In-Package Chemistry Abstraction Rev 02, ICN 00

ANL-EBS-MD-000037

July 2003

1. PURPOSE 
The work associated with the development of this model report was performed in accordance 
with the requirements established in Technical Work Plan for Waste Form Degradation 
Modeling, Testing, and Analyses in Support of SR and LA (BSC 2002a). The in-package 
chemistry model and in-package chemistry model abstraction are developed to predict the bulk 
chemistry inside of a failed waste package and to provide simplified expressions of that 
chemistry. The purpose of this work is to provide the abstraction model to the Performance 
Assessment Project and the Waste Form Department for development of geochemical models of 
the waste package interior. 
The scope of this model report is to describe the development and validation of the in-package 
chemistry model and in-package chemistry model abstraction. The in-package chemistry model 
will consider chemical interactions of water with the waste package materials and the waste form 
for commercial spent nuclear fuel (CSNF) and codisposed high-level waste glass (HLWG) and N 
Reactor spent fuel (CDNR). The in-package chemistry model includes two sub-models, the first 
a water vapor condensation (WVC) model, where water enters a waste package as vapor and 
forms a film on the waste package components with subsequent film reactions with the waste 
package materials and waste formthis is a no-flow model, the reacted fluids do not exit the 
waste package via advection. The second sub-model of the in-package chemistry model is the 
seepage dripping model (SDM), where water, water that may have seeped into the repository 
from the surrounding rock, enters a failed waste package and reacts with the waste package 
components and waste form, and then exits the waste package with no accumulation of reacted 
water in the waste package. Both of the submodels of the in-package chemistry model are film 
models in contrast to past in-package chemistry models where all of the waste package pore 
space was filled with water. The current in-package chemistry model is a film model where both 
dripping and non-dripping cases of water ingress/egress are considered. 
The scope of the model documentation includes detailed descriptions of the process models and 
their inputs and outputs, as well as derivation of the in-package chemistry abstractions and their 
limitations. 
In neither the water vapor condensation model nor the seepage dripping model is the failure 
mode of the waste package specified. The in-package chemistry model and resulting in-package 
chemistry model abstraction are general and independent of waste package failure mode. 
The output from the in-package chemistry model include time series concentrations of the 
elements and their aqueous complexes constituting the waste package, waste form, seepage, and 
gas composition. Also, output will be time series values of pH, ionic strength, and mineralogical 
phase abundance. The in-package chemistry model abstraction simplifies the in-package 
chemistry model outputs for pH, ionic strength, Eh, total carbonate concentration, chloride, and 
fluoride concentration into a Performance Assessment Project compatible format. 
To maximize the applicability while accounting for the uncertainties of the model inputs, a series 
of scenarios were run with variations in temperature, seepage composition, and water distribution 
within the waste package. The abstractions of in-package chemistry are applicable over the

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water volumetric flux (hereafter referred to as flux) range from 0.15 l/yr to 1000 l/yr, a 
temperature range from 15C to 95C and carbon dioxide partial pressure range of 10-4 to 10-2 
atmospheres. Spatially, the applicability of the in-package chemistry model and in-package 
chemistry model abstraction is limited to the waste package interior. 
Temporally, the in-package chemistry model starting time refers to the time of waste package 
breach, or if the waste package should fail while its temperature is above the boiling temperature 
of water, then the starting time refers to when the temperature in the waste package falls below 
the boiling temperature. Therefore, in the remainder of the model report, the term time will be 
relative to signal either of the two cases noted above. Note that the in-package chemistry 
abstraction does not provide information regarding the temperature history of the waste 
packages, therefore, TSPA must decide if the waste package temperature is above or below 
boiling. 
Limitations on the availability of thermodynamic data for certain elements that constitute the 
waste package and SNF (transition metals and radionuclides) prohibited using high-ionic 
strength solutions as initial starting compositions for the in-package chemistry model. This data 
limitation also influenced the boundary conditions used in the model where constant relative 
humidity was required to maintain non-evaporative conditions inside the waste package. 
Further data limitations include the lack of long-term metal alloy corrosion information. Another 
data limitation is the lack of knowledge regarding the change in the surface area of the waste 
package components and SNF as these components degrade in an aqueous environment. The 
specifics of the model limitations will be discussed in Sections 6 and 8. 
This in-package chemistry model abstraction is planned to be used as input to the Total Systems 
Performance Assessment (TSPA)License Application (LA) where the relationships developed 
in this model report will be linked to the principle factors: waste form degradation, dissolved 
concentrations, waste package degradation, cladding degradation, and colloid generation. In 
addition, the in-package chemistry model output may be used as one of the source terms for the 
chemistry in the invert. 
2. QUALITY ASSURANCE 
QA Program Applicability: Development of this model report has been determined to be 
subject to the Yucca Mountain Projects quality assurance program because it will be used to 
support performance assessments; it does not affect any items on the Q-List (BSC 2002a, 
Attachment I). 
Electronic Management of Data: The technical work plan contains the Process Control 
Evaluation used to evaluate the control of electronic management of data (BSC 2002a, 
Attachment III) during the modeling and documentation activities, and this evaluation 
determined that the methods identified in the implementing procedures are adequate. No 
deviations from these methods were performed.

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3. COMPUTER SOFTWARE AND MODEL USAGE 
3.1 COMPUTER SOFTWARE 
The software used in this analysis includes: 
 EQ3/6 v7.2b (EQ3/6 V7.2b, STN: LLNL: UCRL-MA-110662) is qualified on the 
Windows 95 and HP-UX 10.20B operating systems. The EQ3/6 software is composed of 
three component programs, EQPT, EQ3NR, and EQ6. EQPT is used to transform a 
specially formatted thermodynamic database from a text format (data0.* file) to a binary 
format (data1.* file) which is used by EQ3NR and EQ6. EQ3NR is a geochemical 
speciation code which uses as input a water composition and the aforementioned 
thermodynamic database. Output from EQ3NR (*.3o file) provides information 
regarding the saturation state of various minerals and gases and the concentrations of 
aqueous chemical species. EQ3NR also outputs a pickup file (*.3p file) which contains 
the initial water composition in a format that is appended to the end of an EQ6 input file 
to set the initial condition for the EQ6 run. The software was obtained from Bechtel 
SAIC Company Software Configuration Management organization and installed on the 
IBM-compatible computer identified below. It is appropriate for the application and is 
used only within the range of validation in accordance with AP-SI.1Q, Software 
Management. 
 EQ6 V7.2b LV (EQ6 V7.2bLV, STN: 10075-7.2bLV-02) is qualified on the Windows 
95, 98, NT and 2000 operating systems. EQ6 is a geochemical reaction-path modeling 
code that is used to simulate complex systems such as the in-package chemical 
environment. Inputs for EQ6 include the EQ3 pickup file (*.3p), the thermodynamic 
database (data1.*), and reactant information such as surface area, number of moles, and 
reaction rate. Output from EQ6 includes a reaction history or time history of the solid 
and aqueous chemical environment (*.6o, *.txt, *.bin files). The software was obtained 
from Bechtel SAIC Company Software Configuration Management organization and 
installed on the IBM-compatible computer identified below. It is appropriate for the 
application and is used only within the range of validation in accordance with AP-SI.1Q, 
Software Management. 
 Microsoft Excel 2000, a commercially available spreadsheet software package: 
Applications of this software in the current document are restricted to tabulation, visual 
display of results, and use of intrinsic functions (SUM, MAXIMUM, MINIMUM, 
AVERAGE), as well as use of the regression analysis and curve fitting routines which are 
both described in Section 8. No macros or software routines were developed for, or used 
by, this software, and consequently it is an exempt software application in accordance 
with Section 2.1 of AP-SI.1Q. 
 GetEQData, version 1.0.1 (GetEQData V1.0.1, STN: 10809-1.0.1-0) is a Microsoft 
Visual Basic 6.0 macro post-processor, embedded inside a Microsoft Excel 2000 
spreadsheet. This macro was used to extract output from EQ6 (*.6o files) into Microsoft 
Excel 2000 spreadsheets. GetEQData is qualified on Windows NT and Windows 2000

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operating systems. This software was obtained and implemented under AP-SI.1Q, and 
was only used within the range of validation. 
The EQ6 simulations and GetEQData were executed on the following machine using the 
Microsoft Windows 2000 operating system: 
 A Dell Latitude C610, Framatome ANP Inc. Tag# 632MT11 in Albuquerque, New 
Mexico. 
The EQ3NR and EQPT (EQ3/6 V7.2b) simulations were executed on the following machine 
using the Microsoft Windows 95 operating system: 
 A Dell Optiplex GX300 #117728 (Bechtel SAIC Company) in Las Vegas, Nevada. 
4. INPUTS 
4.1 DATA AND PARAMETERS 
All of the information in the following subsections are used as direct input to the in-package 
chemistry model. 
4.1.1 Data 
Table 1 lists the data used in the in-package chemistry model. These data include the 
thermodynamic database used by the EQ3/6 codes and three water compositions. 
Table 1. Data Input and References for In-Package Chemistry Model 
Identifier Input Reference 
Used in 
Section: 
Data0.ymp.R2 Thermodynamic database DTN: MO0302SPATHDYN.000 6.5.4 
J-13 Well Water Water Composition DTN: MO0006J13WTRCM.000 6.5.4 
Ca-porewater Water Composition DTN: GS020408312272.003 6.5.4 
Na-porewater Water Composition DTN: GS020408312272.003 6.5.4 
4.1.1.1 Thermodynamic Database 
The in-package chemistry model used a version of the thermodynamic database Data0.ymp.R2 
(DTN: MO0302SPATHDYN.000) as a necessary component in the execution of the EQ3/6 
software. This database used in the in-package chemistry model contains data for temperatures 
up to 200C. This database is appropriate for the in-package chemistry model because it 
includes the elements that constitute the waste package, waste form, seepage, and gas 
compositions in the temperature range needed for the model. 
The CSNF and HLWG (compositions given in Tables 8 and 9, respectively) were added to the 
database in order to take advantage of EQ3/6's ability to use a pH-dependent and carbonatedependent 
rate law, using the EQ6 transition state theory formalism to describe the degradation. 
Only reactants entered as minerals (solids contained in the database) can specify a range of

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degradation rates based on pH; special reactants (reactants not contained in the database) must 
have a constant or fixed degradation rate. 
The thermodynamic data for the following aqueous species and one gas were unverified: 
(NH4)2Sb2S4(aq); NpSO4
2+(aq); NpO2H2PO4
+(aq); and NO2(g). However, the presence of these 
species did not have any impact on the results of the in-package chemistry model because their 
concentrations were too low to be of any consequence on the results. 
4.1.1.2 Input Water Compositions 
Table 2 summarizes the input water compositions used in the in-package chemistry model as 
direct input. 
Table 2. Input Water Compositions 
Parameter Units 
Ca-porewater 
ECRB-SYS-CS1000/7.3-7.7/UCa 
Na-porewater 
ECRB-SYS-CS2000/16.3-16.5/UCb J-13c 
Ca mg/L 94 81 13.0 
Mg mg/L 18.1 3.3 2.01 
Na mg/L 39 120 45.8 
K mg/L 7.6 6.1 5.04 
Si Mg/L N/A N/A 61.0 
SiO2 mg/L 42 42 N/A 
NO3 mg/L 2.6 0.41 8.78 
HCO3 mg/L 397 362 See Section 6.5.4 
Cl mg/L 21 24 7.14 
F mg/L 3.4 6 2.18 
SO4 mg/L 36 31 18.4 
pH pH 7.6 7.4 7d 
Sources:a Sample: ECRB-SYS-CS1000/7.3-7.7/UC; DTN: GS020408312272.003 
b Sample: ECRB-SYS-CS2000/16.3-16.5/UC; DTN: GS020408312272.003 
c DTN: MO0006J13WTRCM.000 
D Harrar et al. 1990, pp. 4 to 9 
For simplicity, and based on their calcium and sodium concentrations, samples 
ECRB-YS-S1000/7.3-7.7/UC and ECRB-SYS-CS2000/16.3-16.5/UC were termed 
Ca-porewater and Na-porewater, respectively. 
These three water compositions were used as the initial condition(s) in the seepage dripping 
model of the in-package chemistry model for water entering a failed waste package. The 
decision to use these water compositions was based on several lines of reasoning. Although it is 
not expected to enter the repository, the J-13 composition was used for comparison purposes, 
i.e., to maintain continuity between the current work and past in-package chemistry analyses. 
The Ca- and Na-porewater compositions were used because they were obtained from core 
samples proximal to the repository. These waters could represent seepage compositions that 
would enter a failed waste package, especially over the long term. The compositions are 
contained in a DTN from the U.S. Geological Survey (DTN: GS020408312272.003), and a note 
in the data spreadsheet states:

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Chemical composition of pore water extracted from cores of 15 ECRB-SYS-CS 
Series boreholes, USW SD-9, and USW NRG-7/7a, 04/26/2001 to 02/12/2002. 
Thus, identifying the location of the boreholes from which the cores were removed. 
Furthermore, the recent collection date(s) of the samples provides confidence that collection and 
analysis methods were performed under an approved quality assurance program. 
Drift-Scale Coupled Processes (DST and THC Seepage) Models (BSC 2003a, Section 6.2.2.1) 
uses the Ca-porewater (labeled W5 in BSC 2003a) and another composition (labeled W4 
[ECRB-SYS-CS2000/16.5-21/UC]) also from the U.S. Geological Survey DTN 
(DTN: GS020408312272.003), which originates from the next interval in the same core as that 
from which the Na-porewater was extracted. The composition of the Na-porewater used in the 
in-package chemistry model only differs slightly from the W4 composition used in Drift-Scale 
Coupled Processes (DST and THC Seepage) Models (BSC 2003a). Thus, continuity has been 
established for key inputs for the in-package chemistry model and the drift scale processes 
models. 
Compared to the J-13 composition, and with the exception of aqueous silica, the porewater 
compositions are significantly more concentrated (Table 2). Two anions, chloride and fluoride, 
which may be important with regard to steel corrosion are concentrated in the pore waters by 
factors of approximately 2 to 3 compared to J-13. Drift-Scale Coupled Processes (DST and THC 
Seepage) Models (BSC 2003a) states that W5 (Ca-porewater) has the highest (Ca + Mg)/(Na + 
K) ratio of the ECRB samples, and that W4 (Na-porewater) has the lowest (Ca + Mg)/(Na + 
K) ratio of the ECRB samples and has higher fluoride than other samples. Thus, the two 
porewater represent compositional end members and capture the spread of the porewater 
compositions. A Piper diagram (BSC 2003a, Figure 6.2-4) clearly shows that these two samples 
represent compositional end members. 
Since these samples cover the spectrum of observed porewater compositions sampled, and there 
is a possibility that waters of similar composition may potentially enter a drift and contact a 
failed waste package, the use of these compositions is well justified. Furthermore, use of these 
data in the in-package chemistry model, and, thus, the in-package chemistry model abstraction, 
ensures the feeds to the TSPA-LA model, will reflect the compositional variation of the initial 
water composition. 
4.1.2 Parameters 
Table 3 summarizes the parameters and their references used in the in-package chemistry model.

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Table 3. Sources for Fuel and High-Level Waste Glass Reaction Rates 
Identifier Input Reference 
Used in 
Section: 
HLWG Reaction 
Rate 
BSC 2001a, Section 6.2.3.3, Equations 7 and 8 6.5.1.2 
CDNR Reaction 
Rate 
DOE 2000a, Equation 2-39; 
BSC 2001b, Section 6.3.7 
6.5.1.2 
CSNF Reaction 
Rate 
CRWMS M&O 2000a, Equations 16 and 18 6.5.1.2 
Water Diffusion Flux Water Flux BSC 2003b, Section 6.6.2 6.3.2 
Nitric Acid 
Production Rate 
Reaction 
Rate 
BSC 2002b, Table 21, p. 33 Attachment III 
G value of Nitric Acid 
Production 
BSC 2002b, p. 27 Attachment III 
G value of Hydrogen 
Peroxide Production 
Used to 
Calculate 
hydrogen 
peroxide 
production 
IAEA 1998, Table 8.2, p. 214 Attachment III 
NOTE: All inputs used in Attachment III are listed in Attachment III. 
4.1.2.1 High-Level Waste Glass Rate Expression 
For HLWG a transition state theory rate law is recommended (BSC 2001a, Section 6.2.3.3, 
Equations 7 and 8) in which the moles of dissolved glass are a function of the pH: 
for pH < 7.1 (BSC 2001a, Section 6.2.3.3, Equation 7) 
log10 rate = (14.0  0.5) + (-0.6  0.1)  pH + log10(exp((-80  10) kJ/mol/(RT))) 
and for pH = 7.1 (BSC 2001a, Section 6.2.3.3, Equation 8) 
log10 rate = (6.9  0.5) + (0.4  0.1)  pH + log10(exp((-80  10) kJ/mol/(RT))) 
The rate laws are in units of g/m2-day. 
4.1.2.2 CDNR Rate Expression 
For CDNR, a linear (i.e., constant reaction rate) is used to describe the dissolution of the 
N Reactor fuel. This is the best estimate dissolution rate recommended by DSNF and Other 
Waste Form Degradation Abstraction (BSC 2001b, Section 6.3.7), which is five times the 
constant U-metal rate contained in Review of Oxidation Rates of DOE Spent Nuclear Fuel, Part 
1: Metallic Fuel (DOE 2000a, Equation 2-39): 
k1 = 5.03109 
.. 
. 
.. 
.  - 
RT 
kJ/mol 0 . 2 4 . 66 exp mg U/cm2-h 
4.1.2.3 CSNF Rate Expression 
The pH, O2 partial pressure, and carbonate-dependent best estimate rate law used for CSNF in 
this analysis is that recommended in CSNF Waste Form Degradation: Summary Abstraction 
(CRWMS M&O 2000a, Equations 16 and 18) evaluated at 50C.

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for pH > 7 (CRWMS M&O 2000a, Equation 16) 
log10 rate = 4.69 + (-1085)/T + (-0.12)  pCO3 + (-0.32)  pO2 
and for pH = 7 (CRWMS M&O 2000a, Equation 18) 
log10 rate = 7.13 + (-1085)/T + (-0.32)  pO2 + (-0.41)  pH. 
Where, pCO3 = -log10(total carbonate species concentration), pO2 = -log10(oxygen partial 
pressure), and temperature in Kelvin. Rate is in units of mg/m2-day. 
4.1.2.4 Diffusion Flux 
The diffusion flux of water vapor into a waste package was calculated (BSC 2003b, Section 
6.2.2) for ingress of water vapor through stress corrosion cracks. The calculated value of 43.88 
mol H2O/yr (0.79 l/yr) through 25 stress corrosion cracks was used as the water flux term in 
water vapor condensation model runs. 
4.1.3 Other Input 
Table 4 contains the names and shorthand names used in this model and waste package type for 
the metal alloys used throughout this document. 
Table 4. Materials Nomenclature and Waste Package Breakdown 
Waste Package Type 
Material Nomenclature Shorthand CSNF CDNR 
Alloy 22, UNS N06022 Alloy 22 .. .. 
SA-240 S31600 Stainless Steel a S31600 .. .. 
SA-240 S30403 UNS N06625 Stainless Steel 304L .. 
SA-516 Grade 70 Carbon Steel A516 .. .. 
SB-209 6061 T4 Al-6061 .. 
Aluminum Alloy-1100 Al-1100 .. 
Neutronit A976. Neutronit .. 
NOTE: a There are additional chemistry controls applied to the compositional ranges specified for S31600 
stainless steel in the ASME Standard. 
Table 5 summarizes the other direct inputs for the in-package chemistry model and provides the 
references for them.

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Table 5. Input References for the Waste Package Components 
Material/Property Input Reference 
Used in 
Section: 
21-PWR CSNF WP geometric 
information 
WP Component 
Dimensions 
BSC 2002c, Attachment III; 
BSC 2003c; 
Punatar 2001, p. 2-5 
Attachment I 
CSNF.xls 
CDNR WP geometric 
information 
WP Component 
Dimensions 
BSC 2003c; 
DOE 2000b, Section 3 
Attachment I 
CDNR.xls 
Periodic Table of the 
Elements and Mass List of 
Radioactive Isotopes 
Atomic Weight of 
the Elements and 
Isotopes 
Audi and Wapstra 1995; 
Parrington et al. 1996 
Attachment I 
CSNF.xls 
CDNR.xls 
Corrosion rate DTN: MO0303SPAMCRAQ.000 6.5.1.1 
Density ASTM G 1-90 1999, Table XI, p. 7 
Attachment I 
CSNF.xls 
CDNR.xls S31600 
Composition 
ASME 1998, Section II, SA-240, Table 1, p. 
366, for composition of 316; 
ASM International 1987, p. 931, for C and N 
content of 316 
6.5.1.1 
Corrosion rate DTN: MO0303SPAMCRAQ.000 6.5.1.1 
Density ASTM G 1-90 1999, Table XI, p. 7 Attachment I 
CDNR.xls 304L 
Composition ASTM A 240/A 240M-99b 2000, Table 1, p. 2 6.5.1.1 
Corrosion rate DTN: MO0303SPAMCRAQ.000 6.5.1.1 
Density ASTM A 20/A 20M-95a 1995, p. 21 
Attachment I 
CSNF.xls 
CDNR.xls A516 
Composition ASME 1998, Section II, SA-516/SA-516M, 
Table 1, p. 925 6.5.1.1 
Corrosion rate ASM International 1987, Table 12, p. 603 6.5.1.1 
Density ASTM G 1-90 1999, Table X1.1 Attachment I 
CSNF.xls Al-6061 
Composition ASME 1998, Section II, SB-209, Table 1, 
p. 236 6.5.1.1 
Corrosion rate ASM International 1987, Table 12, p. 603 6.5.1.1 
Density ASTM G 1-90 1999, Table XI, p. 7 Attachment I 
CDNR.xls Al-1100 
Composition ASTM B 209-96 1996, Table 1, p. 7 6.5.1.1 
Corrosion rate DTN: MO0303SPAMCRAQ.000 6.5.1.1 
Density Attachment I 
CSNF.xls Neutronit 
Composition 
DTN: MO0109RIB00049.001 Material 
Properties of Neutronit A976/A978 for B 
content; 
ASTM A 887-89 2000, Table 1, S30463, 
S30464, and S30466 
6.5.1.1 
Density BSC 2003c; 
Stout and Leider 1994, Table 6.4 
Attachment I 
CDNR.xls 
HLWG 
Composition BSC 2003c; 
Baxter 1988, Table 10 6.5.1.2 
N-Reactor Fuel Composition BSC 2003c; 
DOE 2000b, Table 3-1 6.5.1.2 
CSNF Isotopic Inventory 
BSC 2003c; 
BSC 2001c, Attachment III Disk 1 of 9, ATT 
III/ LPM1/ uniform_profile/ 3.5/ ft71- 
case10.N04 
6.5.1.2 
NOTE: PWR = pressurized water reactor; WP = waste package.

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4.1.3.1 Waste Package Geometric Information 
Dimensional information about the geometry of the waste package components allows the 
volume and surface area of the waste package components to be calculated. With knowledge of 
the alloy density(s) and the waste package component volumes, the mass for each component is 
calculated, which is converted to moles that are used as input for EQ6. 
The sketch SK-0219 Rev 02 (BSC 2002c, Attachment III) was used as a source for some of the 
21-pressurized water reactor (PWR) CSNF waste package geometry, rather than the baseline 
drawing (BSC 2001d; BSC 2003d). The dimensions used in the model report are tabulated in 
Attachment II, Table II-1. The sketch was used in this model report since it gives more detail 
about the waste package interior geometry, specifically the fuel basket, than the baseline 
drawing. The differences in dimensions between the sketch and the baseline drawing for waste 
package components common to both are insignificant. For example, the dimensions of the 
inner vessel were taken from the sketch, but the only difference from the drawing is in the 
thickness of the inner vessel top and bottom lids with a combined difference of only 5 cm. Such 
small differences in waste package component dimensions should not impact the results of this 
model report as illustrated by the sensitivity analyses (Attachment II). 
Since the interior geometry of these waste packages is complex, the dimensions of the individual 
waste package components will not be included in the text of the document; however, this 
information is available in Attachment I, in CSNF.xls for the CSNF waste package, and 
CDNR.xls for the CDNR waste package. In converting waste package dimensional 
information to quantities that were used as input in the in-package chemistry model, it was 
necessary to use the equations for the calculation of the area of a circle, area of a square, area of 
a rectangle, and surface area of a cylinder. 
4.1.3.2 Atomic Weights 
Atomic weights of the elements and radionuclide isotopes used were taken from Audi and 
Wapstra (1995) and Parrington et al. (1996, p. 50). These references have been used as a source 
for this information throughout the Yucca Mountain Site Characterization Project (CRWMS 
M&O 2001, Section 5.1.1.5; BSC 2002c, Section 4.1.1.6; BSC 2002d, Section 5.1.6) and are 
appropriate as a source for atomic weights in this analysis. As explained in Section 6.5.1.1, the 
atomic weights of the elements are used to convert the weight percent of the elements in the 
metal alloys to moles of elements in the metal alloys, which are used as input in the EQ6 files. 
4.1.3.3 Waste Package Materials Properties 
Table 6 summarizes the composition of the steel and aluminum alloys present in the CSNF and 
CDNR waste packages. While there may be some variability in the composition of the alloys 
used in the construction of the waste packages, they are used in the in-package chemistry model 
as single-value inputs, i.e., the compositions are not varied in the model. In Sections 6.3.4 
and 6.7, variation in the composition of the A516 steel is discussed and a sensitivity analysis 
presented to show the effect on the model response. The alloy compositions represent the best 
available information on the materials that will be used in the construction of the waste package.

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Table 6. Composition of Steel and Al Alloys 
A516 Neutronit Al-6061 S31600 Al-1100 304L 
Element wt% wt% wt% wt% wt% wt% 
C 0.28 0.04  0.02  0.03 
Mn 1.045  0.15 2.00  2.00 
P 0.035   0.045  0.05 
S 0.035   0.03  0.03 
Si 0.29  0.60 0.75 0.45 0.75 
Cr  18.5 0.195 17.00  19.00 
Ni  13  12.00  10.00 
Co  0.2     
Mo  2.2  2.50   
N    0.08  0.10 
Fe 98.3 64.82 0.7 65.58 0.50 68.05 
B  1.245     
Zn   0.25    
Cu   0.275  0.05  
Mg   1.0    
Ti   0.15    
Al   96.68  99.00  
Total 100.0 100.0 100.0 100.0 100.0 100.0 
Source: See Table 5 
Table 7 provides the densities and corrosion rates for the waste package metal alloys described in 
Table 6. The density values are used, as explained in Section 6.5.2, to convert alloy volumes to 
moles, which are used as input in the EQ6 input files. The density values are also used in 
converting the corrosion rates, as explained in Section 6.5.1, to units appropriate for EQ6 input. 
Table 7. Steel and Al Alloy Densities and Corrosion Rates 
A516 Neutronit Al-6061 S31600 Al-1100 304L 
Density (g/cm3) 7.85 7.76 2.70 8.00 2.71 7.94 
Corrosion Rate (m/year) 72 0.1 3.0 0.1 3.0 0.1 
Source: See Table 5. 
The corrosion rates listed in Table 7 fall within the ranges provided in 
DTN: MO0303SPAMCRAQ.000 for temperatures of 25 to 90C. Furthermore, to maintain 
consistency, the use of these corrosion rates is corroborated by Table 6 of Geochemistry Model 
Abstraction and Sensitivity Studies for the 21 PWR CSNF Waste Packages (BSC 2002d). 
4.1.3.4 CSNF, N-Reactor, and HLWG Compositions 
The sources for the composition of the PWR fuel that is used for the CSNF in the in-package 
chemistry model are Repository Design Project, Repository/PA IED Emplacement Drift 
Committed Materials 1 of 2 (BSC 2003c) and Attachment III of PWR Assembly End-Effect 
Reactivity Evaluation (BSC 2001c, Disk 1 of 9, ATT III/ LPM1/ uniform_profile/ 3.5/ ft71-

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 26 of 140 July 2003 
case10.N04). The calculation (BSC 2001c) starts with fresh fuel (UO2) and calculates the 
composition of the irradiated fuel when it is discharged from the reactor (specified by burnup) 
and at specified times after discharge. The calculations are performed using the SAS2H 
sequence and the ORIGEN sequence of the SCALE computer code system. The calculation 
covers initial enrichment of 2 to 5 weight percent (fuels less than 2 percent enriched are bounded 
by this calculation) U-235, and burn-up of 0 to 50 GWd/MTU. The quantity of fuel in one 
assembly is based on the Babcock and Wilcox (B&W) 1515 assembly design with the mass of 
type MK-B2. The MK-B2 design contains the greatest mass of U per assembly (CRWMS M&O 
1998, p. 26). The B&W 1515 fuel assembly is one of the most reactive (B&W Fuel Company 
1991, p. II 6-6). 
For EQ6 runs, the CSNF was specified to have 3.5 weight percent U-235 enrichment, 40 
GWd/MTU fuel burnup, and 10,000 years of decay time. The number of elements in the fuel 
composition was reduced to allow EQ6 to run more efficiently. EQ6 run time increases rapidly 
and non-linearly as the number of elements are increased. Therefore, the moles of Ru in Table 8 
includes the moles of Ru, Rh, Pd, and Ag, and the moles of Gd in Table 8 includes the moles of 
Gd, Y, Pr, Nd, Pm, Sm, Eu, and Ho in the CSNF composition from PWR Assembly End-Effect 
Reactivity Evaluation (BSC 2001c). The elements not included in the fuel composition used in 
the model are Th, Pa, Am, Cm, Li, Be, As, Kr, Nb, and Xe. The concentration of the elements 
excluded from the CSNF composition are estimated to be below levels which have influence 
over the calculated in-package chemistry, thus their exclusion does not impact the results of the 
in-package chemistry model. 
The sources for the CDNR (N Reactor) fuel composition used in the in-package chemistry model 
are Repository Design Project, Repository/PA IED Emplacement Drift Committed Materials 1 
of 2 (BSC 2003c) and Table 3-1 of N Reactor (U-Metal) Fuel Characteristics for Disposal 
Criticality Analysis (DOE 2000b). 
Table 8 summarizes the composition of the CSNF and CDNR fuels, respectively. The CSNF is 
an oxide fuel while the CDNR fuel is a uranium metal type of fuel. 
Table 8. Commercial Spent Nuclear Fuel and Codisposed N Reactor Fuel Compositions 
Element a 
CSNF 
Moles/100g 
CDNR 
Moles/100g 
U 0.3617 0.420 
Np 0.0009 N/A 
Pu 0.0027 N/A 
Zr 0.0005 N/A 
Mo 0.0009 N/A 
Tc 0.0008 N/A 
Ru 0.0020 N/A 
Cs 0.0013 N/A 
Ba 0.0010 N/A 
Gdb 0.0035 N/A 
O 0.7385 N/A

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 27 of 140 July 2003 
The sources for the raw HLWG chemical composition used in the in-package chemistry model 
are Repository Design Project, Repository/PA IED Emplacement Drift Committed Materials 1 
of 2 (BSC 2003c) and Defense Waste Processing Facility Wasteform and Canister Description 
(Baxter 1988, Table 10). Table 3 of EQ6 Calculations for Chemical Degradation of N Reactor 
(U-metal) Spent Nuclear Fuel Waste Packages (CRWMS M&O 2001), which uses Repository 
Design Project, Repository/PA IED Emplacement Drift Committed Materials 1 of 2 (BSC 2003c) 
and Defense Waste Processing Facility Wasteform and Canister Description (Baxter 1988, Table 
10) as its source, provides the HLWG composition in an EQ6-compatible format. Table 9 gives 
the simplified molar composition of the HLWG used in the models as reported in Table 3 of EQ6 
Calculations for Chemical Degradation of N Reactor (U-metal) Spent Nuclear Fuel Waste 
Packages (CRWMS M&O 2001). The Savannah River glass composition was used in the model 
to maintain continuity with other analyses (e.g., CRWMS M&O 2001). Furthermore, the 
Savannah River glass has a higher concentration of potassium, about 23 times more than the 
Hanford glass, and potassium contributes to high pH conditions (BSC 2003c; CRWMS M&O 
2000b, p. 8, Table 5-1). Therefore, use of the Savannah River glass is well justified in this 
model. 
For input into EQ6, several elements in the glass composition from Repository Design Project, 
Repository/PA IED Emplacement Drift Committed Materials 1 of 2 (BSC 2003c) and Defense 
Waste Processing Facility Wasteform and Canister Description (Baxter 1988, Table 10) were: 
 Decayed to other elements (Pu-238 to U-234) 
 Combined with other elements (e.g., Li added to Na) or 
 Excluded to reduce the number of elements for ease in running EQ6. 
The elements not included in the model of the glass composition were Ag, Cs, Cu, Li, Mn, Cl, 
Ni, Pb, Th, and Ti. These minor changes were made to the basic composition to increase the 
efficiency of the calculations, to decrease the EQ6 run time, and to allow the use of a pH 
dependent rate law. The resulting simplified HLWG formula based on 100 g is in Table 9. 
Table 9. High-Level Waste Glass Composition 
Element Moles/100g HLWG 
O 2.70E+00 
U 7.82E-03 
Ba 1.08E-03 
Al 8.63E-02 
S 4.01E-03 
Ca 1.62E-02 
P 4.89E-04 
Si 7.76E-01 
B 2.91E-01 
F 1.66E-03 
Fe 1.72E-01 
K 7.51E-02 
Mg 3.33E-02 
Na 5.77E-01

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ANL-EBS-MD-000037 REV 02 28 of 140 July 2003 
The simplified glass composition was included in the EQ6 database as a mineral named 
SRL_Bulk. These simplifications were necessary because the amounts of any element in the 
composition of minerals added to the database have to exceed 0.0001 moles/100g of glass. 
4.2 CRITERIA 
Project Requirements Document (Canori and Leitner 2003) contains three criteria that are 
relevant to the work documented in this report. They are: 
 PRD-002/T-014 Performance Objectives for the Geologic Repository After Permanent 
Closure 
 PRD-002/T-015 Requirements for Performance Assessment 
 PRD-002/T-016 Requirements for Multiple Barriers. 
Work described in this document will support these requirements, but more specific criteria exist 
in Yucca Mountain Review Plan, Information Only (NRC 2003). Selected acceptance criteria 
(NRC 2003) are presented in order to supplement or clarify the citation from Project 
Requirements Document (Canori and Leitner 2003). 
The following acceptance criteria from Yucca Mountain Review Plan, Information Only (NRC 
2003) were identified as applicable to this technical product: 
1. Quantity and Chemistry of Water Contacting Waste Packages and Waste Forms, 
Acceptance Criteria (NRC 2003, Section 2.2.1.3.3) 
1.1 AC1: System Description and Model Integration are Adequate 
1.2 AC2: Data are Sufficient for Model Justification 
1.3 AC3: Data Uncertainty is Characterized and Propagated Through the Model 
Abstraction 
1.4 AC4: Model Uncertainty is Characterized and Propagated Through the Model 
Abstraction 
1.5 AC5: Model Abstraction Output is Supported by Objective Comparisons 
Technical Work Plan for Waste Form Degradation Modeling, Testing, and Analyses in Support 
of SR and LA (BSC 2002a) does not contain any criteria relevant to this activity. 
4.3 CODES AND STANDARDS 
ASTM C 1174-97 (1998), Standard Practice for Prediction of the Long-Term Behavior of 
Materials, Including Waste Forms, Used in Engineered Barrier Systems (EBS) for Geological 
Disposal of High-Level Radioactive Waste, is used to support the model development 
methodology, categorize the models developed with respect to their usage for long-term total

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 29 of 140 July 2003 
system performance assessment, and relate the information/data used to develop the model to the 
requirements of the standard. See Table 5 for other ASTM standards. 
5. ASSUMPTIONS 
5.1 INERT WASTE PACKAGE COMPONENTS 
It is assumed that Alloy 22 and the structural components (non-fuel materials) of the fuel 
assembly are inert. The rationale for this assumption is that the Zircaloy is virtually nonreactive, 
and the amount of non-Zircaloy materials in the fuel assembly (such as the 304L 
springs, for example) are of negligible mass when compared to the total mass of the waste 
package. It is assumed that the outer shell of the waste package (Alloy 22) is inert. The basis for 
the Alloy 22 assumption is that Alloy 22 corrodes slowly compared to other reactants in the 
waste package. The probability of Alloy 22 corroding quickly is low as shown in Figure 51 of 
General Corrosion and Localized Corrosion of Waste Package Outer Barrier (CRWMS M&O 
2000c, p. 109). This assumption does not require further confirmation because the effects on inpackage 
chemistry would be small and would not significantly alter the results of the in-package 
chemistry model. This assumption is used throughout Sections 6.3 and 6.6. 
5.2 WATER FILM 
It is assumed that there is no time delay between waste package failure and the establishment of 
a continuous film of liquid water on the waste package internals and the chemical reactions, 
which accompany film formation. It is likely that a finite amount of time will be required to 
establish a stable film of water on the internal components of the waste package. Since the inpackage 
chemistry model does not determine the amount of time required to establish a liquid 
water film after a waste package fails, this assumption is the only practical approach to apply and 
one that leads to the earliest release of reacted water from the waste package. Since this 
assumption leads to the earliest release of reacted water from the waste package, it is 
conservative and requires no further confirmation. This assumption is used throughout Sections 
6.3 and 6.6. 
5.3 FILM THICKNESS 
In the water-vapor condensation model the film thickness is 0.1-cm thick for CSNF and 0.2-cm 
thick for CDNR. In the seepage-dripping model the film thickness is 0.25-cm thick for CSNF 
and 0.35-cm thick for CDNR. These film thickness values were chosen based on iterative model 
runs, the goal of which was to minimize the film thickness. EQ6 is an aqueous geochemical 
code; as such it requires the presence of sufficient liquid phase water to run, i.e., the ratio of the 
aqueous phase to the surface area of the solid reactants must exceed some critical value before 
the code executes in an efficient manner and outputs readily reproducible results for a series of 
run scenarios (water flux, temperature, and fuel exposure). While these film thickness values 
may be high with regard to adsorbed layers of water molecules on a solid surface, it was not the 
intent of this model report to examine chemical reactions at such a fine scale. Application of the 
assumed film thickness values is justified because they result in conservative in-package 
chemistry estimates, therefore, no further confirmation is required. This assumption is used 
throughout Sections 6.3 and 6.6.

