ash at the compliance point generated by ASHPLUMO are 

passed to ASHRMOVO from EXEC. The radionuclide inventory of the SF at times after the volcanic event 

is also provided by EXEC. Additionally, the ASHRMOVO code module reads input values such as ash 

blanket characteristics and decay parameters from the ASHRMOVO section of the primary input file. 

3.13.1.2 Information Provided by ASHRMOVO 

For all ground-surface radionuclides, ASHRMOVO transfers the areal radionuclide density in 

activity per unit area as a function of time at the compliance point back to EXEC for subsequent use in 

DCAGS for dose calculation. 

3.13.2 Conceptual Model 

The ASHRMOVO module models the time-dependent radionuclide areal densities of contaminated 

soil surface layers subject to removal by leaching, erosion, and radioactive decay. ASHRMOVO is used in 

concert with ASHPLUMO, which establishes initial radionuclide areal densities for extrusive volcanic events 

at the time of the event that intersects the waste repository. ASHRMOVO performs calculations to decay the 

inventory for succeeding times. The subsequent time history of radionuclide surficial contamination is 

converted to dose by the DCAGS module. 

ASHRMOVO uses generalized analytical solutions to calculate the time-dependent radionuclide 

density in soil considering radioactive decay and ingrowth, and nonradioactive losses by leaching or erosion. 

For the volcanic exposure scenario previously described in ASHPLUMO, ASHRMOVO calculates the time

3-107 NUREG-1 668
history of radionuclide surficial contamination following the event by using analytical solutions to the 

following differential equations: 

dN()- X.•_1N_,(t) - xfiNi(t) - ,iit)- XBlN(t) 

dt (3-80) 

where: 

Ni(t) = time-dependent areal density of radionuclide i [mol/rn2] 

Ni_ 1(t) = time-dependent areal radionuclide density of the parent [mol/m2] 

4• = removal (or generation) of radionuclide i through radioactive decay [l/yr] 

4 = relative leach rate of radionuclide i from the ash blanket [ l/yr] 

B= bulk removal of the blanket through surface erosion [1/yr] 

x = total loss rate of radionuclide i from physical decay, leaching, and surface 

erosion equal to + xi X8,- I l/yr] 

The initial conditions for the system of equations are the areal densities of the radionuclides at the time of 

the event. These densities have been estimated by ASHPLUME. 

The relative leach rate of radionuclide i is based on Eq. (4.6.3) of Napier, et al. (1988). The 

relative leach rate has an upper limit, X4"'u, dependent on the solubility limit of the radionuclide and the 

amount of radionuclide present. When the processes are leach-rate limited, the relative leach rate of 

radionuclide i is given by: 

x L x Lmax N< S3R 

Lmax (3-81) 

and when the leach processes are solubility-limited, it is given by: 

L Sia Ra N.> S a? 

S- Ni gi N1. > -Lmax (3-82) x•ima

NUREG-1668 3-108
where:

S = solubility limit of radionuclide i [mol/m3] 

R = areal recharge of water [m3

/(m2 yr)] 

N, = average areal density over the time step of radionuclide i [mol/m2 ] 

The quantity, X , is a combination of more basic parameters and is given by: 

xLam P (1 - 4e Ma + 1 (1 - .,) .,at = (3-83) 

d (1 + (p/O)Kd,) 

where: 

P = annual precipitation rate [m/yr] 

f, = fraction of precipitation water lost to evapotranspiration 

fi = fraction of year blanket is saturated by precipitation 

I = annual irrigation rate [m/yr] 

fl' = fraction of irrigation water lost to evapotranspiration 

f = fraction of year blanket is saturated because of irrigation 

d = depth of the blanket [m] 

p = soil bulk density [g/cm3] 

0 = soil volumetric water content 

KKd = distribution coefficient for radionuclide i [cm3lg] 

The bulk removal rate of the blanket, )Y, is a constant value of 0.0001/yr based on preliminary 

estimates of ash blanket lifetimes, but may be sampled by specifying a distribution in the primary input file 

(Appendix A). A complete demonstration of the model in ASHRMOVO, including plots of time-dependent 

radionuclide areal densities for an example case, is given in Jarzemba and Manteufel (1997); however, a 

demonstration for the curium 245 (Cm-245) decay chain is provided here as an example. Tables 3-4 and 3-5 

provide a listing of the data used in this example. Using these data and the model described in this section, 

the areal radionuclide densities as a function of time [Ni(t)] are estimated and shown graphically in 

Figure 3-20.

3-109 NUREG-1668
Table 3-4. A Listing of the Nonradionuclide-Specific Data Used in 

Radionuclide Areal Density for Members of the Cm-245 Decay Chain

Estimating the Surficial

Parameter Value 

Initial SF areal density 1 kgl/r2 

Annual precipitation rate 0. 15 mn/yr 

Annual irrigation rate 1.52 m/yr 

Fraction of precipitation lost by evapotranspiration 0.68 

Fraction of irrigation lost by evapoti-anspiration 0.5 

Fraction of year soil is saturated by precipitation 0.0054 

Fraction of year soil is saturated by irrigation 0.2 

Soil bulk density 2 g/cm3 

Soil porosity 0.4 

Fractional blanket erosion rate 0.001 

Depth of the rooting zone I M 

Table 3-5. A Listing of the Radionuclide-Specific Data Used in Estimating the Surficial Radionuclide 

Areal Density for Members of the Cm-245 Decay Chain 

Initial Inventory Physical Decay Solubility 

Nuclide (Bg m-2Kg-) Constant (yr-) (atoms/L) (C 

Curium-245 4.6 x 106 8.2 x 10-' 6.0 x 1017 4.0 x 10' 

(Cm-245) 

Plutonium-241 2.8 x 1012 4.8 x 10-2 3.0 x 1018 5.5 x 102 

(Pu-241) 

Americium-241 6.1 x 10'° 1.6 x 10-' 3.0 x 10'8 1.9 x 103 

(Am-241) 

Neptunium-237 1.1 x 107 3.2 x 10-7 9.5 x 101' 5.0 x 100 

(Np-237) 

Uraniurn-233 8.9 x 102 4.4 x 10-6 2.7 x 10'9 3.5 x 10' 

(U-233) 

Thorium-229 5.1 x 100 9.5 x 10-5 1.9 x 1015 3.2 x 10' 

(Th-229)

NUREG-1668 3-110
1014

S1012 

eg 1010 

S106 N 

108 

102 

100 

100 101 102 103 104 

Time (yr) 

Figure 3-20. A plot of the areal radionuclide density as a function of time for radionuclides in the 

curium-245 (Cm-245) decay chain. 

3.13.3 Assumptions and Conservatism of the ASHRMOVO Approach 

The ASHRMOVO module is an abstraction of the leaching and removal of radionuclides from a 

contaminated soil/ash surface layer after an extrusive volcanic event has distributed radionuclides on the 

Earth's surface. The main processes modeled by ASHRMOVO are removal of radionuclides from the 

contaminated ash blanket by solubilization and leaching by infiltrating water, radioactive decay, and 

blanket erosion. 

Infiltrating water has the ability to dissolve radionuclides in the ash blanket and transport them to 

deeper soil/ash layers. Since only the contamination in the top 15 cm (6 in.) of ash is considered in dose

3-111 NUREG-1668
calculations, the effect of this transport is to remove leached radionuclides from the biosphere. 

Contamination of ground water from this source is not considered. 

Radioactive decay of nuclides in the blanket is accounted for in this model. Any radioactive 

daughters formed from parent decay are also considered. 

Blanket erosion is accounted for in the model as a first-order process. The blanket erosion term 

should be considered as net erosion (deposition from ash eroded from other sections of the blanket minus 

that eroded from the current location). Mechanical redistribution of ash is not specifically accounted for in 

the ASHRMOVO model. 

3.14 DCAGS MODULE DESCRIPTION-FARMING RECEPTOR GROUP 

The purpose of the DCAGS module is to convert the areal radionuclide activity densities (in Ci/m2) 

calculated by the ASHRMOVO module into annual doses (TEDE). This section describes only the 

assumptions and conceptual models, beyond those already described in Section 3.8, for the DCAGW module, 

that are required for accomplishing this purpose. 

3.14.1 Information Flow within TPA 

3.14.1.1 Information Supplied to DCAGS 

Time-varying areal concentrations computed in ASHRMOVO for each ground-surface 

radionuclide and the ash areal density calculated in ASHPLUMO are provided to DCAGS by the EXEC, 

from which ground-surface radionuclide dose (an annual TEDE) is obtained, using ground-surface DCFs in 

two data files: gs_cb_ad.dat and gspb__ad.dat, consistent with the lifestyle of the receptor group and the 

climatic conditions specified in the primary input file. The two data files are identified by the following 

nomenclature: cb--current biosphere; pb-pluvial biosphere; and ad-farming group. The time evolution 

of average annual precipitation and temperature from UZFLOW are passed by the EXEC to DCAGS to 

determine the time when the current condition will switch to pluvial conditions. Other DCAGS input 

parameters such as the depth to which particles can be resuspended and values for the mass load and 

occupancy factor are specified in the DCAGS section of the primary input file. 

3.14.1.2 Information Provided by DCAGS 

The ground-surface doses as a function of time for each radionuclide computed in DCAGS at every 

TPA time step are passed to EXEC. 

3.14.2 Conceptual Model 

The conceptual framework for DCAGS includes processes occurring subsequent to airborne 

transport and deposition of radiological contamination (released from the repository by an extrusive volcanic 

event) to the ground-surface where the farming receptor group is located. Processes included in the

NUREG-1668 3-112
conceptual model for DCAGS at the 20-km (12-mi) receptor location include resuspension of deposited 

contamination, with subsequent inhalation, direct exposure, and ingestion of contaminated plant and animal 

products. This model is implemented in DCAGS (with the exception of the inhalation pathway, which is 

explained below) by using databases of DCFs that convert unit radionuclide areal densities to annual TEDEs, 

which is similar to the implementation in DCAGW described earlier. DCFs were calculated using a 

deterministic approach in the GENII-S pathway/dose assessment code (Leigh, et al., 1993; Napier, 

et al., 1988). These calculations were based on unit radionuclide concentrations in the soil and a combination 

of site-specific and generic input parameters discussed in LaPlante and Poor (1997). 

For each TPA realization, DCAGS multiplies ground-surface radionuclide concentrations 

generated from the ASHRMOVO module by the exposure pathway/climate and radionuclide-specific DCFs. 

For each time step, the products of each radionuclide concentration and DCF are summed within and among 

exposure pathways and radionuclides to calculate total doses. The reporting of peak doses, differentiation 

between climates, and applicable exposure pathways are all the same as described in Section 3.8 for the 

DCAGW module. The only exceptions are that surface contamination, not ground water, is the source of 

radionuclides, and DCAGS does not include the ingestion of contaminated water. 

For inhalation, a mass loading model was developed that is more applicable to expected site 

conditions than available models in GENII-S (Leigh, et al., 1993; Napier, et al., 1988). The mass loading 

model estimates the airborne concentration of radionuclides above the ash blanket after the volcanic event. 

The model multiplies the airborne mass load by the concentration of radionuclides in the ash being 

resuspended. The concentration in ash is calculated from the output of the ASHPLUMO and ASHRMOVO 

modules. The model also accounts for the effect of blanket thickness on the amount of material available for 

resuspension. The areal concentration of the ii radionuclide, C,,• in Ci/m3 is: 

Ca(i) = STli fR (3-84) 

where: 

S = airborne mass load [g/m3] 

7 = activity of radionuclide i per mass of ash [Ci/g] 

f,. = fraction of resuspended mass that emanated from the contaminated volcanic ash 

layer [unitless] 

The activity of radionuclide i per mass of ash at the compliance point is estimated from quantities 

already calculated by the TPA Version 3.1.4 code modules as follows: 

_R,(x,y) riX(xy) 

(3-85) 

where: 

R,(x,y) = quantity of radioactivity per unit area accumulated at location (xy) [Ci/m2] 

X(x,y) = mass of ash per unit area accumulated at location (xy) [g/cm2J 

The fraction of resuspended mass that emanated from the contaminated volcanic ash layer depends 

on the thickness of the ash blanket, T,, and the depth to which particles can be resuspended, T.. In other

3-113 NUREG--1668
words, TR represents the depth from the surface below which no resuspended ash can emanate over the course 

of 1 yr. For blankets thicker than TR, f, is unity. For blankets thinner than TR, fr is given by:

AR -T 

TR

(3-86)

Situations wherefr is equal to unity and where f, is less than unity are shown in Figure 3-21.

where:

The blanket thickness is a function of time and is given by: 

TB =TB-O exp [- XB(t - tent)] (3-87) 

TB- = blanket thickness at the compliance point immediately following the event 

(estimated from X(xy) from ASHPLUMO) [cm]

A = net blanket erosion rate from ASHRMOVO [l/yr] 

t,, = time of the volcanic event from VOLCANO [yr] 

Once the airborne radionuclide concentration has been estimated, the rest of the dose conversion 

process is identical to what is currently implemented in DCAGW. The equation describing the rest of the 

dose conversion process is:

Dj=BIjC(jfe (3-88)

where:

. = inhalation dose rate caused by the airborne pathway for radionuclide i [remlyr] 

B = breathing rate [m3

/yr]

fL = fraction of the year the individual is exposed to contaminated air

inhalation-to-dose conversion factor in rem/Ci [(U.S. Environmental Protection 

Agency, 1988) choices for removal class are as listed in Pacific Northwest National 

Laboratory library] 

The quantity BI, is provided in the DCF tables, in the files gs_.cbjad.dat and gs..pb-ad.dat (inhalation 

parameter column), to the code as input data, where B is assumed to be 1.05 x I10 m3

/yr (2.6 galJmin) 

(Bureau of Radiological Health, 1970).

NUREG--1668 3-114
TBI 

TBj

tamiatedvolcanicas I TR

F R

Figure 3-21. Examples where fraction of resuspended mass that emanted from the contaminated 

volcanic ash layer (fR) (a) is unity and (b) is less than unity.

NUREG- 1668

volcanic govx//z

3-115
For each time step, the products of each radionuclide concentration and DCF are summed within 

and among ground-surface exposure pathways and radionuclides to calculate total doses. In addition to total 

doses, selected output is sorted by realization, time step, and radionuclide. The executive model uses results 

from all realizations to identify and report the peak dose. 

3.14.3 Assumptions and Conservatism of the DCAGS Approach 

DCAGS is an abstraction of complex and uncertain processes occurring in the biosphere. The 

conversion of soil radionuclide concentrations to receptor dose involves assumptions about: (i) the location 

of the receptor group; (ii) the lifestyle characteristics of the receptor group that form the basis for exposure 

pathways; (iii) processes that determine fate and transport of contaminants in the biosphere; and 

(iv) calculation of human doses from factors that convert exposure to contaminated media to effective 

dose equivalents. 

Receptor location is based on the assumption that current physical constraints (i.e., topography, 

depth to water table, and soil conditions) that limit present farming to the Amargosa Valley area will continue 

to limit farming south of YM to within approximately 20 km (12 mi) from the proposed repository site. 

Because the receptor location and lifestyle assumption relate to potential human behavior, it is speculative. 

The use of present knowledge to define the receptor group location helps to limit speculation and is 

consistent with the recommendations of the National Academy of Sciences (1995). 

The conversion of soil concentrations to receptor dose requires conceptualization of potential 

exposure pathways based on the site-specific characteristics of the release pathway, YM biosphere, and 

receptor group. When soil is the source of contamination, the potential exposure pathways are assumed to 

be those resulting from agricultural use of land, consistent with present farming conditions south of YM. 

These pathways include ingestion (of contaminated crops and animal products); inhalation (from 

resuspension of soil); and direct exposure to contaminated soil. 

The assumed diet of locally produced food is important in estimating the dose for a farming 

receptor group. The farmer is assumed to grow alfalfa (for beef and milk cow feed); vegetables, fruits, and 

grains for personal consumption; and feed, for egg-laying hens. The practices the farmer is presumed to 

engage in have been confirmed to exist in areas south of YM; however, it appears unlikely that all activities 

identified would be practiced by a single individual. Therefore, the doses calculated from such assumptions 

are expected to be greater than what might occur under current conditions. Since bartering is known to exist 

among community members (Eisenberg, 1996),'o it is reasonable to project that (for the most highly exposed 

group) a significant portion of diet could be obtained from local sources. Thus, the assumption is considered 

conservative, but not excessively so. These assumptions may change when additional information on local 

consumption patterns is available. 

Modeling the RT in the biosphere involves determination of crop concentrations using soil/plant 

uptake factors, determination of air concentrations from resuspended contamination using a mass load model, 

and accounting for feed intake and transfer of contamination to livestock. 

'

0Eisenberg, N.A. 1996. Staff Visit to Amargosa Valley: Trip Report. Memorandum (May 14) to M. Federline, Nuclear Regulatory 

Commission. Washington DC: Nuclear Regulatory Commission.

NUREG-1668 3-116
Crop concentrations are based on transfer factors that represent the ratio of plant and soil 

concentrations fora class of crops grown in contaminated soil. Food trantsfer factors are generic values based 

primarily on published information (International Atomic Energy Agency, 1994; Baes, et al., 1982). Because 

of the lack of site-specific information, it is reasonable to use generic values; however, they may not be 

representative of arid conditions that exist in Nevada. The selected factors are expected values and have not 

been arbitrarily increased for conservatism. 

Air concentrations from resuspended contamination are calculated by applying a mass loading 

model. This model uises a mass loading factor that is the ratio of airborne and surface soil radionuclide 

concentrations. Mass loading factors are highly uncertain and influenced by a number of processes, including 

wind speed, surface roughness, resuspended particle size and density, and the moisture content of soil. 

Because the GENII-S code does not provide a mass loading model for crop deposition, a resuspension model 

was used with an adjusted mass loading factor. A separate mass loading model for inhalation is used in 

DCAGS, developed to be more applicable to site conditions than the available models in GENII-S 

(Leigh, et al., 1993; Napier, et al., 1988). This mass loading model estimates the airborne concentration of 

radionuclides above the ash blanket after the volcanic event. The model accounts for the effect of blanket 

thickness (as a function of time) on the amount of material available for resuspension. 

Determination of radionuclide concentrations in livestock products is done by quantifying the 

intake of contaminated feed and applying a transfer coefficient. The animal transfer coefficients are based 

on studies that measure radionuclide concentrations in livestock based on measured intake. The coefficients 

are element- and livestock-specific and based on published information (International Atomic Energy 

Agency, 1994; Baes, et al., 1982). 

Calculation of human dose is accomplished through the use of dose factors that convert the 

estimated concentrations in food, air, and soil, together with the corresponding intakes and residence times, 

to TEDEs. The dosimetry models that form the basis for the dose factors are consistent with those described 

in ICRP Publication 30 (ICRP, 1982). The dosimetry models represent state-of-the-art techniques and 

methodologies in existence at the time the publication was released. Refinements to dosimetry models have 

since been published by the ICRP, but have not yet been adopted by the U.S. Government and are therefore 

not used in DCAGS. 

More specific assumptions for DCAGS are provided in the following list and in LaPlante and 

Poor (1997). 

Contamination spread on the ground surface is the sole source of contamination, as 

opposed to contamination dissolved in the ground water used in DCAGW. This 

assumption means that radionuclides leached from the ash blanket to lower soil layers 

and possibly the SZ are removed from the biosphere and thus do not contribute to 

estimated doses. Because, for this exposure pathway, the most significant contributors 

to dose are actinides such as plutonium; americium; and curium, and these radionuclides 

are not expected to be easily leached from ground surface soil/ash layers, this assumption 

is considered reasonable. 

The dose calculation from consumption of crops does not account for washing fruits and 

vegetables before consumption and is, therefore, conservative.

3-117 NUREG-1668
Consumption rates for food are based on national average values, since there is a lack of 

site-specific information. These values may not be representative of local consumption 

practices. Use of average values is not expected to be overly conservative. DOE 

conducted a survey of local consumption practices and, when results are available, DCFs 

may be recalculated if the new consumption rates are found to differ significantly from 

the national averages used for current DCFs. 

For human dosimetry modeling, the average member of the receptor group is an adult 

consistent with the EPA proposed "Federal Radiation Protection Guidance for Exposure 

of the General Public" (U.S. Environmental Protection Agency, 1994). The individual 

is assumed to have physiology consistent with the reference man model (International 

Commission on Radiological Protection, 1975). 

Inhalation and external exposure times are based on generic information provided in 

Kennedy and Strenge (1992). For the farming group, 55 percent of total time is assumed 

spent inside the residence; 20 percent outside of residence; 24 percent at offsite work; 

and 1 percent gardening and farming alfalfa, which is not a labor-intensive crop to farm. 

The remainder of time is assumed to be spent away (not at work) from the residence. 

These activity times are considered conservative for a common residential setting 

(Kennedy and Strenge, 1992), but may not be for a more active farming resident. 

Resuspension of contaminated soil applies to crop deposition, inhalation, and external 

dose modeling. A mass loading model is applicable to a homogeneously mixed layer of 

contamination in the soil and, therefore, a reasonable mass loading factor from the 

literature is used (Kennedy and Strenge, 1992). For crop deposition, the GENII-S code 

includes a resuspension model; therefore, the selected mass loading factor is converted 

to a resuspension factor, using an equation in the GENII-S user manual (Napier, et al., 

1988). A mass loading model is used to determine annual TEDEs from the inhalation 

pathway. 

Airborne mass loading is assumed constant over time. For times long after the time of the 

ash deposition, changes to soil and ash conditions (i.e., hardening of soil) will tend to 

reduce mass loading. However, farming activities (e.g., tilling of soil) are assumed to 

continually disturb the ash and soil. Provided that a reasonably conservative value for the 

airborne mass load is used as input, a reasonably conservative estimate of dose from the 

inhalation pathway for volcanism will be achieved since doses from inhalation are 

linearly dependent on this factor. The mass loading and resuspension models are 

abstractions of complex and varying processes. Thus, considerable uncertainty exists in 

applying them to YM conditions.

NUREG-1668 3-118
3.15 DCAGS MODULE DESCRIPTION-RESUDENTIAL RECEPTOR GROUP 

The purpose of the DCAGS module is to convert the areal radionuclide activity densities (in Ci/m2 ) 

calculated by the ASHRMOVO module into annual dose (TEDEs). This section describes only the 

assumptions and conceptual models beyond those already described in Section 3.9 for the DCAGW module 

required for accomplishing this purpose. 

A residential receptor group located between 5 km (3.1 mi) and 20 km (12 mi) has been included 

in the TPA Version 3.1.4 code to assist timely completion of the sensitivity analyses by allowing shorter 

simulation periods appropriate to the shorter distances. Having the ability to evaluate a residential receptor 

group also provides the staff with the flexibility to address alternative assumptions for the receptor group, 

should that be necessary. 

3.15.1 Information Flow Within TPA 

3.15.1.1 Information Supplied to DCAGS 

Time-varying areal ground-surface radionuclide concentrations, computed in ASHRMOVO, are 

provided to DCAGS by the EXEC. Ground-surface radionuclide dose (an annual TEDE) is obtained, using 

ground-surface DCFs in two data files: gscb&_ci.dat and gs_pbci.dat, associated with the lifestyle of the 

receptor group and the climatic conditions specified in the primary input file. The two data files are identified 

by the following nomenclature: cb--current biosphere; pb-pluvial biosphere; and ci-residential. Other 

DCAGS input parameters such as the depth to which particles can be resuspended and values for the mass 

load and occupancy factor are specified in the DCAGS section of the primary input file. The time evolution 

of average annual precipitation and temperature from UZFLOW are passed by the EXEC to DCAGS to 

determine the time when the current condition will switch to pluvial conditions. 

3.15.1.2 Information Provided by DCAGS 

The ground-surface doses as a function of time for each radionuclide computed in DCAGS at every 

TPA time step are passed to EXEC. 

3.15.2 Conceptual Model 

The conceptual framework for DCAGS includes processes occurring subsequent to airborne transport 

and deposition of radiological contamination (released from the repository by an extrusive volcanic event) 

to the ground surface where the residential receptor group is located. Processes included in the conceptual 

model for DCAGS include resuspension of the deposited contamination at the residential receptor location 

and calculation of dose from inhalation of radionuclides and external exposure. This model is implemented 

in DCAGS (with the exception of the inhalation pathway, which is explained in Section 3.1.4.2) by using 

databases of DCFs that convert unit radionuclide activity areal densities to annual TEDEs. DCFs were 

calculated using a deterministic approach in the GENU-S pathway/dose assessment code (Leigh, et al., 1993; 

Napier, et al., 1988). These calculations were based on unit radionuclide concentrations in the soil and a 

combination of site-specific and generic input parameters discussed in LaPlante and Poor (1997).

3-119 NUREG-1668
For each TPA realization, DCAGS multiplies ground-surface radionuclide concentrations generated 

from the ASHRMOVO module by the exposure pathway/climate and radionuclide-specific DCFs. For each 

time step, the products of each radionuclide concentration and DCF are summed within and among exposure 

pathways and radionuclides to calculate total doses. The reporting of peak doses, the differentiation between 

climates, and the applicable exposure pathways are all the same as described in Section 3.9 for the DCAGS 

module. The only exceptions are that surface contamination, not ground water, is the source of radionuclides, 

and DCAGS does not include the ingestion of contaminated water. 

