CSNF Waste Form Degradation: Summary Abstraction REV 02, ICN 00

ANL-EBS-MD-000015

August 2004
 
 
1. PURPOSE 
The purpose of this model report is to describe the development and validation of models that 
can be used to calculate the release of radionuclides from commercial spent nuclear fuel (CSNF) 
following a hypothetical breach of the waste package and fuel cladding in the repository. The 
purpose also includes describing the uncertainties associated with modeling the radionuclide 
release for the range of CSNF types, exposure conditions, and durations for which the 
radionuclide release models are to be applied. 
This document was developed in accordance with Technical Work Plan for: Regulatory 
Integration Modeling and Analysis of the Waste Form and Waste Package (BSC 2004 
[DIRS 169944]). 
This document considers radionuclides to be released from CSNF when they are available for 
mobilization by gas-phase mass transport, or by dissolution or colloid formation in water that 
may contact the fuel. Because other reports address limitations on the dissolved and colloidal 
radionuclide concentrations (BSC 2004 [DIRS 169944], Table 2-1), this report does not address 
processes that control the extent to which the radionuclides released from CSNF are mobilized 
and transported away from the fuel either in the gas phase or in the aqueous phase as dissolved 
and colloidal species. The scope is limited to consideration of degradation of the CSNF rods 
following an initial breach of the cladding. It considers features of CSNF that limit the 
availability of individual radionuclides for release into the gaseous or aqueous phases that may 
contact the fuel and the processes and events expected to degrade these CSNF features. In short, 
the purpose is to describe the characteristics of breached fuel rods and the degradation processes 
expected to influence radionuclide release. 
The availability of individual radionuclides for release once the CSNF waste package and fuel 
cladding are breached is limited by the structure, microstructure, physicochemical properties of 
the irradiated fuel, distribution of the fission products within the fuel rods, and degradation of the 
fuel rods and fuel matrix. As discussed in more detail in Section 6.2.1.1, most radionuclides, and 
essentially all of the rare earth and actinide radionuclides, are retained within the fluorite 
structure of the fuel matrix. The transition metals (molybdenum, technetium, ruthenium, 
rhodium, and palladium) are partly partitioned into five-metal metallic alloy phases embedded in 
the fuel grains and at the fuel grain boundaries. The part of the radionuclide inventory that is 
present either as a solid solution in the fuel matrix, or embedded as discrete phases within the 
fuel grains, is not available for dissolution until the fuel matrix is dissolved or otherwise altered. 
This part of the inventory is referred to as the matrix inventory ( M
i I , where the subscript i is 
a radionuclide index). A fraction of the inventory of fission gases and of some of the more 
volatile radioelements (e.g., cesium, iodine, and technetium) migrates out of the matrix during 
in-reactor operations and accumulates as gaseous and minor condensed phases in locations 
collectively referred to as the gap region (e.g., the interface between the pellets and the 
cladding, the rod plenum regions, and pellet fracture surfaces). As discussed in Sections 6.2.1.1 
and 6.3, experimental evidence indicates a small fraction of the total radionuclide inventory 
resides at the interfaces between the fuel grains. Because this grain-boundary inventory may 
be released rapidly, it is useful, for modeling purposes, to view the radionuclide inventory in 
CSNF rods as being divided into two fractions: the matrix inventory and the combined gap and 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 1-2 August 2004 
grain-boundary inventories. The latter fraction of the inventory is referred to as the gap and 
grain-boundary inventory ( G
i I ). 
Separate models are developed for release of the gap and grain-boundary inventory ( G
i I ) and the 
matrix ( M
i I ) inventory because the features, events, and processes (FEPs) that affect the release of 
each of these fractions of the inventory are different. The matrix release models address 
release of the fraction of the radionuclide inventory that is in the fuel matrix; while the 
instantaneous release model addresses release of the fraction of the radionuclide inventory that 
is not contained in the fuel matrix (i.e., the gap and grain-boundary inventory). 
The gap inventory is available for immediate gas-phase mass transport or dissolution when the 
cladding is breached. Release of the gap inventory of fission gases is modeled as an 
instantaneous release upon failure of the cladding. The gap inventory of other radionuclides is 
also modeled as an instantaneous release upon contact of the breached fuel with water. 
Although the evidence for preferential degradation at the grain boundaries (compared to the fuel 
grain matrix) and the consequent preferential release of the grain-boundary inventory is limited, 
the grain-boundary inventory is combined with the gap inventory in estimating the instantaneous 
release fraction. This approach overestimates the rate of release of the grain-boundary inventory 
and therefore represents a conservative modeling approach. 
The inventory available for instantaneous release ( G
i I ) can be expressed as a fraction of the total 
inventory of radionuclide, i, in the fuel ( i I ): 
G
i I = i i I f (Eq. 1) 
where 
i f = instantaneous release fraction 
As shown in Technical Work Plan for: Regulatory Integration Modeling and Analysis of the 
Waste Form and Waste Package (BSC 2004 [DIRS 169944], Table 2-1) the total inventory of 
radionuclide i in the fuel ( i I ) is addressed elsewhere. Hence, one purpose of this document is to 
model the instantaneous release by providing a probability distribution function, based on 
available data, to estimate the instantaneous release fraction ( i f ) and the associated 
uncertainties. 
For the radionuclides in the CSNF matrix, the rate of radionuclide release (Ri) can be expressed 
as: 
Ri = M
i I Fi (Eq. 2) 
where 
M
i I = matrix inventory 
Fi = Fractional release rate (i.e., the fraction released per unit time) of any radionuclide 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 1-3 August 2004 
The matrix inventory ( M
i I ) in Equation 2 can be calculated given the instantaneous release 
fraction ( i f ) and the total inventory ( i I ). The matrix inventory can be expressed as: 
(M
i I ) = (1- i f ) i I (Eq. 3) 
This document describes the development and validation of models that can be used to calculate 
the fractional radionuclide release rate from the fuel matrix (i.e., the dependent variable of 
interest is Fi). 
As discussed in Section 6.2.2, the oxidative dissolution is the primary process expected to 
degrade the CSNF matrix in the repository environment. This process begins with the oxidation 
of uranium in the plus four oxidation state, U(IV), to uranium in the plus six oxidation state, 
U(VI), followed by dissolution of U(VI). Using the simplifying assumption (Assumption 5.1) 
that the fractional rate of release of radionuclides from the CSNF matrix is equal to the fractional 
rate of degradation of the matrix, the fractional radionuclide release rate can be expressed as the 
product of the specific surface area (A) of the fuel, and the rate of corrosion of the fuel per unit 
area (Rd): 
Fi = A Rd (Eq. 4) 
where 
A = Specific surface area (i.e., the surface area per unit mass) of the fuel 
Rd = Rate of oxidative dissolution of the fuel per unit surface area. 
This document describes how available experimental data are used for developing and validating 
the models for the fractional release rate of the matrix radionuclide inventory (i.e., the model, 
discussed in Sections 6.4.1 and 7.2, for which the dependent variable is Fi). 
This document also describes how the models parameter values and the associated uncertainties 
are determined by regression analysis of available experimental data. The range of qualified 
experimental data spans CSNF with burnup in the range 15 to 65 (MWd/kgU) and percent 
fission gas release in the range 1percent to 18 percent. It also spans pH values from 2 to 10.3, 
temperatures from 25C to 90C, oxygen partial pressure from 0.002 to 0.2 atmospheres, and 
carbonatebicarbonate concentrations from 0 (i.e., water sparged with CO2-free air) to 2  10-2 
molar. Restrictions on the domain of application of the models (i.e., restrictions on range of 
CSNF characteristics and environmental factors over which the models are valid) are described 
in Section 8.2. With the exception of temperature, for which the upper end of the temperature 
domain is extended to 100C (Section 7.2 for justification), the models application domain is 
restricted to the data domain. A conservative bounding model is used for any fuel exposed to air 
at temperatures greater than 100C (Section 6.2.2.2). 
This report receives no direct inputs from other reports or their technical product output data 
tracking numbers (DTNs). It provides direct inputs to the TSPA-LA model. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 1-4 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 2-1 August 2004 
2. QUALITY ASSURANCE 
This document was developed in accordance with AP-SIII.10Q, Models, and Technical Work 
Plan for: Regulatory Integration Modeling and Analysis of the Waste Form and Waste Package 
(BSC 2004 [DIRS 169944]). Section 8 of the technical work plan (TWP) states that modeling 
activities performed under the TWP are subject to Quality Assurance Requirements Description 
(DOE 2004 [DIRS 171386]). 