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ANL-EBS-MD-000037 REV 02 30 of 140 July 2003 
5.4 TRANSLATION OF MODEL RESPONSE 
It is assumed that variations in a model input will translate to the model output independent of 
the modeled scenario. To clarify, take the example of varying the corrosion rate of the steels 
used in the waste package. If the corrosion rate is decreased for one fuel exposure/flux scenario 
and this decrease has the effect of delaying the timing of a pH response, then it is assumed that 
an equivalent model response will occur for a different fuel exposure/flux scenario. The purpose 
of this assumption is that it allows the use of sensitivity analyses as a method of determining 
uncertainty ranges for parameters defined in the in-package chemistry model abstraction. 
Application of this assumption is justified and therefore needs no further confirmation because it 
is logical that a variation in an input value of a modeled chemical system will cause a similar 
response in a alternate but similar system. This assumption is used in Section 6.7. 
6. MODEL 
6.1 OBJECTIVES 
The objective of the in-package chemistry model and, ultimately, the in-package chemistry 
model abstraction is to predict the chemistry inside of a failed waste package and to provide 
simplified expressions of that chemistry for use by the Performance Assessment Project. The 
outputs from the in-package chemistry model will be used as inputs for the in-package chemistry 
model abstraction, i.e., to generate abstractions of the in-package chemistry parameters of pH, 
ionic strength, Eh, total carbonate, chloride, and fluoride concentration. The in-package 
chemistry model abstraction will be implemented by the Performance Assessment Project in the 
TSPA-LA code, so that the dissolved concentrations, waste package colloid, and the spent 
nuclear fuel (SNF) sub-model abstractions will have their necessary input chemistry parameters. 
Thus, the in-package chemistry model and in-package chemistry model abstraction directly 
address FEP 2.1.09.01.0B Chemical characteristics of water in the waste package. 
The in-package chemistry model simulates the chemistry inside of a failed waste packagethe 
chemistry of the fluid that has reacted with the waste package components and SNF. For the 
in-package chemistry model, two different water ingress models are used: 
 Water vapor ingress and subsequent condensation with film formation (i.e., the water 
vapor condensation model) 
 The seepage dripping model where seepage enters a waste package forming a film, 
reacts with the waste package components and SNF, and exits the waste package. 
Both the water vapor condensation and the seepage dripping model have separate process models 
and separate abstraction models, thus allowing for the implementation of non-dripping (water 
vapor condensation) and dripping (seepage dripping model) models in the TSPA-LA. Thus, 
model uncertainty is propagated through the abstractions to TSPA. 
For both of these models the chemical reaction of the waste package components with a liquid 
phase was simulated as a function of time. The time-dependency of the in package chemistry is 
simulated by assigning kinetic rates to the reactants (i.e., the SNF and waste package 
components).

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ANL-EBS-MD-000037 REV 02 31 of 140 July 2003 
While the in-package chemistry model simulates in-package chemistry, the in-package chemistry 
model abstraction uses the output from the in-package chemistry model and derives simplified 
expressions, either parameter distributions or regression equations, of the in-package chemistry 
for use by total system performance assessment. 
Model inputs for the in-package chemistry model encompass data (e.g., thermodynamic data and 
measured water compositions), parameters (e.g., waste form reaction rates and simulated water 
compositions), and other inputs (e.g., waste package design information and waste package 
component compositions). Inputs to the in-package chemistry model also include variables such 
as the percent of total fuel available for reaction (i.e., the cladding coverage for CSNF). 
The N Reactor codisposal waste package is used as the representative U.S. Department of 
Energy (DOE) SNF in the in-package chemistry model. The rationale for this decision is that 
examination of the dissolution rate literature for other DOE SNF waste forms (DOE 2002, 
Section 6) indicates that the degradation rate of N Reactor fuel generally exceeds that of the 
other types of DOE SNF and that N Reactor SNF comprises approximately 85 percent of the 
total metric ton heavy metal of DOE SNF (DOE 2002, Appendix D). 
6.2 FEATURES, EVENTS, AND PROCESSES 
The development of a comprehensive list of features, events, and processes (FEPs) potentially 
relevant to postclosure performance of the Yucca Mountain repository is an ongoing, iterative 
process based on site-specific information, design, and regulations. The approach for developing 
an initial list of FEPs, in support of Total System Performance Assessment for the Site 
Recommendation (CRWMS M&O 2000d), was documented by Freeze et al. (2001). The initial 
FEP list contained 328 FEPs of which 176 were included in TSPA-SR models (CRWMS M&O 
2000d, Tables B-9 through B-17). To support TSPA-LA, the FEP list was re-evaluated in 
accordance with The Enhanced Plan for Features, Events, and Processes (FEPs) at Yucca 
Mountain (BSC 2002e, Section 3.2). Table 10 provides a list of FEPs that are included in TSPALA 
models described in this model report, summarizes the details of their implementation in 
TSPA-LA, and provides specific references to sections within this model report. 
6.3 BASE-CASE CONCEPTUAL MODEL 
The base-case conceptual model is composed of two conceptual models, the water vapor 
condensation and the seepage dripping model, which collectively are referred to as the 
in-package chemistry model. These conceptual models differ from previous versions of the 
in-package chemistry model (BSC 2001e; CRWMS M&O 2000e; CRWMS M&O 2000f) where 
it was assumed that all of the void space within a waste package is filled with liquid. In essence 
the base-case conceptual model presented herein is a film model and that in previous model 
reports was a bathtub model. In the film model the void space inside of a failed waste package 
is partially occupied by liquid water in thermodynamic equilibrium with atmospheric gases both 
explicitly interacting in the solid-water-gas chemical system inside of a waste package. 
Therefore, the FEP 2.1.02.09.0A, Chemical effects of void space in waste, is addressed in the 
in-package chemistry model and as such in the in-package chemistry model abstraction and 
therefore in TSPA-LA.

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ANL-EBS-MD-000037 REV 02 32 of 140 July 2003 
Table 10. Included FEPs for the In-Package Chemistry Model Report and their Disposition in TSPA-LA 
FEP No. FEP Name 
Section where 
Disposition is 
Described Summary of Disposition in TSPA-LA 
2.1.01.02.0B Interactions 
between 
codisposed 
waste 
6.3.2 
6.3.3 
6.6.2.1 
6.6.2.2 
Included. Interactions of condensed water and seepage with DOE SNF and HLWG is simulated in the IPCM, 
and abstractions of the resulting chemistry are provided for use by TSPA. HLWG tends to generate high pH 
solutions (Section 6.6.2) while DOE SNF generates neutral pH solutions (Section 6.6.2), and together 
combined with the waste package materials the high pH values are buffered to lower values. 
The interactions between codisposed waste is passed to TSPA explicitly via separate abstractions 
depending on the waste package type. The TSPA parameters (pH, ionic strength, total carbonate, Eh, 
chloride, and fluoride) are all impacted by this FEP. 
2.1.02.09.0A Chemical 
effects of void 
space in waste 
package 
6.3 
6.3.1 
6.3.2 
6.3.3 
6.6.1.1 
Included. The IPCM accounts for the unfilled void space in the WP, and thus its impact on the in-package 
chemistry. Void space in the WP is gas filled which has the effect of putting the gas phase in close contact 
with the liquid and solid phase reactants in the system, and is in fact the basis of the conceptual model 
implemented herein. Thus, the increased void space in the waste package decreases distance that gas 
must diffuse to maintain equilibrium with the liquid phase, this is contrast to the bathtub model (section 
6.3.1) where all of the void space is liquid-filled and the gas phase must diffuse much longer distances to 
maintain equilibrium. 
It is included in the abstractions passed to TSPA via the oxygen and carbon dioxide equilibrium with the inpackage 
chemistry solution. The TSPA parameters (pH, ionic strength, total carbonate, Eh, chloride, and 
fluoride) are all impacted by this FEP. 
2.1.09.01.0B Chemical 
Characteristics 
of Water in the 
Waste 
Package 
6.3 
6.3.2 
6.3.3 
6.6.1.1 
Included. The chemical characteristics of water in the waste package is included in the TSPA-LA in-package 
chemistry model abstraction. The IPCM is a fully coupled reaction-path chemical model which includes the 
effects of waste form dissolution, metal alloy corrosion/dissolution, precipitation of metal oxide corrosion 
products, precipitation of complex mineral phases, reaction kinetics, thermal effects, interior WP void space, 
interactions of co-disposed waste forms, oxidationreduction reactions, heterogeneous chemical reactions, 
and seepage composition on the resulting fluid chemistry. 
The output of the IPCM are time varying aqueous inventories of fluid composition, reactant abundances, 
product abundances, and solution properties. Solution parameters are abstracted into a TSPA compatible 
format in the IPCMA; therefore, all of the processes included in the IPCM are reflected in the IPCMA and 
thus in the TSPA. The parameters abstracted in the IPCMA include pH, ionic strength, total carbonate, Eh, 
chloride, and fluoride. 
2.1.09.06.0A Reductionoxidation 
potential in 
EBS 
6.6.1.2 
6.6.2.2 
Included. A partial treatment of this FEP is provided herein. The oxidation-reduction processes inside of the 
WP is explicitly modeled in the IPCM and thus implicitly included in the IPCMA. Oxidation of the metal waste 
package components is the primary process by which the waste packages corrode and the initial water 
composition is altered. Thus, in the absence of redox reactions there would be little alteration of the water 
inside of a failed waste package. 
The redox potential and its effect on the in-package chemistry is implicitly included in the abstractions passed 
to TSPA. The TSPA parameters (pH, ionic strength, total carbonate, Eh, chloride, and fluoride) are all 
impacted by this FEP.

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ANL-EBS-MD-000037 REV 02 33 of 140 July 2003 
FEP No. FEP Name 
Section where 
Disposition is 
Described Summary of Disposition in TSPA-LA 
2.1.09.07.0A Reaction 
kinetics in EBS 
6.3.2 
6.3.3 
6.6.1.1 
6.6.1.2 
6.6.2.2 
6.7 
Included. Reaction kinetics in the EBS, i.e., waste package, are included in the TSPA-LA in-package 
chemistry model abstraction. The IPCM uses kinetic reactants to represent the SNF (Section 4.1.2) and the 
WP components (Section 4.1.3.3). The kinetic rates used in the model were either linear, where a fixed 
amount of reactant is added at each time step, or a transition-state rate law where the amount of reactant 
added to the system depends on chemical properties of the aqueous phase. The effect of varying the 
kinetics on the in-package chemistry was examined in Section 6.7 where the rates were decreased to asses 
the contribution to uncertainty in pH and ionic strength for inclusion in the abstractions of pH for TSPA. 
The variability in the kinetics of the reactants is included in the abstractions passed to TSPA both implicitly by 
their use in the IPCM, and explicitly via the contribution of the kinetics to output uncertainty. The TSPA 
parameters (pH, ionic strength, total carbonate, Eh, chloride, and fluoride) are all impacted by this FEP. 
2.1.09.02.0A Chemical 
interaction with 
corrosion 
products 
6.3.2 
6.3.3 
6.6.1.1 
6.6.1.2 
6.6.2.2 
Included. In-package corrosion products and their effect on in-package chemistry are accounted for directly 
in the IPCM, and thus the IPCMA. The corrosion products of the steel and aluminum alloys in the waste 
package and their control on the concentration of aqueous species is of primary importance in determining 
the pH and ionic strength of the solution. If these corrosion products were not allowed to form during the 
simulations, then the resulting pH and ionic strength values would be much different then the results 
presented through Section 6.6. 
The interaction with the corrosion products is implicitly included in the abstractions passed to TSPA. The 
TSPA parameters (pH, ionic strength, total carbonate, Eh, chloride, and fluoride) are all impacted by this 
FEP. 
2.1.11.08.0A Thermal 
effects on 
chemistry and 
microbial 
activity in the 
EBS 
6.3.3 
6.6.1.2 
6.6.2.2 
6.7 
Included. A partial treatment of this FEP is provided in this model report. The thermal effects on the inpackage 
chemistry are examined in the SDM. Where EQ6 runs were performed at various temperatures to 
examine the temperature effect on the in-package chemistry. In these runs the kinetic reaction rates for the 
SNF were recalculated for runs at 15 to 90C, and temperature appropriate thermodynamic data (Section 
4.1.1.1) were used in the simulations. 
For TSPA-LA the in-package chemistry abstractions of pH, ionic strength, total carbonate, and Eh are cast in 
terms of temperature (Sections 8.2.3,8.2.6, 8.4.3, 8.4.6, 8.5, and 8.6) such that the in-package chemistry 
parameters used by TSPA can be calculated over a range of temperatures. 
2.2.08.12.0A Chemistry of 
water flowing 
into the EBS 
6.3.3 
6.6.1.2 
6.6.2.2 
Included. The chemistry of the water flowing into the EBS, i.e., the waste package, is included in the TSPALA 
LA in-package chemistry model. The SDM examined various input water chemistries (Section 4.1.1.2) 
and their effect on the in-package chemistry. The results showed the parameters passed to TSPA-LA were 
unaffected by changes in the input composition. Thus, while the abstractions for TSPA were derived using 
the output from the three initial water compositions there is no parameter in the abstractions to distinguish 
the initial water composition. 
The variability of the incoming water composition is implicitly included in the abstractions passed to TSPA via 
the use of varying composition waters in the IPCM. The TSPA parameters (pH, ionic strength, total 
carbonate, Eh, chloride, and fluoride) are only slightly impacted by this FEP. 
NOTES:SDM = seepage dripping model; TSPA = total system performance assessment; WP = waste package; IPCMA = in-package chemistry model abstraction; 
IPCM = in-package chemistry model.

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The chemical processes considered in the film models (water vapor condensation and seepage 
dripping model) include the following: 
 Reaction-path tracing of the in-package chemical system 
 Conservation of mass 
 Kinetic dissolution of the waste package components and N Reactor U-metal fuel using 
linear rate laws 
 Kinetic dissolution of CSNF and HLWG using transition-state theory rate laws, i.e., 
non-linear rate laws which are dependant on the fluid chemistry 
 Equilibrium precipitation and dissolution of metal corrosion products and complex 
mineral phases 
 Thermal, i.e., temperature effects on fluid chemistry 
 Equilibrium oxidation and reduction reactions 
 Chemical interaction of input solution with codisposed waste 
 Effect of variable input compositions on the resulting fluid composition. 
The physical processes considered in the film models include the following: 
 Water flow, ingress and egress, through the waste package 
 Mixing of water in contact with waste package components. 
6.3.1 Film versus the Bathtub Model 
Models that employ the EQ6 code to simulate the chemistry inside of a failed waste package all 
use kinetic rate laws, both linear and non-linear, to describe the dissolution of the waste package 
components and require two key input parameters for each reactant: 1) the amount of the 
reactant (i.e., moles); and 2) the surface area of the reactant. It is the method used in the scaling 
of these two quantities that distinguishes between the film and bathtub model. The bathtub 
model uses a volume-based scaling technique, and the film model uses a surface-area-based 
scaling technique. 
EQ6 simulates a system that is composed of 1-liter of waterthe EQ6 volume. Therefore, it is 
necessary to scale the reactant inputs (moles and surface area) to meet this 1-liter volume 
criterion. 
For the bathtub model, a volume scaling scheme is used, with: 
EQ6 volume Volume-Based Scaling Factor = waste package total void volume* 
* The void volume is the space inside of the waste package not occupied by waste package 
components.

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ANL-EBS-MD-000037 REV 02 35 of 140 July 2003 
Multiplying the surface area and moles of each waste package component by this scaling factor 
normalizes these quantities to the 1-liter EQ6 volumethis model considers the total void 
volume of the waste package to be filled with water. 
For the film model, a surface-area-based scaling scheme is used, with: 
EQ6 volume Surface-Area-Based 
Scaling Factor = film thickness * waste package total internal 
surface area 
Thus, multiplying the surface area and moles of each waste package component by this scaling 
factor normalizes these quantities to both the 1-liter EQ6 volume and the film thickness. This 
model spreads the 1-liter EQ6 volume equally over all of the waste package components as 
dictated by the thickness of the film. 
In the water vapor condensation model and seepage dripping model one of the model inputs is 
the percent of SNF exposed to corrosive processes (i.e., the surface area of the SNF). In this 
model where the CSNF cladding is assumed inert (i.e., it does not react with seepage or 
condensation, Section 5.1) the fraction of the CSNF exposed directly influences the input surface 
areas and moles of the other waste package components via the surface area scaling factor. The 
reason for this is the EQ6 volume must be conserved. For example, in the modeled system when 
1 percent of the CSNF is exposed, the water that coats the surfaces of the remaining 99 percent 
of the CSNF rods must be distributed over the other waste package components because it is not 
possible in EQ6 to specify that a fraction of the water in the system be nonreactive (i.e., the 
water coating the cladding). Based on the constraints of EQ6 the cladding remains dry for all 
simulations. The effect of increasing the surface area of the non-SNF components in these 
simulations is that the simulated system will generate a more aggressive chemical environment 
than the real system where the water contacting the cladding is non-reactive. 
In essence, implementing a surface-area-based scaling method increases the surface area and 
moles of the reactants relative to the volume-based scaling method. Thus, the 1-liter EQ6 
volume has both more surface area and moles of reactants to react with, in the film model 
compared to the bathtub model. 
What does not change as a function of the model is the life span of the reactants. The reason for 
this is that the life span is a function of the kinetic rate constant and the ratio of moles to surface 
area, and neither this ratio nor the kinetic rate constant change regardless of the scaling factor. 
In addition to the reactant properties, the water flux into the waste package is also scaled by the 
volume or surface-area-based scaling factor. 
In addition to the effect on the surface area of the reactants, the film model also reduces the 
distances that gases (carbon dioxide and oxygen) must diffuse to maintain equilibrium with the 
waste package solution, i.e., the void space in the waste package is gas filled. In a liquid filled 
waste package (bathtub) the diffusion distance of gases outside the waste package is great and 
the probability of maintaining equilibrium between the gases and the waste package solution is 
low. However, in a partially liquid filled waste package, where the water is present as a film, the

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 36 of 140 July 2003 
diffusion distance of gases is reduced to the film thickness, and thus the probability of 
maintaining liquid  gas equilibrium is increased. It must be noted that in the previous versions 
of the in-package chemistry model (BSC 2001e; CRWMS M&O 2000e; CRWMS M&O 2000f), 
both of which were bathtub models, it was assumed that the waste package solution was well 
mixed and maintained equilibrium with the gases outside of the waste package. Therefore, the 
results of the previous versions of the in-package chemistry model were similar to the current 
model in terms of the system redox potential (Eh) and carbonate species distribution. 
6.3.2 Water Vapor Condensation Model 
The water vapor condensation model is a film model, which examines the in-package chemistry 
when a waste package fails and water vapor enters the package and condenses. The subsequent 
chemical reactions of the condensed water vapor with the waste package components and spent 
nuclear fuel form the water vapor condensation model. In the water vapor condensation model a 
film of water covers the waste package components, the supply rate of water is equal to the 
diffusion rate of water vapor, and there is no advective flow out of the waste package. The inpackage 
chemistry from the water vapor condensation model will be used by TSPA-LA for cases 
when a diffusion model is implemented so that radionuclides may exit a failed waste package via 
diffusion through a film of water. The water vapor condensation model includes scenarios that 
examine the chemistry resulting from water reacting with each waste package component 
individually as well as that resulting from reactions with ensembles of waste package 
components. 
The key components and concepts of the water vapor condensation are summarized below: 
 The water vapor condensation model is executed in EQ6 as a titration (using the 
special reactant type for the water in the EQ6 input file), that is water and reactants are 
added to a reaction vessel at their respective kinetic rate(s) or diffusion flux for water, 
products form and may re-dissolve, but no water exits the system (Wolery and Daveler 
1992, p. 42, Figure 3). 
 Water vapor only condenses and forms a continuous film at 25C. This temperature is 
used as the ambient repository temperature in the in-package chemistry model, and 
condensation may only occur on the waste package components and SNF when their 
temperatures are equal to or below the ambient temperature. With the knowledge that 
the interior of a waste package is the source of heat for the repository, it follows that 
until the temperature of the waste package internal components are equal to or less than 
their surroundings, water vapor will not condense on their surfaces. Furthermore, as was 
demonstrated in Drift-Scale Coupled Processes (DST and THC Seepage) Models (BSC 
2003a, Section 6.5.5.1, Figure 6.5-3), the waste package temperature at 20,000 years 
post-closure is approximately 40C, i.e., above the dew point temperature. However, it 
is also recognized that for the purposes of TSPA, a model describing the in-package 
chemistry for non-dripping conditions may be required at temperatures exceeding 25C, 
as no other model is available, and the water vapor condensation model would provide 
an estimate of the chemistry. Should the water vapor condensation model be applied by 
TSPA at temperatures other than 25C, additional uncertainty should be included as 
described in Sections 8.1.1, 8.1.2, 8.3.1, and 8.3.2.

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ANL-EBS-MD-000037 REV 02 37 of 140 July 2003 
 The relative humidity in the drift and waste package environment is sufficient to 
maintain a film of water on the internal waste package components for the duration of 
the modeled period. Concurrent with the temperature falling below boiling in the 
repository and the surrounding rock, the matrix of the host rock rewets to just less than 
100% saturation, and maintains this value for the remainder of the model period (BSC 
2003a, Section 6.5.5.1, Figure 6.5-3). Thus, given that the matrix maintains high 
constant saturation it follows that the humidity in the drift will also remain high for the 
duration of the model period. While there may be unquantified deviations in the relative 
humidity as a function of time, which would impact the results of the water vapor 
condensation model, the approach of assuming constant and high relative humidity 
ensures that the in-package chemistry is not interrupted for the duration of the model 
period. 
 Water that condenses and reacts with the waste package components and SNF initially 
contains no dissolved solutes other than carbonate species from equilibrium with 
atmospheric carbon dioxide. Water vapor (gaseous phase water) is distilled, i.e., 
contains no solutes. Upon condensation the newly formed liquid phase contains no 
dissolved solutes other than the aforementioned carbonate species. The minor and trace 
gases in the atmosphere were not included in the model because they are either inert or 
of such low concentration to be of no consequence on the model results. This water 
composition was used in the water vapor condensation model because it best represents 
the composition of condensed water vapor. 
 The film thickness is 0.1 cm for CSNF and 0.2 cm for CDNR (Section 5.3). Variations 
in the film thickness in a real system compared to the modeled system could add a small 
degree of uncertainty. Instead, this uncertainty is already captured by the conservative 
nature of the uncertainty range. 
 Water vapor enters the breached waste package and condenses on the waste package 
components at a rate equal to the water vapor diffusion rate into the waste package, 
which is 43.88 moles per year per waste package (BSC 2003b, Section 6.2.2). 
 10 percent of the CSNF is available for reaction; this value was chosen because it 
represents the median value used in the seepage dripping model simulations. Using a 
single value for CSNF exposure is justified because its contribution to the chemical 
system is relatively benign (Section 6.6.1.1 and 6.6.1.2). Although this value was not 
varied in the water vapor condensation model runs its contribution to the uncertainty of 
the model response is expected to be small. 
 100 percent of the HLWG was exposed to corrosive processes. This value is justified 
because HLWG dissolution can result in an aggressive chemical environment via high 
pH conditions (Section 6.6.2.1), and is therefore conservative. Although this value was 
not varied in the water vapor condensation model runs, its contribution to the model 
response is accounted in the abstraction for TSPA-LA (Section 8.3.1). 
 50 percent of the CDNR surface area was exposed to corrosive processes. N Reactor 
(U-Metal) Fuel Characteristics for Disposal Criticality Analysis (DOE 2000b, Section

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 38 of 140 July 2003 
3, Appendix C) states that only about 50 percent of the N Reactor fuel assemblies were 
intact due to underwater storage; thus, an even a higher percentage of the CDNR fuel is 
or may be exposed in the future. This value is justified because the N reactor fuel has a 
minor influence on the in-package chemistry (Section 6.6.2.1). Although this value was 
not varied in the water vapor condensation model runs, its contribution to the uncertainty 
of the model response is expected to be small. 
 The surface area values used in the model for the SNF and waste package components 
are fixed and do not vary with time. The EQ6 code uses the surface area of the reactants 
to calculate the amount of reactant available to react at each time step (Section 6.5.1.1). 
The more surface area available, the faster the reactant degrades. In an actual system, as 
the amount of reactant decreases due to degradation, so too would its surface area. 
However, it is not possible to implement the actual changing surface area in the EQ6 
code. Therefore, the constant surface area provides an average surface area for the 
reactants for the duration of their lifetime. The constant surface area approximation 
could contribute to the uncertainty of the model response. 
 For the ensemble runs the film is well mixed and not flowing. The rationale for this 
concept is twofold; first, the waste package components are evenly distributed and in 
close proximity to one another inside of the waste package, and secondly, the water 
vapor condensation model is developed to examine the chemistry of solutions resulting 
from combinations of waste package components and SNF (i.e., the mixture of any 
intra-waste package flow path dependent chemistries). The results of the single 
component runs compared to the ensemble runs (Sections 6.6.1.1 and 6.6.2.1) show that 
this concept is justified. 
This conceptual model addresses the following FEPs: 
 2.1.09.01.0B Chemical characteristics of water in the waste package 
 2.1.09.07.0A Reaction kinetics in EBS: The reactants that constitute the waste package 
and waste form are represented in the water vapor condensation model as kinetic 
reactants; therefore, this FEP is addressed 
 2.1.02.09.0A Chemical effects of void space in waste package: In the film model, at 
least some of the void space is not filled with liquid; therefore, the conceptual model 
directly addresses the FEP 
 2.1.01.02.0B Interactions between codisposed waste: The film model considers 
codisposed waste and the chemical effects resulting from the codisposal of glass-pour 
canisters and the multi-canister overpacks; therefore, the conceptual model directly 
addresses the FEP 
 2.1.09.02.0A Chemical interaction with corrosion products: Corrosion products are 
generated during the degradation of the metal alloys that make up the waste package 
components. Therefore, the effect of the corrosion products on the in-package chemistry

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 39 of 140 July 2003 
is directly handled by the in-package chemistry model and, thus, the in-package 
chemistry model abstraction that is used in the dissolved concentration calculation. 
Water Vapor Condensation Model CasesThe cases that encompass the water vapor 
condensation model include single reactant runs and multi-component ensemble runs. The 
single reactant runs react a film of water with one alloy or waste form for the purpose of 
determining the resulting chemistry and the contribution of the reactants to the in-package 
chemistry for comparison with the ensemble results. The ensemble runs combine two or more 
reactants for the purpose of determining the effect of multiple reactants and reactant 
combinations on the in-package chemistry. The combination of reactants in an ensemble 
represents the primary source of uncertainty in the water vapor condensation model. The choice 
of reactant ensembles was based on two criteria: spatial proximity of a waste package 
component to the SNF or HLWG and reactivity/surface area of a reactant. For example, A516 
carbon steel is both highly reactive (high corrosion rate) and has a high surface area in CSNF 
packages and is in close proximity to the waste. For these reasons A516 appears in nearly every 
ensemble run. 
Tables 11 and 12 summarize the cases simulated in the water vapor condensation model. The 
EQ6 input file (*.6i) name convention followed was CS = CSNF, CD = CDNR, comp# = 
ensemble ID. For example: 
CS_comp7.6i has the following inputs: CSNF waste package, Al-6061, A516 FBT, and 
A516 stiffeners as reactants. 
6.3.3 Seepage Dripping Model 
The seepage dripping model is a film model that examines the in-package chemistry following 
the ingress of seepage into the waste package. In the seepage dripping model, seepage enters and 
exits the waste package without accumulating or filling all of the waste package void space, as it 
flows the seepage reacts with the internal components of the waste package and deposits 
corrosion products in the waste package interior. 
Table 11. Commercial Spent Nuclear Fuel Water Vapor Condensation Multi-Component Ensembles 
Waste Package 
Component / EQ6 File 
CS_comp1 
CS_comp2 
CS_comp3 
CS_comp4 
CS_comp5 
CS_comp6 
CS_comp7 
CS_comp8 
CS_comp9 
CS_comp10 
CS_comp11 
S31600 NG inner vessel .. .. .. 
Neutronit .. .. .. .. .. 
Al-6061 .. .. .. .. .. .. .. 
FBT (A516) .. .. .. .. .. .. .. .. .. .. .. 
Stiffeners (A516) .. .. .. .. .. .. .. .. .. .. .. 
CSNF .. .. .. .. .. ..

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ANL-EBS-MD-000037 REV 02 40 of 140 July 2003 
Table 12. Codisposed N Reactor Water Vapor Condensation Multi-Component Ensembles 
Waste Package 
Component / 
EQ6 File CD_comp1 CD_comp2 CD_comp3 CD_comp4 CD_comp5 CD_comp6 CD_comp7 CD_comp8 
S31600 Inner 
vessel 
.. 
Al-110 .. .. .. .. 
304L MCO .. .. .. .. 
304L MCO top .. .. .. .. 
304L GPC .. .. .. .. .. .. 
A516 plates .. .. .. .. .. .. 
U-Metal .. .. .. 
HLWG .. .. .. .. .. .. .. .. 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
The key model components and concepts for the seepage dripping model are summarized below: 
 The seepage dripping model of the in-package chemistry model uses the solid-centered 
flow through option in EQ6 (using the displacer reactant type for the water in the EQ6 
input file) which simulates a single cell batch reactor, i.e., seepage water flows into a 
cell at specified rate while reactants are added to the cell at their kinetic rate(s), chemical 
reactions take place, products precipitate and redissolve, and effluent exits the cell 
(Wolery and Daveler 1992, p. 46, Figure 5). 
 Seepage drips onto the upper surface of the waste package and penetrates the waste 
package through an opening. The solution then flows through the waste package coating 
the interior components with a film reacting, mixing, and flushing the dissolved material 
from the waste package. There is no accumulation of water in the waste package. 
 The thickness of the water film(s) is independent of the water flux into the failed waste 
package. For ideal film flow, to maintain a given film thickness, a certain flow rate is 
required. However, with the modeling tools available and the complexity of the system, 
it is not possible to model ideal film flow. Therefore, in this model, the film concept is 
used to scale the amount of water to the surface area of reactants; in essence, taking one 
step closer to the ideal from the previous bathtub model. The film thickness values used 
in the in-package chemistry model are large relative to adsorbed water on a solid 
surface, and depending on the geometry (curved versus planar) and orientation 
(horizontal versus inclined) of a waste package components reacting surface, as well as 
the presence of corrosion products which would act to retain water, a relatively high flux 
value may be required to sustain the film thickness values used in the model. Therefore, 
decoupling the film thickness and the flux is a concept that is critical for the model at 
early time (less than about 20 to 50 years post failure), i.e., at times before a layer of 
corrosion products have developed on the surfaces of the waste package components. 
When the surfaces of the waste package components are fresh, the water film will 
more easily runoff and not fully react. There will be little change in the composition of

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 41 of 140 July 2003 
the incoming seepage. At later times when a layer of corrosion products develops on the 
surfaces, a water film will be more easily sustained under low-flow conditions. 
Furthermore, considering waste package geometry reveals that the key waste package 
components (fuel basket tubes, fuel plate assemblies, and corner guides) have a planar 
geometry and are largely orientated horizontally with their vertical surfaces closely 
adjacent so that capillary forces would maintain a film of water. In the case of the 
CDNR package with the two multi-canister overpacks and two glass-pour canisters there 
are fewer horizontal surfaces as compared to the CSNF; however, the sustained water 
film leads to more complete reaction of the waste package components, SNF, and 
seepage. Since the in-package chemistry model abstraction does not provide total 
system performance assessment with an abstraction of water flux and the decoupling of 
film thickness from water flux leads to conservative estimates of in-package chemistry, 
this model concept is appropriate for TSPA-LA purposes. 
 The inside surface area of the S31600 inner vessel is exposed to degradation within the 
CSNF and CDNR waste package. The rationale for this concept is that the in-package 
chemistry model is only concerned with the chemistry on the interior of the waste 
package (i.e., the chemistry of the solution that either has reacted with the SNF or has 
the potential to react with the SNF). 
 The composition of the seepage water entering the waste package is constant with time. 
Three seepage compositions (Table 2) are used in the seepage dripping model to 
examine the effect of water composition on the resulting in-package chemistry. While it 
is possible that waters of alternate compositions could enter a failed waste package, and 
that composition could vary with time, the results (Sections 6.6.1.2 and 6.6.2.2) show 
that the model response is largely insensitive to the composition of the water entering a 
waste package. The model does not account for the possibility of seepage concentrated 
via evaporative processes entering the waste package, which could add a small degree of 
uncertainty to the model results. Instead, this uncertainty is already captured by the 
conservative nature of the uncertainty range. 
 The water flux through the waste package varies from 0.15 to 15.0 l/y for the seepage 
dripping model. These values are an order of magnitude lower than the correlation 
between percolation flux and drip rate, also called mean seep flow rate (CRWMS M&O 
2000d, Figure 3.2-15). To ensure conservatism, lower water flux terms were used, as 
longer residence times within the waste package and increased contact with waste 
package components allows for extreme chemistries to develop. In Section 6.7 
sensitivity analyses were performed to expand the range of flux values that could be 
used in the abstraction developed in Section 8. 
 The film thickness is 0.25 cm for CSNF packages, and 0.35 cm for CDNR packages 
(Section 5.3). Variations in the film thickness in the real system compared to the 
modeled system could add a degree of unquantified uncertainty to the model output. 
 The CSNF surface area is 1, 10, or 100 percent exposed to corrosive processes. 
Although 100 percent fuel exposure may be a low probability circumstance, the broad 
range of surface area values was used to include all eventualities. The CSNF surface

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 42 of 140 July 2003 
area is used as input parameter for the CSNF pH abstractions (Section 8.2.1 and 8.2.2), 
thus the range used in the model is justified. 
 The CDNR and HLWG are 100 percent exposed to corrosive processes; these values 
were used to maximize the influence of these reactants on the in-package chemistry. No 
parameter is passed to TSPA-LA to describe the CDNR and HLWG exposure, i.e., these 
fuel exposures are justified because they are the TSPA input values. 
 Due to high relative humidity on the interior of the waste package the effects of 
evaporation inside of the waste package will have a negligible influence on the inpackage 
chemistry. For early failures, the openings in a waste package are likely to be 
small and restrictive, thus water entering will quickly increase the relative humidity 
inside of the waste package such that evaporation will be minimized. There will be a 
period immediately after waste package failure (when water first enters and the relative 
humidity increases) where the evaporation inside of the waste package will be high; this 
phenomena was not included in the in-package chemistry model. Depending on the flux 
value of seepage into the waste package, this early time evaporation could add a degree 
of unquantified uncertainty to the model results. At high flux values, the effect of early 
time evaporative processes is likely to have a negligible impact on the model results 
because the humidity inside of the failed waste package would quickly increase, and the 
high water flux would diminish any evaporative effects on the fluid composition. At 
low water flux values the opposite would be true; the time required to increase the 
humidity would be longer, and the low water flux would have less of a dilution effect. 
Thus, the effect of in-package evaporation on model uncertainty is greatest at early times 
and at low water flux values. 
 The film is well mixed. This is justified based on the results presented for the single 
component and ensemble water vapor condensation model (Sections 6.6.1.1 and 
6.6.2.1). 
 Oxygen and carbon dioxide maintain equilibrium with the waste package solution and 
the ambient atmosphere outside of the waste package. The partial pressure of CO2 and 
O2 of the ambient repository atmosphere are set to 10-3.0 and 0.2 atm, respectively (log 
fO2 = -0.7 and log fCO2 = -3). The rationale for the oxygen partial pressure is that it is 
equivalent to that in the atmosphere (Weast 1977, p. F-210). The rationale for choosing 
the carbon dioxide pressure to be higher than the atmospheric value is that ambient 
fluids drawn from boreholes near the repository horizon appear to be in equilibrium with 
above-atmospheric carbon dioxide levels (Yang et al. 1996, Table 8). In the film model 
the waste package void space is only partially occupied with water and the remaining 
void space is filled with gas; therefore, this model concept is well justified. In addition, 
a sensitivity analysis of the model response to the CO2 partial pressure is discussed in 
Section 6.3.4 and the results presented in Section 6.7. The minor and trace gases in the 
atmosphere were not included in the model because they are either inert or of such low 
concentration to be of no consequence on the model results. 
 The EQ3/6 results generated using the B-dot activity coefficient equation for solutions 
with ionic strength greater than 1 molal are sufficiently accurate for the current

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 43 of 140 July 2003 
calculation. The rationale for this is that experimental data (in sulfate, nitrate, and 
chloride solutions) shows that EQ3/6 results using the B-dot activity coefficient equation 
can be used qualitatively up to an ionic strength of about 4 molal to indicate the general 
nature of the reactions that would actually occur (CRWMS M&O 1997, Appendix D). 
Scenarios when the ionic strength exceeded 1 molal occurred for the low flux (0.15 l/yr) 
scenarios. While additional uncertainty may be introduced to the in-package chemistry 
when the ionic strength exceeds one molal, the primary parameter passed to TSPA 
(i.e., pH) does not display unusual trends for high ionic strength simulations compared 
to the low ionic strength simulations (Section 8.2.1). Therefore, a decision was made to 
include these results in the model report and carry them forward in the abstraction with 
no additional uncertainty for two reasons: (1) the potential need by the Performance 
Assessment Project to handle low flux conditions in TSPA-LA; and (2) the submodels 
which use the in-package chemistry abstractions are either not effected by ionic strength 
(CSNF degradation and HLWG dissolutions models) or have flags when the ionic 
strength (solubility and colloid model) exceeds a threshold value. 
 The surface area values used in the model for the SNF and waste package components 
are fixed and do not vary with time. The EQ6 code uses the surface area of the reactants 
to calculate the amount of reactant available to react at each time step (Section 
6.5.1.1).The more surface area available, the faster the reactant degrades. In an actual 
system, as the amount of reactant decreases due to degradation, so too would its surface 
area. However, it is not possible to implement the actual changing surface area in the 
EQ6 code. Therefore, the constant surface area provides an average surface area for the 
reactants for the duration of their lifetime. The constant surface area approximation 
could contribute to the uncertainty of the model response. 
The seepage dripping model conceptual model will aid in the disposition of the following FEPs: 
 2.1.09.01.0B Chemical characteristics of water in the waste package. 
 2.1.09.07.0A Reaction kinetics in EBS: The reactants that constitute the waste package 
and waste form are represented in the seepage dripping model as kinetic reactants; 
therefore, this FEP is addressed. 
 2.2.08.12.0A Chemistry of water flowing into the EBS: The seepage dripping model 
addresses this FEP by using two different seepage compositions in addition to the J-13 
composition. 
 2.1.11.08.0A Thermal effects on chemistry and microbial activity in the EBS: The role 
of elevated temperature on the in-package chemistry is examined in the seepage dripping 
model. 
 2.1.02.09.0A Chemical effects of void space in waste package: In the film model, at 
least some of the void space is not filled with liquid; therefore the conceptual model 
directly addresses the FEP.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 44 of 140 July 2003 
 2.1.01.02.0B Interactions between codisposed waste: The film model considers 
codisposed waste and the chemical effects resulting from the codisposal of glass-pour 
canisters and the multi-canister overpacks; therefore, the conceptual model directly 
addresses the FEP. 
 2.1.09.02.0A Chemical interaction with corrosion products: Corrosion products are 
generated during the degradation of the metal alloys that make up the waste package 
components. Therefore, the effect of the corrosion products on the in-package chemistry 
is directly handled by the in-package chemistry model and thus the in-package chemistry 
model abstraction that is used in the dissolved concentration calculation. 
Seepage Dripping Model CasesA matrix of model runs (Tables 13 and 14) were executed in 
which four key input variables were varied across their expected input range for the purpose of 
generating a broad range of model response. The key parameters included: water flux, fuel 
exposure (clad failure), initial water composition, and temperature. Systematic combinations of 
three input water compositions, three values of fuel exposure (clad failure) for CSNF, and three 
values of water flux were run for both waste package types at two different temperatures. This 
approach is a factorial design approach where a run matrix was implemented (Tables 13 and 14) 
to capture the full range of possible scenarios and model response. 
For the CSNF cases the fuel exposure (clad failure) and water flux input parameters were varied 
because it has been shown (BSC 2001e; CRWMS M&O 2000e; CRWMS M&O 2000f) that the 
results of the in-package chemistry model are sensitive to these inputs, and they are input 
variables in the total system performance assessment to the in-package chemistry model. The 
temperature was varied to increase the range of applicability of the in-package chemistry model 
and in-package chemistry model abstraction. The input water composition was varied to 
determine if the in-package chemistry model response is sensitive to this input, and it answers 
FEP 2.2.08.12.0A Chemistry of water flowing into the engineered barrier system. 
Other model inputs were also varied in sensitivity analyses (Sections 6.3.4 and 6.7) to expand the 
applicability of the model abstraction for TSPA implementation and to quantify model 
uncertainty. 
The EQ6 input file naming convention is as follows: C = CSNF, D = CDNR, 1 = 0.15 l/yr, 2 = 
1.5 l/yr, 3 = 15 l/yr, 1 = 1 percent clad failure, 2 = 10 percent clad failure, 3 = 100 percent clad 
failure (for CDNR always set to 3), J = J-13 water; C = Ca-porewater; N = Na-porewater, 25 = 
25C; 50 = 50C. Tables 13 and 14 summarize the CSNF and CDNR inputs, respectively. For 
example, for EQ6 input file, C22C25.6i, it follows: C = CSNF, 2 = 1.5 l/yr, 2 = 10% clad 
failure, C = Ca porewater, 25 = 25C.

