NUREG-1668, Vol. 2

NRC Sensitivity and 

Uncertainty Analyses 

for a Proposed HLW 

Repository at Yucca 

Mountain, Nevada, 

Using TPA 3.1 

Results and Conclusions 

U.S. Nuclear Regulatory Commission 

Office of Nuclear Material Safety and Safeguards 

Washington, DC 20555-0001
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212-642-4900
NUREG-1668, Vol. 2

NRC Sensitivity and 

Uncertainty Analyses 

for a Proposed HLW 

Repository at Yucca 

Mountain, Nevada, 

Using TPA 3.1 

Results and Conclusions 

Manuscript Completed: March 1999 

Date Published: March 1999 

Division of Waste Management 

Office of Nuclear Material Safety and Safeguards 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001
ABSTRACT 

To quantitatively evaluate the soundness of the safety case that will be made by the U.S. Department of 

Energy (DOE) in its License Application for the proposed Yucca Mountain (YM) repository, the Nuclear 

Regulatory Commission (NRC) has developed a Total-system Performance Assessment (TPA) Code. The 

most recent version of the code, used in evaluation and calculation of YM performance, is TPA 3.1.4; it was 

developed specifically for application to the proposed repository at YM. Although process-level sensitivity 

analyses are summarized in Appendix A, the purposes of this report are to: (i) present the results of system

level sensitivity and uncertainty analyses to determine the parameters that have the most influence on 

repository performance; (ii) estimate the relative importance of the key elements of subsystem abstraction; 

(iii) focus staff reviews of the DOE total-system performance assessments; and (iv) continue improving NRC 

staff capabilities in performance assessment. An influential parameter is one that either drives uncertainty 

in performance, or one to which performance is sensitive. The sensitivity and uncertainty analyses were 

conducted using numerous TPA code runs with results analyzed using several statistical techniques. Results 

of system-level analyses are based on peak total effective dose equivalent in the time period of interest to a 

receptor group 20 km from the repository and are presented as complementary cumulative distribution 

functions and lists of influential input parameters. In general, the lists identify influential parameters for two 

time periods of interest: 10,000 years (10 kyr) and 50 kyr. For a time period of interest of 10 kyr, it was found 

that with an inner overpack material of Alloy 625, parameters dictating the rate of corrosion of the waste 

package and the rate of water ingress into the package were most influential. With an inner overpack material 

of Alloy C-22, the same parameters were found to be influential; however, the parameters dictating how 

seismicity affects the waste package were also found to be influential. For a time period of interest of 50 kyr, 

parameters dictating the transport of two key radionuclides (i.e., neptunium (Np) and americium (Am)) were 

found to be most influential, with parameters dictating the rate of water ingress into the package still being 

influential, but to a lesser extent. The influential parameters were then compared to the current Key Elements 

of Subsystem Abstraction (KESA) used by NRC to focus work on items important to repository performance. 

With the exceptions of two KESAs (distribution of mass flux between matrix and fracture, and flow rates 

in water production zones) all the KESAs had at least one influential parameter, based on the results of the 

TPA 3.1.4 code. The analyses and results are limited by the use of simplifying assumptions, models, and 

sparse data in certain areas. As a consequence, these results are preliminary. Moreover, the manner in which 

these analyses were conducted or the assumptions and approaches used should not be construed to express 

the views, preferences, or positions of the NRC staff regarding the nature of site-specific regulations for YM.

NUREG-1668 iii
CONTENTS 

Section Page 

FIG U R E S ......................................................................... viii 

T A B L E S .......................................................................... x 

A C RO NY M S ....................................................................... xi 

ACKNOW LEDGM ENTS ............................................................ xii 

INTROD UCTION ........................................................... 1-1 

1.1 BACKGROUND ...................................................... 1-2 

1.1.1 Previous Iterative Performance Assessment Analyses ................... 1-2 

1.1.1.1 Iterative Performance Assessment Phase 1 Sensitivity and 

Uncertainty Analyses ............................................ 1-2 

1.1.1.2 Iterative Performance Assessment Phase 2 Sensitivity and 

Uncertainty Analyses ............................................ 1-3 

1.2 PU RPO SE ........................................................... 1-3 

1.3 REPORT ORGANIZATION ............................................. 1-3 

1.4 ASSUMPTIONS AND LIMITATIONS .................................... 1-4 

2 TOTAL-SYSTEM PERFORMANCE ASSESSMENT CODE DESCRIPTION ............ 2-1 

2.1 PROCESSES MODELED ............................................... 2-1 

2.1.1 Water Infiltration into the Subsurface ............................... 2-2 

2.1.2 Near-Field Environment .......................................... 2-2 

2.1.3 Radionuclide Releases from the Engineered Barrier System .............. 2-2 

2.1.4 Transport in the Unsaturated and Saturated Zones ...................... 2-3 

2.1.5 Airborne Transport for Direct Releases .............................. 2-3 

2.1.6 Exposure Scenario and Reference Biosphere .......................... 2-3 

2.2 TPA 3.1.4 CODE STRUCTURE .......................................... 2-3 

2.3 REPOSITORY PERFORMANCE PROCESSES ............................. 2-4 

3 ALTERNATIVE CONCEPTUAL MODELS AND SCENARIO CASES STUDIED AT 

THE SYSTEM LEVEL ....................................................... 3-1 

3.1 DEFINITION OF BASE CASE AND ALTERNATIVE CONCEPTUAL 

M ODELS STUDIED .................................................. 3-2 

3.1.1 Base Case ..................................................... 3-2 

3.1.2 Alternative Conceptual Models Studied with Rationale ................. 3-2 

3.1.3 Scenario Cases Studied ........................................... 3-5 

3.2 CODE OUTPUT FOR ALTERNATE MODEL AND SCENARIO CASE 

AN ALY SES .......................................................... 3-7 

3.2.1 Complementary Cumulative Distribution Functions for the 

Alternative Conceptual M odels .................................... 3-7 

3.2.2 Complementary Cumulative Distribution Functions for the 

Scenario Cases ................................................. 3-8 

3.3 OBSERVATIONS BASED ON ALTERNATIVE CONCEPTUAL 

MODEL AND SCENARIO CASE RESULTS .............................. 3-17

V NUREG-1668
CONTENTS (cont'd) 

Section Page 

4 ALTERNATIVE CONCEPTUAL MODELS AND SCENARIO CASES STUDIED AT THE 

SYSTEM LEVEL ............................................................ 4-1 

4.1 STATISTICAL ANALYSIS TECHNIQUES ................................ 4-1 

4.1.1 Scatter Plot/Single Linear Regression on One Variable .................. 4-1 

4.1.2 Variable Transformations and Their Attributes ........................ 4-4 

4.1.2.1 N orm alization .................................................. 4-4 

4.1.2.2 Rank Transformation ....................................... 4-4 

4.1.2.3 Logarithmic Transformation ....................................... 4-4 

4.1.2.4 Standardization ................................................. 4-5 

4.1.3 Stepwise Multiple Linear Regression ................................ 4-6 

4.1.4 Application of the Kolmogorov-Smimov and Sign Tests 

for Determining Important Parameters ............................... 4-7 

4.1.4.1 The Kolmogorov-Smimov Test .................................... 4-7 

4.1.4.2 The Sign Test .................................................. 4-7 

4.1.4.3 Use of the t-Statistic to Identify Important Variables .................... 4-8 

4.2 RESULTS OF SENSITIVITY AND UNCERTAINTY STATISTICAL 

A N ALY SIS .......................................................... 4-8 

4.2.1 Sensitivity Coefficients for the Nominal Case with Alloy 

625 and Alloy C-22 Inner Overpack and Time Periods of 

Interest of 10 and 50 kyr ......................................... 4-12 

4.2.2 Scatter Plots/Linear Regression for Parameters Correlated 

with Estimated Peak Total Effective Dose Equivalent for 

the Time Period of Interest ....................................... 4-18 

4.3 DIFFERENTIAL ANALYSES .......................................... 4-18 

4.3.1 Introduction to Differential Analysis ............................... 4-18 

4.3.2 Differential Analysis Techniques .................................. 4-18 

4.3.3 Differential Analysis Results ..................................... 4-25 

4.3.4 Comparing Sensitivity Coefficients from Regression 

Analysis and Differential Analysis ................................. 4-28 

4.4 OTHER ANALYSIS RESULTS ......................................... 4-28 

4.4.1 Sensitivities of Peak Total Effective Dose Equivalent for 

the Time Period of Interest to Intermediate Code 

O utputs ...................................................... 4-28 

4.4.2 Most Important Radionuclides Based on These Analyses ............... 4-32 

5 RELATIVE IMPORTANCE OF THE NUCLEAR REGULATORY COMMISSION KEY 

ELEMENTS OF SUBSYSTEM ABSTRACTION .................................. 5-1 

6 CON CLU SION S ............................................................. 6-1 

7 REFEREN CES .............................................................. 7-1

NUREG-1668 Vi
CONTENTS (cont'd) 

Section Page 

APPENDICES 

A SUMMARY OF KEY TECHNICAL ISSUES AND PROCESS-LEVEL SENSITIVITY ANALYSES 

B ALTERNATIVE CONCEPTUAL MODEL AND SCENARIO CASE COMPLEMENTARY 

CUMULATIVE DISTRIBUTION FUNCTIONS 

C IDENTIFICATION OF PARAMETERS LISTED AS SENSITIVE OR UNCERTAIN 

D BASELINE VALUES FOR ALL PARAMETERS EVALUATED IN DIFFERENTIAL ANALYSIS 

E DIFFERENTIAL ANALYSIS RESULTS IN TABULAR FORM

vii NUREG-1668
FIGURES 

Section Page 

2-1 Flow diagram for TPA 3.1.4 code ............................................... 2-5 

3-1 Bar chart of mean peak total effective dose equivalent for a 10-kyr time period of interest 

for the base case and alternative conceptual models ................................. 3-9 

3-2 Bar chart of mean peak total effective dose equivalent for a 50-kyr time period of interest 

for the base case and alternative conceptual models ................................ 3-10 

3-3 Complementary cumulative distribution function for the base case and a 1O-kyr time period 

of interest ................................................................. 3-11 

3-4 Complementary cumulative distribution function for the base case and a 50-kyr time period 

of interest ............................................................... 3-12 

3-5 Complementary cumulative distribution function of the nominal case for a time period 

of interest of 10 kyr .......................................................... 3-15 

3-6 Complementary cumulative distribution function of the nominal case for a time period 

of interest of 50 kyr .......................................................... 3-16 

4-1 A diagram illustrating the use of the Monte Carlo method in performance assessment ...... 4-2 

4-2 An example of a scatter plot/single linear regression ................................. 4-3 

4-3 Plot of the residual sum of squares versus number of parameters included in the fit for the 

Nominal case with alloy 625 inner overpack and a time period of interest of 10 kyr ....... 4-14 

4-4 Plot of the residual sum of squares versus number of parameters included in the fit for the 

Nominal case with alloy C-22 inner overpack and a time period of interest of 10 kyr ...... 4-15 

4-5 Plot of the residual sum of squares versus number of parameters included in the fit for the 

Nominal case with alloy 625 inner overpack and a time period of interest of 50 kyr ....... 4-16 

4-6 Plot of the residual sum of squares versus number of parameters included in the fit for the 

Nominal case with alloy C-22 inner overpack and a time period of interest of 50 kyr ...... 4-17 

4-7 Scatter plots and single linear regressions of the log of peak total effective dose equivalent 

for the time period of interest versus the log of parameter value for the Nominal case with 

alloy 625 inner overpack and a time period of interest of 10 kyr ....................... 4-19 

4-8 Scatter plots and single Linear regressions of the log of peak total effective dose equivalent 

for the time period of interest versus the log of parameter value for the Nominal case with 

alloy C-22 inner overpack and a time period of interest of 10 kyr .................... 4-20 

4-9 Scatter plots and single linear regressions of the log of peak total effective dose equivalent 

for the time period of interest versus the log of parameter value for the Nominal case with 

alloy 625 inner overpack and a time period of interest of 50 kyr ........................ 4-21 

4-10 Scatter plots and single linear regressions of the log of peak total effective dose equivalent 

for the time period of interest versus the log of parameter value for the Nominal case with 

alloy C-22 inner overpack and a time period of interest of 50 kyr ...................... 4-22 

4-11 Differential analysis tree ...................................................... 4-24 

4-12 Plots of the log of peak total effective dose equivalent for the time period of interest versus 

the log of total cumulative release from the engineered barrier system, unsaturated zone, and 

saturated zone for time period of interest of 10 and 50 kyr for the Nominal case .......... 4-31

NUREG-1668 viii
FIGURES (cont'd) 

Section Page 

4-13 A bar chart of the mean peak total effective dose equivalent of 400 realizations per 

radionuclide for the Nominal case (alloy 625 inner/overpack) and a 10-kyr time period of 

interest .................................................................... 4-33 

4-14 A bar chart of the mean peak total effective dose equivalent of 400 realizations per 

radionuclide for the Nominal case (alloy 625 inner overpack) and a 50-kyr time period of 

interest .................................................................... 4-34

ix NUREG-1668
TABLES 

Section Page 

3-1 Alternative conceptual models analyzed in the system-level sensitivity studies using 

T P A 3.1.3 .................................................................. 3-6 

3-2 The 10-kyr time period of interest base case and alternative conceptual 

model results ............................................................... 3-7 

3-3 The 50-kyr time period of interest base case and alternative conceptual 

m odel results ................................................................ 3-8 

3-4 Scenario cases analyzed using TPA 3.1.4 ......................................... 3-13 

3-5 Summary of the 10-kyr time period of interest scenario case results .................... 3-14 

3-6 Summary of the 50-kyr time period of interest scenario case results .................... 3-14 

4-1 Influential parameters on the uncertainty of peak total effective dose equivalent for the 

Nominal case for a 10-kyr time period of interest .................................. 4-10 

4-2 Influential parameter on the uncertainty of maximum total effective dose equivalent for the 

Nominal case with alloy C-22 inner overpack for a 10-kyr time period of interest ........ 4-11 

4-3 A listing of the influential parameters on the uncertainty of peak total effective dose 

equivalent for the Nominal case for a 50-kyr time period of interest ................... 4-11 

4-4 Influential parameters on the uncertainty of peak total effective dose equivalent for the 

Nominal case with a alloy C-22 inner overpack for a 50-kyr time period of interest ....... 4-12 

4-5 Sensitivity coefficients based on log of normalized (1) and standardized (2) variables for the 

im portant param eters ........................................................ 4-13 

4-6 Summary of base value peak total effective dose equivalent for the differential analysis for 

alloy 625 ............................................................. .... 4-26 

4-7 Summary of base value peak total effective dose equivalent for the differential analysis 

of alloy C -22 ............................................................... 4-27 

4-8 Primary sensitive parameters (i.e., mean of sigma-weighted sensitivities Si for the five 

baseline values) ............................................................. 4-29 

4-9 Sensitivity coefficients Si for the identified parameters (Diff.=Differential 

Analysis and Reg.=Regression Analysis with log Si of normalized variables) ............ 4-30 

4-10 The results of the multilinear fit of the log of peak total effective dose equivalent for the 

time period of interest versus the log of total cumulative release from the three subsystems 

for the Nominal case with alloy 625 ............................................. 4-32 

5-1 A crosswalk between the key elements of abstractions, alternative conceptual models, 

parameters affecting uncertainty, and the primary sensitive parameters .................. 5-2 

6-1 The uncertainty driving parameters found from regression analysis for the Nominal case. 

The rankings within a column are based on the value of the t-statistic ................... 6-2 

6-2 Primary sensitive parauneters as determined by the top 10 parameters ranked by arithmetic 

mean of standard-deviation-weighted differentials at the five baseline values ............. 6-3

NUREG-1668 x
ACRONYMS 

ARDR Activities Related to Development of the NRC HLW Regulations 

CCDF complementary cumulative distribution function 

CDF cumulative distribution function 

CLST Container Lifetime and Source Term 

CNWRA Center for Nuclear Waste Regulatory Analyses 

DCF dose conversion factor 

DOE U.S. Department of Energy 

EBS engineered barrier system 

ENFE Evolution of Near-field Environment 

EnPA Energy Policy Act 

EPA U.S. Environmental Protection Agency 

EXEC TPA 3.1.4 Executive module 

HLW high-level waste 

IA Igneous Activity 

IPA Iterative Performance Assessment 

IRSR Issue Resolution Status Report 

K-S Kolmogorov - Smirnov 

KESA Key Element of Subsystem Abstraction 

KTI Key Technical Issue 

LHS Latin Hypercube Sampling 

NRC Nuclear Regulatory Commission 

NWPA Nuclear Waste Policy Act 

PA Performance Assessment 

RDTME Repository Design and Thermal-Mechanical Effects 

RSS residual sum of squares 

RT Radionuclide Transport 

SDS Structural Deformation and Seismicity 

SF spent fuel 

SZ saturated zone 

TCR total cumulative release 

TEDE total effective dose equivalent 

TEF Thermal Effects on Flow 

TPA Total-system Performance Assessment (U.S. Nuclear Regulatory Commission) 

TPI time period of interest 

TSPA Total System Performance Assessment (U.S. Department of Energy) 

TSPAI Total System Performance Assessment and Integration 

USFIC Unsaturated-Saturated Flow under Isothermal Conditions 

UZ unsaturated zone 

UZFT unsaturated zone flow and transport 

VA Viability Assessment 

WP waste package 

YM Yucca Mountain

xi N-UREG-1668
ACKNOWLEDGMENTS 

This report was prepared to document work performed by the Center for Nuclear Waste Regulatory Analyses 

for the Nuclear Regulatory Commission (NRC), under Contract No. NRC-02-97-009, and work performed 

by NRC staff. The reported activities were performed for the NRC Office of Nuclear Material Safety and 

Safeguards, Division of Waste Management. 

The authors wish to thank Drs. Budhi Sagar and Gordon Wittmeyer for their technical reviews, and 

Dr. Wesley Patrick and Dr. Keith McConnell for their programmatic reviews. Thanks are also expressed to 

Ms. Barbara Long and Ms. Ellen Kraus for their editorial reviews and Ms. Cathy Garcia, Ms. Lee Selvey, 

and Ms. Arlene Brune for their secretarial support. 

QUALITY OF DATA AND CODE DEVELOPMENT 

DATA: The Center for Nuclear Waste Regulatory Analyses (CNWRA)-generated data contained in this 

report meet quality assurance (QA) requirements described in the CNWRA QA Manual. Data from other 

sources are freely used. The respective sources of non-CNWRA data should be consulted for determining 

levels of QA. 

CODE: The Total system Performance Assessment (TPA) codes, versions 3.1.1, 3.1.3, and 3.1.4, have been 

developed following the procedures described in CNWRA Technical Operating Procedure-018, which 

implements the QA requirements contained in the CNWRA QA Manual.

NUREG-1668 xii
1 INTRODUCTION 

In accordance with the provisions of the Nuclear Waste Policy Act (NWPA) of 1982, as amended, and the 

Energy Policy Act (EnPA) of 1992, the Nuclear Regulatory Commission (NRC) is responsible for evaluating 

the license application for a proposed geologic repository constructed for emplacement of high-level nuclear 

waste (HLW) [i.e., commercial spent fuel (SF); several types of U.S. Department of Energy (DOE) and Navy 

SF; and vitrified HLW] at Yucca Mountain (YM), Nevada. In support of its regulatory review activities 

outlined in the NWPA and EnPA, the NRC staff is conducting detailed technical performance assessments 

(PAs) to understand and quantify the isolation characteristics and capabilities of the proposed repository 

system at the YM site. 

This PA activity, which is part of an ongoing process at NRC to prepare for the review of a potential DOE 

license application for the proposed HLW disposal facility at YM, includes regular interactions between 

NRC and DOE on the topic of PA. For example, NRC staff comments on model abstraction related to the 

Total System Performance Assessment-Viability Assessment (TSPA-VA) were relayed to DOE in a letter 

to Dr. S. Brocoum dated July 6, 1998 (Bell, 1998a). 

As part of these Iterative Performance Assessment (IPA) activities, NRC and its support contractor, the 

Center for Nuclear Waste Regulatory Analyses (CNWRA), are using the enhanced Total-system Performance 

Assessment (TPA) computer code. The TPA code, which evolves with each IPA phase, is designed to 

simulate the behavior of the geologic repository, taking into account the essential characteristics of the 

natural and engineered barrier systems (EBS), as well as changes in knowledge about the geologic setting and design. This document presents system-level sensitivity and uncertainty analyses using the latest version 

of the TPA code, Version 3.1. TPA 3.1.3 was used to calculate mean doses for alternate conceptual models 

in chapter 3, and TPA 3.1.4 was used in the system-level sensitivity and uncertainty studies in chapter 4. For 

the most part, the codes are very similar. The changes in versions of the code used in the various analyses 

reflect minor enhancements. 

Although the NRC has previously conducted analyses of repository performance (Codell, et al., 1992; 

Wescott et al., 1995), Version 3.1.4 of the TPA computer code was developed as a more general and versatile 

computer code. This version: (i) accommodates the most current repository design [e.g., repository layout 

and waste package (WP) emplacement and design]; (ii) quantifies total system performance in terms of the 

proposed compliance performance measure [i.e., peak Total Effective Dose Equivalent (TEDE) in the time 

period of interest (TPI)] expected in the forthcoming U.S. Environmental Protection Agency (EPA) standard 

and NRC site-specific regulations; and (iii) includes recent site data, and improved conceptual models. In 

addition, because approaches to estimate the performance of geologic repositories and site and repository 

design data continue to evolve, TPA 3.1.4 was developed with the flexibility to: 

0 Evaluate alternative repository and design features. 

0 Specify different areal mass loading. 

0 Assess the significance of various disruptive scenario classes. 

0 Evaluate radionuclide dilution in the saturated zone (SZ). 

0 Compute the peak TEDE for 10,000 years (10 kyr) or longer TPI.

1-1 NUREG-1668
1.1 BACKGROUND

PAs for geologic repositories are based on conceptual models of physical processes (embodied in 

computer codes) and parameters derived from field and laboratory data or from expert elicitation. Because 

of the variabilities and sparsity of measured data and the underlying uncertainty involved with modeling 

physical processes for many 1thousands of years, the results of any PA are uncertain. Therefore, an important 

aspect of conducting a PA is quantifying the sensitivity of the results to, and the uncertainty associated with, 

the input parameters. An analysis of PA code output sensitivity and uncertainty will provide information 

delineating which input parameters most affect the model results. A better understanding of the parameters 

that have the most influence on model results can be used to improve the code. Likewise, identification of 

the most sensitive parameters and of parameters that drive uncertainty provides a means of comparing and 

evaluating different PA models and indicates where future activities should be focused. 

1.1.1 Previous Iterative Performance Assessment Analyses 

To date, two reports on PA for the proposed YM repository have been written by NRC staff. The 

first, referred to as IPA Phase 1 (Codell, et al., 1992), assembled and demonstrated the NRC assessment 

methodology. IPA Phase 1 examined the sensitivity and uncertainty in radionuclide releases to the accessible 

environment for a geologic repository in unsaturated tuff. The second NRC TSPA, IPA Phase 2 (Wescott, 

et al., 1995), was performed using the TPA 2.0 code to investigate the features, events, and processes 

influencing isolation performance of the proposed YM repository. Information obtained in these IPA analyses 

was used in NRC reviews of early DOE TSPAs for YM. The overall performance measures for the geologic 

repository used in IPA Phase 2 were cumulative total releases of radionuclides (normalized release) to the 

accessible environment and radiation dose (effective dose equivalent) to the exposed population. The current 

PA activity implements and updates NRC TSPA capability in preparation for review of the DOE VA. In 

addition, PA tools are being used to better focus NRC activities on technical issues and subissues of greatest 

importance to repository performance. 

1.1.1.1 IPA Phase 1 Sensitivity and Uncertainty Analyses 

Four sensitivity or uncertainty analyses were performed for IPA Phase 1: (i) sensitivity analyses 

demonstrating the effect of individual parameters on the resultant Complementary Cumulative Distribution 

Function (CCDF) for cumulative release to the accessible environment; (ii) analyses using stepwise linear 

regression to estimate sensitivity of key parameters in the consequence models; (iii) determination of relative 

importance of individual radionuclides in the waste; and (iv) sensitivity of CCDFs to performance of the 

natural and engineered barriers. The sensitivity and uncertainty analyses considered only ground-water 

pathway releases, not those from human intrusion or airborne release from volcanism. Gaseous release of 

radionuclides was not part of the IPA Phase 1 TSPA results, but was included as an auxiliary analysis. 

Although IPA Phase I conducted full sensitivity and uncertainty analysis for the ground-water 

pathway, only CCDFs for cumulative release were generated for the scenario cases (base case, base case with 

human intrusion, and base case with pluvial conditions with and without human intrusion). Cumulative 

release refers to the sum of releases of all radionuclides during the TPI. The CCDFs reflected the uncertainty 

in the sampled parameters propagated through the analysis. Peak TEDE was not calculated as a performance 

measure for the IPA Phase 1 study.

NUREG-1668 1-2
1.1.1.2 IPA Phase 2 Sensitivity and Uncertainty Analyses

In IPA Phase 2, model results were evaluated to develop regression equations describing TSPA 

model output and to analyze input parameter sensitivity. Techniques used to develop a regression equation 

that emulated the TPA model included transformation of data (Iman and Conover, 1979; Seitz, etal., 1991); 

test for heteroscedasity or residual variation (Draper and Smith, 1981; Bowen and Bennett, 1988; Sen and 

Srivastava, 1990); and Mallows' CP statistic (Sen and Srivastava, 1990). In addition to techniques used in 

previous PA work (e.g., the stepwise linear regression), several techniques were evaluated for determining 

parameter importance and sensitivity, including Kolmogorov-Smirnov (K-S) and Signs tests (Bowen and 

Bennett, 1988) and differential analysis (Helton, et al., 1991). 

Phase 2 IPA included a number of disruptive scenarios. These scenarios included igneous activity 

(IA), seismicity, faulting, and exploratory drilling. Sensitivity and uncertainty analyses were conducted on 

the undisturbed case as well as the other scenario cases. These analyses were conducted with radionuclide 

release to the accessible environment and integrated population dose, not peak TEDE to a receptor individual 

as the output variable. This differs substantially from the analyses described in the present report. 

1.2 PURPOSE 

Similar to the sensitivity and uncertainty analyses conducted for previous IPA phases, multiple 

sensitivity and uncertainty analyses have been conducted using TPA 3.1. Sensitivity is defined as the relative 

change in output for a unit of input change, and uncertainty is the comparative change in overall output range 

because of input value uncertainty. Sensitivity and uncertainty analyses described in this report are being 

used to: 

Determine the input parameters in the TPA 3.1.4 code that are most influential to the 

estimated peak TEDE for the TPI at the receptor location by using a number of techniques. 

Although process-level sensitivity and uncertainty analyses have been conducted with the 

TPA code to determine the parameters that are most important in a given Key Technical 

Issue (KTI) [e.g., "Unsaturated and Saturated Flow under Isothermal Conditions", IA], this 

report summarizes analyses conducted to determine the most influential parameters at the 

total system level. 

Estimate the relative importance of the key elements of subsystem abstraction. 

Focus staff reviews of the DOE TSPAs on those factors most significant to 

total-system performance. 

Continue improving staff capabilities, including improving the TPA code, for conducting 

independent TSPAs for the proposed YM repository. 

1.3 REPORT ORGANIZATION 

This report documents the most recent system-level sensitivity and uncertainty analyses performed 

by NRC and CNWRA, which were conducted using TPA 3.1. Two versions of TPA 3.1 were used to 

conduct the system-level sensitivity studies, TPA 3.1.3 and TPA 3.1.4. TPA 3.1.4 differs slightly from the 

earlier versions and represents the culmination of a series of desired changes identified during the process1-3 NUREG-1668
level sensitivity studies summarized in Appendix A of this report. Chapter 2 provides a brief description of 

TPA 3.1.4 (see Volume I for a complete description). 

Chapters 3 and 4 describe the system-level sensitivity studies, which were conducted in two parts. 

A set of alternative conceptual niodels and disruptive scenario cases were compared to evaluate the relative 

importance of specific components and assumptions used in the model. Evaluating the influence of individual 

components of the model in this way, where the full set of parameter values is used and a more 

comprehensive range of repository behavior is modeled, allows the relative importance of the components 

to be investigated. These studies were performed using TPA 3.1.3 for the alternative conceptual models and 

TPA 3.1.4 for the scenario cases, and the results are documented in chapter 3. The sensitivity and uncertainty 

of repository performance to specific parameters was evaluated using a number of different statistical tests 

because no single test is completely comprehensive. The use of numerous statistical tests to examine the 

sensitivity of repository performance to individual parameters is intended to identify as comprehensively as 

possible those parameters that are most important for understanding repository performance. This 

information is valuable for estimating the relative importance of key elements of subsystem abstraction and 

focusing prelicensing activities. The evaluation of parameter influence on code output is described in 

Chapter 4. 

Chapter 5 discusses the combined results of the system-level sensitivity studies. Here, the important 

parameters and the alternative conceptual model investigations are related to the NRC key elements of 

subsystem abstraction. The important findings are categorized by inner overpack material (i.e., alloy 625 or 

C-22) and TPI (i.e., 10 or 50 kyr). The conclusions of the sensitivity and uncertainty analysis are presented 

in Chapter 6. 

1.4 ASSUMPTIONS AND LIMITATIONS 

Because it is not possible to model a system as complex as a geologic repository in a complete and 

exhaustive manner, a number of assumptions and limitations are used directly by, or are implicit in, the 

analyses conducted in this report. These assumptions and limitations are listed below: 

Any underlying assumptions, limitations, and bases used to construct the models in 

TPA 3.1.3 (for the alternative conceptual model analyses), or 3.1.4 (for the disruptive 

scenario cases and system-level sensitivity and uncertainty analyses) also apply to these 

analyses. These models are described in Chapter 2 and discussed in greater detail in 

Volume I of this report. 

For the system- level analyses, a 20-km receptor individual location was studied, whereas 

in some of the process-level analyses, a 5 km receptor individual location was examined, 

which allowed for study of individual processes in a more efficient manner. 

The analyses were conducted to provide information that can be used to concentrate review 

of DOE TSPAs and to improve NRC TPA models. 

The results are limited by the use of simplifying assumptions, models, and sparse data in 

certain areas. As a consequence, these results are preliminary. Moreover, the manner in 

which these analyses were conducted or the assumptions and approaches used should not 

be construed to express the views, preferences, or positions of the NRC staff regarding the 

ongoing efforts to develop site-specific regulations for YM.

NUREG-1668 1-4
2 TOTAL-SYSTEM PERFORMANCE ASSESSMENT CODE 

DESCRIPTION 

The TPA code models the postclosure performance of the proposed HLW repository at YM over long time 

periods (e.g., 10 kyr).To attempt to quantify the uncertainty in estimating repository performance over long 

time periods, the TPA code operates in a probabilistic manner (i.e., many realizations are conducted using 

input parameter sets sampled from probability distributions). From the perspective of the subsystem or 

process level, the TPA code models the following: (i) degradation or disruption of the EBS; (ii) release of 

radionuclides from the EBS; (iii) transport of radionuclides from the repository; and (iv) possible exposure 

of an average member of the receptor group to radionuclides. 

TPA 3.1.4 was developed and specifically tailored for evaluating the total-system performance for the 

proposed repository at the YM site to incorporate: 

0 Recent site data and knowledge for conceptual models; 

a Updated abstractions for features, events, and processes; 

a Streamlined methodology for data transfers between the executive and 

consequence modules; 

0 A more flexible design to accommodate future code modifications; 

0 Improved computational algorithms permitting more detailed models; and 

Improved capabilities for determining parameter importance. 

2.1 PROCESSES MODELED 

The primary use of the TPA code is the probabilistic estimation of dose for specified time periods 

(e.g., regulatory compliance time period, TPI) at designated receptor locations (e.g., 20 km downgradient of 

YM). In performing these calculations, system-level simulations of repository performance take into account 

such aspects as 

0 Essential features of the engineered and natural barriers; 

0 Chemical and physical processes affecting barrier degradation and radionuclide releases to 

the biosphere; 

0 Uncertainties and spatial variabilities of system attributes, model parameters, and future 

system states (i.e., scenario classes); and 

0 Lifestyle characteristics of the designated receptor group. 

Taking these into account, the TPA 3.1.4 code includes:

2-1 NUREG-1668
* Water infiltration and deep percolation from the land surface to the subsurface and 

subsequently into the emplacement drifts and onto WPs; 

* The near-field environment around the WPs (temperature, humidity, pH, chloride ion 

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

radionuclide release; 

• Radionuclide release from the EBS, to the groundwater pathway; 

* Transport of radionuclides through the unsaturated zone (UZ) and SZ; 

* Airborne transpor: of radionuclides in volcanic ash from direct releases; and 

* Exposure scenarios and reference biosphere. 

A short description of subprocesses considered in the development and implementation of TPA 3.1.4 

follows. For more complete descriptions of the methods and assumptions used, the reader is referred to 

Volume I. 

2.1.1 Water Infiltration into the Subsurface 

The amount of water infiltrating from the ground surface into the UZ above the repository is 

available for WP corrosion, dissolution of SF in the WP, and transport of material released from the EBS. 

The calculation of percolation flux takes into account changes in precipitation and temperature that may 

occur at the ground surface over the life of the repository and includes the following three components: 

(i) the time history of climatic change; (ii) the variation of shallow infiltration with climate change; and 

(iii) the areal-average percolation flux at the repository level. 

2.1.2 Near-Field Environirrient 

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

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

code, a range of near-field characteristics is depicted in abstracted mathematical models for heat and water 

flow and Table lookups for infiltrating water chemistry parameters. The near-field environment is 

characterized in terms of drift wall rock and WP surface temperatures, relative humidity, water chemistry 

(e.g., pH, chloride, and carbonate concentration), and water reflux during the thermal phase. 

2.1.3 Radionuclide Releases from the EBS 

Estimation of WP failure times and subsequent liquid releases from the EBS depend on the nature 

and extent of corrosion, near-field environment conditions, percolation water flux in the drift, and external 

processes that impose mechanical loads, such as seismically induced rock fall. Radionuclide releases from 

the EBS are calculated by considering the alteration rate of the SF (i.e., rate at which radionuclides in fuel 

become available for release), radionuclide solubility limits, and transport mechanisms out of the WP. After 

radionuclides are leached from the SF waste form, the calculated releases are adjusted to ensure consistency 

with the radioelement solubility limits.

NUREG-1668 2-2
2.1.4 Transport in the UZ and SZ

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

hydrostratigraphic unit at YM. TPA 3.1.4 simulates the transport of radionuclides through the porous rock 

matrix or fractures. Retardation of radionuclides on fracture surfaces, which can increase the transport time 

significantly, is conservatively neglected because significant occurrences of this process on fracture surfaces 

that may be coated with mineral deposits have not been demonstrated. Radionuclide retardation by chemical 

sorption in the rock matrix is included in the model. Although groundwater flow in the SZ is assumed to be 

at steady state in TPA 3.1.4, radionuclide transport (RT) is time-dependent because the release from the EBS 

varies with time. The conceptual model of the SZ assumes that flow in the tuff aquifer is in localized 

production zones (i.e., permeable shear fracture zones), whereas the flow in the alluvium is assumed 

uniformly distributed in the alluvial aquifer. 

2.1.5 Airborne Transport for Direct Releases 

Throughout the past several million years, intrusive and extrusive igneous events have occurred on 

an infrequent basis in the YM Region. Although there is a low-probability that an extrusive igneous event 

would intersect a repository at YM, WPs may fail and material in WPs could be released directly to the air. 

Material ejected from such an event (ash and SF particles) would be deposited on the land surface around 

and downwind of the repository. Radionuclides deposited in the ash blanket would then be available to 

expose receptor populations by pathways other than those encountered via ground-water release. Processes 

to be considered in such a direct release include: determination of the number of WPs failed; the amount of 

SF ejected with the ash; the airborne transport and spatially dependent deposition of ash (and radionuclides 

contained in the ash) on the ground surface; and the degradation, over time, of the contaminated ash blanket 

through natural processes such as wind and water erosion. 

2.1.6 Exposure Scenario and Reference Biosphere 

Dose calculations are performed in the TPA code for an exposure scenario that considers an average 

person of a designated receptor group. Two alternative, hypothetical receptor groups can be modeled: (i) a 

farming community at 20 km from the repository location and (ii) a residential community at an unspecified 

distance less than 20 km from YM. The average member of the designated receptor group is assumed to be 

exposed to radionuclides transported through the ground-water pathway and/or air pathway (as a result of 

direct releases arising from the extrusive component of igneous activity). 

The farming community receptor group is assumed to be composed of persons using the 

contaminated water for drinking (i.e., 2 liters/day) and agriculture typical of the Amargosa Valley, Nevada 

(e.g., growing alfalfa and gardening). In contrast, the residential receptor group is assumed to be composed 

of persons who only use contaminated ground water for drinking. Both groups can receive doses from 

contaminated ash via the airborne pathway; however, the residential receptor is exposed only through the 

inhalation and direct exposure pathways, whereas the farming group may also receive doses through the 

ingestion of contaminated agricultural and animal products grown in the contaminated ash. 

2.2 TPA 3.1.4 CODE STRUCTURE 

The TPA 3.1.4 code is composed of an executive driver program (EXEC), a set of consequence 

modules that model the processes identified above, a library of utility modules, and several static data files. 

The EXEC controls the probabilistic sampling of input parameters, the calculational sequence and data

2-3 NUREG-1668
transfers between modules, and the generation of output files. Various intermediate output files are generated 

for later use, such as postprocessing of time-dependent risk curves and synthesis of statistical distributions 

[e.g., cumulative distribution functions (CDFs)] for appropriate performance measures. The EXEC module 

of the TPA code controls the sequence of consequence and utility module executions. 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. A simplified TPA 3.1.4 flow chart is presented in Figure 2-1. For 

a complete description of the TPA 3.1.4. models and assumptions, the reader is referred to Volume I. 

2.3 REPOSITORY PERFORMANCE PROCESSES 

With the aid of various utility modules, the EXEC reads all inputs from a file including definition 

of repository dimensions and subareas, waste inventory, TPI, etc. After the full set of parameters and data 

files is assembled, the EXEC executes the consequence modules (i.e., modules that simulate physical and 

chemical processes). The norninal (i.e., most likely to occur) case WP degradation and contaminant release 

and transport process consequence modules, include: 

UZFLOW---c-omputes time varying precipitation and percolation flux through the UZ; 

NFENV-provides thermohydrologic and hydrochernical history in the near-field; 

EBSFAIL-simulates corrosion phenomena and computes the container life time; 

SEISMO---estimates the number of containers affected by seismicity induced rock fall; 

EBSREL--cornputes the radionuclide release rate; 

UZFT-simulates flow and transport through the UZ; 

SZFT-simulates flow and transport through the SZ; and 

DCAGW-converts ground-water radioactivity concentrations into dose to a 

specified receptor. 

During the TPA simulation, the EXEC module may invoke other disruptive event consequence 

modules. For instance, if a pailticular disruptive event class is specified, the EXEC module will add to the 

above execution sequence one or more of the following consequence modules: 

FAULTO-simnulates faulting events in the repository and computes number of 

WPs affected; 

VOLCANO---simulates magmatic intrusions and disruptions and computes amount of waste 

released to the surface; 

ASHPLUMC)--simulates airborne transport and contaminated ash particle deposition; 

ASHRMOVO--computes the radionuclide concentrations in the soil; and 

DCAGS--converts the soil concentrations into dose.

NUREG-1668 2-4
Figure 2-1. Flow diagram for TPA 3.1.4 code 

2-5 NUREG-1668
3 ALTERNATIVE CONCEPTUAL MODELS AND SCENARIO 

CASES STUDIED AT THE SYSTEM LEVEL 

A series of process-level sensitivity studies described in Appendix A, were conducted to observe the behavior 

of a particular process or module in isolation from the total system. These sensitivity analyses were 

deliberately limited, to observe the aforementioned behavior. Generally, only those parameters related to the 

process of interest were varied, whereas the other parameters were held constant at their nominal values. The 

same is true for those process-level studies of alternative conceptual models run in the Container Lifetime 

and Source Term (CLST); Evolution of Near-field Environment (ENFE); and Thermal Effects on Flow (TEF) 

analyses. The procedure generally followed in the process-level phase of the sensitivity studies was useful 

from the standpoint of minimizing the confounding influence of other varying parameters for a more direct 

analysis of cause and effect. It is possible, however, that important interactions among different processes 

or modules in the overall system could have been missed by not allowing the parameters of the other modules 

to vary at the same time. 

The system-level sensitivity studies extend the work performed at the process level on specific alternative 

conceptual models (e.g., repository backfill, cladding protection, focused infiltration flow, and releases based 

on natural analogues) and introduce new alternative conceptual models [e.g., alloy C-22 inner overpack 

material, immediate WP failure, flowthrough WP infiltration (as opposed to the bathtub model) with 

increased fuel dissolution, and no radionuclide retardation in the geosphere]. Furthermore, the analyses in 

this chapter include the full ranges of parameter variations for all modules. The system-level studies of 

alternative conceptual models and scenario cases, which consider the full input parameter set, are described 

in this chapter. 

Several series of runs with the TPA code were performed, to determine the sensitivity of dose to parameter 

variability and alternative conceptual models. The results in this document span the evolution of the TPA 

code from Version 3.1.1, used in the process-level sensitivity studies reported in Appendix A; Version 3.1.3, 

for alternative conceptual models reported in this chapter; and finally Version 3.1.4, for the disruptive 

scenario cases reported in this chapter and the sensitivity and uncertainty results in Chapter 4. Differences 

among TPA versions may prevent a direct comparison of results reported in these three chapters. 

Two sets of results are presented in this chapter. The first set of results reflects model runs that compare the 

base case (described in Section 3.1.1) with 11 alternative conceptual models. First, the base case was 

evaluated with a 200-vector run. Alternative conceptual model tests were conducted with 200-vector runs, 

and the results were compared to the base case. The alternative conceptual models were selected to evaluate: 

(i) the effect of several repository design features, currently being considered by DOE, on repository 

performance; (ii) the effect, on repository performance, of plausible alternate thermo-hydrologic conditions 

in the repository near-field; and (iii) bounding engineered or natural system behavior. The second set of 

results reflects the effect of disruptive scenarios, including seismicity (in this case considered to be part of 

the nominal case), volcanism, and major faulting. 

For both sets of analyses, the TPI was limited to 50 kyr. Each 200-vector set required approximately 12 hours 

on a Sun Ultra 1 workstation. The number of realizations was limited to 200, to keep computer resources 

within reasonable limits. Results for the 10-kyr TPI were extracted from the 50-kyr results.

3-1 NUREG-1668
3.1 DEFINITION OF BASE CASE AND ALTERNATIVE CONCEPTUAL 

MODELS STUD][ED 

3.1.1 Base Case 

The base case input data set reflects current repository design features and most-likely parameter 

estimates for evaluation of processes affecting repository performance. The base case set of input parameters 

against which other alternate models are compared does not include seismicity, volcanism, or faulting 

disruptive events. 

The base case for the sensitivity/uncertainty analyses includes: 

• Alloy 625 for the corrosion resistant inner overpack; 

& Carbon steel outer overpack; 

0 No cladding protection; 

0 Dissolution of SF, based on J-13 well-water chemistry; 

* "Bathtub" model (i.e., pooling of water in the WP after failure) for determination of water 

balance and fiuel wetting of failed WP; 

* No matrix diffiusion of contaminants in the UZ; 

* No backfill in repository after closure; 

* 20 km receptor group; 

* 10- and 50-kyr TPI; and 

• No disruptive events (e.g., seismicity, volcanism, or faulting). 

A complete list of the input parameters used for the base case can be found in Appendix A of Volume I. 

3.1.2 Alternative Conceptual Models Studied with Rationale 

Various alternative conceptual models were investigated to determine the sensitivity of repository 

performance (as defined by peak TEDE for the TPI to a 20-km receptor group) to changes in WP design, 

radionuclide release mechanisms, and RT models. These alternative model runs were conducted with 

TPA 3.1.3 and did not include disruptive events (seismicity, faulting, or volcanism). Alternative models used 

in this analysis include: 

• Alloy C-22 

Use of alloy C-22 for the inner overpack material is the WP design for the DOE TSPA-VA. 

Alloy C-22 is a nickel-chromium-molybdenum-tungsten alloy more corrosion-resistant 

(especially against pitting and crevice corrosion) than alloy 625, in the environment

NUREG-1668 3-2
expected in the repository. This alternative model demonstrates the effect of this WP design 

on repository performance. Changes in input data required to invoke the C-22 model are 

listed in Appendix A of Volume I of this report. 

Backfill 

At the time of repository closure, repository drifts containing WPs may be backfilled with 

native materials (i.e., crushed tuff removed during excavation of the repository). In 

TPA 3.1.3, backfill affects only the temperature calculations in the code because backfill is 

a more efficient insulator than air. Although it is likely that backfill will have other tangible 

effects on repository performance, such as protecting the WP from rockfall, altering the 

near-field chemistry, and more uniformly distributing moisture, these phenomena were not 

included in TPA 3.1.3. Invoking the backfill option in these analyses was accomplished by 

setting the time of backfill emplacement to 100 years. Backfill parameters and ranges are 

those listed in Appendix A of Volume I. 

Matrix diffusion 

Models implemented for the base case assume contaminant transport is primarily through 

fractures. Matrix diffusion describes the processes whereby contaminants in the fractures 

diffuse into pores in the intact rock, providing additional storage volume and sorption, 

thereby retarding the arrival of the released contaminant at the points of water use. At the 

present time, evidence suggests that matrix diffusion may occur at YM; however, the 

magnitude of this effect has not been established. Matrix diffusion is implemented in the 

TPA 3.1.3 code by invoking an option in the NEFTRAN II code (Olague et al., 1990), which 

allows a two-compartment model for transfer of contaminants between a mobile phase and 

an immobile phase. The parameters involved in this option are the matrix retardation 

coefficients for the elements (e.g., ImmobileRDSTFFNp), and the mass transfer factor 

between the stationary and mobile compartments (DiffusionRateSTFF). For the alternative 

conceptual model, the latter factor ranged from 0.01 to 1.0 with a log-uniform distribution. 

The retardation coefficients are as listed in Appendix A of Volume I. 

Cladding credit 

The base case assumes that once inner and outer overpack containers have been breached, 

SF is exposed and available for dissolution and transport. This assumption conservatively 

ignores any protection afforded the fuel from intact and partially failed cladding. In this 

alternative model, the effect of cladding protection is simulated by setting the fraction of SF 

wetted (SFWttd%) to a constant value of 0.005. 

C-22 with higher flow 

Building on the alternative model described previously, where alloy C-22 comprises the 

inner overpack, this alternative model assumes a larger water infiltration flow per WP. 

Alloy C-22 is not expected to undergo pitting corrosion, and WPs will fail at later times by 

general corrosion mechanisms resulting in correspondingly larger holes, compared with

3-3 NUREG-1668
pitting-corrosion WP failures. This alternate model was invoked by increasing the range on 

the diversiont factor, Fmult, from 0.01-0.2 to 0.02-0.4, with all other parameters the same 

as the C-22 case described previously. 

Focused flow 

The base case assumes that all parts of a repository subarea will receive an equal quantity 

of infiltrating water. This alternative conceptual model accounts for the possibility that 

water infiltiration reaching the WPs will be focused or funneled by discrete fractures, which 

will wet some of the WPs more heavily than others. This alternative model is evoked by 

increasing the range of Fmult by a factor of 4 (from 0.01-0.2 to 0.04-0.8), while decreasing 

fraction of waste packages wetted, SbArWet%, by a factor of one fourth (from 0-1 to 

0-0.25). This has the effect of funneling the same quantity of water for each subarea to one 

fourth the number of WPs. 

Flowthrough 

This series of runs evaluates the flow-through option for which water enters the WPs 

through corrosion holes but does not pool in the container. In the bathtub model used in the 

base case, the fraction of fuel wetted is determined by the water level in the WP, which 

changes wiith time as the WP fills and reaches a maximum level determined by the parameter 

SFWttd%, uniformly sampled between 0 and 1. In the flowthrough model, the fraction of 

fuel wetted is the same but is unrelated to the water level in the WP. Additionally, the 

fraction of fuel wetted is likely to be much smaller and depend on poorly understood 

phenomena such as dripping patterns, surface tension, and vapor-phase wetting. This 

alternative conceptual model is invoked by specifying a low flow rate for water entering the 

WP by settling Fmult to a range of 0.001 to 0.02 (1/10 of the normal range for the base case) 

to simulate solubility limited contaminant release, where only thin films of water contact the 

fuel. In this model, solubility limits for the radionuclides might become important because 

of the limited amount of water in contact with the fuel. 

Flowthrough with higher fuel dissolution 

TPA 3.1.3 has two internal models for fuel dissolution rate. The base case fuel dissolution 

model (imodel = 2) is based on J-13 well-water chemistry, without significant amounts of 

carbonate ions. The second fuel dissolution model (imodel = 1) used in this alternative has 

an increased fuel dissolution rate at high carbonate concentrations, and silicate and calcium 

concentrations are reduced in the water entering the WP. 

Natural Analog 

In this alternative conceptual model, fuel dissolution and contaminant release rates are based 

on maximum likely rates inferred from measurements at the Pefia Blanca, Mexico, natural 

analog site (Murphy and Pearcy, 1992; Murphy et al., 1997). For this alternative, the 

uranium dissolution rate is set at 24 kg/year from the entire repository. This alternative 

conceptual model was invoked by setting imodel = 3.

NUREG-1668 3-4
Immediate WP failure

This alternative conceptual model demonstrates the contribution to repository performance 

of intact WPs. It is invoked by setting the fraction of initially failed WPs (WPDef%) to 1.0. 

This does not imply that radionuclide releases occur immediately because the bathtub model 

still requires the WPs to fill to their maximum level, and the wetting model requires the WP 

temperature to fall below boiling. 

No retardation 

This alternative conceptual model demonstrates the contribution to repository performance 

of retardation in the geosphere. Once radionuclides are released from failed WPs, they travel 

at the same rate as water through the UZ and SZ to the receptor location. This alternative 

model is invoked by setting all equilibrium coefficients, Kds, to zero and all retardation 

coefficients to unity. 

Table 3-1 summarizes the base case and alternate conceptual model cases analyzed in the 

system-level sensitivity studies. For each alternative conceptual model, only the noted changes to the TPA 

input file were made, with all other input parameters set to the values used in the base case. The base case 

and all alternative conceptual models were evaluated using 200-vector simulations with TPA 3.1.3. 

3.1.3 Scenario Cases Studied 

Several scenario cases were studied in these analyses, which allowed for examination of 

consequences (i.e., receptor doses) should these scenarios occur. These scenario cases, which were studied 

for TPIs of 10 and 50 kyr, are: 

Nominal case 

This scenario case is the same as the base case mentioned previously but includes the effects 

of seismically induced rockfall on the WP failure times. This scenario is invoked by 

activating the seismicity switch in the tpa.inp file (SeismicDisruptiveScenarioFlag). 