For inhalation, a mass loading model was developed that is more applicable to expected site 

conditions than available models in GENII-S (Leigh, et al., 1993; Napier, et al., 1988). The mass loading 

model estimates the airborne concentration of radionuclides above the ash blanket after the volcanic event. 

The model multiplies the airborne mass load by the concentration of radionuclides in the ash being 

resuspended. The concentration in ash is calculated from the output from ASHPLUMO and ASHRMOVO 

modules. The model also accounts for the effect of blanket thickness on the amount of material available for 

resuspension. The areal concentration of the i't radionuclide, C,1) in Ci/m3, is computed from Eq. (3-84). The 

activity of radionuclide i per mass of ash at the compliance point is estimated from quantities already 

calculated by the TPA Version 3.1.4 code, using Eq. (3-85). 

The fraction of resuspended mass emanated from the contaminated volcanic ash layer depends on 

the thickness of the ash blanket, TB , and the depth to which particles can be resuspended, TR. In other words, 

TR represents the depth from the surface below which no resuspended ash can emanate over the course of 

1 yr. For blankets thicker than TR , f, is unity. For blankets thinner than TR, f, is given by Eq. (3-86). 

Situations wheref, is equal to or is less than unity are shown in Figure 3-21. The blanket thickness 

is a function of time and is given by Eq. (3-87). 

Once the airborne radionuclide concentration has been estimated, the rest of the dose conversion 

process is identical to that currently implemented in DCAGW. Equation (3-88) describes the rest of the dose 

conversion process. 

For each time step, the products of each radionuclide concentration and DCF are summed within and 

among exposure pathways and radionuclides to calculate total doses. In addition to total doses, selected 

output is stratified by realization, time step, and radionuclide. The EXEC module uses results from all 

realizations to identify and report the peak dose. 

3.15.3 Assumptions and Conservatism of the DCAGS Approach 

The DCAGS module for the residential receptor converts soil radionuclide concentrations to both 

inhalation and external doses. Therefore, the module includes assumptions about: (i) the location of the 

receptor group; (ii) the lifestyle characteristics of the receptor group that form the basis for exposure 

pathways; and (iii) the conversion of intake to dose. 

The residential receptor is based on an assumption that local topography and the economics of 

ground-water extraction allow for a potential nonfarming residential receptor group to exist closer to the 

potential repository site than a farming receptor group. Although DCAGS does not include the ground-water

NUREG-1668 3-120
pathways (those are addressed in DCAGW), the implicit assumption is that access to water is a precondition 

for the existence of residential dwellings. Because the receptor locitlon and lifestyle assumptions relate to 

potential human behavior, it is speculative. The use of present knowledge to define the receptor group 

location helps to limit speculation and is consistent with the recommendations of the National Academy of 

Sciences (1995). 

Because DCAGS considers the dose from airborne contaminants deposited to soil, exposure 

pathways are limited to inhalation and direct exposure. The inhalation dose is assumed from resuspension 

of contaminated soil, and external dose is primarily caused by outdoor human activities in the vicinity of 

contaminated soils. 

Resuspension is modeled in DCAGS by use of a mass loading model developed to be more 

applicable to site conditions than the available models in GENII-S (Leigh, et al., 1993; Napier, et al., 1988). 

The mass loading model estimates the airborne concentration of radionuclides above the ash blanket after 

the volcanic event. The model accounts for the effect of blanket thickness (as a function of time) on the 

amount of material available for resuspension. The subsequent dose calculation involves the receptor 

breathing rate, exposure time, and inhalation-to-dose conversion factor. Exposure times for inhalation are 

obtained from conservative estimates in Kennedy and Strenge (1992) suggesting residents spend 4 percent 

of their time outdoors and 40 percent indoors (the remainder is away from residence). The inhalation-to-dose 

conversion factors are obtained from Federal Guidance Report No. 11 (U.S. Environmental Protection 

Agency, 1988) and are based on internal dosimetry models consistent with ICRP Publication 30. 

Conversion of soil concentrations to external dose is a simple calculation involving the exposure time 

and the DCF for groundshine. The exposure times for external exposure are based on the aforementioned 

assumptions for inhalation exposure. The DCFs for external exposures are from Federal Guidance Report 

No. 12 (U.S. Environmental Protection Agency, 1993). 

Following are additional details of DCAGS modeling assumptions: 

" Airborne mass loading is assumed constant over time. For times long after the time of the ash 

deposition, changes to soil and ash conditions over time (i.e., hardening of soil) would tend to 

reduce mass loading of radioactive ash. Provided that a reasonably conservative value for the 

airborne mass load is used as input, a reasonably conservative estimate of dose from the 

inhalation pathway for volcanism will be achieved, since doses from inhalation are linearly 

dependent on this factor. 

" Contamination spread on the ground-surface is the sole source of contamination, as opposed to 

contamination dissolved in the ground water. This assumption means that radionuclides leached 

from the ash blanket to lower soil layers and possibly the SZ are removed from the biosphere 

and thus do not contribute to estimated doses. Because, for this exposure pathway, the most 

significant contributors to dose are actinides such as plutonium, americium, and curium, and 

these radionuclides are not expected to be easily leached from ground-surface soil/ash layers, 

this assumption is considered reasonable. It is noted that the parameters in the base case data set 

that determine leaching of radionuclides out of the contaminated ash blanket are appropriate for 

a farming receptor group, (i.e., includes irrigation for farming) which may be nonconservative 

if applied to a residential group.

3-121 NUREG-1668
For human dosimetry modeling, the average member of the receptor group is an adult, consistent 

with the EPA proposed "Federal Radiation Protection Guidance for Exposure of the General 

Public" (U.S. Environmental Protection Agency, 1994). The individual is presumed to have 

physiology consistent with the assumptions of the reference man model (International 

Commission on Radiological Protection, 1975).

NUREG-1668 3-122
4 FUTURE IMPROVEMENTS TO THE TPA CODE 

Analysis of the performance of the proposed repository at YM is an important activity that assists staff in their 

prelicensing interactions with DOE and improves development of review plans by identifying and focusing 

attention on issues most important to performance. The TPA Version 3.1.4 code enhances the staff capability 

to review the DOE TSPA by providing staff with a code more flexible and user friendly and incorporates more 

detailed representations of the proposed site and engineering design relative to previously developed TPA 

codes. Although TPA Version 3.1.4 code represents a significant improvement over previous TPA codes, it 

is recognized that site characterization and engineering design are still evolving, and further refinements of 

this capability are anticipated. Improvements to the code are expected to incorporate new information for 

either the site or engineering design, improve modeling approaches (e.g., reduce conservatism, improve 

credibility of model), or improve the structure of the code (e.g., efficiency, flexibility, and ease of use). 

Some significant improvements to TPA Version 3.1.4 have been identified based on new information. A 

change in the DOE material selection for the inner overpack of the WP (i.e., from Alloy 625 to Alloy C-22) 

occurred between the time of development and documentation of the code. Although the rate of corrosion of 

Alloy C-22, which results in an extended service life of Alloy C-22 compared to that for Alloy 625, has been 

incorporated into the current model, the current model does not account for differences in the mode of 

degradation (e.g., Alloy 625 degrades through pitting corrosion, whereas Alloy C-22 degrades through general 

passive corrosion). Modifications to the TPA Version 3.1.4 code may be needed if differences in these modes 

of degradation of the WP are found to have a significant effect on estimating radionuclide releases from the 

WP. For example, the manner in which water enters, fills, and leaves the WP through the corrosion pits of 

Alloy 625 could be significantly different from Alloy C-22 undergoing uniform corrosion. Therefore, the 

bathtub model for WP failure and the parameter that affects the amount of water entering the WP (i.e., F..) 

need to be evaluated considering the mode of corrosion of Alloy C-22. Additionally, the conservatism in the 

SEISMO module and mechanical failure of the WP, not considered significant to performance for WPs with 

lifetimes of 3000-5000 yrs, need to be examined for their influence on performance of a longer lived WP. 

Sensitivity analyses performed at the process level (i.e., examination of the sensitivity of overall performance 

to variation of parameters and models within a specific process of the TPA Version 3.1.4 code, such as WP 

degradation and infiltration) and the system level (i.e., examination of the sensitivity to overall performance 

because of variation of parameters and models within the TPA Version 3.1.4 code) have been completed and 

are reported in Volume II of this NUREG. Initial use of the TPA Version 3.1.4 code to analyze the proposed 

repository has provided valuable insights assisting identification of code limitations and areas for 

improvement. These initial results have also aided in prioritizing code refinements based on importance to 

modeled repository performance. It should be noted that limitations in the analyses include identification of 

sources of potentially significant sensitivity that could not be evaluated in the sensitivity analysis because of 

their absence in the models and parameters of the code. Radionuclide transport through the emplacement drift 

invert is an example of a potentially significant source of sensitivity not included in the current code. In 

addition to the technical improvements to the modules of TPA Version 3.1.4 code, a number of additions to 

the input options for the code were identified to improve its flexibility and ease of use.

4-1 NUREG-1668
A number of suggestions have been made for improving the TPA Version 3.1.4 code and analyses to evaluate 

the need for or significance of these improvements. The suggestions for these future efforts include: 

The effect of secondary mineral formation could be added to EBSREL. The formation 

of secondary minerals may significantly reduce dissolution rates of the SF. 

The influence of the assumption of the presence of backfill in the diffusive release 

calculation could be reevaluated. Diffusive release through the backfill would not be 

considered if its contribution to the total release from the EBS is insignificant. 

A new module could be added to EBSREL to account for the effect of physical 

properties of a cementitious invert, such as porosity, permeability, and retardation (Kd) 

on the transport of radionuclides. The effect of this change will be radionuclide-specific, 

because of variation in retardation by element, and dependent on the matrix permeability 

of the invert material. 

Aqueous release of the inventory between the fuel pellet and cladding could be invoked 

in EBSREL by developing input values for the fraction of inventory in this gap (because 

of limitations in obtaining values for the gap fraction, aqueous release of the gap 

fraction is included in the current module but was not invoked by setting the gap 

fraction values to zero). Including the release of the gap fraction is expected to increase 

releases at early times. 

New models could be added to provide for more realistic treatment of the transient 

nature of WP and SF wetting. The current approach diverts the additional quantity of 

flow from increased infiltration or reflux to a fixed number of wetted WPs. In addition, 

mechanistic models may replace the currently used abstracted flow factors. 

New parameters account for variability in the bathtub height. A separate bathtub height 

could be used for each repository subarea and failure type (e.g., corrosion, initially 

defective, and seismic failures) as compared with the current approach, which uses a 

single bathtub height for subareas and failure types. 

A new relationship between acceleration and fractional area of rockfall (identified as 

the seismic heterogeneity factor in the TPA Version 3.1.4 code) could be developed in 

the SEISMO module. The number of ground acceleration magnitude classes will be 

increased as a refinement. Thus, the fractional area of rockfall will be a function of the 

magnitude of the acceleration for the seismic event, which will provide a more 

realistic approach.

NUREG-1668 4-2
The height of the yield zone in SEISMO, used in determining the volume of the rockfall 

for impact load calculation, could be made a sampled parameter. In the TPA 

Version 3.1.4 code, the height of the yield zone is specified as a set of constants 

corresponding to acceleration values and rock tyld (these values are specified within an 

external data file for the SEISMO module). This change could provide refinement of 

height of the yield zone versus acceleration, allow a wider range of accelerations, and 

permit the user to account for uncertainty in the estimates of the height of the yield zone 

by specifying a probability distribution and range for the parameters for the height of 

the yield zone. This change is expected to reduce conservatism in the estimates of WP 

failures. 

Consideration of the time of WP failure from seismically induced rockfall could be 

improved by adding distinct time periods for grouping the failures. The TPA 

Version 3.1.4 conservatively uses a single time period to consolidate all the scenario 

failures of the WP (i.e., WP failure from seismicity, faulting, and IA are grouped 

together and assumed to occur at the earliest failure time). Because a number of seismic 

events are possible over 10,000 yr, anew approach could be developed to allow the user 

to specify distinct time periods for which the seismic failures are grouped within each 

specified time period, and failure is assumed to occur at the beginning of the respective 

time period. Because of their low probabilities of occurrence, faulting and igneous 

failures are assumed to occur once during a 10,000-yr compliance period; therefore, a 

single failure time for each event is sufficient. This change will reduce conservatism in 

the time of occurrence of the scenario failures of the WP. 

Distribution functions of particular radionuclides (e.g., uranium, neptunium, plutonium, 

americium, and thorium) are expected to be correlated because of similar water 

conditions, such as pH, that will affect sorption. Inclusion of correlation between 

distribution functions will improve realism of radionuclide transport. It is uncertain 

what effect this change will have on performance, although lower doses will tend to be 

lower and higher doses will tend to be higher for the radionuclides with correlated 

distribution coefficients. 

Evaluation of the TPA Version 3.1.4 code is continuing, and additional improvements are expected as the 

results of the sensitivity analysis are further reviewed and additional calculations performed.

4-3 NUREG-1668
5 REFERENCES 

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NUDOC Accession Number 9508030112. Washington, DC: U.S. Nuclear Regulatory Commission. 

Ahn, T., and P. Soo. 1983. Container Assessment---Corrosion Study of HLW Container Materials, Quarterly 

Progress Report, October-December 1983. BNL-NUREG-34220. Upton, NY: Brookhaven 

National Laboratory. 

Ahn, T., and P. Soo. 1984. ContainerAssessment-Corrosion Study of HLW Container Materials, Quarterly 

Progress Report, January-March 1984, April-June 1984. Upton, NY: Brookhaven 

National Laboratory. 

Baca, R.G., G.W. Wittmeyer, and R.W. Rice. 1999. Analysis of contaminant dilution in groundwater. Scoping 

Calculations for Revisions to the EPA Standard. NUREG-1538. Washington, DC: U.S. Nuclear 

Regulatory Commission. 

Baes, C.F., II, R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1982. A Review and Analysis of Parameters for 

Assessing Transport of Environmentally Released Radionuclides through Agriculture. ORNL-5876. 

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NUREG-1668 5-4
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NUREG-1668 5-6
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NUREG-1668 5-10
APPENDIX A 

REFERENCE DATA SET 

The base case (reference) data set (i.e., primary input file) is presented in the following tables. This input data 

set includes information provided by various key technical issue (KTU) teams. It is expected the base case data 

set will evolve further as the KTI teams continue to use the code and analyze results.

A-1 NUREG-1668
title 

Input file tpa.inp as supplied with TPA Version 3.1 Code. 

Base case data set Rev 3.1.4 4/06/98

NUREG- 1668 A-2
***»>> Disruptive Scenario flags «<<***

ifla ce ari~la~ye Vocansm~ = l no--) sr ptiv~ slf-xpl nat ry 

iflag VoulcangsDisruptiveScenarioFlag(yes= I,no=-O) self-explanatory 0 -* 

iflag Seisi-nicDisruptiveScenarioF ag(yes=],no=-O) sefexplanatory 0 *

A-3 NUREG-1668

** 

** 

**
** ***>>> Problem size specification <<***

** Number and Location of SubAreasim] Based on Fig 3.4-1 in TSPA95 

"**subarea 

**I 

"**ZONE T="ONE RECTANGULAR ZONE Subarea", F=POINT 

** 547405.7 4076362.2 

** 548469.3 4076362.2 

** 548469.3 4079237.8 

** 547405.7 4079237.8 

** 547405.7 4076362.2 

subarea 

7 

ZONE T="Subarea I",1=5,F=POINT 

547472.0, 4079323.7 

548069.2, 4079136.5 

547847.3, 4077816.2 

547318.4, 4077934.0 

547472.0, 4079323.7 

ZONE T="Subarea 2",1=5,F=POINT 

548069.2, 4079136.5 

548609.7, 4078968.6 

548547.9, 4077654.1 

547847.3, 4077816.2 

548069.2, 4079136.5 

ZONE T="Subarea 3",I=5,F=POINT 

547318.4, 4077934.0 

547847.3, 4077816.2 

548322.7, 4077192.2 

547474.7, 4077281.6 

547318.4, 4077934.0 

NUREG-1668 A-4
ZONE T="Subarea 4",I=5,F=POINT 

547847.3, 4077816.2 

548547.9, 4077654.1 

548504.8, 4077170.0 

548322.7, 4077192.2 

547847.3, 4077816.2 

ZONE T="Subarea 5",I=5,F=POINT 

547474.7, 4077282.6 

547887.3, 4077238.1 

547995.0, 4076338.9 

547670.4, 4076435.5 

547474.7, 4077282.6 

ZONE T="Subarea 6",I=5,F=POINT 

547887.3, 4077238.1 

548322.7, 4077192.2 

548319.5, 4076220.2 

547995.0, 4076338.9 

547887.3, 4077238.1 

ZONE T="Subarea 7",I=5,F=POINT 

548322.7,4077192.2 

548504.8,4077170.0 

548473.1, 4076533.7 

548319.5,4076220.2 

548322.7,4077192.2 

iconstant 

StartAtSubarea 

** Stop value should be zero to perform all remaining subareas.

A-5 NUREG-1668
iconstant 

StopAtSubarea 

0 

** Number and Names of Nuclides to be Tracked for Aqueous Pathway 

** NOTE: The order of the nuclide names below should not be changed. 

** This order is used in NEFTRAN for decay chain information. 

aqueousnuclides 

20 

Cm246 

U238 

Cm245 

Arn241 

Np237 

Am243 

Pu239 

Pu240 

U234 

Th230 

Ra226 

Pb210 

Cs135 

1129 

Tc99 

Ni59 

C14 

Se79 

Nb94 

C136

NUREG- 1668 A-6
** 

** 

**

***>>> EXEC <<<***

constant SeedForRandomNumber Seed for random number generator 188910452.0 - ** 

iflag LatinHypercubeSampling(yes= l,no=0) Latin Hypercube Sampling flag I - ** 

iconstant NumberOfRealizations Number of realizations I - ** 

iconstant StartAtRealization Index of first realization to be executed. I - ** 

iconstant StopAtRealization Index of last realization to be executed. 0 - ** 

Should be zero to perform all remaining 

realizations. 

constant MaximumTime[yr] Simulation time period 1.0e4 - ** 

iconstant NumberOtTimeSteps Number of time steps for simulation 201 - ** 

time period 

constant RatioOfLastToFirstTimeStep Ratio of last to first time step. When the 100.0 - ** 

ration is set as i, then all time intervals 

are equal. When the ratio is greater than 

1, the time intervals geometrically 

progress until the maximum time. 

** ***>>> APPEND OPTIONS <<<*** 

iconstant OutputMode(0=None, I =AII,2=UserDefine Flag for appending intermediate output 0** 

d) to files (0 = no files appended, I = all 

files appended, and 2 = all files for 

specified realizations) 

iconstant UserDefinedLowerRealizationAppended For output mode = 2, append files are 2** 

written beginning with this realization. 

iconstant UserDefinedUpperRealizationAppended For output mode = 2, append files are 3 ** written ending with this realization.

A-7 NUREG-1668
** 

** 

**

***>>> UZFLOW <<<***

uniform ArealAverageMeanAnnuallnfi Areally averaged mean annual 1.0, 10.0 Current best estimate by Unsaturated and ** 

ltrationAtStart[mm/yr] infiltration for the initial Saturated Flow under Isothermal Conditions 

(current) climate (mm/yr) (USFIC) team [see the Issue Resolution Status 

Report (IRSR) on shallow infiltration, Nuclear 

Regulatory Commission, 1997)]. 

uniform MeanAveragePrecipitationMu Mean average precipitation 1.5, 2.5 Best estimate by USFIC team (see the IRSR on ** 

ltiplierAtGlacialMaximum (MAP) multiplier at glacial climate, Nuclear Regulatory Commission, 1997). 

maximum 

uniform MeanAverageTemperaturelnc Mean average temperature -10, -5 Best estimate by USFIC team (see IRSR on ** 

reaseAtGlacialMaximum[deg (MAT) increase at glacial climate, Nuclear Regulatory Commission, 1997). 

C] maximum ("C) 

constant TimeStepForClimate[yr] Time step used in the climate 500.0 Value selected on the basis of efficiency of the ** 

model TPA Version 3.1.4 code and the rate for climate 

change 

constant StandardDeviationOfMAPAb Standard deviation of MAP 0.0 Deviation about mean is not considered necessary ** 

outMeaninOneTimePeriod[m about mean in one time period at this point and allows efficient execution of the 

m/yr] (mm/yr) code. 

constant StandardDeviationOfMATAb Standard deviation of MAT 0.0 Deviation about mean is not considered necessary ** 

outMeanlnOneTimePeriod[de about mean in one time period at this point and allows efficient execution of the 

_gC] (1C) code. 

constant CorrelationBetweenMAPAnd Correlation between MAP and -0.8 Best estimate by the USFIC team. ** 

MAT MAT Derived from climate expert elicitation 

(DeWispelare, et al., 1993) 

iconstant ClimatePerturbationSet Options: I and 2; currently both I** 

sets are identical.

NUREG-1668 A-8
** 

** 

**

***>>> NFENV <<<***

ifag TabularTemperatureRHFlag(yes=l,no=0) Flag to use teflti.inp file containing 0** 

temperature and relative humidity (RH) 

data computed external to the TPA 

Version 3.1.4 code. 

iconstant nsetUsedToPickTempRHDataSet Selects the data set in teftli.inp that has I** 

up to four sets of data (options I to 4). 

iconstant UseReflux2 Flag to select reflux model (reflux i = 0 1** 

__ _ __ _ __ _ and reflux2 = I) 

constant LengthOfRefluxZone[m) Length of reflux zone (m) in reflux i 20 Thermal Effects on Flow ** 

model (TEF) team best estimate 

constant MaximumFluxlnRefluxZonc[m/s] Maximum flux in reflux zone (mis) in 1.Oe-9 Center for Nuclear Waste ** 

reflux I model Regulatory Analyses 

(CNWRA) best estimate 

constant PerchedBucketVolumePerSAareatm3/m2] Perched bucket volume per subarea 0.5 TEF team best estimate ** 

(m3/m2) in reflux I model 

constant Reflux2Thickness Thickness of dry-out zone (m) in the 100.0 100 m (328 ft) consistent ** 

reflux2 model with MULTIFLO 

simulations of the dry-out 

zone 

constant Reflux2Porosity Porosity of rock in the reflux zone in the 0.14 CNWRA best estimate ** 

___reflux2 model 

constant Reflux2Satlnit Initial water saturation in the reflux zone 0.9 CNWRA best estimate ** 

in the reflux2 model 

constant Reflux2SatResid Residual water saturation in the reflux 0.1 CNWRA best estimate ** 

zone in the reflux2 model 

constant Reflux2Period Reflux cycle in the reflux2 model; when 100.0 Best estimate, upper value ** 

this factor has a value of 1, dry-out from MULTIFLO runs 

I water recycles each year I 1 _ _1

A-9 NUREG-1668
constant Reflux2LossI Fraction of infiltration-derived water 0.1 TEF team best estimate ** 

that escapes each year 

constant Reflux2LossD Fraction of dry-out zone derived water 0.1 TEF team best estimate ** 

that escapes the reflux cycle each year 

constant WPLength[m] Outer length of waste package (WP) (m) 5.682 Doering (1995) ** 

constant WPDiameter[m] Outer diameter of WP (m) 1.802 Doering (1995) ** 

constant EmplacementDriftDiameter[m] Emplacement drift diameter (in) 5.0 Civilian Radioactive Waste ** 

Management System 

(CRWMS) (1996) 

constant WPSpacingAlongEInplacemenntDrift[nij WP spacing (in) 19.0 TRW Environmental Safety ** 

Systems, Inc. (1995) 

constant ArealMassLoading[MTU/acre Areal mass loading (AML) in metric 83.0 Civilian Radioactive Waste ** 

tons of uranium (MTU)/acre Management System 

Management and Operating 

Contractor (CRWMS 

M&O) (1994) 

constant WastePackagePayload[MTU] WP payload (MTU/package) 9.76 U.S. Department of Energy ** 

(1987) 

constant AgeOfWaste[yrJ Age of waste at the time of emplacement 26.0 ** 

constant AmbientRepositoryTemperature[C] Ambient repository temperature ('C) 20.0 Assumed in TRW ** 

Environmental Safety 

Systems, Inc. (1995) 

constant MassDensityofYMRock[kg/m^3] Mass density of rock (kg/m3) 2580.0 U.S. Department of Energy ** 

(1990) 

constant SpccificHeatotYMRock[J/(kg-K)I Specific heat of rock (J/kg-K) 840.0 U.S. Department of Energy ** 

(1990) 

uniform ThermalConductivityotYMRock[W/(m-K)] Thermal conductivity of rock (W/m-K) 1.8, 2.2 U.S. Department of Energy ** 

(1993) 

constant EmissivityOfDriftWall[-j Emissivity of drift wall 0.8 Incropera & DeWitt (1995) ** 

constant EmissivityOiWastePackage[-] Emissivity of WP 0.7 Incropera & DeWitt (1995) **