Technical Work Plan for: Regulatory Integration Modeling and Analysis of the Waste Form and 
Waste Package (BSC 2004 [DIRS 169944], Appendix A), contains the process control 
evaluation used to evaluate the control of electronic management of data during the modeling 
and documentation activities. The evaluation determined that the methods in the implementing 
procedures are adequate. No deviations from these methods were used. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 2-2 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 3-1 August 2004 
3. USE OF SOFTWARE 
The software applications used to support the CSNF analysis and modeling activities described 
in this document are Microsoft Excel 2002, Service Pack 2, running under Microsoft 
Windows 2000 Professional, Service Pack 2, on a Dell OptiPlex GX240 computer at Argonne 
National Laboratory; SigmaPlot 2002 for Windows, Version 8.0, running on a Dell PW5650 
Workstation in Las Vegas (tag number WSB501356); and Mathcad 2001i Professional, running 
on a Dell PW5650 Workstation in Las Vegas (CRWMS M&O tag WSB 501356). 
Microsoft Excel 2002 was used to perform calculations using user-defined formulas and the 
applications standard functions. Inputs to, outputs from, and formulas and standard functions 
used in the Excel spreadsheets are described at the points where they were used for calculations 
described throughout this document. SigmaPlot was used for the linear and nonlinear regression 
analyses discussed throughout the document. Mathcad 2001i Professional was used for the 
regression analysis documented in Appendix II. The built-in regression functions in the 
SigmaPlot for Windows, Version 8.0, and in Mathcad 2001i Professional were used without 
modification. 
The Microsoft Excel 2002, Mathcad 2001i Professional, and SigmaPlot software applications are 
commercial off-the-shelf software and are not required to be qualified, per Section 2.1.6 of 
LP-SI.11Q-BSC, Software Management. Only standard functions of these software applications 
were used; no software routines or macros were used. 
Regression analysis is used to determine the values of the models parameters that most closely 
describe the data. It does so by minimizing the sum of the squared residuals (i.e., differences 
between the predicted and observed values of the dependant variable) where the sum of the 
squared residuals is given by: 
2 
n
1 i 
c
i 
m
i ) DR DR ( SR .= 
- = (Eq. 5) 
where 
SR = sum of the squared residuals 
m
i DR = log of measured dissolution rate for run i 
c
i DR = log of the calculated dissolution rate for run i 
n = number of runs included in the analyses 
The user interface for the SigmaPlot application is designed for point-and-click implementation 
of functions available in pull-down menus and tool bars, much like those used in Microsoft 
Excel. The inputs for multivariate linear regression analyses are the identification of spreadsheet 
columns containing the data for the dependent and independent variables in the model. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 3-2 August 2004 
The inputs for the nonlinear regression analyses are: 
 The mathematical form of the model to be regressed to the data set (Section 6.4.1.2, 
Table 6-6). This mathematical form identifies the dependent variable, independent 
variables, and model parameters. 
 Initial (estimated) values for each of the model parameters. 
 The name of the dependent variable in the model and identification of a spreadsheet 
column for the corresponding data set to which the dependent variable in the model is to 
be fitted. 
 The spreadsheet columns for each of the independent variable data sets for which 
independent variable data are available. 
The input data for SigmaPlot regression analyses are the data in Tables 4-2, 4-3, and 4-4. The 
outputs from the SigmaPlot for Windows software are the regressed values for each of the model 
parameters, the standard errors in the values of these model parameters, and statistics for the 
goodness of fit of the model to the data set (Tables 6-4, 6-5, 6-6, 6-7, and 6-8). The listing of the 
input data, the form of the model, and the output parameter values allow the results to be 
checked by hand calculation or by comparison with the output from a different software 
application capable of performing multivariate linear and nonlinear regression analyses. 
The regression analysis documented in Appendix II utilizes the matrix and vector manipulation 
features of Mathcad 2001i Professional. The regression equations described in Equation 5 are 
posed in their normal form, which admits to a direct solution, involving only matrix inversion 
and matrix-vector multiplication. The details of the algorithm are given in the two Mathcad 
sheets in Appendix II. 
The input data for the Mathcad analysis is the same as that for the SigmaPlot analysis, 
Tables 4-2, 4-3, and 4-4. The outputs from the Mathcad 2001i Professional software are the 
same regressed values for the model parameters, standard errors and goodness of fit statistics. 
The covariance matrix is an additional output from the Mathcad calculation. The covariance 
matrix is required to implement uncertainty in the regression coefficients, in a way that accounts 
for the correlation between parameters and is consistent with the range of the dissolution rates 
and other data used to obtain the regressed values. Mathcad was also used to calculate the 
Cholesky decomposition of the covariance matrix (Appendix II). The Cholesky decomposition 
facilitates implementation of regression coefficient uncertainty in the TSPA model file. 
The detailed presentation of the Microsoft Excel and Mathcad analyses in Appendices I and II 
allows the results to be checked by hand calculation or by using any other software application 
that performs standard mathematical operations and matrix and vector manipulations. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-1 August 2004 
4. INPUTS 
This section addresses the data inputs directly used for model development. In general, these 
inputs include data and associated measurement uncertainties chosen because they are the most 
current and relevant data available to support the purposes of this report (Section 1). All other 
data and sources of information used in discussing the scientific basis for the models or used for 
corroborative and model validation purposes are included is Sections 6 and 7. 
In the following sections, the appropriateness of the input data is addressed by discussing the use 
of the data and aspects of the testing methods and testing conditions pertinent to considering the 
suitability of the data for the intended use. 
4.1 DIRECT INPUT 
As stated in the Section 1, this document is intended to develop a probability distribution for the 
instantaneous release fraction (fi) for each radionuclide, i, and to derive parameters for modeling 
the matrix fractional release rate (Fi) by analysis of suitable experimental data. Qualified data 
are used as input for these analyses and the model parameter values and associated uncertainty 
estimates are the principal outputs (Sections 8.1 and 8.2). 
The inputs are organized according to the principal model parameters they support and further 
organized according to the test types involved. On this basis, the experiments can be divided 
into three broad categories. The first addresses the gap and grain-boundary inventories and the 
instantaneous release fractions (fi). The second is designed to address the parametric dependence 
of the matrix dissolution rate per unit of exposed surface area (i.e., Rd). The third measures the 
geometric-specific surface area of CSNF pellets. As discussed in Section 6.4.1.5, these 
geometric-specific surface area data are used to scale the area-specific dissolution rates based on 
the effective corroding surface area of CSNF in realistic degraded states and configurations. 
Within these three broad categories, the available test data can be further categorized by the test 
types and test conditions involved. 
4.1.1 Instantaneous Fractional Release Data 
This section includes data from measurements of the gap and grain-boundary inventory fractions 
of fission gases and fission product elements. 
Table 4-1 shows available data for the gap inventory and grain-boundary inventories of cesium, 
technetium, strontium and iodine in CSNF from DTN: MO0407SEPGGBID.000 [DIRS 170619]. 
DTN: MO0407SEPGGBID.000 [DIRS 170619] data were obtained by exposing commercial 
spent fuel to mild leachant solutions and then measuring the concentrations of uranium, 137Cs, 
99Tc, 90Sr, and 129I in the solution. For the gap inventory, the fuel was pushed out of the clad and 
the fuel fragments and cladding were exposed to the leachant (either deionized water or a 
buffered potassium-iodine solution) for periods of 1 day or up to 1 week. The solutions were 
then analyzed to determine the concentrations of the desired radionuclides. The fuel was then 
crushed to obtain individual grain particles and then again exposed to the leachant (either a 
buffered potassium iodine or 0.1 M HCl solution) for periods between two and 24 hours. The 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-2 August 2004 
solutions were then analyzed for the desired radionuclides to determine the grain-boundary 
inventories. The grain-boundary inventory was typically released within the first hour. 
In using the methods described above, a small fraction of the fuel matrix dissolves and 
contributes to the measured 137Cs, 99Tc, 90Sr, and 129I concentrations. The gap and grainboundary 
fractions for these radionuclides are, therefore, determined by subtracting the fractional 
dissolution of uranium (which indicates the fraction of the fuel matrix that dissolved) from the 
fractional dissolution of 137Cs, 99Tc, 90Sr, and 129I. Since the fractions involved are often small 
(the release fractions are typically less than one percent for technetium and strontium), 
subtraction of two small experimentally measured fractions, each of which have some 
measurement error, can result in a negative value. Such negative results are expected due to the 
measurement error inherent in determination of the small dissolution fractions but they have no 
physical meaning for an individual sample, as a negative inventory is not realistic. Such 
negative results are to be expected, especially for technetium where a significant fraction is 
located in the five-metal particles, which are more difficult to dissolve. 