Nominal case with major faulting (Nominal F) 

This scenario case differs from the nominal case by also including the effects of previously 

unknown (or underappreciated) faults on WP failure times. This scenario is invoked by 

activating both the seismicity and faulting (FaultingDisruptiveScenarioFlag) switches in the 

tpa.inp file. 

Nominal case with volcanism (Nominal V) 

This case differs from the nominal case by also including the effects of intrusive and 

extrusive igneous activity. This scenario is invoked by activating both the seismicity and 

volcanism (VolcanismDisruptiveScenarioFlag) switches in the tpa.inp file.

3-5 NUREG-1668
Z Table 3-1. Alternative conceptual models analyzed in the system-level sensitivity studies, using TPA 3.1.3

ON 

00 

ON

Fraction 

Inner SF Amount of Water Matrix Release of WP Dissolu- Retar- Delayed 

Overpack Backfill Wetted Contacting Waste Diffusion Model Wetted tion dation Release 

CASE .625!C22 Y[N 1[.05 1x2x4x .Olx Y N BIFT 1 /4 J[C[A YNY N 

CIA 6 ISE Y7 N I liT I I I I . I I IX' IXI _1 IX 

Backfill X X X X X X X X X X 

Matrix diffusion X X X X X X X X X X 

Cladding credit X X X X X X X X X X 

C-22 with X X X X X X X X X X 

higher flow 

Focused flow X X X X X X X X X X 

Flowthrough X X X X X X X X X X 

Flowthrough with X X X X X X X X X X 

higher dissolution 

Natural analog X X X X X X X X X X 

Inmediate X X X X X X X X X X 

WP failure 

No retardation X __ X X X X X X X X X_

Additional specifications for all cases: Carbon steel as the outer overpack, 10- and 50-kyr TPI, receptor group at 20 km. 

Legend: S = Seismic; V=Volcanism; F=Faulting; 625=alloy 625 inner overpack; C22 = alloy C-22 inner overpack; Y = yes; N = no; J = J-13 water; 

C = carbonate water; A = user-specified for natural analog; B=bathtub; FT=flowthrough.
3.2 CODE OUTPUT FOR ALTERNATE MODEL AND SCENARIO CASE 

ANALYSES 

A number of intermediate results or performance measures may be produced by TPA 3.1.3 at the 

request of the user. In the study, however, the effect of alternate conceptual models and scenarios on overall 

repository performance are compared solely on the basis of the peak TEDE for the 10 and 50 kyr TPIs. 

3.2.1 Complementary Cumulative Distribution Functions for the Alternative 

Conceptual Models 

Tables 3-2 and 3-3 summarize the results of the base case and all alternative models for 10 and 

50 kyr TPIs, respectively. 

Table 3-2. The 10-kyr Time-Period-of-Interest Base Case and Alternative Conceptual Model Results 

Standard 

Deviation of Mean Percentile 

Mean Peak TEDE Peak TEDE Corresponding to 

Case (1Sv) (I'Sv) Mean Value 

Base case 23.0 4.0 87 

C-22 0.57 0.29 81 

Backfill 11.8 5.6 92 

Matrix diffusion 7.30 1.1 88 

Cladding credit 0.12 0.04 90 

C-22 with higher flow 1.6 0.3 80 

Focused flow 18.8 4.5 86 

Flowthrough WP infiltration 4.4 0.6 71 

Flowthrough with higher dissolution 31.0 4.2 72 

Natural analog 3.3 0.9 89 

Immediate WP failure 113 17 77 

No retardation 125 29 86

3-7 NUREG-1668
Table 3-3. The 50 kyr Time,-Period-of-Interest Base Case and Alternative Conceptual Model Results 

Standard Deviation Percentile 

Mean Peak TEDE of Mean Peak Corresponding to 

Case (OjSv) TEDE (OiSv) Mean Value 

Base case 283 16 76 

C-22 140 10 72 

Backfill 226 35 76 

Matrix diffusion 157 20 76 

Cladding credit 1.3 0.19 76 

C-22 with high flow 151 18 72 

Focused flow 78 10 73 

Flowthrough 33.8 4.4 74 

Flowthrough with higher dissolution 97.6 8.0 66 

Natural analog 46.1 3.7 58 

Immediate WP failure 683 93 78 

No retardation 1160 100 67 

These Tables list the mean peak TEDE in the TPI, standard deviation of this quantity, and the distribution 

percentile corresponding to the mean peak TEDE in the TPI for each of the cases evaluated. Figures 3-1 and 

3-2 present bar plots comparing results of the base case and alternative conceptual models for TPIs of 10 

and 50 kyr, respectively. Note that the results presented in Figure 3-1 are ordered according to the mean peak 

TEDE. The order for 10 kyr is preserved for the 50 kyr results in Figure 3-2, which illustrates that the 

importance of some of the phenomena captured in the alternative conceptual models is different for the two 

TPIs. Two CCDFs for the base case with TPIs of 10 and 50 kyr are presented in Figures 3-3 and 3-4. The 

CCDFs for the alternative conceptual models are presented in Appendix B of this report. 

3.2.2 Complementary Cumulative Distribution Functions for the Scenario Cases 

The Nominal case is identical to the base case discussed in Section 3.1.1, with the inclusion of the 

effects of seismically induced rockfall by using the seismicity switch variable and those parameters presented 

in Appendix A of Volume 1. The Nominal case is believed to be the most likely scenario because the 

probability of seismicity at the repository is high enough that it is very likely to occur in 10 kyr. The 

Nominal V scenario differs from the Nominal case by the inclusion of both the intrusive and extrusive 

components of the volcanism scenario. These scenarios are activated by using the volcanism switch variable 

and using the parameters presented in Appendix A of Volume I. The Nominal F scenario case differs from 

the nominal case by inclusion of major faulting by using the faulting switch variable and the parameters 

presented in Appendix A of Volume I. All scenario cases shown here use TPA 3.1.4 rather than TPA 3.1.3. 

Settings for the scenario cases are given in Table 3-4. Results of the scenario cases are summarized in Table 

3-5, for a 10-kyr TPI, and Table 3-6, for a 50-kyr TPI. The CCDFs for the Nominal case are presented in 

Figures 3-5 and 3-6, for 10- and 50-kyr TPIs. It is noted that peak TEDE for these scenario cases has been 

multiplied by the probability of scenario occurrence in the TPI. For the Nominal case, this probability has

NUREG-1668 3-8
NoRd

Totdef 

Flwthru2 

Base 

Clad Cladding protection factor - 0.5% 

Focflow C22base Alloy C-22 

BC22mult Alloy C-22 with less flow diversion 

Bkf'l Natan Natural analog release 

Matdif Flwthru Flowthrough model with 1/100 flow 

Bkfill Backfill emplaced at 100 yr 

C22mult Base Alloy 625, no backfill, no matrix diffusion, 

bathtub model, no cladding 

R lwUU Focflow Flow focused to 1/4 the WP 

Flwthru2 Flowthrough with carbonate dissolution 

Natan Totdef WPs fail at time - 0 

NoRd No contaminant retardation in geosphere 

C22base Matdif Matrix diffusion in geosphere 

Clad 

I p a I, a a a I , , m . I , a I I a a I I a , , a I a a 

0 2 4 6 8 10 12 14 

Mean Annual Peak Dose (mrem) 

0 

SFigure 3-1. Bar chart of mean peak total effective dose equivalent for a 10 kyr time period of interest for the base case and alternative 

0 conceptual models.
z 

rn 0 NoRd 

ONTotdef 00 

Flwthru2 

Base Clad Cladding protection factor = 0.5% 

C22base Alloy C-22 

C22muit Alloy C-22 with less flow diversion 

Bkfil Natan Natural analog release 

Flwthru Flowthrough model with 1/100 flow 

Matdif Bkfill Backfill emplaced at 100 yr 

Base Alloy 625, no backfill, no matrix diffusion, 

C22mult bathtub model, no cladding 

Focflow Flow focused to 1/4 the WP 

Flwthru Flwthru2 Flowthrough with carbonate dissolution 

Totdef WPs fail at time - 0 

Natan NoRd No contaminant retardation in geosphere 

Matdif Matrix diffusion in geosphere 

C22base 

Clad 

0 20 40 60 80 100 120 140 

Mean Annual Peak Dose (mrem) 

Figure 3-2. Bar chart of mean peak total effective dose equivalent for a 50 kyr time period of interest for the base case and alternative 

conceptual models.
100 

10-1 

10-2 

10 -3

106 10-4 10-3 10-2 10-1 100

Peak Annual Total Effective Dose Equivalent 

Figure 3-3. Complementary Cumulative Distribution Function for the base case and a 10-kyr time

period-of-interest

NUREG-1668

(U 

.0 

2u -Ow

3-11
100

10"1 

10-2

10-6 10"5 10"4 10-2 101 100

Peak Annual Total Effective Dose Equivalent (rem) 

Figure 3-4. Complementary cumulative distribution function for the base case and a 50 kyr time 

period of interest.

NUREG-1668

0 

0..

3-12
Table 3-4. Scenario Cases Analyzed Using TPA 3.1.4

z 

00

Disruptive Inner SF Amt of Water Matrix Release Fraction of Delayed 

Events Overpack Backfill Wetted Contacting Waste Diffusion Model WP Dissolution Retardation Release 

CASE S VF625 C22 Y(N 11.05 lx 14x .Olx I Y N n lr 1 1 4 ICIA YJ N Y NI 

Nominal X X X X X X X X X X X 

NominalV X X X X X X X X X X X X 

Nominal F X X X X X X X X X X X 

Additional specifications for all cases: Carbon steel as the outer overpack, 10- and 50-kyr TPI, receptor group at 20 km. 

Legend: S = Seismic; V=Volcanism; F=Faulting; 625=alloy 625 inner overpack; C22 = alloy C-22 inner overpack; Y = yes; N = no; J J-13 water; 

V! carbonate water; A = user-specified for natural analog; B=bathtub; FT=flowthrough.
Table 3-5. Summary of the 10o-kyr Time-Period-of-Interest Scenario Case Results

Table 3-6. Summary of the 50-kyr Time-Period-of-Interest Scenario Case Results 

Probability Mean Standard 

of Scenario Peak Deviation of Percentile 

Occurrence TEDE Mean Peak Corresponding 

Case in the TPI* (Sv) TEDE (pSv) to Mean Value 

Nominal case 0.355 52.0 5.86 77 

Nominal case with volcanism** 4.99 x 10-3 90.3 17.1 87 

(regardless of whether faulting 

occurs) 

Nominal case with faulting 0.629 89.9 10.1 77 

* Note that probabilities need not necessarily add to one because the base case (i.e., no disruptive 

scenarios) is not listed. 

"**Indicates that these mean peak TEDE and standard deviation results have been multiplied by the 

probability that the scenario occurred in the TPI.

NUREG- 1668

Probability Mean Standard 

of Scenario Peak Deviation of Percentile 

Occurrence TEDE Mean Peak Corresponding 

Case in the TPI* (gSv) TEDE (OSv) to Mean Value 

Nominal case 0.818 9.82 4 89 

Nominal case with volcanism** 1.00 x 10-3 52.7 9.87 86 

(regardless of whether faulting 

occurs) 

Nominal case with faulting 0.181 2.35 0.724 89 

*Note that probabilities need not necessarily add to one because the base case (i.e., no disruptive 

scenarios) is not listed. 

"**Indicates that these mean peak TEDE and standard deviations results have been multiplied by the 

probability that the scenario occurred in the TPI.

3-14
100 

o 1 

0 10 

wL Mean Value E c0.982 mrem 

Standard Deviation of Mean Value mrem of0.4 

-2 10~ 

10-3 1211 1 tt ie 

10~ 10~ 10 10 102 101 100 

Peak Annual Total Effective Dose Equivalent 

Figure 3-5. Complementary cumulative distribution function of the nominal case for a time period of 

interest of 10 kyr.

3-15 NUREG-1668
100

10-1 

10-2

10.6 10-4 10 -3 10-2 10-1 100

Peak Annual Total Effective Dose Equivalent 

Figure 3-6. Complementary cumulative distribution function of the nominal case for a time period of 

interest of 50 kyr.

NUREG- 1668

CT.. 

U 

U1,

10 "-3

3-16
been calculated assuming that both faulting and volcanism occur as Poisson processes with recurrence rates 

(XF and Xv) of 2 x 10-' yr-1 and 10-' yr-1 , and that neither has occurred in the TPI using the following 

equation: 

P N = exp(-)4 F TPI) exp(-kv TPI) (3-1) 

For the Nominal V case, the probability of scenario occurrence is calculated as 

PN+V = 1 - exp(-Xv TPI) (3-2) 

Because the effects of extrusive volcanism (the main exposure pathway for this scenario) are unaffected by 

the occurrence of major faulting, this probability has been estimated without considering the occurrence of 

major faulting. Similarly, the scenario probability for major faulting has been estimated as 

PN+F - exp(-Xv TPI)[1 - exp(-exp(-XF TPI)] (3-3) 

CCDFs for the Nominal V and Nominal F scenario cases for TPI of 10 and 50 kyr have been included in 

Appendix B. 

3.3 OBSERVATIONS BASED ON ALTERNATIVE CONCEPTUAL MODEL 

AND SCENARIO CASE RESULTS 

A number of observations can be made based on the calculational results shown in this chapter. They 

are listed as follows: 

" The alternative conceptual model where an equal amount of infiltrating ground water was 

focused to one-quarter of the WPs showed better performance than the base case. This fact 

means that the radionuclides providing the majority of dose are probably release-rate-limited 

in TPA 3.1.3. 

" The relative effects of the alternative conceptual models (based on mean peak TEDE) change 

substantially between 10- and 50-kyr TPIs. 

" For both the alternative conceptual models and the scenario cases, the percentile corresponding 

to the mean peak TEDE is quite high (generally failing between the 80' and 90' percentiles). 

This fact means that the mean peak TEDE is dominated by a relatively small number of large 

dose realizations.

3-17 NUREG-1668
4 ALTERNATIVE CONCEPTUAL MODELS AND SCENARIO 

CASES STUDIED AT THE SYSTEM LEVEL 

This chapter describes the sensitivity and uncertainty analysis techniques used in conjunction with results 

of TPA 3.1.4 system-level runs conducted for this report. In general, a sensitive parameter is defined as one 

that provides a relatively large change in the output variable for a unit change in input parameter. The goal 

of the sensitivity analyses is to determine the parameters to which peak TEDE for the TPI shows the most 

sensitivity. The goal of the uncertainty analyses is to determine the parameters that are driving uncertainty 

in peak TEDE output. The analyses were conducted for the Nominal case (without igneous or faulting 

disruptive events) and also on an alternative model case that used alloy C-22 as the inner overpack material 

since DOE is actively considering this option. The analyses conducted herein rely on the models and 

assumptions used in the TPA 3.1.4 code. For a more detailed description of these models and assumptions, 

the reader is referred to Volume I of this report. Conclusions based on these analyses may be updated as the 

models or assumptions are updated, and certain parameters or processes may become more or less important. 

These analyses complement the work that was performed in the process-level sensitivity studies. 

It should be noted that the alternative conceptual model analyses presented in chapter 3 used a different code 

version with different inputs (e.g., dose conversion factors [DCF' s]; neptunium solubilities), and therefore, 

should not be compared directly to the results in this chapter. Furthermore, the analyses in this chapter 

include seismicity (i.e., in the Nominal case) whereas those in chapter 3 (i.e., in the base case) do not. 

4.1 STATISTICAL ANALYSIS TECHNIQUES 

The techniques used herein (except differential analysis) rely on the use of the Monte Carlo method 

for probabilistically determining system performance. Figure 4-1 illustrates the Monte Carlo method and its 

application to PA. In NRC YM repository PA exercises, the performance measure of the system is the peak 

TEDE in the TPI to an average member of a receptor group located 20 km from the repository. Many of the 

input parameters are not precisely known and/or are spatially variable, so their values are described by 

probability distributions. The Monte Carlo technique makes repeated runs (called realizations) of the possible 

states for the system, choosing values for the input parameters from their probability distributions. Although 

more than 100 input parameters are sampled in the TPA Version 3.1.4 code, only a few of these parameters 

contribute significantly to the uncertainty in peak dose because of the great sensitivity of peak dose to the 

parameter, the large variability of the parameter, or both. This section describes the techniques used to 

determine which input parameters in TPA 3.1.4 are driving either the uncertainty or value of peak TEDE. 

It is noted that not all techniques described below were applied to all cases. 

4.1.1 Scatter Plot/Single Linear Regression on One Variable 

In the scatter plot/single linear regression technique, peak TEDE is plotted versus each of the 

sampled input variables. As shown in Figure 4-2, results of scatter plots give an initial visual indication of 

which variables are likely to be important to further sensitivity and uncertainty analyses. Single linear 

regression (i.e., regression with only the first power of one input variable and an intercept) of the average 

peak TEDE with respect to each of the input parameters can give a quantitative measure of the correlation 

in terms of the coefficient of determination R2, although this figure can be misleading in cases where the 

dependencies are not purely of the first order with respect to the input variable. It is noted that linear here 

and throughout this chapter refers to the functional form of the regression and not the order to which the 

fitting parameters appear (although the regressions are also linear in the fitting parameters).

4-1 NUREG-1668
0 

C7 

00

Stochastic 

Sampling Xl 

Stochastic 

oJaI1pJn11l 

Er

p(x ) 1 

XI-low X l-high 

X2-mean 

-- CYX2 j4

P(X n)I A\ Stochastic 0 

"Sampling n

y(x 1,X2,.*.,Xn)

Xn-low Xn-high 

Probability 

Distributions 

of Input 

Parameters

Realization

Specific 

Input 

Parameters

Conceptual 

Models

Performance 

Measure 

or Output

Figure 4-1. A diagram illustrating the use of the Monte Carlo method in performance assessment.

0
I I I lIllIllIlI jII I

V 

* *. 

4*. * *

Y-=MO+MI*X 

iiiMO -5.3723 

M1 0.77172 

R 0.71393

-3 

-4 

-5 

-6 

-7 

-8

2 3 4 6

Input Variable Being Studied

Figure 4-2. An example of a scatter plot/single linear regression.

0 

-1 

-2

4)

I I a I I a a I I

-2

z 

ON ON

S • . I

-1 0 1

I I I I . . 1. . .. . . . . . . . . . .
Even when the output variable is linearly dependent on the input variable being studied, uni-variate 

linear regression of Monte Carlo results may fail to show unambiguous correlation since other sampled 

parameters that affect the output are varying at the same time and the model is clearly under-specified (i.e., 

the results depend on more thain one variable). Therefore, more sophisticated techniques, which are described 

in the following sections, must be employed to take into account the simultaneous variation of the input 

parameters. 

4.1.2 Variable Transformations and Their Attributes 

The correlation between input and output variables can be enhanced by transforming the variables. 

This section describes variable transformations that have been used in this study. 

4.1.2.1 Normalization 

In normalization, the input variable xi is transformed by dividing by its mean value (or another 

baseline such as the median, 9 0'0 percentile, etc.): 

X1. 

xi= -- (4-1) 

x. 

Normalized variables are dimensionless and are scalar multiples of their baseline values. Dimensionless 

variables allow the comparison of sensitivities to other independent variables with different dimensions. 

Log transformations of the normalized variables were used to produce the best fit of a linear model 

to the TPA 3.1.4 results. As will be shown in Section 4.1.3, normalized variables are a natural outcome of 

sensitivity derived from regression of log-transformed variables. Such sensitivity measures describe only the 

relative change in the dependent variable (peak TEDE) to changes in the independent variables. Although 

this is a useful measure, it treats all sensitivity results equally in spite of the value of peak TEDE. It may be 

more important to weight mere heavily those results where absolute changes in peak TEDE are large. 

Furthermore, sensitivities calculated from normalized variables do not take into account the uncertainty in 

the independent variables. 

4.1.2.2 Rank Transformatfion 

Rank transformation, a dimensionless transform, replaces the value of a variable by its rank (i.e., the 

position in a list that has been sorted from largest to smallest values) (Iman and Conover, 1979). Analyses 

with ranks tend to show a greater sensitivity than results with untransformed variables. 

4.1.2.3 Logarithmic Transformation 

For situations in which input and output variables range over many orders of magnitude, it may be 

advantageous or even necessary to perform analyses on the logarithm of the variables instead of the variable 

values themselves. The log transformation is also valuable for creating regression equations, where the 

subprocesses of the model multiply each other to form the output variable, such as in a transfer function 

approach. For the present situation in which the dose calculation results from radionuclide releases from the

NUREG- 1668 4-4
waste form, transport through the geosphere, and uptake by humans, the processes are indeed largely 

multiplicative rather than additive. Log transforms therefore tend to give better fits to the Monte Carlo results 

than untransformed variables. The log transformation may be used in conjunction with normalization. 

4.1.2.4 Standardization 

The independent and dependent variables can be standardized by subtracting the mean and dividing 

by the standard deviation, that is, 

xi (4-2) X 

The advantage of standardization over normalization is that it inserts the approximate range of the variables 

into the sensitivity analyses. Therefore a variable that has a large per-unit sensitivity, but is well-known and 

has a narrow range, will have an increased sensitivity when standardized. Conversely, independent variables 

with wide ranges will show a reduced sensitivity when standardized. 

Sensitivity measures based on standardized variables (standardized sensitivities) have the advantage 

of taking into account the uncertainty (in terms of the standard deviation) of the independent variable. This 

technique, as stated previously, decreases the sensitivity if the range of the independent variable is large. 

Furthermore, the standardized sensitivities preserve the absolute values of peak TEDE since the derivatives 

are divided by the standard deviation for the entire set of calculations, rather than the mean peak TEDE at 

the evaluation point. Therefore, the absolute value of changes in mean peak TEDE are preserved with 

standardized sensitivities. 

Standardized variables can be greater or less than zero, hence they cannot be used directly in the 

regression analyses using the log-transformed variables. Instead, the standardized sensitivities can be derived 

from sensitivities based on logs of the normalized variables: 

* x ( y y_ (4-3) 

ax,* Cy axi Y) x, 

where y* and x* are the standardized dependent and independent variables as defined by Eq. (4-2). The 

quantity in parentheses is the sensitivity derived from regression analysis with the logs of the normalized 

variables (see Section 4.1.3). Note that since Eq. (4-3) requires the normalized sensitivities, it necessarily 

suffers from some of the same disadvantages as normalized sensitivities. Direct linear regression with 

standardized variables frequently gives higher importance to variables that effect large absolute changes in 

peak TEDE. For example, linear regression with standardized variables in the alloy C-22 case for 10 kyr 

shows that the most important variables are the ones that deal with seismic damage to the WPs, with the top 

variable being the seismic heterogeneity factor SSMOHetF. This is a logical conclusion for this case because 

there are no corrosion failures for Alloy C-22 in 10 kyr and seismic damage is the only likely cause of failure 

other than juvenile failure. However, regression analyses with normalized variables failed to show any 

sensitivity to SSMOHetF. Linear regression analyses with the standardized variables gave generally poor fits

4-5 NUREG-1668
in terms of residual sums of squares, and only the most sensitive variables could be determined 

unambiguously. 

A modified form of the standardized sensitivities approach was also used in the differential analysis 

in Section 4.3. In this case, only 5 points were defined for the parameter space, so the independent variables 

were standardized by the same standard deviations used in the regression analyses (i.e., the standard 

deviation based on 400 samples generated in the Monte Carlo analyses). Peak TEDE values did not need to 

be standardized to show the relative sensitivities to the standardized independent variable. Therefore, those 

sensitivities have units of dose. 

4.1.3 Stepwise Multiple Linear Regression 

Stepwise multiple linear regression (stepwise regression) determines the most influential independent 

variables on output uncertainty according to how much each reduces the residual sum of squares (RSS) 

(Helton et al., 1991). The form of the regression equation is: 

V m= x1 +m2x 2 +mX b (44) 

where 

y - dependent variable 

x1 - independent variables 

m. - regression coefficients 

b' - intercept 

The independent variables may be the raw variables, transformed variables, or ranks. The stepwise algorithm 

calculates the reduction in RSS for the independent variables in the order that gives the greatest reduction 

first. 

The regression coefficients mn are the partial derivatives of the dependent variable with respect to 

each of the independent variables. The form of the linear regression equation that gave the best fit used the 

log of the normalized peak TEDE and the log of the normalized independent variables x.: 

(2 b mI~g +mlog 2 +.+M lo- 2 

log D -b + mllog• + Mn20g ... n og= (4-5) + n 

where 

b - intercept 

m l- mn - coefficients of the regression 

and the overbars denote the value of the quantities used for normalization (generally the mean value). 

When the antilog of both sides of Eq. (4-5) is taken, then the resulting equation becomes:

NUREG-1668 4-6
D _ 1 0 n, m X 2' m 2 .. X_ nn D 1O 0 - () - (..m= (4-6) 

After taking the partial derivative of both sides of Eq.(4-6) with respect to the independent variables and 

rearranging, it reduces to: 

xi aD - m. (4-7) D a~xi Mi(47 

Therefore, the normalized sensitivities are exactly the coefficients of the regression equation using the logs 

of the normalized peak TEDEs and independent variables. The form of the sensitivities given by Eq. (4-7) 

is the same as one of the measures calculated by differential method in Section 4.3. 

4.1.4 Application of the Kolmogorov-Smirnov (K-S) and Sign Tests for Determining 

Important Parameters 

The K-S and Sign tests differ from regression and differential analyses in that they are 

nonparametric; that is, these tests do not require fitting the data to prespecified functional forms. 

4.1.4.1 The K-S Test 

The K-S test determines whether a set of samples has been drawn from a given distribution (Bowen 

and Bennett, 1988). It is used to determine whether an independent variable is important by comparing a 

subset of the independent variable composed of the values from the highest 10 percent of the peak TEDE 

realizations to the theoretical distribution of that variable. If the distributions are equivalent, then peak TEDE 

is not sensitive to the variable in question. Conversely, if the distributions are different, then the variable in 

question does have an effect on peak TEDE. For the present study, there are 400 vectors in the entire set, and 

the subset consists of the 40 vectors corresponding to the top 10 percent of the peak TEDEs. The distribution 

of the variable in the 400-vector set is taken as the theoretical distribution although it would also be possible 

to get the theoretical distribution directly from the generating function specified in the Latin Hypercube 

Sampling (LHS) routine. The significance of the K-S test was determined at the 90-percent confidence level. 

4.1.4.2 The Sign Test 

The Sign test is another nonparametric test used to determine whether a set of data corresponds to 

a given theoretical distribution (Bowen and Bennett, 1988). It is used in a manner similar to the K-S test. In 

the Sign test, each observation of the input variable is represented by either a plus sign (+) or a minus sign 

(-) depending upon whether it is greater than or less than the median value estimated by the theoretical 

distribution. The subset of the input parameter values corresponds to the highest 10 percent of calculated 

peak TEDEs. The subset is compared to the theoretical distribution, which in this case, is assumed to be

4-7 NUREG-1668
represented by the entire set of 400 vectors. The significance of the sign test was determined at the 

90-percent confidence level. 

4.1.4.3 Use of the t-Statistic to Identify Important Variables 

The t-statistic is generally used to estimate to a specified confidence level that an estimated 

parameter value differs from another value. For example, in this report, the t-statistic is used to estimate the 

confidence level that the coefficients for the multilinear fit of the data [e.g., the mi values in Eq. (4-5)] are 

different from zero. A parameler xi is deemed important if there is 95-percent confidence that its slope (mi) 

is different from zero. The t-s:Latistic is given by Draper and Smith (1981) as: 

(M m .en, -m i rest P [ j (X nIli-Xn) 2]II12 

5 = m . (4 -8) 

mi 

where 

ti - t-statistic for regression coefficient i 

m. - estimated value of regression coefficient i 

m. - slope value being tested against; in this case zero 

S - estimated standard deviation of mi; the square root of the variance 

yj -- realized value of parameter x, for the jh realization 

xn mean value of parameter x, over all realizations 

S - estimated standard deviation of the output variable = { (yj _ ) 2 

For the analyses conducted herein, the number of realizations is either 200 or 400, which provides 

essentially an infinite number of degrees of freedom for the t-statistic. The critical value to ensure 95-percent 

confidence that mn differs from zero under these conditions is 1.96 (Mason, et al; 1989). Parameters that were 

identified by either stepwise regression, K-S test, or Sign test, were investigated further by estimating the 

t-statistic for their sensitivity coefficient(s) from a multilinear fit of the data. Parameters with sensitivity 

coefficients whose t-statistic did not exceed the critical value were not further investigated. 

4.2 RESULTS OF SENSITIVITY AND UNCERTAINTY STATISTICAL 

ANALYSIS 

This section presents the sensitivity and uncertainty analysis results generated using methods 

described in the previous section. Six tests were used to screen the probabilistic (Monte Carlo) results for 

the most influential parameters in terms of uncertainty in peak TEDE:

NUREG-1668 4-8
Stepwise regression on the ranks of the variables

0 Stepwise regression on the logs of the normalized variables 

0 K-S test 

0 Sign test 

0 t-test on regression coefficients 

Standardized regression coefficients based on stepwise regression of the logs of the 

normalized variables 

Sensitivity coefficients (i.e., the slopes of the multilinear fit) were also generated. These results will be 

discussed later in Section 4.2.1. 

Tables 4-1 through 4-4 list the most influential parameters, by key word, that were identified by the 

regression fit, or those statistical measures described in Section 4.1. Appendix C contains a full description 

of all parameters. The first two columns for each table were determined from stepwise regression; the third 

and fourth columns were determined from the K-S and Sign test; and the fifth and sixth columns were 

determined by the t-statistic and standardized regression, according to Eq. (4-3), respectively. 

The sixth column in Tables 4-1 through 4-4 present the ranking based on the standardized regression 

coefficients derived from the log normalized regression coefficients. The standardized ranks are significantly 

different from the log normalized ranks because the former considers information about the ranges of the 

input parameters and peak TEDE as well as the ranking based only on reduction of RSS from stepwise 

regression. 

Figures 4-3 through 4-6 show graphically how the RSS is reduced as more variables are added to the 

regression equation. The parameters in these figures correspond to the ordering in the third column of 

Tables 4-1 through 4-4. 

Although adding more terms to the regression always reduces RSS, there is an evident break in the 

slope of the curve connecting the RSS values. Beyond this point, improvement in the regression cannot be 

attributed unambiguously to the new variables. The fifth column in the table contains values of the t-statistic 

determined from a multilinear fit of the data, where any independent variable appearing in the first four 

columns was included in the fit. Only parameters whose slopes had a t-statistic greater than 1.96 are listed 

in the fifth column. The t-statistic results support the results based on reduction of RSS because some 

parameters that were screened based on reduction of RSS were discarded based on low t-statistic values, 

meaning that all the parameters driving uncertainty in peak TEDE were likely captured using these methods. 

The sixth column is derived from the second column, using Eq. (4-3), but the order of the ranking is different 

because the standardized sensitivities take into account the absolute values of peak TEDE and the standard 

deviations of the independent variables. Even though the techniques used in determining the influential 

parameters are significantly different, there is general agreement between the columns in Table 4-1.

4-9 NUREG-1668
Table 4-1. Influential Parameters on the Uncertainty of Peak Total Effective Dose 

Equivalent for the Nominal Case for a 10-kyr Time Period of Interest 

StepwiseStepwise on Log of 

Ranks of Normalized Standardized 

Rank Variables Variables K-S Test Sign Test t-statistic Regression 

1 Fow Sb-PrWet% AAMAI@S AAMAI@S AAMAI@S Fow 

(7.02) 

2 AAMAI@S Fow 00-Coflc MATI@GM FOW (6.12) Fmult 

3 Fmult Fmult Chlorid MixZnT20 SbArWet% 00-Coflc 

1 _(3.78) 

4 SFWttd% c()-Coflc Fow 00-Coflc 00-Coflc AAMAI@S 

(3.69) 

5 Chlorid AAMJAAI@S Fmult Chlorid Fmult (2.98) SbArWt% 

6 00-Coflc I SbArWet% Fow Chlorid 

Because DOE has indicated in its latest design that alloy C-22 will be used as the inner overpack 

material for the WP, this alloy is the alternative focus of the uncertainty analyses whose results are 

summarized in Tables 4-2 and 4-4. Table 4-2 lists the parameters that were found to have the most influence 

on uncertainty of peak TEDE for a TPI of 10 kyr, using the same statistical analysis techniques described 

in the previous section. Table 4-4 presents similar information for a TPI of 50 kyr. As before, there is general 

agreement among the different techniques. 

Table 4-3 lists the parameters that had the most influence on the uncertainty of peak TEDE for the 

nominal case with a 50-kyr Tl']. The sensitive parameters have changed relative to those found for the same 

case with a TPI of 10 kyr. In general, parameters that determine transport of key radionuclides through the 

geosphere have been found to be more important for the longer time period. 

Table 4-4 lists the parameters that had the most influence on uncertainty of the output variable for 

the alloy C-22 inner overpack material and a 50-kyr TPI case. As was the case with the results in the previous 

section, parameters associated with the transport of key radionuclides through the geosphere have been found 

to be more important for the longer time period.

NUREG-1 668 4-10
Table 4-2. Influential Parameters on the Uncertainty of Peak Total Effective Dose Equivalent for the 

Nominal Case with Alloy C-22 Inner Overpack for a 10 kyr Time Period of Interest 

Stepwise on Stepwise-Log 

Ranks of of Normalized Standardized 

Rank Variables Variables K-S Test Sign Test t-statistic _Regression 

1 WPDef% SbArWet% AAMAI@S AAMAI@S FOW (8.25) Fow 

2 SbArWet% Fow Fow Fow WPDef% Fmult 

(6.20) 

3 Fow WPDef% WPDef% SbArWet% SbArWet% SbArWet% 

1_ (5.12) 

4 Fmult Fmult SbArWt% Fmult Fmult (4.51) AAMAI@S 

5 AAMAI@S AAMAI@S Fmult WPDef% AAMAI@S SSMO-JS4 

(3.32) 

6 ARDSAVNp SSMO-JS4 SFWttd% SFWttd%, SSMO-JS4 ARDSAVNp 

WprCg@20, (3.23) 

MRDChvRa 

7 SSMO-JS4 SFWttd% 

SI _ I_ 1_ 1_ (2.70) 

Table 4-3. A Listing of the Influential Parameters on the Uncertainty of Peak Total Effective Dose 

Equivalent for the Nominal Case for a 50-kyr Time Period of Interest 

Stepwise on Stepwise- Log S d i 

Ranks of of Normalized K-S Standardized 

Rank Variables Coefficients Test Sign Test t-statistic Regression 

1 ARDSAVNp SFWttd% ARDSAVNp ARDSAVNp ARDSAVNp OO-Cofic 

(-23.03) 

2 SFWttd% ARDSAVNp SFWttd% SFWttd% SFWttd% WprCg@20 

(22.21) 

3 SbArWet% SbArWet% AAMAI@S SbArWet% SbArWet% ARDSAVNp 

(13.29) 

4 00-Cofc O-Coflc SbArWet% OO-Cofic 00-Coflc SFWttd% 

(11.61) 

5 ARDSAVAm WprCg@20 00-Cofic WprCg@20 Chlorid (7.50) SbArWet% 

6 Chlorid Chlorid WprCg@20 AAMAI@S ARDSAVAm Chlorid 

(-6.34) 

7 WprCg@20 ARDSAVAm MKdUFZPb WprCg@20 ARDSAVAm 

(-5.71) 

8 .... AAMAI@S 

(2.14)

4-11 NUREG-1668
Table 4-4. Influential Parameters on the Uncertainty of Peak Total Effective Dose Equivalent for the 

Nominal Case with an Alloy C-22 Inner Overpack for a 50-kyr Time Period of Interest 

Stepwise

Stepwise on Log of 

Ranks of Normalized K-S Sign Standardized 

Rank Variables Variables Test Test t-statistic Regression 

1 ARDSAVNp A1RDSAVNp ARDSAVNp ARDSAVNp ARDSAVNp ARDSAVNp 

(-31.09) 

2 SFWttd% SFWttd% SbArWet% SbArWet% SFWttd% SFWttd% 

(25.73) 

3 SbArWet% SbArWet% SFWttd% APrsSAV SbArWet% SbArWet% 

(16.90) 

4 AA-2-1 AA 2 1 ARdSAVI AAMAI@s AA-2-1 AA-2-1 

(7.89) 

5 00-Coflc 0-0Cofic WprCg@20 WprCg@20 00-Coflc WprCg@20 

(7.14) 

6 ARDSAVAm WprCg@20 AA_2_1 AA_2_1 WprCg@20 00-Coflc 

(-6.99) 

7 WprCg@20 ARDSAVAm ARDSAVAm SFWttd% ARDSAVAm ARDSAVAm 

1 (-6.22) 

4.2.1 Sensitivity Coefficients for the Nominal Case with Alloy 625 and Alloy C-22 

Inner Overpack and Time Periods of Interest of 10 and 50 kyr 

Table 4-5 displays the sensitivity coefficients [defined as the regression coefficients of Eq. (4-3)] 

for those most important par-ameters identified in Columns 3 and 7 for the four cases in the screening 

analyses presented in the previous section. The number of parameters in a column indicates the number of 

terms in the multilinear fit of peak TEDE for the TPI (not counting the intercept). As a test to indicate 

whether or not the number of sensitive parameters included in the fit was sufficient to describe peak TEDE 

for the TPI, the output data from the Nominal case for 50 kyr, with an alloy C-22 inner overpack, were fit 

first to a multilinear equation, including the 10 most important parameters, and again with the 6 most 

important parameters (the choice of this example was arbitrary). The results of both fits are shown in the last 

column of Table 4-5, with the goodness of fit parameter R2 being 0.843 for 10 parameters and 0.827 with 

six parameters. As indicated by the relatively small changes in R2 and the fitting parameters themselves, it 

appears that the fit is not sigrificantly improved by including more than the top six influential parameters. 

Including only six parameters was the recommended outcome of the stepwise regression of the log of the 

normalized variables.

NUREG-1 668 4-12
Table 4-5. Sensitivity Coefficients Based on Log of Normalized (1) and Standardized (2) Variables for the Important Parameters 

Coefficient for Coefficient for Coefficient for alloy C-22 and 50 kyr 

alloy 625 and 10-kyr Coefficient for alloy alloy 625 and 50 kyr TPI (fit to top 6 parameters in 

TPI C-22 and 10-kyr TPI TPI brackets) 

Variable (1) (2) (1) (2) 1 (1) (2) 1 (1) (2) 

SbArWet% 1.035 0.15 1.057 0.13 0.9803 0.22 1.003 (1.015) 0.26 

Fow 1.79 0.46 1.483 0.32 

Fmult 2 0.25 1.422 0.15 0.959 

00-Coflc 1.26 0.16 1.1578 0.55 0.605 (0.608) 0.13 

AAMAI@S 1.33 0.15 1.036 0.1 0.0158 

MKdTswAm 0.554 0.11 

ARDSAVNp -0.183 0.053 -0.8297 0.44 -0.934 (-0.935) 0.57 

SFWttd% 1.1704 0.26 1.281 (1.28) 0.33 

WPrCg@20 -1.2747 0.49 -1.167 (-1.137) 0.14 

Chlorid 0.13 0.4737 0.1 

ARDSAVAm -0.0925 0.083 -0.0834 0.086 

AA_2-1 1.221 (1.241) 0.16 

SSMO-JS4 1.958 0.07 

WPDef% 1.043 0.12 .... -0.0287

71 

00
z 

0

C 0 

U) 

L'O 

0 0 

Co) 

0 

0~

0

0 0 0

0 

6 * * * 0

5 

5

I 

10 20 25

1 

15 30

Number of Parameters Included in Fit 

Figure 4-3. Plot of the residual sum of squares versus number of parameters included in the fit for the Nominal case with alloy 625 inner 

overpack and a time period of interest of 10 kyr.

*SbArWt% 

Fow 

Fmult 

00-COfic 

AAMAI@S 

MKdTSwAm

Ca 

:3 

C/) CO" 

E 

:) 

a)

0

0
*SbArWt% 

9 Fow

C 

0 

0 

C\1 

0 

0 

CO 

Co 

CD 

0 

0 

C) 

0 

0 

c\

0 s e * . . *. 0 . , 0 

O000S000

10

Number of Parameters Included in Fit 

Figure 4-4. Plot of the residual sum of squares versus number of parameters included in the fit for the Nominal case with alloy C-22 inner 

overpack and a time period of interest of 10 kyr.

(I) 

U) 

0 

E 

:3

e WP-Def% 

* Fmult 

* AAMAI@S 

SSMO-JS4

a

0 0 a 0

0 0.0

0

00

20 30
0 

0 

(0 

0

0

z 

I 

ON 

OrN 

00

10 20 30 40

Number of Parameters Included in Fit

Figure 4-5. Plot of the residual sum of squares versus number of parameters included in the fit for the Nominal case with alloy 625 inner 

overpack and a time period of interest of 50 kyr.

0 

0 

0. 

01

e SFWttd% 

ARDSAVNp 

SbArWt% 

*O0-Coflc 

WprCg@20 

Chlorid 

*ARDSAVAm 

00., ~ *** ***

U) 

09 

E 

U)

0 

0~ 

C\j 

0 

0 

0 

0 

0

CO

ON
0 

0

(0 C\1 

T'

0 

0) 0 

0 

0 

0

I 

0 10 20 30 40

Number of Parameters Included in Fit 

Figure 4-6. Plot of the residual sum of squares versus number of parameters included in the fit for the Nominal case with alloy C-22 inner 

overpack and a time period of interest of 50 kyr.

* ARDSAVNp 

* SFWttd% 

* SbArWt% 

*AA2-1 

* O0-Coflc 

* WprCg@20 " "0 0' ' ' 0 0 ' ' 0*,,,,

C€ 

C,, 

0 

E 

C1

O 

Co 

0 0t

ON 

00
The results for nornaalized and standardized variables are somewhat different for each of the four 

cases. The most sensitive variables using standardization appear to be Fow and Fmult for a TPI of 10 kyr. 

In the alloy C-22 case, there are no failures from corrosion at 10 kyr, so no corrosion parameters appear 

sensitive. Results with stairidardized variables for 50 kyr differ from the 10-kyr results by having more 

emphasis on WP corrosion and retardation factors for long-lived radionuclides such as neptunium and 

americium. 

Table 4-5 also shows the standardized regression coefficients, which were based on the regression 

coefficients for the log of the normalized variables, as calculated in Eq. (4-3). 

4.2.2 Scatter Plots/Liiirear Regression for Parameters Correlated with Estimated Peak 

Total Effective Dose Equivalent for the Time Period of Interest 

Scatterplots and single linear regressions of the log of peak TEDE for the TPI versus the log of 

individual parameter values are shown in Figures 4-7 through 4-10, for six parameters determined from the 

screening to be sensitive parameters. These plots serve as visual authentication that the choice of the most 

influential parameters is reasonable. TPIs of 10 kyr and 50 kyr were considered for alloys 625 and C-22 inner 

overpack material. 

4.3 DIFFERENTIAL ANALYSES 

4.3.1 Introduction to Differential Analysis 

Regression analysis on the Monte Carlo results can only determine the most sensitive parameters 

when those parameters also have large enough correlation coefficients that they are distinguishable from the 

confounding effects of the simultaneous sampling of all other independent variables. Differential analysis 

determines sensitivity unambiguously because it deals with changes in only one independent variable at a 

time. Differential analysis determines sensitivity of parameters only at local points in parameter space and 

does not consider the wide range of parameter variations as does the Monte Carlo method. This section 

describes the results of a diftkrential analysis that was conducted to determine the most sensitive parameters 

with respect to peak TEDE. 

Differential analyses were performed using TPA 3.1.4 with seismicity activated (i.e., the Nominal 

case). Seismicity is anticipated to occur with sufficient frequency that it is considered an integral part of the 

repository evolution through time, not an unlikely disruptive event like volcanism and faulting, and was 

therefore included in all cases. For alloys 625 and C-22, the sensitivity of parameters were evaluated for both 

a 10- and 50-kyr TPI. Cases; where faulting and volcanism were activated in the TPA code were modeled 

separately. 

4.3.2 Differential Analysis Techniques 

Differential analysis tests were conducted through multiple deterministic runs in which a single input 

parameter was changed by a known amount compared to its initial nominal value, and all other input 

parameters were held at a nominal baseline value. The sensitivity of a performance measure (in this case,

NUREG-1668 4-18
0• .

Y 0t . . . . . . . . . = MO + M1*X 

MO -7.732 

S-2 Ml i 4.0874 * - .-. *. 

R 0.41138 ". . A .. *. 

-4*~ 

-6 . ... _ .-. . - : 

.. 8 

-10 

-12 . . . . . . . . . 

0 0.2 0.4 0.6 0.8 

Log of Areal Average Infiltration 

-2 

-6. 

-*. -,4*;' :w' 

9L.9 

[ MO +MI* . 

MO -0.86429 * 

-10 MI M1 1.e466 

0.19~726 

-3.2 -3 -2.8 -2.6 -2.4 -2.2 

Log of Coefficient for Localized Corrosion 

0 

* 9 

-2 - . . 

o JYMO +MI"X 

_1 N.I !'098171 

-12 . 

0.]39 

-102 l0.11

0 

-4 

-1 

-8 

-10 

-12 

-8 

-0

-2

-2.5 -2 -1.5 -1 -0.5 0 

Log of Subarea Wetted Fraction

-3.5 -3 -2.5 -2 -1-5 -1 -0.5 0 

Log of Chloride Multiplication Factor

-2.5 -2 -1.5 -1 -J.b U U.0 -2 -1.8 -1.6 -1.4 -1.2 -1 

Log of Fow Factor Log of Fmult Factor

-0.8 -0.6

Figure 4-7. Scatter plots and single linear regressions of the log of peak total effective dose equivalent 

for the time period of interest versus the log of parameter value for the Nominal case with alloy 625 

inner overpack and a time period of interest of 10 kyr. Only non-zero realizations are displayed.

4-19 NUREG-1668
-2 

-4 

S-8 

S-10 

0-12 

-14 

-16 -4

-16 -3 -3 .. -2.5 2 .. -2 -2 -1.5... -1.5 .. -1 . .-0.5 5 0 0

-2.5 -2 -1.5 -1 -0.5 

Log of SF Wetted Percentage

-2

-4 

S-10 

,_•-1 g =MO + M1 

MO -4. 

-14 M1 0.89 "MRO 0.255 

-16 ....... .. . . 

-2 -2 -1.5 -1 -0.5 0 

Log of Fow Factor

-2

-4 

.- 10 

S-12 

-14

-16 0

-2 

S-8 

Iro 

-12 

~-10 

-14 

-16 

0 -2-2

0.5

0.2 0.4 0.6 0.8 1

-1.8 -1.4 -1 -0.6 

Log of Fmult Factor

Figure 4-8. Scatter plots and single linear regressions of the log of peak total effective dose equivalent 

for the time period of inteirest versus the log of parameter value for the Nominal case with alloy C-22 

inner overpack and a time period of interest of 10 kyr. Only non-zero realizations are displayed.

NUREG- 1668

-3.5 -3 -2.5 

Log of WP Ik:fective Percentage

-2 

-4

-6

-8

-14

-2 

-4 

- -8 

1 -10 

0. 

S-12 

0 

-14

-16 -3

4-20
0 0

- M-2 o2.1898 

i 1.26.01" 

II~ 0.484673| E. [ .* .. .*,. 

0.5 . -4. ""•-.

Y =MO + Ml-X 4 6I• " " 

I~ '9 

-7 Ml -0.78911 -7 

R- 0.4 3, 

Log of Alluvium Maxtrix R dfor Np Log of Spent Fuel Wetted Fraction 

V M* -7° *• • : 

.M -2.323 *,, 

2 0.94619. M 

0.38833 9 27 *w 

E . o •. ,ii Al 

-. . -, . '-. "'*% 

%~~~~~ MI + M -1-*ji + 

M1 1.3792 

S-7 . L 1-"• 

.8_ . .. . .+.. , .. .. 

" "- -4 -3 -2 -1 0 -72 6-4- 21.6 

Log of Subarea Wetted Fractin Log of Coefficient for Localized Comtmo 

o0 

-3

0 0 

-8 ý m & .~ - . . *I .M- . - - I.... ...... 

6.6 6.7 6. 6. 79.%20. . 

Figure 4-9. Scatter plots and single linear regressions of the log of peak total effective dose equivalent 

for the time period of interest versus the log of parameter value for the Nominal case with alloy 625 

inner overpack and a time period of interest of 50 kyr.

4-21 NUREG-1668
-2 

-.3 

-45 

0 

-6 

-7 

0 

-1 

"-2 

•-3 

.4 

-6 

-6 

-7 

0 

-1 

-2 

-6 

-7

0 0.5 1 1.5 2 

Log of Alluvitum Maxtix R d for Np

-3 -2.5 -2 -1.5 -1 -0.5 0 

Log of Subazea Wetted Fraction

4.3 4.4 4.5 4.6 

Log of AA_2_1 Coefficient

0

-1 M -2-4964 

M1i SR 1.1841' 0.47668 ÷•••••' 

°° ~:"$." 'p=•, 

C~-3

*' '* - . • '• °°"* ,. . • 

oo-5 • 

-6 

-7 

2.5 -3 -2.5 -2 -1.5 -1 -0.5 0 

Log of SF Wetted Fraction

0 

-1 

-3 

-4 

,,..J 0 

0 

-7

0

C 

0. 

0• 

0.

-32 -3 -2.8 -2.6 -2.4 -2.2 -2 

Log of Coefficient for Localized Corrosion

4.7 4.8 6.6 6.7 6.8 6.9 7 

Log of Well Pumping Rate

7.1 7.2

Figure 4-10. Scatter plots and single linear regressions of the log of peak total effective dose equivalent 

for the time period of interest versus the log of parameter value for the Nominal case with alloy C-22 

inner overpack and a time period of interest of 50 kyr. Only non-zero realizations are displayed.

NUREG-1668 4-22
peak TEDE for the TPI) to a parameter is estimated as the first derivative of the performance measure with 

respect to that parameter: 

aD D(x+1 Axi)-D(xi) 

S Ax (4-9) 

Usually Axi is relatively small (e.g., 10 percent of the parameter value). These estimates of sensitivity are 

local (i.e., the value of the derivative may change at different points in the sample space). To partially 

alleviate this concern, the derivative may be evaluated at several points in the sample space. In the analyses 

presented herein, the derivative is transformed in one of two ways to allow for comparison of sensitivity 

coefficients between parameters whose units may differ. The first transformation is as described by the 

following equation: 

aD xi 

Si = (4-10) a Xi 15 

where Si is the unitless normalized sensitivity coefficient. These normalized sensitivity coefficients are in 

the same form as the sensitivities defined by the regression analyses with the log of the normalized variables. 

Because S. does not account for the range of the input parameter, a second transformation of the derivative 

is also performed where the derivative is multiplied by the standard deviation of the input parameter 

distribution. 

Baseline cases were run with input parameter values set at the mean, 10 'h percentile of each 

parameter distribution range, 90t percentile of each parameter distribution range, and at two other randomly 

selected points within each parameter distribution range. This choice of baseline values was made in an 

attempt to adequately span the parameter space while limiting the number of computations. 