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ANL-EBS-MD-000037 REV 02 45 of 140 July 2003 
Table 13. EQ6 Input Guide for the CSNF Seepage Dripping Model Runs 
EQ6 RootFile 
Name (*.6i) 
Waste 
Package Type Flux 
Cladding 
Failure Water Type Temperature (C) 
C11C25 CSNF 0.15 1% Ca-Porewater 25 
C12C25 CSNF 0.15 10% Ca-Porewater 25 
C13C25 CSNF 0.15 100% Ca-Porewater 25 
C21C25 CSNF 1.5 1% Ca-Porewater 25 
C22C25 CSNF 1.5 10% Ca-Porewater 25 
C23C25 CSNF 1.5 100% Ca-Porewater 25 
C31C25 CSNF 15.0 1% Ca-Porewater 25 
C32C25 CSNF 15.0 10% Ca-Porewater 25 
C33C50 CSNF 15.0 100% Ca-Porewater 25 
C11C50 CSNF 0.15 1% Ca-Porewater 50 
C12C50 CSNF 0.15 10% Ca-Porewater 50 
C13C50 CSNF 0.15 100% Ca-Porewater 50 
C21C50 CSNF 1.5 1% Ca-Porewater 50 
C22C50 CSNF 1.5 10% Ca-Porewater 50 
C23C50 CSNF 1.5 100% Ca-Porewater 50 
C31C50 CSNF 15.0 1% Ca-Porewater 50 
C32C50 CSNF 15.0 10% Ca-Porewater 50 
C33C50 CSNF 15.0 100% Ca-Porewater 50 
C11N25 CSNF 0.15 1% Na-Porewater 25 
C12N25 CSNF 0.15 10% Na-Porewater 25 
C13N25 CSNF 0.15 100% Na-Porewater 25 
C21N25 CSNF 1.5 1% Na-Porewater 25 
C22N25 CSNF 1.5 10% Na-Porewater 25 
C23N25 CSNF 1.5 100% Na-Porewater 25 
C31N25 CSNF 15.0 1% Na-Porewater 25 
C32N25 CSNF 15.0 10% Na-Porewater 25 
C33N50 CSNF 15.0 100% Na-Porewater 25 
C11N50 CSNF 0.15 1% Na-Porewater 50 
C12N50 CSNF 0.15 10% Na-Porewater 50 
C13N50 CSNF 0.15 100% Na-Porewater 50 
C21N50 CSNF 1.5 1% Na-Porewater 50 
C22N50 CSNF 1.5 10% Na-Porewater 50 
C23N50 CSNF 1.5 100% Na-Porewater 50 
C31N50 CSNF 15.0 1% Na-Porewater 50 
C32N50 CSNF 15.0 10% Na-Porewater 50 
C33N50 CSNF 15.0 100% Na-Porewater 50 
C11J25 CSNF 0.15 1% J-13 25 
C12J25 CSNF 0.15 10% J-13 25 
C13J25 CSNF 0.15 100% J-13 25 
C21J25 CSNF 1.5 1% J-13 25 
C22J25 CSNF 1.5 10% J-13 25 
C23J25 CSNF 1.5 100% J-13 25 
C31J25 CSNF 15.0 1% J-13 25

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ANL-EBS-MD-000037 REV 02 46 of 140 July 2003 
Table 13. EQ6 Input Guide for the CSNF Seepage Dripping Model Runs (Continued) 
EQ6 RootFile 
Name (*.6i) 
Waste 
Package Type Flux 
Cladding 
Failure Water Type Temperature (C) 
C32J25 CSNF 15.0 10% J-13 25 
C33J50 CSNF 15.0 100% J-13 25 
C11J50 CSNF 0.15 1% J-13 50 
C12J50 CSNF 0.15 10% J-13 50 
C13J50 CSNF 0.15 100% J-13 50 
C21J50 CSNF 1.5 1% J-13 50 
C22J50 CSNF 1.5 10% J-13 50 
C23J50 CSNF 1.5 100% J-13 50 
C31J50 CSNF 15.0 1% J-13 50 
C32J50 CSNF 15.0 10% J-13 50 
C33J50 CSNF 15.0 100% J-13 50 
Table 14 provides the CDNR run matrix, note that since there is no fuel exposure (cladding) term 
in the CDNR model the number of runs is greatly reduced compared to the CSNF run matrix 
(Table 13). 
Table 14. EQ6 Input Guide for the Codisposed N Reactor Seepage Dripping Model Runs 
EQ6 RootFile 
Name (*.6i) 
Waste 
Package Type Flux Water Type Temperature (C) 
D13C25 CDNR 0.15 Ca-Porewater 25 
D23C25 CDNR 1.5 Ca-Porewater 25 
D33C50 CDNR 15.0 Ca-Porewater 25 
D13C50 CDNR 0.15 Ca-Porewater 50 
D23C50 CDNR 1.5 Ca-Porewater 50 
D33C50 CDNR 15.0 Ca-Porewater 50 
D13N25 CDNR 0.15 Na-Porewater 25 
D23N25 CDNR 1.5 Na-Porewater 25 
D33N50 CDNR 15.0 Na-Porewater 25 
D13N50 CDNR 0.15 Na-Porewater 50 
D23N50 CDNR 1.5 Na-Porewater 50 
D33N50 CDNR 15.0 Na-Porewater 50 
D13J25 CDNR 0.15 J-13 25 
D23J25 CDNR 1.5 J-13 25 
D33J50 CDNR 15.0 J-13 25 
D13J50 CDNR 0.15 J-13 50 
D23J50 CDNR 1.5 J-13 50 
D33J50 CDNR 15.0 J-13 50 
6.3.4 Sensitivity CasesUncertainty Analyses 
The base-case model inputs have epistemic uncertainty, that is the uncertainty is due either to 
simplifying assumptions regarding geometry or composition, for example, or due to minor

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variations in design versus actual parameters, e.g., engineering drawing dimensions versus actual 
manufactured dimensions. 
Sensitivity analyses were performed to meet the following objectives: (1) to determine 
uncertainty ranges for pH and ionic strength for use in the in-package chemistry model 
abstraction; (2) to expand the boundaries (temperature, flux, and CO2 partial pressure ranges) of 
the in-package chemistry model abstraction without performing a large array of additional 
simulations; and (3) to demonstrate the effects of waste package configuration variations on the 
model output. 
Model runs were executed using the median seepage flux/fuel exposure case (C22C25) for the 
seepage dripping model CSNF at 25C as the starting point input. The underlying assumption 
being that input variations will cause the same response in the modeled output regardless of the 
scenario. 
Key inputs were varied over a range of values, and the outputs were compared to the C22C25 
output. The choice of inputs that were varied in these sensitivity runs was based on the analysts 
knowledge of the phenomena, i.e., inputs that were known to effect the model results were 
chosen. The effects of carbon dioxide partial pressure, sulfur content of the A516, and steel 
corrosion rates were investigated in a series of simulations. Additional simulations varying the 
temperature and water flux values were performed to expand the boundaries of the in-package 
chemistry model abstraction. Finally, runs were completed for both the CSNF and CDNR waste 
packages to show that minor changes in the design configuration had little impact on the model 
response. 
The results of the sensitivity analyses are presented in Section 6.7. 
6.4 ALTERNATIVE CONCEPTUAL MODELS 
6.4.1 Alternative Conceptual Model I 
The interior of a waste package is spatially heterogeneous with respect to composition; therefore, 
a stream of water traveling vertically downward through the cross-sectional interior of a waste 
package would encounter different materials at different times. Along this flow path and at a 
given time, water molecules in the stream of water would react with the solid or solids to which 
they were immediately adjacent, resulting in a water stream that has a variable composition 
along its flow path. This conceptual model could be viewed as a one-dimensional column 
composed of n cells where the reactants in each cell represent the waste package components in 
vertical cross section of the waste packagethis model would eliminate the constraint of the 
EQ6 solid-centered flow through mode, used in the in-package chemistry model, of a well mixed 
batch reactor. This alternative conceptual model would also provide spatial information on the 
chemistry inside of a failed waste package, information that is not used by total system 
performance assessment. 
The water vapor condensation model results showed (Section 6.6.1.1) that the mixing model 
actually provides a good alternative to the more complex Alternate Conceptual Model I. The 
reason being that the waste package materials tend to have an overwhelming effect on the inpackage 
chemistry regardless of mixing.

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6.4.2 Alternative Conceptual Model II 
Seepage entering a failed waste package is likely to vary in composition as a function of time 
(i.e., as the conditions that prevail in the drift and engineered barrier system (EBS) vary over 
time). Therefore, a conceptual model that implemented a continuously varying input fluid 
composition would be more realistic than the current in-package chemistry model, in which the 
input fluid compositions are constant over time. The seepage dripping model showed that model 
response was largely insensitive to input water composition because the effect of the waste 
package materials on the in-package chemistry exerted much more control than the input water 
composition (Section 6.6.1.2). The alternative conceptual models are listed in Table 15. 
Table 15. Alternative In-Package Chemistry Conceptual Models 
Alternative Conceptual Model Key Features Screening Assessment and Basis 
Alternative Conceptual Model I The WP is compositionally discrete The WVC model showed that the resulting 
chemical effects of individual WP 
components were comparable to that of 
their ensembles. See Section 6.6.1.1 
Alternative Conceptual Model II The composition of seepage 
entering a WP is likely to vary as a 
function of changing conditions in 
the UZ and drift environments. 
The SDM showed that wide compositional 
ranges in the seepage composition had 
little influence on the resulting in-package 
chemistry. See Section 6.6.1.2 
NOTES: SDM = seepage dripping model; WP = waste package; WVC = water vapor condensation. 
Although the DOE has considered alternative models, it has been determined (and demonstrated 
in this model report) that the current models (water vapor condensation and seepage dripping 
model) are sufficient, and that this in-package chemistry model is robust, fulfills the stated 
purpose of this model report, and adequate for licensing purposes. 
6.5 BASE-CASE MODEL INPUTS 
6.5.1 Waste Package Component Properties 
6.5.1.1 Metal Alloy Composition and Corrosion Rates 
The in-package chemistry model represents the metal alloys as special reactants, in the EQ6 
input files, with the compositions given in Table 16. The values in Table 16 were calculated 
from the weight percent values in Table 6 by dividing the weight percent of each element by the 
respective atomic weight for each element. This method uses the convention of assigning a 100 
gram/mole molecular weight for each alloy (the same convention was used for expressing the 
compositions of the CSNF, CDNR, and HLWG in Tables 8 and 9). 
Table 17 provides the corrosion rates used in the in-package chemistry model. The corrosion 
rates were calculated by converting to consistent units and multiplying the corrosion values in 
Table 7 by their density and dividing the result by 100g per mole. The steel corrosion rates 
given in Tables 7 and 17 represent values that are robust in the temperature range from 25 to 
50C. The values are supported by the data in DTN: MO0303SPAMCRAQ.000, which cover a 
range of temperatures and corroding water compositions.

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Table 16. Metal Alloy Compositions 
A516 Neutronit Al-6061 S31600 Al-1100 304L 
Element moles moles moles moles moles moles 
C 2.33E-02 3.33E-03 1.67E-03 2.50E-03 
Mn 1.90E-02 2.73E-03 3.64E-02 3.64E-02 
P 1.13E-03 1.45E-03 1.61E-03 
S 1.09E-03 9.36E-04 9.36E-04 
Si 1.03E-02 2.14E-02 2.67E-02 1.60E-02 2.67E-02 
Cr 3.56E-01 3.75E-03 3.27E-01 3.65E-01 
Ni 2.21E-01 2.04E-01 1.70E-01 
Co 3.39E-03 
Mo 2.29E-02 2.61E-02 
N 5.71E-03 7.14E-03 
Fe 1.76E+00 1.16E+00 1.25E-02 1.17E+00 8.95E-03 1.22E+00 
B 1.15E-01 
Zn 3.82E-03 
Cu 4.33E-03 7.87E-04 
Mg 4.11E-02 
Ti 3.13E-03 
Al 3.58E+00 3.67E+00 
Table 17. Metal Alloy Corrosion Rates 
A516 Neutronit Al-6061 S31600 Al-1100 304L 
Corrosion Rate (moles/s-cm2) 1.80E-11 2.54E-14 2.54E-13 2.53E-14 2.54E-13 2.52E-14 
Source: CSNF.xls, worksheetRates 
The amount of metal alloy that EQ6 adds to the reaction during a run is the product of the 
coefficients in Table 17, the duration of the EQ6 time step, and the surface area of the reactant. 
The surface areas used in the simulations (Sections 6.5.2, 6.5.3, and 6.5.4) remain fixed for the 
duration of the reactants existence. 
6.5.1.2 SNF and HLWG Reaction Rates 
Table 18 provides the CSNF reaction rate coefficients at 25, 50, and 90C for the equation 
presented in Section 4.1.2.3. The two expressions given in Section 4.1.2.3 for the dissolution of 
CSNF were combined into a single closed-form expression for use in EQ6 (Table 18). The 
CSNF reaction rate is a transition state theory rate law that is dependent on temperature, pH, and 
bicarbonate concentration. The bicarbonate concentration is used rather than total carbonate 
because CSNF rate law is insensitive to carbonate concentration in the output pH range (3 to 8) 
of the model. Figure II-2 of Attachment II shows the CSNF total degradation constant as a 
function of pH and derived in terms of both total carbonate and HCO3
-. This figure shows that 
from pH 2 to about pH 10 the total degradation constant is not sensitive to carbonate, but above 
pH 10 the degradation constant is sensitive to carbonate. 
The coefficients in Table 18 are from the CSNF.xls spreadsheet, in the sheet Rates.

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Table 18. Commercial Spent Nuclear Fuel Transition State Theory Rate Law and Coefficients 
Total Degradation Rate Constant = k1[H+]0.41 + k2[HCO3-]0.12 
(mole/cm2s) 
Temperature (Celsius) Rate Constant (k1) Rate Constant (k2) 
25 2.14E-11 7.77E-14 
50 4.09E-11 1.49E-13 
90 9.59E-11 3.48E-13 
Source: CSNF.xls, worksheetRates 
The glass dissolution rate law is the pH-dependent, transition state theory rate law presented in 
Defense High Level Waste Glass Degradation (BSC 2001a, Section 6.2.3.3, Equations 7 and 8). 
This rate, in grams/m2day, was converted to a format suitable for input to EQ6 (100 gmoles/
cm2-sec) in the Rates worksheet, of CDNR.xls. The first rate mechanism (described 
with k1) in Table 19 is dominant at pH values = 7.1, while the second rate mechanism (described 
with k2) is dominant at pH values below 7.1. The rate constants (those derived for degradation at 
25, 50, and 90C) are given in Table 19. 
Table 19. High-Level Waste Glass Transition State Theory Rate Law and Constants and 
Codisposed N Reactor Rate Constants 
HLWG 
Total Degradation Rate Constant = k1[H+]-0.4 + k2[H+]0.6 (mole/cm2s) 
Temperature (Celsius) Rate Constant (k1) Rate Constant (k2) 
25 8.86E-19 1.11E-11 
50 1.08E-17 1.35E-10 
90 2.86E-16 3.60E-09 
CDNR 
Temperature (Celsius) Total Degradation constant K (mole/cm2s) 
25 1.63E-10 
50 1.29E-09 
90 1.97E-08 
Source: CDNR.xla, worksheet:DSNF Rates 
6.5.2 Single Component Water Vapor Condensation Model Inputs 
For the single component that water vapor condensation runs it is necessary to scale the 
reactants surface area and moles to the 0.1-cm film thickness. 
Surface area = [EQ6 volume (1000 cm3)] / [Film thickness (0.1 cm)] 
Surface Area = 10,000 cm2 
The moles of each reactant is calculated by dividing moles reactant per waste package by its total 
surface area per waste package and multiplying the result by 10,000 cm2. 
Reactant Moles = [moles reactant per waste package] / [surface area reactant per waste 
package(cm2)] * 10,000 cm2

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Table 20 provides the surface area (column C) and moles (column D) for each of the CSNF 
waste package components. 
Table 20. Commercial Spent Nuclear Fuel Waste Package Single Component Water Vapor 
Condensation Surface Areas and Reactant Moles for 0.1-cm Thick Film at 10 Percent Cladding Failure 
A B C D 
WP Component 10% fuel moles 10%CSNF SA 0.1-cm Film SA 
(cm2) 
EQ6 Input Values 
Moles/10,000 cm2 
S31600 105313.843 241598.779 10000 4359.039 
NEUTRONIT 20983.042 529130.560 10000 396.557 
Al-6061 3419.418 258957.440 10000 132.046 
FBT (A516) 34843.520 1777406.400 10000 196.036 
Stiffeners (A516) 2011.330 27646.044 10000 727.529 
CSNF 10843.178 462421.200 10000 234.487 
D = A/B*C 
Source: CSNF.xls, worksheet Single Component WVC EQ6 Inputs 
NOTE: WP = waste package. 
Table 21 provides the surface area (column C) and moles (column D) for each of the CDNR 
components. 
Table 21. Codisposed N Reactor Waste Package Single Component Water Vapor Condensation 
Surface Areas and Reactant Moles for 0.1-cm Thick Film 
A B C D 
WP Component Total Moles Total Surface Area (cm2) 0.1-cm Film SA (cm2) EQ6 Input Values 
Moles/10,000 cm2 
S31600 Inner 
vessel 
131890.622 269167.135 10000 4899.953 
Al-110 806.883 141782.216 10000 56.910 
304L MCO 27003.677 487054.703 10000 554.428 
304L MCO top 29855.874 36377.355 10000 8207.269 
304L GPC 14830.838 179201.621 10000 827.606 
A516 plates 11457.056 334302.863 10000 342.715 
U-Metal 127455.067 1639433.037 10000 777.434 
HLWG 57655.304 3113959.289 10000 185.151 
D = A/B*C 
Source: CDNR.xls, worksheet Single Component WVC EQ6 Inputs 
NOTES:MCO = multi-canister overpack; GPC = glass-pour canister. 
To convert to EQ6 compatible units and simplify the EQ6 calculations, a mole of special 
reactant fluid is defined as 1000 g ~ 1000 cm3. Thus, by dividing liters per year by 3.16E+07 
seconds per year the EQ6 compatible unit of moles per second result (Table 22).

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Table 22. Water Flux Conversion 
Water Flux Units 
0.79 (l/yr) 
2.5E-08 mols/sec per WP 
Source: CSNF.xls and CDNR.xls, worksheet: Single 
Component WVC EQ6 Inputs 
NOTE: WP = waste package. 
The water flux values for each waste package type is calculated by multiplying the flux by the 
normalization factor, a value unique to each package as defined in Section 6.5.3. Table 23 
provides the normalization factor and water flux for CSNF and CDNR packages. 
Table 23. Single-Component Water Flux Values for Commercial Spent Nuclear Fuel and 
Codisposed N Reactor Waste Package 
Waste Package 
Type 
Film Thickness 
(mm) 
Normalization 
Factor 
Water Flux 
(mol/sec) 
CSNF 1.0 3.0E-03 7.6E-11 
CDNR 1.0 1.6E-03 4.0E-11 
Source: CSNF.xls and CDNR.xls, worksheet: Single Component WVC EQ6 
Inputs 
6.5.3 Multi-Component Ensemble Water Vapor Condensation Inputs 
Tables 24 and 25 list the reactants included in the CSNF and CDNR ensemble runs (Tables 11 
and 20). Since each ensemble has a different total surface area, they each have their own 
normalization factor, and it was necessary to calculate scaled inputs for each ensemble. These 
calculations can be found in "CSNF.xls" and "CDNR.xls." 
Table 24. Commercial Spent Nuclear Fuel Water Vapor Condensation Multi-component Ensembles 
Waste Package 
Component / EQ6 
File comp1 comp2 comp3 comp4 comp5 comp6 comp7 comp8 comp9 comp10 comp11 
S31600 inner vessel .. .. .. .. 
Neutronit .. .. .. .. .. .. 
Al-6061 .. .. .. .. .. .. 
FBT (A516) .. .. .. .. .. .. .. .. .. .. .. 
Stiffeners (A516) .. .. .. .. .. .. .. .. .. .. .. 
CSNF .. .. .. .. .. ..

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Table 25. Codisposed N Reactor Water Vapor Condensation Multi-component Ensembles 
Waste Package 
Component / EQ6 File comp1 comp2 comp3 comp4 comp5 comp6 comp7 comp8 
S31600 Inner vessel .. 
Al-110 .. .. .. .. 
304L MCO .. .. .. .. 
304L MCO top .. .. .. .. 
304L GPC .. .. .. .. .. .. 
A516 plates .. .. .. .. .. .. 
U-Metal .. .. .. 
HLWG .. .. .. .. .. .. .. .. 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
Table 26 contains the surface area and moles of each CSNF reactant for 100 percent CSNF 
exposure and 10 percent CSNF exposure. For the CSNF water vapor condensation ensemble 
runs only the 10 percent fuel exposure values were used (Section 6.3.2). The total surface area 
and total moles of each reactant were calculated in Attachment I CSNF.xls in the moles and 
surface areas worksheet. The surface area and volume for each component was calculated 
based on the geometry of the component as it is represented in Attachment III of EQ6 
Calculation for Chemical Degradation of 21 PWR CSNF Waste Packages (BSC 2002c), 
Repository Design Project, Repository/PA IED Emplacement Drift Committed Materials 1 of 2 
(BSC 2003c), and Summary Report of Commercial Reactor Criticality Data for Crystal River 
Unit 3 (Punatar 2001, pp. 2 to 5). The moles of each component was calculated by multiplying 
the volume (cm3) of the component by its density (g/cm3) and dividing by 100g/mole of material. 
Table 26. Commercial Spent Nuclear Fuel Waste Package Component Surface Areas and Moles 
WP Component Total Surface 
Area(cm2) 
Total Moles 10% fuel SA 10% fuel 
moles 
S31600 241,598.8 105,313.8 241,598.8 105,313.8 
NEUTRONIT 529,130.6 20,983.0 529,130.6 20,983.0 
Al-6061 258,957.4 3,419.4 258,957.4 3,419.4 
FBT (A516) 1,777,406.4 34,843.5 1,777,406.4 34,843.5 
Stiffeners (A516) 27,646.0 2,011.3 27,646.0 2,011.3 
CSNF 4,624,212.0 108,432.0 462,421.2 10,843.2 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
For each ensemble a normalization factor was calculated to normalize the moles and surface area 
of each waste package component to the film thickness (1-mm for CSNF and 2-mm for CDNR) 
and EQ6 volume (1000 cm3). The water flux value was also normalized using the same factor. 
The normalization factor was calculated using the following formula: Normalization Factor = 
[EQ6 volume / film thickness / (Sensemble component surface areas)]. 
The following tables contains CSNF ensemble inputs consistent with Table 24.

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Table 27. Commercial Spent Nuclear Fuel Ensemble 1 Input Values 
Ensemble #1 Surface Area (cm2) Moles 
S31600 732.7 319.4 
NEUTRONIT 1604.8 63.6 
Al-6061 785.4 10.4 
FBT (A516) 5390.7 105.7 
Stiffeners (A516) 83.8 6.1 
CSNF 1402.5 32.9 
Normalization factor 3.0E-03 
Water Flux (mol/sec) 7.6E-11 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 28. Commercial Spent Nuclear Fuel Ensemble 2 Input Values 
Ensemble #2 Surface Area (cm2) Moles 
NEUTRONIT 1731.7 68.7 
Al-6061 847.5 11.2 
FBT (A516) 5817.0 114.0 
Stiffeners (A516) 90.5 6.6 
CSNF 1513.4 35.5 
Normalization factor 3.3E-03 
Water Flux (mol/sec) 8.2E-11 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 29. Commercial Spent Nuclear Fuel Ensemble 3 Input Values 
Ensemble #3 Surface Area (cm2) Moles 
Al-6061 1025.0 13.5 
FBT (A516) 7035.2 137.9 
Stiffeners (A516) 109.4 8.0 
CSNF 1830.3 42.9 
Normalization factor 4.0E-03 
Water Flux (mol/sec) 9.9E-11 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 30. Commercial Spent Nuclear Fuel Ensemble 4 Input Values 
Ensemble #4 Surface Area (cm2) Moles 
FBT (A516) 7838.7 153.7 
Stiffeners (A516) 121.9 8.9 
CSNF 2039.4 47.8 
Normalization factor 4.4E-03 
Water Flux (mol/sec) 1.1E-10 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs

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Table 31. Commercial Spent Nuclear Fuel Ensemble 5 Input Values 
Ensemble #5 Surface Area (cm2) Moles 
NEUTRONIT 2040.5 80.9 
Al-6061 998.6 13.2 
FBT (A516) 6854.3 134.4 
Stiffeners (A516) 106.6 7.8 
Normalization factor 3.9E-03 
Water Flux (mol/sec) 9.7E-11 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 32. Commercial Spent Nuclear Fuel Ensemble 6 Input Values 
Ensemble #6 Surface Area (cm2) Moles 
S31600 852.3 371.5 
NEUTRONIT 1866.6 74.0 
Al-6061 913.5 12.1 
FBT (A516) 6270.1 122.9 
Stiffeners (A516) 97.5 7.1 
Normalization factor 3.5E-03 
Water Flux (mol/sec) 8.8E-11 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 33. Commercial Spent Nuclear Fuel Ensemble 7 Input Values 
Ensemble #7 Surface Area (cm2) Moles 
Al-6061 1254.6 16.6 
FBT (A516) 8611.4 168.8 
Stiffeners (A516) 133.9 9.7 
Normalization factor 4.8E-03 
Water Flux (mol/sec) 1.2E-10 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 34. Commercial Spent Nuclear Fuel Ensemble 8 Input Values 
Ensemble #8 Surface Area (cm2) Moles 
NEUTRONIT 2266.9 89.9 
FBT (A516) 7614.7 149.3 
Stiffeners (A516) 118.4 8.6 
Normalization factor 4.3E-03 
Water Flux (mol/sec) 1.1E-10 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 
Inputs

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Table 35. Commercial Spent Nuclear Fuel Ensemble 9 Input Values 
Ensemble #9 Surface Area (cm2) Moles 
S31600 1180.5 514.6 
FBT (A516) 8684.5 170.2 
Stiffeners (A516) 135.1 9.8 
Normalization factor 4.9E-03 
Water Flux (mol/sec) 1.2E-10 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 36. Commercial Spent Nuclear Fuel Ensemble 10 Input Values 
Ensemble #10 Surface Area (cm2) Moles 
S31600 362.2 157.9 
FBT (A516) 2664.4 52.2 
Stiffeners (A516) 41.4 3.0 
CSNF 693.2 16.3 
Normalization factor 1.5E-03 
Water Flux (mol/sec) 3.8E-11 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 37. Commercial Spent Nuclear Fuel Ensemble 11 Input Values 
Ensemble #11 Surface Area (cm2) Moles 
Neutronit 760.4 30.2 
FBT (A516) 2554.3 50.1 
Stiffeners (A516) 39.7 2.9 
CSNF 664.6 15.6 
Normalization factor 1.4E-03 
Water Flux (mol/sec) 3.6E-11 
Source: CSNF.xls, worksheet WVC Ensemble EQ6 Inputs 
The following table contains the surface area and moles of each CDNR reactant for 100 percent 
N Reactor exposure and 50 percent N Reactor exposure (Section 6.3.2). For the CDNR water 
vapor condensation ensemble runs only the 50 percent fuel exposure values were used. The total 
surface area and total moles of each reactant were calculated in Attachment I CDNR.xls in the 
moles and surface areas worksheet. The surface area and volume for each component were 
calculated based on the geometry of the component as it is represented in Repository Design 
Project, Repository/PA IED Emplacement Drift Committed Materials 1 of 2 (BSC 2003c) and N 
Reactor (U-Metal) Fuel Characteristics for Disposal Criticality Analysis (DOE 2000b, 
Section 3). The moles of each component were calculated by multiplying the volume (cm3) of 
the component by its density (g/cm3) and dividing by 100 g/mole of material.

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Table 38. Codisposed N Reactor Surface Area and Mole Data Used to Generate the Inputs for the 
Multi-Component Ensemble Runs 
WP Component Total Surface Area (cm2) Total Moles 50% N Reactor SA (cm2) 50% N Reactor Moles 
S31600 Inner 
vessel 269167.1 131890.6 269167.1 131890.6 
Al-110 141782.2 806.9 141782.2 806.9 
304L MCO 487054.7 27003.7 487054.7 27003.7 
304L MCO top 36377.4 29855.9 36377.4 29855.9 
304L GPC 179201.6 14830.8 179201.6 14830.8 
A516 plates 334302.9 11457.1 334302.9 11457.1 
N Reactor 1639433.0 127455.1 819716.5 63727.5 
HLWG 3113959.3 57655.3 3113959.3 57655.3 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
Table 39. Codisposed N Reactor Ensemble 1 Input Values 
Ensemble #1 Surface Area (cm2) Moles 
S31600 Inner vessel 250.1 122.5 
Al-110 131.7 0.7 
304L MCO 452.5 25.1 
304L MCO top 33.8 27.7 
304L GPC 166.5 13.8 
A516 plates 310.6 10.6 
U-Metal 761.6 59.2 
HLWG 2893.2 53.6 
Normalization Factor 9.3E-04 
Water Flux (mol/sec) 2.3E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
Table 40. Codisposed N Reactor Ensemble 2 Input Values 
Ensemble #2 Surface Area (cm2) Moles 
Al-110 138.7 0.8 
304L MCO 476.3 26.4 
304L MCO top 35.6 29.2 
304L GPC 175.3 14.5 
A516 plates 327.0 11.2 
U-Metal 801.7 62.3 
HLWG 3045.5 56.4 
Normalization Factor 9.8E-04 
Water Flux (mol/sec) 2.4E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
NOTES:MCO = multi-canister overpack; 
GPC = glass-pour canister.

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Table 41. Codisposed N Reactor Ensemble 3 Input Values 
Ensemble #3 Surface Area (cm2) Moles 
304L MCO 489.9 27.2 
304L MCO top 36.6 30.0 
304L GPC 180.3 14.9 
A516 plates 336.3 11.5 
U-Metal 824.6 64.1 
HLWG 3132.4 58.0 
Normalization Factor 1.0E-03 
Water Flux (mol/sec) 2.5E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
Table 42. Codisposed N Reactor Ensemble 4 Input Values 
Ensemble #4 Surface Area (cm2) Moles 
Al-110 165.1 0.9 
304L MCO 567.3 31.5 
304L MCO top 42.4 34.8 
304L GPC 208.7 17.3 
A516 plates 389.4 13.3 
HLWG 3627.1 67.2 
Normalization Factor 1.2E-03 
Water Flux (mol/sec) 2.9E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
Table 43. Codisposed N Reactor Ensemble 5 Input Values 
Ensemble #5 Surface Area (cm2) Moles 
304L GPC 247.0 20.4 
A516 plates 460.8 15.8 
HLWG 4292.2 79.5 
Normalization Factor 1.4E-03 
Water Flux (mol/sec) 3.5E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
NOTE: GPC = glass-pour canister. 
Table 44. Codisposed N Reactor Ensemble 6 Input Values 
Ensemble #6 Surface Area (cm2) Moles 
304L GPC 272.1 22.5 
HLWG 4727.9 87.5 
Normalization Factor 1.5E-03 
Water Flux (mol/sec) 3.8E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
NOTE: GPC = glass-pour canister.

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Table 45. Codisposed N Reactor Ensemble 7 Input Values 
Ensemble #7 Surface Area (cm2) Moles 
A516 plates 484.7 16.6 
HLWG 4515.3 83.6 
Normalization Factor 1.5E-03 
Water Flux (mol/sec) 3.6E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
Table 46. Codisposed N Reactor Ensemble 8 Input Values 
Ensemble #8 Surface Area (cm2) Moles 
Al-110 217.7 1.2 
HLWG 4782.3 88.5 
Normalization Factor 1.5E-03 
Water Flux (mol/sec) 3.8E-11 
Source: CDNR.xls, worksheet WVC Ensemble EQ6 Inputs 
6.5.4 Seepage Dripping Model Inputs 
For the CSNF seepage dripping model inputs for three CSNF exposure values, 100, 10, and 1 
percent, were generated, i.e., a scaling factor was calculated for each fuel exposure value and the 
reactants surface areas and moles were scaled accordingly. Also scaled were the three values of 
the water flux used in the simulations. The unscaled CSNF inputs for the seepage dripping 
model are listed in Tables 47 and 48. 
Table 47. Commercial Spent Nuclear Fuel Unscaled Surface Areas and Normalization Factors 
WP Component 100% Fuel Surface Area 
(cm2) 
10% Fuel Surface Area 
(cm2) 
1% Fuel Surface Area 
(cm2) 
S31600 241,598.779 241,598.779 241,598.779 
NEUTRONIT 529,130.560 529,130.560 529,130.560 
Al-6061 258,957.440 258,957.440 258,957.440 
FBT (A516) 1,777,406.400 1,777,406.400 1,777,406.400 
Stiffeners (A516) 27,646.044 27,646.044 27,646.044 
CSNF 4,624,211.996 462,421.200 46,242.120 
Total: 7,458,951.219 3,297,160.423 2,880,981.344 
Film thickness (cm) Normalization Factor 
2.50E-01 5.363E-04 1.213E-03 1.388E-03 
Source: CSNF.xls, worksheet SDM EQ6 Inputs.