NUREG- 1668 A-10
A-l I NUREG-1668

constant ThermalConductivityOfFloor[W/(m C)] Thermal conductivity of floor (W/m-°C) 0.6 Incropera & DeWitt (1995) ** 

constant EffectiveThermalConductivityOfUnbackfill Effective thermal conductivity of 0.90 Manteufel (1997) ** 

edDrift[W/(m C)] unbackfilled drift (W/m-*C) 

constant TimeOfBackfillEmplaced[yr] Time of backfill emplacement (yr) 10001.0 Civilian Radioactive Waste ** 

Management System 

Management and Operating Contractor (1996) 

constant EffectiveThermalConductivityOfBackfill[ Effective thermal conductivity of 0.60 U.S. Department of Energy ** 

W/(m C)] backfill (W/m-0C) (1990) 

constant ThermalConductivityOflnnerStainlessStcel Thermal conductivity of inner stainless 15.0 Incropera & DeWitt (1992) ** 

Wall[W/m C] steel container wall (W/m-"C) 

constant ThermalConductivityOfOuterCarbonSteel Thermal conductivity of outer carbon 50.0 Incropera & DeWitt (1992) ** 

Wall[W/m C] steel wall (W/m-"C) 

constant EffectiveThermalConductivityOfBasket&S Effective thermal conductivity of basket 1.0 Assumed based on 

FinWP[W/(m C)] & spent fuel (SF) in WP (W/m-"C) Manteufel and Todreas __ (1994) 

constant ElevationOfRepositoryHorizon[m] Elevation of repository horizon above 1072.0 Civilian Radioactive Waste ** .. _sea level (m) Management System (1996) 

constant ElevationOfGroundSurface[m] Elevation of ground surface above sea 1400.0 Civilian Radioactive Waste ** 

_level (i) ___IIManagement System (1996)
***>>> EBSFAIL <<<***

constant OuterWPThickness[m] Thickness of the outer overpack (m) 0.1 Doering (1995) ** 

constant InnerWPThickness[m] Thickness of the inner overpack (m) 0.02 Doering (1995) ** 

constant MetalGrainRadius[micrometerl Average radius of the metal grains 13.75 Container Life and Source Term ** 

constituting the WP outer overpack (mam) (CLST) meetings 04/11/97, 

04/14/97, and 04/17/97 

constant GrainBoundaryThickness[micrometer] Thickness of grain boundary used in the 7.0e-4 Range about value assumed in ** 

model for calculating coupled oxygen Mohanty, et al. (1996); Lobnig, 

diffusion along grain boundaries in metal et al. (1992) 

constant DryOxidationConstant Constant relating matrix and grain 0.00001 Assumed in Mohanty, et al. (1996) ** 

boundary oxygen diffusivities in metal 

constant CriticalRelativeHumidityHiumidAirCorrosion Critical relative humidity above which 0.55 CLST meeting 04/24/97 ** 

humid-air corrosion may initiate 

normal CriticalRelativeHumidityAqueousCorrosion Critical relative humidity above which 0.75, 0.85 CNWRA staff best estimate ** 

aqueous corrosion may initiate 

uniform ThicknessOfWaterFilm[m] Thickness of water film on WP surface 0.001, Assumed in Mohanty, et al. (1996) ** 

(m) 0.003 

constant BoilingPointOtWater[C] Boiling point of water at Yucca Mountain 97.0 Manteufel (1997) ** 

(YM) (°C) 

constant OuterOverpackErplntercept Outer overpack E_ intercept in mV,,, -620.3 Cragnolino, et al. (1998) ** 

constant TempCoefOfOuterPackErplntercept Temperature coefficient of outer overpack 0.47 Cragnolino, et al. (1998) ** Ern 

constant OuterOverpackErpSlope Outer overpack E,, slope in mV -95.2 Cragnolino, et al. (1998) ** 

constant TempCoefOfOuterPackErpSlope Temperature coefficient of outer overpack 0.88 Cragnolino, et al. (1998) ** 

E,, slope I II

NUREG-1668

** 

** 

**

A- 12
uniform InnerOverpackErplntercept Inner overpack Ep intercept in mV,. 1040.0, Sridhar, et al. (1995). Values listed ** 

1240.0 are for Alloy C-22. For Alloy 625, 

range is 48.5, 148.5. For Alloy 825, 

range is 372.8, 472.8. 

constant TempCoefOflnnerPackErpIntercept Temperature coefficient of inner overpack 0.0 Sridhar, et al. (1995). Values listed ** 

Erp intercept in mV/(°C) are for Alloy 625 and Alloy C-22. 

For Alloy 825, value is -4.1. 

constant InnerOverpackErpSlope Inner overpack Erp slope in mV 0.0 Sridhar, et al. (1995) Value listed is ** 

for Alloy C-22. For Alloy 625, value is -160.8. For Alloy 825, 

value is -64.0. 

constant TempCoefOflnnerPackErpSlope Temperature coefficient of inner overpack 0.0 Sridhar, et al. (1995) Values listed ** 

E,p slope in mV/(°C) are for Alloy C-22 and Alloy 625. 

For Alloy 825, value is -0.80 

constant OuterWPBetaKineticsParameterforOxygen Transfer coefficient for oxygen reduction 0.75 Assumed, based on Calvo and ** 

reaction ( p0o ) for the WP outer Schriffrin (1988) 

overpack I.. 

constant OuterWPBetaKineticsParameterforWater Transfer coefficient for water reduction 0.5 Assumed, based on Bockris and ** 

reaction ( &3,2o ) for the WP outer Reddy (1970) 

overpack 

constant InnerWPBetaKineticsParameterforOxygen Transfer coefficient for oxygen reduction 0.75 Assumed, based on Calvo and ** 

reaction ( 10o2 ) for the WP inner Schriffrin (1988) 

overpack 

constant InnerWPBetaKineticsParameterforWater Transfer coefficient for water reduction 0.5 Assumed, based on Bockris and ** 

reaction (P, o) for the WP inner overpack Reddy (1970) 

constant OuterWPRateConstantforOxygenReduction[ Rate constant for oxygen reduction for the 3.8e12 Assumed, based on Bockris and ** 

coulomb-mnmole/yr] WP outer overpack (C-m/yr/mol) Reddy (1970); Calvo (1979) 

constant OuterWPRateConstantforWaterReduction[co Rate constant for water reduction for the I 1.6e-I Assumed, based on Turnbull and ** 

ulomb-m/mA2/yr] WP outer overpack (C/m21yr) Gardner (1982)

A- 13 NUREG-1668
constant OuterWPActivationEnergyforOxygenReducti Activation energy for oxygen reduction 37300.0 Assumed, based on Calvo (1979) ** 

on[J/mole] for WP outer overpack (J/mol) 

constant OuterWPActivationEnergyforWaterReductio Activation energy for water reduction for 25000.0 Assumed, based on Heusler (1976) ** 

n[J/mole] WP outer overpack (J/mol) 

constant InnerWPRateConstantforOxygenReduction[c Rate constant for oxygen reduction for 3.0eI 0 Assumed, based on Bockris and ** 

oulomb-m/mole/yr] WP inner overpack (C-m/yr/mol) Reddy (1970); Calvo (1979) 

constant InnerWPRateConstantforWaterReduction[co Rate constant for water reduction for WP 3.2 Assumed, based on Turnbull and ** 

ulornb-m/mA2/yr! inner overpack (C/m 2

/yr) Gardner (1982) 

constant InnerWPActivationEnergyforOxygenReducti Activation energy for oxygen reduction 40000.0 Assumed, based on Calvo (1979) ** 

on[J/mole] reaction for WP inner overpack (J/mol) 

constant InnerWPActivationEnergyforWaterReductio Activation energy for water reduction 25000.0 Assumed, based on Heusler (1976) ** 

n[J/mole] reaction for WP inner overpack (J/mol) 

constant AA-I 1[Crm2/yr] Passive current density for WP outer 3.15e5 Assumed, based on Alvarez and ** 

overpack (C/m2

/yr) Galvele (1984) 

uniform AA_2_I[C/in2/yr] Passive current density for WP outer 2.0e4, CNWRA experimental data. Values ** 

overpack (C/m2

/yr) 6.3e4 for Alloys C-22, 625, and 825 

constant MeasuredGalvanicCouplePotential Experimentally measured galvanic couple -0.46 Scully and Hack, 1984 ** 

potential carbon steel and Alloy 625 

uniform CoefForLocCoriOfOuterOveirpack Coefficient for localized corrosion rate of 8.66e-4, Marsh and Taylor (1988) ** 

outer overpack 8.66e-3 

constant ExponetForLocCorrOfOuterOverpack Exponent for localized corrosion rate of 0.45 Marsh and Taylor (1988) ** 

outer overpack 

constant HumidAirCorrosionRate[m/yrl Humid air corrosion rate (m/yr) 1.1 6e-5 Assumed ** 

constant LocalizedCorrRateOflnnerOverpack[m/yr] Corrosion rate for localized corrosion of 2.5e-4 Assumed in Mohanty, et al. (1997). ** 

inner overpack 1997 Sensitivity Analysis indicates 

I low sensitivity. 

constant FractionalCouplingStrength Efficiency factor, varying from 0 to 1, 0.0 Switch: full credit = 1; no credit - ** 

representing galvanic coupling between 0. 1997 Sensitivity Analysis 

the outer and inner overpack not complete indicates high sensitivity. Continue 

sent evaluating.

NUREG- 1668 A- 14
constant FactorForDefiningChoiceOfCritPotentiai Factor for defining choice of critical 0.0 Mohanty, et al. (1997) ** potential (initiation or repassivation) 

constant CritChlorideConcForFirstLayer[moL/L] Critical chloride concentration for 3.Oe-4 Galvele (1978) ** 

localized corrosion of outer overpack 

constant CritChlorideConcForSecondLayer[moIJL] Critical chloride concentration for 1.0 Sridhar, et al. (1995). Value listed is ** 

localized corrosion of outer overpack for Alloy C-22. Value for Alloy 625 

is 3.0e-2. Value for Alloy 825 is 

2.0e-3 

uniform ChlorideMultFactor Factor for changing chloride 1.0, 30.0 Assumed. Note solubility limit for ** 

concentration NaCI = 6 mol/liter. 1997 Sensitivity 

Analysis indicates high sensitivity. Continue evaluating. 

constant ReferencepH pH of water (does not change). 9.0 Assumed in Mohanty, et al. (1997); * 

based on MULTIFLO calculations. 

constant WPsurfaceScaleThickness[m] Scale deposit on WP surface 0.0 A value of 0.0 implies no scale 

deposit. 

constant TortuosityOfScaleonWP Tortuosity of a porous layer scale 1.0 In the case of no deposit, the value * 

deposited on the WP. Thickness does not is 1.0. 

change with time. 

constant PorosityOfScaleonWP Porosity of the layer deposited on the WP. 1.0 In the case of no deposit, the value ** 

Porosity does not change with time. is 1.0. 

constant YieidStrength[MPa] Yield strength of outer overpack 205.0 American Society for Testing ** 

I_ Material (1995) 

constant SafetyFactor Safety factor for residual stresses 1.4 Assumed in Mohanty, et al. (1997) ** 

constant FractureToughness[MPa-m**0.51 Fracture toughness of outer overpack 250.0 Assumed **

A-15 NUREG-1668
** 

** ***>>> SEISMO <<<***

hazardcurve SeismicHazardCurveforSEISMO Minimum peak 4 Wong, et al. (1995), Figure 5, p. 59 ** 

ground 0.1 500.0 

acceleration for 0.22400.0 

bins, return period 0.38000.0 

(yr); number of 

magnitudes for 0.4 20000.0 

recurrence of 

seismic events, 

magnitude, and 

recurrence time 

constant WeightPercentageOfRockFallThat Weight 1.0 Not used ** 

HitsWPforSEISMO percentage of rock 

fall that hits WP 

constant WeightOIWPforSEISMO[N] Weight of WP 1.27D05 Not used ** 

used in impact 

calculation during free fall (N) 

constant WPStiffnessforSEISMO[Pa*mI WP stiffness used 1.21DI0 Calculated based on a simple supported beam with load applied at ** 

for rockfall impact the center of the beam using the following equation: 

calculation (Pa-m) K = 48 E I/L' 

where : 

K is the stiffness of the WPs; 

I = i1 R3

t; 

E is the Young's modulus of the WPs; 

L is the length of the WPs; 

R is the average of inner and outer radii of the WPs; 

t is the thickness of the WPs. 

constant WPModulusOfElasticityforSEISM WP modulus of 2.07D I1 Popov (1970), Appendix Table 1, p. 423 ** I OfPal elasticity (Pa) I I_1___

NUREG-1668 A- 16
normal RockModulusOfElasticityforSEIS Rock modulus of 2.76D10, Brechtel, et a]. (1995), Table 2-4, p. 2-12 ** 

MO[Pa] elasticity (Pa) 4.14D10 

constant WPPoissonRatioforSEISMO[t WP Poisson ratio 0.2D0 Popov (1970), p. 36 ** 

normal RockPoissonRatioforSEISMO[] Rock Poisson 0.15, 0.25 Brechtel, et al. (1995), Table 2-4, p. 2-12 supplies the mean value ** 

ratio for the parameter. The 25% variation for the assumed normal 

distribution of this parameter is adopted from Table 5.1, p. 5-19 of 

the DOE Site Characterization Plan (U.S. Department of Energy, 

1988). 

constant RockFallingDistanceforSEISMO[ Rock falling 2.ODO Estimated based on the diameter of the emplacement drift minus ** m] distance gap the diameter of the WPs minus the height of the pedestal 

between the top of 

WP and the drift 

crown (m) 

constant WPFallingDistanceforSEISMO[m] WP falling 0.3D0 Not currently used in this version of TPA ** 

distance for free 

fall calculations 

(M) 

iconstant WPNumberofSupportPairforSEIS 

MO 

Number of 2.0 The conceptual SEISMO module is based on an assumption that ** 

support pairs on WPs can be represented as simply supported beams. Consequently, the WP pedestal two support beams are assumed. 

constant WPSupportStiffnessforSEISMO[p Stiffness of the 5.5D09 Calculated using the following equation: ** 

a m] pedestal K = A E/L, 

where: 

K is the support stiffness; 

A is the cross-sectional area of the support; 

E is the Young's modulus of the support; L is the height of the support. 

constant DistributionJointSpacing 

MO 

IforSEIS Fractional areal 5.0D-03 Based on Brechtel, et al. (1995), Fig. 7-3, p. 7-9, rock conditions 1, ** 

coverage for rock 2, and 3 take up only 1.5% of the area in total and are each equally condition I subdivided into 0.5% of the repository. 

constant DistributionJointSpacing2forSEIS Fractional areal 5.OD-03 Based on Brechtel, et al. (1995), Fig. 7-3, p. 7-9, rock conditions 1, ** MO coverage for rock 2, and 3 take up only 1.5% of the area in total and are each equally condition 2 subdivided into 0.5% of the repository.

A- 17 NUREG-1668
constant DistributionJointSpacing3forSEIS Fractional areal 5.0D-03 Based on Brechtel, et al. (1995), Fig. 7-3, p. 7-9, rock conditions 1, ** 

MO coverage for rock 2, and 3 take up only 1.5% of the area in total and are each equally 

condition 3 subdivided into 0.5% of the repository. 

constant DistributionJointSpacing4forSEIS Fractional areal 0.629D0 Based on Brechtel, et al. (1995), Fig. 7-3, p. 7-9, about 62.9% of ** 

MO coverage for rock the repository can be characterized as rock condition 4. 

condition 4 

constant DistributionJointSpacing5forSEIS Fractional areal 0.356D0 Based on Brechtel, et al. (1995), Fig. 7-3, p. 7-9, rock condition 5 ** 

MO coverage for rock occupies about 35.6% of the repository. 

condition 5 

normal SEISMOJointSpacingi 1m] Joint spacing (JS) 0.466, 0.600 Not all rocks falling from the roof of the emplacement drift will ** 

for rock condition impact WPs. The effective size of the rock that impacts WPs will 

I be controlled by JS. For abstraction into the TPA Version 3.1.4 

code, the TSw2 thermomechanical unit was subdivided into five 

distinct rock conditions. The rock conditions are estimated using 

available JS (Brechtel, et al., 1995) information for the TSw2 

provided in Brechtel, et al. (1995), which summarizes data 

collected from NRG holes. In the report, the JS information is 

presented in terms of rating of JS (Hoek and Brown, 1982). Three 

JS ratings are observed in the TSw2 Unit: 5, 8, and 10. JS rating of 

5 indicates a JS that is smaller than 0.06 m; JS rating of 8 is for JS 

between 0.06 m to 0.2 m; and JS rating of 10 represents JS of 0.2 to 

0.6 m. For the rock condition scheme used in the SEISMO module, 

rocks related to JS rating of 10 are further subdivided into three 

conditions: conditions 1, 2, and 3 respectively. This subdivision is 

considered reasonable because: (i) the range for JS rating of 10 

between 0.2 m to 0.6 m is large; and (ii) larger JS will induce 

greater impact failures on WPs. A normal distribution of JS is 

assumed in each rock condition category because no additional 

information about the distributions of JS' was compiled when the 

SEISMO module was coded. Future work will examine the validity 

of this assumption.

NUREG- 1668 A- 18
1 ~ ~ ~~~~~~~~I I __ _ _ _ _ _ _ _ _ _ _ __I 

A-19 NUREG-1668

norml S~SMOointpacng2[] J forroc

JS for rock 

condition 2

& i I Iof this assu- 6--n

0.333,0.466 normal SEISMOJointSpacing2[m] Not all rocks falling from the roof of the emplacement drift will impact WPs. The effective size of the rock that impacts WPs will 

be controlled by JS. For abstraction into the TPA Version 3.1.4 

code, the TSw2 thermomechanical unit was subdivided into five 

distinct rock conditions. The rock conditions are estimated using available JS (Brechtel, et al., 1995) information for the TSw2 

provided in Brechtel, et al. (1995), which summarizes data 

collected from NRG holes. In the report, the JS information is 

presented in terms of rating of JS (Hock and Brown, 1982). Three 

JS ratings are observed in the TSw2 Unit: 5, 8, and 10. JS rating of 

5 indicates a JS that is smaller than 0.06 m; JS rating of 8 is for JS 

between 0.06 to 0.2 m; and JS rating of 10 represents JS of 0.2 to 

0.6 m. For the rock condition scheme used in the SEISMO module, rocks related to JS rating of 10 are further subdivided into three 

conditions: conditions 1, 2, and 3 respectively. This subdivision is 

considered reasonable because: (i) the range for JS rating of 10 

between 0.2 m to 0.6 m is large; and (ii) larger JS will induce 

greater impact failures on WPs. A normal distribution of JS is 

assumed in each rock condition category, because no additional 

information about the distributions of JS was compiled when the 

SEISMO module was coded. Future work will examine the validity
I *I I

SEISMOJointSpacing3[m] IS for rock 

condition 3

0.20, 0.333

t t + I

SEISMOJointSpacing4[m] IS for rock 

condition 4

0.06, 0.20

normal

Not all rocks falling from the roof of the emplacement drift will 

impact WPs. The effective size of the rock that impacts WPs will 

be controlled by IS. For abstraction into the TPA Version 3.1.4 

code, the TSw2 thermomechanical unit was subdivided into five 

distinct rock conditions. The rock conditions are estimated using 

available JS information for the TSw2 provided in Brechtel, et al. 

(1995), which summarizes data collected from NRG holes. In the 

report, the JS information is presented in terms of rating of IS 

(Hoek and Brown, 1982). Three IS ratings are observed in the 

TSw2 Unit: 5, 8, and 10. IS rating of 5 indicates a IS that is smaller 

than 0.06 m; IS rating of 8 is for IS between 0.06 to 0.2 m; and IS 

rating of 10 represents IS of 0.2 to 0.6 m. For the rock condition 

scheme used in the SEISMO module, condition 4 is assumed to be 

the rocks with IS rating of 8. A normal distribution of IS is 

assumed in each rock condition category, because no additional 

information about the distributions of IS was compiled when the 

SEISMO module was coded. Future work will examine the validity 

of this assumption.

NUREG-1668

Not all rocks falling from the roof of the emplacement drift will 

impact WPs. The effective size of the rock that impacts WPs will 

be controlled by IS. For abstraction into the TPA Version 3.1.4 

code, the TSw2 thermomechanical unit was subdivided into five 

distinct rock conditions. The rock conditions are estimated using 

available IS information for the TSw2 provided in Brechtel, et al. 

(1995), which summarizes data collected from NRG holes. In the 

report, the IS information is presented in terms of rating of IS 

(Hoek and Brown, 1982). Three IS ratings are observed in the 

TSw2 Unit: 5, 8, and 10. IS rating of 5 indicates a IS that is smaller 

than 0.06 m; IS rating of 8 is for IS between 0.06 to 0.2 m; and IS 

rating of 10 represents IS of 0.2 to 0.6 m. For the rock condition 

scheme used in the SEISMO module, rocks related to IS rating of 

10 are further subdivided into three conditions: conditions 1, 2, and 

3, respectively. This subdivision is considered reasonable because: 

(i) the range for IS rating of 10 between 0.2 m to 0.6 m is large; 

and (ii) larger IS will induce greater impact failures on WPs. A 

normal distribution of IS is assumed in each rock condition 

category because no additional information about the distributions 

of IS was compiled when the SEISMO module was coded. Future 

work will examine the validity of this assumption.

normal

**

**

A-20

m I
I .7 1

SEISMOJointSpacing5[m] JS for rock 

condition 5

normal 0.03,0.06 **

4i C 

normal WPUltimateStrength[N/m2 ] Ultimate strength I. 103448D9 Assumed based on the strength of SS carbon steel (3.5% Ni and ** 

of WP (N/m 2

) 1.655172D9 0.4% C). The mean value of this parameter is obtained from the 

Appendix, Table 1, p. 423 of Popov (1970). The 20-percent 

variation for the assumed normal distribution is based on the expert 

judgment of the authors. 

constant GrainDensityforTSw2SEISMO[g/ Grain density for 2.55 Brechtel, et al. (1995) Table 2-4, p. 2-12 ** 

cm2

] Topopah 

Springs-welded 

(g/cm3) 

lognormal SeismicHeterogeneityFactor Factor that 1.OD-5, 0.5 CNWRA best estimate ** 

accounts for the 

potential effect of 

heterogeneity in 

each rock 

condition

A-21 NUREG-1668

Not all rocks falling from the roof of the emplacement drift will 

impact WPs. The effective size of the rock that impacts WPs will 

be controlled by JS. For abstraction into the TPA Version 3.1.4 

code, the TSw2 thermomechanical unit was subdivided into five 

distinct rock conditions. The rock conditions are estimated using 

available JS information for the TSw2 provided in Brechtel, et al. 

(1995), which summarizes data collected from NRG holes. In the 

report, the JS information is presented in terms of rating of JS 

(Hock and Brown, 1982). Three JS ratings are observed in the 

TSw2 Unit: 5, 8, and 10. JS rating of 5 indicates a JS that is smaller 

than 0.06 m; JS rating of 8 is for JS between 0.06 to 0.2 m; and JS 

rating of 10 represents JS of 0.2 to 0.6 m. For the rock condition 

scheme used in the SEISMO module, corjdition 5 is assumed to be 

equivalent to the rock with JS rating of 5. A normal distribution of 

JS is assumed in each rock condition category because no 

additional information about the distributions of JS was compiled 

when the SEISMO module was coded. Future work will examine 

the validity of this assum tion.
***>>> EBSREL <<<***

iflag FlowThroughModelFlag iflag = 0 for bathtub and iflag=1 for 0** 

flow-through 

lognormal FowFactor Flow convergence/divergence factor 0.01, 3.0 See Appendix B. ** 

lognormal FmultFactor Flow multiplication factor 0.01,0.2 See Appendix B. ** 

uniform SubAreaWetFraction Subarea wet fraction 0.0,1.0 See Appendix B. ** 

uniform DcfcctiveFractionOfWPs/cell Fraction of total WPs in an SA that fails 1.0e-4, No available literature; based on staff ** 

at time t = 0 I.Oe-2 discussions with Electric Power Research 

Institute (EPRI) and National Institute of 

Standards and Technology (NIST). 

constant WPInternalVolume[nm3] Internal WP volume where water can 4.83 Assumed in Mohanty, et al. (1996) ** 

reside (m3

) 

constant FlowOnsetTemperature[C] Flow onset temperature 999 Deliberately set at a large value to allow ** 

flow into the WP, starting at time 0.0 if 

water is available. 

constant SFDensitytkg/m3] Spent fuel density (kg/m 3

) 10600 Wilson (1990); based on UO, ** 

iconstant SurfaceAreaModel Selection of model for computing 1 Mohanty, et al. (1997). 1997 Sensitivity ** 

surface area of SF (options 1 and 2) Analysis indicates high sensitivity. 

Continue evaluating. 

iconstant IModcl Selection of model for computing SF 2 Mohanty, et al. (1997). 1997 Sensitivity ** 

dissolution (1 =absence of Ca and Si, Analysis indicates high sensitivity. 

2=presence of Ca and Si, Continue evaluating. 

3=user-specified). 

constant OxygenPartialPressurelatin] Oxygen partial pressure (over pressure) 0.21 Assumed in Mohanty, et al. (1996). 1997 ** 

(atm) Sensitivity Analysis indicates high 

sensitivity. Continue evaluating. Based on 

atmospheric composition. Stockman (1997).