An examination of the reproducibility of the data in Table 4-1 shows that the majority of tests 
showed reproducibility, which is a measure of all of the uncertainties combined, within a factor 
of about 1.5. In one case (the cesium and iodine grain boundary data in the 0.59 FGR % 
rows), the data differ by factors of about four and seven, respectively. However, it is important 
to note that the absolute values for these fractions is very small and, thus, these differences have 
little effect on total inventory available for instantaneous release (combined gap and grainboundary 
inventory). 
4.1.2 Matrix Dissolution Rate Data: Flow-Through Tests 
CSNF, in the form of grain-sized powders, was tested in single-pass flow-through (SPFT) 
dissolution studies. These studies provide the parametric dependence of the forward matrix 
dissolution rate over a range of conditions that bracket the anticipated groundwater and 
environmental conditions to which CSNF may be exposed in breached waste packages in the 
repository. The test configuration, conditions, and methods are described elsewhere (Gray and 
Wilson 1995 [DIRS 100758], Section 2.0). The flow-through tests were designed to maintain 
constant test conditions and to eliminate the influence of back-reactions or secondary phase 
formation that could inhibit the overall reaction rate (hence the use of the term forward to 
describe the dissolution rates measured in these tests). The effect of feedback from the dissolved 
uranium concentration on the rate of release of 137Cs from fuel grains was examined but the 
results were ambiguous. An increase of the dissolved uranium (U) concentration to 6 to 8 mg/L 
(about half the saturation concentration) in single-pass flow-through leachate resulted in no 
observable effect on the measured dissolution rate, indicating no feedback from the dissolved 
uranium. However, when uranium-free water was again pumped through the system, an increase 
of approximately 50 percent was observed for uranium and 137Cs release (Gray and Wilson 1995 
[DIRS 100758], Section 3.3.5), indicating the dissolved uranium may have some effect on the 
dissolution rate. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-3 August 2004 
Table 4-1. Light Water Reactor SNF Gap and Grain-Boundary Inventories 
137Cs 99Tc 90Sr 129I FGR 
(%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) 
1.1 1.18E+00 1.98E-01 -2.30E-02 5.04E-02 3.97E-02 2.14E-02 
7.85 7.21E-01 1.17E+00 3.44E-05 -7.75E-02 8.70E-03 2.38E-02 
7.85 8.02E-01 3.67E-01 -1.85E-02 4.76E-02 3.68E-02 5.75E-02 
7.85 2.23E+00 1.52E+00 
7.85 1.55E+00 3.28E+00 
7.85 6.24E-01 5.15E+00 
0.59 2.07E-01 1.88E-01 6.63E-02 1.97E-02 8.00E-02 1.68E-02 
0.59 2.78E-01 1.15E-01 
0.59 7.37E-01 2.09E+00 
7.4 1.80E+00 9.37E-02 
7.4 6.96E-01 7.98E+00 
7.4 6.48E-01 7.53E+00 
11.2 2.57E+00 1.00E+00 1.51E-02 1.54E-02 2.30E-02 1.34E-01 
11.2 2.32E+00 8.22E-03 1.22E-02 
11.2 3.21E+00 1.44E+00 
11.2 3.00E+00 1.08E+00 
11.2 7.64E-01 7.48E+00 
11.2 8.14E-01 9.13E+00 
18 4.25E+00 9.34E-01 5.42E-02 1.33E-01 9.70E-02 8.65E-02 
18 5.05E+00 2.55E-02 4.08E-02 
18 7.46E+00 1.82E+01 
18 1.05E+01 1.23E+01 
18 1.10E+00 8.32E+00 
18 1.23E+00 8.54E+00 
Source: DTN: MO0407SEPGGBID.000 [DIRS 170619] 
NOTES: GI = gap inventory; GBI = grain-boundary inventory; FGR = fission gas release. 
Blank table cells indicate no data. 
An inter-laboratory comparison of UO2-dissolution rates measured by the SPFT method was 
conducted using portions of the same batch of unirradiated UO2 and similar test conditions at 
each of the three participating laboratories (Gray et al. 1994 [DIRS 170109]). The dissolution 
rates from each of the three laboratories (2.2 0.5, 1.5 0.9, and 5.5 2.7 mg/m2/day 2s) 
indicated good reproducibility of the results obtained using this test method. 
In the SPFT tests, the principal test response is the oxidative-dissolution rate of the matrix. 
Because the available data set spans a wide range of dissolution conditions, these data are 
suitable for analysis of the functional dependence of the oxidative-dissolution rate (Rd) on 
environmental factors. The range of qualified SPFT data spans the pH range from 2 to 10.3, the 
temperature range from 25C to 78C, oxygen partial pressure from 0.002 to 0.2 atmospheres, 
and carbonatebicarbonate concentrations from 0 to 2  10-2 molar. 
The CSNF flow-through test data for alkaline conditions are shown in Table 4-2. Unirradiated 
UO2 flow-through test data used as input in developing the alkaline conditions model are shown 
in Table 4-3. CSNF flow-through test data for acidic conditions are shown in Table 4-4, which 
includes Run #66, a dissolution rate for ATM-103 of 109 mg/(m2d) at a pH of 3.25 from 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-4 August 2004 
DTN: MO0302PNLDUFTD.000 [DIRS 162385] and data from 
DTN: MO0304PNLLPHDD.000 [DIRS 163441]. It also includes data for one test (Y6-A7B) 
conducted at a pH of 7.29 as part of the acidic conditions test series 
(DTN: MO0304PNLLPHDD.000 [DIRS 163441]). All of the tests listed in Table 4-4 were 
conducted under carbonate-free conditions (i.e., the solutions used were continuously stirred and 
sparged with air containing less than 3 ppm CO2). 
As described in the ReadMe files associated with these input data, the tabulated results are based 
on the steady state test conditions. The initial data, which are associated with transient and 
irrelevant surface phenomena (e.g., dissolution of fuel fines or dissolution of oxidized or 
otherwise altered surface features, or both), are not used. Such initially high and transient 
dissolution rates are commonly observed in laboratory dissolution studies and have been 
attributed to disruption of grain surfaces during sample preparation and/or early dissolution of 
ultrafine particles that adhere to larger grains (e.g., Aagaard and Helgeson 1982 [DIRS 101530], 
p. 244). The effects of oxidized surface layers on the initial dissolution of UO2 are described by 
Jegou et al. (2001 [DIRS 162397], pp. 476 to 478). For acidic conditions, the data obtained after 
a significant fraction (more than approximately 30 percent) of the sample has been dissolved are 
not used because of the uncertain state of the sample material (including its specific surface area 
and the effects of accumulated insoluble residues) after such extensive dissolution. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-5 August 2004 
Table 4-2. CSNF Flow-Through Test Dissolution Data (Alkaline Conditions) 
Run No. 
Burnup 
(MWd/kg U) 
Specific 
Surface 
Area 
(m2/g) 
Temp. 
(C) 
Total 
Carbonate 
(mol/L) 
O2 Partial 
Pressure 
(atm) pH 
Dissolution 
Rate 
Normalized to 
Surface Area 
(mg/m2/day) 
Standard 
Deviation for 
Dissolution 
Rate 
(mg/m2/day) 
Slope of the 
Cumulative 
Release Curve 
(fraction 
released/day) 
1 30 0.0858 49 2.E-03 0.2 9.06 7.58 0.35 6.476E-04 
2 30 0.0858 51 2.E-03 0.2 9.06 8.55 0.76 7.194E-04 
3 30 0.0858 50 2.E-03 0.2 9.06 6.31 0.97 5.177E-04 
4 30 0.0858 24 2.E-02 0.2 8.18 4.15 0.33 3.426E-04 
5 30 0.0858 73 2.E-02 0.2 10.14 13.81 1.36 1.124E-03 
5A 30 0.0858 77 2.E-02 0.2 10.03 21.38 2.39 1.878E-03 
6 30 0.0858 75 2.E-04 0.2 8.13 10.52 1.60 9.265E-04 
6A 30 0.0858 72 2.E-04 0.2 8.13 9.90 0.82 8.683E-04 
7 30 0.0858 23 2.E-04 0.2 10.07 0.58 0.22 4.751E-05 
7A 30 0.0858 20 2.E-04 0.2 10.02 1.08 0.29 8.653E-05 
8 30 0.0858 24 2.E-02 0.2 9.12 3.25 0.31 2.755E-04 
9 30 0.0858 24 2.E-03 0.2 10.11 2.47 0.25 2.128E-04 
10 30 0.0858 27 2.E-04 0.02 8.00 2.12 0.16 1.819E-04 
11 30 0.0858 78 2.E-04 0.02 9.86 1.77 0.15 1.552E-04 
12 30 0.0858 26 2.E-02 0.02 10.04 2.46 0.21 2.082E-04 
13 30 0.0858 77 2.E-02 0.02 8.10 3.57 0.34 3.027E-04 
14 30 0.0858 23 2.E-02 0.002 8.25 2.94 0.09 2.494E-04 
15 30 0.0858 75 2.E-02 0.002 10.12 0.95 0.32 8.624E-05 
16 30 0.0858 76 2.E-04 0.002 8.00 1.41 0.15 1.225E-04 
17 30 0.0858 20 2.E-04 0.002 10.00 0.76 0.19 6.042E-05 
18 30 0.0858 50 2.E-02 0.002 10.05 1.20 0.16 1.076E-04 
19 30 0.0858 22 2.E-03 0.002 8.97 1.95 0.33 1.808E-04 
20 30 0.0858 74 2.E-02 0.02 10.11 5.65 1.05 4.569E-04 
21 31 0.0984 50 2.E-03 0.2 9.05 6.61 0.54 6.578E-04 
22 50 0.277 26 2.E-02 0.2 8.27 1.63 0.32 4.376E-04 
23 31 0.0678 23 2.E-02 0.2 8.02 4.04 0.87 2.837E-04 
24 31 0.0678 76 2.E-02 0.2 8.04 9.41 2.82 6.457E-04 
25 31 0.0678 23 2.E-04 0.2 7.93 2.64 0.34 1.805E-04 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-6 August 2004 
Table 4-2. CSNF Flow-Through Test Dissolution Data (Alkaline Conditions) (Continued) 
Run No. 