Evaluated perturbations were selected to maintain parameter values in their respective defined 

ranges; the 10t' percentile initial constant series input parameters were perturbed by +10 percent, the 90' 

percentile initial constant series input parameters were perturbed by -10 percent; the mean value initial 

constant series input parameters were perturbed by +10 percent, while the first random set was perturbed 

- 10 percent; and the second random set was perturbed by +10 percent. The structure of the differential 

analysis runs is illustrated in Figure 4-11, starting with the analysis type (differential) followed by scenario 

case, TPI, baseline value, and perturbation. The percent perturbations are with respect to the baseline (i.e., 

local) parameter value. 

In TPA 3.1.4, transport through the UZ stratigraphic units is neglected for those units where 

groundwater residence time is less that 10 yr or 10 percent of the residence time for the entire UZ below the 

repository (see Volume I). Differential analyses, in which UZ transport calculations are omitted because of 

this assumption, will result in peak TEDE showing no sensitivity to parameters that describe UZ properties 

in those stratigraphic units excluded from the transport calculations. For example, when all parameters were 

set at their mean values, the UZ portion of NEFTRAN was skipped for a majority of the subareas, so sampled 

unsaturated zone flow and transport (UZFT) parameters did not show any sensitivity in these runs.

4-23 NUREG-1668
NUREG- 1668 4-24
As illustrated in Figure 4-11, the third group of base values set all the parameters at their 

109 percentile. With this set of input values, the WPs did not fail from corrosion in the 10-kyr performance 

period, but did fail within the 50-kyr time period, for alloys 625 and C-22 as the inner overpack material. The 

baseline dose values in these cases are caused solely by initially defective WPs. Again, the UZ portion of 

NEFTRAN was skipped for most subareas. 

The fourth and fifth groups of base values used parameters chosen at two random points in the 

parameter space. The selection of each random value yields runs similar to one realization of a probabilistic 

TPA code run. The first set of values resulted in skipping a few stratigraphic units in the UZ legs of the 

NEFTRAN code, so these runs were able to give a reasonable estimate of the sensitivities of the sampled 

UZFT parameters. For the second random point, the majority of subareas again skipped the UZ in the 

NEFTRAN code, so this set of runs is not expected to evaluate the sensitivity of the sampled UZFT 

parameters. 

4.3.3 Differential Analysis Results 

A total of 158 input parameter values was perturbed for each series. The input parameters perturbed 

are defined by a distribution in the Nominal case tpa.inp input file. The parameters sampled in the tpa.inp 

file are the ones for which there remains a significant amount of uncertainty in their value or that have been 

shown to be potentially significant to estimating peak TEDE in the process-level sensitivity analyses. 

Several parameters were included in the differential analysis that are being treated as constants in 

the Monte Carlo analysis. Previous process-level sensitivity studies showed that the cladding correction 

factor and dispersion fraction in the alluvium and vitric tuff areas of the SZ might have a significant effect 

on peak TEDE but were not among the most sensitive parameters. A list of all the parameters that were 

varied in the differential analysis, and the baseline values for these parameters, can be found in 

Appendix D. 

Each set of base values was used in a TPA code run to determine the reference value of peak TEDE 

necessary to calculate several sensitivity measures. The baseline value peak TEDEs for alloy 625 are shown 

in Table 4-6. The baseline value peak TEDE for alloy C-22 can be found in Table 4-7. These Tables show 

that all runs used C-22 as the inner overpack material result in peak TEDE for the TPIs that are equal to or 

less than the corresponding run using alloy 625. 

The results of the differential analysis are shown in the following tables, for TPIs of 10 and 50 kyr, 

in Appendix E: Tables E-1 and E-2, for the Nominal case for alloy 625; Tables E-3 and E-4, for the Nominal 

case plus faulting for alloy 625; Tables E-5 and E-6, for the Nominal case plus volcanism for alloy 625; and 

Tables E-7 and E-8, for the Nominal case for C-22. Variables that were tested but showed zero sensitivity 

at all baseline values are not shown in the Tables. No disruptive event analyses were conducted for alloy C

22 as the inner overpack material because the results are not expected to differ significantly from the alloy 

625 case. 

Tables E-1 through E-8 show the sensitivity calculated in four different ways: 

(1) The geometric mean of the absolute value of the sensitivity coefficientS, [see Eq. (4-10)] 

was calculated for the five base values (mean, 10d percentile, 90'h percentile, random 1, and 

random 2). The geometric mean is useful for emphasizing parameters that are sensitive over 

the entire range of base values. In cases where the sensitivity coefficient was "zero"

4-25 NUREG-1668
Table 4-6. Summary of Base Value Peak Total Effective Dose Equivalent for the Differential Analysis 

for Alloy 625 

Description of Run Base Value Peak TEDE (Sv)" 

Mean values, no disruptive events, 11 0-kyr time period 7.5 x 10' 

Mean values, no disruptive events, '50-kyr time period 7.0 x 10-' 

Mean values, faulting, 1O-kyr time period 8.0 x 10-7 

Mean values, faulting, 50-kyr time period 7.0 x 10-1 

Mean values, volcanism, 10-kyr time period 0.011 

Mean values, volcanism, 50-kyr timre period 0.0035 

90'" quantile values, no disruptive events, 10-kyr time period 5.9 x 10-' 

90' quantile values, no disruptive events, 50-kyr time period 5.9 x 10-1 

90"' quantile values, faulting, 10-kyr time period 6.1 x 10' 

90' quantile values, faulting, 50-kyr time period 6.1 x 10-1 

90", quantile values, volcanism, 10-kyr time period 0.002 

90"' quantile values, volcanism, 50-kyr time period 7.5 x 10-' 

10' quantile values, no disruptive events, lO-kyr time period 4.3 x 10-10 

10"' quantile values, no disruptive events, 50-kyr time period 7.2 x 10-6 

10"' quantile values, faulting, lO-kyr time period 4.3 x 10"0 

10' quantile values, faulting, 50-kyr time period 7.2 x 10

10' quantile values, volcanism, 10-kyr time period 4.7 x 10" 

10"' quantile values, volcanism, 50-kyr time period 1.6 x 10' 

Random 1 values, no disruptive events, 10-kvr time period 5-2 x 10-' 

Random I values, no disruptive events, 50-kyr time period 1.2 x 10' 

Random i values, faulting, IO-kyr imae period 5.2 x 10-' 

Random I values, faulting, 50-kyr omne period 1.2 xl0' 

Random I values, volcanism, 10-kyr time period 0.011 

Random I values, volcanism, 50-kyr time period 0.0031 

Random 2 values, no disruptive events, 10-kyr time period 5.4 x 10' 

Random 2 values, no disruptive events, 50-kyr time period 2,0 x 10-' 

Random 2 values, faulting, 10-kyr time period 7.9 x 10' 

Random 2 values, faulting, 50-kyr time period 2.0 x10' 

Random 2 values, volcanism, 10-kYr time period 7 3 x 10-' 

Random 2 values, volcanism. 50-kyr time period 4.9 x 10-' 

"Conditional results not weighted by the scenario probability.

NUREG- 1668 4-26
Table 4-7. Summary of Base Value Peak Total Effective Dose Equivalent for the Differential Analysis 

of Alloy C-22

(i.e., smaller than the least significant digit in code output) at a base value, the geometric 

mean is an upper estimate for that parameter. 

(2) The arithmetic mean of the absolute values of S. was calculated for the five base values. 

This measure would be the mean estimate of sensitivity if all points were equally probable. 

It is unlikely that each point is equally probable because the 10 ' and 90'h percentiles for each 

parameter are baseline values that are highly unlikely combinations. Nevertheless, the 

arithmetic average of Si represents the least-biased measure of sensitivity, given that the 

probability of the five points cannot be defined. 

(3) Highest sensitivity of S. is calculated at any of the five points. This sensitivity measure is 

useful to determine if the parameter is sensitive at any of the five points. 

(4) Arithmetic mean of derivative weighted by the standard deviation of the input parameter. 

This sensitivity measures the response of peak TEDE to each of the independent variables 

weighted by their standard deviation. The standard deviations are determined by the 

parameter range and distribution used in the Monte Carlo analyses. This measure takes into 

account the magnitude of the change in peak TEDE and the uncertainty in the independent 

variables. For comparison, the normalized sensitivity measure S, is a relative sensitivity 

where the slope is scaled by the local values of dose and the independent variable. Therefore 

S, does not depend on whether the baseline dose is small or large, but only on the change 

in dose relative to the change in the independent variable.

NUREG-1668

Base Value Peak TEDE 

Description of Run (Sv)* 

Mean values, no disruptive events, 10-kyr time period 1.6 x 10-' 

Mean values, no disruptive events, 50-kyr time period 1.6 x 10-5 

90,h quantile values, no disruptive events, 10-kyr time period 3.7 x 10-6 

90"h quantile values, no disruptive events, 50-kyr time period 1.7 x 10-5 

10

O quantile values, no disruptive events, 10-kyr time period 4.3 x 10-`0 

101h quantile values, no disruptive events, 50-kyr time period 7.2 x 10-6 

Random 1 values, no disruptive events, 10-kyr time period 1.1 x 10-8 

Random 1 values, no disruptive events, 50-kyr time period 3.1 x 10-' 

Random 2 values, no disruptive events, 10-kyr time period 0.0000"* 

Random 2 values, no disruptive events, 50-kyr time period 6.0 x 10-6

*Conditional results not weighted by the scenario probability 

**No dose recorded because no WPs fail or are initially defective. No differential analysis conducted.

4-27
Measure 4 was used to sort the rows in descending order because it reflects both the absolute value 

of peak TEDE and the unce:rtainty in the independent variables. The other three sensitivity parameters are 

also given in the tables provided in Appendix E. The lists of primary sensitive parameters are generated 

based on the top 10 parameters for the four Nominal cases (i.e., alloy C-22 and alloy 625 at 10- and 50-kyr 

TPIs) and are discussed in the following section. 

The tables in Appendix E provide a list of the parameters that showed non-zero sensitivity at any of 

the five baseline values abouat which the derivatives were evaluated. Some of the sensitivities shown, 

however, were exceedingly small. To focus attention on the parameters to which peak TEDE showed the 

largest sensitivity, for the current models on which this report is based, Table 4-8 lists the primary sensitive 

parameters (i.e., the top 10 parameters for the Nominal case based on the mean of sigma-weighted values) 

for the two inner overpack materials and two TPIs. The primary sensitive parameters for the disruptive 

scenarios were determined by including any parameter whose sensitivity was within 1 order of magnitude 

of the most sensitive parameter from the Nominal case using measure 4. For the volcanism scenario, all 

sampled parameters are prirriary, and for the faulting scenario, no sampled parameters are primary. 

Sampling ranges of input parameters may change in the future as more is learned about the proposed 

repository system. Changes to the sampling ranges of the primary sensitive parameters (either expansion or 

contraction) should be made with greater caution than for other parameters, since peak TEDE for the TPI 

shows the largest change per unit deviation in these parameters. 

4.3.4 Comparing Sensitivity Coefficients from Regression Analysis and Differential 

Analysis 

Regression analysis was performed on the results of the probabilistic TPA 3.1.4 runs to estimate the 

sensitivity of peak TEDE to the independent variables. Similar analysis using differentials was conducted 

as described in the preceding section. As noted previously, the normalized sensitivities from the differential 

analysis are analogous to the coefficients of the regression equation using the logs of the normalized doses 

and independent variables. Table 4-9 lists the sensitivity values determined with statistical analysis of 

probabilistic TPA 3.1.4 runs, using the logs of the normalized variables and those calculated from 

deterministic code outputs. Differences between sensitivity coefficients generated using the two methods are 

largely caused by the fact that the differential analysis determines sensitivities to local values of the 

independent variables (with the arithmetic average presented in Table 4-9), whereas the regression analysis 

determines sensitivity averaged over the entire range of the sampled parameters. Again, these sensitivity 

coefficients depend on both the models in the TPA 3.1.4 code and the methods used to calculate them. 

4.4 OTHER ANALYSIS RESULTS 

4.4.1 Sensitivities of Peak Total Effective Dose Equivalent for the Time Period of 

Interest to Intermediate Code Outputs 

Preliminary analyses using the TPA 3.1.2 code showed that peak TEDE for the TPI is correlated with 

cumulative radionuclide release summed over all radionuclides [total cumulative release or (TCR)]. To 

illustrate the correlation of peak dose with TCR, scatter plots and linear regressions of TCR from the EBS, 

UZ, and SZ, for TPIs of 10 and 50 kyr, were made using data generated by the TPA 3.1.4 code from 

400 vector runs of the TPA code for the Nominal case, which includes seismicity.

NUREG- 1668 4-28
Table 4-8. Primary Sensitive Parameters (i.e., Mean of Sigma-Weighted Sensitivities, Si for the Five 

Baseline Values)

Figure 4-12 displays six scatter plots including results of linear regressions. The first row of figure 

4-12 presents plots of the common logarithm of peak TEDE for the TPI versus the common logarithm of 

TCR from the EBS, whereas the second and third rows of Figure 4-12 present the same plots for the UZ and 

Sz.

NUREG-1668

Alloy 625 Alloy C-22* 

10-kyr TPI 50-kyr TPI 10-kyr TPI 50-kyr TPI 

AAMAI@S ARDSAVAm SSMOHetF ARDSAVAm 

Fow ARDSAVNp SSMOJS4 ARDSAVNp 

SbArWet% SFWttd% AAMAI@S SFWttd% 

Fmult MkdCHnNp Fow SbArWt% 

00-Coflc Fow SbArWt% WprCg@20 

SFWttd% SbArWet% ARDSAVAm AA_2_1 

WprCg@20 SolubNp ARDSAVNp SSMOHetF 

InitRSFP WprCg@20 WprCg@20 InitRSFP 

ARDSAVAm InnOvrEI SFWttd% MkdCHnNp 

ARDSAVNp InitRSFP MKdCHnNp 00-Coflc 

Volcanism Parameters 

WindSpd WindSpd 

ABMLFVDC ABMLFVDC 

VE-Power VE-Power 

VE-Dur VE-Dur 

VC-Dia VC-Dia 

AshMnPLD AshMnPLD 

VEROI-Tn VEROI-Tn 

Faulting Parameters 

None None 

*No disruptive event differential analysis was conducted with alloy C-22 because the results were not expected to change 

dramatically

4-29
Table 4-9. Sensitivity Coefficients Sifor the Identified Parameters (Diff. = Differential Analysis and 

Reg. = Regression Analysis with Log Si of Normalized Variables) 

Sensitivity Sensitivity Sensitivity Sensitivity 

Coefficient for Coefficient for Coefficient for Coefficient for 

Alloy 625 and Alloy C-22 and Alloy 625 and 50- Alloy C-22 and 

Variable jq1kyv- TPI 10-kyr TPI kyr TPI 50-kyr TPI [ Diff. Reg. Diff. Reg. Diff. Reg. Diff. Reg. 

SbArWet% 0.99 1.035 1.00 1.057 0.99 0.9803 1.0 1.003 

Fow 10 1.79 2.8 1.483 0.28 -0.038 

Fmult 10 2 2.8 1.422 0.28 0.038 0.959 

00-Coflc 39 1.26 0.41 1.1578 0.028 0.605 

AAMAI@S 11 1.33 2.9 1.036 0.23 -0.043 0.0158 

MKdTswAm 0.554 

ARDSAVNp -0.54 -0.55 -0.183 -0.95 -0.8297 -0.96 -0.934 

SFWttd% 5.4 3.1 0.79 1.1704 -0.98 1.281 

WprCg@20 -1.00 -1.0 -1.00 -1.2747 -0.99 -1.167 

Chlorid 0.4737 

ARDSAVAm -0.024 -. 30 -0.013 -0.0925 -0.012 -0.0834 

AA_2_1 0.91 1.221 

SSMO-JS4 1.2 1.958 0.94 

WPDef% 6.4 1.043 0.0034 -0.022 -0.0287 

The first column of Figure 44-2 is for a TPI of 10 kyr, and the second column is for a TPI of 50 kyr. The first 

column includes only those vectors for which there was non-zero peak TEDE because the logarithm of zero 

is undefined. For a TPI of 50 kyr, no realizations produced zero dose. 

To determine the rel[ative important of the subsystems to system performance, a multilinear fit of the 

output was performed, and the t-statistic was calculated using the log of TCR from the EBS, UZ, and SZ as 

the input variables3 and the l.og of peak TEDE for the TPI as the output variable for TPIs of 10 and 50 kyr. 

The functional form of the fit is given as*:

log(peakdose) = mf log(TCRE~s) + m 2log(TCRuz) + m3 log(TCRsz) + b (4-11)

*TCREBS, TCRuz, and TCRsz, are likely correlated.

NUREG-1668 4-30
4) S-10 

0 

S-20 

-25 

0 

-6 

0-10 

cK -15 

3-20 

-25 

-30

0

4) 

0 

4.. 

0

. A . , 1 I . . . I . . . . -.. . .. . -

I * -1 25 -20 -15 -10 -5 0 5 

-og of Cumulative Release from the EBS

-1 

-2 

-3 

-4 

-5 

-6

-7 

-8 

0 

-1

0 

a) 0

0

-2 

-3 

-4 

-5 

-6 

-7 

-8

Log of Cumulative Release from the UZ

-2 -1 0 1 2 3 4 5 6 

Log of Cumulative Release from the EBS

-2 -1 0 1 2 3 4 5 6 

Log of Cumulative Release from the UZ

0

-1 

S-2 

•-4 

0

- - Y-MO+M1'X 

-6 MO -5.3723 

-7 MI 0.77172 

R 0.71393 

Log of Cumulative Rlaefrom th SZ6

Log of Cumulative Release from the SZ

Figure 4-12. Plots of the log of peak total effective dose equivalent for the time period of interest, 

versus the log of total cumulative release from the engineered barrier system, unsaturated zone, and 

saturated zone for time period of interest of 10 and 50 kyr for the Nominal case.

NUREG-1668

S-10 10 

•-15 

0 

3-20

4-31
Table 4-10. The Results of the Multilinear Fit of the Log of Peak Total Effective Dose Equivalent for 

the Time Period of Interest Versus the Log of Total Cumulative Release from the Three Subsystems 

for the Nominal Case with Alloy 625 

Coefficient 'Value - Standard Error t-statistic R value 

10-kyr TPI* 

m, 0.033 0.038 0.87 

M2 D.655 0.044 14.86 

0.969 

M3 0.412 0.027 15.25 

b -5.985 0.114 52.55 

.. _ 50-kyr TPI* 

Dt,.987 0.004 203.9 

M2 -0.01 0.002 -5.53 0.9996 

M3 1 9.101 0.031 3.21 

b 1 3.064 0.007 9.14 

* Only non-zero realization:; included in fit. 

Table 4-10 displays the results of this fit, along with t-statistic values for the Nominal alloy 625 case. 

The statistical parameters for a TPI of 10 kyr may be a less reliable indication of performance since they only 

include non-zero realizations, and therefore there are fewer results on which to base statistical hypothesis. 

The remainder of this discussion is based on the 50-kyr results. 

Examination of these parameters for the 50-kyr TPI shows the EBS and SZ to be relatively important 

subsystems for repository performance, whereas the UZ is relatively unimportant. Although the t-statistic 

for mn2 (the slope parameter for the UZ) is fairly high, the slope is very small and negative (which is not 

possible based on physical arguments), indicating that TCRuz is probably not important. The relative 

importance of the UZ could be influenced by an assumption made in the TPA 3.1.4 code that could lead to 

bypassing the UZ. Transport through the UZ stratigraphic units was neglected for those units where the 

groundwater residence time :Is less than 10 yr or 10 percent of the residence time for the entire UZ below the 

repository. 

4.4.2 Most Importanit Radionuclides Based on These Analyses 

Two bar charts of the mean peak TEDE by nuclide, for the Nominal case, with alloy 625 as the inner 

overpack material, were prepared for TPIs of 10 kyr and 50 kyr (Figures 4-13 and 4-14). For a time period 

of 10 kyr, the following isotopes contribute to peak TEDE (in descending order): 37Np, 245

Cm, 243Am, 24 6Cm, 2 41

Am, 1291, and 99Tc, with '

31

Np and 245

Cm dominating peak TEDE. For a TPI of 50 kyr, the following 

isotopes contribute to peak TEDE (also in descending order): 237

Np, 24

"Am, 245Cm, 1291, 99Tc, 2 46Cm, 24'Am, 

and 2 39 pU with 2 37Np, 243

Am, and 245Cm dominating peak TEDE. In both cases, 2 37

Np appears to contribute 

the most to peak TEDE.

NUREG-1668 4-32
36C1 

94Nb 

7 9Se 

14C 

59Ni 

99T 

1291 

135Cs 

2 10 pb 

226Ra 

230Th 

234U 

A 240p-U 

239pu 

243Am 

2

37Np 

241AM 

245 Cm 

238U 246CM, 

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 

Mean Peak Dose for Nuclide (reni/yr) 

F igure 4-13. A bar chart of the mean peak total effective dose equivalent of 400 realizations per radionuclide for the Nominal case (alloy 625 

r inner overpack) and a 10-kyr time period of interest. 

C*' 

CO

| ! !
z 

36C] 

0 -- 94Nb 

ON 7 9

Cý Se 00 14C 

59Ni 

99T 

1291 

l

3 5Csr 

210pbo 

22 6

Ra 

230Th 

234U 

,A 240pu 

-A 239pu 

243Am 

237Np 

241 Am 

245CnM i 

238U 

246Cm 

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 

Mean Peak Dose for Nuclide (rem/yr) 

Figure 4-14. A bar chart of the mean peak total effective dose equivalent of 400 realizations per radionuclide for the Nominal case (alloy 625 

inner overpack) and a 50-kyr time period of interest.
5 RELATIVE IMPORTANCE OF THE NUCLEAR REGULATORY 

COMMISSION KEY ELEMENTS OF SUBSYSTEM ABSTRACTION 

Table 5-1 presents a crosswalk between the top parameters affecting uncertainty and the primary sensitive 

parameters to the key elements of subsystem abstraction previously specified in the Rev. 0, of the Total 

System Performance Assessment and Integration Methodology KTI Issue Resolution Status Report (Bell, 

1998b). In addition to the parameters, the alternative conceptual models investigated (see Tables 4-1 through 

4-3, and Figures 4-3 through 4-4) are cross-referenced in Table 5-1. The column labeled "Parameters 

Affecting Uncertainty" lists parameters that had significant impact on reducing RSS in the regression 

analysis. The column labeled "Primary Sensitive Parameters" are parameters that were found to have large 

arithmetic averages for the standard deviation weighted derivatives from the differential analysis described 

in Chapter 4 of this report. 

A few key points should be kept in mind when examining these tabulated results: 

0 All analysis results are based on the models and reference input values used in the 

TPA 3.1.4 code. Volume I of this report gives a description of the conceptual models, lays 

out the key assumptions, and lists the reference input values. 

0 Only two water reflux models (one where matrix waters are immobile during the thermal 

pulse and one where they are boiled off during the thermal pulse) have been incorporated 

in TPA 3.1.4. 

0 No credit is given to retardation or matrix diffusion in the UZ. 

0 The DCFs are kept as constants in all the analyses performed, which implies that the 

lifestyle of the receptor group is more or less fixed. 

0 The receptor group is located 20 km from the repository and uses contaminated groundwater 

for drinking and limited farming activities. 

* Different parameters may be important for different TPIs. These important parameters are 

delineated by TPI in chapter 4 of this report. 

0 Different parameters may be important for different inner overpack materials. These 

important parameters are delineated by TPI in Chapter 4 of this report. 

Note that in Table 5-1. there are a number of parameters that influence variability in peak TEDE for the TPI 

that do not also appear as primary-sensitive parameters. This may be an indication that the parameter only 

contributes to performance because of a relatively large sampling range. Variability of peak TEDE in the TPI 

may be significantly reduced by allocating resources for better defining these parameter ranges. 

The following conclusions provide an indication of which key elements may deserve more attention, given 

limited resources. Because the model abstractions are preliminary and data are continuously being updated, 

results shown in this report provide a snapshot of the existing relative standing and should not be used alone 

to determine the significance of any of the key elements of abstraction.

5-1 NUREG-1668
Table 5-1. A Crosswalk Between the Key Elements of Abstractions, Alternative Conceptual Models, 

Parameters Affecting Uncertainty, and the Primary Sensitive Parameters 

Key Elements Alternative Parameters Primary 

of Models Affecting Sensitive 

Abstractions Investigated Uncertainty Parameters 

Quantity and chemistry of Bkfill, C22mult, Clad, SFWttd%(1-4), Fmult(1,3), Fmult(l), Fow(1,3,4), 

water contacting WPs and Flwthru, Flwthru2, Fow(1,3) SFWttd%(1-4) 

waste forms Focflow 

WP corrosion (temperature, C22base, Totdef AA_2_I, (4) Chlorid (2), AA_2_1(4), OO-Coflc(1,4), 

humidity and chemistry) 00-Coflc (1,2,4), InnOvrEI(2) 

WPDef% (3) 

Mechanical disruption of WPs Nominal-F SSMO-JS4(3), SSMOHetF(3,4), 

(seismicity, faulting, rockfall SSMOHetF(3,4) SSMOJS4(3) 

and dike intrusion) 

Radionuclide release rates and Bkfill, C22mult, Flwthru, InitRSFP(1,3,4), 

solubility limits Flwthru2, Natan SolubNp(2) 

Spatial and temporal Focflow AAMAI@S(I-4), AAMAI@S(l,3), 

distribution of flow SbArWet%( 1-4) SbArWet%(1-4) 

Distribution of mass flux Not evaluated 

between fracture and matrix 

Retardation in fractures in the Not evaluated No retardation or matrix diffusion in the unsaturated zone 

unsaturated zone 

Flow rates in water production Not evaluated 

zones 

Retardation in water Matdif, NoRd ARDSAVAm(2,4), ARDSAVAm (1-4) 

production zones and alluvium ARDSAVNp(2,4) ARDSAVNp (1-4) 

MkdCHnNp (2-4) 

Volcanic disruption of WPs Nominal-V VC-Dia(1,2), 

VE-Power(1,2), 

VE-Dur(1,2) VEROI-Tn( 1) 

Airborne transport of Nominal-V ABMLFVDC(1,2), 

radionuclides AshMnPLD(1,2), 

WindSpd(1,2) 

Dilution of radionuclides in Not evaluated WprCg@20(2,4) WprCg@20(l-4) 

groundwater due to well 

pumping 

Dilution of radionuclides in Not evaluated 

soil due to surface processes DCFs were set as constants 

Critical group location and Not evaluated 

lifestyle 

(1) Parameter found important for alloy 625 and a TPI of 10 kyr. 

(2) Parameter found important for alloy 625 and a TPI of 50 kyr. 

(3) Parameter found important for alloy C-22 and a TPI of 10 kyr. 

(4)Parameter found important for alloy C-22 and a TPI of 50 kyr.

NUREG-1668 5-2
10 kUr and Alloy 625 as the Inner Overpack Material

The results have shown that total system performance is most sensitive to the performance 

of the engineered barriers (WP corrosion, and quantity and chemistry of water contacting 

WPs and waste forms), and the spatial and temporal distribution of flow. 

The alternative conceptual model that assumes failure of WPs at time zero produced a much 

higher peak TEDE than the Nominal case, illustrating the importance of characterizing the 

performance of the WP material and consideration of early failures (WP corrosion). 

The alternative conceptual model that assumes partial cladding protection produced a much 

lower peak TEDE than the Nominal case, which illustrates the need to improve modeling 

capability and focus reviews in this area if DOE decides to take credit for cladding (quantity 

and chemistry of water contacting WPs and waste forms). 

Choice of the release model (bathtub or flowthrough) has an effect on total system 

performance. The fuel dissolution rate also has an effect on total system performance 

(radionuclide release rates and solubility limits). 

Although the most sensitive parameters do not include parameters from the SZ, the 

alternative conceptual model that assumes no retardation in the SZ produced a much higher 

peak TEDE than the Nominal case and illustrates the importance of evaluating RT in the SZ. 

In comparison with retardation, matrix diffusion does not have nearly as pronounced an 

effect on the system performance (retardation in water production zones and alluvium). 

The peak TEDE resulting from the volcanism scenario class is comparable to the Nominal 

case after being weighted by its probability (volcanic disruption of WPs, airborne transport 

of radionuclides). 

10 kyr and Alloy C-22 as the Inner Overpack Material 

This model showed that factors causing WPs to fail by mechanisms other than corrosion 

play a much more important role because of resistance to localized corrosion of alloy C-22. 

Total system performance is sensitive to the percent of initial defective WPs and rockfall 

caused by seismicity. Consistent with the process-level sensitivity studies, repository 

performance is not sensitive to instantaneous fault displacement on new orunderappreciated 

faults (mechanical disruption of WPs). 

Total system performance is still sensitive to the quantity and chemistry of water contacting 

the waste form and spatial and temporal distribution of flow, as in the previous case for 

alloy 625. 

50 kyr and Alloy 625 as the Inner Overpack Material 

Although still sensitive to the performance of the WP and spatial and temporal distribution 

of flow, total system performance is also sensitive to well pumping-induced dilution and 

retardation of Np and Am in the alluvium (dilution of radionuclides in groundwater due to 

well pumping).

5-3 NUREG-1668
The alternative conceptual model that assumes no retardation in the SZ produced a much 

higher peak TEDE than the nominal case, which illustrates the importance of evaluating RT 

in the SZ. By comparison, matrix diffusion does not have a great impact on system 

performance (retardation in water production zones and alluvium). 

The alternative conceptual model where all WPs were assumed to be initially defective 

showed a significant increase in peak TEDE in the TPI (WP corrosion). 

As in the 10-kyr case, partial cladding protection reduced peak TEDE dose significantly 

(quantity and chemistry of water contacting WPs and waste forms). 

The choice of the release model (bathtub or flowthrough) has an effect on system 

performance. The fuel dissolution rate also has an effect on the system performance (see 

natural analog and flowthrough2 alternative conceptual models) (radionuclide release rates 

and solubility limits). 

The peak TEDE resulting from the volcanism scenario class is comparable to the Nominal 

case after being weighted by its probability of occurring during 50 kyr (volcanic disruption 

of WPs, airborne transport of radionuclides). 

50 kyr and Alloy C-22 as the Inner Overpack Material 

0 The system performance is sensitive to the general corrosion rate of alloy C-22 (WP 

corrosion). 

0 The amount of water contacting the waste affects the system performance (spatial and 

temporal distribution of flow, quantity, and chemistry of water contacting WPs and waste 

forms). 

* Retardation of Np and Am in the alluvium is important to system performance (retardation 

in water production zones and alluvium). 

0 Total system performance is sensitive to dilution introduced by well pumping. This 

conclusion is consistent with the findings of the process-level sensitivity studies and extends 

the earlier findings to a larger area of the parameter space describing initial and boundary 

conditions used to model repository performance (dilution of radionuclides in groundwater 

because of pumping).

NUREG-1668 5-4
6 CONCLUSIONS 

This report has described a series of computations performed for the purpose of determining the confidence 

in predictions of future repository performance in light of the uncertainty in conceptual models and 

parameters of those models. These predictions allowed the staff to focus attention on what is likely to be the 

most important phenomena relative to repository performance, and point out deficiencies in the current state 

of knowledge. The results of these analyses also feed back to the iterative performance assessment and the 

issue resolution process, as described in the previous section (Chapter 5) tying the parameter sensitivities 

and alternative conceptual model results to the Key Elements of Subsystem Abstraction (KESAs) identified 

in the TSPA Methodology IRSR. 

Preliminary analyses of process-level sensitivity reported in Appendix A were performed by the individual 

KTIs, focusing mainly on the processes relevant to those KTIs. The knowledge gained in these preliminary 

studies identified the importance of specific modeling assumptions and input parameters, allowing the KTIs 

to focus on those issues needing resolution in the short term. Furthermore, these preliminary analyses were 

used to determine which of the many parameters in each sub-model of the TPA code could be held constant 

and which needed to vary during subsequent sensitivity analyses. In some cases, these preliminary analyses 

led to changes in the TPA code. Actual sensitivity results are not reported in the present document because 

in most cases they were superceded in subsequent analyses using more-refined versions of the TPA code. 

Information derived from these analyses was incorporated into Rev. 1 of the KTI IRSRs. 

Chapter 3 describes the alternative conceptual model studies, in which the results of the "base case" were 

compared with alternative conceptual model cases. The analyses in this chapter used TPA version 3.1.2, and 

a waste package design using an Alloy 625 inner corrosion-resistant barrier. Other stipulations about the 

base case model were no backfilling of repository, no matrix diffusion into rock, no credit for cladding 

protection of fuel, even distribution of infiltrating water to wetted waste packages, and the "bathtub" model 

of fuel wetting. These analyses also did not consider the effects of seismicity or other disruptive forces on 

the repository performance. Alternative conceptual models considered in this study were: (1) Alloy C-22 

corrosion-resistant barrier; (2) backfilling of repository; (3) matrix diffusion of radionuclides into the rock; 

(4) credit for protection of fuel by cladding; (5) alloy C-22, but with higher flow rates to waste package; (6) 

focusing of flow to a smaller number of waste packages; (7) flow-through model with no pooling of water 

in waste package; (8) flowthrough model with faster fuel dissolution; (9) release rate based on a natural 

analog; (10) no credit for retardation; and (11) instantaneous failure of all waste packages. The results of the 

analyses of alternative conceptual models highlight the importance of some of the assumptions being made 

about the processes modeled in the TPA code. The results from analyses in which major barrier components 

such as retardation and waste package integrity were eliminated showed the largest doses. The results also 

pointed out that taking credit for phenomena such as cladding protection and lower release rates based on 

observations of natural analogs could result in considerably smaller doses. 

The sensitivity and uncertainty calculations employed TPA version 3.1.4 and a variety of statistical and 

differential techniques. Because there were substantial differences in the assumptions made between versions 

3.1.2 and 3.1.4, the two sets of results cannot be compared directly. The sensitivity and uncertainty analyses 

were conducted on the Nominal case, without igneous activity or faulting, but considering both alloys 625 

and C-22 for the inner corrosion-resistant barrier: Statistical analysis of the Nominal cases, along with 

differential analysis, was used to identify parameters that have the most effect on uncertainty in performance 

and identify sensitive parameters for which a small input change can have a large effect on estimated 

repository performance. Igneous activity and faulting were considered separately from the Nominal cases, 

using differential analysis only. Analysis indicates that parameters shown in Table 6-1 appear to have the 

most effect on uncertainty in repository performance (delineated by the TPI and inner overpack material

6-1 NUREG-1668
assumed for the tests). The~re is significant overlap between the various statistical tests used to evaluate 

sensitivity in that a number of parameters are influential under more than one TPI and choice of inner 

overpack material. Primary sensitive parameters were identified through deterministic differential analysis 

and the results are listed in 'Table 6-2. Alternative conceptualizations of repository performance were also 

investigated to determine the effects on the mean of the peak TEDE for the TPI for these alternate models. 

Some important conclusions can be drawn by examining the lists of parameters in Tables 6-1 and 6-2. These 

conclusions are as follows 

Numerous parameters dictating the flow of water onto and eventually into the WP (and onto 

SF) are important (e.g., Fow, Fmult, SFWttd%, SbArWt%). There is no mechanistic basis 

for the inpul parameter ranges for these variables used in TPA 3.1.4, and further work 

should be conducted to better define these parameter ranges. 

Table 6-1. The Uncertainty Driving Parameters Found From Regression Analysis for the Nominal 

case. (The Rankings Within a Column are Based on the Value of the t-statistic).

NUREG-1668

Alloy 625 Alloy C-22 

10-kyr TPI 50-kyr TPI 10-kyr TPI 50-kyr TPI 

AAMAI@S (1) ARDSAVNp (2) Fow (1) ARDSAVNp (2) 

Fow (1) SbArWet% (1) WPDef% (3) SFWttd% (1) 

SbArWet% (1) 0O-Coflc (4) SbArWet% (1) AA_2 1 (4) 

00-Coflc (4) Chlorid (4) Fmult (1) 00-Coflc (4) 

Fmult (1) ARDSAVAm (2) AAMAI@S (1) WprCg@20 (5) 

WprCg@20 (5) SSMO-JS4 (6) ARDSAVAm (2) 

AAMAI@S (1) SFWttd% (1) 

(1) parameter used in estimating water contacting waste; (2) parameter used to estimate transport of 

radionuclides; (3) parameter used to quantify WP failure other than corrosion; (4) parameter used for 

estimating corrosion of WlP; (5) parameter used to quantify water usage at 20 km; and (6) parameter 

used to quantify scenario failures.

6-2
Table 6-2. Primary-Sensitive Parameters as Determined by the Top Ten Parameters Ranked by 

Arithmetic Mean of Standard-Deviation-Weighted Differentials at the Five Baseline Values

A relatively large number of parameters dictating corrosive failure of WPs were found to 

either drive uncertainty in peak dose or be parameters to which peak dose was sensitive. 

For a 50-kyrTPI, parameters dictating the transport of two key radionuclei (i.e., Am and Np) 

were greatly increased in importance. 

When the inner overpack material was assumed to be alloy C-22, the fraction of initially 

defective WPs, and some parameters that dictate how seismically induced rockfall affects 

the WP increased in importance, especially for the 10 kyr TPI. It is noted that the models 

that simulate this process in TPA 3.1.4 are being improved for TPA 3.2.

NUREG-1668

Alloy 625 Alloy C-22* 

10-kyr TPI 50-kyr TPI 10-kyr TPI 50-kyr TPI 

AAMAI@S) ARDSAVAm SSMOHetF ARDSAVAm 

Fow ARDSAVNp SSMOJS4 ARDSAVNp 

SbArWet% SFWttd% AAMAI@S SFWttd% 

Fmult MkdCHnNp Fow SbArWt% 

00-Coflc Fow SbArWt% WPRCG@20 

SFWttd% SbArWet% ARDSAVAm AA_2_1 

WPRCG@20 Solub_Np ARDSAVNp SSMOHetF 

InitRSFP WPRCG@20 WPRCG@20 InitRSFP 

ARDSAVAm InnOvrEI SFWttd% MkdCHnNp 

ARDSAVNp InitRSFP MKdCHnNp OO-Coflc 

Volcanism Parameters 

WindSpd WindSpd 

ABMLFVDC ABMLFVDC 

VE-Power VE-Power 

VE-Dur VE-Dur 

VC-Dia VC-Dia 

AshMnPLD AshMnPLD 

VEROI-Tn VEROI-Tn 

Faulting Parameters 

None None I

*No disruptive event differential analysis was conducted with alloy C-22 because the results were not 

expected to change significantly from those found using alloy 625.

6-3
Based on an analysis of plots for TCR versus peak TEDE for a 50-kyr TPI for the various repository 

performance subsystems, the EBS and SZ appear to be relatively important subsystems to overall repository 

performance, as modeled in the TPA 3.1.4 code. The UZ transport beneath the repository does not appear 

to be as important a subsystem as modeled in the TPA 3.1.4 code. 

For a TPI of 10 kyr amd an inner overpack material of alloy 625, the following isotopes contribute 

to peak TEDE (in descending order):237

Np, 24 5 Cm, 24 3

Am, 2 46Cm, 2 4 1

Am, 1291, and 99

Tc, with 237

Np and 245 Cm 

dominating dose. For a TJ?' of 50 kyr with the same inner overpack material, the following isotopes 

contribute to performance (a)lso in descending order): 237

Np, 243Am, 245

Cm, 129 1, 99Tc, 2

46Cm, 24 'Am, and 239pu 

with 237Np, 24 1

Am, and 24 5

Cm dominating dose. In both cases, 237

Np appears to be the most dominant nuclide. 

The contribution of americium to peak TEDE for the TPI may appear unexpected. The results are consistent 

with the range of values chosen for retardation of americium in alluvium. The retardation values used for 

americium are similar to those of Price, et al. (1992) for the Greater Confinement Disposal Facility. Because 

sorption coefficients for americium in alluvium are unavailable at this time, the range of values in Price, et 

al. (1992) which were used to model americium retardation in alluvium in TPA 3.1.4, are intended to provide 

conservative estimates of americium transport. 

Conclusions drawn from these analyses may change as the models and assumptions are updated, and 

certain parameters or processes may become more or less important. Also, the assumptions and limitations, 

as described in Volume I of this report, should be considered when interpreting the results. Preparation by 

NRC to review the DOE TSPA products such as the VA or license application is an iterative process, of 

which this report represents; one facet of the most current iteration.

NUREG- 1668 6-4
7 REFERENCES 

Bell, M.J. 1998a. Letter (July 6) to S.J. Brocoum, U.S. Department of Energy. Nuclear Regulatory 

Commission Comments on the U.S. Department of Energy Total System Performance Assessment. 

Washington, DC: Nuclear Regulatory Commission. 

Bell, M.J. 1998b. Letter (May 11) to S.J. Brocoum, U.S. Department of Energy. Issue Resolution Status 

Report ("Key Technical Issue: Total System Performance Assessment and Integration"). 

Washington, DC: Nuclear Regulatory Commission. 

Bowen, W.M., and C.A. Bennett eds.. 1988. Statistical Methods for Nuclear Material Management. 

NUREG/CR-4604. Washington, DC: Nuclear Regulatory Commission. 

Codell, R.B, et al. 1992. Initial Demonstration of the NRC's Capability to Conduct a Performance 

Assessment for a High-Level Waste Repository. NUREG-1327. Washington, DC: Nuclear 

Regulatory Commission. 

Draper, N.R. and H. Smith. 1981. Applied Regression Analysis. New York: John Wiley and Sons. 

Haan, C.T. 1979. Statistical Methods in Hydrology. Ames, IA: Iowa State University Press. 

Helton, J.C., J.W. Garner, R.D. McCurley, and D.K. Rudeen. 1991. Sensitivity Analysis Techniques and 

Results for Performance Assessment at the Waste Isolation Pilot Plant. SAND90-7103. 

Albuquerque, NM: Sandia National Laboratories. 

Iman, R.L., and W.J. Conover. 1979. "The use of rank transform in regression". Technometrics 21: 499-509. 

Mason, R.L., R.F. Gunst, and J.L. Hess. 1989. Statistical Design & Analysis of Experiments with 

Applications to Engineering and Science. New York: John Wiley and Sons. 

Murphy, W.M., and E.C. Pearcy. 1992. Source-term constraints for the proposed repository at Yucca 

Mountain, Nevada, derived from the natural analog at Pefia Blanca, Mexico. Scientific Basis for 

Nuclear Waste Management XV. C.G. Sombret, ed. Materials Research Society Symposium 

Proceedings. 257: 521-527. 

Murphy, W.M., E.C. Pearcy, and D.A. Pickett. 1997. Natural analog studies at Pefia Blanca and Santorini. 

Seventh CEC Natural Analogue Working Group Meeting Proceedings.H. von Maravic and 

J. Smellie, eds. EUR 17851 EN. Luxembourg. European Commission: 105-112. 

Olague, N.E., D.E. Longsine, J.E. Campbell, and C.D. Leigh. 1990. User's Manual for the NEFTRAN 11 

Computer Code. NUREG/CR-5618, SAND90-2089. Albuquerque, NM: Sandia National 

Laboratories. 

Price, L.L., et al. 1992. Preliminary Performance Assessment of the Greater Confinement Disposal Facility 

at the Nevada Test Site; Volume 3: Supporting Details. SAND91-0047. Albuquerque, NM: Sandia 

National Laboratories. Safety and Risk Assessment Department. 

Sagar, B. 1996. NRC High-Level Radioactive Waste Program Annual Progress Report: Fiscal Year 1996. 

NUREG/CR-6513. San Antonio, TX: Center for Nuclear Waste Regulatory Analyses.

7-1 NUREG-1668
Seitz, R.R., et al. 1991. Sample Application of Sensitivity/Uncertainty Analysis Techniques to a Groundwater 

Transport Problem. DOE/LLW-108. Idaho Falls, ID: Idaho National Engineering Laboratory. 

Sen, A., and M. Srivastava. 1990. Regression Analysis- Theory, Methods and Applications. New York: 

Springer-Verlag, Inc. 

Wescott, R.G., et al. eds. 1995. NRC Iterative Performance Assessment Phase 2. NUREG-1464. 

Washington, DC: Nuclear Regulatory Commission.

NUREG-1668 7-2
APPENDIX A 

SUMMARY OF THE KEY TECHNICAL ISSUES AND PROCESS

LEVEL SENSITIVITY ANALYSES
Al SUMMARY OF THE KEY TECHNICAL ISSUES AND 

PROCESS-LEVEL SENSITIVITY ANALYSES 

This appendix provides a description of the Key Technical Issues (KTIs) around which the Nuclear 

Regulatory Commission (NRC) has focused its High-Level Waste (HLW) program and the process-level 

sensitivity analyses conducted by NRC staff (organized by KTI) which determined the Total-system 

Performance Assessment (TPA) input parameters most important to individual repository subprocesses. 

A1.1 THE KEY TECHNICAL ISSUES 

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

actions for the proposed HLW repository at the Yucca Mountain (YM) site, NRC has focused its program 

on resolving issues most important to overall repository system performance. This has led to structuring the 

NRC HLW program to encompass ten KTIs (Sagar, 1996). Limiting the program scope to these KTIs was 

necessary to focus and streamline the regulatory decision-making process. In addition, the KTI framework 

provides a vehicle to ensure early, clear, and sound technical feedback to the U.S. Department of Energy 

(DOE) regarding possible licensing vulnerabilities in the DOE safety case (Department of Energy, 1998) for 

the proposed YM repository. Continuing the work performed in Iterative Performance Assessment (IPA) 

phases 1 and 2, staff from the KTIs performed process-level sensitivity and uncertainty analyses using TPA 

3.1. 1 to identify parameters that influence the individual processes. Brief descriptions of these 10 KTIs are 

found below. It is noted that interactions between performance assessment (PA) models and the technical 

KTIs are iterative, in that abstracted PA models depend on the more detailed models developed at the KTI 

level and these more detailed models are developed with knowledge about the relative importance of sub

issues gained from modeling the total system. Staff members working on the various KTIs are responsible 

for documenting much of the information used in PA. 

A1.1.1 Container Life and Source Term (CLST) 

The objectives of the CLST KTI are to independently evaluate the waste containment system 

attributes and provide input to the overall PA proposed repository. Among the system attributes recognized 

in this strategy to be most important for predicting performance of the engineered barriers are: (i) waste 

package (WP) lifetime and containment; (ii) rate of release of radionuclides from breached WPs; and 

(iii) radionuclide transport through engineered barriers. Five subissues are being evaluated in the CLST KTI. 

These five subissues are 

The effect of corrosion on the lifetime of the WPs and the subsequent release of 

radionuclides from the engineered barrier system (EBS) taking into consideration 

the influence of weldments and including processes such as dry-air oxidation, 

humid-air corrosion, uniform and localized aqueous corrosion, microbially 

influenced corrosion, and stress corrosion cracking 

The effect of material stability and mechanical failure on the lifetime of the WPs 

and the release of radionuclides, taking into consideration the influence of areal 

mass loading, as well as residual and applied stresses

A-1 NUREG-1668
The effect of spent fuel (SF) degradation on the release of radionuclides, 

considering dry-air oxidation, aqueous oxidative dissolution, and the eventual 

formation of secondary mineral phases 

0 The influence of vitrified reprocessed waste leaching on radionuclide release 

0 The design of the WP for preventing internal criticality 

A1.1.2 Evolution of Near-field Environment (ENFE) 

The objective of the ENFE KTI is to assess all aspects of the evolution of the near-field 

geochemical environment that have the potential to affect the performance of the proposed repository. 

Coupled thermal-hydrologic-chemical and thermal-chemical processes are the primary focus of the ENFE 

KTL. These processes can catlse changes in the values of parameters or conceptual models used within PA 

codes. Effects of coupled processes can influence: (i) seepage into the emplacement drifts and flow in the 

unsaturated zone; (ii) the WP c hemical environment; (iii) the chemical environment for radionuclide release; 

(iv) radionuclide transport (RT) in the near-field; and (v) the potential for criticality in the near-field. 

Evaluation of these potential impacts requires assessment of the natural system (rock, fluids, and gas 

chemistry) and engineered materials introduced into the repository (metal and concrete) as they evolve in 

time in response to thermal energy associated with the radioactive decay of the waste. 

A1.1.3 Igneous Activity (IA) 

The objectives of the 1A KTI are to estimate the probability and consequences of 1A affecting 

the proposed repository in relationship to the overall system performance standard. In the draft "Repository 

Safety Strategy" (U.S. Department of Energy, 1998), DOE hypothesized that igneous events would be rare 

and the consequences of volcanism would be acceptably small. Independent technical investigations by the 

Center for Nuclear Waste Regulatory Analyses (CNWRA) have shown that the annual probability of future 

volcanic eruptions at the proposed repository site is on the order of 10-' per year and that consequences of 

direct HLW release associated with volcanic disruption are significantly higher than estimated in previous 

YM Total System Performance Assessment (TSPAs). 

Al.l.4 Structural Deformation and Seismicity (SDS) 

The main task of the SDS KTI is to analyze the probability and consequences of structural 

deformation and seismicity and related hazards, regarding the disposal of HLW at the candidate site. As part 

of that task, the SDS KTI is evaluating the potential consequences of repository disruption from faulting and 

seismicity. These potential consequences include rupture of WPs directly from fault displacement or 

indirectly from roof collapse m unbackfilled drifts because of seismic shaking. In addition, the SDS KTI is 

providing input to fracture flow modeling by characterizing structural controls of flow under conditions at 

different scales [e.g., drift scale, UZ and saturated zone (SZ) pathways].

NUREG-1668 A-2
Thermal Effects on Flow (TEF)

The objective of the TEF KTI is to understand the effects of spatial and temporal variations 

in the thermal load on UZ temperature as well as on liquid- and gas-phase fluxes in the vicinity of the 

repository and their effects on containment and long-term waste isolation. In particular, it will be necessary 

to understand and predict the flow of moisture in the vicinity of the WPs that originates from either existing 

water in the rock or from infiltration. The TEF KTI develops and evaluates hypotheses concerning: 

(i) temporal variations in heat generation from the waste and humidity in the repository; (ii) engineered 

barriers, including backfill, and their effects on WP integrity and the migration of water and radionuclides 

from the WP into the host rock; and (iii) flow of water in and near the repository. Testing hypotheses on 

these issues will require an understanding of thermally driven flow of moisture through partially saturated 

and fractured porous material. 

A1.1.6 Unsaturated and Saturated Flow under Isothermal Conditions (USFIC) 

The objectives of the USFIC KTI include: (i) evaluation of climatic change and the role of 

vegetation on shallow infiltration at YM; (ii) development of improved estimates of percolation through the 

repository horizon; (iii) evaluation of groundwater mixing and radionuclide dilution in the SZ; and 

(iv) interpretation of the extent of matrix diffusion in the YM region. Unsaturated zone hydrologic studies 

will be conducted to: (i) improve estimates of the magnitude and distribution of flux through the proposed 

repository; (ii) evaluate the corresponding state of saturation of the proposed repository; (iii) improve 

understanding of the formation of perched water bodies; and (iv) interpret the roles of fractures and fracture 

flow in the UZ and the conditions necessary to initiate episodic fracture flow. Saturated zone hydrologic 

studies will be conducted to: (i) develop regional-scale hydrogeologic and hydrochemical models; (ii) 

construct site-scale hydrogeologic; (iii) formulate hydrochemical models from which a three dimensional 

radionuclide transport model can be developed; and (iv) improve understanding of groundwater mixing and 

dilution of dissolved radionuclide. 