NUREG-1668

** 

** 

**

A-22
constant NegativeLoglOCarbonateConcentration[m Negative log1 0 of carbonate 6.71 Equilibrated with CaCO3 and based on 

ol/L] concentration in surrounding water MULTIFLO calculations; no literature data 

(mol/L) available. 1997 Sensitivity Analysis 

indicates low sensitivity. 

constant UserLeachRate[kg/yr/m2] User-provided leaching rate (kg/yr/m2) 3.0e-3 Based on a value for the oxidation rate at ** 

value 3x 10-' only if IModel = 3 the Pefia Blanca natural analog (Murphy, et 

al., 1997) scaled to the mass of uranium in 

the repository, basecase surface area per 

container, and number of containers in the 

repository. 

uniform SFWettedFraction Water level inside the WP expressed as 0.0, 1.0 Assumed in Mohanty, et al. (1996) ** 

a fraction of the WP internal diameter 

constant RDBackfillCm Retardation factor of Cm in backfill 10.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-22 

constant RDBackfill_Pu Retardation factor of Pu in backfill 10.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfillU Retardation factor of U in backfill 1.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-22 

constant RDBackfillAm Retardation factor of Am in backfill 20.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfill.Np Retardation factor of Np in backfill 1.0 Assumed in Nuclear Regulatory 

surrounding the WP Commission (1995), p. A-23 

constant RD_BackfillTh Retardation factor of Th in backfill 10.0 Assumed in Nuclear Regulatory 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfillRa Retardation factor of Ra in backfill 30.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfillPb Retardation factor of Pb in backfill 5.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfillCs Retardation factor of Cs in backfill 8.0 Assumed in Nuclear Regulatory ** 

I surrounding the WP Commission (1995). p. A-23

A-23 NUREG-1668
constant RDBackfillI Retardation factor of I in backfill 1.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfillTc Retardation factor of Tc in backfill 1.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfill_Ni Retardation factor of Ni in backfill 2.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfill_Cl Retardation factor of Cl in backfill 1.0 Based on retardation factor for I ** 

surrounding the WP 

constant RDBackfillC Retardation factor of C in backfill 1.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfill_Se Retardation factor of Se in backfill 1.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant RDBackfill_Nb Retardation factor of Nb in backfill 1.0 Assumed in Nuclear Regulatory ** 

surrounding the WP Commission (1995), p. A-23 

constant GapFractionForCM246 Cm-246/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForU238 U-238/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForCM245 Cm-245/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap . 

constant GapFractionForAM241 Am-24 1/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForNP237 Np-237/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForAM243 Am-243/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForPU239 Pu-239/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForPU240 Pu-240/kg of SF in grain and pellet 0.0 Not used ** 

cladding gapt

NUREG-1668 A -24
constant GapFractionForU234 U-234/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForTH230 Th-230/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForRA226 Ra-226/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForPB2 10 Pb-210/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForCS 135 Cs- 135/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForl 129 1-129/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForTC99 Tc-99/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForNl59 Ni-59/kg of SF in grain and pellet 0.0 Not used 

cladding gap 

constant GapFractionForCL36 CI-36/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForC 14 C-14/kg uf SF in grain and pellet 0.0 Not used ** 

cladding gap ,_ 

constant GapFractionForSE79 Se-79/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

constant GapFractionForNB94 Nb-94/kg of SF in grain and pellet 0.0 Not used ** 

cladding gap 

normal InitialRadiusOfSFParticle[m] Initial radius of U0 2 particle (m) 7.0e-4, Guenther, et. al. (1991); Belle (1961). 1997 ** 

3.0e-3 Sensitivity Analysis indicates high 

sensitivity. Continue evaluating. 

constant RadiusOfSFGrain[in] Radius of U0 2 grain (m) 1.25e-5 Einziger, et al. (1992); Belle (1961). 1997 ** 

Sensitivity Analysis indicates low 

I sensitivity.

A-25 NUREG-1668
constant CladdingCorrectionFactor Cladding correction factor 1.0 ** 

normal SubGrainFragmentRadiusAfterTransFrac[ Subgrain fragment radius of U0 2 5.0e-7, Mohanty, et al. (1997). Distribution based ** 

ml particle after transgranular fracture (m); 2.0e-6 on distribution of grains or particles. 1997 

used only if fuel conversion takes place Sensitivity Analysis indicates high 

from U0 2, to U0 2 4 and U30; used only sensitivity. Continue evaluating. 

by the SF dissolution models that are 

dependent on exposed surface area. 

constant ThicknessOfCladding[m] Thickness of cladding (m) 6.1e-4 Smith & Baldwin (1989) ** 

constant SFC-141nventoryPerKgSF[ciJ (not used) 7.2e-4 Assumed in Mohanty, et al. (1996) ** 

constant CladC-141nventoryPerKlgSF[cil (not used) 4.89e-4 Park (1992) ** 

constant ZyrOxideAndCrudC- 141nvPerKgSF[ciI (not used) 2.48e-5 Park (1992) ** 

constant GapAndGrainBoundaryInventoryPerKgSF (not used) 6.2e-6 Park (1992) ** 

[ci] 

uniform SolubilityAm[kghi3] Solubility limit for Am 2.4e-8, TRW Environmental Safety Systems, Inc. ** 

2.4e-4 (1995), Table 6.3-1, based on expert 

elicitation described in Gauthier (1993), and 

Nitsche, et al. (1993). 1997 Sensitivity 

Analysis indicates high sensitivity. 

Continue evaluating. 

logtriangular SolubilityNp[kg/m3] Solubility limit for Np 1.2e-3, TRW Environmental Safety Systems, Inc. ** 

3.4e-2, (1995), Table 6.3-1, based on expert 

2.4e- I elicitation described in Gauthier (1993); 

Nitsche, et al. (1993); and Dyer (1993)." 

The beta distribution recommended in 

TSPA-95 is approximated by a triangular 

distribution. 1997 Sensitivity Analysis 

indicates high sensitivity. Continue 

evaluating. 

constant SolubilityI[kg/m3] Solubility limit for I 1.29e2 TRW Environmental Safety Systems, Inc. ** 

(1995), p. 6-7. Based on the assumption that 

there is no solubility controlling phase for 

iodine. Conservatively assume 1.0 mol/L.

NUREG-1668 A-26
constant SolubilityTc[kg/m3] Solubility limit for Tc 9.93el Based on CNWRA assumption that there is ** 

no solubility controlling phase for 

technetium. Conservatively assume 1.0 

mol/L. 

constant SolubilityCl[kg/m3] Solubility limit for Cl 3.6eI TRW Environmental Safety Systems, Inc. ** 

(1995), p. 6-7. Based on assumption that 

there is no solubility controlling phase for 

iodine. Conservatively assume 1.0 mol/L. 

constant Solubility_C[kg/m3] Solubility limit for C 1.4el TRW Environmental Safety Systems, Inc. ** 

(1995), p. 6-7. Based on assumption that 

there is no solubility controlling phase for 

carbon (Gauthier, 1993). Conservatively 

assume 1.0 mol/L. 

constant Solubility-U[kg/n3] Solubility limit for U 7.6e-3 TRW Environmental Safety Systems, Inc. ** 

(1995), Table 6.3-1, based on expert 

elicitation described in Gauthier (1993), and 

Wanner and Forest (1992). 1997 Sensitivity 

Analysis indicates low sensitivity. The apex 

of the logtriangular distribution (2.4e-6 to 

2.4e0) used in the sensitivity studies was 

chosen. 

constant SolubilityCm[kg/m3] Solubility limit for Cm 2.4e-4 TRW Environmental Safety Systems, Inc. ** 

(1995), Table 6.3-1, based on expert 

elicitation described in Gauthier (1993). 

Assumed that curium behaves similarly to 

Am

3÷. (See Fuger, 1992). 1997 Sensitivity 

Analysis indicates low sensitivity. The 

maximum value of the uniform distribution 

(2.4e-8 to 2.4e-4) used in the sensitivity 

studies was chosen. 

uniform SolubilityPujkg/m3] Solubility limit for Pu 2.4e-6, TRW Environmental Safety Systems, Inc. ** 

2.4e-4 (1995), Table 6.3-1, based on expert 

elicitation described in Gauthier (1993); 

Nitsche, et al. (1993); and Dyer (1993).' 

1997 Sensitivity Analysis indicates high 

sensitivity. Continue evaluating.

A-27 NUREG-1668
constant SolubilityTh[kg/m3J Solubility limit for Th 2.3e-4 Based on solubility data in ** 

carbonate-containing systems (Osthols, 

et al. (1994), Table 3 and Fig. 6; Rai, et al. 

(1995), Figures 1 & 3 (and appendix). 1997 

Sensitivity Analysis indicates low 

sensitivity. The mean value of the 

loguniform distribution (2.3e-7 to 2.3e-1) 

used in the sensitivity studies was chosen. 

constant SolubilityRatkg/m3l Solubility limit for Ra 2.3e-5 TRW Environmental Safety Systems, Inc. ** 

(1995), Table 6.3-1, based on expert 

elicitation described in Gauthier (1993), and 

Kerrisk (1984). 1997 Sensitivity Analysis 

indicates low sensitivity. The apex of the 

distribution of the logtriangular distribution 

(2.3e-7 to 2.3e-3) used in the sensitivity studies was chosen. 

constant SolubilityPb[kg/m31 Solubility limit for Pb 6.6e-5 TRW Environmental Safety Systems, Inc. ** 

(1995), Table 6.3-1, based on expert 

elicitation described in Gauthier (1993); 

Andersson (1988); and Pei-Lin, et al. 

(1985). 1997 Sensitivity Analysis indicates 

low sensitivity. The apex of the 

logtriangular distribution (2. 1 e-6 to 2. 1 e-3) 

used in the sensitivity studies was chosen. 

constant SolubilityCs[kg/m3] Solubility limit for Cs 1.35e2 Based on CNWRA assumption that there is ** 

no solubility-controlling phase for cesium. 

Conservatively assume 1.0 mol/L. 

constant SolubilityNi[kg/m3] Solubility limit for Ni l.le-I TRW Environmental Safety Systems, Inc. ** 

(1995), Table 6.3-1, based on expert elicitation described in Gauthier (1993); 

Andersson (1988); and Siegel, et al. (1993). 

1997 Sensitivity Analysis indicates low 

sensitivity. The apex of the logtriangular 

distribution (5.9e-5 to 5.9e0) used in the 

sensitivity studies was chosen.

NUREG-1668 A-28
constant SolubilitySe[kg/m3] Solubility limit for Se 7.9el Based on CNWRA assumption that release ** 

of selenium is controlled by waste form 

dissolution. High value of 1.0 mol/L is 

used. 

constant SolubilityNb[kg/m31 Solubility limit for Nb 9.3e-7 TRW Environmental Safety Systems, Inc. ** 

(1995), Table 6.3-1, based on expert 

elicitation described in Gauthier (1993), and 

Andersson (1988). 1997 Sensitivity 

Analysis indicates low sensitivity. The 

mean value of the loguniform distribution 

(9.3e-8 to 9.3e-6) used in the sensitivity 

studies was chosen.

1 Dyer, J.R. 1993. Radionuclide Solubility Working Group (SoIWOG) Meeting Report (SCP:N/A).Letter (August 2) to Lawrence Livermore National Laboratory and Los Alamos 

National Laboratory. Washington, DC: U.S. Department of Energy.

A-29 NUREG-1668
***>>> UZFT <<<***

constant UnsaturatedZoneMinimumVelocity Same velocity is used unless the 0.4 Same as in base case. Current best estimate by ** ChangeFactor[Fraction] velocity changes by this fraction of the unsaturated and saturated zone flow under the initial velocity. However, the isothermal condition team 

minimum time span over which 

velocity does not change is 500 yr. 

constant MatrixLongitudinalDispersivity[Fra Maximum matrix longitudinal 0.1 Same as in base case. Current best estimate by ** ctionOfLayer] dispersivity specified as a fraction of the USFIC team layer thickness 

constant FractureLongitudinalDispersivity[Fr Maximum fracture longitudinal 0.1 Same as in base case. Current best estimate by ** actionOtLayer] dispersivity specified as a fraction of the USFIC team 

layer thickness 

loguniform MatrixKDTSww_Am[m3/kg] Matrix Kd of Am for Topopah 0.100, 2.0 Triay, et al. (1997) ** 

Springs- welded [m3/kg• (UZ) 

*loguniform MatrixKDCHnvAm[in3/kgJ Matrix K, of Am for Calico Hills- 0.100, 1.0 Triay, et al. (1997) ** nonwelded vitric [m3/kg] (UZ) 

loguniform MatrixKDCHnzAm[m3/kg] Matrix V., of Am for Calico Hills- 0.100, 1.0 Triay, et al. (1997) ** nonwelded zeolitic [m3

/kg] (UZ) 

loguniform MatrixKDPPwAin[m3/kgl Matrix K1 of Am for Prow 0.100, 2.0 Triay, et al. (1997) ** Pass-welded [m3/kg•] (UZ) 

loguniform MatrixKDUCFAm[m3/kgl Matrix Kd of Am for Upper Crater 0.100, 2.0 Triay, et al. (1997) ** Flat [m3/kgl (UZ) 

loguniform MatrixKDBFwAm[m3/kg] Matrix 1 d of Am for 0.100, 2.0 Triay, et al. (1997) ** 

Bullfrog-welded [m3

/kg] (UZ) 

loguniform MatrixKDUFZAm[m3/kg] Matrix Kd of Am for Unsaturated 0.100, 2.0 Triay, et al. (1997) ** Fracture Zone [m3/kgj] 

loguniform MatrixKDTSwNp[m3/kgj Matrix K1 of Np Topopah 6.0e-8, Triay, et al. (1997). A value 5 orders of ** Springs-welded [m3

/kg] (UZ) 0.006 magnitude smaller than the maximum value was 

used as the lower bound.

NUREG-1668

** 

** 

**

A-30
loguniform MatrixKDCHnvNp[m3/kgl Matrix Kd of Np for Calico 1.5e-7, Triay, et al. (1997). A value 5 orders of ** 

Hills-nonwelded vitric [m3

/kg] (UZ) 0.015 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDCHnzNp[m3/kgl Matrix Id of Np for Calico 3.0e-8, Triay, et al. (1997). A value 5 orders of ** 

Hills-nonwelded zeolitic [m3

/kgl 0.003 magnitude smaller than the maximum value was 

(UZ) used as the lower bound. 

loguniform MatrixKD_PPw_.Np[m3/kg] Matrix KY, of Np for Prow 6.0e-8, Triay, et al. (1997). A value 5 orders of ** 

Pass-welded [m3

/kg] (UZ) 0.006 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDUCFNpfm3/kgl Matrix K, of Np for Upper Crater Flat 6.0e-8, Triay, et al. (1997). A value 5 orders of ** 

[milkg] (UZ) 0.006 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDBFw_.Np[m3/kgl Matrix K, of Np for 6.0e-8, Triay, et al. (1997). A value 5 orders of ** 

Bullfrog-welded [m3

/kgl (UZ) 0.006 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDUFZNp[m3/kgl Matrix K1 of Np for Unsaturated 6.0e-8, Triay, et al. (1997). A value 5 orders of ** 

Fracture Zone [m3

/kgl (UZ) 0.006 magnitude smaller than the maximum value was 

used as the lower bound. 

constant MatrixKDTSwI[m3/kg] Matrix Kd of I for Topopah 0.0 Triay, et al. (1997) ** 

Springs-welded [m3

/kg] (UZ) 

constant MatrixKDCHnvl[m3/kg] Matrix K. of I for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m 3

/kg] (UZ)_ 

constant MatrixKDCHnzl[m3lkgl Matrix K~d for I for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m3

/kg] 

(UZ)

MatrixKDPPwI[ml3/kgl Matrix K, of I for Prow Pass-welded • n tr T TI ______ _____ _______ _____ I [M'IKgj IjU/)I.'I*

MatrixKDUCF_I[m3/kgl Matrix Kd of I for Upper Crater Flat 0.0

________ ____ I [jmIKRI tI-2)

MatrixKDBFw-l[m3/kgl Matrix Kd of I for Bullfrog-welded 0.0 Triay, et al. (1997)

A-31 NUREG-1668

conslant

constant

constant

Im'lkjzl (UZ) I __ I

0.0 Triay, et al. (1997)

Triay, et al. (1997)
constant MatrixKDUFZ Ijm3/kgj Matrix KI of I for Unsaturated 0.0 Triay, et al. (1997) ** 

Fracture Zone [m3/kgl (UZ) 

constant MatrixKD_TSw_Tc[m3/kg] Matrix Kd of Tc for Topopah 0.0 Triay, et al. (1997) ** 

Springs-welded [m3/kgl (UZ) 

constant MatrixKDCHnvTc[m3/kg] Matrix K, of Tc for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m3

/kg] (UZ) 

constant MatrixKDCHnzTc[in3/kgj Matrix Kd of Tc for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m3

/kg] (UZ) 

constant MatrixKDPPwTc[in3/kgj Matrix K, of Tc for Prow 0.0 Triay, et al. (1997) ** 

Pass-welded [m3

/kgJ (UZ) 

constant MatrixKDUCF_Tc[mn3/kg] Matrix K, of Tc for Upper Crater Flat 0.0 Triay, et al. (1997) ** 

[m3/kg] (UZ) 

constant MatrixKD_BFw.Tc[in3/kgl Matrix Kd of Tc for Bullfrog-welded 0.0 Triay, et al. (1997) ** 

[m3

/kg] (UZ) 

constant MatrixKDUFZTc[m3/kg] Matrix K, of Tc for Unsaturated 0.0 Triay, et al. (1997) ** 

Fracture Zone [m/kg] (UZ) 

constant MatrixKDTSwCl[rn3/kg] Matrix K, of Cl for Topopah 0.0 Triay, et al. (1997) ** 

Springs-welded [m3/kg] (UZ) 

constant MatrixKDCHnvClfm3/kgl Matrix K, of Cl for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m3/kg] (UZ) 

constant MatrixKD_CHnzCl[m3/kg] Matrix Kd of Cl for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m3

/kg] (UZ) 

constant MatrixKDPPwCl[m3Ikg] Matrix Kd of Cl for Prow 0.0 Triay, et al. (1997) ** 

Pass-welded [m3/kg] (UZ) 

constant MatrixKD_UCFCl[m3/kgl Matrix Kd of Cl for Upper Crater Flat 0.0 Triay, et al. (1997) ** 

[m

3

/kg] (UZ)

NUREG- 1668 A-32
constant MatrixKDBFwCl[m3/kg] Matrix Kd of Ci for Bullfrog-welded 0.0 Triay, et al. (1997) ** 

[m3/kg] (UZ) 

constant MatrixKDUFZCl[m3/kg] Matrix Kd of Cl for Unsaturated 0.0 Triay, et al. (1997) ** 

Fracture Zone [m3/kg] (UZ) 

constant MatrixKD_TSwCm[m3/kgJ Matrix Kd of Cm for Topopah 0.0 No Data ** 

Springs-welded [m3/kg] (UZ) 

constant MatrixKDCHnvCmi[m3/kgl Matrix K,, of Cm for Calico 0.0 No Data ** 

Hills-nonwelded vitric [m3-/kg] (UZ) _ 

constant MatrixKDCHnzCm[m3/kg] Matrix Kd of Cm for Calico 0.0 No Data ** 

Hills-nonwelded zeolitic [m-n/kg] 

(UZ) 

constant MatrixKDPPwCm[m3/kg] Matrix K, of Cm for Prow 0.0 No Data ** 

Pass-welded [m3/kg] (UZ) 

constant MatrixKDUCFCmjm3/kg] Matrix Kd of Cm for Upper Crater 0.0 No Data ** 

Flat [m3

/kg] (UZ) 

constant MatrixKD_BFwCm[m3/kg] Matrix Kd of Cm for 0.0 No Data ** 

Bullfrog-welded [m3

/kg] (UZ) 

constant MatrixKD_UFZCm[m3/kg] Matrix Kd of Cm for Unsaturated 0.0 No Data ** 

Fracture Zone [m3/kg] (UZ) 

loguniform MatrixKDTSwU[m3/kgl Matrix Kd of U for Topopah 4.0e-8, Triay, et al. (1997). A value 5 orders of ** 

Springs-welded [m3

/kg] (UZ) 0.004 magnitude smaller than the maximum value was 

I _used as the lower bound. 

loguniform MatrixKDCHnvU[m3/kg] Matrix Kd of U for Calico 3.0e-8, Triay, et al. (1997). A value 5 orders of ** 

Hills-nonwelded vitric [m3

/kg] (UZ) 0.003 magnitude smaller than the maximum value was 

I used as the lower bound. 

loguniform MatrixKDCHnzU[m3/kg] Matrix Kd of U for Calico 3.0e-7, Triay, et al. (1997). A value 5 orders of 

Hills-nonwelded zeolitic [m3

/kg] 0.030 magnitude smaller than the maximum value was 

(UZ) used as the lower bound. 

constant MatrixKDPPwUtm3/kg] Matrix KV. of U for Prow 0.0 Triay, et al. (1997) ** 

_...._ Pass-welded fm 3/kg] (UZ) I I _1_ _1

A-33 NUREG-1668
loguniform MatrixKD_UCFU[m3/kgj Matrix Kd of U for Upper Crater Flat 4.0e-8, Triay, et al. (1997). A value 5 orders of ** 

[m3

/kgl (UZ) 0.004 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKD_BFwUfm3/kg] Matrix Kd of U for Bullfrog-welded 4.0e-8, Triay, et al. (1997). A value 5 orders of ** 

[m3

/kg] (UZ) 0.004 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDUFZ U[m3/kgl Matrix K, of U for Unsaturated 4.0e-8, Triay, et al. (1997). A value 5 orders of ** 

Fracture Zone [m3

/kgl (UZ) 0.004 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKD_TSwPu[m3/kg] Matrix K, of Pu for Topopah 0.004, 0.010 Triay, et al. (1996a) ** 

Springs-welded [m3/kgj (UZ) 

loguniform MatrixKDCHnvPu[m3/kgl Matrix K, of Pu for Calico 0.020, 0.080 Triay, et al. (1996a) ** Hills-nonwelded vitric [m3

/kgl (UZ) 

loguniform MatrixKDCHnzPu[in3/kgl Matrix K, of Pu for Calico 0.090, 0.400 Triay, et al. (1996a) ** 

Hills-nonwelded zeolitic [m3

/kg] (UZ) 

loguniform MatrixKDPPwPu[in3/kgj Matrix Kd of Pu for Prow 0.004,0.010 Triay, et al. (1996a) ** 

Pass-welded [m3

/kg] (UZ) 

loguniform MatrixKD_UCFPu[m3/kg] Matrix K, of Pu for Upper Crater Flat 0.004, 0.010 Triay, et al. (1996a) ** _m 3

/kg] (UZ) 

loguniform MatrixKD_BFwPu[m3/kgJ Matrix K,, of Pu for Bullfrog-welded 0.004, 0.010 Triay, et al. (1996a) ** 

[m3

/kgl (UZ) 

loguniform MatrixKDUFZPu[m3/kg] Matrix K, of Pu for Unsaturated 0.004, 0.010 Triay, et al. (1996a) ** Fracture Zone [m3

/kg] (UZ) 

loguniform MatrixKD_TSwTh[m3/kgj Matrix KV of Th for Topopah 0.100, 2.0 Triay, et al. (1997) ** Springs-welded [m 3

/kg] (UZ) 

loguniform MatrixKD CHnvTh[m3/kgj Matrix Kd of Th for Calico 0.100, 1.0 Triay, et al. (1997) ** I Hills-nonwelded vitric [m3 /kg] (UZ) I III

NUREG-1668 A-34
loguniform MatrixKD_CHnzThfm3/kgj Matrix Kd of Th for Calico 0.100, 1.0 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m

3

/kg] 

(UZ) 

loguniform MatrixKD_PPwTh[in3/kg] Matrix 

K

1 of Th for Prow 0.100, 2.0 Triay, et al. (1997) ** Pass-welded [m3

/kg] (UZ) I 

loguniform MatrixKD.UCF Th[m3/kg] Matrix KY, of Th for Upper Crater Flat 0.100, 2.0 Triay, et al. (1997) ** [m3

/kgJ (UZ) 

loguniform MatrixKDBFwTh[in3/kgj Matrix KV. of Th for Bullfrog-welded 0.100, 2.0 Triay, et al. (1997) ** 

[m3

/kg] (UZ) 

loguniform MatrixKD_UFZTh[in3/kgl Matrix Kd of Th for Unsaturated 0.100, 2.0 Triay, et al. (1997) ** 

Fracture Zone [m3

/kgJ (UZ) 

loguniform MatrixKDTSwRa[m3/kg] Matrix Kd of Ra for Topopah 0.100,0.500 Triay, et al. (1997) ** Springs-welded [m3/kgj (UZ) 

loguniform MatrixKDCHnvRa[m3/kgj Matrix K, of Ra for Calico 0.050, 0.100 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m3 /kgJ (UZ) 

loguniform MatrixKDCHnzRafm3/kg] Matrix Kd of Ra for Calico 1.0, 5.0 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic fm

3

/kgj 

(UZ) 

loguniform MatrixKDPPwRa[m3/kg] Matrix K, of Ra for Prow 0.1, 0.5 Triay, et al. (1997) ** Pass-welded [m3

/kgl (UZ) 

loguniform MatrixKDUCFRa[m3/kg] Matrix Kd of Ra for Upper Crater Flat 0.1, 0.5 Triay, et al. (1997) ** 

[m3

/kgl (UZ) 

loguniform MatrixKDBFwRa[m3/kg] Matrix K1 of Ra for Bullfrog-welded 0.1,0.5 Triay, et al. (1997) ** 

[m

3

/kgl (UZ)

Matrix KDUFZRa[m3/kg]

MatrixKD_TSw-Pb[m3/kg]

I __ ____ ____ ____ ___I

Matrix Id of Ra for Unsaturated lzý•nr 1 '7- T 3,I"- / IT

i ne0L1,0.5L Qý L#11 

Matrix Kd of Pb for Topopah 

Springs-welded [m3

/kg] (UZ)

0.100,0.500

Triay, et al. (1997) 

Triay, et al. (1997)

**

**

A-35 NUREG-1668

loguniform

loguniform

I

0.1,0.5
loguniform MatrixKDCHnvPb[m3/kg] Matrix KV. of Pb for Calico 0.100,0.500 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m3lkg] (UZ) 

loguniform MatrixKDCHnzPb[mn3/kgl Matrix K4 of Pb for Calico 0.100, 0.500 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m3/kgl 

(UZ) 

loguniform MatrixKDPPwPb[m3/kgl Matrix K, of Pb for Prow 0.100, 0.500 Triay, et al. (1997) ** 

Pass-welded [m3

/kgl (UZ) 

loguniform MatrixKD_UCF_Pb[rn3/kg] Matrix K, of Pb for Upper Crater Flat 0.100, 0.500 Triay, et al. (1997) ** 

[m3/kg] (UZ) 

loguniform MatrixKDBFwPbmin3/kgl Matrix K, of Pb for Bullfrog-welded 0.100, 0.500 Triay, et al. (1997) ** 

im3/kgl (UZ) 

loguniform MatrixKD_UFZPb[in3/kgJ Matrix K, of Pb for Unsaturated 0.100, 0.500 Triay, et al. (1997) ** 

Fracture Zone [rn3/kg] (UZ) 

loguniform MatrixKDTSwCs[in3/kgj Matrix K, of Cs for Topopah 0.020, 1.000 Triay, et al. (1997) ** 

Springs-welded [m3/kgl (UZ) 

loguniform MatrixKD_CHnvCs[m3/kg] Matrix K, of Cs for Calico 0.010,0.100 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m3

/kg] (UZ) 

loguniform MatrixKDCHnzCs[m3/kg] Matrix K. of Cs for Calico 0.500, 5.000 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m/kgl 

(UZ) 

loguniform MatrixKD_PPwCs[m3/kgJ Matrix K, of Cs for Prow 0.020, 1.000 Triay, et al. (1997) ** 

Pass-welded [m3

/kg] (UZ) 

loguniform MatrixKD_UCF._Cs[m3/kg] Matrix Kd of Cs for Upper Crater Flat 0.020, 1.000 Triay, et al. (1997) ** 

[m3

/kg] (UZ) 

loguniform MatrixKD_BFwCsfm3/kg] Matrix K4 of Cs for Bullfrog-welded 0.020,1.000 Triay, et al. (1997) 

[rn3

/kgj (UZ) 

loguniform MatrixKD_UFZCs[m3/kg] Matrix Kd of Cs for Unsaturated 0.020, 1.000 Triay, et al. (1997) ** 

I_____ _ I__ Fracture Zone [m/kg] (UZ) I II!