Burnup 
(MWd/kg U) 
Specific 
Surface 
Area 
(m2/g) 
Temp. 
(C) 
Total 
Carbonate 
(mol/L) 
O2 Partial 
Pressure 
(atm) pH 
Dissolution 
Rate 
Normalized to 
Surface Area 
(mg/m2/day) 
Standard 
Deviation for 
Dissolution 
Rate 
(mg/m2/day) 
Slope of the 
Cumulative 
Release Curve 
(fraction 
released/day) 
26 31 0.0678 75 2.E-04 0.2 7.75 10.99 1.72 7.918E-04 
27 44 0.136 23 2.E-02 0.2 8.27 3.62 0.64 4.453E-04 
28 50 0.1023 25 2.E-02 0.2 8.30 3.83 0.22 3.873E-04 
29 50 0.1023 76 2.E-02 0.2 8.30 6.90 1.04 6.939E-04 
30 50 0.1023 25 2.E-04 0.2 7.56 2.85 0.56 2.737E-04 
31 50 0.1023 74 2.E-04 0.2 7.56 9.45 1.37 9.902E-04 
33 50 0.1023 75 2.E-02 0.002 8.06 1.35 0.36 1.552E-04 
34 50 0.1023 27 2.E-04 0.002 7.76 2.03 0.20 2.071E-04 
35 50 0.1023 74 2.E-04 0.002 7.74 3.50 0.46 3.612E-04 
36 15 0.0837 27 2.E-02 0.2 8.02 3.24 0.54 2.739E-04 
37 15 0.0837 76 2.E-02 0.2 7.96 11.94 3.97 9.558E-04 
38 15 0.0837 27 2.E-04 0.2 7.62 3.74 0.47 3.050E-04 
61 65 0.133 26 2.E-02 0.2 8.14 3.94 0.52 5.262E-04 
62 65 0.133 76 2.E-02 0.2 8.12 5.61 1.56 7.769E-04 
63 65 0.133 26 2.E-04 0.2 7.63 2.49 0.89 2.908E-04 
64 65 0.133 76 2.E-04 0.2 7.16 6.77 1.49 9.008E-04 
65 65 0.133 76 2.E-02 0.002 8.07 0.85 0.19 1.217E-04 
Source: DTN: MO0302PNLDUFTD.000 [DIRS 162385] 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-7 August 2004 
Table 4-3. Unirradiated UO2 Flow-Through Test Dissolution Data (Alkaline Conditions) 
Sample 
T 
(C) 
Total CO3 
(mmol/L) 
O2 
(%) pH 
DR 
(mg/m2d) 
4 25 20 20 8.7 2.42 
5 75 20 20 10.3 77.4 
6 75 0.2 20 9.1 10.9 
7 25 0.2 20 9 2.55 
8 25 20 20 9.4 6.72 
9 25 2 20 9.3 9.34 
10 26 0.2 2 7.8 0.12 
11 75 0.2 2 9.7 9.21 
12 26 20 2 10.1 1.87 
13 75 20 2 8.5 5.11 
14 25 20 0.2 8 0.22 
15 75 20 0.2 9.8 5.61 
16 75 0.2 0.2 8.7 0.51 
17 26 0.2 0.2 9.3 0.23 
18 50 20 0.2 9.9 4.6 
19 26 2 0.2 9 1.52 
21 50 2 2 8.9 12.3 
22 50 2 2 8.8 7.96 
23 50 2 2 8.9 10.4 
24 75 0.2 20 9.5 6.48 
25 75 2 20 9.6 23.3 
26 75 20 20 8.5 54 
Source: DTN: MO0407SEPUDISR.000[DIRS 170618]; Appendix I, CSNF MR 
REV2.xls, Sheet A1 
NOTES: T = temperature; Total CO3 = [HCO3
- ] + [CO3
2- ] in millimolar units; 
O2 = oxygen percent in atmospheres; DR = dissolution rate in 
mg/(m2d); 
Table 4-4. CSNF Flow-Through Test Dissolution Data - Acidic Conditions 
Test 
Identification 
Burnup 
(MWd/kg 
M) 
Temp 
(C) 
O2 Partial 
Pressure 
(atmospheres) pH 
Dissolution 
Rate 
Normalized to 
Surface Area 
(mg/m2/day) 
Standard 
deviation for 
Dissolution 
Rate 
Slope of the 
Cumulative 
Release Curve 
(fraction 
released/day) 
Y6-A2B 50 27 0.2 2.02 5.02E+01 6.21E+00 1.406E-02 
Y6-A3B 50 27 0.2 3.01 3.45E+01 5.37E+00 1.045E-02 
Run #66 31 24 0.2 3.25 1.09E+02 6.35E+00 9.043E-03 
Y6-A4B 50 27 0.2 3.80 5.39E+01 1.66E+01 1.756E-02 
Y6-A5B 50 26 0.2 5.07 7.18E+00 7.24E-01 2.019E-03 
Y6-A6B 50 26 0.2 5.82 3.80E+00 1.17E+00 8.932E-04 
Y6-A7B 50 26 0.2 7.29 3.45E+00 1.04E+00 8.381E-04 
Source: DTN: MO0302PNLDUFTD.000 [DIRS 162385] (RUN # 66 Data); MO0304PNLLPHDD.000 [DIRS 163441]; 
Excel Spreadsheet file: CSNF MR REV2.XLS, Sheet A3 of Appendix I 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-8 August 2004 
4.1.3 CSNF Surface Area Data 
Tables 4-5 and 4-6 show available data on the geometric surface area of irradiated CSNF. The 
data in Tables 4-5 and 4-6 were obtained using different methods described in the following 
paragraphs. 
Table 4-5 shows estimated geometric-specific surface areas for irradiated fuel from the 
H. B. Robinson (designated HBR) and from the Turkey Point (designated TP) nuclear power 
plants (Wilson 1990 [DIRS 100793], Appendix E). These estimates were obtained by 
determining the distribution of particle weights in a fuel sample and approximating the particles 
shape as a uniform right-angle wedge. The average geometric-specific surface areas for each 
fuel were calculated by dividing the sum of the particle areas by the sum of the particle weights 
for each fuel type (107 particles for HBR and 89 for TP). The ratio of the height to the length of 
the sides of the square-shaped base (h) of the right-angle wedge shape used to calculate the 
particle areas, was treated as a parameter. Table 4-5 lists the specific surface areas that were 
calculated for three values of h selected on the basis of photographic examination of the shape 
of the fuel fragments. 