A1.1.7 Repository Design and Thermal-Mechanical Effects (RDTME) 

The primary objective of the RDTME KTI is to resolve issues related to repository design 

and thermal-mechanical effects on performance. This will be achieved by developing: (i) an understanding 

of the potential influences of the thermal load on repository design, waste retrievability, the EBS and 

repository seals; and (ii) an independent capability for addressing uncertainties associated with evaluation 

of thermal-mechanical effects on performance of emplacement drifts and EBS. Testing of hypotheses 

(e.g., the engineered barriers will limit migration of radionuclides into the host rock and any sources of 

groundwater, or flow of water into the repository, will be low) will require an understanding of thermal

mechanical effects on the repository, WPs, and repository seals. 

A1.1.8 Total System Performance Assessment and Integration (TSPAI) 

The major objective of the TSPAI KTI is to formulate, develop, and articulate an acceptable 

methodology for compliance determination. The TSPAI KTI supports evaluation of five major subissues 

consisting of: (i) determining acceptable abstractions of critical system elements, processes, couplings, and 

events for a TSPA; (ii) defining an acceptable scenario approach for use in estimating overall risk; 

(iii) identifying a sufficient basis for the TSPA input data sets; (iv) determining intermediate outputs needed

NUREG-1668

A1.1.5

A-3
to understand and explain the TSPA risk calculations; and (v) documenting the TSPA methodology and 

results to ensure they are transparent and traceable. 

Al.1.9 Activities Related to Development of the NRC High-Level Waste 

Regulations (ARDR) 

The primai-y objectives of the ARDR KTI are to: (i) interact with the Environmental 

Protection Agency (EPA) regarding the development of an EPA standard for the YM site; (ii) develop the 

technical bases for revisions 1o the NRC regulations consistent with the new EPA standard; (iii) review and 

comment on the new EPA standard; and (iv) support the revision of the regulation for HLW. 

A1.1.10 Radionuclide Transport (RT) 

The principal objective of the RT KTI is to develop models for transport of radionuclides 

under geochemical and hydrologic conditions specific to YM. A fundamental concern in evaluating the 

suitability of YM as a repository for HLW is the possibility of RT through the subsurface from the repository 

to receptor locations. Processes such as sorption, matrix diffusion, dispersion, mineral precipitation, 

radioactive decay, and basin-scale dilution may help reduce radionuclide concentrations in the groundwater, 

and ultimately lead to a redILIction in potential dose to man. High-priority technical needs identified within 

this KTI include: (i) reviewing YM site data; (ii) investigating alternative conceptual models; and 

(iii) conducting sensitivity analyses. 

A2 THE PROCESS LEVEL SENSITIVITY STUDIES 

A2.1 INTRODUCTION AND BACKGROUND 

In the fall of 1997, NRC conducted process-level sensitivity studies to examine selected 

aspects of the current NRC TSPA model. The analyses used both the TPA 3.1.1 code and separate process

level models, and involved reviewing selected conceptual models, input parameters used to represent the 

system, and events and processes that influence the behavior of the system. Six KTIs performed process-level 

sensitivity studies (i.e., CLST, ENFE, IA, SDS, TEF, and USFIC). Staff from the TSPAI KTI participated 

in the sensitivity studies by both supporting the KTIs in their work and integrating the results. The RT and 

RDTME KTIs did not conduct process-level sensitivity analyses because they were not funded to do so. 

The knowledge gained from the process-level sensitivity studies aided individual KTIs in 

identifying the importance of specific modeling assumptions and input parameters, allowing the KTI to focus 

on those issues needing resolution during prelicensing. Furthermore, these studies identified a number of 

parameters that could be held constant during the system-level sensitivity studies, allowing the system-level 

sensitivity studies to focus on the most important parameters. In addition to reducing the set of sampled 

parameters, the process-level sensitivity studies identified a number of desirable changes to TPA 3.1.1 that 

should be made in TPA 3. 1.3 and 3.1.4 before conducting additional sensitivity studies. The process-level 

studies also identified other changes that will be made in later versions of the TPA code. 

It is noted that the analyses and results presented herein were also intended to demonstrate 

NRC staff capabilities to conduct PA. These results are limited by the use of simplifying assumptions,

NUREG-1 668 A-4
models, and sparse data in certain areas. As a consequence, these results are preliminary. Moreover, the 

manner in which these analyses were conducted or the assumptions and approaches used should not be 

construed to express the views, preferences, or positions of the NRC staff regarding the site-specific 

regulations for YM. 

A2.2 OBJECTIVES 

The objectives of the process-level sensitivity studies were to: 

* Identify parameters and modeling assumptions that have the greatest effects on 

output (i.e., peak TEDE for the TPI to the average member of the receptor group); 

• Provide input to future PA activities, such as 

- parameter ranges for use in the system-level sensitivity studies 

- parameter ranges requiring further investigation 

- short-range and medium-range modeling needs; 

• Support prelicensing activities and issue resolution, such as 

- Identifying issues amenable to resolution and supporting their resolution 

during prelicensing interactions 

- Identifying information needs. 

A2.3 PROCESS-LEVEL SENSITIVITY STUDIES 

Although the process-level studies provided many useful results, the results are not 

exhaustive. They represent but one phase in the current iteration of the NRC PA for YM. As such, future 

work by the KTIs and the TPA 3.1.4 system-level sensitivity studies will be used to confirm the results. 

Notwithstanding this further work to confirm the results, the process-level sensitivity studies contributed to 

the development of Issue Resolution Status Reports (IRSRs) for a number of KTIs. Process-level studies 

were an important precursor to the system-level studies, because they resulted in changes to the conceptual 

models and TPA code that were used for the system-level sensitivity studies. Changes included the addition 

of new models (e.g., different ways of calculating releases from failed WPs, cladding credit) and 

modifications of existing models (e.g., changing the amount of rockfall associated with a single seismic 

event). This section summarizes the six process-level sensitivity studies. 

The sensitivity studies were performed using the mean data set, with some groundwater 

parameters varied. Each KTI varied parameters of particular interest, but kept all others fixed. No constraints 

were placed on the TPI or the distance from the repository to the receptor group. In some cases, to facilitate 

the prompt completion of analyses, the receptor group was located closer than 20 km. Parameter distributions 

for the most significant parameters were carried forward to the system-level studies; less-significant 

parameters were set as constants. This is an expedient to minimize the computational effort to allow the Latin

A-5 NUREG-1668
Hypercube Sampling (LHS) to be effectively used with a smaller number of vectors. Identifying the most 

significant parameters also allows NRC to focus its efforts on those parameters and issues that are potentially 

most important to repository performance. 

One measure of repository performance used by each KTI was the peak Total Effective Dose 

Equivalent (TEDE) in the TP'I, calculated for the average member of the receptor group. The receptor 

locations were assumed to be either 5 or 20 km away from the repository. The travel time is larger for the 

latter group, so TPI for the 20-km receptor group was necessarily longer than for those at 5 km. The number 

of vectors varied between each of the process-level studies. 

A2.3.1 Container Life and Source Term 

CLST analyses involved the modules EBSFAIL and EBSREL, which calculate failure of 

WPs and the radionuclide source term, respectively. A description of the WP performance model and the 

release of radionuclides from failed WPs can be found in Volume I of this report. However, there are 

differences in the TPA 3.1.1 and TPA 3.1.4 models. In TPA 3.1.1, for example, the bathtub model was the 

only available model for advective release of radionuclides. 

CLST studies evaluated the relative importance of input parameters for EBSFAIL and 

EBSREL independently and in combination. The factors controlling the failure of WPs and the release of 

contaminants from the EBS were examined. In these studies, the WP is considered to be made with an inner 

overpack of Alloy 825 and a carbon steel outer overpack. Alternative conceptual models for the dissolution 

of SF and the release of radionuclides from WPs were evaluated. Comparison studies were performed for 

the presence/absence of cladding, backfill, and galvanic coupling. Advective and diffusive releases from 

failed WPs were compared. 

All EBSFAIL and EBSREL parameters were varied concurrently, assuming that backfill was 

emplaced after 100 yr. A number of infiltration-related parameters were also allowed to vary. These 

simulations were performed for receptor groups 5 and 20 km away from the repository. Simulations were 

conducted for 20 and 100 kyr, respectively. Results of these analyses were evaluated using stepwise 

regression with peak TEDE as the dependent variable and confirmed the sensitive parameters identified 

separately in the EBSFAIL or EBSREL sensitivity studies. 

A2.3.1.1 EBSFAIL Stadies 

Sensitivity studies were performed on the corrosion failure of WPs, which are modeled in 

the EBSFAIL module. All ]EIBSFAIL parameters and some infiltration-related parameters were varied 

simultaneously for different Lssumptions about repository design and WP behavior. The following design 

assumptions were examined: (i) backfill is emplaced 100 yr after closure and (ii) galvanic coupling protects 

the inner overpack. Evaluating the first assumption required two sets of calculations: the first assumed that 

backfill was emplaced; and the second that it was absent. Evaluating the NRC model for galvanic coupling 

also involved two sets of calculations; the first assumed galvanic coupling occurred with 100 percent 

efficiency and the second assumed that it was totally ineffective. Efficiency of the galvanic couple is an 

indication of the increase in corrosion potential afforded the inner overpack by virtue of the galvanic couple 

(Mohanty, et al., 1997). These analyses were conducted assuming a receptor group 5 km from the repository 

and a TPI of 20 kyr.

NUREG-1668 A-6
A2.3.1.2 EBSREL Studies

All the EBSREL, and some of the infiltration-affecting parameters, were allowed to vary 

concurrently. It was assumed that a receptor group was located 5 km from the repository. The TPI was 

20 kyr. Sensitivity of the peak TEDE and cumulative release from the EBS were evaluated for each EBSREL 

parameter. 

In addition to the studies described above, where all of the parameters were varied 

simultaneously, CLST also conducted a set of focused investigations, where components of the EBSREL 

conceptual model were examined in detail. A number of these focused examinations could not be performed 

with TPA 3.1.1 without modifying the code. Others required external post-processing of results or changes 

to input parameters to represent different behavior of the system. The detailed studies are presented below. 

Distribution of WP Failures 

The TPA 3.1.1 model assumes one representative WP for each combination of subarea and 

cause of failure, whether caused by corrosion, disruptive events, or manufacturing defects. Similarly, all 

failures from disruptive events and manufacturing defects are modeled as occurring at a single failure time. 

This simplifying assumption was changed in later versions of the code. All failed WPs within a simulation 

are assumed to have the same fraction of wetted fuel, assuming that the WP reaches its carrying capacity. 

The carrying capacity of a failed WP is the maximum amount of water that the WP can hold. Once this 

amount is exceeded, the amount of water flowing into the WP equals the amount of water flowing out. The 

concern with using a representative WP is that there is likely to be a considerable spread of failure times and 

states because of such factors as manufacturing practices, material properties, and differences in environment 

between one part of the subarea and another (e.g., temperature). Simultaneous failure of the WPs might 

overestimate peak TEDE for the TPI because a more random failure sequence might spread out the releases, 

thereby lowering the peak TEDE for the TPI. To test this hypothesis, the TPA code was set up to simulate 

a spread in WP failures within a subarea to compare with the dose estimates using a representative WP. 

Peak TEDE to the receptor individual was calculated with 20 realizations, varying only the 

EBSREL parameters, at a receptor location of 5 kin, and a TPI of 20 kyr, with the balance of the parameters 

taken from the mean data set and the SF wet fraction set to 0.5 (i.e., the mean). The parameter values from 

the realization in the 20-vector set resulting in the largest peak TEDE were used for another set of 

calculations under the same conditions, the exception being that the SF wetted fraction was sampled 

uniformly from 0 to 1. Average dose versus time from this second set of calculations was compared with the 

dose versus time for the realization resulting in the largest peak TEDE in the first calculation. 

SF Dissolution 

There are three models for SF dissolution in EBSREL. The first model assumes matrix 

dissolution under immersion conditions, where dissolution rate is a function of carbonate ion concentration, 

temperature, oxygen fugacity, pH, and particle size. This model uses a functional relationship derived from 

experiments using pure carbonate solutions. The second model uses a functional relationship derived from 

experiments using J- 13 well water containing silica and calcium. The third model is a constant dissolution 

model and corresponds to analog studies or results of unsaturated drip tests. SF particle size is determined 

using one of two models. The first assumes SF particle sizes dictated by irradiation in the reactor (i.e., 

millimeters) and the second assumes sizes dictated by dry oxidation and grain size (i.e., micrometers).

A-7 NUREG-1668
Effective SF Surface Area

The effect of differing SF particle sizes was evaluated. It was found that releases occur much 

earlier for smaller SF particle sizes because radionuclides can more easily escape the U0 2 matrix. 

A separate study of the potential importance of cladding (which may have the effect of 

limiting the effective surface area of the SF) was performed. Normally, the entire surface area of the SF is 

assumed to be exposed. In this study, it was assumed that SF surface area is initially protected by intact 

cladding. A portion of the fuel may be exposed through pits or hairline cracks. The fraction of the surface 

area assumed to be exposed through these pits or cracks was varied from 10-9 to 1.0. 

Performance of the WP after Failure 

Three alternative conceptual models were evaluated for calculating releases from WPs: 

bathtub, flow-through, and pulled plug. The bathtub and flow-through models are described in Volume I. The 

third conceptual model is a hybrid of the other two. It assumes that failed WPs initially behave according to 

the bathtub model; however, additional breaches of the WP result in a 10-fold decrease in the amount of 

water contained in the failed WVP and the amount of wetted fuel. This change is assumed to occur 1 kyr after 

the initial failure and results in a transient increase in release rate as leachate is assumed to drain from the 

WP. 

Advective Versus Diffusive Releases 

The bathtub model assumes advective and diffusive releases through backfill from the WP. 

DOE has proposed a release model in which releases of radionuclides from the WP are controlled by 

molecular diffusion through porous corrosion products. To test this alternative conceptual model, the bathtub 

model was modified to simulate release by molecular diffusion through corrosion products, with no advective 

flow through the WP. The WP was assumed to be filled with water at the time of failure. Diffusion took place 

over 5 percent of the WP surface area through a 10-cm thickness of corrosion products. The gradient across 

the 10 cm of corrosion products was determined by the difference between the concentration inside the WP 

and a zero concentration boundary at the outside surface of the outer overpack. 

Effect of Focused Flow 

TPA 3.1.1 assumes that each WP is contacted by some portion of the water infiltrating to 

the repository horizon. Sensitivity of peak TEDE, for receptor groups located 5 and 20 km away from the 

repository, was evaluated assuming that water was apportioned over 50 percent and 25 percent of the WPs. 

The analysis used 100 realizations and the TPI was 50 kyr. 

Correlated Solubility Coefficients 

Physical phenomena that control solubility-such as temperature, pH, Eh, and ionic strength

will be the same for all radionuclides at that location. Consequently, solubilities of different radionuclides 

are likely to be correlated, especially for elements with similar chemical behavior. Thirty-one runs were made 

using the speciation code EQ 3NR (Wolery, 1992). Correlation coefficients among elemental solubilities were 

developed based on the results as inputs to the TPA code.

NUREG- 1668 A-8
Calculations were performed using a 5-km receptor group, a 20-kyr TPI, and 300 

realizations. Peak TEDE was evaluated by first assuming that the solubilities of americium, neptunium, 

uranium, plutonium, thorium, radium, and lead were correlated. These results were then compared with 

results for the same conditions, but with uncorrelated solubilities. 

A2.3.1.3 Preliminary Conclusions for IRSRs 

EBS performance is influenced by the near-field environment, characteristics of the WP 

design, materials used in the construction of the WP containers, and effective surface area of the fuel that 

is available for release. Important characteristics of the near-field environment include temperature, relative 

humidity, chloride concentration in contact with the WP, and oxygen partial pressure. Important aspects of 

the current WP design and materials appear to be the susceptibility to localized corrosion and the rate of 

localized corrosion of the inner and outer overpack. Effective surface area for the fuel is influenced by the 

surface-area model, contribution of cladding, and amount of fuel wetted. 

Temperature and relative humidity vary over the TPI and are read from tables. In contrast, 

oxygen partial pressure is sampled at the beginning of each run and held fixed throughout the TPI. Assuming 

a low, unchanging, oxygen partial pressure could result in an overestimation of repository performance, 

because oxygen partial pressures are expected to equilibrate with the atmosphere. Oxygen partial pressure 

influences both WP performance and SF dissolution. The significance of oxygen partial pressure is magnified 

by the assumption that it is constant over the TPI. In addition, sensitivity studies on oxygen partial pressure 

were conducted by ENFE KTI and are discussed in Section A2.3.2. 

EBSFAIL 

Galvanic coupling has been shown to provide significantly longer WP life times with TPA 

3.1.1 for Alloy 825 as the inner overpack material. However, the model does not include the throwing power 

for the galvanic protection and is believed to overestimate the contribution of galvanic coupling at long times, 

because of the consumption of the carbon steel overpack by uniform corrosion. Because galvanic coupling 

has the potential to contribute to longer WP lifetimes in certain combinations of materials, it may be a 

component of the DOE PA. The current code could be used to bound the performance of the WP, assuming 

that the coupling efficiency is zero. The results should be treated with caution, however, when the current 

model is used to evaluate the expected contribution of galvanic coupling. 

Backfill has the potential to change the temperature and relative humidity of the near-field 

and, consequently, the performance of the WP. In these analyses, it was found that backfill substantially 

prolonged WP lifetimes and resulted in fewer WP failures during the TPI. 

EBSREL 

Distributing WP failures over time did not result in a substantially lower peak TEDE. This 

result is attributed to the small time required to fill the WP (hundreds to thousands of years), compared with 

the time scale of the peak TEDE (tens of thousands of years). With the present parameters, peak release 

occurs only after very long times (i.e., beyond the TPI of 20 kyr), and is driven by the time scale of the 

infiltration rate. However, TEDEs at times close to the failure of the representative WP may be substantially 

higher than those calculated for distributed failures.

A-9 NUREG-1668
Peak TEDE and cumulative releases from the EBS were found to be sensitive to SF surface 

area. This conclusion is suppxorted by the EBSREL sensitivity analyses- where all parameters were allowed 

to vary simultaneously- and detailed calculations on cladding and the comparison of the models for 

determining the effective surface area (i.e., grain size versus particle size). TPA 3.1.1 assumes that particle 

size and, consequently, surface area, remain constant until the particle is completely dissolved. This results 

in higher releases and higher calculated doses. Although the contribution of surface area may be 

overestimated through this analysis, it is expected to remain important, even if surface area is corrected for 

dissolution. 

Peak TEDEs were largest for release rates derived from experimental results for pure 

carbonate solutions. Peak TEI)Es were lower for release rates derived from J-13 well-water, which contains 

species that inhibit dissolutior (e.g., calcium and silicon). Constant dissolution rates were sampled to include 

both laboratory test results ard analog results. Dissolution rates, assuming constant dissolution, did result 

in peak TEDEs similar to those calculated for J-13 well water, for dissolution rates 100 times less than the 

default value. 

The compaison of alternate conceptual models for the WP after its initial failure indicated 

that the bathtub model and flow-through models result in similar peak TEDEs, when the same amount of fuel 

surface area is exposed. The time delay in the bathtub model, where the WP is filling to its capacity, does 

not significantly influence peak TEDEs. Also, advective and diffusive releases did not result in substantially 

different peak TEDEs over the TPI of 20 kyr. 

The comparison of peak TEDE resulting from redistribution of infiltrating flux to one-half 

and one-quarter the WPs yielded mixed conclusions. For the 5-km receptor group, the mean peak TEDE for 

flux redistributed to one-half and one-quarter of the WPs yielded 86 and 77 percent of the default value, 

respectively. However, peak TEDE for a 20 krn receptor group is directly proportional to the number of WPs 

wetted. The differences in sensitivity are attributed to solubility-limited radionuclides dominating the peak 

TEDE for the TPI at 5 km and release-rate-limited radionuclides controlling the peak TEDE for the TPI at 

20 km. 

Correlated solubilities did not appear to significantly influence peak TEDE. The largest 

contributors to peak TEDE appear to be plutonium, americium, neptunium, iodine, and technetium. Iodine 

and technetium were not assigned correlations, and correlations between the remaining large contributors 

were not very large. A single element dominating the calculated peak TEDE, or the strong contribution of 

technetium or iodine would limit the contribution of correlated solubilities to peak TEDE. 

A2.3.1.4 Significance to TPA 3.1.4 and TPA 3.2 

In response to the CLST process-level sensitivity studies, several changes were made to the 

TPA code. A factor was ini:roduced to allow further examination of cladding protection. Instantaneous 

radionuclide releases from defective WPs and effects of secondary minerals on release are now part of the 

release model. In addition, other long-term changes are being considered, including time-variation effects 

in degradation of engineered barriers, improvements in modeling mechanical damage to WPs, and allowing 

SF surface area to change with time.

NUREG- 1668 A-10
Evolution of The Near-field Environment

ENFE conducted four separate sensitivity studies. The studies included: (i) effects of 

cementitious materials on near-field transport; (ii) effects of colloids on near-field transport; (iii) effects of 

variations in oxygen and chloride concentrations on WP performance; and (iv) alternate release rates based 

on natural analog data from Pefia Blanca. These sensitivity studies involved the following modules: NFENV, 

EBSFAIL, EBSREL, and UZFT (see Volume 1). TPA 3.1.1 did not allow the direct evaluation of 

cementitious materials, colloidal transport, or alternate release models. Therefore, these effects were 

evaluated by modifying appropriate parameter values in the reference data set. 

A2.3.2.1 Cementitious Materials 

Use of cementitious materials, in the form of concrete inverts and linings, is being considered 

for the emplacement drifts in the repository. The effect of concrete on the near-field chemical environment 

of a repository could be pronounced and could be either beneficial or detrimental to repository performance. 

Cements are extremely fine-grained, high surface-area materials containing somewhat soluble and 

thermodynamically metastable phases (e.g., a nearly amorphous gel designated CSH because it contains CaO, 

SiO2, and H20). These properties and the partially interconnected pore network of the solids make these 

materials potentially reactive with the near-field environment and the EBS. 

Hyperalkaline cement pore water is chemically reactive with silicates, a major component 

of the proposed YM repository host rock unit Topopah Spring tuff, which is comprised predominantly of 

alkali-feldspar, quartz, cristobalite, and tridymite (Bish, et al., 1996). Thus, migration of high pH cement pore 

water into the host rock is likely to result in tuff alteration. Because of the low silica concentration of cement 

pore water, the host rock would begin to dissolve on contact with the hyperalkaline fluid (Lichtner and 

Eikenberg, 1995). As the host rock dissolves and the silica concentration increases, calcium-silicate-hydrate 

phases would precipitate and clog the pore spaces. Precipitation of calcite would also occur as low-CO2, 

high-Ca cement pore fluid mixes with ambient groundwater containing high CO2 concentrations (Lichtner 

and Eikenberg, 1995; Steefel and Lichtner, 1994). 

Dissolution of tuff could lead to widening of fractures resulting in enhancement of 

groundwater flow through the repository. Alternatively, precipitation of calcite and calcium-silicate-hydrates 

along the fracture/matrix interface could seal fractures from the matrix, producing isolated channels through 

which radionuclides could be transported relatively unimpeded by matrix diffusion. On the other hand, if 

sufficient amounts of calcite, calcium silicate hydrate, and other calcium-silicate-hydrate phases are 

precipitated along fracture walls, reduction in fracture porosity/permeability or fracture plugging could result 

in diminished groundwater flow through the repository. Conversely, an increase in matrix 

porosity/permeability caused by dissolution of the tuff host rock could lead to enhanced matrix flow and 

reduced fracture flow. 

Four scenarios, which focused on the potential effect of a hyperalkaline plume on hydraulic 

properties and the corresponding influence on repository performance, were considered. DOE sensitivity 

analyses on the consequences of cementitious materials indicate that increases in peak TEDE for the TPI and 

earlier peak TEDE for the TPIs result from decreases in radionuclide sorption in the UZ and/or from 

increases in Np and Pu solubility (Dale, 1996). However, the possible effect of changes in radionuclide 

retardation and solubility on repository performance was not studied here. Literature data indicate that

NUREG-1668

A2.3.2

A-11I
sorption Kds of radionuclides, particularly actinides, in cementitious environments are high (Bradbury and 

Sarott, 1995; Campbell and Krupka, 1997). The four cases considered were: 

Enhanced fracture flow because of dissolution and widening of fractures; 

Increased matrix flow and matrix diffusion caused by dissolution of the tuff 

host rock; 

Reduced diffusion from the fractures into the matrix caused by mineral precipitation 

along, and coating of, fracture walls; and 

Reduced fracture flow caused by mineral precipitation in, and plugging of, 

fractures. 

It is not possible to explicitly model the above cases with TPA 3.1.1. Thus, an indirect 

approach was used in which parameters available in the TPA 3.1.1 input file were varied to approximate the 

effects postulated in each case.. Four types of hydraulic parameters that can be specified in the input file are 

of specific interest, namely:. (i) matrix permeability; (ii) matrix porosity; (iii) fracture permeability; and 

(iv) fracture porosity. Widening of fractures caused by dissolution was represented by increases in the peak 

value in the distribution of both fracture permeability and fracture porosity. These changes to the sampled 

ranges will tend to enhance fracture flow in the affected stratigraphic unit. Dissolution of the tuff host rock 

was represented either by increases in the peak value in the distribution for matrix permeability or an 

expanded range for matrix porosity. These changes to the sampled ranges will tend to increase the amount 

of matrix flow in the affected unit. Reduced fracture flow, caused by mineral precipitation resulting in a 

narrowing or plugging of fractures, was represented by a lower range of values for either fracture 

permeability or fracture porosity. These changes tend to reduce the amount of water that can flow through 

fractures and simulate the effect of fracture narrowing. Reduced diffusion from fractures into the matrix was 

represented by using a range of small values for matrix porosity and matrix permeability. These changes tend 

to result in faster transport through the matrix and a reduced opportunity for diffusion between the fracture 

and the matrix. 

The potential spatial extent of the zone affected by a hyperalkaline plume is unknown. 

Sensitivity analyses with respect to the spatial extent of this zone cannot be done with TPA 3.1.1 because 

its value cannot be specified in the input file. Thus, for the sensitivity calculations, the welded Topopah 

Spring stratigraphic unit was used to represent the altered zone for all seven subareas (see Volume 1). 

A total of 14. simulations were conducted. These simulations, consisting of 100 or 400 

realizations each, were performed to allow several different approaches to be used to evaluate the influence 

of fracture and matrix properties (i.e., permeability and porosity) on repository performance. In these 

analyses, the performance measures were: cumulative release of radionuclides to a location 5 km from the 

repository over 10 kyr and c:umuiative release of radionuclides from the UZ over 10 kyr. One simulation 

allowed all input parameters to vary, whereas others kept most parameters constant (e.g., all parameters 

except one; all parameters except three UZFLOW parameters, three EBSREL parameters, and one parameter 

of interest) at their mean reference data set value.

NUREG-1668 A- 12
A2.3.2.2 Colloids

The effect of colloids was tested by postulating accelerated transport relative to water. The 

objective of this analysis is to use the TPA 3.1.1 code to simulate iron oxyhydroxide pseudocolloid transport 

through the near-field. The scope of the analysis is limited to colloid transport and does not consider aspects 

of colloid formation. 

The conceptual model includes sorption of radionuclides onto an iron oxyhydroxide colloid 

to form a pseudocolloid. Fracture transport of colloids is enhanced relative to nonsorbing tracers and 

groundwater, since groundwater and nonsorbing tracers may diffuse into the matrix, whereas the colloids are 

confined to fracture pathways. It is assumed that colloids are readily filtered in the matrix by physical or 

chemical processes. Enough colloids are filtered by the matrix that sorption behavior of the matrix is 

dominated by the more sorptive iron. 

To simulate the extent of the near-field, pseudocolloid transport is limited to the Topopah 

Spring tuff. Because of large uncertainties in estimating the amount of colloid-size iron oxyhydroxide 

generated by WP degradation, possible effects on solubility or increased loading to levels in excess of 

solubility limits are not considered. In addition, the possibility of plugging fractures and matrix porosity by 

particulates is acknowledged, but is not specifically considered here. 

Colloidal transport is modeled by assuming enhanced transport through fractures and 

assumed matrix sorption coefficients for different radioelements within the Topopah Spring stratigraphic 

unit. Enhanced transport is modeled using reduced retardation coefficients (i.e., 0.9 __ Rd < 1). All 

radioelements, with the exception of those not likely to sorb strongly onto iron oxyhydroxides (i.e., Tc-99, 

1-129, C-14, C1-36, and Cs-135), are assumed to experience enhanced transport as pseudocolloids. Matrix 

sorption coefficient (Kd) values are assumed for each of the radioelements for the Topopah Spring tuff, using 

the Kd distribution developed for TSPA-93 (Wilson, et al., 1994) to identify the minimum, maximum (and 

mean, if appropriate), Kd for iron oxyhydroxides. The same Kd distribution for americium and niobium is 

assumed for curium. Each retardation coefficient (Rd) and Kd were concurrently varied over 220 realizations. 

The TPI was 10 kyr. Sensitivity was measured based on peak TEDE for a receptor group located 5 km away 

from the repository. 

A2.3.2.3 Chloride Multiplication Factor And Oxygen Partial Pressure 

Sensitivity studies were performed on chloride and oxygen concentrations. These studies 

examined the sensitivity of peak TEDE, time of the peak TEDE, and the cumulative EBS release, to chloride 

multiplication factor and oxygen partial pressure (see Volume I). The receptor group was assumed to be 

located either 5 or 20 km away from the repository. The TPI ranged from 10 to 30 kyr. One hundred 

realizations were performed for each simulation. Scatter plots were used to evaluate the influence of 

parameters on the dependent variables. 

Oxygen partial pressure was assumed to vary from 2.1 x 10- atmospheres (atm)

corresponding to conditions approached by careful deaeration of aqueous solutions (Gilroy and Mayne, 

1962)- to 0.21 atm, which corresponds to the maximum value expected in the near-field environment when 

the WP temperature decreases below the boiling point of water. 

Twenty different sensitivity studies were performed. Each sensitivity study differed from 

the others by the values assigned to chloride multiplication factor, oxygen partial pressure, distance to the

A-13 NUREG-1668
receptor group, TPI, or the distribution assumed for selected UZFLOW parameters. Oxygen partial pressure 

was assumed to be either fixed at 0.21 atm or was sampled on the range [2.1 x 10-5, 0.21]. Chloride 

multiplication factor was assumed to be either fixed (i.e., with values of 1, 8.25, 15.5, or 30) or was sampled 

on the range [1,30]. The upper value represents a condition close to solubility of NaCl. Four studies allowed 

oxygen partial pressure and chloride multiplication factor to vary concurrently. 

A2.3.2.4 Natural Analog 

Data from the Nopal I uranium deposit at Pefia Blanca, Chihuahua, Mexico, a natural analog 

to YM, were used to constrain release rates on a geologic time scale by calculating estimates of the maximum 

average rate of oxidative alteration of primary uraninite (an analog of SF) (Murphy and Pearcy, 1992; 

Murphy, et al., 1997). Making use of these analyses, this sensitivity study was based on the premises that 

the oxidation rate of SF in the proposed YM repository is comparable to the oxidation rate of uraninite at 

Pefia Blanca, and uranium solubility at Pefia Blanca was controlled by secondary uranyl minerals. For 

radionuclides incorporated in the matrix of SF, the oxidation rate can be reasonably assumed to be a 

conservative limit on the release rate. Preservation of large quantities of oxidized uranium at Pefia Blanca 

demonstrates that oxidation was rapid relative to removal of uranium from the system (Murphy and Pearcy, 

1992). An additional caveat attached to this alternate model is that consistency among a few dates on related 

oxidized uranium minerals at Pefia Blanca (Pickett and Murphy, 1997) suggests that oxidation may have 

occurred during a relatively brief geologic event with greater rapidity than the average rate. Also, no 

provision is made for radioelements that may be released more rapidly than those limited by the uranium 

oxidation rate (e.g., species in the fuel-cladding gap or on grain boundaries). In this sensitivity analysis, 

repository system performance was evaluated using alternate source term release rates and uranium solubility 

based on natural analog studies at Pefia Blanca. 

Murphy, et al. (1997) calculated a maximum bounding oxidation rate (i.e., the maximum 

average oxidation rate) for uraninite at Pefia Blanca based on the approach of Murphy and Pearcy (1992) and 

a revised minimum time for oxidation based on radiometric ages of late-forming uranophane (an oxidized 

uranium mineral) of 3 million years (Pickett and Murphy, 1997). The maximum average uranium oxidation 

rate for the YM repository, based on the maximum average rate of oxidation at Pefia Blanca, is 24 kg yr-'. 

Murphy and Pearcy (1992) adopted auranium solubility of 10-7 mole liter-' for their analysis 

of maximum average oxidation rate for Pefia Blanca. A variety of possible water chemistries and 

solubility-controlling minerals was examined, leading to a distribution of uranium solubility. A constant 

leach rate from SF was assumed. This leach rate was determined as follows. The maximum average oxidation 

rate for uraninite at Pefia Blanca corresponding to a solubility of uranium was determined. This oxidation 

rate was scaled to the YM inventory. Oxidation rate and effective SF surface area were used to calculate a 

constant leach rate. The release rate for each individual radionuclide is assumed to be equal to the oxidation 

rate times its fractional inventory in SF. Uranium solubility and its corresponding leach rate were sampled, 

assuming perfect correlation, for 100 realizations. Peak TEDE for a receptor group located 5 km away from 

the repository was calculated for TPIs of 100 kyr. 

A2.3.2.5 Preliminary Conclusions for IRSRs 

Cementitious Materials 

Cumulative release is invariant with respect to broad ranges of near-field matrix porosity and 

fracture permeability and the TPA 3.1.1 results are not sensitive to any of the four parameters of interest.

NUREG-1668 A- 14
Lack of sensitivity of repository performance to variations in the parameter of interest is attributed to an 

approximation adopted in TPA 3.1.1 to exclude from the UZ transport calculation stratigraphic units where 

groundwater travel times are less than a specified amount. Performance for these cases is independent of UZ 

transport properties of the excluded units. Using the mean data set resulted in fracture flow and short 

groundwater travel times and repository performance appearing to be independent of matrix porosity and 

fracture permeability in the near-field. The effect of hyperalkaline plume-enhanced radionuclide solubility 

on repository performance, shown by DOE sensitivity analyses to be important (Dale, 1996), still needs to 

be investigated. 

Colloids 

Results from the sensitivity studies indicate that time-to-peak dose, peak TEDE, and 

cumulative UZ releases are not influenced by variations in Rd and Kd for the welded Topopah Spring 

stratigraphic unit in TPA 3.1.1. It is believed that there was no matrix flow in the model as a result of setting 

the parameters at their mean values as described in Section A2.3.2.2, which would bypass the effect of 

changes in matrix sorption. Transport through fractures is sufficiently rapid to preclude fracture property 

changes from influencing the dependent variables. Additional sensitivity analyses with variable flow 

parameters should ensure that at least some realizations with matrix flow are included. Such studies will 

provide useful information on the relative importance of colloidal transport under matrix flow conditions. 

Oxygen Partial Pressure and Chloride Multiplication Factor 

Cumulative EBS release was found to be highly sensitive to chloride multiplication factor 

within a very narrow range of variation, exhibiting a step function response at a value of 10.0, where 

cumulative releases increase by approximately one order of magnitude. A similar step function is also 

apparent for oxygen partial pressure, with higher releases occurring for partial pressures greater than 3.0 x 

102 atm. The chloride concentration needs to exceed a threshold value to initiate localized corrosion. 

Therefore, even in the presence of sufficient oxygen, low chloride concentrations can lead to lower releases 

of radionuclides. Chloride multiplication factor appears to be more important than oxygen partial pressure 

for determining whether localized corrosion occurs and, consequently, cumulative EBS releases. Corrosion 

failure caused by uniform penetration of the WP walls occurs when chloride and oxygen concentrations 

result in corrosion potentials greter than the repasivation potential or chloride concentrations are lower than 

a critical value. Uniform passive corrosion leads to WP failure after approximately 29 kyr. It is important 

to emphasis that these studies were conducted assuming Alloy 825 as the inner overpack material. Results 

for Alloy 625 will be qualitatively similar, but failure of the WP causing radionuclide release would occur 

at higher chloride concentrations, because Alloy 625 has a higher repassivation potential than Alloy 825. 

Use of a chloride multiplication factor to address the uncertainties in chloride concentration, 

as evaluated by MULTIFLO (Lichtner and Seth, 1996) calculations, appears to be appropriate. The decrease 

in oxygen partial pressure to values almost an order of magnitude lower than those prevailing in the 

atmosphere does not appear to significantly influence repository performance as measured by cumulative 

EBS release or peak TEDE for a 20-kyr TPI. Unless oxygen is almost entirely consumed by oxidation 

reactions with man-made materials or components of the rock, the one-order-of-magnitude decrease in 

oxygen partial pressure used in the sensitivity studies is expected to encompass the spatial variability of 

oxygen partial pressure. However, there remains uncertainty in the importance of the spatial and temporal 

variation of the near-field, and the localized humid air or aqueous environment in contact with the WPs, that 

requires further investigation. Episodic wetting and drying cycles can have a significant effect on the aqueous 

concentration of chloride and oxygen, which have been found to be important considerations for repository

A-15 NUREG-1668
performance. DOE thermohydrology experiments could provide insight into the evolution of the near-field, 

which could facilitate efforts to constrain these parameters and reduce uncertainty in the calculated repository 

performance. 

Natural Analog 

Peak TEDE for release rates based on the Pefia Blanca natural analog were lower than those 

calculated using other release models. The results of the natural analog studies were evaluated further 

through the system-level sensitivity studies (see Section 4). 

A2.3.2.6 Significance to TPA 3.1.4. and TPA 3.2. 

The insights gained from the ENFE process-level sensitivity studies have several 

implications for the TPA code and the conduct of future sensitivity studies of the ENFE KTI. The chloride 

concentration factor that attempts to account for the uncertainty in potential dripping and evaporative 

concentration of solutes on the WP was shown to be important to performance of the WP (Alloy 825) and 

peak TEDE. These results support sampling of this parameter in total system performance sensitivity studies 

of Alloy 625 WPs. Oxygen partial pressure is constant and assumed to be at an atmospheric value in total 

system runs; however the results suggest that dose results are somewhat sensitive to its value. Nevertheless, 

process-level modeling using MULTIFLO (Lichtner, 1997) indicates that thermal effects on the oxygen 

partial pressure are limited to early time periods and thus this parameter should remain fixed at atmospheric 

concentration for total system-level sensitivity studies. 

The natural analog study, while somewhat limited in scope and not fully implemented within 

the TPA 3.1.1 code, demonstrated the potential importance of this alternative release model. This alternative 

release model was slightly modified and included within TPA 3.1.4. The results of this alternative release 

model also suggest that the solubility limits for radionuclides used for system-level sensitivity analyses 

should be compatible with the release model that is used. 

The TPA 3.1 .1 code is unable to evaluate the potential effects of some of the near-field 

(i.e.,within the EBS) processes, such as increased sorption caused by WP corrosion products of cementitious 

materials. An attempt to more fully incorporate the near-field processes will be implemented in future 

versions of the TPA code via a transfer function approach. 

A2.3.3 Igneous Activity 

The consequences of extrusive 1A, which involved the modules VOLCANO and 

ASHPLUME (Jarzemba, et at., 1997), were examined. In earlier studies (Hill, et al., 1996, 1997), the 

sensitivity of the volcanic deposit thickness was evaluated for different parameters. In the process-level 

sensitivity studies, parameters were set to mean values and one parameter was allowed to vary over its range. 

Also, all parameters were simultaneously allowed to vary over their range. Sensitivity was measured against 

the peak TEDE for volcanism. The TPI was 10 kyr, and receptor groups at 5 and 20 km were considered in 

the analysis. 

A number of parameters are used to describe the characteristics of the style of volcanic 

eruption. Volcanic eruption through the repository results in an ash deposit contaminated by the inventory

NUREG-1668 A-16
of 1-10 WPs. Doses from exposure to the contaminated ash deposit are controlled by inhalation. Inhalation 

doses are controlled by resuspension which, in TPA 3.1.1, uses the top 15 cm of the contaminated deposit. 

A2.3.3.1 Preliminary Conclusions for IRSRs 

The sensitivity studies show that when all parameters are set to mean values and the time 

of eruption is allowed to vary, peak TEDE exhibits exponential behavior over time. This behavior 

corresponds to radioactive decay of the repository inventory. Also, because mean peak TEDE is dominated 

by a few large-consequence events that occur only sporadically (i.e., output doses range over many orders 

of magnitude), it can be sensitive to the number of realizations used in the analyses if only a limited number 

are performed. 

Peak TEDE has been found to be relatively insensitive to a number of parameters. The tephra 

dispersion model is based on Suzuki (1983). In the Suzuki model, a dimensionless constant referred to as beta 

controls the overall shape of the eruption column; larger values of beta give broader umbrella clouds at the 

top of the column and disperse material farther downwind than smaller values of beta. Previous analyses 

(Hill, et al., 1996, 1997) have shown that tephra deposit thickness is relatively insensitive to column beta. 

Peak TEDE also was found to be insensitive to column beta. The entire inventory of the disrupted WPs may 

be entrained with the ash for small tephra particle sizes. Other parameters have been identified as not 

significantly affecting volcanological transport phenomena. These parameters are related to ash density, 

particle shape and the standard deviation of the ash particle size distribution. 

Eruption duration was found to be important for evaluating dose to the 5 km receptor group. 

Short-duration events result in a more concentrated ash deposit at 5 km. Longer-lived and larger eruptions 

dilute the dispersed inventory over a larger area. For a fixed amount of inventory released, the larger eruption 

will result in a larger ash-deposit area and more dilute concentrations of contaminants and lower values of 

peak TEDE. 

A number of parameters used in the modeling of volcanism vary over many orders of 

magnitude. Over their range, they could result in variations of peak TEDE that vary over one or more orders 

of magnitude. Therefore, there are a number of areas that have been identified as requiring additional 

evaluation. These areas include: wind direction, wind speed, eruption power, eruption duration, and the 

interaction of waste form and WP with the magma. 

A2.3.3.2 Significance to TPA 3.1.4 and TPA 3.2 

The resuspension factor measures the amount of fine-grained contaminated ash that is 

aerially suspended through wind shear and then aspirated. The peak TEDE was found to be insensitive to 

wind speed, once ash deposits are thicker than 15 cm. The sensitivity studies identified additional work on 

the resuspension factor as being necessary. The lower bound of the assumed range was poorly constrained 

and values lower than 2 x 10-9 m-n generally refer to deposits that are not analogous with waste in volcanic 

ash falls or are associated with mass-loading models that are not implemented in TPA 3.1.1. The 

resuspension approach used in TPA 3.1.1 was replaced by a mass-loading model in TPA 3.1.4, which was 

used in the system-level sensitivity studies. However, the current dose conversion factor may need to be 

corrected for thick ash blankets.

A-17 NUREG-1668
Structural Deformation and Seismicity

SDS examined two modules that simulated potential disruptive events for the repository. The 

first is SEISMO, which examines the potential importance of seismically induced rockfall to WP integrity. 

The second is FAULTO, which models direct disruption of WPs by fault displacement. Descriptions of the 

SEISMO and FAULTO modules can be found in Chapters 4.4 and 4.9, respectively, of Volume I of this 

report. 

A2.3.4.1 Seismicity 

Rockfall is modeled for five rock categories. The five categories of rock were derived from 

the quality index for YM rocks. If rockfall is determined to occur for any of the five rock categories, than 

a fraction of the emplaced WPs is assumed to be struck by falling rock. 

Parameters related to WP failure from rockfall were varied one at a time to evaluate the 

influence of each parameter on peak TEDE. Parameters were set at the lower bound, the mean value, and the 

upper bound; all other parameters were held at the mean value for the analysis. The TPI was 20 kyr. The 

receptor group was assumed to be 5 km away from the repository. 

A2.3.4.2 Fault Displacement 

Recurrence intervals for new or underappreciated faults were varied between 10 and 20 kyr. 

In a few analyses the faulting event was forced to occur within the first 500 yr. The threshold displacement 

was set at 0.05 m, which is equal to the minimum simulated fault displacement. All other parameters 

describing the faulting event are sampled over their entire range. The TPI was 20 kyr, and the receptor group 

was assumed to be either 5 or 20 km away from the repository. Many simulations were performed with 

corrosion parameters set to prevent corrosion failures from occurring within 20 kyr. This was done to better 

isolate the potential consequences of faulting. 

Two approaches were used to estimate the correlation between sampled parameters and a 

response variable (the normalized cumulative release from 40 CFR Part 191, peak TEDE, and number of WP 

failures). The first was a linear least-square regression with correlation coefficient as the measure of 

dependency. The second was a rank correlation using the Spearman rank-order correlation coefficient as the 

measure of dependency. 

A2.3.4.3 Preliminary Conclusions for IRSRs 

Waste package rupture from fault displacement or seismically induced rockfall and corrosion 

are competing failure mechanisms. For example, once a WP fails from corrosion, it is no longer available 

to fail by direct disruption from fault displacement or seismicity. Faulting may result in a slight increase in 

the number of early failures of WPs, but does not noticeably increase the total number of WPs that fail. The 

upper bound of fault zone width is not well-constrained. Current analyses of faults in the ESF show that fault 

zones are narrower than the upper bounds of 275 m (NW trending) or 365 m (NE trending) used in the 

process-level sensitivity studies. The probability of a large, or maximum number of WP failures from faulting 

is expected to be less than that found in the sensitivity studies because these studies assumed one faulting 

event per realization, which is more frequent than the upper bound on annual probability (2 x 10-5 yr- 1) 

would dictate. The sensitivity studies did not show faulting to be a significant WP failure mechanism. 

Therefore, the preliminary conclusion is that faulting is not significant to repository performance. Seismicity,

NUREG-1668

A2.3.4

A-18
however, can lead to a significant amount of premature failures when compared with corrosion. Although 

the models and assumptions are assumed to be conservative (i.e., they provide calculated consequences 

greater than expected, if the modeling were more realistic), the potential for spatial or temporal clustered 

activity; the potential for multiple ruptures and co-genetic, co-seismic, and co-volcanic events; increasing 

vulnerability of corroded WPs; strain rates larger than those derived from paleoseismic studies; and the 

combined effects of WP failure and focused flow may need to be considered in evaluating the influence of 

faulting on repository performance. This will be evaluated further in the development of the SDS IRSR Rev. 

1 (Stamatakos, et al., 1998). 

A2.3.4.4 Significance to TPA 3.1.4 And TPA 3.2 

The calculations related to rockfall established a conservative upper bound for the 

consequences of seismically induced rockfall. The potential for large numbers of WP failures early in the 

TPI from rockfall indicate that rockfall may influence dose calculations. Because a number of assumptions 

related to rockfall are believed to be conservative (see Volume I) and rockfall may strongly influence the 

magnitude of some peak dose calculations, it is appropriate to re-evaluate assumptions contributing to the 

conservatism in the calculation. For example, the large numbers of failed WPs are attributable to the 

assumption regarding the number of WPs affected by rockfall from a single seismic event. TPA 3.1.1 

assumes the fraction of WPs affected by rockfall is equal to the fraction of the repository having rock types 

that contribute to rockfall from the event. This assumption was changed in TPA 3.1.4 (see Volume 1). 

The bounding assumption on the fraction of the WPs that could fail from a single seismic 

event was made less conservative in TPA 3.1.4 (see Volume I). This change allows greater resolution when 

evaluating the contribution of seismicity to repository performance. The results of the SDS sensitivity studies 

indicate that applying the bounding assumption could have a sufficient effect on the calculated performance 

to warrant replacement with a more realistic approach. Since faulting was shown to have little effect on peak 

TEDE in the process-level studies, and it was thought that the models are conservative, very little has 

changed in the TPA 3.1.4 and 3.2 models for this process. 

A2.3.5 Thermal Effects on Flow 

TEF examined two thermohydrological conceptual process models (REFLUXI and 

REFLUX2) to investigate the refluxing mechanism. These two models are described in Volume I. 

REFLUXI and REFLUX2 were evaluated using property values taken from TSPA-93 and 

TSPA-95 and infiltration rates of 1.0, 5.5 and 10.0 mm/yr. Parameter values were held constant at the 

TSPA-93ITSPA-95 values. The REFLUX 1 parameters, REFLUX2 parameters, and a limited number of UZ 

flow parameters were allowed to vary. Correlation coefficients were determined for the REFLUX1 and 

REFLUX2 parameters using peak TEDE and cumulative release to the location to the receptor group as the 

dependent variables. The amount of water contacting the WPs was also calculated for each REFLUX 

parameter. The TPIs were 20 and 100 kyr for receptor groups located 5 and 20 km from the 

repository, respectively. 

Three process-level simulations were performed using infiltration rates of 1.0, 5.5, and 10.0 

mm/yr, and property values from TSPA-93 and TSPA-95. Temperature and relative humidity profiles were

A-19 NUREG-1668
calculated at the repository. These profiles were used in place of conduction-only temperature calculations 

used for the other process-level sensitivity studies. Peak TEDE, cumulative releases, time of WP failure, and 

number of WP failures were calculated for each set of temperature and relative humidity profiles. These 

calculations were for a 5 km receptor group location and a 10-kyr TPI. 

A2.3.5.1 Preliminary Conclusions for Issue Resolution Status Reports 

The model for WP corrosion in TPA 3.1.1 uses temperature and relative humidity in the drift 

to determine whether corrosion takes place. Infiltrating water in REFLUXI and REFLUX2 does not result 

in changes to either temperature or relative humidity in the near-field environment. Therefore, the timing of 

WP failure and, consequently, peak TEDE, are not sensitive to these parameters. This lack of sensitivity is 

an artifact of the model and thie significance of refluxing water cannot be ascertained without a connection 

between the refluxing water and WP corrosion. 

WPs would begin to fail, using the mean data set, after approximately 3.3 kyr. Reflux from 

ambient rock water was assumed to end no later than 3 kyr after closure (REFLUX2). Consequently, 

REFLUX2 was not expected to influence the timing of WP failure, peak TEDE, or cumulative release. 

The amount of water contacting WPs was insensitive to the input parameters in REFLUX1. 

The amount of water impacting the WPs in REFLUX2 did show some variability. However, unless dripping 

water results in increased corrosion of the WP or enhanced transport of contamination, this variability in the 

amount of dripping water will not correspond to changes in repository performance, as measured by peak 

TEDE or cumulative release. 