NUREG-1668 A-36
loguniform MatrixKD_TSwNitm3/kgj Matrix Kd of Ni for Topopah 5.0e-6, Triay, et al. (1997). A value 5 orders of ** 

Springs-welded [m3

/kg] (UZ) 0.500 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKD_CHnvNi[m3/kgl Matrix K,, of Ni for Calico 1.Oe-6, Triay, et al. (1997). A value 5 orders of ** 

Hills-nonwelded vitric [m3

/kgl (UZ) 0.100 magnitude smaller than the maximum value was used as the lower bound. 

loguniform MatrixKDCHnzNi[m3/kgJ Matrix Kd of Ni for Calico 5.0e-6, Triay, et al. (1997). A value 5 orders of ** 

Hills-nonwelded zeolitic [m 3

/kg] 0.500 magnitude smaller than the maximum value was 

(UZ) used as the lower bound. 

loguniform MatrixKD_PPwNi[m3/kgJ Matrix K. of Ni for Prow 5.0e-6, Triay, et al. (1997). A value 5 oiders of ** 

Pass-welded [m3

/kg] (UZ) 0.500 magnitude smaller than the maximum value was 

I_ used as the lower bound. 

loguniform MatrixKD_UCFNi[m3/kg] Matrix K, of Ni for Upper Crater Flat 5.0e-6, Triay, et al. (1997). A value 5 orders of ** 

[m3

/kgj (UZ) 0.500 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKD_BFwNi[m3/kg] Matrix Kd of Ni for Bullfrog - welded 5.0e-6, Triay, et al. (1997). A value 5 orders of ** 

[m-/kg] (UZ) 0.500 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDUFZNi[m3/kgJ Matrix Kd of Ni for Unsaturated 5.0e-6, Triay, et al. (1997). A value 5 orders of ** 

Fracture Zone [m3

/kgJ (UZ) 0.500 magnitude smaller than the maximum value was 

used as the lower bound. 

constant MatrixKD_TSwC[m3/kg] Matrix Kd of C for Topopah 0.0 Triay, et al. (1997) ** 

Springs-welded [m3

Ikg] (UZ) 

constant MatrixKDCHnvC[m3/kgJ Matrix Kd of C for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m3

/kg] (UZ) 

constant MatrixKD_CHnzC[m3/kgJ Matrix Kd of C for Calico 0.0 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m3

/kg] 

(UZ) 

constant MatrixKDPPwC[m3/kgl Matrix Kd of C for Prow 0.0 Triay, et al. (1997) ** 

Pass-welded [m3/kg] (UZ) 

constant MatrixKD_UCF._C[m3/kg] Matrix K. of C for Upper Crater Flat 0.0 Triay, et al. (1997) ** I_______ -Im 3I/kg (UZ) I I

A-37 NUREG-1668
constant MatrixKDBFw_C[m3/kgl Matrix K, of C for Bullfrog-welded 0.0 Triay, et al. (1997) ** 

[m3/kgl (UZ) 

constant MatrixKDUFZC[m3/kg] Matrix KV of C for Unsaturated 0.0 Triay, et al. (1997) ** 

Fracture Zone [m3/kg] (UZ) 

loguniform MatrixKDTSwSe[m3/kgl Matrix Kd of Se for Topopah 3.0e-7, Triay, et al. (1997). A value 5 orders of ** 

Springs-welded [m3

/kg] (UZ) 0.030 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDCHnvSe[m3/kgl Matrix Kd of Se for Calico 2.0e-7, Triay, et al. (1997). A value 5 orders of ** 

Hills-nonwelded vitric [m3

/kg] (UZ) 0.020 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDCHnzSe[mn3/kgl Matrix K, of Se for Calico 1.5e-7, Triay, et al. (1997). A value 5 orders of ** 

Hills-nonwelded zeolitic [m3

/kgl 0.015 magnitude smaller than the maximum value was 

(UZ) _used as the lower bound. 

loguniform MatrixKDPPwSe[m3/kg] Matrix KY of Se for Prow 3.0e-7, Triay, et al. (1997). A value 5 orders of ** 

Pass-welded [m3

/kgl (UZ) 0.030 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDUCF.Se[m3/kgl Matrix Kd of Se for Upper Crater 3.0e-7, Triay, et al. (1997). A value 5 orders of ** 

Flats [m3

/kgl (UZ) 0.030 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDBFwSe[m3/kgl Matrix K, of Se for Bullfrog [m3

/kgl 3.0e-7, Triay, et al. (1997). A value 5 orders of ** 

(UZ) 0.030 magnitude smaller than the maximum value was 

used as the lower bound. 

loguniform MatrixKDUFZSe[m3/kg] Matrix K. of Se for Unsaturated 3.0e-7, Triay, et al. (1997). A value 5 orders of ** 

Fracture Zone [m3

/kg] (UZ) 0.030 magnitude smaller than the maximum value was 

used as the lower bound. 

constant MatrixKDTSwNb[m3/kgl Matrix Kd of Nb for Topopah 0.10 Triay, et al. (1997) ** 

Springs-welded [m3

/kg] (UZ) 

constant MatrixKDCHnvNb[m3/kgl Matrix Kd of Nb for Calico 0.10 Triay, et al. (1997) ** 

Hills-nonwelded vitric [m3

/kg] (UZ) 

constant MatrixKDCHnzNb[m3/kgl Matrix Kd of Nb for Calico 0.10 Triay, et al. (1997) ** 

Hills-nonwelded zeolitic [m3

/kg] 

(UZ)

NUREG-1!668 A-38
constant MatrixKD_PPwNb[m3/kg] Matrix Kd of Nb for Prow 0.10 Triay, et al. (1997) ** Pass-welded [m3/kg] (UZ) 

constant MatrixKDUCF._Nb[m3/kg] Matrix Kd of Nb for Upper Crater 0.10 Triay, et al. (1997) ** Flats [m3

/kgJ (UZ) 

constant MatrixKDBFwNb[m3/kg] Matrix K. of Nb for 0.10 Triay, et al. (1997) ** Bullfrog-welded [m3

/kg] (UZ) 

constant MatrixKDUFZNb[m3/kg] Matrix K, of Nb for Unsaturated 0.10 Triay, et al. (1997) ** Fracture Zone [m3

/kg] (UZ) 

constant FractureRDTSwAm Fracture retardation coefficient of Am 1.0 Conservative assumption of no retardation ** for Topopah Springs-welded (UZ) 

constant FractureRDCHnvAm Fracture retardation coefficient of Am 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric (UZ) 

constant FractureRDCHnzAm Fracture retardation coefficient of Am 1.0 Conservative assumption of no retardation 

for Calico Hills-nonwelded zeolitic (UZ) 

constant FractureRDPPwAm Fracture retardation coefficient of Am 1.0 Conservative assumption of no retardation ** for Prow Pass-welded (UZ) 

constant FractureRDUCFAm Fracture retardation coefficient of Am 1.0 Conservative assumption of no retardation ** for Upper Crater Flat (UZ) 

constant FractureRDBFwAm Fracture retardation coefficient of Am 1.0 Conservative assumption of no retardation ** for Bullfrog-welded (UZ) 

constant FractureRDUFZAm Fracture retardation coefficient of Am 1.0 Conservative assumption of no retardation ** for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSwNp Fracture retardation coefficient of Np 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRDCHnvNp Fracture retardation coefficient of Np 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric (UZ)

A-39 NUREG-1668
constant FractureRDCHnzNp Fracture retardation coefficient of Np 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwNp Fracture retardation coefficient of Np 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCF.Np Fracture retardation coefficient of Np 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFwNp Fracture retardation coefficient of Np 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZNp Fracture retardation coefficient of Np 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRD_TSw_I Fracture retardation coefficient of I 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRD_CHnvI Fracture retardation coefficient of I 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRD_CHnzI Fracture retardation coefficient of I 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRD_PPw_I Fracture retardation coefficient of I 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCFI Fracture retardation coefficient of I 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFw_I Fracture retardation coefficient of I 1.0 Conservative assumption of no retardation ** 

for Builfrog-welded (UZ) 

constant FractureRDUFZ_I Fracture retardation coefficient of I 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSwTc Fracture retardation coefficient of Tc 1.0 Conservative assumption of no retardation ** 

for Topopah Springs--welded (UZ) I __II

NUREG-1668 A-40
constant FractureRDCHnvTc Fracture retardation coefficient of Tc 1.0 Conservative assumption of no retardation 

for Calico Hills-.nonwelded vitric 

(UZ) 

constant FractureRDCHnzTc Fracture retardation coefficient of Tc 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

I_ (UZ) 

constant FractureRDPPw_Tc Fracture retardation coefficient of Tc 1.0 Conservative assumption of no retardation ** for Prow Pass-welded (UZ) 

constant FractureRDUCFTc Fracture retardation coefficient of Tc 1.0 Conservative assumption of no retardation ** for Upper Crater Flat (UZ) 

constant FractureRDBFwTc Fracture retardation coefficient of Tc 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZTc Fracture retardation coefficient of Tc 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSw_Cl Fracture retardation coefficient of Cl 1.0 Conservative assumption of no retardation ** for Topopah Springs-welded (UZ) 

constant FractureRDCHnvCl Fracture retardation coefficient of Cl 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FracturcRDCHnzCi Fracture retardation coefficient of Cl 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPw_Cl Fracture retardation coefficient of CI 1.0 Conservative assumption of no retardation ** for Prow Pass-welded (UZ) 

constant FractureRDUCF_Cl Fracture retardation coefficient of Cl 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFw_Cl Fracture retardation coefficient of Cl 1.0 Conservative assumption of no retardation ** 

for Bullfrog -welded (UZ) 

constant FractureRDUFZ_Cl Fracture retardation coefficient of Cl 1.0 Conservative assumption of no retardation ** 

Unsaturated Fracture Zone (UZ) I I _II

A-41 NUREG-1668
constant FractureRD_TSw_Cm Fracture retardation coefficient of Cm 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRD_CHnvCm Fracture retardation coefficient of Cm 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRD_CHnzCm Fracture retardation coefficient of Cm 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRD_PPw_Cm Fracture retardation coefficient of Cm 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRD_UCF_Cm Fracture retardation coefficient of Cm 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRD_BFwCm Fracture retardation coefficient of Cm 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRD_UFZ_Cm Fracture retardation coefficient of Cm 1.0 Conservative assumption of no retardation ** 

Unsaturated Fracture Zone (UZ) 

constant FractureRDTSwU Fracture retardation coefficient of U 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRD_CHnvU Fracture retardation coefficient of U 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRD_CHnzU Fracture retardation coefficient of U 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic (UZ) 

constant FractureRDPPwU Fracture retardation coefficient of U 1.0 Conservative assumption of no retardation ** for Prow Pass-welded (UZ) A 

constant FractureRDUCFU Fracture retardation coefficient of U 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRD_BFw_U Fracture retardation coefficient of U 1.0 Conservative assumption of no retardation ** for Bullfrog-welded (UZ) __ 1 

NUREG- 1668 A-42
constant FractureRD_UFZ_U Fracture retardation coefficient of U 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRD_TSw_Pu Fracture retardation coefficient of Pu 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRDCHnvPu Fracture retardation coefficient of Pu 1.0 Conservative assumption of no retardation 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRDCHnzPu Fracture retardation coefficient of Pu 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwPu Fracture retardation coefficient of Pu 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCF_Pu Fracture retardation coefficient of Pu 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFwPu Fracture retardation coefficient of Pu 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZPu Fracture retardation coefficient of Pu 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSwTh Fracture retardation coefficient of Th 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRDCHnvTh Fracture retardation coefficient of Th 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRDCHnzTh Fracture retardation coefficient of Th 1.0 Conservative assumption of no retardation 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwTh Fracture retardation coefficient of Th 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCFTh Fracture retardation coefficient of Th 1.0 Conservative assumption of no retardation ** 

I _ _ _ _ _ for Uvver Crater Flat (UZ) IId

A-43 NUREG-1668
constant FractureRD_BFw_Th Fracture retardation coefficient of Th 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZTh Fracture retardation coefficient of Th 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRD_TSw_Ra Fracture retardation coefficient of Ra 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRDCHnvRa Fracture retardation coefficient of Ra 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FracturcRDCHnzRa Fracture retardation coefficient of Ra 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwRa Fracture retardation coefficient of Ra 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCFRa Fracture retardation coefficient of Ra 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFwRa Fracture retardation coefficient of Ra 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZRa Fracture retardation coefficient of Ra 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRD_TSw_Pb Fracture retardation coefficient of Pb 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRD_CHnvPb Fracture retardation coefficient of Pb 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRDCHnzPb Fracture retardation coefficient of Pb 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwPb Fracture retardation coefficient of Pb 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ)

NUREG- 1668 A-44
constant FractureRDUCFPb Fracture retardation coefficient of Pb 1.0 Conservative assumption of no retardation ** for Upper Crater Flat (UZ) 

constant FractureRDBFwPb Fracture retardation coefficient of Pb 1.0 Conservative assumption of no retardation ** for Bullfrog-welded (UZ) 

constant FractureRDUFZPb Fracture retardation coefficient of Pb 1.0 Conservative assumption of no retardation for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSw-Cs Fracture retardation coefficient of Cs 1.0 Conservative assumption of no retardation ** for Topopah Springs-welded (UZ) 

constant FractureRD-CHnvCs Fracture retardation coefficient of Cs 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRDCHnzCs Fracture retardation coefficient of Cs 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwCs Fracture retardation coefficient of Cs 1.0 Conservative assumption of no retardation ** for Prow Pass-welded (UZ) 

constant FractureRDUCF_Cs Fracture retardation coefficient of Cs 1.0 Conservative assumption of no retardation ** for Upper Crater Flat (UZ) 

constant FractureRDBFwCs Fracture retardation coefficient of Cs 1.0 Conservative assumption of no retardation ** for Bullfrog-welded (UZ) 

constant FractureRDUFZCs Fracture retardation coefficient of Cs 1.0 Conservative assumption of no retardation ** for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSwNi Fracture retardation coefficient of Ni 1.0 Conservative assumption of no retardation ** for Topopah Springs-welded (UZ) 

constant FractureRDCHnvNi Fracture retardation coefficient of Ni 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric (UZ) 

constant FractureRDCHnzNi Fracture retardation coefficient of Ni 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic (UZ)

A-45 NUREG-1668
constant FractureRDPPwNi Fracture retardation coefficient of Ni 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCFNi Fracture retardation coefficient of Ni 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFwNi Fracture retardation coefficient of Ni 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZNi Fracture retardation coefficient of Ni 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSwC Fracture retardation coefficient of C 1.0 Conservative assumption of no retardation ** 

lor Topopah Springs-welded (UZ) 

constant FractureRDCHnvC Fracture retardation coefficient of C 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRDCHnzC Fracture retardation coefficient of C 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRD_PPw_C Fracture retardation coefficient of C 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRD-UCFC Fracture retardation coefficient of C 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFw_C Fracture retardation coefficient of C 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZC Fracture retardation coefficient of C 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

constant FractureRDTSw_Se Fracture retardation coefficient of Se 1.0 Conservative assumption of no retardation ** 

for Topopah Springs-welded (UZ) 

constant FractureRDCHnvSe Fracture retardation coefficient of Se 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ)

NUREG-1668 A-46
constant FractureRDCHnzSe Fracture retardation coefficient of Se 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwSe Fracture retardation coefficient of Se 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCFSe Fracture retardation coefficient of Se 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFwSe Fracture retardation coefficient of Se 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRD_UFZ_Sc Fracture retardation coefficient of Se 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 4 

constant FractureRD_TSwNb Fracture retardation coefficient of Nb 1.0 Conservative assumption of no retardation ** for- Topopah Springs-welded (UZ) 

constant FractureRDCHnvNb Fracture retardation coefficient of Nb 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded vitric 

(UZ) 

constant FractureRDCHnzNb Fracture retardation coefficient of Nb 1.0 Conservative assumption of no retardation ** 

for Calico Hills-nonwelded zeolitic 

(UZ) 

constant FractureRDPPwNb Fracture retardation coefficient of Nb 1.0 Conservative assumption of no retardation ** 

for Prow Pass-welded (UZ) 

constant FractureRDUCFNb Fracture retardation coefficient of Nb 1.0 Conservative assumption of no retardation ** 

for Upper Crater Flat (UZ) 

constant FractureRDBFwNb Fracture retardation coefficient of Nb 1.0 Conservative assumption of no retardation ** 

for Bullfrog-welded (UZ) 

constant FractureRDUFZNb Fracture retardation coefficient of Nb 1.0 Conservative assumption of no retardation ** 

for Unsaturated Fracture Zone (UZ) 

lognormal MatrixPermeabilityTSw_[m2] Matrix permeability for Topopah 0.2e-19, Numbers based on statistical average of subunits ** 

Springs tuff-welded (UZ) (m2) 0.2e-17 in report by Flint (1996). Distribution assumed 

I__ Ias being typical for vermeabilitv values.

A-47 NUREG-1668
gMatrixPermeability CHnv[m2]

lognormal I MatrixPermeabilityCHnzf m2]

lognormal MatrixPermeabilityPPw_[m2]

lognormal MatrixPcrmeabilityUCF-[m2]

lognormal MatrixPerineability.BFw_[m21

lognormal MatrixPermeability UFZ_[m2]

constant "MatrixPorosityTSw_

constant J MatrixPorosity-CHnv

constant J MatrixPorosityCHnz

constant

lognormal

MatrixPorosity_PPw_ Matrix porosity for Prow 

Pass-welded (UZ)

NUREG- 1668

Matrix permeability for Calico 

Hills-nonwelded vitric (UZ) (m2

) 

Matrix permeability for Calico 

Hills-nonwelded zeolitic (UZ) (M2) 

Matrix permeability for Prow 

Pass-welded (UZ) (m2

) 

Matrix permeability for Upper Crater 

Flat (UZ) (M2

) 

Matrix permeability for 

Bullfrog-welded (UZ) (m2

) 

Matrix permeability for Unsaturated 

Fault Zone (UZ) (M2

) 

Matrix porosity for Topopah 

Springs-welded (UZ) (M2

) 

Matrix porosity for Calico Hills--nonwelded vitric (UZ) 

Matrix porosity for Calico 

Hills--nonwelded zeolitic (UZ)

C

A-48

0.2e-14, 

0.2e-12 

0.5e- 18, 

0.5e-16 

0.le-17, 

0.le-15 

0.3e- 18, 

0.3e-16 

0.2e-19, 

0.2e-17 

1.8e-18, 

2.le-16 

0.12 

0.33 

).32 

).28

Numbers based on statistical average of subunits 

in report by Flint (1996). Distribution assumed 

as bein t ical for ermeabilit values. 

Numbers based on statistical average of subunits 

in report by Flint (1996). Distribution assumed 

as bein t ical for ermeabilit values. 

Numbers based on statistical average of subunits 

in report by Flint (1996). Distribution assumed 

as bein t ical for ermeabilit values. 

Numbers based on statistical average of subunits 

in report by Flint (1996). Distribution assumed 

as bein t ical for ermeabilit values. 

Numbers based on statistical average of subunits 

in report by Flint (1996). Distribution assumed 

as bein t ical for ermeabilit values. 

Flint (1996). Current best estimate, but this 

should be further evaluated in the issue 

resolution status report (IRSR) update for USFIC. 

Numbers based on statistical average of subunits 

in report by Flint (1996). Constant values 

assumed caused by small variability in data set 

Numbers based on statistical average of subunits 

in report by Flint (1996). Constant values 

assumed caused b small variability in data set 

Numbers based on statistical average of subunits * in report by Flint (1996). Constant values 

assumed caused b small variability in data set 

Numbers based on statistical average of subunits * in report by Flint (1996). Constant values 

assumed caused b small variabilit in data set

** 

** 

** 

** 

** 

**

* 

*
constant MatrixPorosityUCF._ Matrix porosity for Upper Crater Flat 0.28 Numbers based on statistical average of subunits ** 

(UZ) in report by Flint (1996). Constant values 

assumed caused by small variability in data set 

constant MatrixPorosity-BFw- Matrix porosity for Bullfrog-welded 0.12 Numbers based on statistical average of subunits ** 

(UZ) in report by Flint (1996). Constant values 

assumed caused by small variability in data set 

constant MatrixPorosityUFZ_ Matrix porosity for Unsaturated Fault 0.12 Flint (1996). Current best estimate, but this ** 

Zone (UZ) should be further evaluated in the IRSR update 

for USFIC. 

constant MatrixBetaTSw_ van Genuchten beta parameter for 1.5 Current best estimate by USFIC team. Also see ** 

Topopah Springs-welded (matrix) Flint (1996). 

(UZ) 

constant MatrixBetaCHnv van Genuchten beta parameter for 1.3 Current best estimate by USFIC team. Also see ** 

Calico Hills- nonwelded vitric Flint (1996). 

(matrix) (UZ) 

constant MatrixBeta_CHnz van Genuchten beta parameter for 2.3 Current best estimate by USFIC team. Also see * 

Calico Hills-nonwelded zeolitic Flint (1996). 