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Table 48. Commercial Spent Nuclear Fuel Unscaled Reactant Moles 
WP Component 100% Fuel Moles 10% Fuel Moles 1% Fuel Moles 
S31600 105,313.843 105,313.843 105,313.843 
NEUTRONIT 20,983.042 20,983.042 20,983.042 
Al-6061 3,419.418 3,419.418 3,419.418 
FBT (A516) 34,843.520 34,843.520 34,843.520 
Stiffeners (A516) 2,011.330 2,011.330 2,011.330 
CSNF 108,431.784 10,843.178 1,084.318 
Source: CSNF.xls, worksheet SDM EQ6 Inputs. 
The values in Table 49 are for a 0.25 cm thick film and three (100, 10, and 1 percent) fuel 
exposure values, and are used because they best represent the 0.25 cm film conceptualization. 
Table 49. Scaled EQ6 Inputs, Commercial Spent Nuclear Fuel Surface Areas, and Reactant 
Moles for 0.25-cm Thick Film at Three Fuel Exposure Values 
Fuel Exposure 100% 10% 1% 
WP Material SA (cm2) Moles SA (cm2) Moles SA (cm2) Moles 
FBT (A516) 953.167 18.685 2156.287 42.271 2467.779 48.377 
Stiffeners (A516) 14.826 1.079 33.539 2.440 38.384 2.793 
NEUTRONIT 283.756 11.253 641.923 25.456 734.653 29.133 
Al-6061 138.871 1.834 314.158 4.148 359.541 4.748 
S31600 129.562 56.476 293.099 127.763 335.440 146.219 
CSNF 2479.819 58.149 560.993 13.155 64.203 1.505 
Source: CSNF.xls, worksheet SDM EQ6 Inputs. 
NOTE: SA = surface area; WP = waste package. 
Table 50 provides the water flux values for three values of fuel exposure (clad failure). 
Table 50. EQ6 Input Water Flux Values for the Commercial Spent Nuclear Fuel Seepage Dripping Model 
Flux(l/yr) 0.15 (l/yr) 1.5 (l/yr) 15 (l/yr) 
Fuel Exposure(%) (mol/sec) (mol/sec) (mol/sec) 
100 2.5490E-12 2.5490E-11 2.5490E-10 
10 5.7664E-12 5.7664E-11 5.7664E-10 
1 6.5994E-12 6.5994E-11 6.5994E-10 
Source: CSNF.xls, worksheet SDM EQ6 Inputs. 
The CDNR seepage dripping model inputs are given in Tables 51 to 53. 
Table 51. EQ6 Input Water Flux Values for the Codisposed N Reactor Seepage Dripping Model 
Water Flux (l/yr) Normalized Water Flux (mol/sec) 
0.15 2.190E-12 
1.5 2.190E-11 
15.0 2.190E-10 
Source: Attachment I, CDNR.xls, worksheet SDM EQ6 Inputs

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Table 52. Codisposed N Reactor Unscaled Surface Areas and Moles and Normalization Factor 
Waste Package 
Component Surface Area (cm2) Moles 
A516 plates 334302.863 11457.056 
304L MCO 487054.703 27003.677 
304L MCO top 36377.355 29855.874 
304L GPC 179201.621 14830.838 
S31600 Inner vessel 269167.135 131890.622 
Al-110 141782.216 806.883 
U-Metal 1639433.037 127455.067 
HLWG 3113959.289 57655.304 
Total 6201278.220 
Film Thickness (cm) 0.35 
Normalization Factor 4.607E-04 
Source: CDNR.xls, worksheet SDM EQ6 Inputs. 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
Table 53. EQ6 Inputs, Codisposed N Reactor Surface Areas, and Reactant Moles for 0.35-cm Thick Film 
Waste Package 
Component Normalized Surface Area (cm2) Normalized Moles 
A516 plates 154.025 5.279 
304L MCO 224.403 12.442 
304L MCO top 16.760 13.756 
304L GPC 82.564 6.833 
S31600 Inner 
vessel 124.015 60.767 
Al-110 65.324 0.372 
U-Metal 755.343 58.723 
HLWG 1434.708 26.564 
Source: CDNR.xls, worksheet SDM EQ6 Inputs. 
NOTES: MCO = multi-canister overpack; GPC = glass-pour canister. 
The water compositions listed in Table 2 were input into EQ3NR and EQ3NR was executed to 
preprocess the water compositions and output an EQ3NR pickup (*.3p) file which is used as 
an integral part of each EQ6 input file. EQ3NR recasts the reported concentrations from Table 2 
into molal units, which are the working units of EQ6. For the J-13 composition the HCO3
- 
concentration was set to as the charge balance constraint in the EQ3 input file. 
Table 54 provides the EQ3NR output molal concentrations of the elements used in the EQ6 input 
files. All of the elements with concentrations of 1.00E-16 molal were included as a necessary 
input in the EQ3NR input files because these elements occur in either the waste package 
component compositions or the waste form component compositions.

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Table 54. EQ6 Input Fluid Compositions 
Constituent 
(molal) 
Ca- 
Porewatera Na-Porewaterb J13c Pure Waterd 
Ionic strength 0.011 0.012 0.0035 N/A 
pH 7.6 7.4 7.0 5.4 
O 5.55E+01 5.55E+01 5.55E+01 5.55E+01 
Al 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
B 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Ba 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Ca 2.35E-03 2.02E-03 3.24E-04 1.00E-16 
Cl 5.92E-04 6.77E-04 2.01E-04 1.00E-16 
Co 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Cr 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Cs 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Cu 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
F 1.79E-04 3.16E-04 1.15E-04 1.00E-16 
Fe 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Gd 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
H 1.11E+02 1.11E+02 1.11E+02 1.11E+02 
C 6.76E-03 8.62E-03 2.49E-03 3.79E-05 
P 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
I 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
K 1.94E-04 1.56E-04 1.29E-04 1.00E-16 
Li 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Mg 7.45E-04 1.36E-04 8.27E-05 1.00E-16 
Mn 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Mo 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
N 4.19E-05 6.61E-06 1.42E-04 1.00E-16 
Na 1.70E-03 5.22E-03 1.99E-03 1.00E-16 
Ni 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Np 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Pb 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Pu 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Ru 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
S 3.75E-04 3.23E-04 1.92E-04 1.00E-16 
Si 6.99E-04 6.99E-04 1.02E-03 1.00E-16 
Tc 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Ti 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
U 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Zn 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Zr 1.00E-16 1.00E-16 1.00E-16 1.00E-16 
Sources: aEQ3NR pickup file Ca_trc.3p (Attachment I) 
bEQ3NR pickup file Na_trc.3p (Attachment I) 
c EQ3NR pickup file J13_trc.3p (Attachment I) 
dEQ3NR pickup file pure_trc.3p (Attachment I)

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6.5.5 Mineral Controls 
In the in-package chemistry model the minerals that precipitate from solution during an EQ6 run 
are determined by the code via consideration of the thermodynamics of the chemical system. 
Based on the modeled conditions, certain minerals will precipitate from solution and others will 
not. In using the EQ6 software the analyst must decide, on the basis of prior knowledge related 
to the phenomena, which mineral phases will not be allowed to form in a simulation; this is 
called mineral suppression. Table 55 lists the phases suppressed in the EQ6 runs. By deciding 
which minerals to suppress, the analyst is implicitly deciding which minerals will form. Often 
trial runs are performed so that the analyst can see first hand what minerals are forming and what 
minerals should be suppressed. By suppressing a phase, a decision is made based on knowledge 
of a phases mode of occurrence and the relative kinetics of formation. For example, amorphous 
silica is more soluble than quartz at low temperatures, hence, quartz reaches saturation first; 
however, low-temperature quartz formation is not observed. Therefore, the analyst would 
suppress quartz formation for a low-temperature simulation. 
Table 56 provides a list of the major minerals, which were not suppressed and were observed to 
precipitate in the EQ6 runs for the in-package chemistry model. Phases in abundance in greater 
than 10-4 moles over the 20,000 year model period are included in the list. 
For certain simulations, the EQ6 code was not able to converge to a solution because of 
difficulties related to finding a stable mineral assemblage for the particular chemical conditions 
of the simulation. In these cases EQ6 would give an error stating that convergence was not 
possible because a phase rule violation occurred, and EQ6 would list the mineral phases involved 
in the violation. Suppressing one of the suspect phases was used as a means of getting the EQ6 
to converge. 
In the following runs, C13N50.6i, C23N50.6i, and C33N50.6i, the uranium phosphate phase 
(UO2)3(PO4)2:6H2O was suppressed and in its place (UO2)3(PO4)2:4H2O formed; thus, the effect 
on the chemistry was negligible. In run CD_Comp6.6i the phase bunsenite (NiO) was 
suppressed because of a phase rule violation and in its place trevorite (NiFe2O4) formed, another 
phase that had an inconsequential impact on the chemistry. In all of the runs the phase 
PuO2(aged, hyd) was suppressed to aid code convergence. No other plutonium phase 
precipitated in its place. Although plutonium is important from a dose perspective, it has no 
impact on the pH of the system because of its relatively low concentration compared to the major 
elements (Ca, Mg, Na, K, Cl, S, and C).

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Table 55. Minerals Suppressed in In-Package Chemistry Model EQ6 Runs 
Mineral Chemical Formula Justification 
Quartz SiO2 Quartz is formed at higher temperatures (around 573C), but its extremely simple elemental makeup makes it resistant 
to corrosion, allowing it to exist widespread as a detrital fragment in many sedimentary rocks, which is why it has been 
noted in so many sedimentary deposits. However, other forms of SiO2 can form as primary sedimentary minerals at low 
temperatures (such as chalcedony) (Klein and Hurlbut 1985, pp. 441 to 442). Therefore, the mineral quartz has been 
suppressed in the EQ6 runs, allowing the lower temperature varieties of SiO2 to form. 
Goethite a-FeOOH Goethite (a-FeOOH) and hematite (a-Fe2O3) are the two most thermodynamically stable Fe minerals under oxidizing 
conditions (Schwertmann and Taylor 1995). Goethite was suppressed in EQ6 runs of the IPCM, however, suppressing 
goethite had no affect on the results of the IPCM since hematite is more thermodynamically stable than goethite, and 
EQ6 would not allow goethite to form unless hematite was suppressed. 
Tridymite SiO2 Tridymite exists as both a and  types. The low temperature a-tridymite forms only from pre-existing -tridymite, which 
forms in the temperature range of 870C to 1470C (Roberts et al. 1990, pp. 881 to 882). 
Muscovite Kal2 (Si3Al)O10(OH,F) 2 Occurs in High T and/or P mineral assemblages and as a detrital mineral in sedimentary rocks (Roberts et al. 1990, p. 
586). 
Celadonite K(Mg,Fe+2)(Fe+3,Al)Si4O10(OH)2 Celadonite is found in altered volcanic rocks (Roberts et al. 1990, p. 149. Celadonite can form at low temperatures, but 
this is due to either diagenesis of pre-existing material, low grade metamorphic processes, or concentration of the liquid 
environment through evaporation (Li et al. 1997). 
Annite KFe3
+2AlSi3O10(OH,F) 2 Synthesized, natural occurrence uncertain (Roberts et al. 1990, p. 32) 
Dolomite Dolomite is usually derived by secondary mineralization, from the replacement of Ca for Mg in the calcite crystal 
structure in Mg rich waters (Klein and Hurlbut 1985, p. 340). Due to the fact that it rarely occurs as a primary mineral, it 
was also suppressed. 
Andradite Andradite is a high P/T mineral found in metamorphic and igneous rocks (Deer et al. 1966, p. 30). 
Phlogopite KMg3AlSi3O10(OH,F) 2 Occurs chiefly in metamorphic limestones and ultrabasic rocks (Roberts et al. 1990, p. 671) 
Talc Mg3Si4O10(OH)2 Talc is characteristically associated with low-grade metamorphic rock and hydrothermal alteration of ultrabasic rocks 
(Kerr 1977, p. 450). 
Ferrite-Ca 
Ferrite-Mg 
CaFe2O4 
MgFe2O4 
Magnesioferrite has been found in sintered magnesite of furnace linings and other refractories (Palache et al. 1944, p. 
705), not expected to form at low T. 
Zircon ZrSiO4 High P/T mineral. Found in sedimentary deposits as a detrital mineral. (Roberts et al. 1990, p. 975) 
PuO2 PuO2 The solubilities of solid Pu(IV) oxide/hydroxide scatter within several orders of magnitude because of the difficulties of 
establishing equilibrium of Pu(IV), polymerization and disproportionation reactions and the strong sorption capacities of 
Pu4+ (Runde 1999). Experimental Pu solution concentrations during PuO2 or PWR SNF degradation have been shown 
to be between the solubility of PuO2 and that of a more soluble phase, Pu(OH)4 (or PuO22H2O) (Rai and Ryan 1982; 
Wilson and Bruton 1989, Section 3.1 and Table 3). 
Soddyite (UO2)2SiO4:2H2O Suppression of soddyite had no effect on the results of this model report, since this mineral is fairly soluble under the 
conditions simulated by the IPCM, and it would not have precipitated in any of the EQ6 runs even if its formation had 
not been suppressed. 
NOTE: P = Pressure; T = Temperature; IPCM = in-package chemistry model.

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Table 56. Major Mineral Phases Formed in EQ6 Runs 
Mineral Chemical Formula Justification 
Hematite a-Fe2O3 Goethite (a-FeOOH) and hematite (a-Fe2O3) are the two most thermodynamically stable and most widespread Fe minerals 
under oxidizing conditions. Hematite is usually found in tropical and subtropical regions where higher temperatures and lower 
water activities aid in its formation. Hematite needs a precursor such as ferrihydrite from which it forms through dehydration and 
rearrangement. (Schwertmann and Taylor 1995). 
Pyrolusite MnO2 Secondary mineral forming from alteration of manganite or other manganese-bearing minerals. (Roberts et al. 1990, p. 704). 
[Manganite forms as a low temperature hydrothermal vein mineral; in circulating meteoric water; or bog, lacustrine, or shallow 
marine deposits. (Roberts et al. 1990, p. 524)]. Found as a secondary mineral in the oxidation zone at the Morro do Ferro site in 
Brazil (Waber 1991). Formed in the system Mn-O2-H2O at 25C and one atmosphere (Bricker 1965, Table 1) 
Trevorite NiFe2O4 High P/T mineral (Roberts et al. 1990, p. 881). Trevorite was allowed to form since Ni-substituted goethite, hematite and 
NiFe2O4 can be synthesized at low P/T (Cornell et al. 1992), and Ni-substituted iron oxides are not in the EQ6 database. 
Powellite Ca(Mo,W)O4 Occurs as a secondary mineral in the oxidation zones of ore deposits (Roberts et al. 1990, p. 692). Secondary mineral, often by 
the alteration of molybdenite, in copper deposits, deposits encountering contact metamorphism (Palache et al. 1951, p. 1080) 
Tenorite CuO Occurs chiefly in the oxidation zone of copper deposits, often associated with other secondary minerals. Also occurs as a 
sublimation product deposited on lavas in volcanic regions (Roberts et al. 1990, p. 856; Palache et al. 1944, p. 509). CuO is a 
component of patina formed in atmospheric and aqueous corrosion of copper alloys, though not specifically given the mineral 
name tenorite (Sequeira 2000) 
Schoepite UO3:2H2O Alteration product of uraninite (UO2); associated with bequerelite, curite and other secondary minerals of U (Palache et al. 
1944, p. 628) 
Gibbsite Al(OH)3 Most common Al(OH)3 polymorph (Hsu 1995). AlOOH are rarer than hydroxides and are considered the product of weathering 
(Allen and Hajek 1995) so the Al(OH)3 polymorphs will be the primary ones expected to form in the WP. 
Kaolinite Al2Si2O5(OH) 4 Most common kaolin, formation at 25C is usually slow, however it can crystallize easily from the alteration of smectites (Dixon 
1995) 
Nontronite Na0.33Fe2 
(Si,Al)4O10(OH)2:nH2O 
One of the three most common smectite minerals found in soils. Smectites are common in temperate and cold climates, but 
may form or be preserved in tropical environments where leaching is limited or drainage is restricted (Allen and Hajek 1995). 
GdPO4 
: 10 H2O 
GdPO4:10 H2O Gadolinium phosphate hydrates are insoluble in near-neutral pH solutions (Firsching and Brune 1991). 
(UO2)3(PO4)2 
: XH2O 
(UO2)3(PO4)2:XH2O X = 4 or 6. (UO2)3(PO4) 2:4H2O is a stable phase under oxidizing conditions, low temperature and pH values relevant for natural 
water systems (Sandino 1991, pp. 16 to 17). (UO2)3(PO4) 2:6H2O was also allowed to form since uranyl phosphates are 
associated with a wide range of weathered U deposits (Finch and Murakami 1999), but few uranyl phosphates are included in 
the EQ6 database. 
Baddeleyite ZrO2 High P/T mineral. Found in sedimentary deposits as a detrital mineral. (Roberts et al. 1990, p. 62) also formed in metamict 
zircon (Deer et al. 1966, p. 15), Baddeleyite was allowed to form in the waste package since hydrolysis of Zr salts leads to 
precipitation of poorly crystalline oxides at low temperatures (Milnes and Fitzpatrick 1995, pp. 1189 to 1190) and soluble Zr may 
be incorporated in or sorb on clay mineral surfaces (Milnes and Fitzpatrick 1995, pp. 1185 to 1186). So, it is likely that some of 
the Zr released by degradation of WP components will precipitate or be sorbed from solution. 
NOTE: P = Pressure; T = Temperature.

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6.6 MODEL OUTPUT 
6.6.1 Commercial Spent Nuclear Fuel Output 
6.6.1.1 Water Vapor Condensation Model Results 
Figure 1 displays the results of the CSNF single component simulations. The pH profiles in 
Figure 1 are plotted as a function of reaction progress, the EQ6 zi (.) variable, to avoid 
confusion as the profiles are not to be interpreted as time functions. This figure provides 
information on how each waste package component contributes to the in-package pH and shows 
the upper and lower pH limits for a CSNF package. 
0.0 
1.0 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 
Reaction Progress (moles) 
pH 
A516 
S31600 
Al-6061 
CSNF 
Neutronit 
Source: Microsoft Excel Spreadsheet CSNF_WVC.xls in DTN: MO0307SPAIPCHM.001. 
Figure 1. Commercial Spent Nuclear Fuel Single Component Water Vapor Condensation pH Outputs 
The low pH resulting from A516 carbon steel corrosion is due to the combination of oxidation of 
elemental sulfur, the large quantity of A516, and its high corrosion rate relative to the other 
waste package components. Both the Al-6061 and the CSNF have neutral to slightly basic pH 
profiles, while the S31600 and Neutronit have slightly acidic pH profiles. The reasons the pH 
profiles are not constant is because the starting pH of the solution differs from the pseudo 
equilibrium pH of the alloy-water system, and, secondly, since the reactants are kinetic reactants 
their dissolution is irreversible, they can only dissolve and not re-precipitate, thus the alloy-water 
system is in a constant state of change as the solution chemistry and solid assemblage evolves. 
The pH profiles of the water vapor condensation multi-component ensemble runs are displayed 
in Figure 2. Comparison of Figures 1 and 2 reveals that pH range exhibited by the single 
component runs (Figure 1) is also covered by the multi-component runs. The similar pH range 
of the two groups of simulations illustrates that the pH is dominated by the reactants and not 
necessarily by the conceptual model. This builds confidence in the robustness of the model.

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The most acid-producing profiles include, in Figure 1, that for A516, and in Figure 2, the comp8 
and comp9 profiles. The low pH observed in the comp8 and comp9 profiles is due to the lack of 
a buffering reactant in the ensemble. Comp8 is composed of A516 and Neutronit, while comp9 
is composed of A516 and S31600, all of which are acid producing reactants (Figure 1). 
The comp3 and comp7 ensembles both contain Al-6061 and A516, and comp3 also contains 
CSNF. For these two ensembles the early-time pH (<100 years) was controlled by A516 
dissolution, the middle period (100 to 1000 years) by Al-6061 dissolution, and for greater than 
1000 years the pH was controlled by equilibrium with the corrosion products. The CSNF in 
comp3 had a minimal effect on the system pH. 
1.0 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
comp1 comp2 
comp3 comp4 
comp5 comp6 
comp7 comp8 
comp9 comp10 
comp11 
Source: Microsoft Excel Spreadsheet CS_comp_runs.xls in DTN: MO0307SPAIPCHM.001. 
Figure 2. Commercial Spent Nuclear Fuel Multi-Component Ensemble pH Profiles 
It must be stated that, for the purpose of total system performance assessment, only reactant 
combinations that include CSNF will be used in the generation of pH abstractions for the colloid, 
solubility, and waste form dissolution sub-models. The reason is that CSNF influences the 
chemistry and it would be an error to not account for its effect on the chemistry. Therefore, 
simulations comp5 through comp9, which do not include CSNF as part of the ensemble, are not 
included in the in-package chemistry model abstraction. 
Furthermore, Figure 2 shows that, with the exception of comp8 and 9, the pH trends are similar 
in shape and magnitude. These two observations build confidence in the robustness of the 
conceptual model compared to Alternate Conceptual Model I (Section 6.4.1). Had the pH 
profiles shown a great degree of variability it would indicate that the in-package chemistry is 
highly path dependent, thus decreasing confidence in the robustness of the water vapor 
condensation model.

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ANL-EBS-MD-000037 REV 02 68 of 140 July 2003 
In the CSNF water vapor condensation model there are no fluoride or chloride abstractions 
because these elements are neither a constituent to the water vapor nor any of the waste package 
components. 
The ensemble simulations and the resulting chemistry of the reacted solutions reflect interactions 
between CSNF, waste package components, the liquid phase water, and the atmosphere in the 
voids in the waste package and drift. In addition, the reactants are kinetic reactants, the metal 
alloys undergo oxidation and precipitate corrosion products that are in contact with the liquid 
phase water thereby having a first order influence on the chemistry. Because the in-package 
chemistry model includes all of these processes, so does the in-package chemistry model 
abstraction and, hence, they are included in TSPA-LA. The following FEPs are addressed by the 
CSNF water vapor condensation model: 
 2.1.09.01.0B Chemical characteristics of water in the waste package 
 2.1.09.07.0A Reaction kinetics in EBS 
 2.1.02.09.0A Chemical effects of void space in waste package 
 2.1.09.02.0A Chemical interaction with corrosion products 
6.6.1.2 Seepage Dripping Model Results 
The pH history and reactant summary for the median flux and fuel exposure seepage dripping 
model case at 25C using the three different seepage compositions are displayed in Figure 3. 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
10.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E+02 
1.0E+03
Moles Reactant 
pH C22C25B pH C22J25B pH C22N25B 
A516 FBT A516 Stiffeners Al-6061 
CSNF Fuel Neutronit S31600 
DTN: MO0307SPAIPCHM.001 
Figure 3. Commercial Spent Nuclear Fuel Seepage Dripping Model Results for Q = 1.5 l/yr, Fuel 
Exposure = 10 Percent, and Three Seepage Compositions at 25C 
An important feature of Figure 3 is the fact that the composition of the seepage (Table 2) has 
virtually no effect on the pH profile. The separation in the early-time (less than approximately

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 69 of 140 July 2003 
0.5 years after waste package breach) pH profiles is a function of the seepage chemistry, 
however, this difference dissipates in approximately 10 years and the pH profiles become nearly 
identical. This addresses FEP 2.2.08.12.0A Chemistry of water flowing into the EBS. Thus, the 
composition of the water flowing into the EBS has negligible impact on the in-package 
chemistry pH. However, the in-package chemistry model and, thus, the TSPA-LA feed are 
developed using the three seepage compositions listed in Table 2; therefore, the effect of the 
chemistry of the water flowing into the EBS is included in the feed to total system performance 
assessment, although there is no parameter for the composition of the water entering the waste 
package. This also addresses the robustness of the in-package chemistry model with respect to 
Alternate Conceptual Model II. Had the seepage dripping model been sensitive to the input 
water composition then an alternate conceptual model with time-varying composition would be 
needed. This, however, was not the case, and the constant seepage assumption is justified. 
Further examination of Figure 3 shows that the reactants dissolve as a function of time, i.e., they 
are kinetic reactants (Tables 15 and 17) and their effect on the in-package chemistry is explicitly 
modeled in the in-package chemistry model and thus is carried over to the feed to TSPA-LA. 
Therefore, FEP 2.1.09.07.0A, Reaction kinetics in EBS, is addressed by the in-package 
chemistry model and the in-package chemistry model abstraction and thus included in total 
system performance assessment. 
Figure 4 displays the Eh (a measure of the redox potential), reactant summary, and oxidation 
products for run C22C25, the median flux and fuel exposure simulation for the Ca-porewater at 
25C. The Eh varies between approximately 0.8 to 1.0 volts, these are oxidizing conditions as 
the presence of metal oxide phases also indicate. Since the metal oxide corrosion products are 
precipitating from the solution that is in contact with the waste form and waste package 
components the corrosion products have a first order influence on the in-package chemistry. 
FEPs 2.1.09.02.0A, Chemical interaction with corrosion products, and FEP 2.1.09.06.0A, 
Reduction-oxidation potential in EBS, are addressed by the in-package chemistry model. The 
implicit inclusion of these FEPs in the in-package chemistry model, and the in-package 
chemistry abstraction provides confidence that these processes are included in TSPA-LA. 
Figure 5 displays mineral phases that precipitated from solution for the C22 runs at 25C for the 
three input water compositions. Hematite, gibbsite, schoepite, pyrolusite, and trevorite constitute 
the five most abundant phases, all of which are expected to form in the oxidizing waste package 
environment. The large amount of gibbsite (Al(OH)3) that formed in these runs is due to the 
relatively low amount of available silica in the incoming waters and waste package components. 
Had more silica been available, then greater quantities of aluminum-silicate phases would have 
formed with proportionately less gibbsite formation. The silica phases that do form include 
kaolinite, nontronite-Mg, and nontronite-Ca, all of which are clay minerals. It is interesting to 
note that the J-13 run produced more kaolinite than the other two water compositions, the reason 
for this being that J-13 has a higher initial silica concentration (Table 2). 
The mineral assemblage plot (Figure 5) provides further information in support of the conclusion 
that the composition of the seepage has little influence over which minerals formed and their 
amounts. The J-13 minerals show some variability compared to the other two water 
compositions, however, the major phases are all present in nearly identical amounts.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 70 of 140 July 2003 
0.0E+00 
2.0E-01 
4.0E-01 
6.0E-01 
8.0E-01 
1.0E+00 
1.2E+00 
1.4E+00 
1.6E+00 
1.8E+00 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Eh (volts) 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E+02 
1.0E+03
Moles 
Eh C22C25B A516 FBT 
A516 Stiffeners Al-6061 
CSNF Fuel Neutronit 
S31600 hematite 
pyrolusite gibbsite 
trevorite 
DTN: MO0307SPAIPCHM.001 
Figure 4. Commercial Spent Nuclear Fuel Seepage Dripping Model C22C25 Results at 25C, 
Showing Eh, Reactant Summary, and Corrosion Products 
1.E-04 
1.E-03 
1.E-02 
1.E-01 
1.E+00 
1.E+01 
1.E+02 
1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 
Time (years) 
Minerals (moles) 
c22c25bH c22j25bH c22n25bH c22c25bG c22j25bG c22n25bG c22c25bS c22j25bS 
c22n25bS c22c25bPy c22j25bPy c22n25bPy c22c25bT c22j25bT c22n25bT c22c25bNMg 
c22j25bNMg c22n25bNMg c22c25bNCa c22j25bNCa c22n25bNCa c22c25bP c22j25bP c22n25bP 
c22c25bK c22j25bK c22n25bK c22c25bU c22j25bU c22n25bU c22c25bZr c22j25bZr 
c22n25bZr c22c25bGd c22j25bGd c22n25bGd 
H
G
S 
Py 
K 
K (J-13) 
T
NMg 
Gd 
P 
P (J-13) NCa 
G
NCa 
Zr 
U 
NMg 
NMg 
G 
Source: Microsoft Excel Spreadsheet CSNF_Plots.xls in DTN: MO0307SPAIPCHM.001. 
NOTES: H=hematite, G=gibbsite, S=Schoepite, Py=pyrolusite, T=trevorite, NMg=nontronite-Mg 
NCa=nontronite-Ca, K= kaolinite, P=powellite, Gd=GdPO4:10H2O, U=(UO2)3(PO4)2:6H2O, 
Zr=Baddeleyite 
Figure 5. Commercial Spent Nuclear Fuel Seepage Dripping Model Mineral phases for the 
Three Seepage Compositions at 25C, Q = 1.5 l/yr, fuel exposure = 10%

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 71 of 140 July 2003 
Figure 6 displays aqueous species for the C22 runs at 25C for the three input water 
compositions. This plot shows that elements which were not present in the initial water 
compositions (Table 2). In other words, those elements that originate from the waste package 
and SNF have aqueous species concentrations that are nearly identical, and independent of the 
initial water composition, for the duration of the simulations. Examples of these elements 
include Ni, Cr, and B, all of which originate from oxidation of the metal waste package 
components. Elements which were present in the initial water composition and were also 
involved in reactions in the waste package also have similar aqueous species concentrations. For 
example, sulfur which was present in all of the water compositions as sulfate (SO4
2-) shows 
similar concentration trends in the simulations, independent of the starting water composition, 
due to the sulfur contained in the A516, which dominates the sulfate concentration trends 
observed in Figure 6. At times great than about 5000 years, the sulfur concentration trends for 
the three waters shows some separation, which is due to the sulfate concentration returning to 
their initial values. 
Figures 5 and 6 show that depending on the initial fluid composition, there is little variability in 
the minerals and aqueous species of the reacted fluid chemistry that may potentially exit a failed 
waste package. However, this variability is slight, and the pH and ionic strength of the reacted 
solutions is unaffected by these variations, and thus variability of the input water compositions 
should not impact TSPA-LA. 
1.E-05 
1.E-04 
1.E-03 
1.E-02 
1.E-01 
1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 
Time (years) 
Major Aqueous Species (molality) 
c22c25b_HCO3 c22j25b_HCO3 c22n25b_HCO3 c22c25b_Na c22j25b_Na c22n25b_Na 
c22c25b_Cl c22j25b_Cl c22n25b_Cl c22c25b_Ca c22j25b_Ca c22n25b_Ca 
c22c25b_SO4 c22j25b_SO4 c22n25b_SO4 c22c25b_SiO2 c22j25b_SiO2 c22n25b_SiO2 
c22c25b_Mg c22j25b_Mg c22n25b_Mg c22c25b_K c22j25b_K c22n25b_K 
c22c25b_F c22j25b_F c22n25b_F c22c25b_Ni c22j25b_Ni c22n25b_Ni 
c22c25b_Cr2O7 c22j25b_Cr2O7 c22n25b_Cr2O7 c22c25b_B(OH)3 c22j25b_B(OH)3 c22n25b_B(OH)3 
B(OH)3(aq) 
Cr2O7-- 
Ni2+ 
Mg2+ 
SO42- 
Na+ 
Na+ 
Cl- 
Cl- 
Cl- 
Ca2+ 
Ca2+ 
Ca2+ 
SiO2(aq) 
HCO3 - 
HCO3 - 
HCO3 - 
HCO3 - 
K+ K+ 
FF- 
FK+ 
Mg2+ 
Mg2+ 
SO4
2- 
Source: Microsoft Excel Spreadsheet CSNF_Plots.xls in DTN: MO0307SPAIPCHM.001. 
Figure 6. Commercial Spent Nuclear Fuel Seepage Dripping Model Aqueous Species for the 
Three Seepage Compositions at 25C, Q = 1.5 l/yr, fuel exposure = 10%

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 72 of 140 July 2003 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E+02 
1.0E+03
Moles pH C22C50 pH C22J50 
pH C22N50 A516 FBT 
A516 Guides Neutronit 
Al-6061 CSNF 
S31600 
DTN: MO0307SPAIPCHM.001 
Figure 7. Commercial Spent Nuclear Fuel Seepage Dripping Model Results for Q = 1.5 l/yr, 
Fuel Exposure = 10 Percent, and Three Seepage Compositions at 50C 
Figure 7 shows a plot of pH and reactant summary for the median seepage flux and fuel exposure 
values at 50C for the three input seepage compositions. Like Figure 3 that shows the same 
simulations but at 25C, the initial composition of the seepage entering the waste package has 
little impact on the composition of the reacted fluid composition as the contents of the waste 
package control the chemistry. Compared to the simulations performed at 25C, the 50C 
simulations use a CSNF degradation rate that is temperature appropriate (Table 17), while the 
metal alloy corrosion rates are the same as the 25C runs (Section 5.2.5). Also, the 
thermodynamic database includes mineral and aqueous species data for 50C, therefore the 
reacted solution chemistry and precipitated mineral phases are temperature appropriate. 
The 50C runs achieve an overall lower pH profile at long time periods and slight variations in 
the stable mineral assemblages compared with their 25C counterparts. Since a suite of 
simulations was carried out at above ambient temperatures and the in-package chemistry model 
results reflect the increased thermal conditions, the TSPA-LA feed also reflects the increased 
thermal conditions, which means that the FEP 2.1.11.08.0A, Thermal effects on chemistry and 
microbial activity in the EBS, has been partially addressed. The abstraction of in-package 
chemistry provides a separate, i.e., different than the 25C abstractions, set of abstractions at 
50C for TSPA-LA, as well as a means of linking the two abstractions

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 73 of 140 July 2003 
6.6.2 Codisposed N Reactor Output 
6.6.2.1 Water Vapor Condensation Output 
Figure 8 displays the pH profiles for the CDNR waste package components. The CDNR 
package displays a much wider variation in pH compared to the CSNF package (Figure 1). The 
HLW generates high pH conditions due the high concentrations of sodium and potassium. The 
Al-1100, S31600, and N Reactor fall in the middle of the pH range at approximately 5.7 to 6.2. 
The A516 and 304L steels are strong acid generators at pH of 1.5 to 2. 
The results of the CDNR multi-component ensemble runs are displayed in Figure 9. The low 
end of the pH range in Figure 8 is not reproduced in the multi-component results (Figure 9). The 
ensembles include either HLWG or N Reactor fuel as a reactant because the TSPA-LA model for 
in-package chemistry does not take cladding credit for N reactor fuel or the glass pour canisters. 
Therefore, the low pH generated by 304L and A516 (Figure 8) is not observed in the ensemble 
runs (Figure 9) because of the pH buffering capacity that HLWG and N Reactor fuel offer. 
0.0 
2.0 
4.0 
6.0 
8.0 
10.0 
12.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 
Reaction Progress (moles) 
pH 
A516 
S31600 
Al-1100 
HLW 
304L 
N reactor 
Source: Microsoft Excel Spreadsheet CDNR_WVC.xls in DTN: MO0307SPAIPCHM.001. 
Figure 8. Codisposed N Reactor Single-Component Water Vapor Condensation Results

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 74 of 140 July 2003 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
10.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
comp1 comp2 
comp3 comp4 
comp5 comp6 
comp7 comp8 
Source: Microsoft Excel Files CD_comp_runs.xls and CD_COMP_WVC.xls in DTN: MO0307SPAIPCHM.001. 
Figure 9. Codisposed N Reactor Multi-Component Ensemble pH Profiles 
The ensemble simulations comp1, comp2, and comp3 include both HLWG and N Reactor fuels 
as reactants, thus, the resulting chemistry of the reacted solutions (pH . 6) reflects interactions 
between the co-disposed waste, the non-SNF waste package components, the liquid phase water, 
and the atmosphere in the voids in the waste package and drift. In addition, the fuel and metal 
alloys are kinetic reactants, which undergo oxidation and precipitate corrosion products that are 
in contact with the liquid phase water, thereby having a first order influence on the chemistry. 
Since the in-package chemistry model and in-package chemistry model abstraction include all of 
these processes, the processes are also included in TSPA-LA. The following FEPs are addressed 
by the CDNR water vapor condensation model: 
 2.1.09.01.0B Chemical characteristics of water in the waste package 
 2.1.09.07.0A Reaction kinetics in EBS 
 2.2.08.12.0A Chemistry of water flowing into the EBS 
 2.1.02.09.0A Chemical effects of void space in waste package 
 2.1.01.02.0B Interactions between co-disposed waste 
 2.1.09.06.0A Reduction  oxidation potential in EBS 
 2.1.09.02.0A Chemical interaction with corrosion products. 
High pH conditions are only realized in the comp7 and comp8 simulations in which HLWG is 
combined with A516 and Al-1100, respectively. In these two cases, the period of elevated pH

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 75 of 140 July 2003 
only occurs after the metal alloys have been exhausted. Because neither A516 nor Al-1100 
provide long-term pH buffering like the S31600 and 304L stainless steels the pH increases when 
these metals are exhausted. In the Comp5 and Comp 6 simulations, where HLWG is paired with 
the various 304L components the resulting pH is just slightly less then pH 7. Given that the 
stainless steel components of the waste package have a longer predicted lifetime then the HLWG 
it is probable that they will moderate the basic conditions generated by HLWG dissolution and 
maintain neutral pH conditions inside the waste package, until the HLWG is exhausted. 
6.6.2.2 Seepage Dripping Model Output 
Figures 10 and 11 display the CDNR seepage dripping model pH profiles for the three seepage 
compositions at 25 and 50C, respectively. Also displayed is the reactant summary. The pH 
profiles converge early in the simulations indicating that the model pH response is insensitive to 
starting water composition, which is similar to the pH profile behavior of the CSNF seepage 
dripping model (Figures 3 and 6). 
The most outstanding feature in Figure 10 is the reactant summary for the N Reactor fuel. The 
figure shows that the N Reactor fuel dissolves over a short duration of just a few years. The 
rapidity of the N Reactor fuel dissolution diminishes its importance to the overall in-package 
chemistry contribution. However, the large quantity of schoepite precipitated in the waste 
package has the capacity to influence the in-package chemistry for an extended duration. 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
8.5 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E+02 
Moles Reactant 
D23C25 D23J25 
D23N25 HLWG 
A516 S31600 
304L GPC 304L MCO 
304L MCO lid N-Reactor 
Al-1100 
Source: Microsoft Excel Spreadsheet CDNR_SDM_25.xls in DTN: MO0307SPAIPCHM.001. 
Figure 10. Codisposed N Reactor Seepage Dripping Model Results for Q = 1.5 l/yr and 
Three Seepage Compositions at 25C