Table 4-5. Estimated Geometric-Specific Surface Area of HBR and TP Fuels 
Fuel Type h = 0.50 h = 0.67 h = 1.00 
HBR (cm2/g) 2.57 2.40 2.29 
TP (cm2/g) 2.37 2.21 2.11 
Source: Wilson 1990 [DIRS 100793], Appendix E 
Table 4-6 shows estimates of the surface area per unit length of the H. B. Robinson fuel 
(designated as ATM-101) and as-fabricated characteristics of this fuel necessary to convert these 
data to specific surface areas (Barner 1985 [DIRS 109194], Tables 4.1 and4.6). In this case, the 
surface area estimates are based on examination of the cracking patterns revealed in transverse 
and longitudinal ceramographic sections of the N-9 fuel rod (Barner 1985 [DIRS 109194], 
Appendix B). A three-step approach was used to estimate the surface area per millimeter length 
of fuel based on the observed cracking patterns. First, the sum of the lengths of the major 
radially oriented longitudinal cracks was estimated from the transverse micrographs. The 
contribution of these cracks to the surface area per unit length of fuel was estimated by 
multiplying the sum of the crack lengths by the pellet radius and by two to account for the two 
facing surfaces defining each crack. Second, the areas of transverse cracks were estimated by 
counting the primarily radial cracks (i.e., cracks oriented approximately normal to the cladding) 
in the longitudinal micrographs. For cracks that were found to intercept the cladding at an angle 
between approximately 45 and 80, a value of half a transverse crack was assigned because of 
the possibility that it had been counted in the transverse micrograph. The area per unit length of 
the transverse cracks was estimated by using the sum of the crack lengths to estimate equivalent 
diameters, calculating the number of equivalent cross sections in the sample, multiplying by two 
to account for the two surfaces associated with each fracture, and dividing by the length of the 
sample. Third, the total surface area per unit length was obtained by adding the fracture surface 
area per unit length to the original pellet surface area per unit length. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-9 August 2004 
Table 4-6. Estimated Surface Area Per Unit Length (mm2/mm) of ATM-101 Fuel 
Fuel Sections 
Area of 
Longitudinal 
Cracks 
Area of 
Transverse 
Cracks 
Total 
Area of 
Cracks 
Area of 
Original 
Surface 
Total Surface 
Area of 
Fragments 
N-9C-C&F 84 30 114 38 152 
N-9C-I&K 74 35 109 38 147 
N-9B-O&M 90 8 98 38 136 
Source: Barner 1985 [DIRS 109194], Table 4.6 
NOTE: For ATM-101 fuel pellets the as-fabricated pellet height =15.2 (mm); diameter = 
9.30(mm); density = 10.08 (g/cm3) (Barner 1985 [DIRS 109194], Table 4.1) 
A number of factors (e.g., surface roughness, preferential corrosion at grain boundaries, and 
oxygen depletion in tight cracks) can influence the extent to which the CSNF geometrical 
specific surface area represents the appropriate value to use for the parameter A in modeling the 
fractional degradation rate of corroding CSNF (Equation 4). Parameter A can be considered to 
be a scaling parameter and will be referred to as the effective specific surface area of corroding 
CSNF to distinguish it from the geometric-specific surface area. The effects of initial surface 
roughness and preferential grain-boundary penetration on the estimated effective specific surface 
area of corroding CSNF have been assessed (Gray and Wilson 1995 [DIRS 100758], 
Sections 2.1.3, 4.1.1, and 4.1.6). Comparison of specific surface area measurements on grainsized 
UO2 powders using Brumauer, Emmett, and Teller (BET) and geometric particle-size 
distribution methods, showed that the BET results were three times larger than the geometric 
results (Gray and Wilson 1995 [DIRS 100758], Section 2.1.3). This factor of three difference 
was attributed to the effects of irregularities in the particles shape and to surface roughness 
effects on the BET specific surface area results. Preferential grain-boundary penetration during 
the corrosion process was estimated to increase the effective specific surface area of corroding 
fuel fragments by a factor of six (Gray and Wilson 1995 [DIRS 100758], Sections 4.1.1 and 
4.1.6). Based on these results, the specific surface area of CSNF corroded in single-pass flowthrough 
tests was estimated to be 3.9  10-3 (m2/g) (Gray and Wilson 1995 [DIRS 100758], 
Section 4.1.1, p. 4.2). This estimate, which includes the effects of surface roughness and grain 
boundary penetration, is used in Section 6.4.1.5 to determine the upper end of the uncertainty 
distribution range for the specific surface area parameter. Its suitability for this purpose is 
discussed in Section 6.4.1.5. 
4.1.3.1 Qualification of CSNF Surface Area Input Data 
The input surface area data (Wilson 1990 [DIRS 100793], Appendix E; Barner 1985 
[DIRS 109194], Tables 4.1 and 4.6; Gray and Wilson 1995 [DIRS 100758], Section 4.1.1, p. 4.2) 
are qualified in Appendices IV and V in accordance with AP-SIII.2Q, Qualification of 
Unqualified Data. The qualification process used for these data involves a combination of the 
following methods (AP-SIII.2Q, Attachment 3): 
 Technical Assessment 
 Corroborating Data. 
The qualification process attributes (AP-SIII.2Q, Attachment 4) used were attributes 1 through 6 
and 10 with most emphasis on attribute 10 (i.e., Extent and quality of corroborating data or 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-10 August 2004 
confirmatory testing results). This qualification process is designed to provide the desired level 
of confidence and its intended use is only for this modeling report. The qualification plan is 
included as Appendix IV and the qualification report is included as Appendix V. 
4.1.3.2 Radionuclide Fractional Release Rate Data - Rod Segment Tests 
This section includes data on fractional radionuclide release rates from short fuel rod segments. 
Five tests were conducted on short segments of ATM-103 (1.5-, 2.6-, and 3.7-inch segments), 
ATM-106 (1.4-inch segment), and ATM-109 (3.2-inch segment) fuel rods in a configuration in 
which a simulated groundwater was allowed to percolate, under a low hydraulic head, through 
the fuel rod segment and the radionuclide release was determined by collecting and analyzing the 
effluent. These tests were conducted at 90C. The test configuration, test matrix, and test 
methods are described in the data report (Goldberg 2003 [DIRS 162410]). The fractional release 
rate data from these tests are used as direct input to this report (Table 4-7). The slopes of 
regression lines fitted to the cumulative release data were used to estimate these fractional 
radionuclide release rates (Goldberg 2003 [DIRS 162410]). The zero intercepts of the linear 
regression lines through the cumulative release data for each nuclide gave the instantaneous 
fractional releases used for validating the instantaneous fractional release model in Section 7.1.1. 
Overall, uncertainties in the measured fractional radionuclide release rate data from these tests 
are estimated to be bounded by 80 percent relative standard deviation (Goldberg 2003 
[DIRS 162410]). Section 6.4.1.4 discusses other components of the uncertainty of these data 
pertinent to estimate the effective specific surface areas used in scaling CSNF matrix release 
rates. 
The data in Table 4-7 are used as inputs to determine the lower end of uncertainty range for the 
specific surface area parameter (Sections 6.4.1.4 and 6.4.1.5). These data are appropriate for this 
purpose because, as discussed in Section 6.2.2.1, short fuel rod segments represent a plausible 
state of degraded fuel rods. 
Table 4-7. Average Fractional Radionuclide Release Rates (d-1) from Rod Segment Tests 
Fuel 137Cs 129I 90Sr 99Tc 
ATM-103, 1.5 7.09E-7 8.94E-6 7.01E-7 4.01E-7 
ATM-103, 2.6 8.01E-7 1.52E-5 1.05E-6 5.17E-7 
ATM-103, 3.7 7.96E-7 1.37E-5 2.82E-7 6.31E-7 
Average ATM-103 7.69E-7 1.26E-5 6.78E-7 5.16E-7 
ATM-106, 1.4 1.95E-6 1.03E-5 9.09E-7 1.93E-7 
ATM-109, 3.2 5.21E-7 5.07E-6 NDa 2.60E-8 
Source: DTN: MO0301ANLSF001.450 [DIRS 162384] 
NOTE: a Concentration not determined in test solution. 
4.1.4 Input Data Summary 
The input data described above in Sections 4.1.1, 4.1.2, and 4.1.3 are summarized in Table 4-8. 