(matrix) (UZ) II 

constant MatrixBetaPPw_ van Genuchten beta parameter for 1.5 Current best estimate by USFIC team. Also see ** 

Prow Pass-welded (matrix) (UZ) Flint (1996). 

constant MatrixBeta_UCF_ van Genuchten beta parameter for 1.4 Current best estimate by USFIC team. Also see ** 

Upper Crater Flat (matrix) (UZ) Flint (1996). 

constant MatrixBeta_BFw_ van Genuchten beta parameter for 1.7 Current best estimate by USFIC team. Also see ** 

Bullfrog-welded (matrix) (UZ) Flint (1996). 

constant MatrixBeta_UFZ_ van Genuchten beta parameter for 2.3 Current best estimate by USFIC team. Also see ** 

Unsaturated Fault Zone (matrix) (UZ) Flint (1996). 

constant MatrixGrainDensityTSw_[kg/m3] Matrix grain density for Topopah 2460.0 Best estimate by the USFIC team. Based on ** 

Springs-welded (UZ) (kg/I 3

) averaging of subunit data reported by Flint 

(1996). Constant values assumed caused by little 

I variation in reported data

A-49 NUREG-1668
constant MatrixGrainDensityCHnv[kg/m3] Matrix grain density for Calico 2260.0 Best estimate by the USFIC team. Based on ** 

Hills-nonwelded vitric (UZ) (kg/m3) averaging of subunit data reported by Flint 

(1996). Constant values assumed caused by little 

variation in reported data 

constant MatrixGrainDensityCHnz[kg/m3] Matrix grain density for Calico 2400.0 Best estimate by the USFIC team. Based on ** 

Hills-nonwelded zeolitic (UZ) averaging of subunit data reported by Flint 

(kg/m3) (1996). Constant values assumed caused by little 

variation in reported data 

constant MatrixGrainDensityPPw_[kg/hn3 Matrix grain density for Prow 2540.0 Best estimate by the USFIC team. Based on ** 

Pass-welded (UZ) (kg/mr3) averaging of subunit data reported by Flint 

(1996). Constant values assumed caused by little 

variation in reported data 

constant MatrixGrainDensityUCF_[kg/m3] Matrix grain density for Upper Crater 2420.0 Best estimate by the USFIC team. Based on ** 

Flat (UZ) (kg/m3) averaging of subunit data reported by Flint 

(1996). Constant values assumed caused by little 

variation in reported data 

constant MatrixGrainDensityBFw_[kg/m3] Matrix grain density for 2570.0 Best estimate by the USFIC team. Based on ** 

Bullfrog-welded (UZ) (kg/m3) averaging of subunit data reported by Flint 

(1996). Constant values assumed caused by little 

variation in reported data 

constant MatrixGrainDensityUFZ_[kg/m3] Matrix grain density for Unsaturated 2630.0 Best estimate by the USFIC team. Based on ** 

Fault Zone (UZ) (kg/m3) averaging of subunit data reported by Flint 

(1996). Constant values assumed caused by little 

variation in reported data 

lognormal FracturePermeabilityTSw_[m2] Fracture permeability Topopah 8.0e-15, Current best estimate by the USFIC team based ** 

Springs-welded (m2) (UZ) 8.0e- I1 on Schenker, et al. (1995) 

lognormal FracturePermeabilityCHnvjm2] Calico Hills-nonwelded vitric 8.0e-15, Current best estimate by the USFIC team based ** 

fracture permeability (m2) (UZ) 8.0e- I I on Schenker, et al. (1995) 

lognormal FracturePermeabilityCHnz[m2] Calico Hills-nonwelded zeolitic 6.0e- 15, Current best estimate by the USFIC team based ** 

fracture permeability (M2

) (UZ) 6.0e-I 1 on Schenker, et al. (1995) 

lognormal FracturePermeabilityPPw_[m2] Prow Pass-welded fracture 6.0e- 15, Current best estimate by the USFIC team based ** 

permeability (d2) (UZ) 6.0e- I1 on Klavetter and Peters (1986) and Nuclear 

I Regulatory Commission (1995)

NUREG- 1668 A-50
lognormal FracturePermeabilityUCF_[m2] Upper Crater Flat fracture 6.0e-15, Current best estimate by the USFIC team based ** 

permeability (m2) (UZ) 6.0e- 11 on Klavetter and Peters (1986) and Nuclear 

Regulatory Commission (1995) 

lognormal FracturePermeabilityBFwjm2] Bullfrog- welded fracture 3.0e-I 5, Current best estimate by the USFIC team based ** 

permeability (m2

) (UZ) 3.0e-I I on Klavetter and Peters (1986) and Nuclear 

_...._Regulatory Commission (1995) 

lognormal FracturePermeabilityUFZ_[in2 Unsaturated Fracture Zone fracture I.Oe- 13, Current best estimate by the USFIC team based ** 

permeability (m2

) (UZ) I.Oe- II on Klavetter and Peters (1986) and Nuclear 

I_ Regulatory Commission (1995) 

loguniform FracturePorosityTSw_ Fracture porosity Topopah I .Oe-3, Current best estimate by the USFIC team. Upper ** 

Springs-welded (UZ) I.Oe-2 value based on Geldon, et al. (1997), citing 

Streltsova (1988), and lower values based on 

consideration of Geldon (1993) 

loguniform FracturePorosityCHnv Calico Hills - nonwelded vitric I.Oe-3, Current best estimate by the USFIC team. Upper ** 

fracture porosity (UZ) 1.0e-2 value based on Geldon, et al. (1997), citing 

Streltsova (1988), and lower values based on .. _ consideration of Geldon (1993) 

loguniform FracturcPorosityCHnz Calico Hills-nonwelded zeolitic 1.Oe-3, Current best estimate by the USFIC team. Upper ** 

fracture porosity (UZ) I.Oe-2 value based on Geldon, et al. (1997), citing 

Streltsova (1988), and lower values based on 

consideration of Geldon (1993) 

loguniform FracturePorosityPPw_ Prow Pass-welded fracture porosity 1.Oe-3, Current best estimate by the USFIC team. Upper ** 

(UZ) 1.0e-2 value based on Geldon, et al. (1997), citing 

Streltsova (1988), and lower values based on 

I consideration of Geldon (1993) 

loguniform FracturePorosityUCF._ Upper Crater Flat fracture porosity 1.Oe-3, Current best estimate by the USFIC team. Upper ** 

(UZ) I.Oe-2 value based on Geldon, et al. (1997), citing 

Streltsova (1988), and lower values based on 

consideration of Geldon (1993) 

loguniform FracturePorosityBFw_ Bullfrog-welded fracture porosity I.Oe-3, Current best estimate by the USFIC team. Upper 

(UZ) I.Oe-2 value based on Geldon, et al. (1997), citing 

Streltsova (1988), and lower values based on 

I consideration of Geldon (1993)

A-51 NUREG-1668
loguniform FracturePorosityUFZ. Unsaturated Fault Zone fracture 1.Oe-3, Current best estimate by the USFIC team. Upper ** 

porosity (UZ) l.Oe-2 value based on Geldon, et al. (1997), citing 

Streltsova (1988), and lower values based on 

consideration of Geldon (1993) 

constant FractureBetaTSw_ van Genuchten beta parameter for 3.0 Best estimate by USFIC team based on Geldon ** 

I Topopah Springs-welded (UZ) (1993) 

constant FractureBetaCHnv van Genuchten beta parameter for 3.0 Best estimate by USFIC team based on Geldon ** 

Calico Hills-nonwelded vitric (UZ) (1993) 

constant FractureBetaCHnz van Genuchien beta parameter for 3.0 Best estimate by USFIC team based on Geldon ** 

Calico Hills-nonwelded zeolitic (1993) 

(UZ) 

constant FractureBetaPPw_ van Genuchten beta parameter for 3.0 Best estimate by USFIC team based on Geldon ** 

________________Prow Pass--welded (UZ) ______(1993) 

constant FractureBetaUCF_ van Genuchten beta parameter for 3.0 Best estimate by USFIC team based on Geldon ** 

Upper Crater Flat (UZ) (1993) 

constant FractureBeta-BFw_ van Genuchten beta parameter for 3.0 Best estimate by USFIC team based on Geldon ** 

Bullfrog-welded (UZ) (1993) 

constant FractureBeta&UFZ_ van Genuchten beta parameter for 3.0 Best estimate by USFIC team based on Geldon ** 

Unsaturated Fault Zone (UZ) (1993) 

constant InletArea.lSubArea[m2] Inlet area (M2

) - subarea I (UZ) (not 5.4e5 Center for Nuclear Waste Regulatory Analyses ** 

used) (CNWRA) staff best estimate 

constant InletAreaM2SubArea[m21 Inlet area (i 2) - subarea 2 (UZ) (not 5.4e5 CNWRA staff best estimate ** 

used) 

constant lnletArea_3SubArea[m2] Inlet area (m2

) - subarea 3 (UZ) (not 5.4e5 CNWRA staff best estimate ** 

used) 

constant InletArea_4SubArea[m2] Inlet area (in) - subarea 4 (UZ) (not 5.4e5 CNWRA staff best estimate ** 

used) 

constant InletArea_5SubArea[m2l Inlet area (m2) - subarea 5 (UZ) (not 5.4e5 CNWRA staff best estimate ** 

used) I_ __ _ 

constant InletArea_6SubArea[m2] Inlet area (M2

) - subarea 6 (UZ) (not 5.4e5 CNWRA staff best estimate ** 

I used) I ti

NUREG-1668 A-52
constant InletArea_7SubArea[m2] Inlet area (m2) - subarea 7 (UZ) (not 5.4e5 CNWRA staff best estimate ** 

used) 

constant TSwThicknessI SubArea[m] Topopah Springs-welded thickness 33.0 Derived directly from the CNWRA 3-D ** 

Subarea] (m) (UZ) Geological Framework Model (Stirewalt and 

I_ I I Henderson, 1995, pp. 3-5) 

constant CHnvThickness_l SubArea[m] Subareal Calico Hills-nonwelded 0.0 Derived directly from the CNWRA 3-D 

vitric thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant CHnzThicknessi SubArea[m] Subarea I Calico Hills-nonwelded 163.0 Derived directly from the CNWRA 3-D ** 

zeolitic thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1. 1.3 in Bodvarsson and F _ _Bandurraga (1996). 

constant PPw_Thickness_I SubAreafm] Subareal Prow Pass-welded 34.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UCF_Thickness_ ISubArea[m] Subarea I Upper Crater Flat thickness 67.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

I Henderson, 1995, pp. 3-5) 

constant BFwThickness_lSubArea[m] Subarea] Bullfrog-welded thickness 0.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UFZ_Thickness_ ISubArea[m] Subareal Unsaturated Fracture Zone 0.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant TSwThickness_2SubArea[m] Subarea2 Topopah Springs-welded 116.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pt. 3-5)

A-53 NUREG-1668
constant CHnvThickness_2SubArea[m] Subarea2 Calico Hills-nonwelded 0.0 Derived directly from the CNWRA 3-D ** 

vitric thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant CHnzThickness_2SubArea[m] Subarea2 Calico Hills-nonwelded 154.0 Derived directly from the CNWRA 3-D ** 

zeolitic thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant PPwThickness_2SubArea[m] Subarea2 Prow Pass-welded 39.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UCFThickness_2SubArea[m] Subarea2 Upper Crater Flat thickness 20.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant BFw_Thickness_2SubArea[m] Subarea2 Bullfrog-welded thickness 0.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UFZThickness_2SubArea[m] Subarea2 Unsaturated Fracture Zone 0.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant TSw_Thickness_3SubArea[m] Subarea3 Topopah Springs-welded 20.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant CHnvThickness_3SubArea[m] Subarea3 Calico Hills-nonwelded 0.0 Derived directly from the CNWRA 3-D ** 

vitric thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996).

NUREG-1 668 A-54
constant CHnzThickness_3SubArea[m] Subarea3 Calico Hills-nonwelded 122.0 Derived directly from the CNWRA 3-D ** zeolitic thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant PPw_Thickness_3SubArea[m] Subarea3 Prow Pass-welded 40.0 Derived directly from the CNWRA 3-D ** thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UCF_Thickness_3SubArea[m] Subarea3 Upper Crater Flat thickness 158.0 Derived directly from the CNWRA 3-D ** (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant BFwThickness_3SubArea[m] Subarea3 Bullfrog-welded thickness 0.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UFZh_Tickness_3SubArea[m] Subarea3 Unsaturated Fracture Zone 0.0 Derived directly from the CNWRA 3-D ** thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant TSwThickness_4SubArea[mI Subarea4 Topopah Springs-welded 110.0 Derived directly from the CNWRA 3-D ** thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant CHnvThickness .4SubArea[mj Subarea4 Calico Hills-nonwelded 0.0 Derived directly from the CNWRA 3-D ** vitric thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1. 1.3 in Bodvarsson and 

Bandurra-ga (1996). 

constant CHnzThickness_4SubArea[m] Subarea4 Calico Hills-nonwelded 132.0 Derived directly from the CNWRA 3-D ** zeolitic thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraza (1996).

A-55 NUREG-1668
constant PPwThickness_4SubArea[m] Subarea4 Prow Pass-welded 34.0 Derived directly from the CNWRA 3-D ** 

thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UCF_Thickness_4SubArea[m] Subarea4 Upper Crater Flat thickness 57.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

I I_ Henderson, 1995, pp. 3-5) 

constant BFw_Thickness_4SubArea[m] Subarca4 Bullfrog-welded thickness 0.0 Derived directly from the CNWRA 3-D ** 

(mn) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UFZThickness_4SubArea[mJ Subarea4 Unsaturated Fracture Zone 0.0 Derived directly from the CNWRA 3-D ** 

thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant TSwThickness_5SubArea[m] Subarea5 Topopah Springs-welded 20.0 Derived directly from the CNWRA 3-D ** 

thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant CHnvThickness_5SubArea[m] Subarca5 Calico Hills-nonwelded 113.0 Derived directly from the CNWRA 3-D ** 

vitric thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga. (1996). 

constant CHnzThickncss_5SubArea[im] Subarea5 Calico Hills-nonwelded 0.0 Derived directly from the CNWRA 3-D ** 

zeolitic thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant PPwThickness_5SubArea[m] Subarea5 Prow Pass-welded 38.0 Derived directly from the CNWRA 3-D ** 

thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UCFThickness_5SubArea[m] Subarea5 Upper Crater Flat thickness 158.0 Derived directly from the CNWRA 3-D ** 

(in) (UZ) Geological Framework Model (Stirewalt and 

L_ ____ I_ IIIHenderson, 1995, pp. 3-5)

NUREG-1668 A-56
constant BFwThickness_5SubArea[m] Subarea5 Bullfrog-welded thickness 32.0 Derived directly from the CNWRA 3-D ** 

(i) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UFZThickness_5SubArea[mJ Subarea5 Unsaturated Fracture Zone 0.0 Derived directly from the CNWRA 3-D ** thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant TSwThickness_6SubArea[mj Subarea6 Topopah Springs-welded 53.0 Derived directly from the CNWRA 3-D ** thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant CHnvThickness_6SubArea[mj Subarea6 Calico Hills-nonwelded 125.0 Derived directly from the CNWRA 3-D ** vitric thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurragaa (1996). 

constant CHnzThickness_6SubArea[m] Subarea6 Calico Hills-nonwelded 0.0 Derived directly from the CNWRA 3-D ** zeolitic thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant PPwThickness_6SubArea[mn] Subarea6 Prow Pass-welded 26.0 Derived directly from the CNWRA 3-D ** thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UCFThickness_6SubArea[m] Subarea6 Upper Crater Flat thickness 136.0 Derived directly from the CNWRA 3-D ** (i) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant BFw_Thickness_6SubArea[m] Subarea6 Bullfrog-welded thickness 0.0 Derived directly from the CNWRA 3-D 

(i) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UFZThickness_6SubArea[m] Subarea6 Unsaturated Fracture Zone 0.0 Derived directly from the CNWRA 3-D ** thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson. 1995, DD. 3-5)

A-57 NUREG-1668
constant TSwThickness_7SubArea[m] Subarea7 Topopah Springs-welded 121.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant CHnvThickness_7SubArea[m] Subarea7 Calico Hills-nonwelded 0.0 Derived directly from the CNWRA 3-D ** 

vitric thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant CHnzThickness_7SubArea[m] Subarca7 Calico Hills-nonwelded 114.0 Derived directly from the CNWRA 3-D ** 

zeolitic thickness (m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5). The identification of 

the vitric portions of the Calico Hills was 

developed from Fig. 1.1.3 in Bodvarsson and 

Bandurraga (1996). 

constant PPw_Thickness_7SubArea[m] Subarea7 Prow Pass-welded 43.0 Derived directly from the CNWRA 3-D ** 

thickness (in) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UCFThickness_7SubArea[mj Subarea7 Upper Crater Flat thickness 63.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

I_ Henderson, 1995, pp. 3-5) 

constant BFwThickness_7SubArea[m] Subarea7 Bullfrog-welded thickness 0.0 Derived directly from the CNWRA 3-D ** 

(m) (UZ) Geological Framework Model (Stirewalt and 

Henderson, 1995, pp. 3-5) 

constant UFZ_Thickness_7SubArea[m] Subarea7 Unsaturated Fracture Zone 0.0 Derived directly from the CNWRA 3-D ** 

thickness (m) (UZ) Geological Framework Model (Stirewalt and 

I Henderson, 1995, pp. 3-5)

NUREG-1668 A-58
** 

** 

**

***>>> SZFT <<<***

constant MixingZoneDispersionFraction Dispersion fraction of the mixing 0.01 CNWRA best estimate. Assumed to be ** 

zone (SZ)-Iongitudinal dispersion conservative 

specified as a fraction of the path 

length 

constant DispersionFractionSTFF Dispersion fraction of tuff 0.01 CNWRA best estimate. Assumed to be ** 

(SZ)-iongitudinal dispersion conservative 

specified as a fraction of the path 

length 

constant DispersionFractionSAV Dispersion fraction of Amargosa 0.1 de Marsily (1986) citing work of Lallemand- ** 

Valley (SZ)-Iongitudinal Barfrs and Peaudcerf (1978) 

dispersion specified as a fraction of 

the path length 

constant MinimumResidenceTime_ Minimum residence time in tuff 10.0 Value selected for code efficiency reasons ** 

STFF[yr] (SZ) (yr) 

constant MinimumResidenceTimeSAV[yr Minimum residence time in 10.0 Value selected for code efficiency reason ** 

I alluvium (SZ) (yr) 

constant FractureRDSTFFAm Fracture retardation coefficient for 1.0 Conservative assumption of no retardation ** 

Am (SZ) 

loguniform AlluviumMatrixRDSAVAm Alluvium retardation coefficient for 1.0, 4.0e5 Wolfsberg, 1978; (Eu analog); Price, et al., ** 

Am (SZ) 1992 

constant FractureRDSTFFNp Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Np (SZ) 

loguniform AlluviumMatrixRDSAVNp Alluvium retardation coefficient for 1.0, 300.0 Triay, et al., 1996b No data for alluvium, ** 

Np (SZ) crushed tuff used as analog 

constant FractureRDSTFFI Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for I (SZ) 

loguniform AlluviumMatrixRDSAV-I Alluvium retardation coefficient for 1.0,4.0 Daniels, 1981 ** 

I__ _ _ I_ I (SZ) I I

A-59 NUREG-1668
constant FractureRDSTFFTc Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Tc (SZ) 

loguniform AlluviumMatrixRDSAVTc Alluvium retardation coefficient for 1.0,30.0 Meijer, 1990; crushed tuff analog ** 

_Tc (SZ) 

constant FractureRDSTFF_Ci Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Cl (SZ) 

constant AlluviuniMatrixRDSAV_Cl Alluvium retardation coefficient for 1.0 Conservative assumption of no retardation ** 

Cl (SZ) 

constant FractureRDSTFFCm Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Cm (SZ) 

constant AlluviumMatrixRDSAVCm Alluvium retardation coefficient for 1.0 No data and conservative assumption of no ** 

Cm (SZ) retardation 

constant FractureRDSTFFU Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for U (SZ) 

loguniform AlluviumMatrixRDSAVU Alluvium retardation coefficient for 1.0, 300.0 Triay, et al., 1996a ** 

_U (SZ) 

constant FractureRDSTFF_Pu Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Pu (SZ) 

loguniform AlluviumMatrixRDSAVPu Alluvium retardation coefficient for 9.0, 2.0e4 Meijer, 1990; Triay, 1996a ** 

Pu (SZ) 

constant FractureRDSTFFTh Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Th (SZ) 

loguniform AlluviumMatrixRDSAV_Th Alluvium retardation coefficient for 2.0e3, 3.0e4 Triay, et al., 1997 ** 

Th (SZ) 

constant FractureRDSTFFRa Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Ra (SZ) 

loguniform AlluviumMatrixRDSAVRa Alluvium retardation coefficient for 2.0e3, 8.0e3 Triay, et al., 1997; crushed tuff analog ** 

Ra (SZ) _ __1_1_1 

constant FractureRDSTFF_Pb Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

I tuff for Pb (SZ) I II

NUREG-1668 A-60
loguniform AlluviumMatrixRDSAV_Pb Alluvium retardation coefficient for 2.0e3, 8.0e3 Triay, et al., 1997; crushed tuff analog ** 

constant FractureRDSTFF_Cs Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff for Cs (SZ) 

constant FractureRD_STFF_Ni 

s 

Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** llvimatixDCsVC f 

loguniform AlluviumMatrixRD_SAVNi 

Alluvium retardation coefficient for i.0, 8.0e3 Triay, et al., 1997; crushed tuff analog ** 

constant FractureRDSTFF_C 

Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tufffori (SZ) 

constant AlluviumMatrixRD 

_SAV_C Alluvium retardation coefficient for 1.0 Conservative assumption of no retardation ** 

tufffor (SZ) C 

constant FractureRDSTFF_Se 

Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

tuff forC(SZ) 

loguniform AlluviumMatrixRD_SAV_Se 

Alluvium retardation coefficient for 1.0, 500.0 Triay, et al., 1997; crushed devitrified tuff ** 

Se (SZ) 

analo g 

constant FractureRD_STFF_Nb 

Fracture retardation coefficient of 1.0 Conservative assumption of no retardation ** 

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ t u f f f o r N b ( S Z ) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

loguniform AlluviumMatrixRDSAV_Nb 

Alluvium retardation coefficient for 2.0e3, 3.0e4 Triay, et al., 1997; crushed devitrified tuff ** 

tuff for Nb (SZ) 

Nb (SZ) analog 

loguniform FracturePorositySTFF Fracture porosity of saturated tuff I .Oe-3, l.e-2 Best estimate by USFIC Team. For lower ** 

bound of Ie-3, see Geldon (1993). For upper 

bound of le-2, see Geldon (1997) citing 

uniform AlluviumMatrixPorositySAV Alluvium porosity of Amargosa 1.Oe-1, 1.5e-I Best estimate by USFIC team. Also see Geldon ** Valley alluvium (1993) and Oatfield and Czarnecki (1989). 

constant ImmobileRDSTFFAm Retardation factor for Am in matrix 1.8e4 Triay, et al., 1997 ** 

I during matrix diffusion II_1___

A-61 NUREG- 1668
constant ImmobileRDSTFFNp Retardation factor for Np in matrix 19.0 Triay, et al., 1997 ** 

during matrix diffusion 

constant ImmobileRDSTFF_I Retardation factor for I in matrix 1.0 Triay, et al., 1997 ** 

during matrix diffusion 

constant ImmobileRDSTFFTc Retardation factor for Tc in matrix 1.0 Triay, et at., 1997 ** 

during matrix diffusion 

constant ImmobileRDSTFF_CI Retardation factor for CI in unatrix 1.0 Triay, et al, 1997 ** 

during matrix diffusion 

constant ImmobilcRDSTFFCm Retardation factor for Cm in matrix 1.0 No Data ** 

during matrix diffusion 

constant ImmobileRDSTFFU Retardation factor for U in matrix 37.0 Triay, et al., 1997 ** 

during matrix diffusion 

constant hmmobileRDSTFF_Pu Retardation factor for Pu in matrix 1.8e3 Triay, et al., 1997 ** 

during matrix diffusion 

constant ImmobileRDSTFFTh Retardation factor for Th in matrix 1.8e4 Triay, et al., 1997 ** 

during matrix diffusion 

constant ImmobileRD-STFF_Ra Retardation factor for Ra in matrix 5.4e3 Triay, et al., 1997 ** 

during matrix diffusion 

constant lmmobileRDSTFF_Pb Retardation factor for Pb in matrix 5.4e3 Triay, et al., 1997 ** 

during matrix diffusion 

constant lmmobileRDSTFFCs Retardation factor for Cs in matrix 9.0e3 Triay, et al., 1997 ** 

during matrix diffusion 

constant lmmobileRDSTFFNi Retardation factor for Ni in matrix 1.8e3 Triay, et al., 1997 ** 

during matrix diffusion 

constant ImmobileRD-STFFC Retardation factor for C in matrix 1.0 Triay, et al., 1997 ** 

during matrix diffusion 

constant ImmobileRD-STFFSe Retardation factor for Se in matrix 55.0 Triay, et al., 1997 ** 

I_ I during matrix diffusion

NUREG-1668 A-62
A-63 NUREG-1668

constant ImmobileRD_STFF_Nb Retardation factor for Nb in matrix 1.8e4 Triay, et al., 1997 ** 

during matrix diffusion 

constant ImmobilePorositySTFF Matrix porosity in tuff 0.01 CNWRA best estimate 

constant DiffusionRateSTFF Diffusion rate in tuff. This 0.0 CNWRA best estimate 

parameter is set to a constant value 

of 0.0 for the base case in order to 

bypass matrix diffusion 

calculations.
** 

** 

**

***>>> DCAGW <<<***

constant DistanceToCriticalGroup[km][should-bec5 Distance to receptor 20.0 Farming is assumed to occur at a distance 20 km or greater at ** 

or_20] group (krm) YM. 

uniform WellPumpingRateAtCriticalGroup5km[gal./ Well pumping rate 1.35e4, Based on an assumed population of 100 to 880 (Fedors and ** 

day] for residential 2.64e5 Wittmeyer, 1998) and a range of per capita water use from 150 

receptor group gpd for Tucson, AZ, to 300 gpd for Las Vegas, NV, water use 

located for less than (van der Leeden, et al., 1990; Table 5-16, P. 319). 