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 76 of 140 July 2003 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
8.5 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E+02 
Moles Reactant 
D23C25 
D23J25 
D23N25 
HLWG 
A516 
S31600 
304L GPC 
304L MCO 
304L MCO top 
N Reactor 
Al-1100 
Source: Microsoft Excel Spreadsheet CD_SDM_50.xls in DTN: MO0307SPAIPCHM.001. 
Figure 11. Codisposed N Reactor Seepage Dripping Model Results for Q = 1.5 l/yr and 
Three Seepage Compositions at 50C 
Another important feature of the pH profiles in Figures 10 and 11 is the absence of a period of 
sustained high pH (greater than 9) that might be expected from the dissolution of the HLWG. 
The CDNR water vapor condensation models, single reactant and ensemble, both predicted that 
high pH conditions are possible in the absence of an acid producing reactant, i.e., steel alloy. 
However, in the seepage dripping model, seepage is allowed to stream through the waste 
package reacting with waste package components, and it is unlikely that seepage which has 
reacted with HLWG could exit a waste package without contacting a steel component along its 
flow path. 
The HLWG is more quickly depleted at 50C then 25C because of the higher rate constants 
(Table 19) at elevated temperature. 
The CDNR seepage dripping model simulations include both high-level radioactive waste and 
N Reactor fuels as reactants, thus, the resulting chemistry of the reacted solutions reflects 
interactions between co-disposed waste, the non-SNF waste package components, the liquid 
phase water, and the atmosphere in the voids in the waste package and drift. In addition, the 
reactants are kinetic reactants, the fuel and metal alloys undergo oxidation and precipitate 
corrosion products that are in contact with the liquid phase water thereby having a first order 
influence on the chemistry. Since the in-package chemistry model and in-package chemistry 
model abstraction include all of these processes, the processes are also included in TSPA-LA.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 77 of 140 July 2003 
The following FEPs are addressed by the CDNR water vapor condensation model: 
 2.1.09.01.0B Chemical characteristics of water in the waste package 
 2.1.09.07.0A Reaction kinetics in EBS 
 2.2.08.12.0A Chemistry of water flowing into the EBS 
 2.1.02.09.0A Chemical effects of void space in waste package 
 2.1.01.02.0B Interactions between co-disposed waste 
 2.1.09.06.0A Reduction  oxidation potential in EBS. 
1.E-04 
1.E-03 
1.E-02 
1.E-01 
1.E+00 
1.E+01 
1.E+02 
1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 
Time (years) 
Minerals (moles) 
d23c25H d23j25H d23n25H d23c25G d23j25G d23n25G d23c25S d23j25S 
d23n25S d23c25Py d23j25Py d23n25Py d23c25T d23j25T d23n25T d23c25NMg 
d23j25NMg d23n25NMg d23c25NCa d23j25NCa d23n25NCa d23c25P d23j25P d23n25P 
d23c25NK d23j25NNa d23n25NNa d23c25U d23j25U d23n25U 
S
H 
G 
T NNa 
NK 
NMg 
P 
U 
NCa 
Py 
Source: Microsoft Excel Spreadsheet CDNR_Plots.xls in DTN: MO0307SPAIPCHM.001. 
NOTES: H=hematite, G=gibbsite, S=Schoepite, Py=pyrolusite, T=trevorite, NMg=nontronite-Mg 
NCa=nontronite-Ca, P=powellite, Gd=GdPO4:10H2O, U=(UO2)3(PO4)2:6H2O, Zr=Baddeleyite 
Figure 12. Codisposed N Reactor Seepage Dripping Model Mineral Phases Results for 
Q = 1.5 l/yr and Three Seepage Compositions at 25C 
Figure 12 shows the minerals which formed for the CDNR seepage dripping model for the three 
seepage compositions at 25C. This plot shows that schoepite, hematite, and gibbsite constitute 
the three most abundant mineral phases that form from the degradation of a CDNR waste 
package. The abundance of schoepite (UO3:2H2O) results from the complete reaction of the 
uranium metal N Reactor fuel, while the hematite and gibbsite are products of steel and 
aluminum alloys dissolution, respectively. Over the duration of the simulations, less gibbsite 
forms in the CDNR runs compared to the CSNF runs (Figure 5), while greater amounts of the 
nontronite clay minerals form in the CDNR runs compared to the CSNF runs. The reason for 
this is the greater availability of silica in the CDNR compared to the CSNF waste package from

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 78 of 140 July 2003 
the presence and degradation of the HLWG. This additional silica leads to greater amounts of 
nontronite formation in the CDNR waste packages. 
The CDNR mineral assemblages (Figure 5) for the three seepage compositions provides further 
support of the conclusion that the composition of the seepage has little influence over which 
minerals formed and their amounts. 
1.E-05 
1.E-04 
1.E-03 
1.E-02 
1.E-01 
1.E+00 
1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 
Time (years) 
Major Aqueous Species (molality) 
d23c25_HCO3 d23j25_HCO3 d23n25_HCO3 d23c25_Na d23j25_Na d23n25_Na 
d23c25_Cl d23j25_Cl d23n25_Cl d23c25_Ca d23j25_Ca d23n25_Ca 
d23c25_SO4 d23j25_SO4 d23n25_SO4 d23c25_SiO2 d23j25_SiO2 d23n25_SiO2 
d23c25_Mg d23j25_Mg d23n25_Mg d23c25_K d23j25_K d23n25_K 
d23c25_F d23j25_F d23n25_F d23c25_Ni d23j25_Ni d23n25_Ni 
d23c25_Cr2O7 d23j25_Cr2O7 d23n25_Cr2O7 d23c25_B(OH)3 d23j25_B(OH)3 d23n25_B(OH)3 
Na+ 
Cr2O7-- 
B(OH)3(aq) 
Ni2+ 
K+ 
K+ 
Mg2+ 
SO4
2- 
Cl- 
FCl- 
Ca2+ 
Ca2+ 
SiO2(aq) 
HCO3- 
Source: Microsoft Excel Spreadsheet CDNR_Plots.xls in DTN: MO0307SPAIPCHM.001. 
Figure 13. Codisposed N Reactor Seepage Dripping Model Aqueous Species Results for 
Q = 1.5 l/yr and Three Seepage Compositions at 25C 
Figure 13 displays the aqueous species for the CDNR runs at 25C for the three input water 
compositions. Similar to CSNF, Figure 13 shows that elements which were not present in the 
initial water compositions (Table 2), i.e., those elements that originate from the waste package 
and SNF, have aqueous species concentrations that are nearly identical, and independent of the 
initial water composition, for the duration of the simulations. Sodium, chromium, and boron 
have the highest concentrations, and either originated from HLWG dissolution (in the case of the 
sodium) or from the oxidation of the metal waste package components (in the case of boron and 
chromium). While those elements that do not originate from the waste package components or 
SNF, and do not participate in the formation of minerals, they do have aqueous species 
concentrations set by the incoming water composition (e.g., chloride). However, the 
concentrations of the majority of aqueous species are nearly identical and independent of the 
seepage composition. Therefore, it may be concluded that the in-package chemistry is set by the 
waste package components and SNF and not the composition of the water entering the failed 
waste package

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6.7 SENSITIVITYUNCERTAINTY OUTPUT 
Sensitivity analyses were performed to meet the following objectives: (1) to determine 
uncertainty ranges for pH and ionic strength for use in the in-package chemistry model 
abstraction; (2) to expand the boundaries (temperature, flux, and CO2 partial pressure ranges) of 
the in-package chemistry model abstraction without performing a large array of additional 
simulations; and (3) to demonstrate the effects of waste package design configuration variations 
on the model output. 
6.7.1 Uncertainty Range 
To assess the magnitude of the model response to variability in model inputs, sensitivity analyses 
were performed. The inputs that were identified as potentially important included: the partial 
pressure of carbon dioxide (PCO2), metal alloy corrosion rates, and the sulfur content of A516. 
A simple approach was taken where the median fuel exposure (cladding)/flux combination Caporewater 
CSNF seepage dripping model simulation was used as the reference case, and 
simulations were performed by varying these inputs independently. The results were plotted and 
compared to the reference case simulation and error bars were used to cover the range of the 
output. 
The results of the sensitivity analyses are presented in Figure 14 for pH and Figure 15 for ionic 
strength. The sensitivity run results show that the model was not sensitive to changes in carbon 
dioxide partial pressure from 10-4 to 10-2 atmospheres, labeled low PCO2 and high PCO2, 
respectively, on the figures. The model was sensitive to decreases (factor of 5) in the metal alloy 
corrosion rates (labeled low alloy rates on the figures), which had the effect of delaying the pH 
response compared to the reference case. The metal alloy corrosion rates were not increased for 
the sensitivity analyses because the base-case rates are considered to be high as the steels are 
likely to become coated with corrosion products which would slow corrosion. Changes in the 
sulfur content ( a factor of five) of the A516 had the expected result of decreasing the pH when 
the sulfur content was increased and increasing the pH when the sulfur content was decreased. 
For pH, the error bars had to meet two criteria: 1) To encompass the extreme values of pH (i.e., 
pH values which had the greatest deviation from neutral, which is a pH of 7); and 2) to capture 
the pH uncertainty over the long duration of the model period. The first criterion was set based 
on the reasoning that large excursions from neutral pH, either acidic or basic, are likely to have 
the greatest impact on the sub-models which use parameter feeds from the in-package chemistry 
model abstraction. The second criterion was set because the in-package chemistry tends towards 
stabilized conditions over long time periods and accounting for the uncertainty over long term 
has the potential for the greatest impact on the sub-models which use parameter feeds from the 
in-package chemistry model abstraction. 
Figure 14 shows the reference case with error bars of  0.5 pH units. The error bars capture the 
pH low at about 20 years for the high sulfur run (yellow symbols). The high sulfur pH profile 
was used as the defining profile because it deviates the most from neutral. Between about 300 
and 1000 years the error bars do not envelop the pH trend of the high sulfur case. Since the error 
bars would have had to be increased in magnitude to 1-pH unit, which over estimates the 
uncertainty over the long term, it was decided not to include this period in the estimation of error

In-Package Chemistry Abstraction 
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bars. The period of nearly constant pH between 1000 and 20,000 years, the longest period, the 
low alloy corrosion rate simulation defines the error bars. 
It is the recommendation based on this analysis of the variability in the model output, that for the 
pH abstractions (Section 8) in the in-package chemistry model abstraction a sampled uniform 
uncertainty range of -0.5 to 0.5 pH units be added to the abstracted pH value(s). A uniform 
distribution was chosen because of the equal probability of any variable contributing to the 
uncertainty. 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
10.0 
1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
pH 
C22C25 
low PCO2 
high PCO2 
low alloy rates 
low Sulfur A516 
high Sulfur A516 
Source: Microsoft Excel Spreadsheet CS_Sensitivity.xls in DTN: MO0307SPAIPCHM.001. 
Figure 14. Sensitivity Analysis of pH 
For ionic strength a criterion was implemented for setting the error bars. Abstraction of the 
in-package ionic strength is used exclusively by the colloid sub-model in TSPA-LA. The colloid 
model report (BSC 2003e, Section 6.3.2.4) defines the upper limit of ionic strength for colloid 
stability, the value above which colloids are no longer stable as dispersed particles in solution, as 
0.05 molal. Therefore, below this 0.05 molal ionic strength threshold value colloids are stable in 
solution and available for radionuclide adsorption. The criterion used to fit the error bars was to 
honor the low ionic strength (less than 0.05 molal) side of the reference case. For example, in 
Figure 15 the low sulfur and the low alloy rate cases tend toward lower ionic strength compared 
to the reference case, and the error bars cover the range of these sensitivity cases. 
The ionic strength error bars are a percentage value rather than a constant additive value like that 
used for pH. Thus, the uncertainty range of -0.6 to 0.6 (as shown in Figure 15, is multiplied by 
an ionic strength value and the product is added back to the ionic strength to obtain the ionic

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strength with uncertainty. This uncertainty range and its application in the TSPA-LA ionic 
strength abstractions is further discussed in Section 8. 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
Ionic Strength (molal) 
C22C25 
low PCO2 
high PCO2 
low alloy rates 
high Sulfur A516 
low Sulfur A516 
Source: Microsoft Excel Spreadsheet CS_Sensitivity.xls in DTN: MO0307SPAIPCHM.001. 
Figure 15. Sensitivity Analysis of Ionic Strength 
6.7.2 Limits of the In-Package Chemistry Model Abstraction 
Two suites of additional simulations were performed, one using a series of elevated 
temperatures, and a second using a series of elevated seepage flux values. The purpose of these 
additional simulations was to aid in the determination of the application limits of the abstractions 
derived in Section 8. For the results, discussion, and application of these simulations see 
Sections 8.2.3, 8.2.6, 8.4.1, 8.4.3, and 8.4.6. 
The runs performed at alternate carbon dioxide partial pressures (Section 6.7.1) are also used to 
expand the limits of the abstractions and are discussed in Section 8.8. 
6.7.3 Effect of Waste Package Design 
Variations in the waste package design configurations for the CSNF packages were examined to 
determine if the model response was sensitive. This sensitivity analysis was performed because 
not all of the A516 that made up the A516_Guides was included in the EQ6 input files, so the 
additional A516 was included and model runs were performed. This change affects the moles 
and surface area for all of the components because the additional volume of A516 changed the 
calculated surface area, which is used to normalize surface area of these components. The EQ6 
input values are included in Attachment I, CSNF.xls, worksheet SDM A516 Guides. Run 
C11C25.6i was used as a comparison case because the effect of increased A516 on the model

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response would be the greatest at low water flux and low fuel exposure. The results are shown in 
Figure 16.
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
pH 
C11C25 
Corrected A516 Guides 
Source: Microsoft Excel Spreadsheet CS_Sensitivity.xls in DTN: MO0307SPAIPCHM.001. 
Figure 16. Sensitivity Analysis with Corrected A516 Guide Information 
Figure 16 shows that increasing the mass and surface area of the A516 by a factor of 
approximately 10 had little influence on the pH profile. 
7. MODEL VALIDATION 
The purpose of the in-package chemistry model and the in-package chemistry model abstraction 
is to predict the bulk chemistry inside of a failed waste package and to provide simplified 
expressions of that chemistry for use in TSPA-LA. The technical work plan (BSC 2002a, 
Section 2.1.4.4) states that the post-development validation of the model will focus on the two 
sub-models. The fuel-degradation sub-model will be validated by comparison with results from 
laboratory experiments published in refereed journals or literature. The types of minerals formed 
and the aqueous concentrations predicted by the model will be compared to experimental results 
to quantify the uncertainty range of the model. The steel-degradation sub-model will be 
validated based on results published from laboratory experiments on man-made analog materials. 
The validation efforts outlined in the technical work plan and cited above were subsequently 
considered to provide insufficient confidence for predicting in-package chemistry over the much 
longer time spans to which the model is to be applied. Consequently, model validation efforts

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avoided laboratory evidences and instead focused on natural analogue observation which might 
provide confidence in model predictions over near geologic time spans. 
The primary output of the in-package chemistry model abstraction to total system performance 
assessment are ranges of pH and ionic strength. Adequate validation of the in-package chemistry 
model, therefore, entails showing, on the basis of natural analogue evidence, that the pH and 
ionic strength ranges are comparable. 
The salient critical features of the in-package chemistry model are: 
1. Production of alkaline waters (8 < pH < 10) by interaction of dilute solutions with 
waste form glass components, 
2. Production of mildly acidic (3 < pH < 5) waters by interaction of incoming solutions 
with waste form metallic components (primarily A516 low-carbon and S31600 
stainless steel), 
3. Production of high ionic strength solutions (greater than 1 M) by reaction with waste 
form components. 
The technical work plan (BSC 2002a, Section 2.1.4.2) indicates that the in-package chemistry 
model recommends a higher level of confidence (moderate or Level II) than other waste form 
degradation models which require a low level of confidence. The technical work plan criteria 
(BSC 2002a, Section 2.1.4.2) used to establish the level are: 
1. The in-package chemistry model is extrapolated over long time periods. 
2. The in-package chemistry model itself does not have large uncertainties, however, the 
in-package chemistry model must be robust enough to cover uncertain initial 
conditions. 
3. The in-package chemistry model will be used to demonstrate compliance for licensing 
as it is an input to TSPA-LA. 
4. The in-package chemistry model is not a primary contributor to the determination of 
dose. 
Level II (moderate) validation, recommends satisfying Level I criteria (a) through (f) (BSC 
2002f, Appendix B), and a single post-development model validation method as per APSIII.
10Q, Models. The additional, single post-development model validation criterion that is 
addressed for each salient feature above is that of corroboration with data published in refereed 
journals or literature in accordance with Section 5.4.1 in AP-SIII.10Q, Models. Specifically, 
validation of the in-package chemistry model requires that the broad ranges of pH and ionic 
strength predicted by the model be shown to be consistent with the pH values and ionic strengths 
observed in natural systems and documented in refereed journals or literature, wherein the same 
underlying mechanisms prevail 
Level I criteria recommend that model development efforts entailed:

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(a) Evaluating and selecting input parameters and data: Section 4.1 provides the inputs 
used in the in-package chemistry model, which include waste package design 
information, material compositions, atomic weights, densities, water compositions, 
SNF compositions, corrosion/reaction rates, and thermodynamic data, these inputs were 
chosen to best represent all of the components of the in-package chemistry model. 
Therefore, this criterion has been met for all of the salient features. 
(b) Formulating defensible assumptions and simplifications: Sections 5, 6.3.2 and 6.3.3 
provide the assumptions (Section 5) and simplifications (Sections 6.3.2 and 6.3.3) used 
in the in-package chemistry model. The in-package chemistry model has been updated 
and subdivided into two scenario driven sub-models both of which eliminate a previous 
implausible conceptualization. Therefore, the in-package chemistry model assumptions 
and simplifications are defensible. This criterion has been met for all of the salient 
features of the model. 
(c) Ensuring consistency with physical principles, such as conservation of mass, energy, 
and momentum: Sections 6.3, 6.3.2 and 6.3.3 provide brief discussions of the EQ6 
geochemical modeling software used for the in-package chemistry model simulations. 
EQ6 preserves mass, both of solids and water; therefore the in-package chemistry 
model also conserves these properties. This criterion has been met for all of the salient 
features. 
(d) Representing important future state (aleatoric), parameter, and alternative model 
uncertainties: Unexpected random (aleatoric) events are outside of the realm of the 
in-package chemistry model. However, the in-package chemistry model was designed 
to be robust enough to handle large changes in temperature, flux, and seepage 
composition. Alternate conceptual models were discussed in Section 6.4 and the 
current in-package chemistry model robustness eliminates the need for these alternate 
conceptual models. This criterion has been met for all of the salient features. 
(e) Ensuring simulation conditions have been set up to span the range of intended use and 
the avoidance of inconsistent outputs: Sections 6.3.2 and 6.3.3 outline the wide range 
of inputs (flux, fuel exposure, temperature, reactant combinations) that were used in the 
in-package chemistry model. These conditions span the range of intended use in 
TSPA-LA for which the in-package chemistry model will be used. This criterion has 
been met for all of the salient features. 
(f) Ensuring that model predictions (performance parameters) adequately represent the 
range of possible outcomes, consistent with important uncertainties: Sections 6.3.2 and 
6.3.3 provide the input conditions which span the range of the intended use of the 
model; thus logic dictates that the range of outputs (performance parameters) also 
represents the range of possible outcomes. Additionally, sensitivity analyses have been 
performed to expand the performance parameters to values consistent with important 
conditional uncertainties. Therefore, this criterion has been met for all of the salient 
features.

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7.1 PRODUCTION OF ALKALINE WATERS BY GLASS DISSOLUTION 
Is the production of alkaline waters consistent with the peer reviewed or industry literature? 
Glass dissolution causes pH values greater than 7 when protons from solution exchange with 
alkalis, typically Na+, K+, or Ca2+, present at the dissolving glass-solution interface, e.g., 
Glass-Na+ + H+ . Na+ + Glass-H+ 
The general link between dissolution of alkali-bearing silicate minerals and glasses with acidity 
destruction (alkalinity production) is amply documented in the peer-reviewed scientific 
literature. Typically, large-scale weathering of alkali-bearing silicates ultimately leads to high 
pH values in alkali lakes (Berner and Berner 1987, pp. 280 to 281). Experiments seeking to 
mimic interaction of seawater with basaltic glass on the ocean floor occasionally observe 
alkaline pH values (note that basaltic glass is generally considered to be an appropriate natural 
analogue for nuclear waste glass (Ewing and Haaker 1979). The highest pH values in natural 
waters (up to 12) tend to be observed in deep groundwaters in contact with dissolving ultramafic 
rocks isolated from atmospheric CO2 (Hem 1985, p. 64). 
Values of pH above approximately 10 in fluid-mineral systems tend to be severely limited by the 
influx of carbon dioxide, precipitation of metal-carbonate minerals, by deprotonation of aqueous 
species (e.g., silica, Al(OH)3aq, etc.), and by proton loss from mineral surface groups. Indeed it 
is exceedingly difficult to find natural waters with pH values of 10. Those that exhibit higher pH 
values do so under conditions different from those expected to prevail in the waste packages, 
namely they are out of contact with atmospheric carbon dioxide. For the purposes of model 
validation, the above discussion does two things: (1) It states the upper limit of pH to be roughly 
10; and (2) it shows that the conditions in the waste form will be similar to natural systems with 
upper pH bounds of approximately 10. 
In short, the two together indicate that the high-end pH values predicted by the in-package 
chemistry model are corroborated by natural observations documented in the peer-reviewed 
literature. This, in tandem with the satisfaction of Level I criteria (a) through (f), therefore 
provides sufficient justification for a medium level of confidence in this particular feature of the 
in-package chemistry model. On the basis of meeting the criteria for this feature, the level of 
confidence has been met. 
7.2 ACID PRODUCTION FROM STEEL DISSOLUTION 
Does the corrosion of steel and generation of acidic conditions agree with natural observations 
documented in the peer-reviewed literature? Oxidation of sulfur in A516 is predicted to be the 
primary source of acidity in the in-package chemistry model. The composition for the low 
carbon steel (A516) is given in Table 6. Upon complete oxidation, the form of each element that 
is found in natural waters will be as follows for elements present in greater than trace amounts 
(Hem 1985; p. 109 (C), p. 69 (Si), pp. 127 to 128 (P), pp. 84 to 88 (Mn)): C will become carbon 
dioxide (a weak acid), or bicarbonate; the Si will become aqueous silica; the P will form 
phosphoric acid, monohydrogen phosphate, or dihydrogen phosphate (weak acids) except under 
highly alkaline conditions; the S will become sulfate, except under highly acidic conditions. The 
Fe and Mn will form Mn and Fe oxides, which are largely insoluble. Note that local oxidation

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 86 of 140 July 2003 
will lead to local deviations from the picture outlined below. By the same token other oxidized 
iron phases other than those mentioned below might be observed to form for limited time 
periods. The assemblage may ultimately react with other components from the incoming 
solution (e.g., the phosphate species might react with divalent cations to form a phosphate salt 
such as hydroxyapatite). 
For the purposes of validating acidity production by long-term steel degradation, note that for 
both of the metal steel components oxidation reactions involve either water, or free oxygen, or 
both, but that neither produces (or consumes) protons. 
Oxidation of Fe, the primary component, is: 
2Fe + 3/2O2 . Fe2O3 
Oxidation of P under sufficiently acid conditions (i.e., pH < about 5) similarly doesnt produce 
acid: 
Metal-P + 3/2H2O + 5/4O2 . H3PO4. 
Oxidation of S in the steel is different because it produces a strong acid, sulfuric acid, which 
ultimately produces protons: 
Metal-S + H2O + 3/2O2 . SO4
-2 + 2H+ 
(Metal-P and Metal-S denote P and S, respectively, in the metal). The reaction above is 
constrained by two natural and experimental observations: (1) Sulfur is in reduced form in steel; 
and (2) sulfate is the typical oxidized form of S in oxidizing natural waters (Hem 1985, pp. 112 
to 117). 
The closest natural analogue for long-term low carbon steel dissolution is probably the alteration 
of pyrite, FeS2, under oxidizing conditions. Pyrite oxidation is the process that generates acid 
mine drainage (Langmuir 1997, pp. 457 to 461) producing pH values from 2 to 5 (Drainage from 
coal mines show the same pH range for the same reason). There are significant differences 
though, the most important being that the Fe/S ratio in pyrite is orders of magnitude smaller than 
it is in low carbon steel. Since S oxidation produces acid, but Fe oxidation does not, this means 
that pH values observed in acid mine drainage would tend to be lower than those expected in the 
waste form. (Also the presence of alkalinity-producing glass dissolution reactions in some waste 
forms will drive pH higher.) The inverse connection between S content and pH in natural waters 
has been noted by Langmuir (1997, p. 457). Because of the predominance of iron, the principal 
degradation product will be some form of iron oxide or hydroxide. In either case, the reaction 
with acid can be characterized as dissolution of the solid with the production of Fe3+ and water, 
as in: 
Fe2O3 + 6H+ . 2Fe3+ + 3H2O 
Addition of acid, e.g., from the oxidation of reduced sulfur, will tend to be neutralized by the 
iron oxide or hydroxide. Conversely, addition of alkali will be neutralized by the precipitation of 
the iron. Thus, over some moderate range of iron concentrations, these systems will behave as

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buffers. The range is limited by the amount of ferric solid (or saturation in another ferric solid, 
such as ferric sulfate). The supply of dissolved iron is limited and would be depleted by 
excessive addition of alkali. The effective buffering range can be seen by examining 
Figures 12.8 and 12.10 in Langmuir (1997) to lie in the general range of pH 2 to 4 for Fe(OH)3. 
For FeOOH the range shifts to lower pH values because of its greater thermodynamic stability. 
Again, since the iron to sulfur ratio is so high in low carbon steel, acidity production from sulfur 
oxidation below pH 3 is counteracted by acidity consumption from iron oxidation. Note lastly, 
that many of the reactions in the Fe-S-O-H system are microbiologically mediated. 
Consequently, there is likely to be a strong kinetic component to any estimations of pH control in 
natural systems. 
For the purposes of model validation, it is sufficient to demonstrate that natural waters having pH 
values below 3 are rare, and below 2 exceedingly so (Langmuir 1997, pp. 410 to 411) and tend to 
form due to oxidation of sulfursometimes in the presence of fumaroles. In other words, because 
the conditions that would favor pH less than 3 (low acid-buffering capacityFe, and high S) are 
absent in the in-package chemistry model, the lower limit of pH approximately 3 estimated by 
the in-package chemistry model is corroborated by natural observations documented in the peerreviewed 
literature. Moreover, this, combined with satisfaction of Level I criteria (a) through (f), 
provides broad support for a medium level of confidence in this feature of the in-package 
chemistry model. On the basis of meeting the criteria for this feature, the level of confidence has 
been met. 
7.3 PRODUCTION OF HIGH IONIC STRENGTH SOLUTIONS 
Is the production of high ionic strength solutions consistent with observations documented in the 
technical literature? High ionic strength solutions are predicted to occur at high pH in the inpackage 
chemistry model and in nature, primarily because such solutions in contact with 
atmospheric CO2 tend to have high levels of high charge carbonate species (Morel and Hering 
1993, Chapter 3; Stumm and Morgan 1996, Chapter 3): 
CO2 + H2O . CO3
-2 + 2H+ 
For example, a pH 10 solution in equilibrium with 10-3 atm of CO2 (the in-package chemistry 
model carbon dioxide level in the waste package) can be roughly estimated to have an ionic 
strength of at least 0.45 (ignoring activity coefficient effects) corresponding to 10-0.7 M HCO3
-, 
0.1 M CO3
2- with Na+ as the charge-balancing cation (Stumm and Morgan 1996, Chapter 3). At 
the same time, cations such as Na+ produced by glass dissolution, and Cs+, precipitate from 
natural waters only when they become highly concentrated, as in evaporative lakes (Rai and 
Zachara 1984), and, therefore accumulate in solution, raising the ionic strength. The closest 
natural analogue to the modeled situation are alkali lakes that have high pH and are exposed to 
the atmosphere (pCO2 = 10-3.5). An example of this is Alkali Valley, Oregon, which has a pH of 
10.1 and an ionic strength that exceeds 4 M (Drever 1982, p. 206). To summarize, the close 
linkage between extensive degradation of waste form and high ionic strengths is predicted to be 
most apparent when glass degradation produces high pH solutions. The high salt content of high 
pH solutions is corroborated by textbook documentation and natural observations documented in 
the peer-reviewed literature. A medium level of confidence in this feature of the in-package 
chemistry model is therefore justified based on both the independent corroboration from the

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technical literature and satisfaction of Level I criteria (a) through (f). On the basis of meeting the 
criteria for this feature, the level of confidence has been met. 
7.4 SUMMARY 
To summarize, the in-package chemistry model is a numerical titration of water with acidproducing 
solids (steel components), and alkali-producing solids (glass). The outputs are timedependent 
solution pH and ionic strength trajectories. Documenting that these trajectories are 
within the bounds of pH values and ionic strengths observed in natural situations, as documented 
in the peer-reviewed literature, where similar processes prevail is critical to model validation of 
the in-package chemistry model. Specifically, achieving a medium level of confidence requires 
documenting: (1) production of low-pH waters by steel degradation; (2) production of high-pH 
waters by glass degradation; and (3) the accumulation of dissolved salts with prolonged waste 
form degradation, in addition to satisfying Level I criteria (a) through (f). The evidences 
specifically enumerated above are believed collectively to provide broad justification for 
assignment of a medium level of confidence to the in-package chemistry model. The validation 
of in-package chemistry model meets the requirements for implementation in TSPA-LA, and 
does not require any further validation. 
8. RESULTS AND CONCLUSIONS 
The results of the in-package chemistry model are the in-package chemistry model abstractions, 
which are used by the Performance Assessment Project. The in-package chemistry model 
abstraction simplifies the detailed complex time-dependent chemistry into response surfaces and 
parameter distributions for the in-package chemical parameters that are to be directly 
implemented in the TSPA-LA. 
The following subsections outline the chemical parameters, the methods and reasoning used in 
abstracting the parameters, the abstractions themselves, and the recommended limits of the 
abstractions. 
8.1 COMMERCIAL SPENT NUCLEAR FUEL WATER VAPOR CONDENSATION 
IN-PACKAGE CHEMISTRY MODEL ABSTRACTION 
For TSPA-LA the CSNF water vapor condensation model abstractions should be implemented 
when water vapor enters a CSNF waste package, condenses, and then reacts with the waste 
package components. This is a non-dripping model. There is no flux dependence in the water 
vapor condensation model abstractions since water does not exit the waste package. 
8.1.1 Commercial Spent Nuclear Fuel Water Vapor Condensation pH Abstraction 
Figure 17 displays the pH profiles and abstraction for the non-dripping water vapor condensation 
model for CSNF packages. Two time periods are apparent based on the sharp break in pH (0 to 
1000 and 1000 to 20,000 years), and for the purpose of the abstraction, an additional transition 
time period was included. This transitional period (450 to 1000 years) accounts for delays in the 
pH response which could result from variation in the metal alloy corrosion rates (Section 6.7), 
and uses the minimum pH from the first period and the maximum pH from the third period as 
limits.

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3.0 
3.5 
4.0 
4.5 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
comp1 
comp2 
comp3 
comp4 
comp10 
comp11 
pH Minimum 
pH Maximum 
DTN: MO0307SPAIPCHM.001 
Figure 17. Non-Dripping Commercial Spent Nuclear Fuel Water Vapor Condensation pH Abstraction 
Note that comp5 through comp9 were not included in the abstraction because those simulations 
did not contain CSNF as a reactant. 
In the TSPA-LA, for non-dripping CSNF, the pH should be uniformly sampled between the 
minimum and maximum values for the three time periods listed in Table 57. At 25C a uniform 
uncertainty range of -0.5 to 0.5 pH units should be uniformly sampled and added to the sampled 
pH. This one pH unit uncertainty envelope accounts for variation in the EQ6 input parameters 
(Section 6.7). At temperatures other than 25C a uniform uncertainty range of -2 to 2 pH units 
should be uniformly sampled and added to the sampled pH. The pH limits are 1 and 12, i.e., if 
the abstracted pH with included uncertainty is less than pH = 1, or greater than pH = 12, then the 
pH should be set to the limiting value. 
8.1.2 Commercial Spent Nuclear Fuel Water Vapor Condensation Ionic Strength 
Abstraction 
In an attempt to provide realistic interpretations of the ionic strength data for use in abstractions 
that are fed to total system performance assessment a cumulative distribution approach has been 
taken in abstracting this information. This approach uses the knowledge that colloids are not 
stable above ionic strength values of 0.05 molal, and combines the time functionality of the ionic 
strength as a means to assign probabilities to ionic strength ranges.

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Table 57. Non-Dripping Commercial Spent Nuclear Fuel pH Abstraction 
0 - 450 years 450 to 1000 years 1000 to 20,000 years 
Minimum Maximum Minimum Maximum Minimum Maximum 
Comp1 3.58 5.41 N/A N/A 5.21 5.68 
Comp2 3.57 5.41 N/A N/A 4.94 5.82 
Comp3 3.53 5.41 N/A N/A 4.77 7.71 
Comp4 3.47 5.41 N/A N/A 4.19 4.62 
Comp10 3.80 5.41 N/A N/A 4.97 5.24 
Comp11 3.82 5.41 N/A N/A 4.24 5.63 
Distribution uniform uniform uniform 
Minimum 3.47 N/A 3.47 4.19 N/A 
Maximum N/A 5.41 7.71 N/A 7.71 
DTN: MO0307SPAIPCHM.001 
Figure 18 displays the ionic strength profiles for the non-dripping CSNF ensemble cases. The 
ionic strength steadily increases for approximately the first 40 years for all of the ensembles, and 
from 40 to 20,000 years the ionic strength is stable for most of the ensembles. Therefore, the 
abstraction will be divided into two periods, 0 to 40 years, and 40 to 20,000 years. 
To generate realistic abstractions of ionic strength, criteria or break points for each period, were 
set to ensure that the cumulative distributions best reflected the model output. For example, in 
all cases where a cumulative distribution abstraction was implemented the minimum and 
maximum ionic strength for the given time period was used to set the lower and upper limits of 
the distribution. Additionally, the upper stability limit for colloids, 0.05 molal, was used because 
this value provides the cutoff for colloids. One other value, 0.01 molal, is sometimes used for 
the first period of the abstraction because the ionic strength is usually above 0.01 for the greater 
duration of the period. 
Inspection of Figure 18 reveals that the ionic strength increases to greater than 0.01 molal 
between about one and three years, or the probability of the ionic strength being less than 0.01 
molal at any point in time ( between 0 and 40 years) is about 2 in 40, or 5 percent. Likewise, the 
probability of the ionic strength being less than 0.05 molal is about 14 in 40 or 35 percent, and 
finally the probability of the ionic strength being less than 0.21 (the upper limit for the 0 to 40 
year period) is 100 percent.

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ANL-EBS-MD-000037 REV 02 91 of 140 July 2003 
1.0E-06 
1.0E-05 
1.0E-04 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Ionic Strength (molal) 
comp1 
comp2 
comp3 
comp4 
comp10 
comp11 
Lower IS Limit 
Upper IS Limit 
0.05 molal limit of colloid stability 
2 years 
900 years 
5000 years 
14 years 
DTN: MO0307SPAIPCHM.001 
Figure 18. Non-Dripping Commercial Spent Nuclear Fuel Water Vapor Condensation 
Ionic Strength Abstraction 
For the second period from 40 to 20,000 years, four of the six simulations maintain ionic 
strengths greater than 0.05 molal for the entire period, while comp3 and comp4 fall below 0.05 
molal at 5000 and 900 years, respectively. For a total duration of 6  20,000 years = 120,000 
years, only 5000 to 20,000 years and 900 to 20,000 years, or 15,000 + 19,100 = 34,100 years are 
below the 0.05 molal value. Therefore, the probability of the ionic strength being less than 0.05 
molal is 34,100/120,000 = 28 percent, and the probability of the ionic strength being less than 
1.02 (the upper limit for the period) is 100 percent. 
The cumulative distributions for ionic strength for the CSNF water vapor condensation model for 
use in TSPA-LA are recorded in Table 58. 
Table 58. Non-Dripping Commercial Spent Nuclear Fuel Ionic Strength Cumulative 
Distribution Parameters 
Time Period 
Post WP 
Breach 
(years) Ionic Strength (molal) Probability 
3.9E-06 0.0 
0.01 0.05 
0.05 0.35 
0 to 40 
0.21 1.0 
6.4E-03 0.0 
0.05 0.28 40 to 20,000 
1.02 1.0 
DTN: MO0307SPAIPCHM.001

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 92 of 140 July 2003 
For TSPA-LA the ionic strength should be sampled using the information in Table 58, and 
uncertainty should assigned to the sampled value. At 25C a uniformly sampled uncertainty 
value from the range of -0.6 to 0.6 (Section 6.7) multiplied by the sampled value should be added 
to the sampled value. At temperatures other than 25C, a uniformly sampled uncertainty value 
from the range of 0.99 to 0.99 multiplied by the sampled value should be added to the sampled 
value. 
Ionic strength = ISsampled + (Uncertaintysampled  ISsampled) 
For example, using fictitious values, if the sampled ionic strength (IS) value is 0.72, and the 
sampled uncertainty is -0.04, then the ionic strength should be: 
IS = 0.72 + (-0.04  0.72) 
= 0.69 
8.2 COMMERCIAL SPENT NUCLEAR FUEL SEEPAGE DRIPPING MODEL INPACKAGE 
CHEMISTRY MODEL ABSTRACTION 
For TSPA-LA the CSNF seepage dripping model abstractions should be implemented when 
seepage water enters a commercial spent nuclear fuel waste package and reacts with the waste 
package components; this is a dripping model. The seepage dripping model abstractions are 
organized based on parameter and temperature. 
8.2.1 Commercial Spent Nuclear Fuel Seepage Dripping Model 25C pH Abstraction 
The pH profiles for the CSNF seepage dripping model are displayed in Figure 19 together with 
time divisions for the abstraction. The criteria used for abstraction are listed in Table 59. The 
time periods and the applied pH criteria were chosen based on the observed pH trends and the 
role of the reactants during that period. For example, during period 1 in Figure 19 all of the pH 
profiles achieve a minimum by 50 years, the time at which the A516 is depleted, thus the time 
division is set at 50 years and pH criteria are set at the minimum. 
A brief discussion of the procedure used to generate the pH response surfaces will be provided 
using Period 1 for the CSNF seepage dripping model at 25C as an example. 
The criteria in Table 59 were chosen because they best represent the pH trend for the given time 
period. The minimum pH was used for the first period because the pH was low for the greatest 
amount of time during that period, where the log time scale exaggerates the early time pH trends 
even though the duration of these trends was short. The average pH was used for the second 
time period because this period can be characterized as transitional and the average best captures 
this trend. Finally, the third and longest duration period is characterized by stabilized pH, where 
the average was used again because it provided the best representation of the model output. 
Section 8.8 provides a discussion of the abstraction validation, and the Figures II-3 and II-5 
display the CSNF pH abstractions at 25 and 50C, respectively, plotted together with the model 
output.