The input data uncertainties and the effects of these uncertainties on the model output are 
discussed in Sections 6.3.1, 6.3.2, 6.4.1.1, and 6.4.1.4. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-11 August 2004 
Table 4-8. Direct Input Data Summary 
Data Name Data Source DTN 
Data Use in this 
Model Report 
Instantaneous Release Fraction 
Gap/Grain Boundary 
Inventory Fractions 
MO0407SEPGGBID.000 
[DIRS 170619] 
Section 6.3.1 
Matrix Dissolution Rate Per Unit Area 
CSNF Flow-Through Test 
Data - Alkaline Conditions 
MO0302PNLDUFTD.000 
[DIRS 162385] 
Sections 6.4.1.2 
and 6.4.1.3 
UO2 Flow-Through Test Data MO0407SEPUDISR.000 
[DIRS 170618] 
Section 6.4.1.2 
CSNF Flow-Through Test 
Data - Acidic Conditions 
MO0304PNLLPHDD.000 
[DIRS 163441] 
Section 6.4.1.3 
CSNF Geometric Surface Area Data 
Geometric-Specific Surface 
Area (cm2/g) of HBR and TP 
Fuels 
Wilson 1990 [DIRS 100793], 
Appendix E 
N/A  Qualified per 
AP-SIII.2Q 
Sections 6.4.1.4 
and 6.4.1.5 
Surface Area Per Unit Length 
(mm2/mm) of ATM-101 Fuel 
Barner 1985 [DIRS 109194], 
Tables 4.6 and 4.1 
N/A  Qualified per 
AP-SIII.2Q 
Sections 6.4.1.4 
and 6.4.1.5 
Rod Segment Tests - 
Fractional Release Rates 
Goldberg 2003 [DIRS 162410], 
Tables 6, 7, 8, and 9 
MO0301ANLSF001.450 
[DIRS 162384] 
Sections 6.4.1.4 
and 6.4.1.5 
Specific Surface Area of 
Corroded CSNF 
Gray and Wilson 1995 [DIRS 
100758], Section 4.1.1, p. 4.2 
N/A  Qualified per 
AP-SIII.2Q 
Section 6.4.1.5 
4.2 CRITERIA 
Project Requirements Document (Canori and Leitner 2003 [DIRS 166275]) contains 
requirements relevant to this document. The requirements are primarily contained in Section 3.4 
of that document. The key requirements (referred to by their requirement identifier) are: 
1. PRD-002/T-014, Performance Objectives for the Geologic Repository After Permanent 
Closure (Canori and Leitner 2003 [DIRS 166275], pp. 3 to 14) 
This section specifies the repository performance objectives that must be met following 
permanent closure. It includes a requirement for multiple barriers and limits on 
radiological exposure. 
2. PRD-002/T-015, Requirements for Performance Assessment (Canori and Leitner 2003 
[DIRS 166275], pp. 3 to 14) 
This section specifies the technical requirements to be used in performing a performance 
assessment. It includes requirements for calculations, including data related to site 
geology, hydrology, and variability in the models, and deterioration or degradation 
processes, including waste form degradation. 
3. PRD-002/T-016, Requirements for Multiple Barriers (Canori and Leitner 2003 [DIRS 
166275], pp. 3 to 15). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 4-12 August 2004 
This section specifies the requirements for the identification of the repository multiple 
barriers, describing the capabilities of the barriers to isolate waste, and provide the 
technical bases for the capability descriptions. 
Work described in this document will support these requirements. More specific criteria exist in 
Yucca Mountain Review Plan, Final Report (NRC 2003 [DIRS 163274]). Applicable Yucca 
Mountain Review Plan acceptance criteria are presented below. Section 8.3 quotes the full text 
of the applicable acceptance criteria with pointers to the information within this report that 
pertains to the criteria. 
System Description and Demonstration of Multiple Barriers Acceptance Criteria (NRC 
2003 [DIRS 163274], Section 2.2.1.1.3) 
 Acceptance Criterion 1Identification of Barriers Is Adequate 
 Acceptance Criterion 2Description of Barrier Capability to Isolate Waste Is Acceptable 
 Acceptance Criterion 3Technical Basis for Barrier Capability Is Adequately Presented. 
Degradation of Engineered Barriers (NRC 2003 [DIRS 163274], Section 2.2.1.3.1.3) 
 Acceptance Criterion 1System Description and Model Integration are Adequate 
 Acceptance Criterion 2Data Are Sufficient for Model Justification 
 Acceptance Criterion 3Data Uncertainty is Characterized and Propagated through the 
Model Abstraction 
 Acceptance Criterion 4Model Uncertainty is Characterized and Propagated Through 
the Model Abstraction 
 Acceptance Criterion 5Model Abstraction Output Is Supported By Objective 
Comparisons. 
Radionuclide Release Rates and Solubility Limits Acceptance Criteria (NRC 2003 [DIRS 
163274], Section 2.2.1.3.4.3) 
 Acceptance Criterion 1System Description and Model Integration Are Adequate 
 Acceptance Criterion 2Data Are Sufficient for Model Justification 
 Acceptance Criterion 3Data Uncertainty Is Characterized and Propagated Through the 
Model Abstraction 
 Acceptance Criterion 4Model Uncertainty Is Characterized and Propagated Through 
the Model Abstraction 
 Acceptance Criterion 5Model Abstraction Output Is Supported by Objective 
Comparisons. 
4.3 CODES AND STANDARDS 
Standard Practice for Prediction of the Long-Term Behavior of Waste Package Materials, 
Including Waste Forms, Used in Engineered Barrier Systems (EBS) for Geologic Disposal of 
High-Level Radioactive Waste (ASTM C 1174-97 [DIRS 105725]) applies to this report. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 5-1 August 2004 
5. ASSUMPTIONS 
Three assumptions were used in the development of the CSNF models. 
5.1 FRACTIONAL DEGRADATION RATE BOUNDS RADIONUCLIDE RELEASE 
Assumption: In developing the models for the rate of fractional radionuclide release from the 
CSNF matrix, it is assumed that the fractional degradation rate of the CSNF matrix 
conservatively bounds the rate of fractional release of radionuclides located in the fuel matrix. 
Rationale: This assumption is based on the fact that there is no process, other than those 
involving prior degradation of the fuel matrix, which can enable fission products dissolved or 
embedded in the fuel matrix to be released. The processes that could cause some mobilization of 
fission products embedded in the matrix are discussed in Section 6.2.1.2. These processes will 
have a negligible effect on radionuclide release compared to the matrix oxidation and oxidative 
dissolution processes that are modeled (Table 6-1). This assumption is conservative because the 
CSNF matrix degradation models assume that the fission product and actinide elements 
embedded in the fuel matrix are made available for mobilization and behave (e.g., dissolve) as 
individual elements as the fuel matrix degrades. Fission product technetium is known to be 
partly in the form of noble five-metal alloy particles. The properties of the five-metal alloy 
particles are likely to control technetium release rate at a level much lower than the CSNF matrix 
degradation rate. Assuming that technetium release from the CSNF matrix is limited only by the 
matrix degradation rate is, therefore, conservative. 
Confirmation Status: This assumption is conservative and does not require further confirmation. 
Use in the Model: Section 6.2.1.2. 
5.2 FUEL FRAGMENTS AND SHORT FUEL ROD SEGMENTS CONSERVATIVELY 
REPRESENT DEGRADED CSNF 
Assumption: In assessing the effective specific surface area of corroding CSNF in fuel rods 
following breaching of the cladding, it is conservatively assumed that the configuration of the 
fuel is represented by fuel pellet fragments and short fuel rod segments. 
Rationale: The basis for assessing the evolution of the state of fuel rods after the cladding is 
breached is provided in Section 6.2.2.1. As pointed out in Section 6.2.2.1, several scenarios are 
plausible for the progression of the degradation of fuel rods once the cladding is breached. 
However, because there is insufficient information to discriminate between the evolution 
scenarios, the conservative assumption is made that the degradation progresses rapidly leaving 
the fuel in the form of fuel pellet fragments or short rod segments. 
Confirmation Status: This assumption is conservative and does not require further confirmation. 
Use in the Model: Section 6.2.2.1. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 5-2 August 2004 
5.3 SIMILARITY BETWEEN OXYGEN AND TEMPERATURE DEPENDENCE FOR 
ACIDIC AND ALKALINE CONDITIONS 
Assumption: In developing the model for the specific dissolution rate of CSNF under acidic 
conditions, it is assumed that the model parameter values expressing dependence on temperature 
and oxygen fugacity and associated uncertainties developed for alkaline conditions also apply 
under acidic conditions. 
Rationale: As discussed in Section 6.4.1.3, the rationale for using the same temperature and 
oxygen fugacity dependence under alkaline and acidic conditions is that the same redox reaction 
steps are likely to be involved under acidic and alkaline conditions in carbonate solutions. The 
effects of low pH are to promote dissolution of the partially oxidized UO2.33 surface layer. This is 
also the effect of carbonate under alkaline conditions. The rate-controlling steps under acidic 
conditions are probably similar to those in alkaline carbonate solutions when the dissolution of 
the UO2.33 surface layer does not control the overall reaction rate. 
Confirmation Status: Evidence for this assumption is provided by the fact that the oxygen 
dependence reported for the rate of oxidative dissolution of UO2 under acidic conditions 
(Torrero et al. 1997 [DIRS 114439]; Shoesmith 2000 [DIRS 162405], Section 3.1.2), which 
shows an oxygen concentration exponent value of 0.31, is similar to that in the base-case model 
for alkaline conditions (Section 6.4.1.2). 