Process-level model simulations using MULTIFLO (Lichtner and Seth, 1996), did not use 

the current TPA reference data set. At the time of the sensitivity studies, MULTIFLO did not successfully 

run to completion with the reference data set values for the Topopah Spring welded unit. Therefore, TSPA-93 

and TSPA-95 values were used instead. The MULTIFLO simulations using parameter values derived from 

DOE PAs resulted in new temperature and relative humidity profiles, which could influence WP corrosion 

and time of failure. These simulations did show changes in the peak TEDE for the TPI, cumulative release, 

and time of WP failure. Efforts to incorporate the TPA base case parameter values in MULTIFLO are 

ongoing. 

A2.3.5.2 Significance to TPA 3.1.4 and TPA 3.2 

A better linkt between thermal reflux and WP performance is required before the importance 

of thermal effects on flow subissues can be ascertained. 

A2.3.6 Unsaturated and Saturated Flow under Isothermal Conditions 

USFIC examined the influence of hydrologic parameters in the flow and transport modules 

of TPA 3.1.1 and the modeling of peak TEDE, specifically the well pumping rate. The modules UZFLOW, 

UZFT, SZFT, and DCAGW were evaluated. They are described in Sections 4.1, 4.6, 4.7, and 4.8 of 

Volume I. The contributions of the hydrologic parameters to repository performance were evaluated using 

the measures peak TEDE, cumulative release to the location of the critical group, UZ groundwater travel 

time, and SZ groundwater travel time. Receptor groups were located 5 and 20 km away from the repository. 

Simulations were carried out to 50 and 100 kyr for the respective groups.

NUREG-1668 A-20
All 64 hydrologic parameters were varied concurrently and treated as uncorrelated variables. 

The response of each of the four measures of repository performance to each of the hydrologic parameters 

was evaluated. Stepwise regression was used to rank the relative importance of each of the independent 

variables to peak TEDE. Multiple and univariate linear regression models were constructed with the 

estimated linear model coefficients and were then scaled. USFIC also conducted sensitivity studies where 

parameters were varied one at a time, while all other parameters were fixed. 

Local measures of sensitivity were evaluated using perturbation and chord-slope methods. 

These calculations were performed for the four variables determined, through stepwise regression, to have 

the greatest explanatory value for a measure of repository performance. Chord-slope measures of sensitivity 

were performed at the minimum and peak value of the independent variable. A perturbation method was used 

to determine a scaled sensitivity at the mean data set values of the independent variables. 

In each UZ transport leg, it was assumed that water either moves within the matrix or 

fractures, and remains the same for each simulation (see Volume I). Parameters are sampled for three matrix 

properties and three fracture properties within each unit. If the saturated hydraulic conductivity (K.) of the 

matrix is larger than the percolation flux, then water is assumed to flow exclusively in the matrix, whenever 

this condition occurs. However, if matrix K., is smaller than the percolation flux, then all water is assumed 

to flow in the fractures. Many of the layers have maximum matrix K. that exceed the expected fluxes, but 

the geometric mean of the assumed distributions is less than the maximum expected flux. This will result in 

matrix flow through the unit for some realizations and fracture flow in others. 

A2.3.6.1 Preliminary Conclusions for IRSRs 

Infiltration-affecting parameters were found to be important contributors for most of the 

performance measures examined. In particular, ArealAverageMeanAnnuallnfiltrationatStart and 

MeanAnnualPrecipitationMultiplieratGlacialMaximum were important in explaining peak TEDE at 5 km. 

Peak TEDE for 20 km away from the repository was the only performance measure where the infiltration

affecting parameters were not among the most important contributors. In contrast, parameters affecting 

dilution at the wellhead (i.e., well pumping rate and production zone thickness) appeared to have the greatest 

influence on peak TEDE at 20 km, and were important for explaining peak TEDE at 5 km. 

Groundwater flow in the UZ units was in the matrix for some realizations and in fractures 

for others. This arises from assumed ranges, for matrix K•, that overlap the assumed range of the expected 

infiltration rates. If infiltration is greater than the sampled K. for a given unit, then flow is through fractures; 

otherwise, groundwater flow is through the matrix. For a unit experiencing matrix flow, fracture properties 

do not enter into the calculation. A similar situation arises for matrix properties, when flow is through 

fractures. Because many of the sampled hydrologic properties are only used for some of the calculations, it 

is more difficult to gauge their importance. However, the transition between matrix flow and fracture flow 

is important so the assumed range of matrix K.at for some units may require greater scrutiny. 

Fracture flow tends to be orders of magnitude faster than matrix flow, with water passing 

through a layer in a matter of decades or less. On the time scale of repository performance and decay of the 

controlling radionuclides, travel times through fractures are almost negligible. In the sensitivity studies, the 

majority of the transport time radionuclides spent in the UZ was in the nonvitric layer of the Calico Hills 

(CH,v), where matrix flow dominated. Therefore, hydrologic properties of the CHn, matrix are important. 

Since flow through other stratigraphic units often occurred in fractures, UZ travel time and peak TEDE are 

not very sensitive to properties in these stratigraphic units.

A-21 NUREG-1668
SZ groundwater travel time is most sensitive to fracture porosity values. Relative 

groundwater travel times through segments in the SZ streamtubes depend on segment length, mean cross

sectional area, effective porosity, and hydraulic conductivity. Therefore, longer streamtube legs and legs with 

higher effective porosities or lower hydraulic conductivity will tend to have longer groundwater travel times. 

Parameters that strongly influence travel times through these segments also may influence total SZ 

groundwater travel time, and potentially, peak TEDE. Peak TEDE is sensitive to the effective porosity of the 

composite tuff-alluvium leg, tfhe fractured tuff leg, and the Amargosa Valley (alluvium) leg. Saturated zone 

dispersion length also has some significance. 

The USFIC sensitivity studies treated flow parameters as uncorrelated variables. Because 

some flow parameters found to be important contributors to groundwater travel time and, consequently, peak 

TEDE are correlated with other parameters, the influence of these correlations on the transport calculations 

needs further investigation. The transport calculations also are influenced by assumed distributions of 

parameters, which are a functlion of the available data. In particular, the amount of hydrologic information 

for the saturated alluvium is extremely sparse and is insufficient to adequately constrain the performance of 

alluvium. To bound the performance of saturated alluvium, more information is needed (e.g., hydraulic 

conductivity, porosity, measured head, location of the tuff/alluvial contact, and its ability to retard 

radionuclides). 

A2.3.6.2 Significance to TPA 3.1.4 and TPA 3.2 

Americium and plutonium were significant contributors to peak TEDE for a receptor group 

5 km away from the repository. Although these radionuclides have high sorption in porous media, nearly all 

transport to the 5 km receptor group is through fracture flow pathways, for which the model assumes no 

retardation. 

The following aspects of TPA 3.1.1 were identified as requiring additional examination and, 

possibly, modification: well pumping rates (5-kmn and 20-km receptor groups); mixing zone thickness (5 km 

and 20 kin); and width of the streamtubes at 20 km. Also, the treatment of matrix diffusion and/or the 

parameters related to sorption of radionuclides such as americium and plutonium need confirmation.

NUREG-1668 A-22
APPENDIX A REFERENCES 

Bish, D.L., et al., 1996. Summary and Synthesis Report on Mineralogy and Petrology Studies for the Yucca 

Mountain Site Characterization Project. LA-EES-1-TIP-96-009. Los Alamos, NM: Los Alamos 

National Laboratory. 

Bradbury, M.H., and F.A. Sarott. 1995. Sorption Databases for the Cementitious Near-Field of a LIJLW 

Repository for Performance Assessment. PSI 95-06. Villigen, Switzerland: Paul Scherrer Institute. 

Campbell, A.C., and K.M. Krupka. 1997. Application of geochemical data and modeling in performance 

assessments of low-level radioactive waste disposal facilities. Symposium on Hydrogeologic and 

Geochemical Aspects of Waste Disposal, Waste Management 97, Tucson, Arizona. 

Dale, T.F. 1996. RIP Simulations to Investigate the Possible Consequences of Cement Placement within the 

Potential Yucca Mountain Repository. Attachment in Status/Summary Report for Fiscal Year 1996 

Activities Within the Performance Assessment Overview Study on the Consequences of Cementitious 

Materials. LV.PA.DCS.09/96-038. Las Vegas, NV: TRW Environmental Safety Systems. 

Gilroy, D., and J.E.O. Mayne. 1962. The deaeration of aqueous solutions. Journal ofApplied Chemistry 12: 

382-384. 

Hill, B.E., C.B. Connor, and J.S. Trapp. 1996. Igneous Activity. NRC High-Level Radioactive Waste 

Program Annual Progress Report, Fiscal Year 1996. CNWRA 96-01A. San Antonio, TX: Center 

for Nuclear Waste Regulatory Analyses: 2-1 to 2-32. 

Hill, B.E., et al., 1997.1995 Eruptions of Cerro Negro Volcano, Nicaragua, and Risk Assessmentfor Future 

Eruptions. San Antonio, TX: Center for Nuclear Waste Regulatory Analyses. 

Jarzemba, M.S., P.A. LaPlante, and K.J. Poor. 1997. Ashplume Code Version 1.0 Model Description and 

User's Guide. CNWRA 97-004. San Antonio, TX: Center for Nuclear Waste Regulatory Analyses. 

Lichtner, P.C., and J. Eikenberg. 1995. Propagation of a Hyperalkaline Plume into the Geological Barrier 

Surrounding a Radioactive Waste Repository. PSI-Report 95-01. Wurenlingen, Switzerland: Paul 

Scherrer Institute. 

Lichtner, P.C., and M.S. Seth. 1996. User's Manualfor MULTIFLO: Part II-MULTIFLO 1.0 and GEM 1.0, 

Multicomponent-Multiphase Reactive Transport Model. CNWRA 96-010. San Antonio, TX: Center 

for Nuclear Waste Regulatory Analyses. 

Lichtner, P.C. 1997. Estimate of Near-Field Chemistry Based on MULTIFLO Calculations. San Antonio, 

TX: Center for Nuclear Waste Regulatory Analyses. 

Mohanty, S., et al., 1997. Engineered Barrier System Performance Assessment Code: ESPAC Version 1.1: 

Technical Description and User's Manual. CNWRA 97-006. San Antonio, TX: Center for Nuclear 

Waste Regulatory Analyses.

A-23 NUREG-1668
Murphy, W.M., and E.C. Pearcy. 1992. Source-term constraints for the proposed repository at Yucca 

Mountain, Nevada, derived from the natural analog at Pefia Blanca, Mexico. Scientific Basis for 

Nuclear Waste Management XV. C.G. Sombret, ed. Materials Research Society Symposium 

Proceedings: 257: 521-527. 

Murphy, W.M., E.C. Pearcy, and D.A. Pickett.. 1997. Natural analog studies at Pefia Blanca and Santorini. 

Seventh CEC Natural Analogue Working Group Meeting Proceedings.H. von Maravic and 

J. Smellie, eds. EUR 1.7851 EN. Luxembourg. European Commission: 105-112. 

Pickett, D.A., and W.M. Murphy. 1997. Isotopic constraints on radionuclide transport at Pefia Blanca. 

Seventh CEC Natural Analogue Working Group Meeting Proceedings. H. von Maravic and 

J. Smellie, eds. EUR 1.7851 EN, Luxembourg. European Commission: 113-122. 

Sagar, B. 1996. NRC High-Level Radioactive Waste Program Annual Progress Report: Fiscal Year 1996. 

NUREG/CR-6513. San Antonio, TX: Center for Nuclear Waste Regulatory Analyses. 

Stamatakos, J.A., D.A. Ferrill, D. Simms, R. Chen, and H.L. McKague. 1998. Technical Input on Review 

and Acceptance Criteria for Issue Resolution Status Report, Rev. 1.0: Key Technical Issue: 

Structural Deformation and Seismicity. San Antonio, TX: Center for Nuclear Waste Regulatory 

Analyses. 

Steefel, C.I., and P.C. Lichtner. 1994. Diffusion and reaction in rock matrix bordering a hyperalkaline fluid

filled fracture. Geochimica et Cosmochimica Acta 58: 3,595-3,612. 

Suzuki, T. 1983. A theoretical model for the dispersion of tephra. Arc Volcanism: Physics and Tectonics. 

D. Shimozuru and I. Yokyama, eds. Tokyo, Japan: Terra Scientific Publishing Company 95-113. 

U.S. Department of Energy. 1998. Repository Safety Strategy: U.S. Department of Energy's Strategy to 

Protect Public Health and Safety After Closure of a Yucca Mountain Repository. Revision 1. 

YMP/96-01. U.S. Department of Energy: Office of Civilian Radioactive Waste Management. 

Wilson, M.L., et al., 1994. Total-System Performance Assessment for Yucca Mountain - SNL Second 

Iteration (TSPA-1993). Volume 2. SAND3-2675. Albuquerque, NM: Sandia National Laboratories. 

Wolery, T.J. 1992. EQ3NR, A Computer Program for Geochemical Aqueous Speciation-Solubility 

Calculations: Theoretical Manual, User's Guide, and Related Documentation (Version 7.0). 

UCRL-MA-1 10662-PT-InI. Livermore, CA: Lawrence Livermore National Laboratory.

NUREG-1668 A-24
APPENDIX B 

ALTERNATIVE CONCEPTUAL MODEL AND SCENARIO 

CASE COMPLEMENTARY CUMULATIVE 

DISTRIBUTION FUNCTIONS
101 10 / 

-o 

Mean Value =12.5 marem 

Standard Deviation of Mean Value = 2.9 mrem_ 

2 10

1 -3 

10-6 10-5 10-4 10-3 10-2 10-1 10°0 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-1. A CCDF for the no-retardation alternative conceptual model for a TPI of 10 

kyr.

B-1 NUREG-1668
100 0.. 

10 "1 

V alue 116 m rem 

oo0. Standard Deviation of Mean Value = 10 mrem 

X 

O -2 10

1 0 -3 1 . . .1.1 R I , I , R ill- , . I a.. pood.. I I. . ...... I a. . ... 

10a6 10-5 10-4 10- 10-2 10-1 10° 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-2. A CCDF for the no retardation alternative conceptual model for a TPI of 50 

kyr.

NUJREG-1668 B-2
1•U . . .... I . . .. ... . . ... 5 .... I S ...... I If j . . ..... mlS~ . ' S 

1 0

S 10 

-o 

X 

w. Mean Value =11.3 mnremn 

o Standard Deviation of Mean Value 1.7 mrem 

0 04 1 0-2 

10. 

0-31 

10-6 10-5 10-4 10-3 10-2 10"1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-3. A CCDF for the all WP initially defective alternative conceptual model for a 

TPI of 10 kyr.

B-3 NUREG-1668
10°0 

. 1 0 "1 

Mean Value = 68.3 mrem 

Standard Deviation of Mean Value = 9.3 mrem 

0 

0 -2 

102 

1 0 -3 1 ,,,,,I . I .a .I .1, ... 1,1 1 1 , loll.. , I ,1,, 1. I 1 1 1 . ...

10-6 10-5 10-4 10-3 10-2 101 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-4. A CCDF for the all WP initially defective alternative conceptual model for a 

TPI of 50 kyr.

NUREG-1668 B-4
10 0 

0= 10-1 

-o 

Mean Value = 3. 10 marem 

Standard Deviation of Mean Value = 0.42 mrem 

0 

0 

-3 

10 -6 -4-2

1 0 - 1 a.. . a I a , , , , , , I . . . . . . . . I . I. ... 

10-6 105 10-4 10"3 10-2 101 1 0 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-5. A CCDF for the flow through with carbonate dissolution alternative 

conceptual model for a TPI of 10 kyr.

B-5 NUTREG-1668
100

10-1 / 

Mean Value = 9.76 mrem 

-D Standard Deviation of Mean Value = 0.80 mrem 

X 

10-00 10-6 10-5 10-4 1 0-3 10-2 1 01 1 00 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-6. A CCDF for the flow through with carbonate dissolution alternative 

conceptual model for a TPI of 50 kyr.

NUREG-1668 B-6
10 0 

o 10 

Mean Value = 1.88 mrem 

4-4W Standard Deviation of Mean Value = 0.45 mrem 

0 

0 10-2 

10-311- ii I 

10-6 10-5 10-4 10-3 10-2 10-1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-7. A CCDF for the focused flow (4 times flow to 1/4 the WPs) alternative 

conceptual model for a TPI of 10 kyr.

B-7 NUREG-1668
100

l- / 

o 10

Mean Value = 7.8 mrem 

Standard. Deviation of Mean Value = 1.0 mrem 

-2 

10 

10"6 10-5 10-4 10-3 10-2 10-1 10 0 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-8. A CCDF for the focused flow (4 times flow to 1/4 the WPs) alternative 

conceptual model for a TPI of 50 kyr.

NUREG-1668 B-8
100

S 10-1 

U 10" 

0 

-Mean Value = 1.18 mrem 

Standard Deviation of Mean Value = 0.56 mremn 

0 91 10

1n-3 , , ,,,,,I . . .. . . .1 .* . *...... * . . ,...... * . .......i . . .. . 

1 - 6 -5-1

10-6 10-5 10-4 10-3 10-2 10"1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-9. A CCDF for the backfill emplaced at 100 years alternative conceptual model for 

a TPI of 10 kyr.

B-9 NUREG-1668
100 I0° 

10-1 

7) Mean Value --22.6 mremr 

Standard Deviation of Mean Value = 3.5 mnremrn 

4-4 

0 

-o 

2 10 

10 -3 1 C C t - I I 1 i. 1 . . .. .CI. .. .I . . . .. 

10-6 10-5 10-4 10-3 10-2 10-1 1 0 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-10. A CCDF for the backfill emplaced at 100 years alternative conceptual model 

for a TPI of 50 kyr. 

NUREG-1668 B-10
1 00

-1 S 10 

-o 

10-2 

1 "3 .g..., * , I , , , , , a . I .. * . . .w.. .I . . . ..i . .I . . . .. 

10-6 10.5 10-4 10-3 10-2 10-1 1 00 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-11. A CCDF for the matrix diffusion alternative conceptual model for a TPI of 10 

kyr.

B-11 NUREG-1668
100

~. 10-1 / 1 0 Mean Value = 15.7 mrem 

Standard deviation of Mean Value = 2.0 mrem 

0 

0 10-2 

1 0 -3 . 1. 1 1, . ,2 1. 1 1 1 1 1 1 1 I 1..... . . .. 1 . . . .... 

10-6 10-5 10-4 10-3 10-2 101 1 0 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-12. A CCDF for the matrix diffusion alternative conceptual model for a TPI of 50 

kyr.

NUREG-1668 B-12
10° 

Mean Value = 0.057 mrem 

Standard Deviation of Mean Value = 0.029 mrem 

o 10

-o 4 

2-2 

102 

i0-3 , . ... I .. I , , ,,,,.I , s s..... a .. . . . .. .... 

10-6 10-5 10-4 10-3 10-2 10-1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-13. A CCDF for the Alloy C-22 inner overpack alternative conceptual model for a 

TPI of 10 kyr.

B-13 NUREG-1668
10°

10-1/ 

Mean Value = 14.0 mrem 

"Standard Deviation of Mean Value = 1.0 mrem 

4-) 

X 

0-2 

1 i" 1"0 

1 0 -3 ,1.. . .1 .. .. 1 m ild.. .1 11... 1,,I . . . .. . . 1.. 

1 0-6 1 0"5 10-4 10-3 1 0-2 1 0"1 10 0 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-14. A CCDF for the Alloy C-22 inner overpack alternative conceptual model for a 

TPI of 50 kyr.

NUREG-1668 B-14
100

0• 10

01 S0-2 

10-3 . 1...,. 

10-6 10-5 10-4 10-3 10-2 10-1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-15. A CCDF for 1/100 flow through alternative conceptual model for a TPI of 10 

kyr.

B-15 NUREG-1668
1 0 

10I 

V alue = 3.38 m rem 

Standard Deviation of Mean Value = 0.44 mrem 

4-. 

0 

O -2 10

0 - 6 1 -1 10 1 100a 

106 105 104 10-31- 011 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-16. A CCDF for 1/100 flow through alternative conceptual model for a TPI of 50 

kyr.

NUREG-1668 B-16
100

O 101 

0 

4-o 

0 

01O-2 

00

i n o - . . .3 , . . . . . .. . * , . . .. . .a . . .... a .a. .. . . .a . a... .. 

10-6 10-5 10-4 10"3 10-2 10-1 1 00 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-17. A CCDF for the natural analogue release alternative conceptual model for a 

TPI of 10 kyr.

B-17 NB7TREG-1668
10°

10-1/ 

Mean Value = 4.61 mrem 

Standard Deviation of Mean Value = 0.37 mrem 

0 

(U 

, 10-2 

i0o 3 1 1~ a I Id I I ., ,I . .1 .31 t 1 Id1.. I I I333 a I. I 1 2 

10-6 10-5 10-4 10-3 10-2 10-1 1 00 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-18. A CCDF for the natural analogue release alternative conceptual model for a 

TPI of 50 kyr. 

NUREG-1668 B-18
"• 0 0 1 ... .. a 4 ' ... 1 .... 

Mean Value = 0.16 mrem 

Standard Deviation of Mean Value = 0.03 mrem 

10-1 

-o a.,) 

-o 

0 10 

1 0 -3 12 1 1 

10- 10- - 16 10-2 10-1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-19. A CCDF for the Alloy C-22 inner overpack with less flow diversion alternative 

conceptual model for a TPI of 10 kyr.

B-19 NUTREG-1668
10 0

1 

o Mean Value = 15.1 mrem 

SStandard Deviation of Mean Value = 1.8 mrem 

X ° 

O "$- 10 -2 

103 

1 0 " .,. . . .. . . . . , , . .t , , , , ,,,,I .3.. . ... I I , 333,, 

10-6 10-5 10-4 10-3 10-2 10-1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-20. A CCDF for the Alloy C-22 inner overpack with less flow diversion alternative 

conceptual model for a TPI of 50 kyr.

NUREG-1668 B-20
1 00

00 

-o 

100 

-D

10-3 

10-6 10-5 10-4 10-3 10-2 1o01 1 00 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-21. A CCDF for the cladding protection alternative conceptual model for a TPI of

B-21 NUREG-1668
1 00

0 0-1 

o 101 

50kr., 

0 

t., 

• ] 0-2 

3 .*n . . . . . . . . ., ,, . ... i . . . .. . , , , p, i. . , , .. 

/ 06 1-5 1 -4 1 -3 1 -2 1 01 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-22. A CCDF for the cladding protection alternative conceptual model for a TPI of 

50 kyr. 

NUREG- 1668 B-22
10-3

4 

o 10

-o 

0 

S 10 

1 0 "6 . . .. . . .. . . . t . I .... I Bil . . .. . d I . . ...... I talks[ 

10-3 10-2 10-1 100 101 102 103 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-23. A CCDF for the nominal case with volcanism for a TPI of 10 kyr. 

B-23 NUREG-1668
10-2

-3 

o 10 

-o 

I.Q 

0 

1• -4 4 10 

10 -5 1 .1. t , 1, 1 -,1 .1 .I.. I1... .1 .. I . .aI .. a 1. . . 

10 110 10 100 101 102 10 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-24. A CCDF for the nominal case with volcanism for a TPI of 50 kyr.

NUREG-1668 B-24
"10u . . . .. I . . . I .. .. . ... a a a .. a... a . .. ..... I . . .. . 

S 10-4 

0 

0 

0-4 10 

10 -3 , ,1,, 1 11 , , 1 , , I. 2 . .1 .1. .. . . 1 .. 1.. . ..... .... 

100 10 4 10-3 102 10-1 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-25. A CCDF for the nominal case with faulting for a TPI of 10 kyr.

B-25 NUREG-1668
1 0.,..0 . .. . .J .. I . . . . .... I . . . .... *U I . . . .... * IU . . . .UU ... I B. .. . 

o 10 

(Ul 

-o 

0 1 

10 -3 . . . ..... UI 1 111 . , 1 , , , fil. .... I . . .....t 1 , , ,m,, 

10 101 10 10 10 10- 100 

Peak Annual Total Effective Dose Equivalent (rem) 

Figure B-26. A CCDF for the nominal case with faulting for a TPI of 50 kyr.

NUREG-1668

I

B-26
APPENDIX C 

IDENTIFICATION OF PARAMETERS LISTED AS 

SENSITIVE OR UNCERTAIN
The TPA 3.1.4 code input parameters that may be influential on peak dose or to which peak dose is 

sensitive are identified in the text of this document as nicknames of the long name used in the code. This 

appendix lists the short name, the name used in the TPA 3.1.4 code (full name), and gives a short 

description of what the parameter represents.

Short Name: 

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AA2 1 

AA_2_ I [C/m2/yr] 

A corrosion rate (passive current density) for the Waste Package (WP) inner overpack in 

EBSFAIL. 

AAMAI@S 

ArealAverageMeanAnnuallnfiltrationAtStartjmm/yr] 

Mean areal average infiltration into the subsurface at the start of a TPA 3.1.4 run. 

ABMLFVDC 

AirborneMassLoadForVolcanismDoseCalculation[g/m3] 

Mass load of ash/SF from volcanic event in the air available for inhalation by a receptor. 

APrs SAV 

AlluviumMatrixPorositySAV 

Amargosa Valley alluvium saturated zone matrix porosity. 

ARDSAV I 

AlluviumMatrixRDSAV_I 

Matrix retardation for iodine in the saturated zone of Amargosa Valley alluvium. 

ARDSAVAm 

AlluviumMatrixRDSAVAm 

Matrix retardation for americium in the saturated zone of Amargosa Valley alluvium. 

ARDSAVNp 

AlluviumMatrixRDSAVNp 

Matrix retardation for neptunium in the saturated zone of Amargosa Valley alluvium. 

AshMnPLD 

AshMeanParticleLogDiameter[d_inscm] 

Relative size of ash/SF particulates from a volcanic event. 

Chlorid 

ChlorideMultFactor 

Chloride concentration in matrix pore water is multiplied by this factor to compensate 

for dripping and drying that would lead to salt accumulation. 

CladCorF 

CladdingCorrectionFactor 

A variable allowing for increased SF protection, in a WP, because of the presence of 

cladding on the SF.

C-1 NUREG- 1668
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CritRHAC 

CriticalRelati veHumidityAqueousCorrosion 

The relative humidity at which corrosion of the outer overpack goes from dry air to 

humid air conrosion or to the more rapid aqueous corrosion. 

DispSAV 

DispersionFractionSAV 

Dispersion fraction in saturated zone of Amargosa Valley alluvium. 

Fmult 

Fmult 

The fraction of water infiltrating to the repository from the unsaturated zone above the 

repository that will enter the WP and contribute to the release of radionuclides. Water 

dripping toward the drifts may be diverted around the drift due to capillary action, may 

be diverted down the side of the drift, or may not enter the WP for other reasons. 

Fow 

Fow 

A flow focusing factor which expresses the flow potentially reaching a wetted WP (can 

be greater or less than 1.0) 

InitRSFP 

InitialRadiusOfSFParticle 

Initial Radius of Spent Fuel Particle affects SF alteration rate and transport out of a 

failed WP in EBSREL. 

MAPM@GM 

MeanAnnualPrecipitationMultiplierAtGlacialMaximum 

Mean annual precipitation increase at glacial maximum affects infiltration from the land 

surface in UZFLOW. 

MATI@GM[ 

MeanAnnualTemperaturelncreaseAtGlacialMaximum 

Magnitude of mean annual temperature change at glacial maximum affects infiltration 

from the land surface in UZFLOW. 

MixZnT20 

MixingZoneThickness[20km] 

Mixing zone thickness in a well at a receptor group 20 km from YM. 

MKdChvRa 

MatrixKD_CHnvRa[m3/kg] 

Matrix Kd for radium in the Calico Hills (non-vitric) stratigraphic unit 

MKdTSwAin 

MatrixKD_TswAmfm3/kg] 

Matrix Kd for americium in the Tonopah Spring (welded) stratigraphic unit.

NUREG-1668 C-2
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MKdUFZPb 

MatrixKDUFZPb[m3/kg] 

Matrix Kd for lead in the unsaturated zone fault zone. 

00-Cofic 

CoefForLocCorrOfOuterOverpack 

Coefficient in EBSFAIL equation for time-dependent pit penetration. 

SbArWet% 

SubAreaWetFraction 

Fraction of a subarea wetted by water infiltrating into the repository. 

SFWttd% 

SFWettedFraction 

Fraction of the SF wetted by moisture entering a failed WP. 

SSMOHetF 

SeismicHeterogeneityFactor 

Factor relating earthquake magnitude at surface to magnitude at repository depth. 

SSMO-JS2 

SEISMOJointSpacing2[m] 

Length and width of the falling rock for rock type 2 in SEISMO. 

SSMO-JS4 

SEISMOJointSpacing4[m] 

Length and width of the falling rock for rock type 4 in SEISMO. 

VC-Dia 

DiameterOfVolcanicCone[m] 

Diameter of volcanic cone. 

VE- Power 

VolcanicEventPower[W] 

Volcanic event power. 

VEROI-Tn 

TimeOfNextVolcanicEventRegionOflnterest[yr] 

Time of the next volcanic event that intersects the YMR. 

WindSpd 

WindSpeed[cm/s] 

Wind speed used in dispersal of ash ejected during an extrusive volcanic event. 

WPDef% 

DefectiveFractionOfWPs/cell 

Fraction of WPs in each subarea that are initially defective.

C-3 NUREG- 1668
Short Name: 

Full Name: 

Description: 

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Description:

WprCg@20 

WellPumpingRateAtCriticalGroup2Okm[gal/day] 

Well pumping rate at the 20 km receptor group location. 

YMR-TC 

ThermalConductivityofYMRock[W/(m-K)] 

Thermal conductivity of rock in the YM repository near field.

NUREG- 1668 C-4
APPENDIX D 

BASELINE VALUES FOR ALL PARAMETERS EVALUATED 

IN DIFFERENTIAL ANALYSIS
Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quantile'Values 10th Quantile Random 1 Random 2 

Values Values Values 

AA_.2_I [C/m2/yr] 41500 58700 24300 31100 55200 

AirborneMassLoadForVolcanismDoseCalculation[g/m3] 1.00e-03 6.31 e-03 1.60e-04 1.90e-03 1.50e-04 

AlluviumMatrixPorositySAV 0.125 0.145 0.105 0.144 0.137 

AlluviumMatrixRDSAVSe 22 269 1.86 490 2.79 

AlluviumMatrixRDSAVNi 89 3257 2.46 1.13 37.1 

AlluviumMatrixRDSAVTc 5.5 21.4 1.41 2.81 8.5 

lluviumMatrixRDSAVPu 420 925 19.5 20.9 3450 

lluviumMatrixRD-SAVTh 7700 22883 2622 2398 6022 

lluviumMatrixRD_SAVAm 630 1.1Oe+05 3.63 17600 68.8 

AlluviumMatrixRDSAVNb 7700 22900 2622 11300 2466 

lluviumMatrixRDSAVRa 4000 6964 2297 2951 2555 

,lluviumMatrixRDSAVU 17 170 1.77 5.48 8.91 

lluviumMatrixRDSAVCs 95000 99000 91000 94000 93700 

lluviumMatrixRDSAVPb 3601 6964 2297 2156 3004 

lluviumMatrixRDSAVI 2 3.5 1.15 2.29 3.1 

lluviumMatrixRDSAVNp 17 170 1.77 1.85 245.3 

AngleOfVolcanicDikeMeasuredFromNorthClockwise[degrees] 7.5 13.5 1.5 7.9 5.93 

ArealAverageMeanAnnuallnfiltrationAtStart[mm/yr] 5.5 9.1 1.9 2.94 8.98

00
C, 

00

Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quantile Values 10th Quantile Random 1 Random 2 

Values Values Values 

AshMeanParticleLogDiameter[dcin.cml 0.1 0.357 0.028 0.05 0.42 

ChlorideMultFactor 15.5 27.1 3.9 28.6 16.4 

CladdingCorrectionFactor 1 1 1 1 1 

koefForLocCorrOfOuterOverpack[m/yrl/2] 4.76e-03 7.88e-03 1.64e-03 6.08e-03 2.21e-03 

CriticalRelativeHumidityAqueousCorrosion 0.8 0.821 0.78 0.8 0.78 

DefectiveFractionOfWPs/cell 5.05e-03 9.Ole-03 1.09e-03 4.28e-03 2.38e-04 

DiameterOfVolcanicCone[m] 51.25 72.57 29.93 37.23 24.7 

DispersionFraction-SAV 0.1 0.1 0.1 0.1 0.1 

DispersionFractionSTFF 0.01 0,01 0.01 0.01 0.01 

multFactor 0.045 0.083 10.024 0.082 0.0287 

FowFactor 0.173 0.565 0.053 0.054 0.528 

FracturePermeabilityPPw_[m2] 6.00e- 13 4.10e-12 8.90e-14 2.80e- 13 3.67e- 12 

FracturePermeabilityjTSw_[m2] 8.00e-13 5.40e-12 1.20e-13 7.40e- 13 3.84e-14 

FracturePermeabilityCHnz[m2] 6.00e-1 3 4.10e-12 8.90e-14 2.30e-13 8.59e- 13 

FracturePermeability.BFw[m2] 3.00e- 13 2.00e-12 4.40e-14 1.30e-13 6.14e-14 

FracturePermeabilityCHnv[m2] 8.00e-13 5.40e-12 1.20e-13 3.30e-13 2.42e-12 

FracturePermeabilityUFZ_[m2] 1.00e-12 5.20e- 17 3.80e- 13 5.80e- 13 7.44e- 13 

FracturePermeabilityUCFjm2] 6.00e- 13 4.10e-12 8.90e-14 3.70e- 13 1.93e-11

F
Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quantile Values 10th Quantile Random I Random 2 

Values Values Values 

FracturePorosityBFw_ 3.20e-03 7.90e-03 1.30e-03 2.1Oe-03 8.05e-03 

FracturePorosityCHnz 3.20e-03 7.90e-03 1.30e-03 3.20e-03 6.30e-03 

FracturePorosityCHnv 3.20e-03 7.90e-03 1.30e-03 2.90e-03 5.38e-03 

FracturePorosityPPw. 3.20e-03 7.90e-03 1.30e-03 1.50e-03 6.82e-03 

FracturePorositySTFF 3.20e-03 7.90e-03 1.30e-03 2.1Oe-03 6.05e-03 

FracturePorosityTSw_ 3.20e-03 7.90e-03 1.30e-03 2.30e-03 2.47e-03 

FracturePorosity_.UCF_. 3.20e-03 7.90e-03 1.30e-03 5.80e-03 5.06e-03 

FracturePorosityUFZ_ 3.20e-03 7.90e-03 1.30e-03 1.20e-03 2.37e-03 

[nitialRadiusOfSFParticle[m] 1.85e-03 2.33e-03 1.37e-03 1.54e-03 2.04e-03 

[nnerOverpackErplntercept (value for alloy 625, value for alloy C- 98.5, 1140 138.5, 1220 58.5, 1060 56.4, 1056 54.2, 1051.5 

22) 

LengthOfVolcanicDike[m] 6500 10100 2900 5561 6075 

MatrixKDBFwAm[m3/kgl 0.45 1.48 0.135 1.73 0.31 

MatrixKDBFwCs[m3/kg] 0.14 0.676 0.0295 0.025 0.614 

MatrixKDBFw.Ni[m3/kg] 1.60e-03 0.16 1.60e-05 0.078 2.20e-05 

MatrixKD BFw.Np[m3/kgl 1.90e-05 1.90e-03 1.90e-06 3.00e-03 1.90e-03 

MatrixKDBFwPb[m3/kg] 0.22 0.43 0.117 0.39 0.224 

MatrixKD BFwPu[m3/kg] 6.30e-03 9.12e-03 4.40e-03 7.00e-03 5.57e-03 

MatrixKD BFwRa[m3/kg] 0.22 0.43 0.117 0.27 0.16

ON 00
00

Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quantile Values 10th Quantile Random 1 Random 2 

Values Values Values 

MatrixKDBFwSe[m3/kg] 9.50e-05 9.50e-03 9.50e-07 4.70e-06 5.1Oe-04 

MatrixKDBFwTh[m3/kg] 0.45 1.48 0.135 0.35 1.07 

MatrixKD BFwU[m3/kg] 1.26e-05 1.26e-03 1.26e-06 2.88e-03 2.93e-05 

atrixKDCHnvAm[m3/kg] 0.32 0.794 0.126 0.94 0.33 

MatrixK DCHnvCsIm3/k-0 0.0312 10079 1001976 0065 0.016 

MatrixKDCHnvNi[m3/kg] 3.20e-04 0.032 3.20e-06 1.90e-04 1.21e-04 

MatrixKDCHnvNp[m3/kg] 4.70e-05 4.70e-03 4.70e-06 1.80e-05 4.90e-06 

MatrixKDCHnvPb[m3/kg] 0.22 0.43 0.117 0.23 0.194 

MatrixKD CHnvPu[m3/kg] 0.04 0.0696 0.023 0.043 0.072 

MatrixKD CHnvRa[m3/kg] 0.07 0.093 0.0536 0.053 0.095 

MatrixKDCHnvSe[m3/kg] 6.30e-05 6.30e-03 6.30e-07 5.30e-03 3.28e-07 

MatrixKD_CHnvTh[m3/kg] 0.32 0.794 0.126 0.32 0.334 

MatrixKD_CHnvU[m3/kg] 9.50e-06 9.50e-04 9.50e-07 2.1Oe-05 5.75e-04 

latrixKDCHnzAm[m3/kg] 0.32 0.794 0.126 0.6 0.75 

WatrixKDCHnzCs[m3/kg] 1.6 3.97 0.629 1.12 0.625 

atrixKDCHnzNi[m3/kg] 1.60e-03 0.16 1.60e-05 0.023 8.96e-04 

atrixKDCHnzNp[m3/kgl 9.50e-06 9.50e-04 9.50e-06 1.60e-07 1.40e-07 

atrixKDCHnzPb[m3/kg] 0.22 0.43 0.117 0.34 0.4

I
Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quantile Values 10th Quantile Random 1 Random 2 

Values Values Values 

MatrixKDCHnzPul[m3/kg] 0.19 0.345 0.1 0.12 0.174 

latrixKDCHnzRa[m3/kgl 2.23 4.26 1.17 1.53 2.57 

MatrixKDCHnzSe[m3/kg] 4.70e-05 4,70e-03 4.70e-07 6.40e-07 1.61e-06 

MatrixKDCHnzTh[m3/kgl 0.32 0.794 0.126 0.3 0.12 

MatrixKDCHnzU[m3/kgl 9.50e-05 9.50e-04 9.50e-06 1.30e-02 1.60e-03 

MatrixKDPPwAm[m3/kg] 0.45 1.48 0.135 1.09 0.2 

MatrixKDPPwCs[m3/kgl 0.14 0.676 0.0295 0.028 0.022 

latrixKDPPwNi[m3/kg] 1.60e-03 0.16 1.60e-05 5.60e-05 1.28e-03 

latrixKDPPwNp[m3/kg] 1.90e-05 1.90e-03 1.90e-06 5.60e-03 5.80e-07 

SatrixKDPPwPb[m3/kg] 0.22 0.43 0.117 0.46 0.188 

IatrixKDPPw Pu[m3/kg] 6.30e-03 9.12e-03 4.40e-03 9.1 Oe-03 7.00e-03 

latrixKDPPw Ra[m3/kg] 0.22 0.43 0.117 0.18 0.44 

MatrixKD PPw Se[m3/kg] 9.50e-05 9.50e-03 9.50e-07 0.027 6.86e-05 

MatrixKDPPw..Th[m3/kg] 0.45 1.48 0.135 1.23 1.89 

atrixKDTSwAm[m3Ikg] 0.45 1.48 0.135 1.09 0.23 

MatrixKDTSw Cs[m3/kg] 0.14 0.676 0.0295 0.45 0.526 

MatrixKDTSw Ni[m3/kgl 1.60e-03 0.16 1.60e-05 0.093 8.26e-06 

MatrixKDTSw-Np[m3/kgj 1.90e-05 1.90e-03 1.90e-06 1.50e-03 2.30e-04

01 

00

I
00

Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quan-tile Values 10th Quantile Random I Random 2 

Values Values Values 

MatrixKDTSww_Pb[m3/kgl 0.22 0.43 0.117 0.44 0.153 

MatrixKDTSwPu[m3/kg] 6.30e-03 9.12e-03 4.40e-03 8.50e-03 8.80e-03 

MatrixKDTSwRa[m3/kg] 0.22 0.43 0.117 0.34 0.15 

atrixKD TSwSe[m3/kg] 9.50e-05 9.50e-03 9.50e-07 1.20e-06 3.14e-04 

MatrixKDTSwnTh[m3/kg] 0.45 1.48 0.135 0.6 0.201 

MatrixKDfTSw-U[m3/kg] 1.30e-05 1,30e-03 1.30e-06 6.40e-08 4.20e-06 

MatrixKDUCFAm[m3/kg] 0.45 1.48 0.135 1.3 0.47 

MatrixKD_.UCF_-Cs[m3/kg] 0.14 0.676 0.0295 0.03 0.078 

MatrixKD.UCF_-Ni[m3/kg] 1.60e-03 0.16 1.60e-05 1.50e-03 0.0368 

atrixKD_UCF._Np[m3/kg] 1.90e-05 1.90e-03 1.90e-06 4.50e-06 2.54e-03 

MatrixKDUCFPb[m3/kgl 0.22 0.43 0.117 0.3 0.131 

MatrixKDUCFPu[m3/kg] 6.30e-03 9.12e-03 4.40e-03 4.50e-03 6.05e-03 

MatrixKD-UCF_-Ra[m3/kg] 0.22 0.43 0.117 0.22 0.128 

MatrixKDUCFSe[m3/kg] 9.50e-05 9.50e-03 9.50e-07 8.80e-04 0.0276 

MatrixKDUCFTh[m3/kg] 0.45 1.48 0.135 0.85 1.36 

MatrixKDUCFUfm3/kg] 1.26e-05 1.26e-03 1.26e-06 6.50e-07 2.87e-04 

MatrixKD_UFZ_-Am[m3/kg] 0.45 1.48 0.135 0.98 0.105 

PlatrixKDUFZCs[m3/kg] !0.14 0.676 0.0295 0.63 0.196
Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quantile Values 10th Quantile Random 1 Random 2 

Values Values Values 

MatrixKDUFZNi[m3/kg] 1.60e-03 0.16 1.60e-05 6.20e-06 0.0113 

MatrixKDUFZ Np[m3/kgj 1.90e-05 1.90e-03 1.90e-06 3.70e-05 5.24e-05 

MatrixKDUFZPb[m3/kg] 0.22 0.43 0.117 0.13 0.218 

latrixKDUFZPu[m3/kgl 6.30e-03 9.12e-03 4.40e-03 4.20e-03 7.78e-03 

latrixKDUFZRa[m3/kgj 0.22 0.43 0.117 0.35 0.26 

MatrixKDUFZ-Se[m3/kg] 9.50e-05 9.50e-03 9.50e-07 0.013 5.04e-05 

MatrixKDUFZh [m3/kg] 0.45 1.48 0.135 0.27 0.76 

MatrixKDUFZU[m3/kg] 1.26e-05 1.26e-03 1.26e-06 5.90e-04 4.36e-07 

MatrixPermeabilityCHnz[m2] 5.00e-18 1.30e-17 1.90e-18 1.40e-17 5.60e-18 

MatrixPermeabilityUFZ_[m2] 1.90e-17 5.20e- 17 1.20e-13 1.20e-16 8.00e- 18 

MatrixPermeabilityPPw_[m2] 1.o00e-17 2.60e-17 3.80e-18 4.90e-17 1.80e-17 

MatrixPermeabilityTSw_[m2] 2.00e-19 5.20e-19 7.70e-20 3.10e-19 2.80e-19 

klatrixPermeability_.UCFJm2] 3.00e- 18 7.80e- 18 1.20e- 18 6.10e-18 1.19e- 18 

WatrixPermeabilitylCHnv[m2] 2.00e-14 5.20e- 14 7.70e- 15 2.40e-14 9.89e-15 

WatrixPermeability.BFw_[m2j 2.00e-19 5.20e-19 7.20e-18 6.60e-19 1.77e-19 

leanAveragePrecipitationMultiplierAtGlacialMaximum 2 2.4 1.6 2.44 1.99 

MeanAverageTemperaturelncreaseAtGlacialMaximum[degC] -7.5 -5.5 -9.5 -9.1 -6.3 

MixingZoneThickness20km[m] 125 185 65 192 95

0C' 

00
00 

00

Parameter Name Base Value 

- Base Value 

- 90th Base Value 

- Base Value 

- Base Value 

Mean Values Quantile Values 10th Quantile Random 1 Random 2 

Values Values Values 

NEFaultZoneWidth[m] 28.5 60.4 5.06 21.2 51.3 

4WaultZoneWidth[mI 21.6 45.6 3.934 36.3 14.5 

OxygenPartialPressure[atm] 0.21 0.21 0.21 0.21 0.21 

ntoDetermineFaultOrientation 0.5 0.9 0.1 0.052 0.035 

RockModulusOfElasticityforSEISMO (Pal 3,45e+10 3.74e+10 3.16e+ 10 3.65e+10 3.13e+10 

RockPoissonRatioforSEISMO[0 0.2 0.221 0.18 0.2 0.2 

SeismicHazardCurveforSEISMO a a a a a 

SeismicHeterogeneityFactor 2.20e-03 0.021 2.40e-04 5.57e-04 3.1Oe-03 

3EISMOJointSpacingl [m] 0.533 0.561 0.505 0.52 0.496 

,EISMOJointSpacing2[m] 0.4 0.427 0.372 0.39 0.391 

SEISMOJointSpacing3[m] 0.266 0.294 0.239 0.252 0.284 

;EISMOJointSpacing4[m] 0.13 0.159 0.101 0.165 0.124 

0EISMOJointSpacing5[m] 0.045 0.051 0.039 0.046 0.05 

SFWettedFraction 0.5 0.9 0.1 0.114 0.86 

SolubilityAm[kg/m3] 1.20e-04 2.16e-04 2.40e-05 1.70e-04 5.10e-05 

SolubilityNp[kg/m3] 0.021 0.08675 4.54e-03 0.032 0.103 

SolubilityPu[kg/m3] 1.20e-04 2.16e-04 2.62e-05 2.30e-04 5.30e-06 

SubAreaWetFraction 0.5 .0.9 0.1 0.66 0.9
Parameter Name Base Value - Base Value - 90th Base Value - Base Value - Base Value 

Mean Values Quantile Values 10th Quantile Random 1 Random 2 

Values Values Values 

SubGrainFragmentRadiusAfterTransFrac[m] 1.25e-06 1.56e-06 9.39e-07 1.80e-06 1.38e-06 

ThermalConductivityofYMRock[W/(m-K)] 2 2.16 1.84 2.12 1.98 

lhicknessOfWaterFilm[m] 2.00e-03 2.80e-03 1.20e-03 2.30e-03 2.30e-03 

ThresholdDisplacementforFaultDisruptionOfWP[m] 0.25 0.4 0.1 0.4 0.4 

TimeOfNextFaultingEventinRegionOflnterest[yr] (10,000 year time 2000, 2000 2000, 2000 1006, 3361 1660, 1660 1027, 1027 

period, 50,000 year time period) 

TimeOfNextVolcanicEventinRegionOflnterest[yr] (10,000 year time 5049, 25019 9010, 44999 1090, 5079 7013, 34922 3622, 16225 

period, 50,000 year time period) 

VolcanicEventDuration [s] 6.66e+05 4.49e+06 9.88e+04 9.16e+04 1.92e+06 

VolcanicEventPower[W] 3.02e+10 2.17e+11 4.21e+09 1.20e+1I 1.28e+10 

WellPumpingRateAtCriticalGroup2Okm[gal/day] 8.75e+06 1.22e+07 5.35e+06 9.20e+06 1.18e+07 

WidthOfVolcanicDike[m] 5.5 9.1 1.9 4.2 7.1 

WindSpeed[cm/s] 1200 2800 127 338 1080 

WPUltimateStrength[N/mA2] 1.38e+09 1.41e+09 1.35e+09 1.43e+09 1.36e+09 

XLocationOfFaultingEventlnRegionOflnterest[m] 548000 548480 547520 548440 547590 

YLocationOfFaultingEventlnRegionOflnterest[m] 4077620 4078800 4076500 4078500 4077800 

a - The base values for the hazard curve are the same as the master input file for all runs.