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ANL-EBS-MD-000037 REV 02 93 of 140 July 2003 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
C11C25B C12C25B C13C25B 
C21C25B C22C25B C23C25B 
C31C25B C32C25B C33C25B 
C11J25B C12J25B C13J25B 
C21J25B C22J25B C23J25B 
C31J25B C32J25B C33J25B 
C11N25B C12N25B C13N25B 
C21N25B C22N25B C23N25B 
C31N25B C32N25B C33N25B 
Period 1 Period 2 Period 3 
DTN: MO0307SPAIPCHM.001 
Figure 19. Seepage Dripping Model pH Profiles for Commercial Spent Nuclear Fuel at 25C, Showing 
the Three Time Periods of the Abstraction 
Table 59. Criteria Used to Define the pH for Each Abstracted Time Period 
Period 
Time Period 
(years post waste 
package breach) pH Criteriaa 
1 0 to 50 Minimum 
2 50 to 500 Average 
3 500 to 20,000 Average 
NOTE: a pH criteria is the value used for each scenario to generate a point on the 
response surface. 
The pH values for each flux/fuel exposure scenario representing each time period were compiled 
into three matrices in Microsoft Excel so that a response surface, since there were two 
independent variables, could be generated. For three-dimensional response surfaces three data 
columns were specified in Microsoft Excel (Table 60), where x1 = water flux, x2 = log fuel 
exposure, and y = pH, and the regression wizard was used from the Data Analysis  Tools 
menu tree to fit a 3-dimensional plane to the data. The output consisted of the coefficients of the 
equation of the plane and various goodness-of-fit statistical parameters (Table 61). Only the 
input matrix for the 0 to 50 year time period is included in the text, the matrices for the other 
time periods are included in DTN: MO0307SPAIPCHM.001.

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ANL-EBS-MD-000037 REV 02 94 of 140 July 2003 
Table 60. Input Matrix for the Commercial Spent Nuclear Fuel Seepage Dripping Model, 
0 to 50 year pH Response Surface 
CSNF pH Abstraction, 0 to 50 years 
EQ6 Root File Name 
(*.6i) Flux(l/yr) 
Log (fuel 
exposure) Minimum pH 
C11C25 0.15 -2.00 3.28 
C12C25 0.15 -1.0 3.68 
C13C25 0.15 0.0 4.03 
C21C25 1.5 -2.0 3.28 
C22C25 1.5 -1.0 3.70 
C23C25 1.5 0.0 4.03 
C31C25 15 -2.0 3.30 
C32C25 15 -1.0 3.72 
C33C25 15 0.0 4.07 
C11J25 0.15 -2.00 3.26 
C12J25 0.15 -1.0 3.68 
C13J25 0.15 0.0 4.03 
C21J25 1.5 -2.0 3.27 
C22J25 1.5 -1.0 3.68 
C23J25 1.5 0.0 3.98 
C31J25 15 -2.0 3.29 
C32J25 15 -1.0 3.71 
C33J25 15 0.0 4.00 
C11N25 0.15 -2.00 3.28 
C12N25 0.15 -1.0 3.66 
C13N25 0.15 0.0 4.05 
C21N25 1.5 -2.0 3.28 
C22N25 1.5 -1.0 3.66 
C23N25 1.5 0.0 4.05 
C31N25 15 -2.0 3.31 
C32N25 15 -1.0 3.68 
C33N25 15 0.0 4.10 
DTN: MO0307SPAIPCHM.001 
Note that the minimum, maximum, and average pH values for the time period were calculated in 
Microsoft Excel using either the MIN, MAX, or AVERAGE intrinsic function, 
respectively, for the pH values in a given time period. Note that minimum pH for the 0 to 50 
year post breach is 3.3, this is also the minimum pH for all of the 25C simulations for the entire 
20,000 year model period. 
Table 61 lists the pH relationships for the three time periods. The input matrices and the output 
data can be found in Attachment I (Microsoft Excel Spreadsheet CSNF_SDM_25.xls in 
DTN: MO0307SPAIPCHM.001).

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ANL-EBS-MD-000037 REV 02 95 of 140 July 2003 
Table 61. Total System Performance Assessment Input pH Input Parameters for the 
Response Surfaces at 25C 
Z = yo + ax + bya 
Period yo a b R-Squared 
1 4.04 1.86E-03 3.78E-01 0.99 
2 4.71 4.80E-02 2.58E-01 0.78 
3 5.57 4.25E-02 5.74E-02 0.82 
DTN: MO0307SPAIPCHM.001 
NOTE: aZ = pH,  = water flux (l/yr), y =Log10 (Fuel Exposure (fractional value)), yo, a, and b are coefficients 
of the equation. 
A uniform uncertainty distribution of -0.5 to 0.5 should be added to the calculated pH values. 
As the seepage flux increases, the residence time of water in a waste package decreases, which 
lowers the potential for reaction of that water with the waste package components. Meaning that 
at some upper limit of flux, seepage will enter and exit a waste package with virtually no 
alteration of its original composition. This upper threshold is related to the ratio of seepage flux 
to the kinetic rate coefficient of the most reactive reactant. A516 has both a high corrosion rate 
and a strong effect on the chemistry, so attempts have been made using sensitivity analyses to 
determine this upper flux limit by increasing the seepage flux until there is little change in the 
effluent chemistry. Attachment II CS_Sensitivity.xls shows that only minimal reaction of 
seepage with A516 occurs at 1000 l/yr, therefore, this is the process model limiting flux. 
Table 62 provides the limits of seepage flux and fuel exposure for the regression equations in 
Table 61. Should the TSPA-LA seepage flux exceed the upper limit in Table 62, then the pH 
should be set to a constant value of 8.3, the maximum pH in Figure 19. Note that even at the 
limits of the abstraction the uncertainty should still be applied. 
The possibility exists that in the TSPA-LA model runs the flux and fuel exposure could be less 
than the minimum values in Table 62. If the flux is lower than 0.15 l/yr then the pH should be 
calculated using 0.15 l/yr and the uncertainty range should be uniformly sampled and expanded 
to -1.0 to 1.0 pH units. The additional uncertainty will account for pH variations in the low flux 
scenarios. If the fuel exposure falls below the minimum value of 0.01, then the pH should be 
calculated using 0.01, and the -0.5 to 0.5 sampled uncertainty range should be applied. At low 
values of fuel exposure the CSNF has virtually no influence on the in-package chemistry, 
therefore, no additional uncertainty is required. If the both the fuel exposure and water flux fall 
below the minimum values, 0.01 and 0.15 l/yr, respectively, then the expanded uncertainty range 
of -1.0 to 1.0 pH units should be applied. 
Table 62. Flux and Fuel Exposure Limits for the Commercial Spent Nuclear Fuel Seepage Dripping 
Model 25C pH Abstraction 
Period Flux Limits (l/yr) Fuel Exposure Limits 
Lower Uppera Lower Uppera 
1 0.15 1000 0.01 1.0 
2 0.15 75 0.01 1.0 
3 0.15 70 0.01 1.0 
NOTE: aUpper Limit pH = 8.3

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ANL-EBS-MD-000037 REV 02 96 of 140 July 2003 
8.2.2 Commercial Spent Nuclear Fuel Seepage Dripping Model 50C pH Abstraction 
The method outlined in Section 8.2.1 for the generation of pH response surfaces at 25C was 
also applied to the 50C abstractions. The same time periods and pH criteria were used for the 
50C abstractions as they are listed in Table 59. The pH matrices for each time period and the 
statistical output for the regression analyses are contained in Attachment I in 
CSNF_SDM_50.xls. 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
C11C50 C12C50 C13C50 C21C50 C22C50 
C23C50 C31C50 C32C50 C33C50 C11J50 
C12J50 C13J50 C21J50 C22J50 C23J50 
C31J50 C32J50 C33J50 C11N50 C12N50 
C13N50 C21N50 C22N50 C23N50 C31N50 
C32N50 C33N50 
Period 1 Period 2 Period 3 
DTN: MO0307SPAIPCHM.001 
Figure 20. Seepage Dripping Model pH Profiles for Commercial Spent Nuclear Fuel at 50C, 
Showing the Three Time Periods of the Abstraction 
The minimum pH for the 50C CSNF simulations is 2.8, and occurs during the first 50 years post 
breach. Table 63 contains the TSPA-LA input parameters for the 50C pH response surfaces. 
Table 63. Total System Performance Assessment Input pH input Parameters for the 
Response Surfaces at 50C 
Z = yo + ax + bya 
Period yo a b R-Squared 
1 4.01 2.05E-03 5.97E-01 1.00 
2 4.97 2.61E-02 4.61E-01 0.89 
3 5.61 4.37E-02 2.30E-01 0.83 
MO0307SPAIPCHM.001 
NOTE: aZ = pH,  = water flux (l/yr), y =Log10 (Fuel Exposure (fractional value)), yo, a, and b are coefficients of 
the equation. 
A uniform uncertainty distribution of -0.5 to 0.5 should be added to the calculated pH values.

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ANL-EBS-MD-000037 REV 02 97 of 140 July 2003 
Table 64 provides the limits for seepage flux and fuel exposure for the regression equations in 
Table 63. Should the TSPA-LA seepage flux exceed the upper limit in Table 64, then the pH 
should be set to a constant value of 8.5, the maximum pH in Figure 20. Note that even at the 
limits of the abstraction the uncertainty should still be applied. 
The possibility exists that in the TSPA-LA model runs the flux and fuel exposure could be less 
than the minimum values in Table 64. If the flux is lower than 0.15 l/yr then the pH should be 
calculated using 0.15 l/yr and the uncertainty range should be uniformly sampled and expanded 
to -1.0 to 1.0 pH units. The additional uncertainty will account for pH variations in the low flux 
scenarios. If the fuel exposure falls below the minimum value of 0.01, then the pH should be 
calculated at 0.01 with no additional uncertainty required, i.e., the -0.5 to 0.5 sampled range 
should be used. At low values of fuel exposure the CSNF has virtually no influence on the inpackage 
chemistry, therefore, no additional uncertainty is required. If the both the fuel exposure 
and water flux fall below the minimum values, 0.01 and 0.15 l/yr, respectively, then the 
expanded uncertainty range of -1.0 to 1.0 pH units should be applied. 
Table 64. Flux and Fuel Exposure Limits for the Commercial Spent Nuclear Fuel Seepage Dripping 
Model 50C pH Abstraction 
Period Flux Limits (l/yr) Fuel Exposure Limits 
Lower Uppera Lower Uppera 
1 0.15 1000 0.01 1.0 
2 0.15 135 0.01 1.0 
3 0.15 65 0.01 1.0 
NOTE: a Upper limit pH = 8.5 
The flux limits provided in Table 62 and 64 differ because of the difference in the pH range of 
the 25C versus 50C simulations. 
8.2.3 Commercial Spent Nuclear Fuel Seepage Dripping Model pH Temperature 
Dependence 
Figure 21 shows the pH histories of simulations executed over the temperature range from 
15 to 90C using the initial input of C22C25. The CSNF rate law coefficients were recalculated 
for this temperature range. These sensitivity analyses show that for CSNF (Attachment II, 
CS_sensitivity.xls) the pH varies systematically with temperature, this however does not 
legitimize the abstraction up to 90C without increasing the uncertainty with the temperature. 
The high-temperature simulations shown in Figure 21 do not account for the effects of 
evaporation on the composition of the seepage, which would increase the ionic strength of the 
reacting solution while decreasing the flux out of the waste package. While the general trends 
displayed in Figure 21 for the high-temperature runs are expected to be preserved, the level of 
uncertainty is increased compared to the low-temperature simulations.

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ANL-EBS-MD-000037 REV 02 98 of 140 July 2003 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
pH 
15C 
C22C25 
50C 
75C 
90C 
Source: Microsoft Excel Spreadsheet CS_sensitivity.xls in DTN: MO0307SPAIPCHM.001. 
Figure 21. Effect of Temperature on the pH of Run C22C25 
To implement a temperature functionality into the pH abstraction a linear interpolation should be 
applied between 25C and 50C, this interpolation may be used to extrapolate the pH down to 
15C and up to 95C. However, it is recommended that the uncertainty range for temperatures 
greater than 50C be expanded to -2.0 to 2.0 pH units, sampled uniformly and added to the 
abstracted pH value. For temperatures above 95C and below 100C, the pH calculated at 95C 
should be used. 
For certain combinations of fuel exposure (clad failure), water flux, and temperature, it may be 
possible for TSPA to calculate pH values of approximately 2. Likewise if the sampled 
uncertainty were equal to 2, then the pH would be zero. Therefore, it is necessary to set a lower 
limit on pH for TSPA purposes, that pH value is 1. If the TSPA calculates a pH value less than 
one, then TSPA must set the pH equal to one. Likewise the upper limit of pH should be set not to 
exceed 12. 
8.2.4 Commercial Spent Nuclear Fuel Seepage Dripping Model 25C Ionic Strength 
Abstraction 
The ionic strength abstractions are a hybrid of cumulative distributions and regression curves. 
The 25C CSNF seepage dripping model ionic strength abstraction will be presented as an 
example. 
Figure 22 displays the ionic strength profiles for the CSNF seepage dripping model runs at 25C. 
The period from 0 to 40 years is characterized by increasing ionic strength and is best

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ANL-EBS-MD-000037 REV 02 99 of 140 July 2003 
represented by a cumulative distribution type of abstraction. The period from 40 to 20,000 years 
can be represented as a function, where ionic strength is a function of seepage flux. It should be 
noted that for the low end seepage flux (0.15 l/yr) the ionic strength exceeds 1 molal, due to the 
accumulation of aqueous chromium species in the waste package effluent. These high chromium 
concentrations occur in the EQ6 simulations because there is no chrome bearing mineral phase in 
the thermodynamic database available to precipitate under the conditions of the simulation. 
Since the ionic strength abstraction is used strictly by the colloid sub-model in the TSPA-LA and 
colloidal suspensions are unstable at ionic strength values much less than 1 molal the 
significance of exceeding the limits of the B-dot activity coefficient equation is diminished. 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Ionic Strength (molal) 
C11C25B C12C25B 
C13C25B C21C25B 
C22C25B C23C25B 
C31C25B C32C25B 
C33C25B C11J25B 
C12J25B C13J25B 
C21J25B C22J25B 
C23J25B C31J25B 
C32J25B C33J25B 
C11N25B C12N25B 
C13N25B C21N25B 
C22N25B C23N25B 
C31N25B C32N25B 
C33N25B Lower IS Limit, 0-40 yrs 
Upper IS Limit, 0-40 yrs 0.05 molal upper limit 
20 years 
2 years 
DTN: MO0307SPAIPCHM.001 
Figure 22. Commercial Spent Nuclear Fuel Seepage Dripping Model 25C Ionic Strength Profiles 
Table 65 lists the criteria used in setting the ionic strength ranges. 
Table 65. Ionic Strength Criteria for the Commercial Spent Nuclear Fuel Seepage 
Dripping Model Runs at 25C 
Period post WP breach Ionic Strength Criteria 
0 - 40 years Min, 0.01, 0.05, Max 
40 to 20,000 years Max 
Inspection of Figure 22 reveals that the ionic strength increases to greater than 0.01 molal 
between about one and three years, or the probability of the ionic strength being less than 0.01 
molal at any point in time (between 0 and 40 years) is about 2 in 40, or 5 percent. Likewise, the 
probability of the ionic strength being less than 0.05 molal is about 20 in 40 or 50 percent, and

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ANL-EBS-MD-000037 REV 02 100 of 140 July 2003 
finally the probability of the ionic strength being less than 0.13 (the upper limit for the 0 to 40 
year period) is 100 percent. 
Table 66 provides the cumulative distribution of the ionic strength for the period from 0 to 40 
years, which should be used as input for TSPA-LA. 
Table 66. Commercial Spent Nuclear Fuel Seepage Dripping Model 25C 0 to 40-year 
Ionic Strength Cumulative Distribution for TSPA-LA 
Time Period post 
WP Breach 
(years) 
Ionic Strength 
(molal) 
Probability 
2.5E-03 0.0 
0.01 0.05 
0.05 0.50 
0 - 40 
1.3E-01 1.0 
DTN: MO0307SPAIPCHM.001 
Figure 23 displays the ionic strength abstraction for the CSNF seepage dripping model at 25C, 
for the period from 40 to 20,000 years. This abstraction, and the ionic strength abstractions to 
follow, were all generated by plotting in Microsoft Excel the maximum ionic strength for the 
specified period versus the base ten logarithm of seepage flux and then using the Add 
Trendline from the Chart submenu to fit the exponential function to the data. 
y = 0.5711e-2.1931x 
R2 = 0.9964 
0.0 
0.5 
1.0 
1.5 
2.0 
2.5 
3.0 
3.5 
4.0 
4.5
-1.0 0.0 1.0 2.0 
log (flux(l/yr)) 
Ionic Strength (molal) 
Max IS, 40 - 20k years 
Expon. (Max IS, 40 - 20k 
years) 
DTN: MO0307SPAIPCHM.001 
Figure 23. Commercial Spent Nuclear Fuel Seepage Dripping Model 25C 40 to 20,000-Year 
Ionic Strength Abstraction

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The ionic strength abstraction for the CSNF seepage dripping model at 25C from 40 to 20,000 
years is given in Table 67. 
Table 67. Commercial Spent Nuclear Fuel Seepage Dripping Model 25C Ionic Strength 40 to 
20,000-Year Abstraction 
y = aEXP(bx)a 
a b 
0.5711 -2.1931 
R2 0.9964 
DTN: MO0307SPAIPCHM.001 
NOTE: ay = ionic strength,  = log10(flux(l/yr)) 
An uncertainty factor such as that applied in Section 8.1.2 should also be used here for TSPA-LA 
purposes for both the 0 to 40 year and 40 to 20,000 year abstractions. A uniform uncertainty 
range of -0.6 to 0.6 multiplied by the sampled value should be added to the sampled value. Ionic 
strength = ISsampled + (Uncertaintysampled  ISsampled). 
The limits of flux for the abstraction are given in Table 68. The upper flux limit was calculated 
using the ionic strength of J-13 (Table 54), i.e., at flux values greater than 210 l/yr the abstracted 
ionic strength would be less than that of the J-13. Note that even at the limits of the abstraction 
the uncertainty should still be applied. 
Table 68. Commercial Spent Nuclear Fuel Seepage Dripping Model 25C Ionic Strength 
Flux Limits for 40 to 20,000-Year Abstraction 
Flux Limits (l/yr) 
Lower Uppera 
0.15 210 
DTN: MO0307SPAIPCHM.001 
NOTE: aUpper Limit set by J-13 at 0.0035 molal 
If the flux is less than the minimum, then the ionic strength should be evaluated using the lower 
flux limit from Table 68. Likewise, if the flux is greater than the upper limit, then the ionic 
strength should be evaluated using the upper limit value from Table 68. 
8.2.5 Commercial Spent Nuclear Fuel Seepage Dripping Model 50C Ionic Strength 
Abstraction 
The CSNF seepage dripping model 50C ionic strength abstraction takes the same form as the 
25C abstraction.

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ANL-EBS-MD-000037 REV 02 102 of 140 July 2003 
Table 69. Commercial Spent Nuclear Fuel Seepage Dripping Model 50C 0 to 40-Year 
Ionic Strength Cumulative Distribution for TSPA-LA 
Time Period post 
WP Breach 
(years) 
Ionic Strength 
(molal) 
Probability 
2.3E-03 0.0 
0.01 0.05 
0.05 0.50 
0 - 40 
1.3E-01 1.0 
DTN: MO0307SPAIPCHM.001 
Table 70. Commercial Spent Nuclear Fuel Seepage Dripping Model 50C Ionic 
Strength 40 to 20,000-Year Abstraction 
y = aEXP(bx)a 
a b 
0.5551 -2.2731 
R2 0.988 
DTN: MO0307SPAIPCHM.001 
NOTE: ay = ionic strength,  = log10(flux(l/yr)). 
An uncertainty factor such as that applied in Section 8.1.2 should also be used here for TSPA-LA 
purposes for both the 0 to 40 year and 40 to 20,000 year abstractions. A uniform uncertainty 
range of -0.6 to 0.6 multiplied by the sampled value should be added to the sampled value. Ionic 
strength = ISsampled + (Uncertaintysampled  ISsampled). 
The flux limits for the CSNF seepage dripping model 50C ionic strength abstraction are listed in 
Table 71. 
Table 71. Commercial Spent Nuclear Fuel Seepage Dripping Model 50C Ionic Strength 
Flux Limits for 200 to 20,000 Year Abstraction 
Flux Limits (l/yr) 
Lower Uppera 
0.15 170 
NOTE: aUpper Limit set by J-13 at 0.0035 molal 
If the flux is less than the minimum, then the ionic strength should be evaluated using the lower 
flux limit from Table 71. Likewise, if the flux is greater than the upper limit, then the ionic 
strength should be evaluated using the upper limit value from Table 71. 
8.2.6 Commercial Spent Nuclear Fuel Seepage Dripping Model Ionic Strength 
Temperature Dependence 
Figure 24 shows the ionic strength results of EQ6 simulations at progressively higher 
temperatures. The results plotted in Figure 24 show that temperature had a minor influence on 
ionic strength. Although evaporation from the interior of the waste package is not expected to be 
great, due to high humidity in the waste package interior, evaporation could still influence the

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ANL-EBS-MD-000037 REV 02 103 of 140 July 2003 
ionic strength as the temperature increases. Therefore, fully decoupling the temperature and 
ionic strength could result in misleading ionic strength values. 
For TSPA-LA at temperatures from 15C to 50C the 0 to 40 year abstraction should use the 
cumulative distribution in Table 69 (50C abstraction), which covers a slightly broader range of 
ionic strength than the 25C abstraction. For the 40 to 20,000 year abstraction in the same 
temperature range, a linear relationship should be established between the values calculated 
using the relationships provided in Tables 67 and 70, honoring the limits of these relationships as 
defined in Tables 68 and 71. An uncertainty factor such as that applied in Section 8.1.2 should 
also be used here, for TSPA-LA purposes, for both the 0 to 40 year and 40 to 20,000 year 
abstractions. A uniform uncertainty range of -0.6 to 0.6 multiplied by the sampled value should 
be added to the sampled value. Ionic strength = ISsampled + (Uncertaintysampled  ISsampled). 
For TSPA-LA, at temperatures greater than 50C, implementation of a cumulative distribution 
that encompasses the range of ionic strength values displayed in Figure 22, of 2E-03 to 4 molal, 
with the following ionic strength  probability pairs: (2E-03, 0), (0.05, 0.33), and (4.0, 1.0). A 
uniform uncertainty range of -0.6 to 0.6 multiplied by the sampled value should be added to the 
sampled value. Ionic strength = ISsampled + (Uncertaintysampled  ISsampled). 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
Ionic Strength (molal) 
15C 
C22C25 
50C 
75C 
90C 
Source: Microsoft Excel Spreadsheet CS_sensitivity.xls in DTN: MO0307SPAIPCHM.001. 
Figure 24. Effect of Temperature on the Ionic Strength of Run C22C25

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 104 of 140 July 2003 
8.2.7 Commercial Spent Nuclear Fuel Seepage Dripping Model 25C and 50C Fluoride 
Abstractions 
The fluoride profiles for the 25C CSNF seepage dripping model simulations are plotted in 
Figure 25 together with the limits of the abstraction. Note that other than the input seepage 
compositions, there are no other sources of fluorine in the CSNF waste package. The dip in 
fluoride concentration at about one year (Figure 25) is the result of fluorapatite precipitation with 
subsequent dissolution as the concentration recovers to approximately 1E-04 molal. This early 
fluoride minimum was not considered for the abstraction because it occurs so early, and the 
minimum of the average fluoride concentrations was set as the lower bound of the abstraction 
instead.
1.0E-16 
1.0E-15 
1.0E-14 
1.0E-13 
1.0E-12 
1.0E-11 
1.0E-10 
1.0E-09 
1.0E-08 
1.0E-07 
1.0E-06 
1.0E-05 
1.0E-04 
1.0E-03 
1.0E-02 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Fluoride (molal) 
C11C25B C12C25B C13C25B 
C21C25B C22C25B C23C25B 
C31C25B C32C25B C33C25B 
C11J25B C12J25B C13J25B 
C21J25B C22J25B C23J25B 
C31J25B C32J25B C33J25B 
C11N25B C12N25B C13N25B 
C21N25B C22N25B C23N25B 
C31N25B C32N25B C33N25B 
Lower F Limit Upper F Limit 
DTN: MO0307SPAIPCHM.001 
Figure 25. Commercial Spent Nuclear Fuel Seepage Dripping Model 25C Fluoride 
Profiles and Abstraction 
If the fluoride abstraction is implemented in TSPA-LA then the fluoride values should be 
uniformly sampled within the range specified in Table 72. Given that the abstraction covers a 
wider range than what Figure 25 suggests, it is not necessary to apply an uncertainty value to the 
sampled fluoride value.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 105 of 140 July 2003 
Table 72. Commercial Spent Nuclear Fuel Seepage Dripping Model 25 and 50C Fluoride Abstraction 
Temperature Lower F Limit Upper F Limit 
Distribution Uniform 
(C) Mol/kg 
25 3.01E-05 1.21E-03 
50 1.05E-04 1.83E-03 
DTN: MO0307SPAIPCHM.001 
Table 72 also provides the range of fluoride concentrations for the CSNF seepage dripping 
model 50C abstraction. Like the 25C fluoride profiles, there is a low concentration dip at early 
time, which has been omitted from the abstraction. No temperature functionality is included for 
the fluoride abstraction. 
8.2.8 Commercial Spent Nuclear Fuel Seepage Dripping Model 25C and 50C Chloride 
Abstractions 
Figure 26 shows the CSNF seepage dripping model 25C chloride profiles and abstraction limits. 
The CSNF seepage dripping model 50C chloride profiles are not plotted because they show the 
same trends as the 25C profiles. 
Table 73. Commercial Spent Nuclear Fuel Seepage Dripping Model 25 and 50C Chloride Abstractions 
Temperature Lower Cl Limit Upper Cl Limit 
Distribution Uniform 
(C) Mol/kg 
25 2.01E-04 2.60E-03 
50 2.01E-04 3.93E-03 
DTN: MO0307SPAIPCHM.001 
The chloride abstractions for 25 and 50C are provided in Table 73. No temperature 
functionality is provided for the chloride abstractions.

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ANL-EBS-MD-000037 REV 02 106 of 140 July 2003 
1.0E-04 
1.0E-03 
1.0E-02 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Chloride (molal) 
C11C25B C12C25B C13C25B 
C21C25B C22C25B C23C25B 
C31C25B C32C25B C33C25B 
C11J25B C12J25B C13J25B 
C21J25B C22J25B C23J25B 
C31J25B C32J25B C33J25B 
C11N25B C12N25B C13N25B 
C21N25B C22N25B C23N25B 
C31N25B C32N25B C33N25B 
Lower Cl Limit Upper Cl Limit 
DTN: MO0307SPAIPCHM.001 
Figure 26. Commercial Spent Nuclear Fuel Seepage Dripping Model 25C 
Chloride Profiles And Abstraction 
8.3 CODISPOSED N REACTOR WATER VAPOR CONDENSATION IN-PACKAGE 
CHEMISTRY MODEL ABSTRACTION 
For TSPA-LA the CDNR water vapor condensation model abstractions should be implemented 
when water vapor enters a codisposal waste package and condenses and reacts with the waste 
package components-this is a non-dripping model. There is no flux dependence in the water 
vapor condensation model abstractions since water does not exit the waste package. 
8.3.1 Codisposed N Reactor Water Vapor Condensation pH Abstraction 
Figure 23 displays the CDNR water vapor condensation model pH profiles and abstraction.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 107 of 140 July 2003 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
10.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
comp1 
comp2 
comp3 
comp4 
comp5 
comp6 
comp7 
comp8 
Lower pH Limit 
Upper pH Limit 
DTN: MO0307SPAIPCHM.001 
Figure 27. Codisposed N Reactor Water Vapor Condensation pH Profiles and Abstraction 
The pH range in the abstraction is not discretized in time because the relative time when waste 
package components will interact is unknown. Thus, in TSPA-LA the entire pH range specified 
in Table 74 should be uniformly sampled, and at 25C a uniformly distributed uncertainty range 
of -0.5 to 0.5 should be added to the sampled value of pH. At temperatures other than 25C a 
uniform uncertainty range of -2 to 2 pH units should be uniformly sampled and added to the 
sampled pH. The pH limits are 1 and 12, i.e., if the abstracted pH with included uncertainty is 
less than pH = 1, or greater than pH = 12, then the pH should be set to the limiting value.

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ANL-EBS-MD-000037 REV 02 108 of 140 July 2003 
Table 74. Codisposed N Reactor Water Vapor Condensation pH Abstraction 
Minimum pH Maximum pH 
Comp1 5.20 5.84 
Comp2 5.09 5.99 
Comp3 5.34 6.05 
Comp4 4.88 6.22 
Comp5 4.11 7.21 
Comp6 4.11 7.21 
Comp7 4.12 9.56 
Comp8 5.41 9.04 
TSPA-LA Input Range 
Uniform Distribution 
Maximum 9.56 
Minimum 4.11 
DTN: MO0307SPAIPCHM.001. 
8.3.2 Codisposed N Reactor Water Vapor Condensation Ionic Strength Abstraction 
The ionic strength profiles for the CDNR water vapor condensation model are displayed in 
Figure 28, as are the break points used in defining the cumulative distribution. The lower limit of 
the range is not plotted in Figure 28 because it is less than the minimum value of the Y-axis. The 
ionic strength abstraction for the CDNR water vapor condensation model was broken into three 
periods due to the large spread in the data, e.g., the profiles cross the 0.05 threshold from 12 to 
300 years. 
1.0E-04 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
time(yrs) 
Ionic Strength (molal) 
comp1 
comp2 
comp3 
comp4 
comp5 
comp6 
comp7 
comp8 
Upper IS Limit 
0.05 molal colloid stability limit 
300 years 
55 years 
DTN: MO0307SPAIPCHM.001 
Figure 28. Codisposed N Reactor Water Vapor Condensation Ionic Strength Profiles and Abstraction

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 109 of 140 July 2003 
The first period runs from 0 to 55 years, the second period from 55 to 300 years, and the third 
from 300 to 20,000 years. The probability was calculated using the number of simulations that 
were below the threshold rather the duration below the threshold. For example, for the 0 to 55 
year period, three simulations, comp4, comp5, and comp8 were all less than 0.05 molal, so that 
the probability of encountering an ionic strength value less than 0.05 molal is 3/8 or 0.38. For 
the 55 to 300 year period the probability of encountering an ionic strength value between 0.01 
and 0.05 molal is 2/8 or 0.25. The parameters for the cumulative distributions for the three 
periods are listed in Table 75. 
Table 75. Codisposed N Reactor Water Vapor Condensation Model Ionic Strength 
Cumulative Distribution 
Time Period post 
WP Breach 
(years) 
Ionic Strength 
(molal) 
Probability 
3.9E-06 0.0 
0.05 0.38 0 to 55 
0.85 1.0 
0.01 0.0 
0.05 0.25 55 to300 
0.85 1.0 
0.05 0 
0.1 0.13 300 to 20,000 
0.85 1.0 
DTN: MO0307SPAIPCHM.001. 
An uncertainty factor, as described in Section 8.1.2, should be applied to the CDNR water vapor 
condensation model ionic strength abstraction. At 25C a uniformly sampled uncertainty value 
from the range of -0.6 to 0.6 (Section 6.7) multiplied by the sampled value should be added to 
the sampled value. At temperatures other than 25C, a uniformly sampled uncertainty value 
from the range of 0.99 to 0.99 multiplied by the sampled value should be added to the sampled 
value. Ionic strength = ISsampled + (Uncertaintysampled  ISsampled). 
8.3.3 Codisposed N Reactor Water Vapor Condensation Fluoride Abstraction 
Although there is no dissolved fluoride in the water vapor, the HLWG does contain fluorine 
(Table 9), therefore, a fluoride abstraction is provided for the CDNR water vapor condensation 
model. Figure 29 displays the fluoride profiles for the CDNR ensembles. The variation in the 
fluoride concentration (Figure 29) is due to precipitation/dissolution reactions of minor fluoride 
bearing phases (fluorite and fluorapatite). 
Table 76 provides the minimum and maximum fluoride concentrations. The minimum fluoride 
concentration is set by the incoming water, which by convention, is set to 1E-16 molal. If the 
fluoride abstraction should be implemented in TSPA-LA, then the range between the minimum 
and the maximum fluoride values (Table 76) should be uniformly sampled. Since the range to be 
sampled encompasses 12 orders of magnitude, it is unnecessary to include any further 
uncertainty on the estimated fluoride concentration.