Use in the Model: Section 6.4.1.3. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-1 August 2004 
6. MODEL DISCUSSION 
6.1 MODELING OBJECTIVES 
The modeling objectives are threefold. The first objective is to develop a probability distribution 
for the instantaneous release fractions (fi). The second objective is to derive parameter values for 
modeling the matrix fractional release rate (Fi) by analysis of suitable experimental data and to 
assess and represent the uncertainty in these model parameter values. The third objective is to 
define the domain of application (i.e., range of CSNF characteristics and environmental factors 
for which the models and uncertainty analyses are expected to be valid); the output from this 
report (Section 8.2) includes a limitation that the models should not be applied outside this 
domain. A list of supporting and corroborative data and information is in the associated 
Document Input Reference System (DIRS) report. 
6.2 BASIS FOR MODELING RADIONUCLIDE RELEASE 
Modeling the release of radionuclides from failed CSNF requires a conceptual framework for 
understanding the state of the fuel rods and the processes likely to alter this state and cause 
radionuclide release. The important processes will depend on the environments to which the fuel 
is exposed. Until the waste package and the CSNF cladding are breached, the CSNF pellets and 
radionuclides in the fuel rods will be isolated from the external repository environment. If these 
barriers are eventually breached, the CSNF will be exposed to humid air or dripping 
groundwater, or both. 
Section 6.2.1 discusses the basis for assessing the state of CSNF when the waste package and 
cladding are eventually breached. Section 6.2.2 discusses processes influencing the evolution of 
the state of CSNF and radionuclide release after the waste package and cladding are breached 
and, as appropriate, it also discusses the CSNF state and environmental factors that influence 
these processes. Section 6.2.3 summarizes the FEPs included in the CSNF models. 
6.2.1 State of CSNF When Cladding is Breached 
The state of CSNF rods, when the cladding is eventually breached, is assessed here by 
summarizing pertinent information on the state of freshly irradiated CSNF (i.e., up to a few 
decades out of light water reactors) and assessing how the known state of freshly irradiated fuel 
may change as a result of long-term evolution in the repository before the waste package and fuel 
cladding are breached. 
Section 6.2.1.1 describes pertinent features of the initial state of freshly irradiated CSNF. 
Processes that may cause the initial CSNF state to change in the closed system environments to 
which the fuel will be exposed while either the waste package or the fuel cladding remain intact 
are discussed in Section 6.2.1.2. 
6.2.1.1 Initial State of Irradiated CSNF 
The term initial state is used here to refer to the characteristics of CSNF after it is discharged 
from the reactor and before it is emplaced in the repository. The state (microstructure, 
physicochemical condition, and distribution of fission products and actinide elements) in freshly 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-2 August 2004 
irradiated CSNF has been extensively investigated in postirradiation examinations conducted 
throughout the world. It is beyond the scope of this document to provide a comprehensive 
summary of the extensive literature in this area. However, such a comprehensive review has 
been performed recently (Dehaudt 2001 [DIRS 164019], Section 5.2). The following is intended 
to summarize the characteristics (features) relevant to modeling radionuclide release in the 
repository and is mainly abstracted from the review by Dehaudt (2001 [DIRS 164019], p. 49). 
Features of irradiated CSNF that may influence the instantaneous release and the matrix release 
rate are the chemical composition of the fuel matrix, the macro- and micro-scale structure of the 
fuel (gap dimensions, fuel pellet fracturing, fuel connected porosity, and grain boundary 
structure), and the distribution of the fission product inventory within these fuel features. 
After irradiation, the macroscale structure of CSNF rods is changed from the as-fabricated 
condition. The gap between the fuel pellets and the cladding decreases with increasing burnup 
(Dehaudt 2001 [DIRS 164019], p. 196) and the fuel pellets are extensively cracked with the 
cracks running radially and axially through the irradiated fuel pellets. This cracking results from 
the differential expansion and the associated tensile stresses caused by the radial temperature 
gradients in the fuel rod, mostly when it is subjected to initial power increase in the reactor. 
Although the fuel pellet fragments formed initially may subsequently undergo further 
fragmentation, much of the cracking occurs early in the irradiation history (Dehaudt 2001 
[DIRS 164019], p. 87). The radial and transverse cracking of the fuel pellets result in the 
creation of 15 to 20 fragments per pellet with the number of cracks somewhat dependent on the 
fuel burnup (Dehaudt 2001 [DIRS 164019], pp. 106 and 176). Section 6.4.1.5 discusses specific 
surface area estimates for CSNF in its cracked condition following irradiation. 
Although the as-fabricated fuel pellets have a few percent porosity, this porosity is mostly due to 
pore formers added during the pellet fabrication and is in the form of isolated pores usually 
referred to as closed porosity (Pelletier 2001 [DIRS 164034], Section 5.4.3.1.1). However, the 
release of fission gasses in CSNF irradiated at high power, or to high burnup, generates lensshaped 
bubbles (often associated with metallic fission product inclusions) that can become 
interconnected on the grain boundary surfaces and form gas tunnels that conduct the fission gas 
to the gap region of the fuel rod. The fission gas release leaves some residual grain boundary 
decohesion and connected or open porosity at grain boundaries in the fuel pellets. At burnups 
higher than approximately 45 MWd/kgU, a rim region (150 to 250m thick) is formed at the 
outer surface of the pellets characterized by a very fine-grained fuel microstructure, up to 10 to 
15 percent porosity, and local burnup up to three times higher than the pellet average burnup. 
Further details of the microstructural features and effects of fission gas release on irradiated 
CSNF are summarized elsewhere (Dehaudt 2001 [DIRS 164019], Sections 5.2.5.2 and 5.2.5.3; 
Barner 1985 [DIRS 109194], pp. 4.14 and 4.15). The effects of these microstructural features of 
CSNF (particularly the effects of the connected porosity) on the specific surface area of CSNF 
are discussed in Section 6.4.1.5. 
The chemical state and distribution of fission product elements in irradiated CSNF have been 
studied using thermodynamic equilibrium calculations and experimental measurements 
(Dehaudt 2001 [DIRS 164019], Section 5.2.6.3). Most fission product elements are retained 
within the fluorite lattice structure of the fuel matrix. However, because some of the fission 
product elements are not soluble in the CSNF matrix, a fraction of the inventory of these 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-3 August 2004 
elements migrates out of the fuel grains under normal reactor operating conditions 
(Pelletier 2001 [DIRS 164034], Section 5.4). The extent of migration of these fission products 
out of the fuel grains and the subsequent migration and accumulation at the grain boundaries and 
in gap regions of the fuel depend on the diffusion coefficients of the individual fission product 
elements in the CSNF matrix and the available mechanisms for migration and accumulation at 
the grain boundaries and in the gap region of the fuel rods. The factors controlling the diffusion 
and release of fission gasses from CSNF grains are described by Pelletier (2001 [DIRS 164034], 
Sections 5.4.5 and 5.4.6). The factors and processes that are involved in migration from the 
grain boundaries to the gap regions are also discussed (Dehaudt 2001 [DIRS 164019], 
Section 5.2.9). Correlation of relative release rates of fission product elements with their 
electronegativity (Pelletier 2001 [DIRS 164034], Section 5.4.6.2 and Table 41) provides a basis 
for assessing their release relative to the release of the fission gasses. The percentage of the 
fission gas (krypton and xenon) released from the fuel during irradiation has been measured in 
CSNF postirradiation studies (Barner 1985 [DIRS 109194], p. 4.6; Guenther et al. 1988 
[DIRS 109206], Section 4.4; Guenther et al. 1988 [DIRS 109205], Section 4.4.2). 
Dehaudt (2001 [DIRS 164019], Section 5.2.9.4) states that for high-burnup CSNF 
(approximately 60MWd/kgU), the overall range of fission gas release to the free volume of the 
fuel rod (i.e., to the gap region) is about 1 to 4 percent. The percent fission gas release to the 
grain boundaries is about 10 percent (Dehaudt 2001 [DIRS 164019], Table 29). As discussed in 
Section 6.3.1, these data are useful in assessing the plausible upper bound for the gap and grainboundary 
inventories of other radioelements. 
Like the fission gasses, some of the fission product cesium and iodine diffuses out of the fuel 
grains and is found at the grain boundaries and in the gap region between the fuel pellets and the 
cladding (Dehaudt 2001 [DIRS 164019], Section 5.2.9.6). After irradiation, part of the fission 
product molybdenum, ruthenium, technetium, rhodium, and palladium inventory is found in the 
form of metallic alloy particles located within the fuel grains and at grain boundaries. This 
accumulation of noble metals in alloy particles is more evident in the higher-powered regions of 
the fuel (Barner 1985 [DIRS 109194], p. 4.17; Guenther et al. 1988 [DIRS 109206], Section 4.6). 