00
APPENDIX E 

DIFFERENTIAL ANALYSIS RESULTS IN TABULAR FORM
Table E-1. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 10 kyr 

Parameter Name - S - - High krithmeOti - No 3 - No - No 3 - No 3 - No y/dx * y/dx * y/dx * y/dx * y/dx 

3eometric vithzetic alue Mean of DEs, Mean )Es, 90th )Es, 10th )Es, DEs, Sig - NO iig - No Sig - No 

Mean Mean ;igma values, )uantile uantile kandoml Random2 DEs, Es, 90'h DEs, 10"' )Es, DEs, 

4eighted L0k years lalues 10k lalues, Jalues, lalues, Means, uantile Quantile Randoml Random2 

lalues (ears LOk years 10k years 10k years 

ArealAverageMeanAnnualInfiltrat 7.5e+00 .le+0l 2.4e+0l 3,8e-04 18.1 a.1 4.5 5.8 23.6 5.7e-04 3.7e-03 2.8e-07 2.8e-05 3.9e-05 

ionAtStart[mm/yr] I I I I 

FowFactor 5.0e+00 .0e+0l 2.3e+0l 8.4e-04 17.9 0.4 .1 2.8 23.4 2.7e-03 1.4e-03 2.0e-06 9.4e-05 8.4e-05 

ubAreaWetFraction 9.9e-01 9.9e-01 O.0e+00 4.le-04 1.0 1.0 1.0 1.0 1.0 4.6e-05 .0e-03 1.3e-07 .3e-06 1.8e-06 

FmultFactor 5.0e+00 I.Oe+0l 2.3e+Ol 3.2e-04 17.9 0.4 7.1 2.9 23.0 8.le-04 6.9e-04 3.2e-07 4.6e-06 l.le-04 

oefForLocCorrOfOuterOverpack <4.9e-01 3.9e+01 19e+02 F.7e-04 1.0 .2e-02 ý2.3e-04 .6 F86.8 B.e-04 .4e-04 P.0 .le-06 P.le-03 

FWettedFraction 3.6e+00 .4e÷00 a.7e+0O 2.6e-04 -7.9 0.5 8.7 -2.6 -7.2 -3.4e-04 .3e-04 -l.le-06 -3.4e-05 -1.3e-05 

•ellPumpingRateAtCriticalGroup 2 1.0e+00 L.0e+00 I.le÷00 .6e-04 0.9 -1.1 -0.9 1.1 -1.0 -1.9e-05 1.3e-03 -1.8e-08 -1.5e-06 -l.le-06 

Okm[gal/day] I 

InitialRadiusOfSFParticle[m] 9.le-01 9.le-01 1.le+00 2.le-04 0.9 -1.1 -0.9 .0.8 -0.9 -1.4e-05 l.le-03 -l.0e-08 -9.8e-07 9.2e-07 

%lluviumMatrixRDSAVNp <3.7e-03 5.4e-01 .6e÷00 l.le-04 1.3e-04 <1.7e-04 -6.4e-02 -2.6 1.8e-04 I.0 ).0 -l.le-07 -5.3e-04 0.0 

FlluviumMatrixPorositySAV 1.7e+00 2.2e+00 .4e+00 1.Oe-04 -2.5 -0.8 -2.6 -4.4 0.6 -2.2e-05 -4.9e-04 -1.6e-08 -2.4e-06 -3.7e-07 

rhermalConductivityofYMRock[W/( 1.8e+00 2.6e+00 .6e+00 .0e-05 .6 -0.8 -1.4 1.6 1.4 3.3e-05 -2.7e-04 -3.7e-09 4.7e-07 -4.4e-07 

IaatrixKD_CHnvNp[m3/kg] <7.9e-04 <6.8e-02 ,4e-01 5.8e-05 1.3e-04 1.7e-04 ý2.3e-04 0.3 1.8e-04 0.0 0.0 D.0 -2.9e-04 0.0 

geanAveragePrecipitationMultipl 1.6e+00 .7e+00 .2e+00 3.9e-05 3.0 -0.2 .1.5 1.5 7.2 3.3e-05 -1.5e-04 1.2e-08 8.9e-07 -5.7e-06 

ierAtGlacialmaximum 

hlluviousMatrixRD._SAVAm ý9.9e-04 <2.4e-02 1.2e-01 2.2e-05 -2.7e-04 <1.7e-04 -0.1 <1.9e-04 -9.2e-04 -2.3e-06 0.0 -i.0e-04 0.0 5.2e-06 

seanAverageTemperatureIncreaseA 6.3e-02 6.7e-02 9.6e-02 2.0e-05 9.6e-02 -6.4e-02 -4.3e-02 -4.6e-02 a.4e-02 -1.4e-06 1.0e-04 2.8e-10 3.9e-08 -l.le-07 

tGlacialMaximum[degc] I 

CriticalRelativeHumidityAqueous <2.4e-02 <4.8e-01 1.5e+00 1.8e-05 0.2 -0.7 <2.3e-04 -1.5 <1.8e-04 2.7e-07 -8.9e-05 .0 .1.7e-07 0.0 

Zorrosion 

EracturePorositySTFF .3e-02 6.2e-02 1.2e-01 1.7e-05 -0.1 -4.4e-02 -4.5e-02 -7.8e-02 -2.2e-02 [7.3e-06 -8.0e-05 -3.4e-09 F4,4e-0 7 -4.6e-08 

klluviursMatrixRD_-SAVI <1.9e-02 <8.3e-02 3.0e-01 1.6e-05 13.8e-02 -5.3e-02 -0.3 -2.3e-02 <1.8e-04 -1.3e-06 -7.7e-05 -9.9e-09 I4.6e-08 0.0 

4PUltimateStrengthIN/m^21 <3.7e-03 <l.4e-01 3.7e-01 .6e-06 2.3e-04 0.3 <2.3e-04 -0.4 <1.8e-04 0.0 -3.3e-05 ).0 -3.2e-08 0.0 

InnerOverpackErplntercept 1.6e-03 <2.0e+00 9.9e+00 5.6e-06 <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 -9.9 .0 ).0 ).0 0.0 -2.8e-05 

DefectiveFractionOfWPs/cell 7.le-02 4.7e+00 2.3e÷0l 5.3e-06 0.2 .3e-03 23.0 0.3 <1.8e-04 7.0e-06 1.6e-05 Z.6e-06 1.2e-06 0.0 

SatrixPermeabilityCHnv[m2

] <3.9e-03 <2.2e-01 l.le+00 .le-06 2.4e-03 O.0e-03 12.3e-04 k.1 1.8e-04 1.6e-07 .6e-05 .0 4.2e-0 6 .0 

SeismicHeterogeneityFactor <2.2e-04 ý2.5e-04 5.le-04 7.7e-07 <1.3e-04 kle-04 I2.3e-04 L1.9e-04 -1.8e-04 Reg. .8e-06 Feg. 9e. eg. 

00
Table E-1. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 10 kyr (cont'd) 

SolubilityAm[ kg/r3] <2.7e-04 :4.5e-04 1.5e-03 5.9e-07 :1.3e-04 1.5e-03 2. 3e-04 <1.9e-04 <1.8e-04 Neg. 2.9e-06 D.0 3.8e-10 Neg. 

P- SolubilityNp[kg/m3] <1.6e-03 <6.0e-02 2.9e-01 3.5e-07 c1.3e-04 =1.7e-04 9.3e-03 0.3 <1.8e-04 D.0 0.0 .2e-09 1.8e-06 0.0 

ON RockModulusOfElasticityforSEISM :3.3e-04 <9.7e-04 4.le-03 .9e-07 <1.3e-04 4.le-03 <2.3e-04 <1.9e-04 1.8e-04 Reg. 1.5e-06 0.0 0.0 Neg. 003[Pa]o 

EISMOJointSpacing3[m] 2.2e-04 2.5e-04 5.le-04 4.7e-08 1.3e-04 5.le-04 <2.3e-04 1 9e-04 <1,8e-04 Neg. .3e-07 Neg. Neg. leg. 

klluviumMatrixRDSAVTC <3.9e-04 <2.0e-03 9.4e-03 3O.e-08 -1.3e-04 <1.7e-04 :2.3e-04 9.4e-03 <1.8e-04 -1.4e-08 0.0 0.0 -1.4e-07 0.0 

4atrixPermeabilityCHnzW2 <3.9e-04 ý2.1e-03 1.0e-02 4.0e-09 <1.3e-04 <1.7e-04 <2.3e-04 O.e-02 <1.8e-04 0.0 0.0 0.0 2.0e-08 0.0 

FracturePorosity _CHnz :5.5e-04 ý1.4e-02 F Be-0 2 I.Ie-09 ,1.3e-04 <1.7e-04 -6.8e-02 1.9e-04 -1.8e-04 0.0 0.0 -5.7e-09 0.0 D.0 

klluviumMatrixRD _SAV _Se :3,4e-04 <1.3e-03 5.7e-03 3.0e-09 <1.3e-04 1.7e-04 5.7e-03 1.9e-04 <1.8e-04 Ieg. D.0 -1.5e-08 k.0 D.0 

DispersionFractionSTFF 3.4e-02 .9e-0 2 1.4e-01 'eg. 0.1 1.5e-03 5.2e-02 0.1 4.Oe-02 k.0 0.0 0.0 D.0 D.0 

SeismicHazardcurveforSEISMO -2.9e-04 <5.3e-04 .9e-03 Neg. :1.3e-04 -1.9e-03 <2.3e-04 <1.9e-04 <1.8e-04 Neg. 0.0 0.0 D.0 leg. 

DispersionFraction-SAV 5.3e-01 .6e.00 1.6e ÷01 Neg. ).5 1.5e-02 0.4 0.8 16.5 D.0 .0 0.0 ).0 0.0 

-laddingCorrectionFactor 9.2e-01 .3e-01 I.Oe+00 •eg. 1.0 1.0 1.0 0.7 1.0 Neg, D.0 'eg. Neg. 'eg. 

FracturePermeabilityUCF_ m2l <1.8e-04 ý1.8e-04 2.3e-04 leg. <1.3e-04 <1.7e-04 :2.3e-04 ý1.9e-04 1.8e-04 Neg. Neg. Neg. 'eg. Neg. 

SlixingZoneThickness20km(mJ .1.8e-04 <1.8e-04 <2.3e-04 Neg. 1.3e-04 <.17e-04 <2.3e-04 <1.9e-04 <1.8e-04 leg. 'eg. 'eg. Neg. Neg. 

EISMOJointSpacing4]m] <1.8e-04 I.Be-04 F2.3e-04 Neg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 Neg. Neg. 'eg. Neg. Neg. 

SEISMOJointSpacingl mj <1.8e-04 1.8e-04 2.3e-04 Neg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 Neg. eg. Neg. Neg. Neg. 

klluviumMatrixRD_-SAVNi -1.8e-04 1.8e-04 <2.3e-04 Neg. 1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 Ne. Ne. eg. 'leg. Neg. 

kAA2 _1I[C/m2/yr] :1.8e-04 1.8e-04 <2.3e-04 Neg. ý1.3e-04 <1.7e-04 :2.3e-04 1.9e-04 <1.8e-04 leg. Neg. Neg. 'eg. 'eg. 

4atrixKD_CHnvU [m3/ekg 1.8e-04 ý1.8e-04 <2.3e-04 Neg. <1.3e-04 <1.7e-04 :2.3e-04 <1.9e-04 •1 8e-04 Feg. leg. Neg. 'eg. leg. 

k1luviumMatrixRDSAVPu <1.8e-04 <1.8e-04 :2.3e-04 Neg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 Negq Neg. Neg. 'eg. Neg. 

1luviumMatrixRD-SAV.Cs <1.8e-04 <1.8e-04 <2.3e-04 Neg. <1.3e-04 <1.7e-04 <2.3e-04 ý1.9e-04 :1.8e-04 Neg. Neg. Neg. 'eg. leg. 

ýracturePermeabilityUFZ.[m2] <1.8e-04 1.8e-04 <2.3e-04 leg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 1. 8e-04 leg. Neg. Ieg. leg. Neg. 

7racturePermeability-CHnv im2] :1.8e-04 1.8e-04 <2.3e-04 eq. 1.3e-04 ý1.7e-04 :2.3e-04 <1.9e-04 < 8e-04 Neg. Neg. 'eg. Neg. 'eg. 

:hlorideMultFactor <1.8e-04 <1.8e-04 <2.3e-04 Reg. :1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 'eg. leg. Neg. Neg. Neg. 

racturePermeabilityBFiw.- [ m2l 1.8e-04 €1.8e-04 <2.3e-04 Neg. <1.3e-04 <1.7e-04 <2.3e-04 ý1.9e-04 1. 8e-04 leg, leg. leg. 'eg. leg. 

1IluviumMatrixRD_SAV_U :1.8e-04 <1.8e-04 42.3e-04 Neg. <1.3e-04 <1.7e-04 2.3e-04 <1.9e-04 1 8e-04 Neg. Neg. 4eg. 'eg. leg. 

ockPoissonRatioforSEISMO[] <1.8e-04 <1.8e-04 12.3e-04 Peg. <1.3e-04 [1.7e-04 [2.3e-04 11.9e-04 1.8e-04 feg. ýeg. ýeg. Feg. egq.
Table E-1. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 10 kyr (cont'd) 

hlluviumMatrixRD-SAVTh ý1.8e-04 1.8e-04 <2.3e-04 leg. <1.3e-04 -1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 eg. Ieg. leg. ,eg. leg. 

SubGrainFragmentRadiusAfterTran <1.8e-04 1.8e-04 2.3e-04 leg. <1.3e-04 <1.7e-04 <2.3e-04 <.19e-04 <I.Be-04 eg. 'eg. leg. 'eg. leg. 

sFrac (M) 

SEISMOJointSpacing5(m[ <1.8e-04 1.8e-04 -2.3e-04 9eg. <1.3e-04 <1.7e-04 :2,3e-04 <1.9e-04 <1.8e-04 eg. 'eg. Seg. 'eg. 4eg. 

SolubilityPu[kg/m32 <1.8e-04 1.8e-04 :2.3e-04 Feg. 1.3e-04 1.7e-04 2.3e-04 1.9e-04 1.8e-04 leg. Seg. leg. leg. 1eg. 

FracturePermeability_PPw rm2 ] <1.8e-04 1.8e-04 :2.3e-04 leg. <1.3e-04 <1.7e-04 2.3e-04 I1.9e-04 <1.8e-04 eg. eg. leg. e. leg. 

S11uviumMa trixRDoSAVRa <1.8e-04 <1.8e-04 <2.3e-04 leg. <1.3e-04 ý1.7e-04 2.3e-04 <1.9e-04 <1.8e-04 leg. leg. leg. 'eg. 'eg. 

IatrixPermeability-UFZ_..m2) <1.8e-04 -1.8e-04 :2.3e-04 leg. <1.3e-04 <1.7e-04 2.3e-04 <1.9e-04 <1.8e-04 legq. eg. leg. 'eg. leg. 

latrixPermeabiItyPPw_.[m21 1.8e-04 :1.8e-04 <2.3e-04 leg. 1.3e-04 -1.7e-04 2.3e-04 <1.9e-0 4 <1.8e-04 leg. leg. 'eg. leg. leg. 

latrixPermeabilityBFw.. Am2 < 1.8e-04 <1.8e-04 2.3e-04 leg. :1.3e-04 <1.7e-04 2.3e-04 1.9e-04 ý1.8e-04 leg. leg. 'eg. leg. leg. 

1lluviumMatrixRDO SAV _Pb <i.8e-04 1.8e-04 :2.3e-04 leg. <1.3e-04 1.7e-04 :2.3e-04 1.9e-04 <1.8e-04 e. eg. Reg. leg. leg. 

.aatrixPermeabiliityTSw [ m2] <1.8e-04 <1.8e-04 2.3e-04 leg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 €1.8e-04 leg. leg. e. pegq. eg. 

rhicknessOfWaterFilm[m] <1.8e-04 <1,8e-04 <2.3e-04 egq. <1.3e-04 1.7e-04 <2.3e-04 <1.9e-0 4 1.8e-04 leg. 'eg. 'eg. leg. 'eg. 

OxygenPartialPressure(atml <1.8e-04 :1.8e-04 <2.3e-04 leg. <1.3e-04 <1.7e-04 .2.3e-04 <1.9e-04 <1.8e-04 eg. eg. le. eg. leg. 

SEISMOJointSpacing2 [m <1.8e-04 1.8e-04 <2.3e-04 leg. <1.3e-04 1.7e-04 <2.3e-04 1.9e-04 <1.8e-04 legq. eg. leg. Reg. 'eg. 

.racturePorosityUFZ _- <1.8e-04 :1.8e-04 <2.3e-04 leg. <1.3e-04 1.7e-04 =2.3e-04 <1.9e-04 .e-4 eq. leg. Reg. Reg. 'eg. 

BFw <racturePorosity 1.8e-04 <1.8e-04 12.3e-04 legq <1.3e-04 <1.7e-04 :2.3e-04 <1.9e-04 1i.8e-04 leg. Aeq. leg. leg. leg. 

%lluviumMatrixRD._SAV_Nb 1.8e-04 <1.8e-04 <2.3e-04 leg. 1 3e-04 ý1.7e-04 <2,3e-04 < 9e-04 <1.8e-04 'eg. leg. 'eg. 'eg. leg. 

,racturePorosity _UCF _ <1.8e-04 1.8e-04 <2.3e-04 'eg. 1 3e-04 1.7e-04 :2.3e-04 1 9e-04 1 8e-04 leg. leg. leg, leg. leg. 

"racturePermeabilityTSw__Jm2] <1.8e-04 <1.8e-04 <2.3e-04 leg. ý1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 < 8e-04 leg. leg. egq. 'eg. leg. 

fatrixPermeabi1iyUCF_[m22 <1.8e-04 1.8e-04 :2.3e-04 leg. <1.3e-04 1.7e-04 :2.3e-04 <1.9e-04 <1.8e-04 leg. legq. eg. 1eq. 4eg. 

<ractureporosityPPw. 1.8e-04 1.8e-04 -2.3e-04 leg. 1 3e-04 1 7e-04 :2.3e-04 1 9e-04 < 8e-04 leg. 'eg. leg. ýeg. 'eg. 

racturePorosity-TSw_ 1.8e-04 1.8e-04 :2.3e-04 leg. <1.3e-04 1 7e-04 2.3e-04 1 9e-04 1 8e-04 leg. leg. leg. egq. leg. 

MatrixKD__CHnzU(m3/kgq ]1.Se-04 <1.8e-04 :2.3e-04 eg. <1,3e-04 <1.*e-04 <2.3e-04 ý1.9e-04 < 8e-04 leg. ,eg. leg. 'eg. 'eg. 

FracturePorosityMCHnv <1.8e-04 <1.8e-04 :2.3e-04 leg. 1.3e-04 <1.7e-04 <2.3e-04 < 9e-04 <1.8e-04 Neg. leg. leg. leg. leg. 

IatrixKD__UFZ__U [m3/kgq .1.8e-04 1.8-e-04 leq 13e -204e-04 04 1.7e-04 <2.3e-04 ý1.9e-04 <1.8e-04 leg. leg. leg. 'eg. leg. 

4atri xKDTSwU[m3/kgq <1.8e-04 <1.8e-04 ý2.3e-04 legq <1.3e-04 <1.7e-04 <2.3e-04 1. 9e-04 ý1.8e-04 leq. leq. e. e. e•q. 

ýatrixKD_UCFTh(m3 /kV 11.8e-04 <1.8e-04 <2.3e-04 eq. :1.3e-04 -,.7e-04 12.3e-04 1,9e-04 <1.8e-04 leg. e. eg. Leg. eg.

00
Table E-1. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 10 kyr (cont'd)

C0 

00

4atrixKDfUCF_Ni(m3/kgj ý1.8e-04 (1.8e-04 2.3e-04 leg. ý1.3e-04 1.7e-04 ý2.3e-04 1.9e-04 1.8e-04 leg. leg. leg. leg. leg. 

4atrixKD_..BFwPu[m3/kg) ý1.8e-04 <1.8e-04 :2.3e-04 leg. <1.3e-04 -1.7e-04 <2.3e-04 21.9e-0 4 1.8e-04 leg. 'eg. leg. leg. leg. 

•atrix~D.UFZ_.Pu~3/kg] €1.8e-04 1.8e-04 2.3e-04 eq. 13e-04 41.7e-04 <2.3e-04 1 9e-04 1.8e-04 Neg. Neg. leg. leg. leg. 

IatrixKD_TSwTh[m3/kgq <1.8e-04 <1.8e-04 <2.3e-04 Neq <e1 13e-04 1.7e-04 2.3e-04 1 9e-04 1.8e-04 eq eg. e. eq. 

IatrixKDCHnvrThIm3/kg) <1.8e-04 <1.8e-04 r2.3e-04 leg. r1,3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 .eg. Neg. leg. Neg. leg. 

racturePermeabilityCHnz(2] <1.8e-04 <1.8e-04 2.3e-04 Neg. ý1.3e-04 <1.7e-04 ;2.3e-04 ý1.9e 04 <1 .e-04 Neg. Neg. e eg. leg. 

FatrixKD_. abilCSe[eC3/ky] <1.8e-04 <1.8e-04 2.3e-04 Neg. ý1.3e-04 ý1.7e-04 <2,3e-04 I 9 e-0 4 <1.8e-04 Neg. egqg. e eg. Neg. 

AatrixKDUCF..SeNp(m3/kq I 1.8e-04 <1.8e-04 12.3e-04 leg. <1.3e-04 1.7e-04 :2.3e-04 1 9e-04 <1.8e-04 Neg. Neg. Neg. Neg. Neg. 

MatrixKDFPPwNp[m3/kg] <1.8e-04 :1.8e-04 12.3e-04 leg. <1.3e-04 I1.7e-04 ý2.3e-04 <1.9e-04 I1.8e-04 leg. Ieg. Neg. leg. Ieg. 

MatrixKDTPSwNb([m3/kg] <1.8e-04 <1.8e-04 :2.3e-04 legq. 1.3e-04 .1.7e-04 <2.3e-04 <1.9e-04 1.8e-04 Negg.. 'eg. eg. eqg. 

atrixKD.TSw.Ra[m3/kqI <1.8e-04 1.8e-04 2.3e-04 •eq. 1.3e-04 <1.7e-04 .2.3e-04 <1.9e-04 -1.8e-04 Neg. leg. eg. e. leg. 

IatrixKDPPw jNi~m3/kg] I<.e-04 <1.8e-04 :2.3e-04 leg. .1.3e-04 .1.7e-04 <2.3e-04 <1.9e-04 :1.Be-04 Neg. leg. q. eq. e. 

[atrixKD_CHnzRa(m3/kg] <1.8e-04 <1.8e-04 :2.3e-04 eg.. 1.3e-04 :1.7e-04 .2.3e-04 1.9e-04 1.Be-04 Ieg. Neg. Neg. peg. Neg. 

MatrixKDPPwRa(m3/kg] <1.8e-04 :1.8e-04 :2.3e-04 Neg. 1.3e-04 <1.7e-04 <2.3e-04 1.9e-04 :1.8e-04 Ieg. leg. Neg. leg. Neg. 

MatrixKDUCFRa[m3/kgq <1.8e-04 .1.8e-0 4 <2.3e-04 eg. 1.3e-04 ý1.7e-04 2.3e-04 <1.9e-04 ý1.8e-04 leg. Neg. 'eg. qeg. Neg. 

4atrixKD_..CHnzNp(m3/kg] 1.8e-04 1.8e-04 <2.3e-04 Neg. 1.3e-04 :1.7e-04 2.3e-04 <1.9e-04 <1.8e-04 .eg. 'eg. eg. Neg. Neg. 

,aatrixKDUFZNp[Em3/kgI 1.8e-04 <1.8e-04 12.3e-04 leg. 1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 Neg. 'egq. eg. Reg. 'eg. 

atrixKDCHnvCs(m3 /kg) 1.8e-04 <1,8e-04 2.3e-04 leg. 1.3e-04 1.7e-04 <2.3e-04 1.9e-04 -1.8e-04 Negq. eg. 'eg. leg. Neg. 

IaatrixKDCHnvPbh(m3/kq1 1.8e-04 <1.8e-04 2.3e-04 leg. 1.3e-04 1.7e-04 <2.3e-04 1 9e-04 <1.8e-04 Neg. 'eg. Negq leg. 'eg. 

[aerixKD.CHrzPb~m3/kg] 1.8e-04 2.3e-04 eq <1.e-04 1.3e-04 c1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 eqg. 'eg. Negq leg. Neg. 

IatrixKD_pPwPbjm3/kg] <1.8e-04 1.8e-04 2.3e-04 eq 1.3e-04 1.7e-04 2.3e-04 1.9e-04 :.8e-04 eq eq eq eq eq 

HatrixKD.UCFPb(m3/kg] <1.8e-04 :1.8e-04 2.3e-04 Neg. 1.3e-04 41.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 Neg. legq leg. Neg. Negq 

latr ixKD...BFW.Pb (nO / kgI 1.8e-04 ý1.8e-04 2.3e-04 Neq. 1.3e-04 41.7e-04 <2.3e-04 1.9e-04 <1.8e-04 Neq. 'eq. 'eq. leq. eq. 

IatrixKDBFwZPb~m3/kg] <1,8e-04 <1.8e-04 :2.3e-04 'eg. 1.3e-04 ý1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 Neg. Neg. 'eg. leg. eg. 

HIa trixKD_UFZ_Pbf[m3 /kg ] <1.8e-04 1. 8e-04 <2.3e-04 Neg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1. 8e-04 Neg. fleg, 'leg. leg. Neg. 

IatrixKDTSw_-Csm3/kgqj <1.8e-04 1, 8e-04 <2.3e-04 Neg. ý1,3e-04 <1.7e-04 <2.3e-04 1 9e-04 1 8e-04 Neg. Neg. 'egq leg. Neg. 

S<itrixKDCHnvNiJe/kg] 1.8e-04 <1.8e-04 -2.3e-04 leg. <1.3e-04 I1.7e-04 ý2.3e-04 <1.9e-04 .1.8e-04 Neg. egq Neg. leg. Neg. 

•atrixKDwcHnzCs[m3/kg) <1.8e-04 1.8e-04 2.3e-04 leg. <1.3e-04 <1.7e-04 <2.3e-04 < 9e-04 1.8e-04 'egq. eg. leg. legq Neg. 

4atrixKDPPwCs(m3/kgI <1.8e-04 1.8e-04 2.3e-04 leg. <1.3e-04 ,1.7e-04 <2.3e-04 <1.9e-04 _1.8e-04 Eeg. pegq leg. leg. eg.

trl
Table E-1. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 10 kyr (cont'd) 

AatrixKD..UCF..CS(e3/kgl ý1.8e-04 ý1.8e-04 <2.3e-04 eq~. ý1.3e-04 <1.le-04 <2.3e-04 ý1.9e-04 1.8e-04 leg. leg. eg. leg. leq. 

4atrixKD..BFw..Cs[rn3/kgj ý1.8e-04 <1.Se-04 ý2.3e-04 leg. <1.3e-04 ý1.7e-04 ý2.3e-04 ý1.9e-04 1I.Be-04 leg. leg. leg. eg. leg. 

IatrixKDUFZCs[m3/kg] <1.8e-04 <1.8e-04 <2.3e-04 'eg. 1.3e-04 <1.7e-04 2.3e-04 <1.9e-04 1.Be-0 leg. eg. leg. leg. leq. 

IatrixKD_.TSw_.Ni(rmj/kgj ý1.8e-04 <1.8e-04 ý2.3e-04 qeq. .1.3e-04 ý1.7e-04 <2.3e-04 <1.9e-04 ý1.8e-04 leg. 'eg. leg. leg. leq. 

4atrixd(D..BFwJI'h(fl3/kg] <1.8e-04 1I.Be-04 <2.3e-04 leg. ý1.3e-04 <1.7e-04 ý2.3e-04 ý1.9e-04 <1.8e-04 leg. eg. leg. eg. leg. 

4atrixKD_.CHnvAe(m3!kg1 ý1.8e-04 <1.8e-04 -2.3e-04 'eg. ý1.3e-04 ý1.7e-04 ý

2

.3e-04 <1.9e-04 -1.8e-04 leg. leg. leg. eg. leg. 

IatrixKO..BFWNp(m3/kgl <1.Be-04 <1.8e-04 ý2.3e-04 leg. <1.3e-04 <1.7e-04 ý2.3e-04 ý1.9e-04 <1.8e-04 leq. leq. leg. leg. leg. 

AatrixKDO..B1FW..Ra[m3/kg1 1.8e-04 ý1.8e-04 1 2.3e-04 'eg. F. 3e-04 1.7e-04 2.3e-04 <1.9e-04 <1.8e-04 leq. 'eg. 'eg. leg. leg. 

.4atrixKD..UCF_.Se~m3/kgl .1.8e-04 <1.8e-04 2.3e-04 leg. 1.3e-04 <1.7e-04 ý2.3e-04 1.9e-04 <1.8e-04 leq. leq. 'eq. leq. eq.. 

IatrixKD..PPw..Se[m3/kgj <1.8e-04 <1.8e-04 <2.3e-04 leg. -1.3e-04 -1.7e-04 <2.3e-04 ý1.9e-04 <1.8e-04 eq.eq. eq. leq. leg. 

IatrixKD...BFw_.Se(m3/kgj <1.8e-04 <1.8e-04 <2.3e-04 'eg. ý1.3e-04 <1.7e-04 ý2.3e-04 ý1.9e-04 <1.8e-04 leq. leg. eq. leq. 'eq. 

IatriXKO...Tsw..Nbtm3/kg) I.Be-04 1.8e-04 <2.3e-04 'eg. 1<. 3e-04 ý1.7e-04 2.3e-04 ý1.9e-04 1.8e-04 leq. 'eq. '.eq. leq. leq. 

4atrix1<D..UFZ-.Se~rn3/kg) ý1.8e-04 1.8e-04 <2.3e-04 leg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 leq. Seq. eq. qeq. leq. 

OatrixKD...CHnvNb(m3/kgl <1.8e-04 <1.8e-04 -2. 3e-04 leg. ý1.3e-04 1.7e-04 ý2.3e-04 <1.9e-04 ý1.8e-04 leq. eg. 4eq. 'eq. leg. 

I.1 atrixKDPlwNb(m3/kgl ý1.8e-04 1.8e-04 ý2.3e-04 leg. 1.3e-04 ý1.7e-04 2.3e-04 ý1.9e-04 1.8e-04 neq. .eq. 4eq. 4eq. eq. 

4atrixl(D...CHnzNb(M3/kgj <1.8e-04 <1.8e-04 ý2.3e-04 leq. -.1.3e-04 ý1.7e-04 ý2.3e-04 <1.9e-04 <1.8e-04 leq. eq. leq. 'eq. leq. 

etiK..C..be/ký1S-4 1B-4 2.3e-04 leq. ý1.3e-04 -1.7e-04 <2.3e-04 <1.9e-04 ý1.8e-04 leg. eq. leg. 'eg. leg. 

IatricKD..VFZ..Nbje3/kgj 1.8e-04 ý1.8e-04 .e0 eq .- 0 .e0 23-4 19-4 I8-0 e.e.e.e.e. 

IatrixKD....Fw...Nb(m3/kg) <1,8e-04 ý1.Be-04 ý2.3e-04 .eq. <1.3e-04 ý1.7e-04 <2.3e-04 ý1.9e-04 <1.8e-04 leq. 'eq. 'eq. eq. leq. 

fatrixKD...T~w..Nb~m3/kgl 1.8e-04 <1.Be-04 <2.3e-04 leq. <1.3e-04 1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 leq.eqe.e.e. 

AatrixKD....CwxuNp(r3/kgl ý1.8e-04 <1.8e-04 <2.3e-04 leg. <1.3e-04 <1.7e-04 ý2.3e-04 <1.9e-04 J..8e-04 ý,eq. eq. leq. leq. eq. 

Iatri.xKD..CHnz~i(m3/kgj 1.8e-04 <1.8e-04 <2.3e-04 leq. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 leq. leq. eq. eq. leq. 

IatrixKD..UFZ...Am3e/kg1 ý1.8e-04 I.Be-04 ý2.3e-04 leq. ý1.3e-04 ý1,7e-04 <2.3e-04 1.9e-04 ý1.8e-04 leq. leq. eq. leq. 'eq. 

IatrixKD..BFw..Amnii3/kg) <1.8e-04 ý1.8e-04 ý2.3e-04 leg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 leq. leq. eq. leq. leq. 

atrixKDj)CP..AMjm3/kg] <1.8e-04 <1.8e-04 2.3e-04 leq. <1.3e-04 ý1.7e-04 2.3e-04 <1.9e-04 1.8e-04 eq. Req. 'eq. 'eq. 'eq. 

IatrixKo....PAwJen3/kyl 1.8e-04 ý1.8e-04 <2.3e-04 leg. ý1.3e-04 ý1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 leq. leq. leq. leq. eq. 

4 atrixKELCj2nzAem(ei/kg] I R8e-04 <1.8e-04 <2.3e-04 eq. I1.3e-04 l.7e-04 ý2.3e-04 ý1.9e-04 <1.8e-04 leq. eq. leq. leq. eq. 

atrixKfl..BFw...Nitm3/kgj ý1.8e-04 <..Be-04 2.3e-04 eq. 11.3e-04 I2. 7e-04 12.3e-04 1.9e-04 <1.8e-04 eq ýeq. eq7. eq. leq. 

0 

rg 

00
Table E-1. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 10 kyr (cont'd)

00

1atrixKDUFZjTh(m3/kgq <1.8e-04 1.8e-04 ý2.3e-04 leg. <1.3e-04 <1.7e-04 2.3e-04 <1.9e-04 <1.8e-04 eg. eg. leg. leg. _ eg. 

IatrixKDCHnzPu(m3/kgl 1.ge-04 1.8e-04 <2.3e-04 leg. 1.3e-04 <1.7e-04 <2.3e-04 <1.9e04 <1.8e-04 eg. eg. e. eg. eg. 

natrixKD__UFZNifm3/kgq <1.8e-04 <1.8e-04 2.3e-04 leg. <1.3e-04 <1.7e-04 ý2.3e-04 <1.9e-04 <1.8e-04 eg. eg. leg. leg. 'eg. 

•atrixKD_.CHnvSe~m3/kg] 1.8e-04 1.8e-04 <2,3e-04 •e. 1.3e-04 1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 eg. leg. leg. qeg. Neg. 

•atrixKD_.TSwSe[m3/kgj <1.8e-04 1.8e-04 r2.3e-04 leg. 1.3e-04 1.7e-04 I2.3e-04 1.9e-04 18e-04 eq eg. eg. eq. eq. 

1atrixKDCHnvRa[m3/kg] :1.8e-04 <1.8e-04 2.3e-04 'eg. :1.3e-04 .1.7e-04 ý2.3e-04 I1.9e-04 <1.8e-04 eg. leg. leg. jeg. leg. 

IatrixKD_PPw.Pu[m3/kg] <1.8e-04 <1.8e-04 :2.3e-04 qeg. <1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 eg. leg. leg. .eg. leg. 

,atrixKDP.UCFUm3/kg] <1.8e-04 1.8e-04 :2.3e-04 'eg. - 1.3e-04 1.7e-04 ý2.3e-04 <1.9e-04 <1 8e-04 'eg. leg. 'eg. eqg. leg. 

fatrixKDPPwTh[m3/kg] <1.8e-04 <1.8e-04 :2.3e-04 Ieg. <1.3e-04 1.7e-04 ý2.3e-04 li.9e-04 [1.8e-04 leg. leg. leg. Ieg. seg. 

•atrixKD_PPTSwAm3/kg] 1.8e-04 <1.8e-04 2.3e-04 'eg. <1.3e-04 <1.7e-04 <2.3e-04 <i.9e-04 <1.8e-04 leg. leg. leg. eg. 4eq. 

•atrixKD.BFw.U[rm3/kg] 1.8e-04 <1.8e-04 <2.3e-04 Neg. 1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 <1.8e-04 leg. leg. leg. ýeg. leg. 

SatrixKDUFZRa(m3/kgl :1.8e-04 <1.8e-04 :2.3e-04 leg. 1.3e-04 <1.7e-04 <2.3e-04 <1.9e-04 1.8e-04 leg. leg. leg. Seg. leg. 

fatrixKD_.UCFPu[m3/kgl <1.8e-04 1.8e-04 :2.3e-04 leg. 1.3e-04 <1.7e-04 <2.3e-04 1.9e-04 :1.8e-04 leg. 'eg. leg. •eg. 'eg. 

latrixKD_CHnzTh[m3/kg] li.8e-04 <1.8e-04 :2.3e-04 leg. 1.3e-04 <1.7e-04 <2.3e-04 <l.9e-04 1.8e-04 4eg. 4eg. leg. 'eg. eqg. 

AatrixKD.TSwPu[m3/kg] <1.8e-04 1.8e-04 <2.3e-04 qeg. 1.3e-04 1.7e-04 ý2.3e-04 ý1.9e-04 :1.8e-04 4eg. Peg. eq. leq. . 

ote: The use of Neg. in a column indicates that the value of dy/dx sigma for that parameter was zero for that given run. Inclusion of the minimum measureable value in the 

alculation would have a negligible impact on the arithmetic average value of dy/dx sigma.

T1
Table E-2. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 50 kyr 

Parameter Name S - Geom. S - S - High hrith. 3 - No 3 - No $ - No 3 - No 3 - No ly/dx * / * y/dx y/dx * y/dx 

Nverage hrith. value hverage DEs, )Es, )Es, DEs, )Es, sigma - igma igma - sigma - igma 

Nverage of Sigma lean )0th 0th Randoml landom2 ao DEs, 40 DEs, go DEs, Randoml, Random2, 

seighted Values, ýuantile ýuantile Jalues, Jalues, lean g0 h I0h 50k 50k 

Value 50k lalues, Falues, 50k 50k 7alues, Quantile, 2uantile, 

,ears 50k Wk ýears ,ears 50k 50k 50k 

jears rears 

AlluviumMatrixRD._SAVA.<1.Se-03 <1.3e-02 5.5e-02 .6e-01 1.4e-04 <1.7e-04 -5.5e-02 <8.6e-04 -6.6e-03 Neg. 'eg. -0.8 Neg. -1.4e-02 

AlluviumMatrixRDSAVN <3.le-02 <9.5e-01 4.le+00 6.4e-02 -4.1 <1.7e-04 -0.2 -0.4 <5.le-04 -0.1 leg. -5.6e-03 -0.2 Meg. 

SFWettedFraction 6.2e-01 7.9e-01 1.6e+00 .Oe-02 0.3 0.3 1.2 .6 0.5 1.2e-03 5.4e-04 2.4e-03 4.7e-02 .6e-04 

MatrixKDCHnvNpfm3/kg] <1.3e-03 <4.4e-03 .le-02 .6e-03 -l.le-02 5.1e-04 <1.4e-04 9.8e-03 <5.le-04 -4.6e-03 .0e-06 Neg. .8e-02 leg. 

FowFactor l.le-01 .8e-01 6.9e-01 .3e-03 F.7 0.2 -2.7e-03 1.4e-01 0.4 9.6e-03 6.5e-04 -1.3e-04 -i.le-02 .le-04 

SubAreaWetFraction 9.9e-01 9.9e-01 .0e+00 .9e-03 1.0 1.0 1.0 .0 1.0 4.3e-03 2,0e-03 2.2e-03 5.2e-03 .5e-04 

SolubilityNp(kg/m3] <9.le-03 <1.4e-01 6.le-01 .5e-03 0.6 <1.7e-04 -1.6e-02 -7.3e-02 <5.1e-04 6. 2 e-03 Neg. -9.7e-05 -9.7e-04 qeg. 

VellPumpingRateAtCriticl.Oe+00 .Oe+00 .le+00 .4e-03 -0.9 -1.1 -0.9 -1.1 -1.0 -1.8e-03 -1.3e-03 -3.0e-04 -3.4e-03 3.9e-04 

alGroup20km[gal/day] 

InnerOverpackErpInterce<l.8e-03 <1.4e+00 6.9e+00 14e-03 <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 -6.9 Neg. Meg. leg, Neg. -7.0e-03 Pt 

InitialRadiusOfSFPartic8.3e-01 9.0e-01 1.2e+00 .le-03 -0.4 -1.1 -0.9 -1.2 -0.9 -5.0e-04 -I.Oe-03 -1.8e-04 -3.4e-03 -3.4e-04 

lem(] 

ArealAverageMeanAnnuall .2e-01 .3e-01 6.7e-01 .7e-04 0.7 .2e-02 -2.4e-02 -0.2 0.2 2.3e-03 5.6e-05 -2.5e-05 2.3e-03 .3e-04 

nfiltrationAtStart[(m/! r]III 

FmultFactor .2e-01 .8e-01 6.9e-01 .Oe-04 0.7 .2 -2.8e-03 -0.2 0.4 2.9e-03 3.3e-04 -2,le-05 -5.2e-04 6.8e-04 

CoefForLocCorrOfOuterOv ,7e-01 .le-01 .le+00 .9e-04 0.1 .3e-02 0.5 0.2 1.1 4.4e-04 1.6e-04 .6e-04 .Oe-03 2.3e-03 

erpack 

hlluviumMatrixPorosity_ .4e-01 1,le+00 3.4e+00 .6e-04 -3.4 -0.6 -0.3 -0.7 -0.5 -2.9e-03 -3.9e-04 2.6e-05 -8.8e-04 -1.2e-04 

SAV 

klluviumMatrixRDSAV_.SE <.Oe-03 <5,8e-03 2.6e-02 4.3e-04 -2.0e-03 <1.7e-04 -1.4e-04 <8.6e-04 -2.6e-02 -7.le-05 Neg. 5.9e-06 9eg. -2.le-03 

4eanAveragePrecipitatic .4e-01 3.5e-01 7.8e-01 2.8e-04 .8 -0.5 -4.6e-02 1.4e-01 .3 .9e-04 -3.2e-04 6.0e-06 1.9e-04 9.7e-05 

a•lultiplierAtGlacialMay 

imum 

rracturePorositySTFF 2.5e-02 5.5e-02 1.9e-01 F.7e-04 .2 -4.le-02 -3.4e-03 -1.3e-02 P3.Oe-02 .le-03 -7.4e-05 -4.3e-06 -1.6e-04 -2.2e-05 

hermalConductivityofY 5.9e-01 7.4e-01 1.2e+00 .6e-04 0.2 1.2 0.2 -1.2 0.9 9.6e-05 3,7e-04 l.le-05 7.4e-04 I.Oe-04 

ock[W/(m-K)] _ _ I 1 1 1_1

O0
Table E-2. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 50 kyr (cont'd)

ON 00

FracturePermeability-U(<6.5e-04 <1.8e-03 6.5e-03 .le-04 <1.4e-04 <1.7e-04 1.5e-03 6.5e-03 <5.le-04 Neg. leg. 3.3e-05 5.3e-04 leg. 

leanAverageTemperature]2.6e-02 4.9e-02 1,5e-01 5.le-05 .2 5.8e-03 -2.6e-02 1.0e-02 5.2e-02 -2.0e-04 -9.0e-06 2.9e-06 -1.9e-05 -2.4e-05 

acreaseAtGlacialMaximun 

[degC] 

AA2-1[C/m2/yrj <1.5e-03 <1.4e-01 7.2e-01 5.2e-05 <1.4e-04 <1.7e-04 .7 <8.6e-04 <5.le-04 Ieg. leg. 2.6e-04 Neg. Ieg. 

CriticalRelativeHumiditl.9e-01 3.0e-01 8.9e-01 3.6e-05 -6.4e-02 -0.9 .2 -0.2 .1 -9.7e-06 -l.le-04 3.4e-06 -5.le-05 .9e-06 

yAqueousCorrosion 

MatrixPermeability CHn3.le-03 4.6e-03 1.3e-02 3.le-05 -2.6e-03 2.4e-03 .3e-04 -1.3e-02 -4.le-03 -1.6e-05 .8e-06 1.4e-06 -1.2e-04 -1.5e-05 

lluviumMatrixRDSAVI <4.4e-03 <3.3e-02 i.le-Ol 2.8e-05 2.6e-03 -5.0e-02 -1.4e-04 <8.6e-04 -0.1 8.le-06 -7.le-05 -7.9e-08 Neg. -6.le-05 

AlluviumMatrixRD. SAVTC<1.6e-03 <2.8e-03 5.7e-03 3.le-05 5.7e-03 <1.7e-04 -8.6e- 0 4 -2.6e-03 -4.6e-03 5.7e-05 Neg. -3.5e-06 -8.4e-05 -8.4e-06 

SEISMOJointSpacing4[m] <9.7e-04 <2.0e-02 1.0e-01 1.8e-05 <1.4e-04 .1 <1.4e-04 <8.6e-04 <5.le-04 Neg. .9e-05 Neg. 'eg, Neg. 

SeismicHeterogeneityFac 6.2e-04 <2.5e-03 l.le-02 .7e-05 <1.4e-04 .le-02 <1.4e-04 <8.6e-04 <5.le-04 Neg. 8.4e-05 le. eg. eg. 

tor 

DefectiveFractionOfWPs/l .9e-03 <3.4e-03 .5e-03 1,2e-05 7.le-04 9.5e -03 1.9e-03 4.3e-03 <5,le-04 2.9e-06 .8e-05 .7e-06 .4e-05 qeg. 

cellIII 

AlluviumMatrixRDSAV_Ni• 2.7e-04 <3.6e-04 <8.6e-04 1.4e-05 <1.4e-04 <1.7e-04 -1.4e-04 <8.6e-04 <5.le-04 Neg. Neg. -6.9e-05 Neg. leg. 

CladdingCorrectionFact B.4e-01 8.9e-01 1.le+00 leg. 0.4 1.0 1.0 1.1 .0 Negg.e. eg. eg. 'eg. 

PUltimateStrength(N/m' 1.7e-03 <5.le-02 .5e-01 .9e-07 <1.4e-04 -0.3 <1.4e-04 -6.2e-03 <5.le-04 Neg. -2.7e-07 Neg. -1.2e-06 Neg. 

)ispersionFractionSTFF ,5e-02 .le-02 .7e-01 Peg. 0.2 .3e-02 V.2e-03 -5.3e-02 [.Oe-02 leg. Neg. Neg. Neg. Neg. 

SeismicHazardCurveforS l.0e-03 <2.4e-02 .2e-01 Neg. <1.4e-04 -0.1 <1.4e-04 <8.6e-04 <5.le-04 Neg. leg. Neg. Neg. Neg. 

0SMO 

RockModulusOfElasticit) <9.9e-04 <2.2e-02 .le-01 .4e-08 <1.4e-04 .1 <1.4e-04 8.6e-04 <5.le-04 leg. 4.2e-07 Neg. Neg. Neg. 

forSEISMO[Pa] 

)ispersionFraction-SAV .5e-02 .2e-01 .5e-01 Neg. 1.0 .le-02 1,7e-03 -5.9e-02 .6e-02 Neg. Neg. Neg. Neg. legq 

3olubilityAm(kg/m3] <5.4e-04 <1.4e-03 .3e-03 .0e-06 <1.4e-04 ,3e-03 1.4e-04 8.6e-04 <5.le-04 leg. 1.0e-05 Neg. Neg. Neg. 

AatrixKD CHnvU(m3/kg] <3.le-04 <4.0e-04 <8.6e-04 .4e-07 <1.4e-04 -3.4e-04 <1.4e-04 <8.6e-04 <5,le-04 leg. -1.2e-06 Negq. eg. leg. 

lluviumMatrixRDSAVPI '2.7e-04 r3.6e-04 8.6e-04 .3e-06 <1.4e-04 <l.7e-04 -1.4e-04 8.6e-04 F5.le-04 Neg. leg. 2.2e-05 Neg. leg. 

EISMOJointSpacinql[m] •2.7e-04 _3.6e-04 8.6e-04 eg. 1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 5.1e-04 eq. eq. eg. eg. e

60
Table E-2. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 50 kyr (cont'd) 

SEISMOJointSpacing2[m] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8,6e-04 <5.le-04 Neg. leg. eg. eg. Neg. 

SEISMOJointSpacing3[m] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 le. eg. Neg. leq. leg. 

FracturePermeabilityCH<2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 eg. eg. eg. eg. eg. nz [m2 ] 

MixingZoneThickness20krr<2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <'1.4e-04 <8.6e-04 <5.1e-04 4eg. 4eg. qeg. ieg. leg. 

AlluviumnMatrixRD-SAV.C <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.1e-04 Neg. Negq. eg. Neg. Neg. 

FracturePermeabilityUE 2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.le-04 Neg. Neg. Neg. Neg. Neg. 

Z- [m2] 

FracturePermeabilityCH 2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 5.1e-04 Neg. eg, Neg. Neg. Neg. 

nv[m2 r 

ChlorideMuItFactor <2.7e-04 3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 5.1e-04 Neg. Neg. Neg. Neg. Neg. 

FracturePermeabilityBS 2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 :5.le-04 Neg. Neg. Neg. 'eg. leg. 4_[m2] 

AlluviumMatrixRDSAVU <2.7e-04 <33.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 5.le-04 Neg. Neg. leg. leg. Neg. 

RockPoissonRatioforSEI :2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. Neg. leg. Neg. Neg. 

AlluviumMatrixRDSAV_T. •2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8,6e-04 <5.1e-04 Neg. Neg. Neg. leg. Neg. 

SubGrainFragmentRadius 2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 eg. 'egqg. e Neg. 'eg. 

fterTransFrac [mi] 

SEISMOJointSpacing5[m] <2.7e-04 <3.6e-04 <8.6e-04 Neg. 1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. Neg. 'leg. Neg. leg. 

SolubilityPutkg/m3] 2.7e-04 F3.6e-04 <8.6e-04 Neg. 1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 <5.le-04 legq. eg. leg. egq. Neg. 

MatrixPermeability_UFZ <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 c1.7e-04 <1.4e-04 8.6e-04 5.1e-04 leg. leg. Neg. Neg. leg. 

[m2] 

AlluviumMatrixRDfSAVRa<2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 5.1e-04 Neg. Neg. Neg. 'eg. leg. 

FracturePorosityUFZ_ <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1,4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.1e-04 Neg. Neg. Neg. Neg. Neg. 

MatrixPermeabilityPPw_<2.7e-04 3.6e-04 <8.6e-04 Neg. 1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-0.4 eg. leg. Neg. eg. Neg. 

[m2l rI 

AatrixPermeabilityBFw <2.7e-04 <3.6e-04 :8.6e-04 Neg. <1,4e-04 <1.7e-04 1.4e-04 <8.6e-04 -5.le-04 eg. Neg. Neg. eg. Neg. 

klluviumMatrixRDSAV_PL 2.7e-04 <3.6e-04 8.6e-04 Neg. <1.4e-04 <1.7e-04 e1.4e-04 8.6e-04 5.le-04 eq. eg. geg. egq. Ieg,

CN 

00
Table E-2. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 50 kyr (cont'd)

00 0%~ 

00

HatrixPermeability.TSw_<2.7e-04 3.6e-04 8.6e-04 eg. <1.4e-04 <1,7e-04 <1.4e-04 c8.6e-04 <5.1e-04 eg. eg. eg. eg. eq. 

Im~l 

ThicknessOfWaterFilm[m]<2,7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 Neg. leg. neg. Neg. 'eg. 

OxygenPartialPressure[a<2.7e-04 <3.6e-04 <8.6e-0 4 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. Reg. Neg. eg. eg. 

imI I 

MatrixPermeability-CHnz 2.7e-04 <3.6e-04 <8,6e-04 leg. <1.4e-04 cI.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. leg. leg. ne. eg, 

[m21 

FracturePorosity_BFw_ <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 8,6e-04 <5.le-04 Neg. eq eg. eg. Neg. 

klluviumMatrixRDSAVNh-2.7e-04 <3.6e-04 .8.6e-04 peg. <.4e-04 i.e-uc 1.ee-04 o~oe-v' . .. ._.- _ _ ..

FracturePorosity.UCF_ <2.7e-04 <3.6e-04 <8.6e-04 Neg. 1.4e-04 <1.7e-04 <1.4e-04 8,6e-04 5.e-04 eg. 'eg. eg. leg. Ieg. 

FracturePermeabilityT <2.7e-04 <3.6e-04 <8.6e-04 Reg. 1.4e-04 <1.7e-04 <1,4e-04 <8.6e-04 <5.le-04 eg. Reg. Ne. eg. Neg. 

- (m2] 

MatrixPermeabilityUCF <2.7e-04 <3.6e-04 <8.6e-04 neg. 1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 eg. Neg. Neg. eg neg. 

[m2l I I 

•racturePorosityPPw_. -2.7e-04 <3.6e-04 .8.6e-04 4eg. <1.4e-04 '1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 leg. eq. eg. eg. eg. 

FracturePorosityTSw. <27e-04 <1.7e-04 <3.6e-04 -8.e-04 eq. <1.4e-04 1.4e-04 <8.6e-04 leeg4 . eeg. Neg. neg. 

FracturePorosityCHnv <2.7e-0 4 <3.6e-04 <8.6e-04 leg. <1.4e-04 1.7e-04 1.4e-04 <8.6e-04 5.le-04 neg. Neg. Neg. Neg. neg. 

FracturePorosityCHnz <2.7e-04 3.6e-04 <8.6e-04 eg. 1.4e-04 1.7e-04 1.4e-04 <8.6e-04 55.1e-04 eg. Peg. Neg. Neg. Neg. 

MatrixKDCHnzU[m3/kg] <2.7e-04 3.6e-04 <8.6e-04 leg. 1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.le-04 eg. Neg. Reg. Neg. eg. 

YracturePermeabilityP<2.7e-04 3.6e-04 <8.6e-04 Neg. 1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 5.1e-04 eg. Reg. Neg. 'eg. eg. 

4atrixKDTSw_.U[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 .5.le-04 egq. e eg. Peg, Neg. 