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ANL-EBS-MD-000037 REV 02 110 of 140 July 2003 
Table 76. Codisposed N Reactor Water Vapor Condensation Model Fluoride Abstraction 
Lower F Limit Upper F Limit 
Minimum Maximum 
(mol/kg) 
1.0E-16 8.3E-04 
DTN: MO0307SPAIPCHM.001. 
1.0E-16 
1.0E-15 
1.0E-14 
1.0E-13 
1.0E-12 
1.0E-11 
1.0E-10 
1.0E-09 
1.0E-08 
1.0E-07 
1.0E-06 
1.0E-05 
1.0E-04 
1.0E-03 
1.0E-02 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
time(yrs) 
Fluoride (molal) 
comp1 
comp2 
comp3 
comp4 
comp5 
comp6 
comp7 
comp8 
DTN: MO0307SPAIPCHM.001 
Figure 29. Codisposed N Reactor Water Vapor Condensation Fluoride Profiles 
8.4 CODISPOSED N REACTOR SEEPAGE DRIPPING MODEL IN-PACKAGE 
CHEMISTRY MODEL ABSTRACTION 
In the TSPA-LA model the CDNR seepage dripping model abstractions should be implemented 
when seepage enters a codisposal waste package, reacts with the waste package components, and 
then exits the waste package-this is a dripping model. 
8.4.1 Codisposed N Reactor Seepage Dripping Model 25C pH Abstraction 
Figure 30 displays the CDNR pH profiles for the seepage dripping model at 25C, also displayed 
is the 0 to 60 years pH abstraction range. The pH abstraction was split into two time periods, 0 
to 60 years, and 60 to 20,000 years. Since the low end of the pH range for first time period was 
relatively high (approximately pH = 5.2, Figure 30) compared to that observed for the CSNF 
(approximately pH = 3.2, Figure 17) a decision was made to not parameterize the early-time 
CDNR pH abstraction. Instead, the maximum and minimum pH for the 0- to 60-year period was

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ANL-EBS-MD-000037 REV 02 111 of 140 July 2003 
calculated for each simulation and a range was set. Table 77 provides the minimum and 
maximum pH values and the values to be uniformly sampled in TSPA-LA. A uniform 
uncertainty range of -0.5 to 0.5 pH units should be sampled and added to the sampled pH value. 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
8.5 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
D13C25 
D13J25 
D13N25 
D23C25 
D23J25 
D23N25 
D33C25 
D33J25 
D33N25 
Upper pH Limit, 0 - 60 yrs 
Lower pH Limit, 0 - 60 yrs 
DTN: MO0307SPAIPCHM.001 
Figure 30. Codisposed N Reactor Seepage Dripping Model 25C pH Profiles and 0 to 60 Year 
Abstraction Limits

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ANL-EBS-MD-000037 REV 02 112 of 140 July 2003 
Table 77. Codisposed N Reactor Seepage Dripping Model 25C pH Abstraction, 0 to 60 Years 
Flux (l/yr) Minimum Maximum 
D13C25 0.15 5.45 8.09 
D23C25 1.5 5.40 8.09 
D33C50 15 5.30 8.09 
D13J25 0.15 5.22 8.09 
D23J25 1.5 5.22 8.09 
D33J50 15 5.41 8.09 
D13N25 0.15 5.41 8.36 
D23N25 1.5 5.41 8.36 
D33N50 15 5.31 8.36 
TSPA-LA Input Range 
Uniform Distribution 
Minimum 5.22 
Maximum 8.36 
DTN: MO0307SPAIPCHM.001 
Figure 30 shows three distinct groups in the pH profiles for the 60 to 20,000 year time period, 
these groupings are independent of the initial seepage composition but are a function of the flux. 
Figure 31 is a plot of the stabilized pH versus the log10 of the flux (l/yr), and the regression line. 
Rather than use the maximum or minimum pH as the guiding criteria the stable pH for the 60- to 
20,000-year period was used in generating the abstraction. 
y = 0.3815x + 5.7584 
R2 = 0.9704 
5.3 
5.4 
5.5 
5.6 
5.7 
5.8 
5.9 
6.0 
6.1 
6.2 
6.3 
-1 -0.5 0 0.5 1 1.5 
log [flux(l/yr)] 
pH 
Stable pH 
Linear (Stable pH) 
DTN: MO0307SPAIPCHM.001 
Figure 31. Codisposed N Reactor Seepage Dripping Model 25C pH and Abstraction for the 60 to 20,000 
Year Period

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ANL-EBS-MD-000037 REV 02 113 of 140 July 2003 
The pH abstraction (Table 78) was generated by plotting the stabilized pH versus the log10 of 
the flux and selecting the Add Trendline from the Chart submenu and picking the type as 
linear. The results are the regression line, equation and R-squared value displayed in Figure 31. 
A uniform uncertainty range of -0.5 to 0.5 pH units should be sampled and added to the 
calculated pH value. 
Table 78. Codisposed N Reactor Seepage Dripping Model 25C pH Abstraction, 60 to 20,000 Years 
y = mx + ba 
m b 
0.3815 5.7584 
R2 = 0.9704 
DTN: MO0307SPAIPCHM.001. 
NOTE: ay = pH,  = log10(flux(l/yr)) 
Examination of Figures 8 and 9 shows that the CDNR water vapor condensation model has the 
potential to generate pH values near 10. Although the output from the CDNR seepage dripping 
model (Figure 30) for the 60 to 20,000 year period shows the pH does not exceed approximately 
6.3, it is possible that high pH values (8 to 10) could also result from the CDNR seepage 
dripping model should seepage contact the HLWG and exit the waste package without 
contacting metal components. This scenario also includes the possibility that a waste package 
may contain only HLWG, which would have a much greater potential for generating high-pH 
solutions. 
Therefore, to include the possibility of occurrence of high-pH conditions in the TSPA-LA model, 
a mixed-relationship type abstraction is recommended, where 85 percent probability is assigned 
to the relationship in Table 78, and 15 percent probability is assigned to the 6 to 11 pH range. 
The 6 to 11 pH range should be sampled uniformly, and no uncertainty term should be added to 
this sampled range. 
Table 79 provides the flux limits for the pH abstraction. At flux values greater than 1000 l/yr the 
pH should be set to a constant value of 8.3. The upper flux limit was set by the sensitivity 
analysis (Attachment II, CS_Sensitivity.xls) which showed that above 1000 l/yr the seepage 
chemistry was largely unchanged. 
The possibility exists that in the TSPA-LA model runs the flux could be less than the minimum 
value in Table 79. If the flux is lower than 0.15 l/yr then the pH should be calculated using 0.15 
l/yr and the uncertainty range should be uniformly sampled and expanded to -1.0 to 1.0 pH units. 
The additional uncertainty will account for pH variations in the low flux scenarios. 
Table 79. Codisposed N Reactor Seepage Dripping Model 25C pH Abstraction Flux Limits for 60 to 
20,000 Year Abstraction 
Flux Limits (l/yr)a 
Lower Upper 
0.15 1000 
NOTE: aUpper Limit pH = 8.3

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ANL-EBS-MD-000037 REV 02 114 of 140 July 2003 
8.4.2 Codisposed N Reactor Seepage Dripping Model 50C pH Abstraction 
The pH profiles for the CDNR seepage dripping model simulations at 50C are plotted in 
Figure 32. The 0 to 60 year pH range is provided in Table 80, this range should be sampled for 
TSPA-LA and an uncertainty range of -0.5 to 0.5 pH units should be sampled and added to the 
sampled pH value. 
Table 80. Codisposed N Reactor Seepage Dripping Model 50C pH Abstraction, 0 to 60 Years 
TSPA-LA Input 
Uniform Distribution 
Lower pH Limit Upper pH Limit 
5.93 8.49 
DTN: MO0307SPAIPCHM.001. 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
8.5 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
D13C50 
D13J50 
D13N50 
D23C50 
D23J50 
D23N50 
D33C50 
D33J50 
D33N50 
Upper pH Limit, 0-60 yrs 
Lower pH Limit, 0-60 yrs 
DTN: MO0307SPAIPCHM.001 
Figure 32. Codisposed N Reactor Seepage Dripping Model 50C pH Profiles and 0 to 60-Year 
Abstraction Limits 
Note that the pH drop at 20,000 years (Figure 32) for the D13 runs results from the depletion of 
the HLWG, and only occurs in the low flux runs because the acid generating potential of the 
304L components is more exaggerated at low flux values. 
The regression equation describing the pH as a function of the log10 of the flux is provided in 
Table 81. Like the 60 to 20,000 year CDNR seepage dripping model 25C pH abstraction the 
stabilized pH was also used for the 50C abstraction to generate the regression equation. A

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ANL-EBS-MD-000037 REV 02 115 of 140 July 2003 
uniform uncertainty range of -0.5 to 0.5 pH units should be sampled and added to the calculated 
pH value. 
Table 81. Codisposed N Reactor Seepage Dripping Model 50C pH Abstraction, 60 to 20,000 Years 
y = mx + ba 
M B 
0.4847 6.1844 
R2 = 0.9967 
DTN: MO0307SPAIPCHM.001. 
NOTE: ay = pH,  = log10(flux(l/yr)) 
Examination of Figures 8 and 9 shows that the CDNR water vapor condensation model has the 
potential to generate pH values near 10. Although the output from the CDNR seepage dripping 
model (Figure 32) for the 60 to 20,000 year period shows the pH does not exceed approximately 
6.3, it is considered possible that high pH values (8 to 10) could also result from the CDNR 
seepage dripping model should seepage contact the HLWG and exit the waste package without 
contacting metal components. This scenario also includes the possibility that a waste package 
may contain only HLWG, which would have a much greater potential for generating high-pH 
solutions. 
Therefore, to include the possibility of occurrence of high-pH conditions in the TSPA-LA model, 
a mixed-relationship type abstraction is recommended, where 85 percent probability is assigned 
to the relationship in Table 81, and 15 percent probability is assigned to the 6 to 11 pH range. 
The 6 to 11 pH range should be sampled uniformly, and no uncertainty term should be added to 
this sampled range. 
Table 82 provides the flux limits for the pH abstraction. At flux values greater than 1000 l/yr the 
pH should be set to a constant value of 8.5. The upper flux limit was set by the sensitivity 
analysis (Attachment II, CS_Sensitivity.xls) which showed that above 1000 l/yr the seepage 
chemistry was largely unchanged. 
The possibility exists that in the TSPA-LA model runs the flux could be less than the minimum 
value in Table 82. If the flux is lower than 0.15 l/yr then the pH should be calculated using 0.15 
l/yr and the uncertainty range should be uniformly sampled and expanded to -1.0 to 1.0 pH units. 
The additional uncertainty will account for pH variations in the low flux scenarios. 
Table 82. Codisposed N Reactor Seepage Dripping Model 50C pH Abstraction Flux 
Limits for 60 to 20,000 Year Abstraction 
Flux Limits (l/yr) 
Lower Uppera 
0.15 1000 
DTN: MO0307SPAIPCHM.001. 
NOTE: aUpper Limit pH = 8.5

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8.4.3 Codisposed N Reactor Seepage Dripping Model pH Temperature Dependence 
Figure 33 shows how the pH history for D23C25 varies as a function of temperature. Both the 
HLWG and N reactor fuel rate coefficients were corrected for temperature. Unlike the CSNF pH 
histories (Figure 21), the CDNR pH response is not systematic, i.e., it is not possible to 
extrapolate the regression equations beyond their range without the risk of abstracting pH values 
which are out of agreement with process model output. Therefore, it is recommended that only a 
limited temperature functionality be implemented for the CDNR seepage dripping model pH 
abstraction. 
4.0 
4.5 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
8.5 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
pH 
D23C25 
90C 
50C 
75C 
15C 
Source: Microsoft Excel Spreadsheet CDNR_sensit.xls in DTN: MO0307SPAIPCHM.001. 
Figure 33. Effect of Temperature on the pH of Run D23C25 
The early-time distribution (0 to 60 years post breach) should include the extreme values from 
Figure 33. The minimum pH value is 4.7 for the 15C simulation, and the maximum pH is 8.1 
for the 75C simulation. Uniform sampling in the pH range from 4.7 to 8.1 will provide 
adequate coverage for temperatures between 15 and 95C. For pH values sampled between 25 
and 50C. with a uniformly sampled uncertainty range of -0.5 to 0.5 should be applied. For 
temperatures below 25 and above 50C a uniformly sampled uncertainty range of -2 to 2 should 
be applied. 
For the period beyond 60 years post waste package breach, the mixed-relationship pH abstraction 
will be used. The TSPA-LA code should use a single selector function to choose either the pHflux 
relationship (Tables 78 and 81) or a uniform distribution for the 25 and 50C abstractions.

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If the distribution is chosen, then a single distribution uniformly sampled in the pH range from 6 
to 11 independent of temperature should be implemented in TSPA-LA. Using a single 
distribution to represent both temperatures is preferred over using two distributions and then 
interpolating between the sampled values because interpolating between sampled values has no 
basis for the temperature functionality. The expanded sampled range of 6 to 11 is sufficient to 
account for uncertainty and temperatures up to 95C ; thus, it will not be necessary to include an 
additional uncertainty term to this sampled pH value. 
If the pH-flux relationships are chosen, then the 25C and 50C regression equations (Tables 78 
and 81) should be used, and the pH values calculated at 25C and 50C can define a linear (pH 
versus temperature) relationship to estimate the pH at temperatures between 15C and 95C. If 
the temperature is below 25C, or above 50C, then the extrapolated pH value should be 
considered as more uncertain than pH values calculated within 25C to 50C temperature range. 
Additional uncertainty should be applied to pH values extrapolated outside of the 25C to 50C 
range, and a uniformly sampled distribution in the range from -2.0 to 2.0 pH units should be 
applied. Application of a uniformly sampled distribution from 0.5 to 0.5 pH units should be 
added for pH values interpolated in the temperature range between 25C to 50C unless the flux 
is less than 0.15 l/yr in which case the uniformly sampled distribution should be expanded to -1.0 
to 1.0 pH units. The lower and upper limits of pH should be set by TSPA as equal to 1 and 12, 
respectively. In other words, should a pH value with added uncertainty be less than one, or 
greater than 12, TSPA should set that pH value to its limiting value. If the temperature is less 
than 15C, or greater 95C, then the pH should be evaluated at 15C, or 95C, respectively. 
8.4.4 Codisposed N Reactor Seepage Dripping Model 25C Ionic Strength Abstraction 
Figure 34 displays the ionic strength profiles for the CDNR seepage dripping model runs at 
25C. The period from 0 to 80 years is characterized by increasing ionic strength and is best 
represented by a cumulative distribution abstraction (Table 83). The period from 80 to 20,000 
years can be represented by the stabilized ionic strength as a function of flux. 
Table 83 provides the cumulative distribution data for the period between 0 and 80 years for the 
CDNR seepage dripping model 25C abstraction. Where the 0.01 ionic strength cutoff was set to 
7/80, or 0.09 probability, and the 0.05 ionic strength cutoff set to 14/80, or 0.18 probability. 
Table 83. Codisposed N Reactor Seepage Dripping Model 25C Ionic Strength 0 to 80 Years Cumulative 
Distribution 
Time Period 
Post WP breach 
(years) 
Ionic Strength 
(molal) 
Probability 
2.4E-03 0.0 
0.01 0.09 
0.05 0.18 
0 to 80 
0.35 1.0 
DTN: MO0307SPAIPCHM.001.

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ANL-EBS-MD-000037 REV 02 118 of 140 July 2003 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Ionic Strength (molal) 
D13C25 
D13J25 
D13N25 
D23C25 
D23J25 
D23N25 
D33C25 
D33J25 
D33N25 
Upper IS Limit, 0 - 80 yrs 
Lower IS Limit, 0 - 80 yrs 
0.05 molal upper colloid stability limit 
7 years 
14 years 
DTN: MO0307SPAIPCHM.001 
Figure 34. Codisposed N Reactor Seepage Dripping Model 25C Ionic Strength Profiles and 0 to 80 Year 
Abstraction Limits 
The ionic strength abstraction function for the CDNR seepage dripping model at 25C from 80 to 
20,000 years is given in Table 84. 
Table 84. Codisposed N Reactor Seepage Dripping Model 25C Ionic Strength 80 to 20,000 Year 
Abstraction 
y = aEXP(bx)a 
a b 
0.5206 -2.2168 
R2 = 0.9996 
DTN: MO0307SPAIPCHM.001 
NOTE: ay = ionic strength,  = log10(flux(l/yr)) 
An uncertainty factor such as that applied in Section 8.1.2 should also be used here for TSPA-LA 
purposes for both the 0 to 80 year and the 80 to 20,000 year abstractions. A uniform uncertainty 
range of -0.6 to 0.6 multiplied by the sampled value should be added to the sampled value. Ionic 
strength = ISsampled + (Uncertaintysampled  ISsampled). 
The limits of flux for the abstraction are given in Table 85. The upper flux limit was set using 
the ionic strength of J-13 (Table 54) as the lower ionic strength limit, i.e., at flux values greater 
than 180 l/yr the abstracted ionic strength would be less than that of the most dilute input 
solution, in this case J-13.

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ANL-EBS-MD-000037 REV 02 119 of 140 July 2003 
Table 85. Codisposed N Reactor Fuel Seepage Dripping Model 25C Ionic Strength Flux 
Limits for 80 to 20,000 Year Abstraction 
Flux Limits (l/yr) 
Lower Uppera 
0.15 180 
NOTE: a Upper Limit set by J-13 at 0.0035 molal 
If the flux is less than the minimum, then the ionic strength should be evaluated using the lower 
flux limit from Table 85. Likewise, if the flux is greater than the upper limit, then the ionic 
strength should be evaluated using the upper limit value from Table 85. 
8.4.5 Codisposed N Reactor Seepage Dripping Model 50C Ionic Strength Abstraction 
Figure 35 displays the ionic strength profiles for the CDNR seepage dripping model runs at 
50C. The period from 0 to 80 years is characterized by increasing ionic strength and is best 
represented by a cumulative distribution abstraction. The period from 80 to 20,000 years can be 
represented by the stabilized ionic strength as a function of flux. The ionic strength abstraction 
for the CDNR seepage dripping model at 50C from 0 to 80 years is given in Table 86. Where 
the 0.01 ionic strength cutoff was set to 1.8/80, or 2.3E-02 probability, and the 0.05 ionic 
strength cutoff set to 10/80, or 0.13 probability. 
Table 86. Codisposed N Reactor Seepage Dripping Model 50C Ionic Strength Abstraction 0 to 80 Years 
Time Period post 
WP Breach 
(years) 
Ionic Strength 
(molal) 
Probability 
2.2E-03 0.0 
0.01 2.3E-02 
0.05 0.13 
0 to 80 
0.33 1.0 
DTN: MO0307SPAIPCHM.001

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ANL-EBS-MD-000037 REV 02 120 of 140 July 2003 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Ionic Strength (molal) 
D13C50 
D13J50 
D13N50 
D23C50 
D23J50 
D23N50 
D33C50 
D33J50 
D33N50 
Upper IS Limit, 0-80 yrs 
Lower IS Limit, 0- 80 yrs 
0.05 molal upper stability limit for colloids 
10 years 
1.8 years 
DTN: MO0307SPAIPCHM.001 
Figure 35. Codisposed N Reactor Seepage Dripping Model 50C Ionic Strength Profiles and 
0 to 80 Year Abstraction Limits 
The ionic strength abstraction for the CDNR seepage dripping model at 50C from 80 to 20,000 
years is given in Table 87. 
Table 87. Codisposed N Reactor Seepage Dripping Model 50C Ionic Strength 80 to 20,000 
Year Abstraction 
y = aEXP(bx)a 
a b 
0.6293 -2.1549 
R2 = 0.9996 
DTN: MO0307SPAIPCHM.001 
NOTE: ay = ionic strength,  = log10(flux(l/yr)) 
An uncertainty factor such as that applied in Section 8.1.2 should also be used here for TSPA-LA 
purposes for both the 0 to 80 year and the 80 to 20,000 year abstractions. A uniform uncertainty 
range of -0.6 to 0.6 multiplied by the sampled value should be added to the sampled value. Ionic 
strength = ISsampled + (Uncertaintysampled  ISsampled). 
The limits of flux for the abstraction are given in Table 88. The upper flux limit was set using 
the ionic strength of J-13 (Table 54) as the lower ionic strength limit, i.e., at flux values greater 
than 250 l/yr the abstracted ionic strength would be less than that of the most dilute input 
solution, in this case, J-13.

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ANL-EBS-MD-000037 REV 02 121 of 140 July 2003 
Table 88. Codisposed N Reactor Seepage Dripping Model 50C Ionic Strength Flux 
Limits for 80 to 20,000 Year Abstraction 
Flux Limits (l/yr) 
Lower Uppera 
0.15 250 
DTN: MO0307SPAIPCHM.001 
NOTE: aUpper Limit set by J-13 at 0.0035 molal 
If the flux is less than the minimum, then the ionic strength should be evaluated using the lower 
flux limit from Table 88. Likewise, if the flux is greater than the upper limit, then the ionic 
strength should be evaluated using the upper limit value from Table 88. 
8.4.6 Codisposed N Reactor Seepage Dripping Model Ionic Strength Temperature 
Dependence 
Figure 36 shows that the ionic strength does not show a temperature functionality. Therefore, 
the 25 and 50C abstractions should not be interpolated for intermediate temperatures. However, 
examination of the early time distributions (Tables 83 and 86), and the late-time flux 
relationships (Table 84 and 87) reveals that both the distributions and the relationships are 
approximately the same. Therefore, it is recommended that TSPA use the 25C abstractions 
(Tables 83 and 84) for temperatures ranging from 15C to 37.5C, and the 50C abstractions 
(Tables 86 and 87) for temperatures ranging from 37.5C to 100C. For temperatures between 
15 and 50C an uncertainty range of -0.6 to 0.6 should be uniformly sampled, and for 
temperatures greater than 50C an uncertainty range of 0.99 to 0.99 should be uniformly 
sampled. For all temperatures the sampled uncertainty value should be multiplied by the ionic 
strength value and the result added to the ionic strength. Ionic strength = ISsampled + 
(Uncertaintysampled  ISsampled). 
8.4.7 Codisposed N Reactor Seepage Dripping Model 25C and 50C Fluoride 
Abstraction 
The fluoride profiles for the CDNR seepage dripping model 25C simulations are plotted in 
Figure 37. The minimum fluoride spike at about 5 years was not included in the abstraction 
because of its short duration. Compared to the CSNF fluoride profiles (Figure 25) the CDNR 
fluoride profiles are much more complex. The reason for the increased complexity is that 
fluorine is a component of the HLWG so that the aqueous fluorine concentration is controlled by 
the HLWG dissolution rate which is pH dependent (Table 19).

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ANL-EBS-MD-000037 REV 02 122 of 140 July 2003 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
Ionic Strength (molal) 
15C 
D23C25 
50C 
75C 
90C 
Source: Microsoft Excel Spreadsheet CDNR_sensit.xls in DTN: MO0307SPAIPCHM.001. 
Figure 36. Effect of Temperature on the Ionic Strength of Run D23C25 
1.0E-06 
1.0E-05 
1.0E-04 
1.0E-03 
1.0E-02 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) Fluoride (molal) 
D13C25 D13J25 
D13N25 D23C25 
D23J25 D23N25 
D33C25 D33J25 
D33N25 Upper F Limit 
Lower F Limit 
DTN: MO0307SPAIPCHM.001 
Figure 37. Codisposed N Reactor Seepage Dripping Model 25C Fluoride Profiles and Abstraction Limits

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 123 of 140 July 2003 
The CDNR seepage dripping model fluoride abstractions at 25 and 50C are provided in 
Table 89. 
Table 89. Codisposed N Reactor Seepage Dripping Model 25 and 50C Fluoride Abstraction Limits 
Temperature Lower F Limit Upper F Limit 
Distribution uniform 
(C) Mol/kg 
25 9.2E-05 3.0E-03 
50 1.2E-04 2.9E-03 
DTN: MO0307SPAIPCHM.001 
8.4.8 Codisposed N Reactor Seepage Dripping Model 25C and 50C Chloride 
Abstraction 
The CDNR seepage dripping model chloride profiles are plotted in Figure 38 together with the 
abstraction limits. The abstractions for 25 and 50C are provided in Table 90. 
1.0E-04 
1.0E-03 
1.0E-02 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Chloride (molal) 
D13C25 
D13J25 
D13N25 
D23C25 
D23J25 
D23N25 
D33C25 
D33J25 
D33N25 
Upper Cl 
Limit 
Lower Cl 
Limit 
Source: Microsoft Excel Spreadsheet CDNR_SDM_25.xls in DTN: MO0307SPAIPCHM.001. 
Figure 38. Codisposed N Reactor Seepage Dripping Model 25C Chloride Profiles and Abstraction Limits

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ANL-EBS-MD-000037 REV 02 124 of 140 July 2003 
Table 90. Codisposed N Reactor Seepage Dripping Model 25 and 50C Chloride Abstraction Limits 
Temperature Lower Cl Limit Upper Cl Limit 
Distribution uniform 
(C) Mol/kg 
25 2.01E-04 6.11E-03 
50 2.01E-04 6.16E-03 
DTN: MO0307SPAIPCHM.001 
8.5 TOTAL CARBONATE ABSTRACTION 
Total carbonate is used in the kinetic rate expression for the dissolution of CSNF; therefore, 
abstracted values are needed for the TSPA-LA. In a system where the partial pressure of carbon 
dioxide (CO2) is constant over the modeled period and the pH and temperature known, the total 
carbonate can be calculated using the equilibrium mass action expressions. 
Where the total carbonate (0<pH<14) is equal to: 
0) SC = [CO2 (aq)] + [HCO3
-] + [CO3
2-] 
1) CO2(g) . CO2 (aq) logK = K1 
[CO2 (aq)] = PCO2(g) 10K
1 
2) CO2 (aq) . H+ + HCO3
- logK = K2 
[HCO3
-] = (PCO2(g) 10K
1 10K
2)/10-pH 
3) HCO3
- . H+ + CO3
2- logK = K3 
[CO3
2-] = [(PCO2(g) 10K
1 10K
2)/10-pH] 10K
3/ 10-pH 
Combining terms and substituting back into expression (0), the total carbonate for the system is 
equal to the expression in Table 91. 
Table 91. Expression for Total Carbonate as a Function of pH to be Used in the Total System 
Performance Assessment 
Total C = PCO2(10K
1 + 10(pH + K
1
+ K
2
) + 10(2pH + K
1
+ K
2
+ K
3
)) 
DTN: MO0307SPAIPCHM.001 
Since the equilibrium constants for the carbonate species vary as a function of temperature it is 
possible to derive expressions for K1, K2, and K3 that include this functionality. The 
thermodynamic data from 0 to 100C for the carbonate species was extracted from 
Data0.ymp.R2 (DTN: MO0302SPATHDYN.000) and is compiled in Table 92. Note that log K1 
(Table 92) is the result of combined expressions.

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Table 92. Log K Values for the Carbonate Species at Temperature 
Log K at temperature Log K Reaction 
0C 25C 60C 100C 
K1 
CO2(g) + H2O = H+ + HCO3
- 
HCO3
- + H+ = CO2(aq) + H2O 
CO2(g) = CO2(aq) 
-7.6765 
+ 6.5804 
-1.0961 
-7.8136 
+ 6.3447 
-1.4689 
-8.0527 
+ 6.2684 
-1.7843 
-8.3574 
+ 6.3882 
-1.9692 
K2 CO2(aq) + H2O = HCO3
- + H+ -6.5804 -6.3447 -6.2684 -6.3882 
K3 HCO3
- = CO3
2- + H+ -10.6241 -10.3288 -10.1304 -10.0836 
Source: DTN: MO0302SPATHDYN.000. 
Log K temperature expressions were derived by plotting the log K values from 0 to 100C 
(Table 92) versus their respective temperatures and fitting a polynomial to the data in Microsoft 
Excel. This is the same method used by the EQPT code when it compiles the log K grid of an 
EQ3/6 data0 file (Daveler and Wolery 1992, p. 12). Therefore consistency is maintained 
between the approach used in the model report and that used in EQ3/6. 
Table 93. Log K Temperature Interpolation Functions for Use in the Total Carbonate Abstraction 
Log K Log K expressiona R2 
K1 Log K1 = 7E-05T2 - 0.0159T - 1.1023 0.9992 
K2 Log K2 = 5E-07T3 - 0.0002T2 + 0.0132T - 6.5804 1.0 
K3 Log K3 = -8E-05T2 + 0.0128T - 10.618 0.9977 
DTN: MO0307SPAIPCHM.001 
NOTE: aT = temperature in degrees Celsius 
Plots of the log K temperature interpolations are included in DTN: MO0307SPAIPCHM.001. 
The expressions in Table 93 are valid for temperatures in the range of 0 to 100C. This range is 
larger than that for the pH abstractions (15 to 90C) in Sections 8.1 to 8.4. 
Three feeds are required for the total carbonate abstraction as it should be implemented in 
TSPA-LA. The first feed is the temperature, which is required to calculate the appropriate log K 
values. The second feed is the in-package pH, which is estimated using the relationships and 
conditions specified in Sections 8.1 to 8.4. The third input is the pCO2. Once the log K values 
and in-package pH are calculated they can be used to calculate the total carbonate via the 
expression in Table 92. 
Since the pH abstractions account for propagated uncertainty there is no need to introduce further 
uncertainty at the level of the total carbonate abstraction. Therefore, the carbonate abstraction 
can be used as is with no uncertainty term added to it.

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1.0E-05 
1.0E-04 
1.0E-03 
1.0E-02
3.0 4.0 5.0 6.0 7.0 8.0 9.0 
pH 
total carbonate 
C22C25 
Total Carbonate Function 25 C 
C22C50 
Total Carbonate Function 50 C 
Source: Microsoft Excel Spreadsheet carbonate_Eh_abstraction.xls in DTN: MO0307SPAIPCHM.001. 
Figure 39. Plot of Total Carbonate Function at Two Temperatures Compared to Model Output 
Figure 39 shows the carbonate abstraction calculated at two temperatures compared to output 
from the model. The close agreement of the abstraction and the model output illustrates that the 
abstraction adequately reproduces the model output. 
8.6 Eh ABSTRACTION 
In aqueous systems in equilibrium with a constant partial pressure of oxygen, the Eh may be 
calculated directly from the pH. For the formation of water the pE may be calculated from the 
following expression, where .G is the Gibbs free energy of formation, F is the Faraday 
constant (23.06 kcal/volt-gram equivalent), Eh is the electron activity expressed in units of volts, 
and pE is the negative log base 10 of electron activity. 
1) 1/2O2 + 2H+ + 2e- = H2O .G = -56.688 kcal/mol (DTN: MO0302SPATHDYN.000) 
2) .G = -nFEh (Drever 1982, p. 254, Equation 11-6) 
3) pE = F/(2.303RT)Eh (Drever 1982, p. 254) 
Solving (3) for Eh and substituting into (2) results in (4), establishing a relationship between .G 
and pE 
4) .G = -2.303nRT(pE), 
Where, n = number of electrons [2], R = Gas Constant (1.987E-03 kcal/mol.K), PO2 is the partial 
pressure of oxygen, and T = absolute temperature (Kelvin).

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Solving for pE with temperature dependency results in: 
pE = 6193.973/T 
Thus for the formation of water in terms of one electron mole we have: 
1/4O2 + H+ + e- = 1/2H2O 
pE = pE + Log(PO2
1/4[H+]) 
pE = 6193.973/T  pH + 0.25log(PO2) 
Converting to Eh we get the expression in Table 94. The expression for Eh as a function of pH, 
oxygen partial pressure, and temperature (Table 94) is applicable for both CSNF and CDNR 
waste packages. The temperature should be input in degrees Kelvin, and the output is in volts. 
Table 94. Expression for Eh as a Function of pH, Oxygen Partial Pressure, and 
Absolute Temperature for use in the TSPA-LA 
Eh = 2.303RT/F(6193.973/T  pH + 0.25logPO2) 
Figure 40 shows the Eh abstraction function plotted together with the model output from the 
25 and 50C runs for the median case. Like the carbonate abstraction, uncertainty is built in to 
the Eh abstraction by virtue of the input pH, which already has associated uncertainty. 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
1.1
2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 
pH 
Eh (volts) 
C22C25 
25C function 
C22C50 
50 C function 
Source: Microsoft Excel Spreadsheet carbonate_Eh_abstraction.xls in DTN: MO0307SPAIPCHM.001. 
Figure 40. Plot of Eh Function at Two Temperatures Compared to Model Output

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8.7 OXYGEN AND CARBON DIOXIDE PARTIAL PRESSURE RANGE OF 
APPLICABILITY 
Both oxygen and carbon dioxide partial pressures were set to constant values for the full duration 
of the process model simulations (Section 6). However, sensitivity analyses (Section 6.7) 
demonstrated that varying the PCO2 between 10-4 to 10-2 had little or no impact on the pH and 
ionic strength, and furthermore, the uncertainty assigned to the abstractions incorporates this 
variation in CO2 partial pressure. Therefore, the PCO2 used in TSPA-LA may be expanded to 
accommodate this range (10-4 to 10-2 atmospheres) without extending beyond the uncertainty 
range of the abstractions. No simulations were done to determine the effects of PO2 on the 
system response; therefore, TSPA-LA is limited to atmospheric conditions. Table 95 provides 
the gas composition. 
Table 95. Gas Abstraction Information 
Gas Log partial pressure 
Oxygen -0.7 
Carbon Dioxide -4.0 to 2.0 
NOTE: Partial pressure in atmospheres 
8.8 ABSTRACTION VALIDATION 
This abstraction model uses mathematical relationships and statistical distributions to fit product 
output i.e., product output is used as input for the abstraction. Thus, product output was used to 
generate the abstraction model, i.e., to generate the mathematical relationships and statistical 
distributions that will be used in TSPA-LA. Therefore, the acceptance criteria, for the 
abstraction relationships, is that they reflect the product output used in their generation. In other 
words, validation of the abstraction model requires comparison of abstracted parameter values to 
their corollary product output parameter values. It must be emphasized that this does not 
constitute validation of the product output, but only that the abstraction can reproduce a subset of 
the process model output through the use of mathematical relationships. 
Validation of the CSNF and co-disposal pH and ionic strength abstractions is implicit in the 
method by which the pH response surfaces and distributions were developed. The validation 
process used for the pH abstractions was a comparison of the abstracted pH for each time period 
to the product output value for a given set of input parameters. The response surfaces are 
mathematical constructs whose limits were set by the process models inputs and outputs. The 
pH abstractions represent a best fit to the process model data, and are therefore able to reproduce 
the process model output for given set of input conditions within a range of uncertainty, thus 
rendering them self validating. The pH and ionic strength abstraction validation plots for the 
response surfaces, linear, and non-linear relationships are contained in Attachment II, Figures II- 
3 to II-9. The distributions do not require any further validation because their limits are 
extracted directly from the product output and as such are self validated. 
The total carbonate and Eh relationships use process model input data and an abstracted pH 
value to calculate a total carbonate concentration and Eh values in solution at a given time. The 
ability for these relationships to reproduce process model output was demonstrated in Sections

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8.5 and 8.6, respectively. In other words the total carbonate and ionic strength abstractions were 
validated by comparison to product output (Figure 39 and 40, respectively). 
The remaining parameters, fluoride and chloride concentrations were set to distributions where 
the values were taken directly from the product output for both CSNF and co-disposal and are 
thus self-validated. 
8.9 LINKING THE WATER VAPOR CONDENSATION AND SEEPAGE DRIPPING 
MODEL FOR TSPA-LA 
In a potential repository situation one could imagine a scenario where a waste package could fail 
and water vapor would enter, condense, and react and then at some future time it is possible that 
seepage might also enter this same waste package and react with the remaining materials. The 
opposite sequence might also occur, where seepage enters a waste package and at some future 
time the seepage might cease and water vapor could enter the same package. This would be a 
scenario where the water vapor condensation model and the seepage dripping model would need 
to be linked. In Sections 6.6.1.2 and 6.6.2.2 it was demonstrated that the initial composition of 
the seepage entering a failed waste package did not exert a significant influence on the resulting 
in-package chemistry. As the following figures demonstrate the results of the seepage dripping 
model follow similar behavior as the ensemble outputs from the water vapor condensation 
model. 
Based on the comparison of the results in Figures 41 and 42 it is recommended that for the 
purposes of TSPA-LA should it be necessary to link the water vapor condensation model and the 
seepage dripping model the change from one model to the other should occur synchronously. If 
a package is initially receiving water vapor and at some later time seepage begins to enter the 
package, then the water vapor condensation abstraction should cease and the seepage abstraction 
should begin at that in point in time.