20 km from YM 

(gal./day) 

uniform WellPumpingRateAtCriticalGroup20km[gal./ Well pumping rate 4.5e6, Equivalent number of quarter-section center-pivot irrigation ** 

day] for farming receptor 1.3e7 plots under cultivation with alfalfa. From 1989 LANDSAT 

group located image (Wittmeyer, et al., 1996). Low value is based on actual 

greater than 20 km irrigated area of 13 quarter-section plots. High value is based 

from YM (gal./day) on evidence of as many as 27 quarter-section plots 126 acres 

per quarter-section center-pivot irrigation plot. Low value is 

based on consumptive water use for alfalfa in semiarid climates 

of 3.1 ft/yr near Los Angeles, CA (Linsley and Franzini, 1979, 

Table 14-2, P. 377). High value is based on 4.38 ft/yr in Mesa, 

AZ (van der Leeden, et al., 1990, Table 2-50, P. 99). 

uniform PlumeThickncss5km[m] Plume thickness at 10.0, ** 

1_5 km (m) 100.0 

uniform AquiferThickness5km[m] Aquifer thickness at 300.0, Luckey, et al. (1996), Table 2, P. 19. Computed by subtracting ** 

5 km (m) 700.0 the altitude of the base of the lower volcanic aquifer from the 

altitude of the water level in the well. 

uniform MixingZoneThickness20km[m) Mixing zone 50.0, Based on data on well screen depths obtained from the USGS ** 

thickness at 20 km 200.0 GWSI database (Wittmeyer, et al., 1995), the distance from the 

(mn) water table to the bottom of the first screened section for wells 

in the Amargosa Farms area ranges from approximately 40 to 

245 m.

NUREG-1668 A-64
***>>> FAULTO <<<***

finite TimeOfNextFaultingEventinRegi Time of next faulting event (years 100.0, Based on PSHA data, USGS (1996) 

exponential onOflnterest[yr] from present) 10000.0, 2.0e

5 

user ThresholdDisplacementforFaultDi Threshold fault displacement for 4 Estimated values because no direct analyses of ** 

distribution sruptionOfWP[m] disruption (m) 0.1 WP stability in fault zones exist. The 0.2 m value 

Data input order: number of fault 0.2 is the mean value of the original distribution and 

displacement values to be 0.3 is consistent with the proposed fault zone 

provided followed by 04 displacement. 

equiprobable displacement values 

uniform XLocationOtFaultingEventlnRegi X location of center of faulting 547400.0, Within repository ** 

onOflnterest[m] event in region of interest (m) 548600.0 

uniform YLocationOfFaultingEventlnRegi Y location of center of faulting 4076200.0, Within repository ** 

onOflnterest[m] event in region of interest (m) 4079040.0 

constant ProbabilityForNWOrientationOtF Probability for NW orientation of 0.05 Based on map of Scott and Bonk (1984) ** 

aults faults 

uniform RNtoDetermineFaultOrientation A random number to determine 0.0, 1.0 ** 

fault orientation 

constant NWFaultStrikeOrientationMeasur NW strike orientation measured -32.5 Based on map of Scott and Bonk (1984) ** 

edfromNorthClockwise[degrees] from N clockwise (degrees) 

constant NEFaultStrikeOrientationMeasure NE strike orientation measured 10.0 Based on map of Scott and Bonk (1984) ** 

dfromNorthClockwise[degrees) from N clockwise (degrees) 

constant NWFaultTraceLength[mj NW fault trace length (m) 4000.0 Based on maps of Scott and Bonk (1984) and 

Simonds, et al. (1995) 

constant NEFaultTraceLength[m] NE fault trace length (m) 4000.0 Based on maps of Scott and Bonk (1984) and ** 

I _Simonds, et al. (1995) 

beta NWFaultZoneWidth[m] NW fault zone width (M) 0.5,275.0, Based on observational data from ESF, Ghosh, ** 

1 11.25, 15.0 et al. (1997), pp. 2-5; Stirewalt, et al. (1995)

A-65 NUREG-1668

** 

** 

**
NUREG-1668

beta NEFaultZoneWidth[m] NE fault zone width (m) 0.5, 365.0, Based on observational data from ESF, Ghosh, ** 

1.25, 15.0 et al. (1997), pp. 2-5; Stirewalt, et al. (1995) 

constant NWAmountOfLargestCredibleDi NW largest credible displacement 0.5 U.S. Geological Survey (1996), ** 

splacement[mJ (m) Chapters 4.3-4.13. Median value of measured 

paleoseismic ruptures 

constant NEAmountOtLargestCredibleDis NE largest credible displacement 0.5 U.S. Geological Survey (1996), ** 

placement[ml (m) Chapters 4.3-4.13. Median value of measured 

paleoseismic ruptures 

constant NWCumulativeDisplacementRate NW cumulative displacement rate 0.00005 Based on Electric Power Research Institute (1993) ** 

[mnVyr] (mlyr) 

constant NECumulativeDisplacementRatel NE cumulative displacement rate 0.00005 Based on Electric Power Research Institute (1993) 

mm/yrl (mlyr) ___

A-66
***>>> VOLCANO <<<***

finite TimeOfNextVolcanicEventin Time of next 100.0, Technical basis for. probability of volcanic disruption of the proposed ** 

exponenti RegionOflnterest[yr) volcanic event 10000.0, repository site is explained in the Connor, et al. (1997) issue resolution status 

al (yr) 1.0e-7 report on probability of igneous events. Additional methodologies developed 

in Hill, et al. (1996), pp. 2-6 to 2-16, and Connor and Hill (1995), inclusive. 

constant XLocationlnRegionOflnterest X-coordinate of 548000.0 Represents the x-coordinate of the center of the repository to ensure that the ** 

[m] the center of the volcano will strike the repository when the volcanism scenario flag is turned 

I dike on. 

constant YLocationlnRcgionOf]ntcrcst Y-coordinate of 4078000.0 Represents the y-coordinate of the center of the repository to ensure that the ** 

[m] the center of the volcano will strike the repository when the volcanism scenario flag is turned 

dike on. 

uniform RNtoDeterminelfExtrusiveOr Random 0.0,1.0 This parameter was developed for previous versions of TPA but not used in ** 

IntrusiveVolcanicEvent numbers to TPA 3.1.4. If iflag = I for volcanic disruption, there is no reason to assume 

determine that a random fraction of igneous events is not volcanic but instead is wholly 

volcanic event intrusive. This parameter currently is maintained as a place holder for future 

type versions of TPA that will evaluate effects associated with intrusive igneous 

activity. 

constant FractionOfTimeVolcanicEven Fraction of 0.999 User-specified value to investigate simplistic effects of igneous events that are ** 

tlsExtrusive extrusive intrusive rather than extrusive. Results in failure of WPs based solely on area 

volcanic events directly intersected by a dike that is constructed using subsequent uniform 

parameter distributions. This parameter was developed for previous versions 

of TPA and currently is maintained as a placeholder for future versions of 

TPA that will evaluate effects associated with intrusive igneous activity. 

uniform AngleOfVolcanicDikeMeasur Dike angle 0.0, 15.0 The orientation of planar, vertical igneous intrusions (i.e., dikes) at shallow ** 

edFromNorthClockwise[degr (degree) crustal levels is controlled by the 3-D distribution of crustal stress (Anderson, 

ees] 1938; Delaney, et al., 1986). In addition, pre-existing crustal structures, such 

as faults, may serve as conduits for ascending magma at shallow crustal levels 

(Young, et al., 1994; Jolly and Sanderson, 1997). Although the current 

direction of maximum horizontal compressive stress at YM is oriented 280 

from north, faults at the proposed repository site are oriented between 00 and 

150 from north (Morris, et al., 1996, p. 277). This orientation serves as the 

most likely orientation direction for future igneous intrusions at the proposed 

repository site.

A-67 NUREG-1668

** 

** 

**
uniform LengthOfVolcanicDike[mJ Volcanic dike 2000.0, Few data are available on dike lengths for the Yucca Mountain region (YMR). ** length (m) 11000.0 Connor, et al. (1997, pp. 4-29-4-34) summarize volcanic alignment data that 

are consistent with subsurface dike lengths between 2 km and 11 km for 5 Ma 

and younger YMR volcanoes. A detailed geologic map by Gartner and 

Delaney (1988) of the San Rafael volcanic field, Utah, provides length 

information for more than 200 basaltic dikes that were originally emplaced 

0.5-1.5 km from the surface (Delaney and Gartner, 1997). The length range of 

2-8 km accounts for the range observed by Gartner and Delaney (1988) and 

YMR vent alignment lengths (Connor, et al., 1997). 

uniform WidthOtlVolcanicDike[mj Volcanic dike 1.0, 10.0 Few data are available on dike widths for YMR. Staff have observed that ** width (i) dikes between 0.5 and 10 m currently are exposed at 3.8-12 Ma YMR basaltic 

volcanic and intrusive centers. A detailed geologic map by Gartner and 

Delaney (1988) of the San Rafael volcanic field, Utah, provides width 

information for more than 200 basaltic dikes that were originally emplaced 0.5-1.5 km from the surface (Delaney and Gartner, 1997, pp 1180-1185). The 

width range of 1-10 m accounts for the range observed by Delaney and 

Gartner (1988) and YMR dike widths. 

uniform DiameterOfVolcanicCone[m] Cone diameter 24.6, 77.9 Hill (1996, inclusive) provides the technical basis to conclude that subsurface ** 

(i) conduits for the youngest YMR volcanoes likely widened from initial 

diameters of meters to tens of meters in diameter during late eruption stages. Lithologic and structural information constrains this widening to 49±7 m for 

an analog volcano site at Tolbachik, Russia. Insufficient deposits remain at 

YMR volcanoes to measure this parameter directly. The number of WPs disrupted, however, depends on the WP dimensions, WP spacing, and 

centerline spacing between emplacement drifts. Average repository waste 

loading values may significantly underestimate the number of WPs disrupted 

by a volcanic conduit. For example, using an average waste loading of 83 

MTU/acre and a 9.76 MTU WP loading, a 50-m diameter conduit will only disrupt 4.1 WPs. Geometric relationships show that disruption of I to 10 WPs 

presents a reasonably conservative range for volcanic conduits that may vary between I and 50 m in diameter. This number of WPs corresponds to effective 

conduit diameters of 24.6-77.9 m using an average waste loading of 83 MTU/acre and a 9.76 MTU WP loading.

NUREG-1668 A-68
** 

** 

**

***>>> ASHPLUMO <<<***

constant DensityOfAirAtSTP[g/cm3] Air density at standard 0.00129 Weast (1976), p. F-I1 ** 

temperature and 

pressure (g/cm 3

) 

constant ViscosityOfAirAtSTP[g/cm-s] Air viscosity (g/cm/s) 0.00018 Weast (1976), pp. F-13 to F-16 ** 

constant ConstantRelatingFallTimeToEdd Constant relating eddy 400.0 Suzuki (1983), p. 99 ** 

yDiffusivity[cm2/s5/21 diffusion to particle 

fall time (cm2/s52) 

constant MaximumParticleDiameterForPar Maximum particle 10.0 Eruptions that are many orders of magnitude larger than the ** 

ticleTransport[cm] diameter for transport largest <10 Ma YMR basaltic eruption rarely transport particles 

(cm) >10 cm in diameter through convective dispersal (Pyle, 1989, 

pp. 7-1 1). Grain-size analyses at YMR analog deposits have 

average maximum particle diameters of <4 cm at distance 1-10 

kmn from the vent (Hill and Connor, 1995, p. 149), which 

decreases to <1 cm at distances > 10 km from the vent (e.g., 

Hill, et al., 1997). Incompletely preserved YMR fall deposits 

exposed 0.5-3 km from the vent have maximum particle 

diameters <4 cm (Hill, et al., 1995, pp. 7-14 to 7-15). 

constant MinimumFueIParticulateSize[cm] Minimum fuel particle 0.0001 SF has initial grain-size average diameters on the order of 100s ** 

diameter (cm) of pm (e.g., U.S. Department of Energy, 1988). Crush-impact 

studies (Nuclear Regulatory Commission, 1988) indicate an 

average diameter of crushed HLW on the order of 100 pm. The 

thermal, mechanical, and chemical loads of a basaltic volcanic 

eruption clearly exceed those imposed by a falling ceiling panel. 

Basaltic eruption processes will thus fragment HLW further 

than crush-impact processes, supporting the basic assumption of 

a 10-pm median diameter for HLW transported by a basaltic 

volcanic eruption. Minimum diameter for a log-triangular 

distribution is assumed to be 1 log unit below the median 

diameter, based on grain-size distributions in U.S. Department 

of Energy (1988) and Nuclear Regulatory Commission (1988).

A-69 NUREG-1668
constant ModeFuelParticulateSize[cm] Median fuel particle 0.001 SF has initial grain-size average diameters on the order of 100s ** 

diameter (cm) of pm (e.g., U.S. Department of Energy, 1988). Crush-impact 

studies (Nuclear Regulatory Commission, 1988) indicate an 

average diameter of crushed HLW on the order of 100 pm. The 

thermal, mechanical, and chemical loads of a basaltic volcanic 

eruption clearly exceed those imposed by a falling ceiling panel. 

Basaltic eruption processes will thus likely fragment HLW 

"further than crush-impact processes, supporting the basic 

assumption of a 10-pm median diameter for HLW transported 

by a basaltic volcanic eruption. 

constant MaximumFueiParticulateSize[cm Maximum fuel particle 0.01 SF has initial grain-size average diameters on the order of ** 

diameter (cm) hundreds of microns (e.g., U.S. Department of Energy, 1988). 

Crush-impact studies (Nuclear Regulatory Commission, 1988) 

indicate an average diameter of crushed HLW on the order of 

100 p m. The thermal, mechanical, and chemical loads of a 

basaltic volcanic eruption clearly exceed those imposed by a 

falling ceiling panel. Basaltic eruption processes will thus likely 

fragment HLW further than crush-impact processes, supporting 

the basic assumption of a 10-pm median diameter for HLW 

transported by a basaltic volcanic eruption. Maximum diameter 

for a log-triangular distribution is assumed to be 1 log unit 

above the median diameter, based on grain-size distributions in 

U.S. Department of Energy (1988) and Nuclear Regulatory 

Commission (1988). 

constant MinimumAshDensityForVariatio Minimum ash density 1.2 Basaltic tephra ranges in density from 2600 kg/in 3 when non- ** 

nWithSize[g/cm3] (g/cm3

) vesiculated at 1100 'C, to about 1200 kg/m3, when vesiculated 

at 25 *C (e.g., Hill, et al., 1997, p. 10). 

constant MaximumAshDensityForVariatio Maximum ash density 2.0 Non-vesiculated YMR-type basalt has a density of around 2700 ** 

nWithSize[g/cm3] (g/cm3) kg/in3 at 25 TC (e.g., Hill, et al., 1997, p. 10). The minimum 

amount of vesiculation (i.e., void spaces caused by volatile 

exsolution during depressurization) typically found in basaltic 

tephra is around 25% (e.g., Walker and Croasdale, 1972), which 

reduces the particle density to about 2.000 kg/m3

.

A-70 NUREG-1 668
constant MinimumAshLogdiameterForDe Minimum value of -2.0 The density of basaltic scoria is strongly controlled by average ** nsityVariation logarithm of ash grain size. Basaltic scoria break along vesicle (i.e., bubble) walls 

diameter in cm during transport fragmentation, which results in a net loss of 

vesicle void-space and concomitant increase in bulk density. For 

example, Walker, et al. (1984) measured a 40-percent increase 

in scoria density as average grain size decreased from about 1 

cm to about 1 mm. Jarzemba and LaPlante (1996, pp. 6-10) 

concluded that a range of I log unit in average grain size would 

effectively encompass the range in size-dependent density variations commonly observed in basaltic tephra-fall deposits 

(e.g., Hill, et al., 1997, p. 10). 

constant MaximumAshLogdiamctcrForDc Maximum value of - 1.0 The density of basaltic scoria is strongly controlled by average ** nsityVariation logarithm of ash grain size. Basaltic scoria break along vesicle (i.e., bubble) walls 

diameter during transport fragmentation, which results in a net loss of 

vesicle void-space and concomitant increase in bulk density. For 

example, Walker, et al. (1984) measured a 40 percent increase 

in scoria density as average grain-size decreased from about 1 

cm to about I mm. Jarzemba and LaPlante (1996, pp. 6-10) 

concluded that a range of I log unit in average grain size would 

effectively encompass the range in size-dependent density variations commonly observed in basaltic tephra-fall deposits (e.g., Hill, et al., 1997, p. 10). 

constant ParticleShapeParameter Ash particle shape 0.5 The shape of irregular particles with axial lengths a, b, and c can ** 

parameter be described as [b+cJ/2a, where a is greater than b or c (Suzuki, 

1983, p. 100). Hill, et al. (1996, pp. 2-23) concluded shape 

parameters between 0.5 and 0.25 describe irregularly shaped 

basaltic tephra and that variations within this range do not affect 

transport properties in the ASHPLUME module. 

constant IncorporationRatio SF incorporation ratio 0.3 Assumed value of 0.3 in Jarzemba and LaPlante (1996, ** 

pp. 9-12). For critical group locations 20 km from the proposed 

repository site, Hill and Trapp (1997, pp. 5-6) found that a 

uniform distribution of U[0. 1, 1.01 for this parameter did not 

affect dose significantly for the default HLW grain-size characteristics used in the primary input file.

A-71 NUREG- 1668
WindDirection[degrees] Wind direction relative 

to due east in the 

counter-clockwise 

direction (degrees)

-90.0

I I + I

WindSpeed[cm/sl Wind speed (cm/s) 0.00083

constant

Wind speed data are not available for altitudes of interest (2 km 

to about 6 km) above YM (U.S. Department of Energy, 1988; 

U.S. Department of Energy, 1997, inclusive). The nearest wind 

speed data to YM and the receptor group location are from 

Desert Rock airstrip, located about 50 km SE of YM. For 

altitudes of about 2 km (700 mbar pressure level), wind speed 

above Desert Rock within the NNW-NNE sector averages 

6 m/s (U.S. Department of Energy, 1997, pp. 3-63-3-71). For 

altitudes of about 4 km (500 mbar pressure level), wind speed 

above Desert Rock within the NNW-NNE sector averages 

12 m/s (U.S. Department of Energy, 1997, pp. 3-63-3-71). 

Basaltic eruption column associated with YMR-type eruptions 

likely reached above-ground altitudes of at least 6 km (e.g., 

Jarzemba, 1997). A mean wind speed for 4-km altitudes thus 

presents a reasonable mean value for 2-6 km high eruption 

columns.

loguniform VolcanicEventDuration[s] Duration of volcanic 6.13e4, Baca and Jarzemba (1997), pp. 4-3 to 4-5 

IIevent (s) I7.24e6 II

**

**

NUREG-1668

Setting wind direction to 90* clockwise from east (i.e., south) 

ensures that the wind blows to the receptor group during each 

run. This provides a conservative basis for evaluating the effects 

of volcanic eruptions on nuclear facilities (International Atomic 

Energy Agency, 1997, pp. 19-20). In addition, Hill and Trapp 

(1997, p. 6) calculated that ground-surface dose is within one 

order-of-magnitude of peak dose when the wind blows within a 

500 sector centered about a main dispersion axis of 90*. Surface 

topography strongly controls wind direction and speed 

measured at surface weather stations to altitudes of at least 100 

m (U.S. Department of Energy, 1997, pp. 3.63-3.71). Wind 

direction data are not available for altitudes of interest (2 km to 

about 6 kin) above YM (U.S. Department of Energy, 1997, 

inclusive). The nearest wind directiorf data to YM and the 

receptor group location are from Dessert Rock airstrip, located 

about 50 km SE of YM. For altitudes of about 2 and 4 km (700 

and 500 mbar pressure level), wind direction above Desert Rock 

is within the 500 sector centered on south 18-15% of the time.

exponential

A-72
A-73 NUREG-1668

loguniform VolcanicEventPower[W] Event power (W) 2.57e9, Baca and Jarzemba (1997), pp. 4-3 to 4-5 ** 

3.55el 1 

constant VolcanicColumnConstantBeta Shape parameter for 10.0 Hill and Trapp (1997, p. 10) concluded that this parameter was a ** 

the volcanic column relatively insensitive parameter and that setting this parameter to: 

a constant value of 10 was sufficient for modeling YMR-type basaltic eruptions using ASHPLUME. This result was 

confirmed by sensitivity studies conducted by Hill, et al. (1997) 

on the 1995 Cerro Negro eruption deposits. 

logtriangul AshMeanParticleLogDiameter[d_ Logarithm of the mean 0.01,0.1, Few data are available to characterize the mean particle diameter ** ar incm] ash particle size in cm 1.0 for an entire basaltic fall deposit. Hill, et al. (1997, pp. 4-5) calculated a mean diameter of 0.7 mm for the 1r-mm and thicker 

fall deposits from the 1995 Cerro Negro eruption. Although this 

eruption was about an order of magnitude smaller than the 

smallest YMR eruptions, it is likely that overall grain-size 

characteristics remain fairly scale-independent for these types of basaltic eruptions (e.g., Walker and Croasdale, 1972). 

constant AshParticleSizeDistributionStand Ash particle size 1.0 Hill, et al. (1996, pp. 2-21-2-23) showed that this parameter ** ardDeviation distribution standard does not demonstrably affect tephra-deposit thicknesses 

deviation modeled using the ASHPLUME code. Hill and Trapp (1997) recommended setting this parameter to a constant value of 1.
I I I

RelativeRateOfBlanketRemo 

val[ l/yr]

Bulk removal rate from 

blanket (I/yr)

0.0001 Only traces of the tephra-fall deposit are preserved at the youngest 

YMR volcano, Lathrop Wells, which is around 100,000 years old. 

Recent work by Delgado-Granados, et al. (1998) at Xitle volcano, 

Mexico, has shown that substantial amounts of basaltic tephra-fall deposits are preserved from this 2,000-year-old eruption. The area 

around Xitle volcano receives around 500-700 mm of annual rainfall, 

in contrast to the roughly 100-mm annual rainfall in the YMR. 