4atrixKDUCFTh[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1,7e-04 1.4e-04 <8.6e-04 <5.1e-04 Neg. Neg. Neg. Neg. eg. 

atrixKD__UCF._Ni[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 egq. V1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 :5.le-04 Neg. leg. Neg. 'eg. Neg. 

1atrixKD_BFwPu[m3/kg] 2.7e-04 3.e-04 <8.6e-04 Neg. <1.4e-04 1.7e-04 1.4e-04 <8.6e-04 <5.le-04 Neg. leg. Neg. 'eg. leg. 

IatrixKDtUFZ_Purm3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 Neg. leg. Neg. Neg. eg. 

•atrixKDTSwTh[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Peg. _1.4e-04 1.7e-04 <1.4e-04 <8.6e-04 :5,le-04 leg. 'eg. e neg. eg. 

etrixKDCHnvTh[m3/kg] <2.7e-04 3.6e-04 <8.6e-04 eg. l.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. eg. Neg. leg. eg. 

atrixKDUFZU[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 eg. 1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.le-04 eq. eq. eg. eq eq

0D
Table E-2. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 50 kyr (cont'd) 

9atrixKDCHnzSe(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 8.6e-04 <5.le-04 Neg. 'eg. Meg. Meg. Meg. 

atrixKDUCFp(m3/kg <2.7e-04 <3.6e-04 <8.6e-04 Neg 1.4e-04 <1,7e-04 1.4e-04 <8.6e-04 5e-04 leg. eg. Neg. Neg. eg. 

4atrixKDUPPwCp[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 5.1e-04 Reg. leg. Neg. 'eg. 'eg. 

4atrixKDTSwPb(m3/kg] <2.7e-04 <3.6e-04 r8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 leg. feg. Meg. Neg. Peg. 

4atrixKD TSwPRam3/kg) <2.7e-04 <3.6e-04 <8.6e-04 Meg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. Meg. Meg. Meg. Meg. 

MatrixKDPPw_Ni(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 leg. Meg. Meg. Meg. Meg. 

atrixKDwNi[m3/kg] 2.7e-04 3.6e-04 8.6e-04 eg. 1.4e-04 <1,7e-04 <1.4e-04 <8.6e-04 -5.le-04 Neg. eg. eg. eg. eg. 

atrixKDCHnzRa[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Meg. -1.4e-04 .7e-04 1.4e-04 8.6e-04 e. M5.1e-04 eg. Neg. Meg. leg. 

4atrixKD_PPw_Ra(m3/kgJ <2.7e-04 <3.6e-04 r8.6e-04 Neg. -1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Meg. Neg. keg. Neg. 'eg. 

4atrixKD_UCF Ra(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 eg. eg. Meg. 'eg. 'leg. 

eatrixKDCHnzNp[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 -5.le-04 Neg. Neg. Neg. 'eg. Meg. 

•atrixKD_UFZCNp[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8,6e-04 -5.le-04 Neg. Neg. Meg. Meg. Meg. 

IatrixKDCHnvCs(m3/kg] <2.7e-04 3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 V8.6e-04 <5.1e-04 Neg. Neg. Neg. Neg. Neg. 

IatrixKD CHnvPb[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.1e-04 leg. qeg. Neg. Meg. Neg. 

AatrixKD_CHnzPb(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.1e-04 Aeg. Neg. Meg. Meg. Meg. 

4atrixKD_PPw_Pb(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.1e-04 Peg. 'eg. Neg. Peg. Neg. 

4atrixKDUCF_Pb[m3/kg] <2.7e-04 13.6e-04 <8.6e-04 Meg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. Meg. Neg. Meg. leg. 

AatrixKD._BFw_Pb[m3/kg] <2.7e-04 3.6e-04 <8.6e-04 Neg. <1,4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Neg. Neg. leg. leg. Neg. 

AatrixKD_UFZPbSm3/kg] <2.7e-04 3.6e-04 <8.6e-04 leg. 1.4e-04 1.7e-04 <1.4e-04 8.6e-04 <5.1e-04 leg. eg, eg. 'eg. Meg. 

4atrixKDTSwCs[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 Reg, leg. Neg. Peg. leg. 

atrixKCHn [m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 17e-04 14e-04 <8.6e-04 5.e-04 leg. eg. eg. Neg. eg. 

AatrixKD_CHniCs[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 eg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 5.1e-04 leg. 'eg. 'eg. Neg. Neg. 

AatrixKDPCnzCs[m3/kgJ <2.7e-04 13.6e-04 <8.6e-04 Meg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5. le-04 leg. 'eg. Neg. Neg. Neg. 

AatrixKD._Pw._Cs[m3/kg] <2.7e-04 3,6e-04 =8.6e-04 Meg. <1.4e-04 1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 Meg. Neg. Neg. leg. 'eg. 

AatrixKDCF_ýCS(1m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 Meg. leg. 'eg. Meg. Neg. 

atrixKDjBFwCs[m3/kgJ <2.7e-04 3.6e-04 8.e-04 Neg. <1.4e-04 <1.7e-04 1.4e-04 8.6e-04 5le-04 leg. Neg. eg, eg. Neg. 

katrixKDTSwNi (m3/kg] <2.7e-04 I 3.6e-04 .'8.6e-04 19eg. r1.4e-04 <1.7e-04 11.4e-04 k8.6e-04 r5,1le-04 Fleg. Meg. keg,, Meg. leg.

00 

00
00 

tOl

Table E-2. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 50 kyr (cont'd) 

IatrixKDBFwTh[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 leg. leg. 'leg. leg. leg. 

atrixKDCHnvAm[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 5.1e-04 leg. leg. 'leg. leg. leg. 

MatrixKD_BFw._Np[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 -5.le-04 leg. eg. leg. leg. leg. 

MatrixKDBFwRajm3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1,4e-04 <8.6e-04 5,le-04 leg. eeg. eg. leg. peg. 

MatrixKD_UCFSe[m3/kg) <2.7e-04 <3.6e-04 <8.6e-04 Meg. <1.4e-04 <1.7e-04 <,4e-04 <8.6e-04 <5.1e-04 'eg. leg. leg. leg. Neg. 

MatrixKDPPwSerm3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 leg. 'eg. leg. leg. leg. 

AatrixKDBFwLSetm3/kg] <2.7e-04 3.6e-04 B.6e-04 qeg. 1. 4 e-04 <1.7e-04 <1.4e-0 4 <8.6e-04 <5.le-04 leg. eg. leg. e. eg. 

MatrixKDTSwNb(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 leg. leg. Me. eg. 'eg. 

MatrixKDUFZSe[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.1e-04 leg. Neg. leg. leg. leg. 

MatrixKD CHnvNb(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 leg, leg. 'eg. leg. leg. 

MatrixKD PPwNb[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 r1.7e-04 <1.4e-04 F8.6e-04 ,5.le-04 leg. eg. eg. e. peg. 

MatrixKDCHnzNb[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 .5.le-04 leg. 'eg. eq legq. eg. 

MatrixKDUCFNb[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 leg. leg. 4eg. le. eg. 

.atrixKD_UFZNb(m3/kgj <2.7e-04 <3.6e-04 <8.6e-04 qeg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5S.le-04 leg. leg. Neg. leg. Aeg. 

4atrixKDBFw.Nb~m3/kg1 <2.7e-04 <3.6e-04 <8.6e-04 leg. 1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.le-04 peg. leg. leg. leg. leg. 

KatrixKDTSwNp(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 5.1e-04 leg. legq. eg. leg. leg. 

CHnzNi~m3/kq] 2.7e-04 3.6e-04 8.6e-04 eq. <atrixKD 1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.1e-04 leg. 'eg. 'eg. 'eg. leg. 

.4tiK-Izim/g <27-0e36e0g..e04lg 

4atrixKDCHnvPu[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.le-04 leg. eg. eg. 'eg, e. 

4atrixKD_UFZAm(m3/kgj <2.7e-04 <3.6e-04 8.6e-04 leg. <1,4e-04 1.7e-04 F1.4e-04 <8.6e-04 <5.le-04 egq. e. eg. eg. leg. 

4atrixKD._BFw._Am(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 eq. 1.4e-04 1.7e-04 1.6e-048.6e-04 e. l.ee-04 eq. leg. eg. 'eg. 

4atrixKDUCF_Amnm3/kg) <2.7e-04 3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 <5.le-04 Neg. leg. leg. peg. leg. 

AatrixKDOPPw Am[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 <5.1e-04 egq. 'leq. e. eg. 'eg. 

AatrixKDCHnzAm[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. F1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 <5.le-04 leg. leg. 'eg. leg. leg. 

AatrixKD BFwNi(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.le-04 peg. Aeg. leg. Aeg. leg. 

MatrixKD_UFZTh[m3/kg] I'2.7e-04 :3.6e-04 r8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 Iegqg.eq. eg, eg. eg. 

4atrixKDCHnzPu[m3/kg] ý2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 5.1e-04 eg. Ieg. Feg. Feg. Ieg.

K
Table E-2. Differential analysis results for the nominal case with Alloy 625 for a time period of interest of 50 kyr (cont'd) 

MatrixKDUFZNi(m3/kg] <2.7e-04 <3.ýe-04 <8.6e-04 meg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 <5.le-04 Neg. Neg. Neg. Neg, Neg. 

MatrixKD_CHnvSe[m3/kg] <2.7e-04 <3.6e-04 <8,6e-04 leg. <1.4e-04 1.7e-04 <1.4e-04 8.6e-04 <5.le-04 eg. le. eg. 'leg. leg. 

MatrixKD -TSwSejm3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 geg. eg, eg. 'eg. 'eg. 

MatrixKDCHnvRalm3/kg] 2.7e-04 r3.6e-04 <8.6e-04 leg. 1.4e-04 <1.7e-04 1.4e-04 8.6e-04 <5.le-04 Pe. eg.eg. Peg. Neg. 

.latrixKDPPw.Pu(m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. 1.4e-04 <1.7e-04 1.4e-04 <8.6e-04 <5.le-04 Neg. leg. leg. keg. leg. 

natrixKDUCFU(m3/kg] c2.7e-04 <3.6e-04 <8.6e-04 'leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 Neg. leg. leg. keg. leg. 

4atrixKDPPwTh(m3/kg] <2. 7 e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 eg. eg. eq. egq. eg. 

latrixKDTSwAmfm3/kg] 2.7e-04 3.6e-04 <8.6e-04 leg. 1.4e-04 1.7e-04 1.4e-04 8.6e-04 <5.le-04 eg. Neg. eg. Neg. Neg. atrixKDBFw Um3/kg] <2.7e-04 <3.6e-04 <8.6e-04 Neg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 5,1e-04 eq. eg. eg. eg. eg. 

MatrixKDUFZ_.Ra[m3/kgJ <2.7e-04 <3.6e-04 8.6e-04 eg. <1.4e-04 <1.7e-04 <1.4e-04 8.6e-04 <5.le-04 [egq. eg. Neg. Feg, leg. 

4atrixKDUCFPu[m3/kg] <2.7e-04 <3.6e-04 <8.6e-04 leg. <1.4e-04 <1.7e-04 <1.4e-04 <8.6e-04 <5.le-04 egq leg. Neg. Neq e. 

4atrixKDCHnzTh[m3/kgj <2.7e-04 <3.6e-04 <8.6e-04 leg. I1.4e-04 I1.7e-04 1.4e-04 8.6e-04 <5.le-04 Meg, leg. leg. Negq. eg. 

4atrixKDT2.:Pu[m3/kg] I2.7e-04 <3.6e-04 <8.6e-04 leg. 1.4e-04 11.7e-04 <1.4e-04 18.6e-04 <5.le-04 eleg. eq. eg. legq. eg. 

lote: The use of Neg. in a column indicates that the value of dy/dx * sigma for that parameter was zero for that given run. Inclusion of the minimum neasureable value in the calculation would have a negligible impact on the arithmetic average value of dyldx * sigma.

ri

00
Table E-3. Differential analysis results for the nominal case with Alloy 625 and faulting for a time period of interest of 10 kyr

0N 

00

Parameter Name - Geom. - 3 - High ,rithmetic - - - - - //dx y/dx * * ly/* * lyldy yldx 

Pverage krithmetic alue lean of Faulting, aulting, aulting, Faulting, Faulting, igma - igma - igma - igma - sigma 

of 10,000 Hean sigma 4ean 0th 0th landoml Random2 aulting, aulting, aulting, aulting, aulting, 

years eighted lalues, uantile uantile lalues, lalues, [ean 0t" 0' Random 1, Random 2, 

Value l0k years alues 10k alues, 10k years .Ok years alues, l0k uantile, uantile, LOk years 0k years 

ears Ok years ears Ok years Ok years 

rimeofNextFaultingEventinReg 1.6e-02 L.7e-01 .8e-01 2.le-05 -0.3 .0e-04 1.6e-03 -4.le-02 -0.5 3.6e-06 .0 -1.9e-10 -3.5e-07 -1.0e-04 

LonOfInterest(yr] I 

IEFaultZoneWidth (m] 3. le-03 <1.8e-02 4.8e-02 1.9e-05 4.8e-02 3.7e-02 k.3e-04 5.7e-03 <1.3e-04 3.3e-06 9.2e-05 4.8e-11 3.4e-08 D.0 

NWFaultZoneWidth[m] <9.2e-04 <1.4e-01 7.2e-01 1.4e-05 <1.3e-04 :1.6e-04 <2.3e-04 <1.9e-04 0.7 0.0 0.0 0.0 0.0 7.2e-05 

ntopetermineFaultOrientatio r5.le-04 r1.2e-02 5.7e-02 .4e-07 <1.3e-04 <1.6e-04 <2.3e-04 .7e-02 ý1.3e-04 0.0 0.0 0.0 1.7e-06 0.0 

FhresholdDisplacementforFaul 1i6e-04 1.7e-04 ý2.3e-04 eg. <1.3e-04 1.6e-04 :2.3e-04 1.9e-04 1.3e-04 0 D. 0 D.0 3.0 ).0 

tDisruptionOfWP(mt I 

YLocationOfFaultingEventInRe :1.6e-04 <1.7e-04 ý2.3e-04 eg. <1.3e 04 ý1.6e-04 <2.3e-04 <1.9e-04 <1.3es04 0 ).0 0.0 00 0.0 

gionOfInterest(m] 

ocationOfFaultingEventnRe <1.6e-04 <1.7e-04 <2.3e-04 eg. <1.3e-04 1.6e-04 12.3e-04 <1.9e-04 <1.3e-04 0 ).0 0.0 1,0 3.0 

•ioonOfInterest[m]

4-

'Tote: The use of Neg. in a column indicates that the value of dy/dx * sigma for that parameter was zero for that given run. Inclusion of the minimum measureable value in the 

:alculation would have a negligible impact on the arithmetic average value of dy/dx * sigma.
Table E-4. Differential analysis results for the nominal case with Alloy 625 and faulting for a time period of interest of 50 kyr

?arameter Name - Geom. S - - High rithmetic - 3 - - - - /ydx * y/dx y/dx * ly/dx * ly/dx * sigma 

Pverage Arithmetic Value ean of Faulting Faulting Faulting Faulting, Faulting, igma - 3igma - iigma - Faulting, 

Average igma lean )0th 10th ýandoml Random2 aulting, Faulting, aulting, Faulting, Random 2, 50k 

eighted Jalues, ýuantile uantile lalues, Values, ean ? oh Random 1, years 

alues 50k years •alues, Values, 50k years 50k years alues, uantile, uantile, 0k years 

50k years 50k years Ok years 50k years Ok years 

ZntoDetermineFaultOrientation ý6.4e-04 1.4e-02 6.8e-02 .9e-04 1.4e-04 cl.6e-04 I1.4e-04 S.8e-02 <5.0e-04 .0 ).0 .0 .5e-03 0.0 

4EFaultZonewidth(m] 4.5e-03 lie-02 4.0e-02 5.2e-05 5.4e-03 .0e-02 .8e-03 9.0e-03 <5.0e-04 .3e-05 9.8e-05 .3e-06 1.2e-04 0.0 

rimeOfNextFaultingEventinRegi ý8.5e-04 <5.Oe-03 2.3e-02 4.4e-06 -2.3e-02 <1.6e-04 2.8e-04 <8.6e-04 <5.0e-04 2.2e-05 0.0 -1.6e-07 0.0 0.0 

)nOfInterest yr] I 

MFaultZoneWidth[m] 4.7e-04 <1.9e-03 8.4e-03 .2e-06 O 1.4e-04 1.6e-04 1.4e-04 8.6e-04 .4e-03 0.0 0.0 0.0 0.0 2.le-05 

PhresholdDisplacementforFault <2.7e-04 <3.6e-04 <8.6e-04 4eg. 1.4e-04 <1.6e-04 1.4e-04 8.6e-04 :5.0e-04 0.0 D.0 D.0 0.0 0.0 

)isruptionOf i_:Pnm) _ 

(LocationOfFaultingEventInReg <2.7e-04 :3.6e-04 <8.6e-04 ieg. 1.4e-04 <1.6e-04 <1.4e-04 8.6e-0 4 :5.0e-04 0.0 0.0 0.0 0.0 0.0 

LonOfInterestm(] 

MLocationOfFaultingEventInReg 2.7e-04 <3.6e-04 8.6e-04 Meg. 1.4e-04 <1.6e-04 t1.4e-04 <8.6e-04 <5.0e-04 0.0 0.0 0.0 0.0 0.0 

ionOflnterestim] 

Jote: The use of 14eg. in a column indicates that the value of dy/dx sigma for that parameter was zero for that given run. Inclusion of the minimum measureable value in the 

,alculation would have a negligible impact on the arithmetic average value of dy/dx * sigma.

~T1

00
Table E-5. Differential analysis results for the nominal case with Allo 625 and volcanism for a time period of interest of 10 kvr

00

Parameter Name S - Geom. - S - High krithmetic - I - S - 3 - - dy/dx * y/dx * y/dx * y/dx * y/dx hverage hrithmetic Value Mean of Faulting, 7aulting, Faulting, Faulting, Faulting, sigma - igma - igma - sigma - igma of 10,000 Mean sigma Aean ?0th 10th andoml landom2 Faulting, aulting, Faulting, aulting, aulting, ears seighted ralues, ýuantile 2uantile lalues, lalues, lean 0th F andom 1, andom 2, ralue 10k years lalues 10k Values, LOk years LOk years falues, 10k uantile, ýuantile, 10k years Ok years rears 0k years Iears Ok years 10k years 

rimeOfNextFaultinlgEventinReg 1.6e-02 1.7e-01 4.8e-01 .le-05 -0.3 1.Oe-04 -1.6e-03 -4.le-02 -0.5 3.6e-06 .0 -l.9e-10 -3.5e-07 -l.Oe-04 

ionOfInterest yr] 

ý48-aultZoneWidth~mJ 3.le-03 1.8e-02 4e02 1.9e-05 4.8e-02 3.7e-02 2.3e-04 5.7e-03 1.3e-04 3.3e-06 9.2e-05 4.8e-1l 3.4e-08 0.0 

'.lFaultZoneWidthlm] [9.2e-04 <1.4e-01 .2e-01 1.4e-05 <1.3e-04 <1.6e-04 <2.3e-04 <1.9e-04 0.7 0.0 0.0 0.0 0.0 7.2e-05 

RntoDetermineFaultOrientatio l5.1e-04 <1.2e-02 5.7e-02 3.4e-07 li,3e-04 1.6e-04 2.3e-04 5.7e-02 1.3e-04 0.0 ).0 0.0 1.7e-06 0.0 

rhresholdDisolacementforFaul 1.6e-04 1.7e-04 L23r-04 Lc- 1 -O4 1 A-04 1-64 - L Q5. 04 Ll -nt I I . . tDisruptionOfWP(m) I I I I I

iLocationOfFaultingEventlnRe 1.6e-04 :1.7e-04 <2.3e-04 leg. <1.3e-04 <1.6e-04 <2.3e-04 <1.9e-04 <1.3e-04 0.0 0.0 0.0 .0 0.0 ;ionofInterest[m] 

4LocationOfFaultingEventInRe [1.6e-04 <1.7e-04 [2.3e-04 leg. l1.3e-04 <S.6e-04 <2.3e-04 :1.9e-04 -1.3e-04 0.0 0 0.0 [.0 _.0 lionOfInterest~m] 

4ote: The use of Neg. in a column indicates that the value of dy/dx sigma for that parameter was zero for that given run. Inclusion of the minimum measureable value in the :alculation would have a negligible impact on the arithmetic average value of dy/dx * sigma.

Ir 

-4
Table E-6. Differenti analysis results for the nominal case with Alloy 625 and volcanism for a time eriod of interest of 50 kr 

Parameter Name s - S - 9 - High rithmetic - - - - - Iy/dx * y/dx * y/dx * ly/dx * y/dx * 

Oeometric irithmetic Jalue fean of Jolcanism, Iolcanism, lolcanism, lolcanism, lolcanism, sigma, igma, igma, sigma, sigma, 

Average Mean Sigma Mean )0th L0th Randoml kandom2 lolcanism, olcanism, olcanism, 1olcanism, Volcanism, 

50,000 4eighted alues, uantile uantile alues, 50k lalues, iean 00 Oandom 1, Random 2, 

years Jalues 50k years falues, Jalues, jears 50k years ]alues, uantile, uantile, 50k years 50k years 

50k years 50k years 30k years Ok years Ok years 

lindSpeed(cm/s] 4.2e-01 .2e-01 1.6e+00 4.5e-01 3.8e-02 ).4 ).8 1.6 ).7 5.7e-03 l.3e-02 1. 3.1 4.7e-03 

z.irbornetlassLoadForVolcanis 7.3e-01 3.3e-01 1.3e+00 .2e-01 1.0 0.6 1.0 1.3 0.3 0.8 1.6e-02 0.2 0.5 2.2e-02 

ýDoseCalculation(g/m3) 

lolcanicEventPower[W) 2.9e-01 .0e-01 9.0e-01 .Oe-01 -0.9 -0.7 ).1 0.2 -0.2 -0.9 -2.0e-02 3.9e-02 3.9e-02 -5.7e-05 

VolcanicEventDuration(s] <3.2e-02 <3.6e-01 9.0e-01 .7e-01 "0.9 -0.6 <6.4e-04 <3.2e-04 -0.3 -0.8 -1.9e-02 0.0 0.0 -1.2e-03 

ý)ameterOfVolcanicConelm] 1.8e+00 1.8e+00 .lee00 .7e-02 2.1 1.9 .1 1.9 1.3 0.2 2.9e-02 1.6e-02 0.2 3.7e-03 

4shIeanParticleLogDiameter( 1.3e-01 1.7e-01 3.7e-01 3.6e-02 -5.3e-02 -7.2e-02 -0.4 -0.2 -0.2 -2. le-02 -I.7e-03 -2.3e-02 -0.1 -2.2e-04 

-in cm] 

TimeOfNextVolcanicEventinRe 7.5e-01 8.3e-01 1.2e+00 9.7e-03 -1.0 -1.1 -0.4 -1.2 -0.5 -4.3e-03 -5.3e-03 -3.3e-03 -3.0e-02 -4.le-04 

gionOfInterest(yr) 

hngleOfVolcanicDikeMeasured <2.7e-04 <3.2e-04 :6.4e-04 leg. <2.8e-04 <1.3e-04 <6.4e-04 <3.2e-04 <2.le-04 0.0 0.0 D.0 .0 .0 

FromNorthClockwise(degrees] 

5engthOfVolcanicDike~m) <2.7e-04 <3.2e-04 :6.4e-04 eg. ý2.8e-04 =1.3e-04 -6.4e-04 <3.2e-04 <2.1e-04 ).0 0.0 0.0 0.0 0.0 

IidthOfVolcanicDikeimJ <2.7e-04 €3.2e-04 <6.4e-04 eg. <2,8e-04 =1.3e-04 <6.4e-04 <3.2e-04 <2.le-04 0.0 0.0 T.0 .0 0.0 

lote: The use of Neg. in a column indicates that the value of dy/dix sigma for that parameter was zero for that given run. Inclusion of the minimum measureable value in the 

ialculation would have a negligible impact on the arithmetic average value of dy/dx * sigma.

OS 

ON 

00
Table E-7. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 10 kyr

00

Parameter Name - - - High krithmetic S - No - No - No 5 - No 3 - No ly/dx * y/dx yy/dx /dX y/dx * 

3:ometric krithmetic Value 4ean of DEs, Mean )Es, 90th DES, 10th DES, )ES, isiga Sigma - No Sigma - No igma - No Sigma - No 

4ean Aean igma Values, ýuantile uantile zandoml Iandom2 'o DEs, DEs, 90" DEs, 10 h Es, Random DEs, 

geighted 0k years Values Values, Values, falues, ean uantile, )uantile, l, 0k Random 2, 

Values 10k years L0k years 10k years 10k Values, 10k .Ok lOk 

ýears l0k 

SeismicHeterogeneityFactor :3.0e-03 .1.7e-01 5.7e-01 5.4e-05 6.3e-04 ).7 2.3e-04 I8.9e-04 4/A Jeg. 3.2e-04 4eg. eg. Neg. 

SEISNOJointSpacing4)[m1 5.0e-03 <1.2e,00 4.8e*00 5.2e-05 <6.3e-04 5.8 <2.3e-04 8.9e-04 N/A Neg. 2.6e-04 eeg. Ie. 4eg. 

ArealAverageMeanAnnuallnfiltrat 2.7e+00 2.9e+00 4.5e+00 4.3e-05 1.7 1.8 4.5 .8 N/A 1.4e-05 2.0e-04 2.8e-07 3.9e-06 \eg. 

ionAtStart [rss/yr] 

FowFactor [.8e÷00 2.8e+00 7.2e+00 3.3e-05 1.7 ).4 7.2 2.1 T/A 5.2e-05 9.4e-05 2.Oe-06 1.5e-05 ieg. 

S'IbArea1)etFreac t ion I.0e+00 1.0-00 . Oe÷- 0 2.7e-05 1.0 1.0 L.1.0 .I/A V. , -0 . - .3 e--U --e p.±e-u, qeg. 

kI1uviumlatrixRDPSAVAm .2.1e-03 3 ,0e-02 1L.2e-Ol .e-05 -6.3e-04 :2.7e-04 -0.1 08.9e-04 4/A -1.le-06 Neq. -1.Oe-04 leg. qeg. 

klluviumMatrixRDSAVNp 1.2e-02 <S.5e-01 2.le÷00 1.9e-05 6.3e-04 :2.7e-04 -6.4e-02 -2.1 4/A Neg. Neg. -1.le-07 -9.4e-05 leg. 

aellPumpingRateAtCriticalGroup2 1.0e00 Oe+00 .le+00 1.7e-05 0.9 -1.. -0.9 -1.1 I/A -4.0e-06 -7.9e-05 .1.8e-08 -3.3e-0 7 leg. 

Okm[gal/day) 

SFWettedFraction 1.8e+00 le+00 F.7e+00 1.6e-05 -1.9 .5 -8.7 1.1 /A -1.7e-05 6.0e-05 l.le-06 3.2e-06 Teg. 

14atrixKD-CHnvNp[m3/kg) <2.0e-03 <9.3e-02 3.7e-O1 1.4e-05 <6.3e-04 <2.7e-04 2.3e-04 -0.4 N/A peg. Neg, leg. -

7 .1e-05 leg. 

IritialRadiusCf FParticle(m) .2e-01 ;.Se-01 Lle+00 1.4e-05 -0.9 -1.1 -0.9 -0.5 4/A 2.9e-06 -6.6e-05 "1.Oe-08 -1.4e-0 7 leg. 

FmultFactor .8e+00 2.8e+00 7.2e+00 1.3e-05 1 .7 0.4 .2 2.1 •/A 1.6e-05 4.8e-05 3.2e-07 .3e-07 Neg. 

DefectiveFractionOfWPs/cell 1.8e+00 . 4e,00 2.3e+01 i.le-05 0.8 0.4 3.0 1.5 7/A .e-06 4.6e-05 2.6e-06 1.2e-06 leg. 

rhermalConductivityofYMRock[W/( 1.2e÷00 1.3e+00 1Se+00 7.4e-06 -1.2 -1.8 -1.4 -0.8 I/A -l.le-06 -3.6e-05 -3.7e-09 -5.0e-08 Neg. 

klluviumMatrixPorositySAV :2.5e-01 <1.6e+00 2.8e+00 Oe-06 <6.3e-04 -0.9 -2.6 -2.8 /A eg. -3.5e-05 -1.6e-08 -3.2e-07 leg. 

SEISMOJointSpacing3im3 .2.9e-03 ý1.4e-01 5Se-Ol .2e-06 <6.3e-04 3.6 :2.3e-04 48.9e-04 I/A ýeg. .6e-05 leg. 'eg. 'eg. 

4eanAveragePrecipitationMultipl .l1e-01 e-01 .5e+00 1.8e-0 -0.5 -0.2 1.5 0.5 I/A -1.2e-06 -7.6e-06 -1.2e-08 .6.5e-08 'eg. 

[erAtGlacialMaximum 

FracturePorositySTFF 3.5e-02 -Se02 4.4e-02 .le-06 -3.2e-02 -4.4e-02 -2.9e-02 -3.6e-02 /A -4.2e-07 4.9e-06 -3.4e-09 -5.5e-08 Aeg. 

4PUltimateStrength[N/m^2] 8.4e-02 :1.7e+01 6.0e+01 Se-07 <6.3e-04 -60.4 :2.3e-04 -5.8 /A leg. -4.le-06 leg. -1.le-07 leg. 

NlluviumMatrixRDSAVI .6e-02 .le-01 2.9e-01 7.0e-0 7 -9.2e-02 3.1e-02 -0.3 -4.0e-02 /A 6.5e-07 2.8e-06 -9.9e-09 -1.8e-08 eg. 

•atrixPermeabilityCHnv[m2j <1.2e-02 1e-01 5.9e-01 4.3e-07 II le-02 .2e-02 2.3e-04 0.7 T/A 1.6e-07 1.5e-06 leg. 5.3e-07 qeg. 

kockModulusOfElasticityforSEISM 5.3e-03 <1.5e+00 5.9e÷00 .0e-07 <6.3e-04 5.9 <2.3e-04 ý8.9e-04 I/A Neg. S.5e-06 geg. Neg. 'eg. 

(Pal _ 

eanAverageTemperatureIncreaseA .8e-02 .5e-02 .7e-01 2.6e-07 -0.2 -7.Se-03 -4.3e-02 -4.0e-02 IA 5.3e-07 .4e-07 2.8e-10 .2e-09 leg. 

JGacialMaximum(degC] .......

00
Table E-7. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 10 kyr (cont'd) 

Solubilityllpfk/m3l <5.4e-03 <1.4e-01 5.4e-01 1.3e-07 <6.3e-04 c2.7e-04 9.3e-03 0.5 IIA Neg. e. 3.2e-09 6.7e-07 eq. 

SolubilityAxn[kg/m31 <5.le-04 ý5.7e-04 ý8.9e-04 1.3e-08 ý6.3e-04 -5.4e-04 <2.3e-04 -8.9e-04 IA Neg. -6.5e-08 'eg. 3.8e-10 'eg. 

FracturePorosity-.Cl-nz ý1.6e-03 1le-0 2 4.4e-02 1.1e-09 <6.3e-04 <2. 7e-04 -4.4e-02 <8.9e-04 I/A Neg. eg. I5.?e-09 'leg. Neq. 

A l Iuviuml'latrixRD-SAV-Se ý9.7e-O4 ý1.9e-03 5.8e-03 3.e-09 <6.3e-04 ý2.7e-04 -5.8e-02 :8. 9e-04 VlA Neq. leg, -1.5e-08 'eg. Neg. 

'atrixPermeability...CHnz(m2) ý1.1e-03 ý1.le-02 4.1e-02 4.e-09 <6.3e-04 <2.7e-04 ý2.3e-04 .le-02 IA Neg eq leg. 2.e-08 Req. 

ll1uviuxnMatrixRD_.SAVTC <6.le-03 0.3e-02 2.6e-01 3.5e-08 -6.3e-04 <2.7e-04 -0.3 -3.1e-02 I/A -1.4e-08 Neg. -6.5e-08 -9.8e-08 Req. 

CladdingCorrectionFactor 8.2e-01 3.6e-01 1.0e00 Req. 1.0 1.0 1.0 0.5 /A leq. 'eg. leg. leq. Neg. 

DispersionFraction_.SAV .,le-02 .3e-01 4le-01 Neg. 9.7e-02 -1.6e-03 0.4 0.4 4/A leg. eg. leg. Neq. :leg. 

SeismicliazarclCurveforSEISllO 2 .3e-03 5.3e-02 2.1e-01 :leg. 6.3e-04 -0,2 <2.3e-04 <8.9e-0, NI/A Neq. 'eg. leg. Neg. Neg. 

DispersionFraction-.S'rF 1.5e-02 0.4e-02 7.1e-02 Neg. 1.3e-02 -1.le-03 5.2e.-02 7.1e-02 N/A Neq. 'eg. leq. Neq. 'eq. 

InnerOverpackErplntercept ý4.3e-04 ý5.1e-04 ý8.9e-04 Req. 6.3e-04 <2.7e-04 <2.3e-04 ý8.9e-04 N/A 'eq. Req. eq. leg. leq. 

Fracturelpermeability....CF..e2l <4.3e-04 ý5.1e-04 <8.9e-04 leg. ý6.3e-04 2.7e-04 <2.3e-04 8.9e-04 IA Peq. 'eg. 'eq. Peq. Neq. 

dixingZon~eThickness20kmjmji ý4.3e-04 <S.1e-04 ý8.9e-04 Neg. <6.3e-04 <2.'7e-04 2.3e-04 ý8.9e-04 IA Neg. Neq. eq. Neq. eq. 

..riticalP.elativeHumidityAqu~eous <4.3e-04 <5.1e-04 ý8.9e-04 leg. ý6.3e-04 ý2.7e-04 <2.3e-04 <8.9e-04 VA Neq. eq. eq. Neq. leq. 

.orrosion 

2oef~orLocCorrOfOuter0verpack <4.3e-04 <5.1e-04 ý8.9e-04 Neg. <6.3e-04 <2.7e-04 <2.3e-04 ý8.9e-04 WA Neq. 'eq. 'leg. leq. leq. 

EISI4OJoi1LSpacingl[m) <4.3e-04 ý5.1e-04 F8.9e-04 Neq. r63e-04 <2.?e0 .e-04 8.9e-04 N/A Peq. Neq. eq. eq. leq. 

A11uviue44atrixRD..SAV...Ni ý4.3e-04 5.1e-04 ý8.9e-04 Req. ý63e-04 <2.7e-04 ý2.3e-04 <8.9e-04 9/A Neq. 'eq. eq. 'eq. Req. 

kA.2-.1[C/ze2/yrj 4.3e-04 <5.le-04 <8.9e-04 eq. 6.3e-04 2.7e-04 <2.3e-04 -8.9e-04 N/A eq. leq. eq. 'eq. eq. 

latrixKD..CHnvU(m3/kg] ý4.3e-04 S5.le-04 ý8.9e-04 Req. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 I/A leg. leq. eq. 'eq. Neq. 

k11uviumgl~atrixRfL...SAV...Pu 4.3e-04 5.1e-04 ý8.9e--04 eq. ý63e-04 ý2,7e-04 2.3e-04 ý8.9e-04 IA leq. 'eq. Neq. eq. leq. 

h1luviueqdatrixRD..SAV..Cs <4.3e-04 5t.le-04 ý8.9e-04 Neq. 6.3e-04 <2.le-04 <2.3e-04 k8.9e-04 N/A Req. 'eq. eq. eq. eq. 

FracturePermeabiity_.UFZ-[.A2] <4.3e-04 ý5.1e-04 ý8.9e-04 leq. <6.3e-04 ý2.7e-04 ý2.3e-04 <8.9e-04 I/A 'eq. Req. eq. leq. Neq. 

Fracture~eeneability...Clnvfie2l <4.3e-04 ý5.1e-04 8.9e-04 leq. ý6.3e-04 ý2.7e-04 ý2.3e-04 <8.9e-04 I/A 'eq. 'eq. Neq. Neg. leq. 

hlorideMultPactor 4.3e-04 5.1e-04 ý8.9e-04 Neq. <6.3e-04 ý2.7e-04 ý2.3e-04 <8.9e-04 F/A Neq. leq, 'eg. Reg. leq. 

FracturePerrneability...Bl~w-m2] <4.3e-04 <5.le-04 <8.9e-04 Neq. <6.3e-04 2.7e-04 <2.3e-04 <8.9e-04 9/A Neg. leq. Neq. Neq. leq. 

U1uvium~atrixRD..SAV..U <4.3e-04 <S.le-04 <8.9e-04 Neq. <6.3e-04 <2.'7e-04 ý2.3e-04 <8.9e-04 I/A Neq. Neq, leg. leg. leq. 

lock~oissonRatioforSEISMHJ .4.3e-04 <5.le-04 <8.9e-04 leq, <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 I/A Req. Peg. eg. Reg. eq.

trl 

0 

09 09 

00
Table E-7. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 10 kyr (cont'd)

00

P,1uviuw~atrixRD-.SAV_.Th ý4.3e-04 Sý.1e-04 ý8.9e-04 leq. ý6.3e-04 ý2.7e-04 <2.3e-04 ý8.9e-04 R/A Req. eq. - eq. 'eg. 'eq. 

subGrainFragmentlladiusAfterTral <4.3e-04 Sic1-0 4 .8.9e-04 Reg. ý6.3e 04 ý2.7e-04 ý2.3e-04 ý8.9e-04 I/A eq. leg leq. eq. Reg. 

sFrac (m) 

SEISMOJointSpaCin95[m] ý4.3e-04 -5.le-04 <8.9e-04 Neq. <6.3e-04- <2.7e-04 ý2.3e-04 <8.9e-04 N/A Neq. eq Req. Neq. Neq. 

*colubilityPu~kg/m31 4.3je-04 c5.1. 04 8.9e-04 leg. 6.3e-04 2.7e-04 2.3e-04 <8,9e-04 .4/A leg. eg. leg. leg. leg. 

FracturePermeability..yPw..Ar2) 4.3e-04 ý5.l1e-04 <8.9e-04 Req. ý6.3e-04- <2.7e-04 <2.3e-04 <8.9e-04 VA 'eq. Req. leq. Req. Reg. 

P,1uviwek~atrixRfl..SAV...Ra 4.3e-04 5S.le-04 <8.9e-04 Req. ý6.3e-04 ý2.7e-04 ý2.3e-04 ý8.9e-04 I/A Req. 'eq. eq. 'eq. 'eq. 

matrixPerreeability-.IJIZ-Am2l 4 .3e-0

4 ý5.le-0 4 9. 9e-04 eg. 6.3e-04 <2.7e-04 2.3e-04 <8.9e-04 I/A eg. lTg Neg. Reg. 'leg. 

ljW'.Prilereablijt. -P.(r~ 4,3e-04 ý5 Se 04 ý8.9~-04 leg. 0.3e- 14 I ie-04 -2ýC4 8.9e.O4 II/A lag. Ig leg. 46j.-. ligu 

latrixPermeabi~ity_.BF'w...l2) 4.3e-04 45.le-04 ý8.9e-04 ::eg. 6.3e-04 ý2.7e 04 <2.3e-04 8.9e-04 VIA leg. leg. leg. leg. leg. 

A11uviumMatrixRD-.SAV..Pb 4.3e-04 05.1e-04 <8.9e04 'eq. 6.3e-04 ý2.le-04 <2.3e-04 8.9e-04 IA leg. eq. Reg. leg. Req. 

t-atrixPerrneability-TSW-CAe2) 14.3e-04 ý5.le-O4 <8.9e-04 Reg. 6.3e-04 2.7e-04 <2.3e-04 ý8.9e-04 /A eq. eg. eg. Re. Req. 

ThicknessOfWaterFilie~mn 4.3e-04 5.1e-04 8.9e-04 Reg. 6..3e-04 ý2,7e-04 ý2.3e-04 8.9e-04 I/A leq. Neq. eg. 'eg. ',eq. 

DxygenPartialPressurelatm] ý4.3e-04 I5.1e-04 ý8.9e-04 leq. ý6.3e-04 ý2.7e-04 <2.3e-04 ý8.9e-04 VA eq. eq. Ieq. 'eq. eq. 

SEISHOJointSpacing2[m) ý4.3e-04 ý5.1e-04 ý8.9e-04 leg. E6.3e 04 ý2.7e-04 ý2.3e-0

4 <8.9e-04 4/A leg. leg. leg. 'eg. leg. 

ýractureporosity..uFZ. <4.3e-04 ý5.1e-04 <8.9e-04 leg. ý6.3e-04 <2,7e-04 <2.3e-04 <8.9e-04 VA leg. eg. Neg. Neg. Reg. 

FracturePorosity...1w.. <4.3e-04 ý5.1e-04 I 8.9e-04 Req. <6.3e-04 <2.7e-04 2.3e-04 F8.9e-04 IIA eq leq. eq. eq. Req. 

A11uvium~atrixRD_..SAVNb ý4.3e-04 r5.1e-04 <8.9e-04 Reg. <6.3e-04 <2.7e-04 <2.3e-04 ý8.9e-04 IA req. eg. Reg. eg. Req. 

FracturePorosityjJCF.. <4.3e-04 <5.le-04 <8,9e-04 Req. <6.3e-04 ý2.7e~-04 <2.3e-04 <8.9e-04 I/A 'eg. Req. Reg. Reg. 'eq. 

ýracturePereieability....Sw..m2l <4.3e-04 ý5.1e-04 <8.9e-04 Req. <6.3e-04 ý2.7e-04 ý2.3e-04 <8.9e-04 /A 'eq. leq. eq. Req. Req. 

datrixPerreeability...UCF.Am2J <4.3e-04 <5.1e-04 F8.9e-04 Req. <6.3e-04 <2.7e-04 2.3e-04 r8.9e-04 I/A 'eq. leq. eq.- eq. Req. 

Fractureporosity...PPw.. 4.3e-04 <S.le-04 <8.9e-04 Req. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 /A Req. 'eq. Req. Neq. 'eq. 

FracturePorosity...TSw.. ý4.3e-04 <S.le-04 <8.9e-04 Req. <6,3e-04 <2.7e-04 <2.3e-04 ý8.9e-04 IIA eq-. Req. eq. Req. 'eq. 

atrixKD...cHnzU(1f3/kgj <4.3e-04 ý5.le-04 <8.9e-04 Req. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 IIA INeq. 'eq. Req. Req. Req. 

FracturePorosity...CHnv 4.3e-04 5.1e-04 <8.9e-04 Req. <6.3e-04 2.7e-04 2.3e-04 r8.9e-04 I/A 'eq. leq. eq. Req. leq. 

,atrixXD.SJFZ...U[M3Ikgl <4.3e-04 5.1e-04 8.9e-04 Req. 46.3e-04 ý2.7e 04 2.3e-04 8.9e-04 /A 'eq. 'eq. Req. 'eq. Req. 

4atrixKO..TSW..j(103/kg) ý4.3e-04 5.1e- 04 <8.9e-04 leq. <6.3e-04 <2.7e-04 2.3e-04 8.9e-04 I/A Req. leq. leq. Req. Req. 

attvixKfl..UCFjrhtm3/kg) <4,3e-04 5.1e-04 <8.9e-04 leq. <6.3e-Od <2 7e04 2.3e-04 8.9e-04 VA Req. leq. leg. Reg. leq.

M
Table E-7. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 10 kyr (cont'd) 

IatrixKD...UCF..Ni(1e3/kg) ý4.3e-04 5.1e-04 ý8.9e-04 leg. ý6.3e-04 <2.7e-04 ý2.3e-04 <8.9e-04 I/A 'eg. 'eg. leg. leg. eq. 

latrixKD..BFW..yu[r3/kgj ý4.3e-04 ý5.le-04 ý8.9e-04 leg. ý6.3e-04 <2.7e-04 ý2.3e-04 <8.9e-04 I/A eq. leg. leg. leg. leg. 

datrixKEDUFZ-PU[ei3/kg1 ý4.3e-04 5.1e-04 8.9e-04 leq. <6,3c-04 <2.7e-04 <2.3e-04 <8.9e-04 /A leg. leg. leg. eg. leg. 

latriXKD -PSW-Th(m3/kg] 4.3e-04 ý5.1e-04 ý8. 9e-04 leg. ý6.3e-04 ý2.le-04 2.3e-04 8.9e-04 V/A leq. ,eg. eg. leg.e 

datrixKtýCI-nvThjm3/kg] 4.3e-04 <5.le-04 <8.9e-04 Meg. <6.3e-04 ý2.7e-04 ý2.3e-04 <8.9e-04 M/A .eg. eg. [eq. leg. eg. 

FraeturePerieeability...Clnz[ie2] 4.3e-04 ý5.1e-04 ý8.9e-04 Reg. <6.3e-04 ý2.7e-04 <2.3e-04 <8.9e-04 9/A eq. Req. Neq. eq. eq. 

latrixKD..CHnzSe(re3/kgl 4.3e-04 ý5.1e-04 ý8.9e-04 leg. ý6.3e-04 ý2.?e-04 ý2.3e-04 8.9e-04 T/A 'eq. leq. eq. leq. leq. 

latrixKD UCF lp~m3/kqi 4.3e-04 5.1e-04 -8.9e-04 leg. <6.3e-04 2.7e-04 ý2.3e-04 48.9e-04 V/A leg. :leg. leg. leg. leg. 

latrixKD-PPw..NP[M3/kgj 4.3e-04 <5.le-04 8.9e-04 leg. <6.3e-04 2.7e-04 <2.3e-04 ý8.9e-04 4/A leg. leg. .Ieg. leg. leg. 

4atrixKD...TSw..Pbje3/kg] ý4.3e-04 ý5.1e-04 <8.9e-04 leg. <6.3e-04 ý2.7c.-^4 <2.3e-04 <8.9e-04 I/A Meq. leg. leg. 'eq. leq. 

latriXKD..TSW..Ra~m3/kg) ý4.3e-04 <5.1e-04 <8.9e-04 leg. <6.3e-04 <2.7e-04 <2.3e-04 8.9e-04 lAeq. leq. leg. leq. eg. 

eItrixK(O..PPw..Ni(re3/kg] <4.3e-04 5.1e-04 r8.9e-04 leg. <6. 3e-04 <2.7e-04 ý2.3e-04 ý8.9e-04 /A eq. leq. peq. eq. leq. 

atrixKD...C~neRa(nQ/kgj ý4.3e-04 5.1e-04 <8.9e-04 leq. <6.3e-04 ý2.7e-04 <2.3e-04 <8.9e-04 I/A leq. 'eq. eq. 'eq. leq. 

IatrixKo...PPw..Ra(z3/kgl <4.3e-04 5.Ie-04 <8.9e-04 leq. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 IIA leq. eq. leq. 'eq. leq. 

IatrixKO...UCF...Ra(ie3/kgj ý4.3e-04 5.1e-04 <8.9e-04 leq. <6.3e-04 ý2.7e-04 <2.3e-04 <8.9e-04 I/A eq. eq. leq. Neq.eq 

4IatrixKD_.CHnzNp(e3lkg] ý4.3e-04 ý5.1e-04 ý8.9e-04 eq. 6.3e-04 2.7e-04 2.3e-04 8.9e-04 4/A leq. eq. leq. 'eq. leq. 

4atriXKD...tFZ..Np~m3/kgj ý4.3e-04 <5.le-04 ý8.9e-04 Neq. <6.3e-04 <2.7e-04 2.3e-04 <8.9e-04 N/A Neq. Neq. INeq. eq. Neq. 

latrixKo..C~nVCStre3/kg] <4.3e-04 <5.1e-04 ý8.9e-04 leq. 6.3e-04 <2.7e-04 2.3e-04 <8.9e-04 IIA leq. 'eq. leq. leq. eq. 

4atrixKD...CHnvPb~re3/kgl ý4.3e 04 ý5.1e 04 <8.9e-04 leg. ý6.3e 04 ý2.7e 04 2.3e-04 <8.9e-04 IIA leq. 'eq. leq. leq. leq. 

IatriXKo.DCnzPbfre3/kg) ý4.3e-04 S.le-04 F8. 9e-04 leq. V6.3e-04 ý2.le-04 ý2.3e-04 ý8.9e-04 I q q q q q 

ati~..Pw.Pte3kl43-4 5l-4 8.e0 q .e0 .l-4 23-4 89-4 /A 'eq. leq. eq. peq. leq. 

IatrixKPO...Bw..ybjr3/kgj <4.3e-04 ý5.1e-04 ý8.9e-04 leq. ý63e-04 <2.7e-04 ý2.3e-04 .8.9e-04 N/A 'eq. Neq. leq. leq. eq. 

.atrixKD.tWZ...FPbCM3 /kg) 4.3e-04 ý5.1e-04 ý8.9e-04 leq. ý63e 04 <2.7e-04 <2.3e-04 ý8.9e-04 I/A 'eq. eq. leq.eqe. 

latrix...row....C[r3/kgl ý4.3e-04 <5.le-04 <8.9e-04 leq. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 I/A eq. leq. leq. leq. leq. 

latrixKDFl...CPvbiM3/kqj ý4.3e-04 5.1e-04 <8.9e-04 eq <6.3e-04 2.7e-04 <2.3e-04 <8.9e-04 I/A eq leq. eq. leq. eq. 

4atrixKD..C~nzCe(M3/kg1 <4.3e-04 5S.Ie-04 68.9e-04 leq. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 IA eq. eq. leq. leq. leg. 

atrixKfl..PPw...Cstre3/kg) 4.3e-04 <5.le-04 8.9e-04 eq. <6.3e-04 2.7e-04 <2.3e-04 8.9e-04 /A leg. leq. Peq. leq. eq.

00
r

Table E-7. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 10 kyr (cont'd)

00

iatrixKDUCFCs(m3/kg] 4.3e-04 5.1e-04 ý8.9e-04 leg. <6.3e-04 <2.7e-04 2.3e-04 <8.9e-04 I/A eg. leg. leg. eg. Ieg. 

IatriXKDOBFW_4CS[m3/kg] <4.3e-04 <5. e-04 0 9e-04 Neg. :6.3e-04 2,7e-04 :2.3e-04 <8,9e-04 N/A eg. Neg. leg. eg. leg. 

4atrixKD_UFZCs(m3/kg) <4.3e-04 5. e-04 08.9e-04 Neg. 6.3e-04 2.7e-04 .2.3e-04 8.9e-04 N/A leg. Neg leg. eg. leg. 

elatrixKD.TSwNiem3/kgq 4.3e-04 :5.1e-04 <8.9e-04 peg. <6.3e-04 -2.7e-04 <2.3e-04 <8.9e-04 I/A leg. 1eg. leg. leg. leg 

a4trixKDBFwTh (3/ kg 4.3e-04 5. le-04 <8.9e-04 Neg. <6.3e-04 2.7e-04 12.3e-04 <8.9e-04 A/A 'eg. leg. Reg. ,eg. Ieq. 

4atrixKD.CHnVAM [m3/kgq :4.3e-04 :5.1e-04 <8.9e-04 Reg. <6.3e-04 <2.7e-04 <2.3e-04 68.9e-04 I/A Neg. leg. Neg. 'eg. leg. 

laetrixKDBFwNp[em3/O kg ]4.3e-04 5 le-04 <8.9e-04 'eg. <6,3e-04 <2.7e-04 _2.3e-04 :8.9e-04 I/A Reg. leg. Neg. Reg. leg. 