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1.0 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
comp1 
comp2 
comp3 
comp4 
comp5 
comp6 
comp7 
comp8 
comp9 
comp10 
comp11 
SDM output C22C25 
DTN: MO0307SPAIPCHM.001 
Figure 41. Commercial Spent Nuclear Fuel Water Vapor CondensationSeepage 
Dripping Model Comparison 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
10.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
comp1 
comp2 
comp3 
comp4 
comp5 
comp6 
comp7 
comp8 
SDM output D23C25 
DTN: MO0307SPAIPCHM.001. 
Figure 42. Codisposed N Reactor Water Vapor CondensationSeepage Dripping Model Comparison

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8.10 OUTPUT UNCERTAINTY 
The in-package chemistry model combines two approaches to model uncertainty: (1) Application 
of a factorial design approach to account for known large potential variations in model input 
(reactant combinations, water flux, fuel exposure, temperature, and seepage composition; and (2) 
sensitivity analysis of lesser known, not as well defined input variations (carbon dioxide partial 
pressure, A516 sulfur content, corrosion rates, extreme temperatures and flux values). Where (1) 
provided the functional basis of the in-package chemistry model abstraction, (2) provided the 
uncertainty ranges of the abstracted parameters. Thus, the information to be used in TSPA-LA 
directly incorporates uncertainty exterior to the in-package environment (1) and interior to the inpackage 
environment in a form that can be readily implemented in TSPA-LA (i.e., model 
uncertainty is propagated through the abstractions). Thus, the only restrictions on the subsequent 
use of the in-package chemistry model abstraction in TSPA-LA are that all of the abstractions 
presented in Section 8 must be applied within the stated limits (flux, temperature, PCO2, and fuel 
exposure) as specified in this model report. 
8.11 CONCLUSIONS 
This model report provides the documentation of the in-package chemistry process models as 
well as the in-package chemistry abstractions. The abstractions are to be used as input to 
TSPA-LA and will feed the solubility, colloid, CSNF, and HLWG submodels. The application 
of the abstractions and their limits and uncertainties are documented in detail in this section. 
The in-package chemistry process models are developed and documented in Section 6, and 
Attachment III. Based on the results of the in-package chemistry process models, it may be 
concluded that the following inputs and/or processes have only minor impact on the model 
response such that the abstractions are not affected. 
 Initial water composition 
 Partial pressure of carbon dioxide 
 Radiolysis (Attachment III, DTN: MO0304SPAICSCR.000) 
 Decreased corrosion rate of the waste package alloys 
 Minor modifications to waste package design configuration 
 Sulfur content of the carbon steel (A516) waste package components. 
The effect of the above factors on the model response is finite and accounted for in the 
abstractions via applied uncertainty terms. 
The following variables have a large influence on the in-package chemistry. 
 Waste package type (CSNF versus codisposed) 
 Water flux 
 Fuel exposure (clad failure) 
 Temperature. 
These four parameters are used as input to the in-package chemistry abstractions, i.e., the 
abstractions are functions of these variables. Therefore, in TSPA-LA the in-package chemistry

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 132 of 140 July 2003 
is implemented as a function of external variables, which directly links the in-package chemistry 
to the repository environment. 
The acceptance criteria from Section 4.2 relating to the quantity and chemistry of water 
contacting the waste packages and waste form have been met in the following sections: 
 The system description and model integration were described in Sections 6.3, 6.3.1, 
6.3.2, and 6.3.3. 
 The sufficiency of the data is discussed in Section 4 and sub-sections of Section 4. 
 The data uncertainty and its propagation through the model abstraction is described in 
Section 6.3.4, 6.7 and its sub-sections, and in Sections 8.1 through 8.7. 
 The model uncertainty and its propagation through the model abstraction is contained in 
Sections 6.1 and 8.10. 
 The objective comparisons of the model abstraction outputs is contained in Section 8.8. 
Two DTNs were developed in this model report: 
DTN: MO0307SPAIPCHM.001, which is the primary output of this model report. 
DTN: MO0304SPAICSCR.000, which is developed in Attachment III and documents the 
effect of radiolysis on the in-package chemistry. The radiolysis analyses (Attachment III) 
provided an additional sensitivity study on processes that may occur and influence the inpackage 
chemistry model results.
9. REFERENCES 
9.1 REFERENCES CITED 
Allen, B.L. and Hajek, B.F. 1995. Mineral Occurrence in Soil Environments. Chapter 5 of 
Minerals in Soil Environments. 2nd Edition. Dixon, J.B. and Weed, S.B., eds. SSSA Book 
Series, No. 1. Madison, Wisconsin: Soil Science Society of America. TIC: 237222. 
ASM International 1987. Corrosion. Volume 13 of Metals Handbook. 9th Edition. Metals 
Park, Ohio: ASM International. TIC: 209807. 
Audi, G. and Wapstra, A.H. 1995. Atomic Mass Adjustment, Mass List for Analysis. Upton, 
New York: Brookhaven National Laboratory, National Nuclear Data Center. TIC: 242718. 
B&W Fuel Company 1991. Final Design Package Babcock & Wilcox BR-100 100 Ton 
Rail/Barge Spent Fuel Shipping Cask. Volume 2. 51-1203400-01. DBABE0000-00272-1000- 
00014 REV 00. Lynchburg, Virginia: B&W Fuel Company. ACC: MOV.19960802.0083. 
Baxter, R.G. 1988. Defense Waste Processing Facility Wasteform and Canister Description. 
DP-1606, Rev. 2. Aiken, South Carolina: E.I. du Pont de Nemours & Company, Savannah 
River Plant. TIC: 8704.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 133 of 140 July 2003 
Berner, E.K. and Berner, R.A. 1996. Global Environment: Water, Air, and Geochemical 
Cycles. Upper Saddle River, New Jersey: Prentice Hall. TIC: 253965. 
Bricker, O. 1965. Some Stability Relations in the System Mn-O2-H2O at 25 and One 
Atmosphere Total Pressure. American Mineralogist, 50, 1296-1354. Washington, D.C.: 
Mineralogical Society of America. TIC: 238855. 
BSC (Bechtel SAIC Company, LLC) 2001a. Defense High Level Waste Glass Degradation. 
ANL-EBS-MD-000016 REV 00 ICN 02. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20011015.0502. 
BSC 2001b. DSNF and Other Waste Form Degradation Abstraction. ANL-WIS-MD-000004 
REV 01 ICN 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20010316.0002. 
BSC 2001c. PWR Assembly End-Effect Reactivity Evaluation. CAL-UDC-NU-000006 REV 00. 
Las Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20010412.0158. 
BSC 2001d. Repository Design, Waste Package, Project 21-PWR Waste Package with Absorber 
Plates, Sheet 1 of 3, Sheet 2 of 3, and Sheet 3 of 3. DWG-UDC-ME-000001 REV A. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20020102.0174. 
BSC 2001e. In-Package Chemistry Abstraction. ANL-EBS-MD-000037 REV 01. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20010315.0053. 
BSC 2002a. Technical Work Plan for Waste Form Degradation Modeling, Testing, and 
Analyses in Support of SR and LA. TWP-WIS-MD-000008 REV 02. Las Vegas, Nevada: 
Bechtel SAIC Company. ACC: MOL.20020819.0359. 
BSC 2002b. Gamma and Neutron Radiolysis in the 21-PWR Waste Package from Ten to One 
Million Years. 000-00C-DSU0-00700-000-00A. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20021004.0002. 
BSC 2002c. EQ6 Calculation for Chemical Degradation of 21 PWR CSNF Waste Packages. 
CAL-UDC-MD-000002 REV A. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20020601.0195. 
BSC 2002d. Geochemistry Model Abstraction and Sensitivity Studies for the 21 PWR CSNF 
Waste Packages. MDL-DSU-MD-000001 REV 00. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: MOL.20021107.0154. 
BSC 2002e. The Enhanced Plan for Features, Events, and Processes (FEPs) at Yucca 
Mountain. TDR-WIS-PA-000005 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20020417.0385. 
BSC 2002f. Scientific Processes Guidelines Manual. MIS-WIS-MD-000001 REV 01. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20020923.0176.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 134 of 140 July 2003 
BSC 2003a. Drift-Scale Coupled Processes (DST and THC Seepage) Models. MDL-NBS-HS- 
000001 REV 02C. Las Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20030507.0274. 
TBV-5120. 
BSC 2003b. EBS Radionuclide Transport Abstraction. ANL-WIS-PA-000001 REV 01A. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20030617.0222. TBV-5179. 
BSC 2003c. Repository Design Project, Repository/PA IED Emplacement Drift Committed 
Materials 1 of 2. 800-IED-WIS0-00301-000-00A. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: ENG.20030627.0003. 
BSC 2003d. Repository Design Project, RDP/PA IED Typical Waste Package Components 
Assembly 1 of 9. 800-IED-WIS0-00201-000-00A. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: ENG.20030702.0001. 
BSC 2003e. Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: 
Abstraction and Summary. MDL-EBS-PA-000004 REV 00. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: DOC.20030626.0006. 
Canori, G.F. and Leitner, M.M. 2003. Project Requirements Document. TER-MGR-MD- 
000001 REV 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20030404.0003. 
Christensen, H. 1995. Remodeling of the Oxidant Species During Radiolysis of High- 
Temperature Water in a Pressurized Water Reactor. Nuclear Technology, 109, 373-382. 
La Grange Park, Illinois: American Nuclear Society. TIC: 253765. 
Christensen, H. and Bjergbakke, E. 1982. Radiolysis of Ground Water from Spent Fuel. SKB 
TR-82-18. Stockholm, Sweden: Svensk Krnbrnslefrsrjning A.B. TIC: 205896. 
Christensen, H.; Sunder, S.; and Shoesmith, D.W. 1994. Development of a Kinetic Model to 
Predict the Rate of Oxidation and Dissolution of Nuclear Fuel (UO2) by the Radiolysis of Water. 
AECL-11102. Pinawa, Manitoba, Canada: Atomic Energy of Canada Limited. TIC: 242350. 
Cornell, R.M.; Giovanoli, R.; and Schneider, W. 1992. The Effect of Nickel on the Conversion 
of Amorphous Iron(III) Hydroxide into More Crystalline Iron Oxides in Alkaline Media. 
Journal of Chemical Technology and Biotechnology, 53, (1), 73-79. Oxford, England: 
Blackwell Scientific Publishing. TIC: 254448. 
CRWMS M&O (Civilian Radioactive Waste Management System Management and Operating 
Contractor) 1997. Degraded Mode Criticality Analysis of Immobilized Plutonium Waste Forms 
in a Geologic Repository. Predecisional Document. A00000000-01717-5705-00014 REV 01. 
Las Vegas, Nevada: CRWMS M&O. ACC: MOL.19980422.0911. 
CRWMS M&O 1998. Summary Report of Commercial Reactor Criticality Data for Crystal 
River Unit 3. B00000000-01717-5705-00060 REV 01. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.19980728.0189.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 135 of 140 July 2003 
CRWMS M&O 2000a. CSNF Waste Form Degradation: Summary Abstraction. ANL-EBSMD-
000015 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20000121.0161. 
CRWMS M&O 2000b. Source Terms for HLW Glass Canisters. CAL-MGR-NU-000002 REV 
01. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20000823.0004. 
CRWMS M&O 2000c. General Corrosion and Localized Corrosion of Waste Package Outer 
Barrier. ANL-EBS-MD-000003 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000202.0172. 
CRWMS M&O 2000d. Total System Performance Assessment for the Site Recommendation. 
TDR-WIS-PA-000001 REV 00 ICN 01. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001220.0045. 
CRWMS M&O 2000e. In-Package Chemistry Abstraction. ANL-EBS-MD-000037 REV 00. 
Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20000418.0818. 
CRWMS M&O 2000f. Summary of In-Package Chemistry for Waste Forms. ANL-EBS-MD- 
000050 REV 01. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20010129.0134. 
CRWMS M&O 2001. EQ6 Calculations for Chemical Degradation of N Reactor (U-metal) 
Spent Nuclear Fuel Waste Packages. CAL-EDC-MD-000010 REV 00. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL.20010227.0017. 
Daveler, S.A. and Wolery, T.J. 1992. EQPT, A Data File Preprocessor for the EQ3/6 Software 
Package: User's Guide and Related Documentation (Version 7.0). UCRL-MA-110662 PT II. 
Livermore, California: Lawrence Livermore National Laboratory. TIC: 205240. 
Deer, W.A.; Howie, R.A.; and Zussman, J. 1966. An Introduction to the Rock-Forming 
Minerals. New York, New York: John Wiley & Sons. TIC: 245492. 
Dixon, J.B. 1995. Kaolin and Serpentine Group Minerals. Chapter 10 of Minerals in Soil 
Environments. 2nd Edition. Dixon, J.B. and Weed, S.B., eds. SSSA Book Series, No. 1. 
Madison, Wisconsin: Soil Science Society of America. TIC: 237222. 
DOE (U.S. Department of Energy) 2000a. Review of Oxidation Rates of DOE Spent Nuclear 
Fuel, Part 1: Metallic Fuel. DOE/SNF/REP-054, Rev. 0. Washington, D.C.: U.S. Department 
of Energy. TIC: 248978. 
DOE 2000b. N Reactor (U-Metal) Fuel Characteristics for Disposal Criticality Analysis. 
DOE/SNF/REP-056, Rev. 0. Washington, D.C.: U.S. Department of Energy, Office of 
Environmental Management. TIC: 247956. 
DOE 2002. DOE Spent Nuclear Fuel Information in Support of TSPA-SR. DOE/SNF/REP-047, 
Rev. 2. Idaho Falls, Idaho: U.S. Department of Energy, Idaho Operations Office. TIC: 252089. 
Drever, J.I. 1982. The Geochemistry of Natural Waters. Englewood Cliffs, New Jersey: 
Prentice Hall. TIC: 240706.

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Ewing, R.C. and Haaker, R.F. 1979. Naturally Occurring Glasses: Analogues for Radioactive 
Waste Forms. PNL-2776. Richland, Washington: Pacific Northwest Laboratory. 
ACC: NNA.19900315.0277. 
Finch, R.J. and Murakami, T. 1999. Systematics and Paragenesis of Uranium Minerals. 
Chapter 3 of Uranium: Mineralogy, Geochemistry and the Environment. Burns, P.C. and Finch, 
R.J., eds. Reviews in Mineralogy Volume 38. Washington, D.C.: Mineralogical Society of 
America. TIC: 247121. 
Firsching, F.H. and Brune, S.N. 1991. Solubility Products of the Trivalent Rare-Earth 
Phosphates. Journal of Chemical Engineering, 36, 93-95. Washington, D.C.: American 
Chemical Society. TIC: 240863. 
Freeze, G.A.; Brodsky, N.S.; and Swift, P.N. 2001. The Development of Information Catalogued 
in REV00 of the YMP FEP Database. TDR-WIS-MD-000003 REV 00 ICN 01. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20010301.0237. 
Harrar, J.E.; Carley, J.F.; Isherwood, W.F.; and Raber, E. 1990. Report of the Committee to 
Review the Use of J-13 Well Water in Nevada Nuclear Waste Storage Investigations. UCID- 
21867. Livermore, California: Lawrence Livermore National Laboratory. ACC: 
NNA.19910131.0274. 
Hem, J.D. 1985. Study and Interpretation of the Chemical Characteristics of Natural Water. 
3rd Edition. Geological Survey Water-Supply Paper 2254. Washington, D.C.: U.S. Government 
Printing Office. ACC: NNA.19940427.0181. 
Hsu, P.H. 1995. Aluminum Hydroxides and Oxyhydroxides. Chapter 7 of Minerals in Soil 
Environments. 2nd Edition. Dixon, J.B. and Weed, S.B., eds. SSSA Book Series, No. 1. 
Madison, Wisconsin: Soil Science Society of America. TIC: 237222. 
IAEA (International Atomic Energy Agency) 1998. Waterside Corrosion of Zirconium Alloys in 
Nuclear Power Plants. IAEA-TECDOC-996. Vienna, Austria: International Atomic Energy 
Agency. TIC: 248234. 
Kerr, P.F. 1977. Optical Mineralogy. 4th Edition. New York, New York: McGraw-Hill. TIC: 
252886. 
Klein, C. and Hurlbut, C.S., Jr. 1985. Manual of Mineralogy. 20th Edition. New York, New 
York: John Wiley & Sons. TIC: 242818. 
Langmuir, D. 1997. Aqueous Environmental Geochemistry. Upper Saddle River, New Jersey: 
Prentice Hall. TIC: 237107. 
Li, G.; Peacor, D.R.; Coombs, D.S.; and Kawachi, Y. 1997. Solid Solution in the Celadonite 
Family: The New Minerals Ferroceladonite, K2Fe2
2+Fe3
2+Si8O20(OH)4, and 
Ferroaluminoceladonite, K2Fe2
2+Al2Si8O20(OH)4. American Mineralogist, 82, (5-6), 503-511. 
Washington, D.C.: Mineralogical Society of America. TIC: 252472.

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Milnes, A.R. and Fitzpatrick, R.W. 1995. Titanium and Zirconium Minerals. Chapter 23 of 
Minerals in Soil Environments. 2nd Edition. Dixon, J.B. and Weed, S.B., eds. SSSA Book 
Series, No. 1. Madison, Wisconsin: Soil Science Society of America. TIC: 237222. 
Morel, F.M.M. and Hering, J.G. 1993. Principles and Applications of Aquatic Chemistry. 
New York, New York: John Wiley & Sons. TIC: 248465. 
NRC (U.S. Nuclear Regulatory Commission) 2003. Yucca Mountain Review Plan, Information 
Only. NUREG-1804, Draft Final Revision 2. Washington, D.C.: U.S. Nuclear Regulatory 
Commission, Office of Nuclear Material Safety and Safeguards. TIC: 254002. 
Palache, C.; Berman, H.; and Frondel, C. 1944. Elements, Sulfides, Sulfosalts, Oxides. Volume I 
of The System of Mineralogy of James Dwight Dana and Edward Salisbury Dana, Yale 
University 18371892. 7th Edition. New York, New York: John Wiley & Sons. TIC: 209331. 
Palache, C.; Berman, H.; and Frondel, C. 1951. Halides, Nitrates, Borates, Carbonates, Sulfates, 
Phosphates, Arsenates, Tungstates, Molybdates, Etc. Volume II of The System of Mineralogy of 
James Dwight Dana and Edward Salisbury Dana, Yale University 18371892. 7th Edition. New 
York, New York: John Wiley & Sons. TIC: 209332. 
Parrington, J.R.; Knox, H.D.; Breneman, S.L.; Baum, E.M.; and Feiner, F. 1996. Nuclides and 
Isotopes, Chart of the Nuclides. 15th Edition. San Jose, California: General Electric Company 
and KAPL, Inc. TIC: 233705. 
Punatar, M.K. 2001. Summary Report of Commercial Reactor Criticality Data for Crystal River 
Unit 3. TDR-UDC-NU-000001 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20010702.0087. 
Rai, D. and Ryan, J.L. 1982. Crystallinity and Solubility of Pu(IV) Oxide and Hydrous Oxide 
in Aged Aqueous Suspensions. Radiochimica Acta, 30, 213-216. Munchen, Germany: 
R. Oldenbourg Verlag. TIC: 219107. 
Rai, D. and Zachara, J.M. 1984. A Critical Review. Volume 1 of Chemical Attenuation Rates, 
Coefficients, and Constants in Leachate Migration. EPRI EA-3356. Palo Alto, California: 
Electric Power Research Institute. TIC: 237846. 
Roberts, W.L.; Campbell, T.J.; and Rapp, G.R., Jr. 1990. Encyclopedia of Minerals. 2nd Edition. 
New York, New York: Van Nostrand Reinhold. TIC: 242976. 
Runde, W. 1999. Letter Report on Plutonium Thermodynamic Database. Letter from W. 
Runde (LANL) to P. Dixon (LANL), August 1, 1999, with attachments. ACC: 
MOL.19991214.0624. 
Sandino, A. 1991. Processes Affecting the Mobility of Uranium in Natural Waters. Ph.D. thesis. 
Stockholm, Sweden: Royal Institute of Technology. TIC: 246941.

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Schwertmann, U. and Taylor, R.M. 1995. Iron Oxides. Chapter 8 of Minerals in Soil 
Environments. 2nd Edition. Dixon, J.B. and Weed, S.B., eds. SSSA Book Series, No. 1. 
Madison, Wisconsin: Soil Science Society of America. TIC: 237222. 
Sequeira, C.A.C. 2000. Corrosion of Copper and Copper Alloys. Chapter 42 of Uhlig's 
Corrosion Handbook. 2nd Edition. Revie, R.W., ed. New York, New York: John Wiley & 
Sons. TIC: 248360. 
Stout, R.B. and Leider, H.R., eds. 1994. Preliminary Waste Form Characteristics Report, 
Version 1.0. UCRL-ID-108314, Rev. 1. Livermore, California: Lawrence Livermore National 
Laboratory. ACC: MOL.19960314.0440. 
Stumm, W. and Morgan, J.J. 1996. Aquatic Chemistry, Chemical Equilibria and Rates in 
Natural Waters. 3rd Edition. New York, New York: John Wiley & Sons. TIC: 246296. 
Waber, N. 1991. Mineralogy, Petrology and Geochemistry of the Pocos de Caldas Analogue 
Study Sites, Minas Gerais, Brazil. II. Morro do Ferro. SKB TR-90-12. Stockholm, Sweden: 
Svensk Krnbrnslefrsrjning A.B. TIC: 206350. 
Weast, R.C., ed. 1977. CRC Handbook of Chemistry and Physics. 58th Edition. Cleveland, 
Ohio: CRC Press. TIC: 242376. 
Wilson, C.N. and Bruton, C.J. 1989. Studies on Spent Fuel Dissolution Behavior Under Yucca 
Mountain Repository Conditions. PNL-SA-16832. Richland, Washington: Pacific Northwest 
Laboratory. ACC: HQX.19890918.0047. 
Wolery, T.J. and Daveler, S.A. 1992. EQ6, A Computer Program for Reaction Path Modeling of 
Aqueous Geochemical Systems: Theoretical Manual, User's Guide, and Related Documentation 
(Version 7.0). UCRL-MA-110662 PT IV. Livermore, California: Lawrence Livermore 
National Laboratory. ACC: MOL.19980701.0459. 
Yang, I.C.; Rattray, G.W.; and Yu, P. 1996. Interpretation of Chemical and Isotopic Data from 
Boreholes in the Unsaturated Zone at Yucca Mountain, Nevada. Water-Resources Investigations 
Report 96-4058. Denver, Colorado: U.S. Geological Survey. ACC: MOL.19980528.0216. 
9.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
AP-2.22Q, Rev. 0, ICN 1. Classification Criteria and Maintenance of the Monitored Geologic 
Repository Q-List. Washington, D.C.: U.S. Department of Energy, Office of Civilian 
Radioactive Waste Management. ACC: DOC.20030422.0009. 
AP-SI.1Q, Rev. 05, ICN 01. Software Management. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: DOC.20030708.0001. 
AP-SIII.10Q, Rev. 1, ICN 2. Models. Washington, D.C.: U.S. Department of Energy, Office of 
Civilian Radioactive Waste Management. ACC: DOC.20030627.0003.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 139 of 140 July 2003 
ASME (American Society of Mechanical Engineers) 1998. 1998 ASME Boiler and Pressure 
Vessel Code. 1998 Edition with 1999 and 2000 Addenda. New York, New York: American 
Society of Mechanical Engineers. TIC: 247429. 
ASTM A 20/A 20M-95a. 1995. Standard Specification for General Requirements for Steel 
Plates for Pressure Vessels. West Conshohocken, Pennsylvania: American Society for Testing 
and Materials. TIC: 240026. 
ASTM A 240/A 240M-99b. 2000. Standard Specification for Heat-Resisting Chromium and 
Chromium-Nickel Stainless Steel Plate, Sheet, and Strip for Pressure Vessels. West 
Conshohocken, Pennsylvania: American Society for Testing and Materials. TIC: 248529. 
ASTM A 887-89 (Reapproved 2000). 2000. Standard Specification for Borated Stainless Steel 
Plate, Sheet, and Strip for Nuclear Application. West Conshohocken, Pennsylvania: American 
Society for Testing and Materials. TIC: 249544. 
ASTM B 209-96. 1996. Standard Specification for Aluminum and Aluminum-Alloy Sheet and 
Plate. West Conshohocken, Pennsylvania: American Society for Testing and Materials. 
TIC: 247078. 
ASTM C 1174-97. 1998. Standard Practice for Prediction of the Long-Term Behavior of 
Materials, Including Waste Forms, Used in Engineered Barrier Systems (EBS) for Geological 
Disposal of High-Level Radioactive Waste. West Conshohocken, Pennsylvania: American 
Society for Testing and Materials. TIC: 246015. 
ASTM G 1-90 (Reapproved 1999). 1999. Standard Practice for Preparing, Cleaning, and 
Evaluating Corrosion Test Specimens. West Conshohocken, Pennsylvania: American Society 
for Testing and Materials. TIC: 238771. 
9.3 SOFTWARE CODES 
Software Code: EQ3/6. V7.2b. LLNL: UCRL-MA-110662. 
Software Code: EQ6. 7.2bLV. PC. 10075-7.2bLV-02. Windows NT, 2000. 
Software Code: GetEQData. V1.0.1. PC w/Windows 2000. 10809-1.0.1-0. 
9.4 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
GS020408312272.003. Collection and Analysis of Pore Water Samples for the Period from 
April 2001 to February 2002. Submittal date: 04/24/2002. 
MO0006J13WTRCM.000. Recommended Mean Values of Major Constituents in J-13 Well 
Water. Submittal date: 06/07/2000. 
MO0109RIB00049.001. Waste Package Material Properties: Neutron Absorbing Materials. 
Submittal date: 09/17/2001.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 140 of 140 July 2003 
MO0302SPATHDYN.000. Thermodynamic Data Input Files - Data0.YMP.R2. Submittal date: 
02/05/2003. 
MO0303SPAMCRAQ.000. Materials Corrosion Rates in Aqueous Environments. Submittal 
date: 03/05/2003. 
9.5 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
MO0304SPAICSCR.000. In-Package Chemistry Sensitivity for CSNF with Radiolysis. 
Submittal date: 04/10/2003. 
MO0307SPAIPCHM.001. Abstractions of In-Package Chemistry for TSPA-LA. Submittal date: 
03/18/2003.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 I-1 June 2003 
ATTACHMENT I 
Volume in drive D is 030719_1720 
Volume Serial Number is 0AC8-A9B7 
Directory of d:\ 
07/19/2003 10:08a 23,207,507 CDNR.zip 
07/19/2003 10:08a 71,299,139 CSNF.zip 
04/10/2003 10:26a 606,335 Database.zip 
07/19/2003 10:03a <DIR> files for Attachment II 
07/19/2003 03:20p <DIR> files for Attachment III 
07/19/2003 03:21p <DIR> files for Attachment IV 
07/19/2003 04:52p 4,395,799 spreadsheets.zip 
04/10/2003 10:27a 215,216 waters.zip 
04/10/2003 10:27a 1,476,546 WVC_singlereact.zip 
6 File(s) 101,200,542 bytes 
Directory of d:\files for Attachment II 
07/19/2003 10:03a <DIR> . 
07/19/2003 10:03a <DIR> .. 
07/19/2003 03:35p 12,041,505 AttachmentII.zip 
1 File(s) 12,041,505 bytes 
Directory of d:\files for Attachment III 
07/19/2003 03:20p <DIR> . 
07/19/2003 03:20p <DIR> .. 
04/10/2003 08:42a 4,480,834 AttachmentIII.zip 
1 File(s) 4,480,834 bytes 
Directory of d:\files for Attachment IV 
07/19/2003 03:21p <DIR> . 
07/19/2003 03:21p <DIR> .. 
04/10/2003 08:25a 314,559 AttachmentIV.zip 
1 File(s) 314,559 bytes 
Total Files Listed: 
9 File(s) 118,037,440 bytes 
9 Dir(s) 0 bytes free

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 II-1 July 2003 
ATTACHMENT II 
Attachment II is comprised of additional plots. The computer files are included in Attachment 
V. 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
pH 
C22C25 
50 l/yr 
75 l/yr 
150 l/yr 
500 l/yr 
1000 l/yr 
Source: Attachment V, CS_Sensitivity.xls 
Figure II-1. Plot of pH Versus Time for C22C25 Showing the Effect of Increasing Flux

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 II-2 July 2003 
0 
5E-13 
1E-12 
1.5E-12 
2E-12 
2.5E-12 
3E-12 
3.5E-12
1 3 5 7 9 11 13 15 
pH 
CSNF Total Degradation 
Rate Constant at 25C 
(mol/s-cm2) 
uses total carbonate 
uses only HCO3- 
Source: Attachment V, carbonate_abstraction.xls, worksheet CSNF Rate Sensitivity 
Figure II-2. Plot of CSNF Total Degradation Rate Constant as a function of pH

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 II-3 July 2003 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
C11N25B C11N25 Abstracted pH 
C22N25B C22N25 abstracted pH 
C33N25B C33N25 Abstracted pH 
Period 1 
0-50 years 
Period 2 
50-500 years 
Period 3 
500-20K years 
Source: Attachment V, CSNF_SDM_25.xls 
Figure II-3. Validation of pH abstraction to model output for 25C CSNF 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Ionic Strength (molal) 
C11N25B 
C11N25 ionic strn abstraction 
C22N25B 
C22N25 ionic strn abstraction 
C33N25B 
C33N25 ionic strength abstraction 
40 years 
0.05 molal limit 
Source: Attachment V, CSNF_SDM_25.xls 
Figure II-4. Validation of ionic strength abstraction to model output for 25C CSNF

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 II-4 July 2003 
2.0 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
C11N50 C11N50 pH abstraction 
C22N50 C22N50 pH abstraction 
C33N50 C33N50 pH abstraction 
Period 1 Period 2 Period 3 
Source: Attachment V, CSNF_SDM_50.xls 
Figure II-5. Validation of pH abstraction to model output for 50C CSNF 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04 1.00E+05 
Time(yrs) 
Ionic Strength (molal) 
C11N50 process model output 
C11N50 ionic strength abstraction 
C22N50 process model output 
C22N50 ionic strength abstraction 
C33N50 process model output 
C33N50 ionic strength abstraction 
Source: Attachment V, CSNF_SDM_50.xls 
Figure II-6. Validation of ionic strength abstraction to model output for 50C CSNF

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 II-5 July 2003 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
8.5 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
D13C25 
D13NC25 pH abstraction 
D23J25 
D23J25 pH abstraction 
D33N25 
D33N25 pH abstraction 
Upper pH Limit, 0 - 60 yrs 
Lower pH Limit, 0 - 60 yrs 
Source: Attachment V, CDNR_SDM_25.xls 
Figure II-7. Validation of pH abstraction to model output for 25C CDNR 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Ionic Strength (molal) 
D13C25 
D13C25 abstraction 
D23J25 
D23J25 abstraction 
D33N25 
D33N25 abstraction 
Source: Attachment V, CDNR_SDM_25.xls 
Figure II-8. Validation of ionic strength abstraction to model output for 25C CDNR

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 II-6 July 2003 
5.0 
5.5 
6.0 
6.5 
7.0 
7.5 
8.0 
8.5 
9.0 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
pH 
D13C50 
D13C50 abstraction 
D23J50 
D23J50 abstraction 
D33N50 
D33N50 abstraction 
Source: Attachment V, CDNR_SDM_50.xls 
Figure II-9. Validation of pH abstraction to model output for 50C CDNR 
1.0E-03 
1.0E-02 
1.0E-01 
1.0E+00 
1.0E+01 
1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time(yrs) 
Ionic Strength (molal) 
D13C50 
D13C50 abstraction 
D23J50 
D23J50 abstraction 
D33N50 
D33N50 abstraction 
Source: Attachment V, Attachment V, CDNR_SDM_50.xls 
Figure II-9. Validation of ionic strength abstraction to model output for 50C CDNR 
Table II-1. 21-PWR Waste Package Internal Component Dimensions 
Component Subcomponent Dimension Value (mm) 
Inner Shell 
(1 per waste 
package) 
Inner Diameter 1424

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 II-7 July 2003 
Component Subcomponent Dimension Value (mm) 
Outer Diameter 1524 
inner height 4585 
Inner Shell Assembly height 4762 
bottom lid thickness 88.9 
top lid thickness 50.8 
Fuel Basket A,B-Plate Height 1396 
Fuel Basket A,B-Plate Width 1134 
Fuel Basket C-Plate Height 733.2 
Fuel Basket C-Plate Width 1134 
Fuel Basket D,E-Plate Height 1394 
Fuel Basket D,E-Plate width 1132 
A,B,& C plates thickness 7 
Fuel plate assembly 
(4 per waste 
package) 
D & E Plates thickness 5 
Length 4575 
exterior dimension width, height 236.4 
Fuel Basket Tube 
(21 per waste 
package) interior dimension width, height 226.4 
height 77.8 
width 209.2 
Basket A Stiffener 
(64 per waste 
package) thickness 10 
height 91.7 
width 216.4 
Basket B Stiffener 
(32 per waste 
package) thickness 10 
height 227.3 
width 227.3 
Side 
Corner-Guide 
Stiffeners 
(4 per waste 
package) Basket C Stiffener 
(32 per waste 
package) thickness 10 
width 236.3 
height 89.7 
length 1134 
Basket A-Sideguide 
(32 per waste 
package) 
thickness 10 
width 236.4 
height 93.4 
length 1134 
Basket B-Sideguide 
(16 per waste 
package) 
thickness 10 
width 243.4 
height 243.4 
length 1134 
Basket Cornerguide 
(16 per waste 
package) 
thickness 10

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 III-1 July 2003 
ATTACHMENT III. 
Radiolysis contribution to in-package chemistry 
Radiolysis is a potentially important process which could influence the results of the in-package 
chemistry model. A non-mechanistic treatment in the form of a sensitivity analysis is provided 
here to examine how radiolysis could effect the pH and concentration of certain aqueous species. 
All of the pertinent computer files may be found in Attachment V. 
Data and Parameters 
 Gamma and Neutron Radiolysis in the 21-PWR Waste Package from Ten to One Million 
Years (BSC 2002b). Nitric acid (HNO3) production rate at 500 years (assuming waste 
package failure) is 3.60E+13 molecules/yr-cm3 (air) (BSC 2002b, Table 21, p. 33). This 
time was selected because it is the earliest failure, representing juvenile failure of the 
waste package. Figure 5 of this reference shows that the production is constant after 500 
years so the selection of this time is not critical. 
 Geometry of CSNF waste package: Volume = 4968751 cm3, 10 percent fuel exposed 
surface area = 3297160 cm2, void volume with 0.25-cm thick water film = 4144461 cm3, 
from CSNF.xls, Attachment I. 
 Surface area normalization factor for 10 percent fuel exposure = 1.213E-03, from 
CSNF.xls, Attachment I. 
 G value for nitric acid production is 1.5 molecules/100 ev energy deposition (BSC 2003b, 
p. 27). The G value represents the number of molecules formed from radiolysis in a 
material and is linear with energy deposition. This number is used to calculate hydrogen 
peroxide (H2O2) generation by scaling (first bullet). 
 Density of moist air (40 percent RH, 30C) is 1.16E-3 gm/cm3 (see Attachment I, 
Microsoft Excel file csnf_radiolysis.xls, Sheet MoistAir, Cell H14). This number is used 
to calculate hydrogen peroxide generation by scaling the first bullet, above. 
 G value for hydrogen peroxide production is 0.72 molecules/100 ev energy deposition. 
This value is presented in Waterside Corrosion of Zirconium Alloys in Nuclear Power 
Plants (IAEA 1998, Table 8.2, p. 214). This value is corroborated by Christensen (1995, 
p. 375, Table II), Christensen et al. (1994, p. 6, Table 1) and Christensen and Bjergbakke, 
(1982, Table 1, p.16) G value is 0.74 molecules/100 ev energy. 
Other accepted engineering numbers: 
 Avogadro constant = 6.02E+23 molecules/Mole 
 Density of water at 27C = 1 gm/cm3

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 III-2 July 2003 
Assumptions 
 It is assumed that the nitric acid production and hydrogen peroxide production is 
represented by the production rates at 500 years. This assumption is conservative 
because 500 years is the earliest waste package failure time because of early failures and 
radiolysis generally decreases with time. Figure 5 of Gamma and Neutron Radiolysis in 
the 21-PWR Waste Package from Ten to One Million Years (BSC 2002b) shows that 
gamma dose and therefore most of the radiolysis is nearly constant after 500 years. This 
assumption needs no further validation. 
 It is assumed that the nitric acid that is produced in the humid air by radiolysis is 
absorbed into the films of water on the fuel and none escapes the waste package through 
the corrosion patches. This assumption is conservative because it is the upper limit of the 
amount of nitric acid that can be absorbed on the fuel. This assumption is necessary 
because modeling of chemical diffusion rates on this scale is not available. This 
assumption is conservative and needs no further validation. 
 It is assumed that the radiolysis production in the outer eleven fuel assemblies of the 21- 
PWR design can be approximated by the radiolysis in the middle nine assemblies. 
Gamma and Neutron Radiolysis in the 21-PWR Waste Package from Ten to One Million 
Years (BSC 2002b) gives analysis of the production in the center assembly of the waste 
package only. Radiation fluxes and therefore radiolysis production rates are less in the 
outer assemblies. This assumption is conservative and needs no further validation. 
 It is assumed that other than the generation of nitric acid and hydrogen peroxide there are 
no other collateral chemical effects on the in-package environment. This assumption is 
justified because HNO3 and H2O2 have the greatest potential to alter the in-package 
chemistry. 
Model Discussion 
When radiation passes through a material, some energy is deposited in the medium and chemical 
reactions can occur from the local deposition of energy (radiolysis). The G value represents the 
number of molecules of a substance that is formed when 100 ev of energy is deposited in the 
media. When gamma and fast neutron energy pass through moist air, nitric acid is produced. 
Gamma and Neutron Radiolysis in the 21-PWR Waste Package from Ten to One Million Years 
(BSC 2002b) predicts the amount of nitric acid that is produced per cm3 of humid air in the waste 
package (3.6E+13 molecules/yr-cm3 at 500 years). The amount of nitric acid formed in the 
waste package is calculated in EQ6 compatible input units via: 
HNO3 production rate = 3.6E+13 molecules/yr-cm3 * (void volume waste package) 
= 3.6E+13 molecules/yr-cm3 * (4144461 cm3) 
= 1.49E+20 molecules/yr-waste package * (1 mole/6.02E+23 molecules) 
= 2.48E-04 moles/yr-waste package * (1.213E-03) * (1 year/3.16E+07 seconds)

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 III-3 July 2003 
= 9.53E-15 moles/second 
Where, 1.213E-03 is the normalization factor from Table 47. 
When gamma and fast neutron energy pass through water, hydrogen peroxide is produced. The 
production rate for hydrogen peroxide (H2O2) in water is calculated by adjusting the nitric acid 
production rate by the ratio of G values and densities and converting to EQ6 compatible input 
units: 
H2O2 Production Rate = PRHNO3 * (GH2O2 / GHNO3) * (Density water/Density air) 
= 3.6E+13 molec./yr-cm3 * (0.72/1.5) * (1.0 gm/cm3 / 1.16E-3 gm/cm3) 
= 1.5E+16 molec./yr-cm3 * (4144461 cm3) 
= 6.22E+22 molecules/yr-waste package * (1 mole/6.02E+23 molecules) 
= 1.03E-01 moles/yr-waste package * (1.213E-03) * (1 year/3.16E+07 seconds) 
= 3.97E-12 moles/second 
Where, 1.213E-03 is the normalization factor from Table 47. 
EQ6 Implementation 
The effect of radiolysis on the in-package chemistry was handled in a non-mechanistic manner, 
i.e., EQ6 does not have the facility to model the radiolysis process directly, therefore, only the 
products of radiolysis are included as inputs in EQ6 simulations. However, EQ6 does have the 
capability to model the effect of H2O2 and HNO3 on the in-package chemistry. 
A series of runs were performed where nitric acid and hydrogen peroxide were included as 
inputs in EQ6 runs and the results of these simulations were compared to their non-radiolysis 
counterparts. 
Two base-case runs (C12C25 and C22C25, Section 6.5.4) were used to test the effects of 
radiolysis. These runs were chosen because they represent the median fuel exposure(clad 
failure) value and the low and median flux values. It would be expected that the effects of nitric 
acid and hydrogen peroxide additions would be greater at the low end of the flux range. 
Two simulations were performed for each file, the first using the base-case nitric acid and 
hydrogen peroxide rates listed above, and a second where the HNO3 and H2O2 rates were 
multiplied by a factor of ten. The EQ6 inputs are listed in Table III-1.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 III-4 July 2003 
Table III-1. EQ6 File Names and Nitric Acid and Hydrogen Peroxide Production Rates 
EQ6 Input File HNO3 Rate (mol/s) H2O2 Rate (mol/s) 
C22C25 0.0 0.0 
C22CBC 9.53E-15 3.97E-12 
C22BC10 9.53E-14 3.97E-11 
C12C25 0.0 0.0 
C12CBC 9.53E-15 3.97E-12 
C12BC10 9.53E-14 3.97E-11 
Results 
The results of the simulations are displayed in Figure III-1, where the pH profiles for the various 
runs are displayed versus time. These results show that neither the base case nor the 10X basecase 
generation rates of HNO3 and H2O2 had an impact on the in-package pH. Therefore, it may 
be concluded that if radiolysis only effects the chemistry via HNO3 and H2O2 generation, then it 
will not be a significant process with regard to influencing the in-package chemistry. 
Table III-2 provides the ferric iron, chloride and hydrogen peroxide maximum concentrations for 
the simulations. Again the runs with HNO3 and H2O2 input show little deviation compared to the 
runs without HNO3 and H2O2. 
Table III-2. Chloride, Ferric Iron, and Hydrogen Peroxide Molality 
Maximum Molalities 
EQ6 Input File Cl- Fe+++ H2O2(aq) 
C22C25 6.6E-04 8.8E-11 3.7E-19 
C22CBC 6.6E-04 8.8E-11 3.7E-19 
C22BC10x 6.5E-04 8.8E-11 3.7E-19 
C12C25 9.7E-04 5.3E-11 3.7E-19 
C12CBC 9.6E-04 5.3E-11 3.7E-19 
C12BC10x 9.2E-04 5.4E-11 3.7E-19 
Based on this non-mechanistic approach to modeling the effects of radiolysis on in-package 
chemistry it may be stated that radiolysis will not effect the in-package chemistry in a manner 
that would impact TSPA. The results of these analyses are compiled in DTN: 
MO0304SPAICSCR.000.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 III-5 July 2003 
3.0 
4.0 
5.0 
6.0 
7.0 
8.0 
9.0 
1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 
Time (yrs) 
pH 
C22C25 
C22C base case 
C22C base case 10x 
C12C25 
C12C base case 
C12C base case 10x 
Source: csnf_radiolysis.xls, DTN: MO0304SPAICSCR.000. 
Figure III-1. pH Profiles Showing how the Radiolysis Inputs Affect the pH

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 IV-1 July 2003 
ATTACHMENT IV 
EQ6 CSNF seepage dripping model input files for J-13 (C22J25.6i), Ca-porewater (C22C25.6i) 
and Na-porewater (C22N25.6i) run at three values of carbon dioxide partial pressure (10-4, 10-3, 
and 10-2). The EQ6 input files and a summary excel spreadsheet are provided in Attachment V.

In-Package Chemistry Abstraction 
ANL-EBS-MD-000037 REV 02 V-1 July 2003 
ATTACHMENT V. 
CD-ROM