Although many factors, such as topographic slope, degree of incision, 

and eolian sedimentation, affect tephra-fall erosion, areas of greater 

annual rainfall should experience higher erosion rates of 

nonconsolidated fall deposits than areas of lower annual rainfall. Thus, 

tephra-fall deposits in the YMR likely are significantly preserved for

constant FractionOfPrecipitationLostT Fraction of precipitation 0.68 Jarzemba and Manteufel (1997), p. 924. Based on the converse of the ** oEvapotranspiration lost to fraction of precipitation that can reach the deep soil 

evapotranspiration 

constant FractionOflrrigationLostToE Fraction of irrigation 0.5 Jarzemba and Manteufel (1997), p. 924 ** 

vapotranspiration water lost to 

evapotranspiration 

constant AnnualPrecipitation[m/yr] Annual precipitation rate 0.085 Wilson, et al. (1994) ** I (m/yr) 

constant Annuallrrigation[m/yr] Annual irrigation rate 1.52 Nuclear Regulatory Commission (1995) pp. 7-10 ** 

(m/yr) 

constant FractionOfYearSoillsSaturat Fraction of year blanket 0.0054 Jarzemba and Manteufel (1997), p. 924 ** 

edDueToPrecipitation saturated due to 

precipitation 

constant FractionOtYearSoillsSaturat Fraction of year blanket 0.2 LaPlante, et al. (1995), Table 2.1, pp. 2-6, based on average growing ** edDueTolrrigation saturated due to time of crops from Chambers and May (1994) irrigation I I 

constant AshBulkDensity[g/cm3J Bulk density for 1.4 Based on data in Fisher and Schmincke (1984), Table 5-6, p. 118 and ** volcanic ash (g/cm3) I Fig. 5-28, p. 120

* e

**

NUREG- 1668

** 

** 

**

***>>> ASHRMOVO <<<***

constant

A-74
constant AshVolumetricMoistureFract Volumetric fraction of 0.4 Calculated from bulk density of ash ** 

ionAtSaturation moisture in volcanic ash 

at saturation 

constant DepthOfTheRootingZone[m] Depth of rooting zone 0.15 Napier, et al. (1988), p. 4.58 ** 

constant KdOfUraniumInVoicanicAsh U distribution 35.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

[cm3/g] coefficient in volcanic 

ash (cm3

/g) 

constant KdOfCuriumlnVolcanicAsh[ Cm distribution 4000.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

cm3/g] coefficient in volcanic 

ash (cm3/g) 

constant KdOfPlutoniumlnVoicanicA Pu distribution 550.0 Sheppard and Thibault (1990), Table I, p. 472 ** 

sh[cm3/gl coefficient in volcanic 

ash (cm3/g) 

constant KdOfAmericiumlnVolcanic Am distribution 1900.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

Ash[cm3/g] coefficient in volcanic 

ash (cm3

1g) 

constant KdOfThoriumlnVolcanicAsh Th distribution 3200.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

[cm3/gJ coefficient in volcanic 

ash (cm3/g) 

constant KdOIRadiumlnVolcanicAsh[ Ra distribution 500.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

crn3/gj coefficient in volcanic 

ash (cm 3

/g) 

constant KdOfLeadlnVolcanicAsh[cm Pb distribution 270.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

3/gI coefficient in volcanic 

ash (cm 3

/g) 

constant KdOfProtactiniumlnVolcanic Pa distribution 550.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

Ash[cm3/g] coefficient in volcanic 

ash (cm3

/g) 

constant KdOfActiniumlnVolcanicAs Ac distribution 450.0 Sheppard and Thibault (1990), Table 1, p. 4 72 ** 

h[cm3/gj coefficient in volcanic 

I _________ _ Iash (cm 3

/R) I III

A-75 NUREG-1668
constant KdOfNeptuniumrlnVolcanic Np distribution 5.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

Ash[cm3/gj coefficient in volcanic 

ash (cm3

/g) 

constant KdOfSamariurnlnVolcanicA Sm distribution 245.0 Sheppard and Thibault (1990), Table 1, p. 47 2 ** 

sh[cm3/g] coefficient in volcanic 

ash (cm3/g) 

constant KdOfCesiumlnVolcanicAsh[ Cs distribution 280.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

cm3/gJ coefficient in volcanic 

ash (cmn3/g) 

constant KdOflodinelnVolcanicAsh[c I distribution coefficient 1.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

m3/gj in volcanic ash (cm '/g) 

constant KdOtTinlnVolcanicAsh[cm3 Sn distribution 130.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

/g] coefficient in volcanic 

ash (cm 3/g) 

constant KdOfSilverlnVolcanicAsh[c Ag distribution 55.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

m3/g] coefficient in volcanic 

ash (cm3/g) 

constant KdOfPaladiumlnVolcanicAs Pd distribution 55.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

hlcm3/gJ coefficient in volcanic 

ash (cm 3

/g) 

constant KdOfTechnetiumlnVolcanic Tc distribution 0.1 Sheppard and Thibault (1990), Table 1, p. 472 ** 

Ash[cm3/gJ coefficient in volcanic 

ash (cm 3/g) 

constant KdOfMolybdenumInVolcani Mo distribution 10.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

cAsh[cm3/g] coefficient in volcanic 

ash (cm 3

/g) 

constant KdOfNiobiumlnVolcanicAsh Nb distribution 160.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

[cm3/g] coefficient in volcanic 

ash (cm3/g) 

constant KdOfZirconiumlnVolcanicA Zr distribution 600.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

sh[cm3/gJ coefficient in volcanic 

I ash (cm 3

/g) J_

NUREG-1668 A-76
constant KdOfStrontiumlnVolcanicAs Sr distribution 15.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

h[cm3/g] coefficient in volcanic 

ash (cm3lg) 

constant KdOfSeleniumlnVolcanicAs Se distribution 150.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

h[cm3/g] coefficient in volcanic 

ash (cm3

/R) 

constant KdOfNickellnVolcanicAsh[c Ni distribution 400.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

m3/gJ coefficient in volcanic 

ash (cm 3

/g) 

constant KdOfChlorinelnVolcanicAsh Cl distribution 0.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

[cm3/g] coefficient in volcanic 

ash (cm3/g) ,., 

constant KdOfCarbonlnVolcanicAsh[ C distribution coefficienw 5.0 Sheppard and Thibault (1990), Table 1, p. 472 ** 

cm3/gj in volcanic ash (cm-/g) 

constant SolubilityOfUraniumlnVolca U solubility (mol/L) 4.5e-5 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

nicAsh[moles/liter] 

constant SolubilityOfCuriumlnVolcan Cm solubility (mol/L) I.Oe-6 Kerrisk (1985), Table 9, p. 14 ** 

icAsh[moles/liter] 

constant SolubilityOfPlutoniumlnVol Pu solubility (rnol/L) 5.0e-6 Wilson, et al. (1993), Table 9-2b, p. 9-7; Kerrisk (1985), Table 9, p. 14 ** 

canicAsh[moles/liter] 

constant SolubilityOfAmericiumlnVol Am solubility (mol/L) 1.Oe-6 Kerrisk (1985), Table 9, p. 14 ** 

canicAsh[moles/liter] 

constant SolubilityOfThoriumlnVolca Th solubility (mol/L) 3.2e-9 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

nicAsh[moles/liter] 

constant SolubilityOfRadiumlnVoican Ra solubility (mol/L) 1.Oe-7 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

icAsh[moles/liter] 

constant SolubilityOfLeadlnVolcanic Pb solubility (mol/L) 3.2e-7 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

Ash[moles/liter] I 

constant SolubilityOfProtactiniumlnV Pa solubility (mol/L) 3.2e-8 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** olcanicAsh[moles/literl

A-77 NUREG-1668
constant SolubilityOfActiniumInVolc Ac solubility (mol/L) 1.Oe-6 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

anicAsh[moles/literl 

constant SolubilityOfNeptuniumlnVol Np solubility (moi/L) i.0e-4 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

canicAsh[moIcs/liter] 

constant SolubilityOfSamariumrlnVolc Sm solubility (mol/L) 5.0e-6 Kerrisk (1985), Table 9, p. 14 ** 

anicAsh[moles/Iiterl 

constant SolubilityOlCesiumlnVolcan Cs solubility (mol/L) 1.0 Wilson, et al. (1993), Table 9-2b, p. 9-7; Kerrisk (1985), Table 9, p. 14 ** 

I icAshimoles/Iiter] 

constant SolubilityOflodinelnVolcani I solubility (mol/L) 1.0 Wilson, et al. (1993), p. 9-9; Kerrisk (1985), Table 9, p. 14 ** 

cAsh[moles/liter] 

constant SolubilityOfTinlnVolcanicA Sn solubility (mol/L) 5.0e-8 Wilson, et al. (1993), Table 9-2b, p. 9-7; Kerrisk (1985), Table 9, p. 14 ** 

sh[moles/liter] 

constant SolubilityOfSilverlnVolcanic Ag solubility (tool/L) 1.0 Conservative estimation due to lack of data ** 

_Ash[moles/liter] 

constant SolubilityOfPaladiumlnVolc Pd solubility (tool/L) 9.5e-4 Wilson, et al. (1993), Table 9-2b, p. 9-7; Kerrisk (1985), Table 9, p. 14 ** 

anicAsh[moles/liter] 

constant SolubilityOfTechnetiumlnVo Tc solubility (mol/L) 1.0 Wilson, et al. (1993), p. 9-9; Kerrisk (1985), Table, p. 14 ** 

lcanicAsh[moles/liter _ 

constant SolubilityOfMolybdenumln Mo solubility (mol/L) 1.0 Conservative estimation due to lack of data ** 

VolcanicAsh[moles/liter _ 

constant SolubilityOfNiobiumlnVolca Nb solubility (moi/L) 1.Oe-8 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

nicAsh[imoles/liter] 

constant SolubilityOfZirconiumlnVol Zr solubility (mot/L) 3.2e-10 Wilson, et at. (1993), Table 9-2b, p. 9-7 ** 

canicAsh[moles/liter] 

constant SolubilityOfStrontiumlnVolc Sr solubility (tool/L) 1.3e-4 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

anicAsh[motes/liter] 

constant SolubilityOfSeleniumlnVolc Se solubility (mol/L) 1.0 Wilson, et al. (1993), p. 9-

9 ** 

anicAshlmoles/literl I I 

constant SolubilityOfNickellnVolcani Ni solubility (molIL) 2.0e-3 Wilson, et al. (1993), Table 9-2b, p. 9-7 ** 

cAshrmoles/literl I

NUREG-1668 A-78
A-79 NUREG-1668

constant SolubilityOfChlorinelnVolca Cl solubility (mol/L) 1.0 Wilson, et al. (1993), p. 9-9 ** 

nicAsh[moles/liter] 

constant SolubilityOfCarbonlnVolcan C solubility (tool/L) 1.0 Wilson, et al. (1993), p. 9-9; Kerrisk (1985), Table 9, p. 14 ** 

_ _ 1 icAsh[moles/literl .... I _I_ _ _ _
***>>> DCAGS <<<***

NUREG-1668

** 

** 

**

constant DistanceCutoffForDoseConversionDualitylnDCA Cutoff point between a farming 19.99 Jarzemba and Weldy (1997), ** 

GS[kmj and a residential group p. 8 

loguniform AirborneMassLoadForVolcanismDoseCalculation[ Mass of soil in the air .Oe-4, Best estimate based on data ** g/m3j I.0e-2 from Sehmel (1977) and 

Tegen and Fung (1994) 

constant OccupancyFactorForVolcanismDoseCalculation[-] Fraction of time that receptor is 0.24 Based on 40 hours per week ** 

exposed to airborne volcanic ash 

constant DepthOtResuspendableLayer[cm] Thickness of the upper soil layer 0.3 ** 

that is available for resuspension (cm)

A-80
correlateinputs SubareaWetFraction ArealAverageMeanAnnuallnfiltrationAtStart[mm/yr] 0.631 

correlateinputs SubareaWetFraction MatrixPermeability-TSw-[ni2l -0.623 

correlateinputs FowFactor ArealAverageMeanAnnualinfiltrationAtStart[mm/yrI -0.224 

correlateinputs FowFactor MatrixPermeabilityjSw-[m2] 0.13 

correlateinputs FowFactor SubareaWetFraction -0.366 

endoffile

***>>> correlated parameters <<<***

A-81 NUREG-1668
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A-89 NUREG-1668
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NUREG-1668 A-90
Wolfsberg, K. 1978. Sorption-desorption studies of Nevada Test Site alluvium and leaching studies of nuclear 

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Intrusion. CNWRA 94-025. San Antonio, TX: Center for Nuclear Waste Regulatory Analyses.

A-91 NUREG-1668
APPENDIX B 

EBSREL FLOW FACTOR DERIVATION 

The factors F., F., and F.,. are used in the Total-system Performance Assessment (TPA) code to describe 

how waste packages (WPs) and the waste form interact with infiltrating water. The factors Fh and F., could 

have been combined into a single factor for use in the TPA Version 3.1.4 code; however, these factors were kept 

separate because F0,. is strongly correlated with the parameters <I> and <K,.>, whereas Fm, is considered to 

be an independent random variable. F. , is also correlated to F&, which is the fraction of WPs that could be 

intercepted by dripping water (less than 1). The specified initial infiltration rate at Yucca Mountain (YM), <I>, 

and the specified matrix saturated hydraulic conductivity for the TSw layer at YM, <K,.>, are used in the 

derivation of these flow factor values as described below. 

Derivation of Estimates for F.. and F.. 

F.. is the probability of a WP getting a dripping flux greater than zero; [i.e. P(qdp) >0]. The TPA code 

requires the assumption that the number of wetted WPs is invariant, and does not consider changing dripping 

locations. Some data (Wilder, 1996) indicate that drip locations in tunnels move over time. Therefore, the 

number of wetted WPs could be considered to spread the dripping eventually over all WPs, producing a lower 

average flow rate to each. 

Wilder (1996) proposed that the net infiltration to the repository level flows in either the rock matrix 

or fractures depending on the local values of infiltration and matrix hydraulic conductivity. Variabilities of the 

local values for I and KJ, within a realization are characterized by their coefficients of variation C, and C,. Both 

I and K, are assumed to be lognormally distributed within the subarea and correlated with each other. 

The dripping model requires that the spatial scale of variability in Km. and I be greater than the size of 

an individual WP. If not (for example, the flowing fractures are closely spaced), then variability from point to 

point would, in the limit, cause every WP to always be wetted. This is similar in some respects to the "weeps" 

model of TSPA93 (Wilson, et al., 1994) that assumes that in the limit of many small seeps, all WPs would be 

wetted. 

If the scale of the dripping phenomenon is larger than the WP scale, then only the interaction of the 

variability of I and K, needs to be considered. Correlation between I and K. may be important if there are 

definite paths with high conductivity, so infiltration at the drift would be determined by the high conductivity 

of these paths. Although the present model allows correlation between Km and !, only the uncorrelated case is 

considered here. 

Assumption about the distribution of model parameters 

For the derivation of F.. and F, the mean infiltration <I> is uniformly distributed from I to 20 mm/yr 

with a coefficient of variation (C,,) range from 0 to 2, and <Kin> is uniformly distributed from 0.1 to 20 mm/yr 

with a CA, range from 0 to 2. The local parameters K,. and I are described by the mean and standard deviation 

in most reported data on YM. However, this conceptualization assumes that these local parameters are 

lognormally distributed; it is, therefore, necessary to convert between the two distributions. As an example using

B-1 B-NUREG-1668
K,, given the arithmetic mean and coefficients of variation, the standard deviation in the log-transformed space can be described (Benjamin and Cornell, 1970) as follows: 

ln(Cvk + 1) (B-1) 

p = In(<K,> e (B-2) 

The F, and F,, factors are generated from Monte Carlo runs, which sample the parameters <I>, <K,>, C,•, and CAk. Referring to Figure B- 1, consider the repository subarea to be broken into n equal parts (subareas). For each Monte Carlo sample of </>, <K.,,>, Ci,, and C., a lognormal distribution is generated for the local values I and K. for each of the subareas. If (I - K,,) is greater than zero, the number of wet areas N,,• is incremented, and the sum of the positive values of (I - K.) is collected into q,., which represents the potential dripping water. For the current Monte Carlo sample of </>, <K.>, C,, and CAI the factor F,. is the ratio NWJN, where N is the total number of WPs. The ratio of q,,/llN,,, averaged over the entire area is Fo. The fraction of wetted cells represents the potential fraction of wetted WPs. 

The histograms of Fw, and F,, based on 6000 samples, with 1000 subareas per sample, are plotted in 

Figures B-2 and B-3. The covariances among I, K,, Fw, and F.. are presented in Table 3-2. 

Determination of the Flow Diversion Factor F.. 

WP 

The flow diversion factor Fm,, is defined as the fraction of potentially dripping water that will enter the and contribute to the release and transport of radionuclides. Only a fraction of the water intercepting the drifts is expected to come into direct contact with the WPs. Only a portion of the water dripping onto the WPs is expected to get inside where it can interact with the waste form. Factors considered in estimating Fmo,,, include: 

Flow to WPs is reduced because of diversion of water around the drift by the capillary barrier. This is estimated as a uniform distribution between 0.8 and 1.0. Water flow into the drift will face a capillary barrier if the fractures are small enough. Water can easily move across the capillary break, and can be diverted in the fracture around the drift opening. If the drift were in a homogenous, unsaturated porous medium, there would be a precise relationship between infiltration rate and flow, with a clear diversion around the opening, and perching of water at the crest of the opening [e.g, Phillip and Watcher (1989); Ross (1990)]. However, most of the flow is expected to occur in fractures, and may be largely episodic in nature. Under these conditions, the flow may appear to be 

more saturated than unsaturated (Birkholzer, et al., 1997). Flow in large fractures may occur as unstable fingers or rivulets on one face or the other (Glass, 1993), which, for all intents and 

purposes, is not tractable by mathematical models. The relatively minor importance attached to this 

factor (0.8 to 1.0 range) reflects the large uncertainties in the phenomena.

NUREG-1668 B-2
<I> <Kst> for subarea

Sub-Subarea 

Fu 

Figure B-i. Conceptual dripping model.

CD 

1--

2.

0ý

IL) 

0-

Waste 

Packages

ibarea

0.0 0.2 04 

Fwet Factor

0.6 0.8 1.0

Figure B-2. Fwet histogram.

NUREG-1668

'I

t I

0.8 1.0

B-3
Q 

Q

° 

z 

L0 

LO 

-4 -2 0 2 

Natural Log of Fow Factor 

Figure B-3. Fo. histogram. 

Water may be diverted by flowing down the drift wall. The fraction not diverted down the drift wall 

is estimated to be uniformly distributed from 0.5 to 0.8. Water flow managing to break the capillary 

barrier into the drift can drip from the ceiling or from protuberances along the drift. Much of the 

water, however, is likely to flow as a sheet along the drift walls rather than drip from the ceiling onto 

the containers. The closer to the crest of the tunnel, the greater the propensity to drip. Away from 

the crest, the slope of the tunnel walls diverts water to sheet flow along the walls. The placement 

of the canisters affect this factor. Some designs have the canisters aligned with the tunnel centerline, 

whereas other designs have the canisters offset from the centerline. The current design has the 

canister along the center of the placement drifts. 

Some fraction of dripping water will fall directly onto an open WP hole. This factor is assumed to 

be uniformly distributed with a range of 0.1 to 0.5. Water dripping from the ceiling would have to 

fall onto the WP in such a way that it could enter an open hole. If the drop were directly in the path 

of an open hole (e.g., the hole was caused by the corrosion from dripping water), then this condition 

would be fulfilled. However, the current corrosion model does not require dripping water, and, in 

fact, such a condition might actually diminish corrosion by lowering the chloride concentration. 

Considering that in the present model drip impingement has no direct bearing on the location of the 

corrosion holes, not all drips falling onto the container surface would fall directly into a hole. As 

more corrosion holes occur, this condition increases the likelihood of a drop falling into a corrosion 

hole. Otherwise, a large fraction of the water will run off the WP and not enter a hole. As time goes 

on, more pits form on a WP and the likelihood that water drips on a hole increases.

NUREG-1668 B-4
Some fraction of drops may be poised to enter a corrosion hole, but may be unable to do so because 

of the presence of corrosion products. This factor is estimated as a uniformly distributed variable 

from 0.2 to 0.5. Products from the corrosion of the steel overpack and inner barrier may be flaky, 

porous, or gel-like. The density of the corrosion products will be considerably less than the steel 

itself, and, without a high rate of water flux, there will be no mechanism to dislodge them. If this 

is the case, then water dripping into the corrosion hole will have difficulty entering the canister and 

will simply flow off. Holes at the crest will have a higher probability that water will enter because 

there is a reduced propensity for water to flow off. Holes on the side will have higher probability that the water would flow off rather than enter. 

Combining Factors into F.. 

The factor, F., shown in Figure B-4, was formulated by sampling each of the above factors and forming 

their product. The histogram of the resulting factor is approximately lognormal and is represented in the TPA 

input by a lognormal function ranging between 0.01 and 0.2. The fraction of WPs getting wetted and the average 

flow per WP are determined from the above procedure for fixed values of infiltration. Water infiltration at the 

repository horizon is determined by module NFENV and is time-varying because of changes in climate and 

thermal reflux. Under conditions of increased infiltration or reflux, it is expected both the wetting fraction and 

average flow to the WPs will increase. For purposes of computational expediency, the model currently assumes 

the factors F.,,, Fo,,, and F, remain constant through the simulation period. Under conditions of increased 

infiltration or reflux, the wetting model should predict that the number of WPs wetted and the average flow per WP would increase. The current code, however, diverts the additional quantity of flow from increased infiltration 

or reflux to the initial number of wetted WPs, which partially compensates for this model shortcoming. Future 

versions of the code might allow for more realistic treatment of the transient nature of WP and fuel wetting. 

However, increasing the number of WPs wetted as a function of time in the middle of the calculation will make 

the code more complex and will demand significantly greater run times. 

Fmult Factor for TPA Version 3 

38 

E 

Z 

0. 1 .5 Dsso I h IIII ,u E~ 

0.0 0.05 0.10 0.15 

Dierston Factor Fmuit 

Figure B-4. F... histrogram

B-5 NUREG-1668
APPENDIX B REFERENCES 

Benjamin, J.R., and C.A. Cornell. 1970. Probability, Statistics, and Decision for Civil Engineers. New York, 

NY: McGraw-Hill. 

Birkholzer, J., G. Li, C. Tsang, T. Tsang. 1997. Drifts in unsaturated rock at Yucca Mountain. FTAM Workshop. 

December 15-16. Lawrence Berkeley Laboratories. 

Glass, R.J. 1993. Modeling gravity-driven fingering in rough-walled fractures using modified percolation theory. 

Proceeding of the 4th Annual International Conference of High-Level Radioactive Waste Management. 

La Grange Park, IL: American Nuclear Society: 2042-2053. 

Phillip, J.R. and R.T. Watcher. 1989. Unsaturated seepage and subterranean holes: conspectus and the exclusion 

problem for circular cylindrical cavities. Water Resources Research 25: 16-18. 

Ross, B. 1990. Quasi-linear analysis of water flow in the unsaturated zone at Yucca Mountain, Nevada USA. 

Memories of the 22' Congress IAH, Vol XXII, Lausanne, Switzerland: 166-173. 

Wilder, D.G. 1996. Near-field and altered zone environmental report, Volume II. UCRL 124998. Lawrence 

Livermore National Laboratory. 

Wilson, M.L., et al. 1994. Total-System Performance Assessment for Yucca Mountain-SNL Second Iteration 

(TSPA-93). SAND 93-2675, Vols. 1 and 2. Albuquerque, NM: Sandia National Laboratories.9

NUREG-1668 B-6
NRC FORM 335 U.S. NUCLEAR REGULATORY COMMISSION 1. REPORT NUMBER 

(2.89) VAsained by NRC. Add Vol., S~p.. Ray.. (2-m)o Iml Addendsm Nowlars, If my.) 

320M 1102. BIBUIOGRAPHIC DATA SHEET 

3201.3202 

(See k ueis an frem" NUREG-1668 

2. TITLE AND SUBTITLE Vol. 1 

NRC Sensitivity and Uncertainty Analyses for a Proposed Repository 3. DATE REPORT PUBLISHED 

at Yucca Mountain, Nevada, Using TPA 3.1 MONTH YEAR 

Conceptual Models and Data February 2001 

4. FIN OR GRANT NUMBER 

5. AUTHOR(S) 6. TYPE OF REPORT 

S. Mohanty*, T. McCartin** Technical 

7. PERIOD COVERED (cW DOuWe 

8. PERFORMING ORGANIZATION -NAME AND ADDRESS (f NRC, prod. Diion. 0ke orRegio, U.S. NMclwRegulatroyComimssioanng f ooa ,,add , 

*Center for Nuclear Waste Regulatory Analyses, 6220 Culebra Road, San Antonio, TX 7828-0510 

"*Office of Nuclear Material Safety and Safeguards, U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001

9. SPONSORING ORGANIZATION &NRC. - NAME AND ADDRESS WeSare n aoms) Afcorwaao, PvA* N; Dnaloo, Oft or ), U.'. • , .. 

Division of Waste Management 

Office of Nuclear Materials Safety and Safeguards 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001

10. SUPPLEMENTARY NOTES

11. ABSTRACT (200 wods or e-) 

The total-system performance assessment computer code (TPA Version 3.1.4) was developed to assist the U.S. Nuclear 

Regulatory Commission staff in evaluating the safety case for the proposed geologic repository at Yucca Mountain, Nevada. 

The TPA Version 3.1.4 code is designed to estimate total-system performance measures of annual individual dose or risk. The 

TPA Version 3.1.4 code consists of an executive driver, a set of consequence modules, and a library of utility modules. The 

executive driver controls the probabilistic sampling of input parameters, the calculational sequence and data transfers among 

consequence modules, and the generation of output files. The various output files are used in parameter sensitivity analyses, 

post-processing of time-dependent risk curves, and synthesis of statistical distributions (e.g., cumulative distribution functions 

and complementary cumulative distribution functions) for appropriate performance measures. Consequence models simulate 

physical processes and events such as unsaturated zone infiltration; evolution of the near-field thermal-hydrologic environment; 

failure of waste packages; dissolution and release of waste; transport of waste in the ground-water system; extraction of ground 

water, and consumption of ground water which, if contaminated, would expose future populations to radiation. In addition to 

considering climate change, the TPA Version 3.1.4 code is designed to calculate the effects of disruptive events such as 

faulting, seismicity, and volcanism. Utility modules ensure the consistency of algorithms and data sets used repeatedly by 

various consequence modules.

12. KEY WORDS&DESCRIPTORS (List words or •p s"a dw ,ass mearchers in caow V*e reoart.) 13. AVAILABILITY STATEMENT 

unlimited 

Computer code Disruptive Consequences unlimited 

Dose DoseGeolgic Geologic repository epostory14. SECURITY CLASSIFICATION 

Ground water High-level radioactive waste MAW POP) 

Performance Assessment Probabilistic risk assessment unclassified 

Receptor group Scenario jt ROPO, 

Source term Waste package unclassified 

Yucca Mountain 15. NUMBER OF PAGES 

16. PRICE

NRC FORM 335 (2-89) This form was elcrr• Produced bY r-. -•w r ,•- ",
paper 

Federal Recycling Program
UNITED STATES 

NUCLEAR REGULATORY COMMISSION 

WASHINGTON, DC 20555-0001 

OFFICIAL BUSINESS 

PENALTY FOR PRIVATE USE, $300