4atrixKDBFwRae[m3/kgi 4.3e-04 5. le-04 8.9e-0 4 Reg. 6.3e-04 2.7e-04 2.3e-04 [8.9e-0 4 V/A leg leg. leg. Peg. leg, 

4atrixKD.UCFSe[m3/kg) ]4.3e-04 5.1e-04 8.9e-04 Reg. 6.3e-04 <2.7e-04 ý2.3e-04 <8.9e-04 4/A 'leg. leg. leg. leg. leg. 

latrixKDPPw.Sejra3/kg) -4.3e-04 5.1e-04 8.9e-04 Reg. <6.3e-04 ý2.7e-04 2.3e-04 -8.9e-0 4 /A Neg. leg. leg. .eg. leg. 

.aatrixKD_.BFw_-Se[m3/kg] :4.3e-04 5.1e-04 8.9e-04 N' <6.3e-04 2.7e-04 <2.3e-04 .8.9e-04 I/A Neg. Neg. leg. Reg. leg. 

<atejxKDTrw...b~m3/kql 4.3e-04 5.1e-04 9.9e-0 4 'eg. ý6.3e-0 4 2.7e-04 <2.3e-04 9.9e-04 1/A Neg. Neg. 'eg. , eg. Reg. 

IaatrixKDUFZSe m3/kgq <4.3e-04 5, le-04 8.9e-0 4 Neg. 6.3e-04 2.7e-04 2.3e-04 <8.9e-04 •/A eg.. eq eg. eg. Neg. 

•atrixKD.CI~nv~bfl3/k] •4.3e-04 5 le-04 8.9e-04 Reg. <6.3e-04 :2.7e-04 <2.3e-04 -8.9e-04 I/A 'eg. leg eg. Reg. e. 

tatrixKD.PPwNb(1e3/kgq 4.3e-04 5. le-04 R89e-04 eq <63e-04 2.7e-04 :23e-04 5Se-04 •/A eq eq eq. eq. eg. 

IatrixKDCHnzNb(m3/kgI 4.3e-04 5. le-04 18.9e-0 4 Reg. <6.3e-04 :2.7e-04 <2.3e-04 ý8.9e-04 R/A Reg. 'eg. eg. Reg. Reg. 

,latrixKDtCFINb([m3/kg3 <4.3e-04 5 le-04 8.9e-04 eg. <6.3e-04 2.7e-04 2.3e-04 8.9e-04 I/A Neg. Neg. eg. 'eg. leg. 

AatrixKDUFZJNb [ m3 /kg) <4.3e-04 5. le-04 8.9e-0 4 Neg. <6.3e-04 ý2.7e-04 2.3e-04 8.9e-0 4 I/A Pegq egq eg. Neg. 'eg. 

atrixKDLBFwNb[ 3/ kg 4.3e-04 e04 85. 8.9e-04 Reg. <6.3e-04 .2.7e-04 2.3e-04 8.9e-04 I/A eg. eg. eg. Neg. Neg. 

•atrixKDTSw.Npe[m3/kg] 4.3e-04 5.1e-04 8.9e-04 eq 6.3e-04 2.7e-04 2.3e-04 8.Se-04 /A eq eq eq. eq. eq. 

IatrixKD_.CHnzN•(m3/kg] 4.3e-04 <5. le-04 I 8.9e-04 leg. <6.3e-04 ý2.7e-04 2.3e-04 68.9e-04 I/A Neg. egq eg. Neg. Peg. 

latrixKD_..CHnvPu(m3/kg] <4.3e-04 <5.1e-04 <8.9e-04 Neg. 86.3e-04 <2.7e-04 2.3e-04 <8.9e-04 R/A leg. Reg. eg. Neg. e. 

atrixKD UFZAle[ m3 / k 4.3e-04 5. le-04 <8.9e-04 Regq <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 /A Reg. leg. eg. Neg. Neg. 

JatrixKDBFw_.AnmIr3/kg} <4.3e-04 <5. e-04 <8.9e-04 eg. :6.3e-04 <2.7e-04 <2.3e-04 48.9e-04 I/A Reg. 'eg. Reg. Reg. Neg. 

MatrixKDUCF-Am(m3/kgj 4.3e-04 5.1e-04 8.9e-04 eg. <6.3e-04 <2.7e-04 2.3e-04 I8.9e-04 I/A Ieg. Neg. 'eg. 'eg. 'eg. 

IatrixKDPPwAm m3/kgJ <4.3e-04 5. le-04 ý8.9e-04 'eg. <6.3e-04 <2.7e-04 2. 3 e-04 8.9e-04 /A Reg. Neg. Peg. Neg. 'eg. 

•atrixKDCHnzAm0m3/kq] <4.3e-04 <5. le-04 48.9e-04 Reg. <6.3e-04 <2.7e-04 <2.3e-04 8.9e-04 I/A Reg. eg.q leg. Legq. eg. 

,•atr ixKD.BFW.Ni(m3/kgq ]4.3e-04 <5. le-04 <8.9e-04 Reg. <6.3e-04 2.7e-04 <2.3e-04 I8.9e-04 |/A R•e eq. e. -eq. Ieg.

tQ 

k)
Table E-7. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 10 kyr (cont'd) 

4atrixKD._UFZTh(m3/kg] ý4.3e-04 ý5.le-04 <8.9e-04 leg. €6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 /A 'eg. leg. leg. eg. Ieg. 

4atrixKD._CHnzPu[m3/kgj <4.3e-04 5.le-04 <8.9e-04 leg. ý6.3e-04 <2.7e-04 <2.3e-04 :8.9e-04 T/A leg. leg. eg. eg. leg. 

4atrixKU.UFZNi[m3/kg' 4.3e-04 <5.le-04 :8.9e-04 leg. 6.3e-04 2.7e-04 <2.3e-04 <8.9e-04 A/A Neg. Aeg. leg. eg. leg. 

IatrixKD_CHnvSe[m3/kgl <4.3e-04 ý5.1e-04 r8.9e-04 .eg. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 T/A Neg. leg. 'eg. eg. 'eg. 

1atrixKDTSWSe[mr3/kg] <4.3e-04 <5.le-04 8.9e-04 leg. <6.3e-04 <2.7e-04 <2.3e-04 8.9e-04 I/A Neg e eg . leg. eg. 

4atrixKDCHnvRa[m3/kg] <4.3e-04 5.le-04 6.9e-04 Neg. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 I/A ýegq leg. eg. leg. leg. 

latrixKDPPwPu[m3/kgj <4.3e-04 5.le-04 :8.9e-04 Neg. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 /A 'eg. leg. eg. 'eg. leg. 

1atrixKD..UCFU[m3/kg] <4.3e-04 5.le-04 :8.9e-04 leg. <6.3e-04 c2.7e-04 <2.3e-04 <8.9e-04 I/A leg. leg. eg. 'eg. leg. 

4atrixKDEPPwTh[m3/kg] <4.3e-04 5.le-04 <8.9e-04 eg. <6.3e-04 :2.7e-04 <2.3e-04 8.9e-04 WA 4eg. leg. eg. leg. leg. 

4atrixKD..TSwAm[m3/kg) <4.3e-0 4 <5.le-04 <8.9e-04 leg. <6.3e-04 ý2.7e-04 <2.3e-04 18.9e-04 VA leg. leg. eg. leg. 4eg. 

.atrixKD_.BFwUtm3/kgl 4.3e-04 <5.le-04 <8.9e-04 4eg. <6.3e-04 2.7e-04 <2.3e-04 <8.9e-04 •/A leg. leg. eg. •4eg. eq. 

4atrixKDUFZtRafm3/kg] <4.3e-04 <5.le-04 <8.9e-04 eg. <6,3e-04 <2.7e-04 <2.3e-04 <8.9e-04 /A leg. leg. eg. eq. Neg. 

latrixKDUCFPulm3/kgl <4.3e-04 <5.le-04 :8.9e-04 leg. <6.3e-04 <2.7e-04 <2.3e-04 <8.9e-04 /A legq. eg. leg. leg. leg. 

,atrixKDCHnzTh[m3/kg] <4.3e-04 5.le-04 <8.9e-04 leg. <6.3e-04 <2.7e-04 12.3e-04 8.9e-04 R/A leg. 'eg. leg. leg. leg. 

1atrixKDTSw-Pu(m3/kg] 4.3e-04 5.le-04 k8.9e-04 leg. <6.3e-04 <2.7e-04 k2.3e-04 8.9e-04 1/A Iegq. eg. 'eg. 'eg. leg. 

ote: The use of Neg. in a column indicates that the value of dy/dx sigma for that parameter was zero for that given run. Inclusion of the minimum measureable value in the 

alculation would have a negligible impact on the arithmetic average value of dy/dx * sigma.

~t1

ON 

0
Table E-8. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 50 kyr 

Parameter Name S - - - High rithmatic - No - No S - No - No - No Iy/dx * iy/dx - 1y/dx y/dx :y/dx :eom. rithmatic alue ean of DEs, Mean Es, 90th DEs, 10th Es, DEs. sigma - No 5igma - No sigma - No sigma - No sigma - No Wverage lean igma values, 2uantile nuantile andoml :andomZ DEs, Mean )Es 90 Es, 10" )Es. DEs, Random 

Jeighted 50k years Values alues, , alues, Jalues alues, 2uantile, 2uantile, landom 1, 2, 50k 

- alues 5Ok years 50k years 50k years 0k years 50k 50k 50k 50k 

Cs 

00 lluviumMatrixRD_SAVAm <2.ie-03 :1,3e-02 .5e-02 .6e-01 ý6.2e-04 <5.9e-04 -5.5e-02 c3.2e-04 -6.5e-03 leg. 4eg. -0.8 leg. .4.1e-03 

IiluviumMatrixRDSAVNp O2.6e-02 49.6e-01 4.5e+00 .0e-02 -4.5 :5,9e-04 -0.2 0.1 1.7e-04 -3.0e-02 ýeg. -5.6e-03 -1.6e-02 ýeg. 

$FWettedFraction 9.4e-01 9.8e-01 .4e+00 3.0e-03 0.9 0.7 .2 .4 .7 8.4e-04 3.5e-04 2.4e-03 L.le-02 1.5e-04 

SubAreaWetFraction .0e+00 1.0e L Oe+00 .le-03 .0 1.0 .0 .0 .0 9.8e-04 5.7e-04 2.2e-03 1.4e-03 2.0e-04 

ellPum~pingRateAtCriticalGroup20 .e-01 ).9e-01 1,aS÷00 .2c-0 4 0.9 1-,1 -0.9 1.1 0.9 -4.1a-04 -3.6e-04 -3.0e-04 -9.1- 0 4 -1.2e-04 dLiyai/day ] 

2._2 (lC/m2/yr] .4e-01 .le-01 2.2e÷00 3.8e-04 2.2 -0.2 .7 .4 1.2 1.Oe-03 -5.4e-05 2.6e-04 4.3e-04 1.5e-04 

aeismicHeterogeneityFactor 1.3e-03 =1.7e-Oi 8.6e-01 3.7e-04 <6.2e-ý4 0.9 1.4e-04 .0 <1.7e-04 Neg. 1.8e-03 Neg. Neg. 'eg. 

InitialRadiusOfSFParticle(m] 9.8e-01 9. e-01 I.le+00 .4e-04 -0.9 -1.1 -0.9 1.1 -0.9 -2.9e-04 -3.0e-04 -1.8e-04 -8.4e-04 -1.0e-04 

4atrixKDoCHnvNp~m3/kg) 6.4e-04 Be-03 1.2e-02 2.5e-04 1.2e-02 1.2e-03 v1.4e-04 <3.2e-04 1.7e-04 -1.2e-03 1.4e-06 ieg. reg. leg. 

oefForLocCorrofouterOverpack 3.4e-02 2.Se- 6.3e-01 .le-04 .e-02 5.9e-04 .5 .3e-03 0.8 l.Oe-05 Jeg. 1.6e-04 . 6e-06 .2e-04 

lluviumMatrixRDSAVSe <9.1e-04 8.le-03 3.9e-02 1.9e-04 -6.2e-04 :5.9e-04 -1.4e-04 3.2e-04 -3.9e-02 -5.ie-06 Neg. -5.9e-06 Neg. -9.4e-04 

k) lluviumMatrixPorositySAV .0e-01 S .Oe.00 3.8e+00 1.8e-04 -3.8 -0.4 -0.3 0.2 -0.5 -7.2e-04 -6.4e-05 -2.6e-05 -5.0e-05 -3.6e-05 

FowFactor c.8e-02 ý3.8e-02 1.2e-01 I.le-04 -2.7e-02 -0.1 -2.8e-02 I.le-02 -2.0e-03 8.7e-05 -1.2e-04 -i.3e-04 -2.le-04 -7.9e-07 

Rock/1odulusOfElasticityforSEISNO -9.6e-03 ,8e-01 4.9e+00 1.0e-04 6.2e-04 4.9 ý1.4e-04 1.2e-02 1.6e-02 Neg. 5.0e-04 Neg. 2.4e-06 O .- 07 

[Pa] 

hlluviumMatrixRDSAVPu <3.6e-04 4.3e-04 6.5e-04 8.4e-05 <6.2e-04 :5.9e-04 -1.4e-04 -6.5e-04 <1.7e-04 Neg. Reg. -2.2e-05 -4.0e-04 Neg. 

rhermalConductivityofYMRock(W/( 4.7e-01 46.7e-01 1.9e÷00 6.5e-05 -0.5 .1.9 -0.2 -0.5 -0.2 -4.9e-05 -1.7e-04 "1.le-05 -8.8e-05 -6.6e-06 

n-K) ) 

FracturePorositySTFF 1.4e-02 4.7e-02 1.7e-01 .3e-05 ).2 3.6e-02 -2.2e-03 -1.6e-03 2.3e-02 .3e-04 -1.8e-05 -4.3e-06 6.7e-06 5.3e-06 

h11uviumMatrixRDSAVTc 3.8e-03 le-02 3.9e-02 .5e-05 3.9e-02 :5.9e-04 8.3e-04 -2.9e-03 -1.4e-02 -9.0e-05 Neg. -3.5e-06 -2.5e-05 -7.6e-06 

SEISMOJointSpacing4[m] :8.9e-03 9.4e-01 4.6e÷00 2.7e-05 5.4e-02 4.6 <1.4e-04 9.4e-03 <1.7e-04 1.6e-05 l.le-04 'eg. 4.2e-06 seg. 

krealAverageMeanAnnuallnfiltrati 2.3e-02 :4.3e-02 1.4e-01 2.6e-05 2.8e-02 0.1 -2.4e-02 -3.9e-03 -1.7e-02 2.2e-05 i.se-05 -2.5e-05 -l.e-05 3.le-06 

nAtStart[mm/yr) 

SolubilityNpfkg/m33 <3.5e-03 <8.6e-03 2.0e-02 2,1e-05 2.0e-02 Oe-03 -1.7e-02 -2.90-03 1.7e-04 .6e-05 2.ie-06 -9.7e-05 9.9e-06 eg. 

racturePermeabilityUCFCm2) <8.5e-04 1.5e-03 ,8e-03 .9e-05 6.2e-04 5.9e-04 .5e-03 4.8e-03 <1.7e-04 geg. sg. 3e-05 I.le-04 Neg

FmultFactor .e-02 .8e-02 .2e-01 S 4e-05 2.7e-02 0.1 -2.8e-02 -i.1e-02 -2.0e-03 -2.6e-05 -6.ie-05 -2.le-05 -1.0e-05 -1.0e-06 

klluviumMatrixRDSAV_I 1.9e-03 k.3e-02 1.8e-01 ,4e-06 k.1e-03 -2.9e-02 -1.4e-04 -1.3e-03 D.2 2.2e-06 -1.2e-05 -7.9e-08 -l.6e-06 -3.1e-05
Table E-8. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 50 kyr (cont'd) 

4atrixPermeabilityCHnv(m2

1 4.4e-03 5.7e-03 1.0e-02 7.5e-06 1.0e-02 5.9e-03 .3e-04 ,8e-03 7.2e-03 1.4e-05 .4e-06 1.4e-06 1.0e-05 7.8e-06 

DefectiveFractionOfWPs/cell :4.3e-03 <2.2e-02 8.9e-02 1.5e-05 1.5e-02 8.9e-02 1.9e-03 3.6e-03 <1.7e-04 1.4e-05 .8e-05 .7e-06 7.4e-06 'eg. 

4eanAveragePrecipitationlMultipli 5.8e-02 6.9e-02 1.le-01 1.2e-05 6.9e-02 -9.8e-02 4.6e-02 -2.0e-02 0.1 1.6e-05 -2.0e-05 -6.0e-06 -7.2e-06 9.9e-06 

ýrAtGlacialMaximum 

h11uviumMatrixRDSAVNi X3.1e-04 <3.7e-04 S6.2e-04 4e-05 V6.2e-04 5.9e-04 -1.4e-04 <3.2e-04 <1.7e-04 Neg. leg. -6.9e-05 Peg. leg. 

SeismicHazardCurveforSEISMO <2.6e-03 3.0e-01 1.5e+00 legq 6.2e-04 -1.5 =1.4e-04 5.5e-03 <1.7e-04 leg. leg. leg. ýeg. leg. 

DispersionFractionSAV .3e-02 3.le-01 1.4e+00 Neg. 4 6.5e-02 I.7e-03 -2.3e-02 3.9e-02 Reg. 'eg. eg. 'eg. 'eg. 

•riticalRelativeHumidityAqueousC <1.3e-02 .5e-02 2.3e-01 2.4e-06 6.2e-04 0.2 0.2 ý3.2e-04 3.3e-02 Neg. .3e-06 ,4e-06 eg. .3e-0 7 

Drrosion I 

AeanAverageTemperature IncreaseAt J.e-03 1.1e- 02 Se 02 1e-06 2e02 -1.2e-03 -26e-02 1.9e-03 L.2e-02 -3.7e-06 5.3e-07 .9e-06 .6e-07 -1.le-06 

31acialMaxeium[degCJ 

SEISMOJointSpacing3(m) 8.6e-04 2 .Oe-02 1.0e-01 27e-06 O 6.2e-04 ).1 1. 4e-04 :3.2e-04 1.7e-04 leg. 1.3e-05 Neg. 'leg. leg. 

:laddingCorrectionFactor 1.0e÷00 1.0e,00 .0e+00 Neg. 1.0 1.0 1.0 1.0 1.0 Neg. Neg. leg. 'leg. 'eg. 

4PultimateStrength[N/m^ 2

] S6.3e-03 1.9e+00 S.6etOO 1.3e-06 6.2e-04 -9.6 1.4e-04 -7?.e-02 ý1.7e-04 leg. -3.0e-06 Neg. -3.7e-06 leg. 

SEISMOJointSpacing2(m] <6.4e-04 <1.6e-03 .3e-03 4.2e-08 6.2e-04 5.9e-04 1.4e-04 ý3.2e-04 6.3e-03 legq. eg. leg. Neg. 2.1e-07 

.ockPoissonRatioforSEISMO(] <6.4e-04 <1.6e-03 5.3e-03 5.9e-08 <6.2e-04 5.9e-04 <1.4e-04 3.2e-04 6.3e-03 Negq. eg. leg. peg. .0e-07 

DispersionFractionSTFF 1.7e-02 4.8e-02 2.0e-01 Neg. 0.2 1.le-02 8.2e-03 -4.2e-03 1.7e-02 teg. |eg, Neg. Neg. Reg. 

.l 1uviuwiIatrixRD-SAV..U -3.le-04 F3.7e-04 .6.2e-04 .6e-06 6.2e-04 -5.9e-04 1.4e-04 -3.2e-04 -1.7e-04 'leg. leg. 'eg. -1.3e-05 :leg. 

SolubilityAm[kg/m3) 3.6e-04 4.9e-04 1.2e-03 1.6e-07 <6.2e-04 1.2e-03 <1.4e-04 ý3.2e-04 <1.7e-04 leg. -6.5e-07 Neg. Neg. 1.3e-0 7 

innerOverpackErplntercept ý3.le-04 <3.7e-04 <6.2e-04 leg. <6.2e-04 5.9e-04 1. 4e-04 <3.2e-04 < 7e-04 Neg. Neg. 'eg. Neg. Neg. 

FracturePermeabilityCflnz[m2] 3.le-04 :3.7e-04 6.2e-04 eq 562e-04 5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 eg. eg. eg. 1e.. e. 

NixingZoneThickness20km[M) <3.le-04 <3.7e-04 <6.2e-04 Neg. <6.2e-04 5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 Peg. leg. Negq. eg. qeg. 

klluviumMatrixRDSAVCs -3.le-04 <3.7e-04 <6.2e-04 Neg. 6.2e-04 <5.9e-04 <1.4e-04 V3.2e-04 <1.7e-04 1eq. leg. 'eg. Neg. 'eg. 

?racturePermeability_.UFZ_-m2] <3.le-04 :3.7e-04 <6.2e-04 leg. 6.2e-0 4 5.9e-04 <1.4e-04 3.2e-04 :1.7e-04 leg. 'egq 'eg. Neg. leg. 

FracturePermeabilityCHnv[m2l] 3.le-04 :3.7e-04 <6.2e-04 leg. <6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 leg. leg.q 'eg. 'eg. peg. 

:hloridefultFactor <3.1e-04 <3.7e-04 S6,2e-04 leg. 56.2e-04 5.9e-04 <1.4e-04 23.2e-04 .1,7e-04 leg. leg, legq eg. leg. 

-racturePermeabiity_BFW_(m2j <3.1e-04 <3.7e-04 <6.2e-04 leg. <6,2e-04 5.9e-04 ý1.4e-04 <3.2e-04 1.7e-04 Neg. leg. leg. leg. leg. 

1atrixKDTSw__Pu~m3/kg] <3,1e-04 <3.7e-04 <6.2e-04 leq. 6.2e-04 45.9e-04 <1.4e-04 I3.2e-04 I1.7e-04 ýeg. leg. leg. teg. leg. 

4atrixKDCHnzThtm3/kg] <3. le-04 3,e-04 <6.2e-04 leg. 6.2e-04 5.5e-04 <1.4e-04 3.2e-04 1. 7e-04 _eg. ieg. leg. Peg._ eg.

LQ 

0% 

00
!4Table E-8. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 50 kyr (cont'd)

0' 

05 

00

A1IuviuzelatrixRD_.SAV_.Th 0.1e-04 ý3.7e-04 ý6.2e-04 'eg. <6.2e-04 <5.9e-04 ý1.4e-04 <3.2e-04 ý1.7e-04 Neg. leg. eg. 'eg. eg. 

Su.bGrainFraqmentRadiusAfterTrsans ý3.le 04 <3.7e-04 ý6.2e-04 Neg. <

6 .2e-04 ý5.9e-04 ý1.4e-04 ý3.2e-04 <1.7e-04 :leg. leg. leg. leg. leg. 

Frac (m) 

SEISMOJOintSpacing5(m) <3.le-04 3.7e-04 ý6.2e-04 Neg. <6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 A.e-04 Neg. Neg. Neg. eg. eg. 

SolubilityPu~kq./m31 ý3.1e-04 <3.7e-04 <6.2e-04 Neg. ý6.2e-04 ý5.9e-04 1.4e-04 3.2e-04 <1.7e-04 Neg. peg. 'eg. Peg. Neq. 

latrixPermeabi1itY...tFZ...Ae2l O.Ie-04 ý3.7e-04 6.2e-04 leg. <6.2e-04 <5.9e-04 ý1.4e-04 0.2e-04 ý1.7e-04 leg. 'eg. eg. leg. leg. 

h1IuviumlatrixRD_.SAV...Ra <3.le-04 3.7e-04 <6.2e-04 Neq, <6.2e-04 <5.9e-04 <1.4e-04 <3,2e-04 <1.7e-04 Neg. 'eg. 'eg. Neg. Neq. 

PracturePerrneability..yPw..m2] ý3.1e-(4 P3.7e-04 <6.2e-04 eq. <6.2e-04 <5.9e-04 <1.4e-04 3.2e-04 <1.7e-04 Neg. Neg. 'eg. eg. Neg. 

MatrixPermeability..yPw...m31 <3.le-04 0.7e-04 <6.2e-04 leg. ý6.2e-04 ý5.9e-04 <1.4e-04 <3 .2e-04 1.7e-04 leg. 'eg. leg. seq. eg. 

latrixPermeabilit~YB~w_[m21 <3.le 04 <3.7e-04 <6. 2e-04 leg. <6 .2e-04 ý5.9e-04 0.4e-04 ý3.2e-04 ý1.7e-04 leq. eq. leg. leg. eg. 

:.11~uvuna4latrixRDSAVPb ý3.1e-04 .3 .7-04 6.2e-04 leg. <6.2e-04 5. 9e-04 .1.4e-04 <3.2e-04 <1.7e-04 qrO. leg. leg. leg. eq. 

latrixerreabi~ity...TSw ln2l ý3.1e 04 3.7e-04 .6,2e-04 leg. ý6 .2e-04 -5.9e-04 ý1.4e-04 03.2e-04 li.e 04 leg. leg. leg. leg leg. 

Thickness~f¶4aterl~m(ml ý3.1e-04 3.7e-04 6.2e-04 leg. <6.2e-04 <5.9e-04 <1.4e-04 3.2e-04 ý1.7e-04 Neg. Neg. Neg. 'eg. Neg. 

DxygenPartialPressurelatml <3.le-04 ý3.7e-04 ý6.2e-04 leg. <6.2e-04 <5.9e-04 <1.4e-04 3,2e-04 <1.7e-04 Neq. Meg. Neq. 'eq. Neq. 

4atrixPermeability..CHnz(m23 <3.le-04 <3.7e-04 ý6.2e-04 leg. ý6.2e-04 <5.9e-04 ý1.4e-04 3 .2e-04 <1.7e-04 Neq. Neq. Neg. Neg. Neq. 

FracturePorosity_.BFw.. <3.le-04 3 .7e-04 ý6.2e-04 leg. 6. 2e-04 <5.9e-04 ý1.4e-04 3 .2e-04 <1.7e-04 leq. 'eq. 'eg. leg. leq. 

1.11-rxul4atr, ixRl)SAV~]jb ý3.1e 04 -. e-04 <6. 2e- 04 leg. ý6.2e-04 5.9e-04 ~1.4e-C, 3.2e-04 li.e 04 leq. Meg. Meg. leg. .eg. 

FracturePorosity...LCF_. ~ 3.le-04 3.7e-04 <6.2e-04 Neg. ý6.2e-04 -5.9e-04 0.4e-04 3 ,2e-04 ý1.7e-04 Neq. Neq. 'eq. leq. Neq. 

FracturePermeabilityM.T ý_..lz2j 3.le-04 3.7e-04 <6.2e-04 leg. ý6.2e-04 ý5.9e-04 ý1.4e-04 .3,2e-034 <1.7e-04 Neg. Neg. Neg. 'leg. Neg. 

katrixPermeabiitY...UCF..Am2] 0.1e-04 07e-04 3 6.2e-04 .seq. 6.2e-04 5.9e-04 <1.4e-04 <3,2e-04 cl.7e-04 leq. Neq. Neq. eq. qe. 

FracturePorosity...PPw.-. ý3.1e-04 3 7e-04 ý6.2e-04 [eq. ý6.2e-04 I5.9e-04 <1.4e-04 1 .2e-04 <1.7e-04 Neq. eq. Neq. leq. Jeq. 

FracturePorosity...TSw... 3.Ie-04 3.7e-04 -6,2e-04 Neq. ý6.2e-04 ý5.9e-04 <1.4e-04 0.2e-04 <1.7e-04 leq. eq. 'eq. ,eq. Neq. 

FracturePorosity-.C~lav 0.1e-04 3.7e-04 ý6.2e-04 Neq. <6.2e-04 <5.9e-034 <1.4e-04 <3.2e-04 <1.7e-04 Neq. Neq. leq. eq. Neq. 

SElSMOJointSpacingl(m) 3.1e-04 ý3.?e-04 ý6.2e-04 Neq. ý6.2e-04 S5.9e-04 ý1.4e-04 <3.2e-04 <1.7e-04 leq. 'eq. leq. leq. Neq. 

FracturePorosity...UFZ.. 3.le-04 <3.7e-04 <6.2e-04 Neg. c62e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 Neq. eq. Neq. 'eq. Neq. 

IatrixKD..TSw...U(nO/kgJ 3 le-04 ý3.7e 04 ý6.2e-04 leq. <6.2e-04 <5.9e-04 ý1.4e 04 0.2e-04 <1.7e-04 Neq. Neq. leg. leq. leq. 

ýatrixKfl..UCP...Thjre3/kg) <3 le-04 3.7e-04 .6.2e-04 Peq. 6.2e-04 <5.9e-04 1.4e-04 3.2e-04 <1.7e-04 leq. Neq. eq. Feq. seq.

M.1 

05j
Table E-8. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 50 kyr (cont'd) 

.atrixKD_UCF_Ni[m3/kg) ]3.le-04 <3.7e-04 ý6,2e-04 leg. <6.2e-04 ý5.9e-04 <1. 4e-04 <3.2e-04 <1.7e-04 Neg. Neg. Neg. Neg. leg. 

Iatrix.D_PFwPu m3,kgq :2.1e-04 3 .

7 e-04 <6. 2e-04 leg. -6.2e-04 ý5,9e-04 1.4e-04 :3.2e-04 1.7e-04 leg. leg. leg. eg. leg. 

9atrixKDjUFZPu [m3/kgj 03.e-04 <3.7e-04 6.2e-04 Reg. <6.2e-04 <5.9e-04 1.4e-04 =3.2e-04 <1.7e-04 Neg. eg. Neg. Neg. Neg. 

4atrixKDTSwThm3/1kg] 3.lie-04 <3.e-4e-04 6.2e-04 .eg. <6.2e-04 I5.9e-04 <1.4e-04 <3.2e-04 <1.2e-04 Neg. Neg. Neg. leg. Reg. 

4atrixKD CHnvTh(m3/kg] 3.1e-04 3.7e-04 <6.2e-04 Reg. <6.2e-04 ý5.9e-04 <i.4e-04 <3.2e-04 <1.7e-04 'eg. eg. Reg. leg. Reg. 

4atrixKDUFZU[m3/kgl :3.1e-04 3 7e-04 6.2e-04 'eg. ý6.2e-04 5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 Neg. eg. Neg. Neg. leq. 

latrixKDCHnzSeim3/kg] 3.1e-04 [3.7e-04 <6.2e-04 Reg. <6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 'eg. eg. Neg. Reg. Neg. 

4atrixKDUCFNp[m3/kg) 3.1e-04 3.7e-04 .6.2e-04 Neq. <6.2e-04 <5.9e-04 1.4e-04 <3.2e-04 <1.7e-04 'eg. Reg. Reg. Neg. 'eg. 

4atrixKDýpPwNp(m3/kq] 3.le-04 [3.7e-04 <6.2e-04 Neg. <6.2e-04 IS.9e-04 1.4e-04 <3.2e-04 1. 7

e-04 Neg. leg. 'eg. 'eg. 'eg. 

latrixKD.TSw_.Pbjm3/kg] .3.ie-04 <3.7e-04 <6.2e-04 Neg. <6.2e-04 <5.9e-04 1.4e-04 3.2e-04 <1.7e-04 Neq. Reg. Neq. Neg. Neg. 

4atrixKD_]TSw_-Ra m3/kg] i3.1e-04 <3.7e-04 -6.2e-04 Reg. <6.2e-04 <5.9e-04 1.4e-04 <3.2e-04 1.7e-04 Neg. Neg. Neg. Neg. Neg. 

4atrixKDOFPw_..Ni m3/kgl 3.1e-04 3.e-04 <6.2e-04 Reg. <6.2e-04 <5.9e-04 1.4e-04 =3.2e-04 <i.7e-04 Ieg. 'eq. leg. Ieg. Neg. 

4atrixKDCHnzRa(m3/kg3 le-04 3.7e-04 <6.2e-04 Neg. <6.2e-04 5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 keq Reg. leg. leg. Req. 

atrixKDPPwRa[m3/kq] 3.le-04 3.?e-04 .e-04 Neg. 6.2e-04 5.9e-04 :1.4e-04 <3.2e-04 <1.7e-04 Neg. Neg. leg. leg. Neg. 

4atrixKDUCFRa[m3/kg i3.1e-04 33.7e-04 <6.2e-04 leg. 6.2e-04 <5.9e-04 <1.4e-04 3.2e-04 <1.7e-04 Neg. eq. Re. eeg. eg. 

Aatr i xKDCHrizNp(m3/kgj 3.1e-04 3.7e-04 6.2e-04 leg. 6.2e-04 5. 9e-04 <1.4e-04 33.2e-04 1.7e-04 leg. leg. leg. leg. Neg. 

4atrixKDUFZ_Ra(m3/kg] L3.ie-04 3.7e-04 <6.2e-04 Neg. 6.2e-04 <5.9e-04 <1.4e-04 •3.2e-04 <1.7e-04 Neg. Neg. Neg. leq. 'eg. 

4atrixKD_.CHnvcs[m3/kg] <3.1e-04 3.7e-04 <6.2e-04 Neg. 6.2e-04 <5.9e-04 <1.4e-04 3.2e-04 <1.7e-04 Neg. Reg. Neg. leq. Neg. 

4atrixKD.CHnvPbim3/kg .3.1e-04 3.7e-04 <6.2e-04 e. 6.2e-04 <5.9e-04 <1.4e-04 3.2e-04 <1.7e-04 Reg. eq. eg. Ne. eg. 

ýatrlKDCHnzPb~m3/kg3 3.1e-04 3.7e-04 <6.2e-04 Neq. 6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 Neq. eg. 'eg. leg. Reg. 

4atrixKD._PPwPb(r3/kg]j 0.1e-04 3.7e-04 <6.2e-04 leq. <6.2e-04 <5.9e-04 :1.4e-04 <3.2e-04 <i,7e-04 _eg. Neg. Neg. Ieq9. Neg. 

IatrixKDUCFPb(m3/kg] :3.1e-04 3.7e-04 <6.2e-04 leq. <6.2e-04 <5.9e-04 :1.4e-04 -3.2e-04 <1.7e-04 'eq. Neg. Neq. 'eg. leq. 

•atrixKDBFwPb[m3/kgj 3.1e-04 3 .7e-04 <6.2e-04 eq 6.le-04 <5.9e-04 :1.4e-04 <3.2e-04 <2.7e-04 'eg. Neq. Reg. Neg. Neq. 

4atrixKDUFZPb(rm3/kgq ]3.le-04 3.7e-04 <6.2e-04 Neg. <6.2e-04 5.9e-04 :1.e-04 3.2e-04 <2.7e-04 Req. Neg. Neq. Neg. Neq. 

4atrixKD3.1TSwCs~m3/k] 31e-04 3.7e-04 <6.2e-04 Reg. 6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 Reg. Neg. leg. leq. Neg. 

IatrixXD_CHnvzi[m3/kg] <3.le-04 3.7e-04 <6.2e-04 Neq. <6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 ý1.7e-04 'eg. Neg. 'eg. leg. Neg. 

{atrivKDCHnzCs(m3/kgI <3.1e-04 [3.7e-04 6 .2e-04 Reg.6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 Neg. eg. Neg. Neg. eg. 

IlatrixKDPPwCs(m3/kgI r31e0 <3.7e-04 <6.2e-04 Neg. <6.2e-04 1<5.9e-04 rl,4e-04 <3.2e-04 <1.'7e-04 • eg. 'el. eg. Regf. INeg.

00
Table E-8. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 50 kyr (cont'd)

00

RatrixKD....CF-CS~rn3/lk) ý3.1e-04 3.7e-04 ý6.2e.-04 leg. ý6.2e-04 <5.9e-04 1A4e-0 ' 3.2e-04 ý1.7e-04 eg. [eg. leg. Reg. Req. 

Ia.trixKD-2FVI-CSjm3/kgJ ý3.1e-01 ý3.7e-04 ý.C2e-04 leg, 6.2-- 04 '5.9e-04 1.4e-04 ý3.2e-04 1,7e-04 leg. leg. leg. leg. leg. 

4atrixKD..UFZ-.Cs[m3/kg) 3.1e-04 <3.7e-04 <6.2e-04 leg. <6.2e-04 ý5.9e-04 <1.4e-04 3.2e-04 <1.7e-04 eq q e.e.eg. 

4atrixl(D..TSw..Ji~m3/kg1 .3.e-04 <3.7e-04 6.2e-04 peg. <6.2e-04 5.9e-04 <1.4e-04 3 .2e-04 <1.7e-04 Reg. eg. 'eq. 'eq. Reg. 

Iatrixl(D..BFw..Th~m3/kgJ ý3.1e-04 <3.7e-04 <6.2e-04 Reg. 6.2e-04 <5.9e-04 ý1.4e-04 0.2e-04 ý1.7e-04 Req. eq. 'eg. leq. leg. 

lfatrixKD..CHnvAm~m3/kgi ý3.1e-04 <3.7e-04 <6.2e-04 'eg. ý6.2e-04 5,9e-04 <1.4e-04 <3.2e-04 <1,7e-04 'eq. eq. eq. Req. Req. 

4atrixKDý_SFw_-Np~m3/kgJ <3.le-04 <3.7e-04 <6.2e-04 Reg. <6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 'eq. e. 'Ja. Reg. Reg. 

IatrixKo.BFw...Ra[ie3/kg) O.Ie-04 <3.7e-04 ý6.2e-04 Ieq. <6.2e-04 I5,9e-04 <1.4e-04 3.2e-04 F1.7e-04 Req. Req. Reg. 'eq. Req. 

lairixKD...UCF...Selm3/kgj <3.le-04 ý3. 7e-04 <6.2e-04 Reg. <6.2e-04 ý5.9e-04 -1.4e-04 3 .2e-04 <1.7e-04 eqa. Reg. leg. '.eg. 'eg. 

4atrixl<O..PPw..Se[rn3/kg) ý3.1e-04 0.7e-04 <6.2e-04 Reg. ý6.2e-04 ý5.9e-04 ý1.4e-O4 3.2e-04 <1.7e-04 eqa. eqa. Req. 'eg. eg. 

latriel(D..BFw...Setrr3/kg) <3.1e-04 3 .7e-04 <6.2e-04 Reg. ý6.2e-04 <5.9e-04 <1.4e-0 4 3 .2e-04 <1.7e-04 eq. Req. Req.eqe. 

4atrixl(D..TSw...Nb(e3/kg] ý3.1e-04 <3.7e-04 .6.2e-04 Req. ý6.2e-04 5.59e-04 ý1.4e-O4 <3.2e-04 ý1.7e-04 leq. Req. Req. eg. eq. 

IatrixKD..UFZ..Se(M3/kg) ý3.le 04 ý3.7e-04 6. 2e-04 leq. ý6.2e-04 5.9e-04 0.4e-04 3 .2e-04 <1.7e-04 leg. leg. 'leg. leg. leq. 

latrixKD...ClnvNb(m3/kgJ <3.le-04 ý3. 7e-04 6.2e-04 leq. ý6.2e-04 ý5.9e-04 1.4e-04 ý3.2e-04 <1.7e-04 leq. eq. Req. eqa. leg. 

IetriKD_.PPe. Nblm3/kcj) <3.e-04 :.3 e-04 <6.2e-04 leg. -6.2e-04 <5.9e-04 ý1.4e-O4 0.2e-04 <1.7e-04 leg. leg. leg. Aeg. leg. 

IatrixKD..CHnzNb~m3/kg) -.31-c04 ý3.7e 04 <6.2e-04 leg. 6 .2e-04 <5.9e-04 <1.4e-04 3.2e-04 <.7l~e-04 leg. leg. leg. leg, eq. 

latrixKDýUCF llb[e3/kgj <3.le-04 <3 .7e-04 <6. 2e-04 4eq. .6.2e-04 ý5.9e-04 1.4e-04 <3 .2e-04 ý1.7e-04 eqa. \eq. 'eg. 4eg. Reg. 

4atrixKD..IFZ..Nb~m3/kgj ý3.1e-04 ý3.7e-04 6.2e-04 eqa. .6.2e-04 <5.9e-04 ý1.4e-04 3.2e-04 <1.7e-04 Req. Req. eq. leq. Req. 

latrixKDBFw-jlb(m3/kg) <3.le-04 I 3.7e-04 <6.2e-04 '.eq. ý6.2e-04 -5.9e-04 ý1.4e-04 ý3.2e-04 <1.7e-04 Req. leq. eqa. eqa. 'eq. 

fatrixKl3..TSW..Np(m3/kgl <3.le-0437e4 .- q eq .e4 .e0 4e4 3.-4 17e4 e.e.e.e.e. 

atIxlD.Cn~in/ 3 1e-4 3.7e-04 ý6.2e-04 'eq. ý6.2e-04 <5.9e-04 <1.4e-04 <3.2e-04 <1.7e-04 Req. Req. eq. Req. 'eq. 

latrixxo...Clnzinvu3/kg) <3.le-04 <3.7e-04 <6.2e~-04 Req. <6.2e-04 <5.9e-04 ý1.4e-04 3.2e-04 <1.7e-04 Req. eq. Req. Req. eq. 

eItrixl(DC.. vuF AIlm3/kgl <3.1e-04 ý3.7e-04 ý6.2e-04 Req. <6.2e-04 <5.9e-04 l1.4e-04 ý3.2e-04 <1.7e-04 4eq. eq. \eg. Req. eq. 

atrixKD...Fw..Aie~m3/kgJ ý3.1e-04 ý3.7e-04 ý6.2e-04 leq. ý62e 04 ý5.9e 04 1. 4e-04 ý3.2e-04 <1.7e-04 'eq. eq. leq. Req. Req. 

eltrixKD...UFwjeemei3/kg] <3.1e-04 3.7e-04 <6.2e-04 Req. <6.2e-04 -5.9e-04 1.4e-04 ý3.2e-04 <2.7e-04 'eq. Req. 'eq. Req. Req. 

IatrixKD_.PPw_.Am[mf3ikg3 <3.1e-04 <3.7e-04 <6.2e-04 Req. ý62e-q4 -5.9

e-0 4 <1.4e-04 <3.2e-04 <1.7e-04 Req. 'eq. leq. leq. eq. 

<ti~l.Cn~en~kl3.le-04 <3.7e-04 6.2e-04 leq. 6.2e-04 <5.9e-04 2l.4e-04 <3.2e-04 1.7e-04 eq. leq. leq. eq. 'eq. 

IatrixKD_.BFW-Ni~e3/kg) 3.1e-04 3.7e-04 16.2e-04 keq 16.2e-04 S .9e-04 ý1.4e-04 0.2e-04 * 1.7e-04 [eq. eq13. eq-. eq.9 Req.

00
Table E-8. Differential analysis results for the nominal case with Alloy C-22 for a time period of interest of 50 kyr (cont'd) 

.atrixKO...UFZ...Thtm3/kg) <3. le-04 <3. ?e-04 <6.2e-04 Reg. <6.2e-04 <5.9e-04 1. 4e-04 ý3. 2e-04 <1.7e-04 'eg. Ne. 'eg. Neg. Neq. 

iatLixKD-CInzPu (m3ikg) 2.e-04 ý3. 7 -- 6.2e 04 :leg. ý6.2e-04 .5.9e 04 1l.4e-04 '3.2e-04 1.7e-04 leg. :leg. leg. leg. leg. 

4atrixltDUFZ_1Nifm3/kgJ ý3.le-04 ý3.7e-04 <6.2e-04 'leg. ý6.2e-04 S5.9e-04 1.4e-04 <3.2e-04 <1.7e-04 Neg. 'eg. eg. 'eg. leg. 

IatrixKO...CHnvSe~m3/kgl <3.le-04 <3.7e-04 <6.2e-04 Reg. ý6.2e-04 ý5.9e-04 l1.4e-04 <3.2e-04 <1.7e-04 Neg. 'eg. Neg. Reg. 'eq. 

,atrixKDjrSw..Setm3/kgl ý3.le-04 <3.7e-04 6.2e-04 Neg. .6.2e-04 <S.9e-04 <1.4e-04 3.2e-04 ý1.7e-04 Neg. 'eg. Reg. leg. eg.. 

AatrixKD...CHnvRa~ze3/kgj <3.le-04 <3.7e-04 <6.2e-04 Reg. <6.2e-04 <5.9e-04 ý1.4e-04 0.2e-04 ý1.7e-04 Neg. Reg. eg. leq. eg. 

4atrixKD...PPw_.Pufm3/kg] <3.le-04 0.7e-04 ý6.2e-04 leg. <6.2e-04 ý5.9e-04 <1.4e-04 <3,2e-04 <1.7e-04 Neg. 'eg. eg. leg. Neg. 

.Iatr ixKD..UCF- U[m3 / kg <3le-04 0.7e-04 <6.2e-04 Reg. ý6.2e-04 ý5.9e-04 <1.4e-04 -0.2e-04 <1.7e-04 eq. Neg, Neg. leq. 'le. 

tlatrixKD PPW..rh(m3Ikgl 3.1e-04 <3.7e-04 ý6.2e-04 Neg. ý6.2e-04 <5.9e-04 1.4e-04 0.2e-04 <1.7e-04 Reg. 'le. 'eg. Neg. Reg. 

Matr ix KtlTSWAojIm3/ kg) ý3.le-04 0.7e-04 ý6.2e-04 Neg. <6.2e-04 .5.9e-04 1.4e-04 0.2e-04 <1.7e-04 Neq. Neq. Reg. eg. Reg. 

datrixKD_.BFw tl~m3/kqj3l-4 .e0 6.2e-04 Reg. ý6.2e-04 <5.9e-04 1.4e-04 0.2e-04 ý1.7e-04 'eg. 'eg. 'eg. Neg. 'eg. 

FracturePorosity..CHnz 3.le-04 3.7e-04 6.2e-04 Reg. <6.2e-04 5.9e-04 <1.4e-04 <3.2e-04 ý1.7e-04 leg. Neq. Reg. Reg. Neq. 

Matrixl(DHnvUfm3/kg] 0.1e-04 3.7e-04 ý6.2e-04 leg. ý6.2e-04 <5.9e-04 <1.4e-014 <3.2e-04 ý1.7e-04 neg. Reg. 'eg. Reg. leg. 

4atrixl<D...ClP..Pu~m3/kg] .3.le-04 0.7e-04 ý6.2e-04 Reg. ý6.2e-04 <5.9e-04 <1.4e-04 0.2e-04 l1.7

e-04 Neq. 'eg. Reg. leg. eq. 

latrixl<DCHnzU(m3/kgjj -3.ic-04 ý3.7e 04 ý6.2e-04 leg. <6.2e-04 <5.e-04 <1.e-04 <3.2e-Gl t1.7e-04 leg. leg. leg. leg. eo. 

7 te: The use C' neg in a clu~rn i,'3x-ates that the value of dy.'dx sigma fir that parameter aas zer0 for tli~t gi-en run. Inclusion of the minimum measureable value in the -aliulation -,d have a~ negli.cjbl- imract on the arithmet ' .&*araqe value of dy/dx *sigma.

tll

00
NRC FORM 335 U.S. NUCLEAR REGULATORY COMMISSION 1. REPORT NUMBER 

(2-89) (Assigned by NRC, Add Vol, Supp., Rev, 

NRCM 102, BIBLIOGRAPHIC DATA SHEET and Addendum Numbers, If any.) 

3201, 3202 

(See instnxcons on #)e reverse) NUREG-1668 

:2. TITLE ANO SUBTITLE Vl 

Vol. 2 

NRC SensitiVity and Uncertainty Analyses for a Proposed HLW Repository 3. DATE REPORT PUBLISHED 

at Yucca Mountain, Nevada, Using TPA 3.1 MONTH YEAR 

Results and Conclusions March 1999 

4. FIN OR GRANT NUMBER 

5. AUTHOR(S) 6. TYPE OF REPORT 

M.S. Jarzemba*, R.B. Codell**, L.M. Deere*, J.R. Firth**, C. Lui**, S. Mohanty*, K. Poor*, Technical 

J.Weldy*, V. Colten-Bradley** 7. PERIOD COVERED (Inclusive Dates) 

8. PERFORMING ORGANIZATION - NAME AND ADDRESS (if NRC, provide Diviso, Ofltce or Region. U.S Nuc•w Reguleboy Comrision, nd mei/ng eddies, if conhca-o, 

provide name and meg adues.) 

Center for Nuclear Waste Regulatory Analyses, 6220 Culebra Road, San Antonio, TX 78228-0510 

Office of Nuclear Material Safety and Safeguards, U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001

9. SPONSORING ORGANIZATION - NAME AND ADDRESS (If NRC, type Same asabove' if contractor, provide NRC Division, Office or Region, U.S. Nuclear Regulatory Commission, 

and mailing address.)

Division of Waste Management 

Office of Nuclear Material Safety and Safeguards 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001

10. SUPPLEMENTARY NOTES

11. ABSTRACT (200 words orless) 

The total-system performance assessment computer code was developed to assist t he U.S. Nuclear Regulatory Commission 

staff in evaluating the safety case for the proposed geologic repository at Yuc ca Mountain, Nevada. This report: (i) presents 

results of process and system-level sensitivity and uncertainty analyses to det ermine the parameters that have the most 

influence on repository performance; (ii) estimates the relative importance of the key elements of subsystem abstraction; and 

(iii) focuses staff reviews of the U.S. Department of Energy total-system perf ormance assessments. Results of system-level 

analyses are based on peak dose in the time period of interest (TPI) to a receptor group 20 kilometers from the proposed 

repository. For a TPI of 10,000 years, and an inner waste package of Alloy 625, the most influential parameters were the rates 

of corrosion and water ingress into the package. With Alloy C-22, the same para meters were found to be influential, along with 

those affecting damage to waste packages from seismicity. For a TPI of 50,000 y ears, parameters dictating the transport of 

neptunium and americium from the waste form and through the geosphere were foun d to be most influential, with parameters 

dictating the rate of water ingress into the package somewhat less important. T he influential parameters resulting from these 

analyses were compared to the current key elements of subsystem abstraction use d by the NRC staff to focus work on items 

important to overall repository performance.

12. KEY WORDS/DESCRIPTORS (List words or phrases that will assist researchers in locating Ihe report) 13. AVAILABILITY STATEMENT 

Computer code Disruptive Consequences unlimited 

Dose EPA Standard 14. SECURITY CLASSIFICATION 

Geologic repository Ground water (This Page) 

High-level radioactive waste Parameter unclassified 

Performance assessment Probabilstic risk assessment (This Report) 

Receptor group Scenario unclassified 

Sensitivity and uncertainty analysis Source term 15. NUMBER OF PAGES 

Transport Yucca Mountain 

Waste package 

16. PRICE

NRC FORM 335 (2-89)
Federal Recycling Program
SPECIAL STANDARD MAIL 

POSTAGE AND FEES PAID 

USNRC 

PERMIT NO. G-67

UNITED STATES 

NUCLEAR REGULATORY COMMISSION 

WASHINGTON, DC 20555-0001 

OFFICIAL BUSINESS 

PENALTY FOR PRIVATE USE, $300