The extent to which the metallic fission products, cesium and iodine migrate depends on the fuel 
burnup, operating temperatures, and temperature gradients, as determined by the fuels linear 
power history in the reactor (Guenther et al. 1988 [DIRS 109205], Sections 4.3 and 4.5.2). 
6.2.1.2 Evolution of CSNF State Before Cladding is Breached 
This section addresses the effects of processes that may influence the state of CSNF while the 
cladding is intact and while the internals of the fuel rods remain in a closed system that isolates 
them from the external repository environment. Consideration of these effects is important 
because test data from freshly irradiated CSNF testing materials is used to predict the behavior of 
CSNF after potential cladding failure in the distant future. The possible effects of radioactive 
decay of short-lived fission products (e.g., 137Cs and 90Sr) and alpha decay of the actinide 
elements in the fuel must be considered. Also, although the interior gas-phase environment in 
the fuel rods is an inert noble gas environment, the closed system (consisting of the irradiated 
fuel matrix, embedded fission products, and cladding envelope) is not in thermodynamic 
equilibrium. Hence, possible changes in the physicochemical state must be assessed. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-4 August 2004 
The long-term closed system evolution of fuel rods has been assessed (Piron 2001 
[DIRS 162396], Section 5.1). This reference examined the chemistry evolution (specifically 
oxygen potential) of the fuel due to decay of radionuclides (principally 137Cs . 137Ba and 90Sr . 
90Y . 90Zr). It also examined the evolution of the physical state of the pellets and the potential 
migration of radionuclides and changes in the radionuclide distribution. 
On the basis of thermal-chemical and oxygen balance calculations (Piron 2001 [DIRS 162396], 
Section 5.1), it was concluded that the chemical composition of CSNF is not sufficiently altered 
by radioactive decay to change the oxidation state of the fuel matrix. Although thermally 
activated diffusion of radionuclides within the grains is negligible, the effects of accumulation of 
alpha radiation damage in the matrix could play a role in activating diffusion and, also, long-term 
thermal annealing of radiation damage could cause some mobilization of fission products and 
thereby influence the gap and grain-boundary inventories. Also, the accumulation of helium 
from alpha decay (particularly for high-burnup fuel) may cause microcracking and decohesion at 
grain boundaries (Piron 2001 [DIRS 162396], Section 5.1.4). 
To accommodate these uncertainties, the grain-boundary inventories are combined with the gap 
inventory estimates in estimating the instantaneous release fractions and the range of specific 
surface area estimates used allows for uncertainty in the extent of grain boundary decohesion 
(Sections 6.3.2 and 6.4.1.5). 
6.2.2 Processes Influencing CSNF State and Radionuclide Release after Cladding Breach 
After the cladding is breached within a failed waste package, the interior of the fuel rod will be 
exposed to the repository environment. Section 6.2.2.1 addresses the evolution of the fuel rod 
degradation when a breached fuel rod is exposed to humid air. Section 6.2.2.2 addresses the 
corrosion of the fuel matrix exposed to humid air, and Section 6.2.2.3 addresses the oxidative 
dissolution of the fuel exposed to humid air and groundwater. The experimental basis for 
assessing the degradation processes involved and the rates at which they proceed, is based in 
large part on experiments involving freshly irradiated CSNF test materials. However, the 
conclusions from these data are conservatively biased because the radiolytic effects due to the 
short-lived fission product (137Cs and 90Sr) will not be significant after about 300 years. The 
effects of beta and gamma radiolysis (associated with 137Cs and 90Sr decay) on the oxidative 
dissolution rate of CSNF are, therefore, discussed in Section 6.2.2.3. 
6.2.2.1 Degradation of Fuel Rods after the Cladding is Breached 
The rate of radionuclide release from CSNF rods with breached cladding depends on the 
evolution of the CSNF rod degradation following the initial cladding breach. The objective of 
this section is to discuss plausible evolution scenarios for the state of CSNF rods following initial 
breaching of the cladding. The effects of corrosion of the CSNF pellets and fuel-side corrosion 
of the cladding are considered; effects of external forces (e.g., forces associated with seismic 
events and external mechanical loads) are addressed in other model reports and are not 
considered here. 
If the cladding is eventually breached, the fuel rod will be exposed to ingress of water or humid 
air, or both. Under these conditions, several distinct scenarios are considered plausible for the 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-5 August 2004 
evolution of the degradation of the fuel rods (fuel pellets and cladding). Results from accelerated 
tests that were designed to identify potential evolution scenarios for fuel rods with breached 
cladding are discussed below. 
Accelerated tests were conducted to examine the corrosion behavior of fuel and cladding. The 
test conditions were selected to determine how the state of CSNF, with breached cladding, may 
evolve under unsaturated repository conditions (Cunnane et al. 2003 [DIRS 162406]). The 
results discussed here were obtained from two test samples, each consisting of approximately 
3.5-in. segments of ATM-103, a moderate-burnup (approximately 30 GWd/MTU) pressurized 
water reactor fuel. These tests were conducted in closed test vessels with humid air at 175C to 
accelerate the degradation processes so that their effects could be observed in reasonable 
experimental time frames. The test conditions (i.e., 175C in combination with 100 percent 
relative humidity) cannot occur in the repository because the water vapor pressure in the 
repository cannot exceed one atmosphere; the maximum relative humidity at 175C is 
approximately 11%. However, the results are relevant for assessing fuel and fuel-side cladding 
degradation processes that may occur at lower temperatures (<100C) when the relative humidity 
approaches 100 percent. Visual examination of the samples after 1.5 years revealed that each 
had developed an axial crack that passed through a drilled hole in the cladding and ran the full 
length of the sample. These tests are described more fully in Section 2a of Yucca Mountain 
Project Report, Waste Form Testing Work (Cunnane et al. 2003 [DIRS 162406]). As illustrated 
in Figure 6-1, destructive examination of the fuel and cladding showed that the cladding had 
experienced regions of extensive fuel-side corrosion. Transmission electron microscopy and 
electron diffraction analyses showed that the corrosion products were monoclinic ZrO2. The 
results indicate that the fuel-side corrosion of the cladding and the specific volume increase 
associated with the formation of the corrosion products caused the hoop stresses that resulted in 
the observed axial splitting. Also, the extent of through-wall penetration of the cladding, at the 
locations of the observed fuel-side corrosion, was estimated to be about 18 percent. This result 
indicated that regions on the fuel side of the cladding had corroded actively under the humid 
(100 percent relative humidity) 175C test conditions. It is likely that this corrosion was caused 
by the occurrence of some water vapor condensation in local regions of the fuelcladding 
interface that served as an electrolyte within which corrosion potentials exceeded the Zircaloy 
repassivation potential or the passive layer breakdown potential. This condition may have been 
promoted by deliquescent fission product salts at those regions. Conditions under which 
breakdown of the Zircaloy passive layer may occur are described by Pan et al. (2001 
[DIRS 162412], Section 3, p. 33). 
Several hypothetical scenarios for the evolution of the state of a fuel rod following breach of its 
cladding are considered plausible. While the corrosion of the fuel and precipitation of alteration 
products in a breached fuel rod could quickly lead to axial splitting, or unzipping, of the 
cladding, it is also possible that the precipitating alteration phases will seal the gap and fracture 
openings and, as a result, limit the rate of radionuclide release. Another possibility is that the 
fuel-side cladding corrosion will cause extensive cladding degradation and exposure of the fuel 
pellet fragments. Because of the widely different implications of these evolution scenarios for 
radionuclide release, conservative assumptions are adopted for the state of the breached fuel 
rods. Specifically, it is assumed that the state of a breached fuel rod will degrade rapidly 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-6 August 2004 
following initial breaching of the cladding to a condition in which the fuel pellet fragments or 
short fuel rod segments are exposed directly to the external repository environment. 
NOTES: Figure 6-1a shows a polished cross section illustrating two regions of fuel-side corrosion. Figure 6-1b 
illustrates the corrosion layer and the precipitated hydrides in the cladding adjacent to the corrosion layer. 
Figure 6-1. Cladding Fuel-Side Corrosion Observed in Tests Conducted in Humid Air at 175C 
6.2.2.2 Corrosion of the CSNF Matrix in Humid Air 
If the waste package and the cladding are breached, the CSNF will be exposed to the humid-air 
oxidizing environment expected in the repository. This section addresses the reactions of UO2 
and CSNF with the oxygen, water vapor and water films that may interact with the exposed 
CSNF under these conditions. 
At temperatures well above the normal water boiling point, the air to which the fuel can be 
exposed has a maximum water pressure of 1 atm and can be characterized as dry air (dew 
point = 100C). UO2 is known to be oxidized by pure steam under high temperature and 
pressure conditions (Olander 1986 [DIRS 170078]). For example, fuel in rods that fail during 
reactor service is known to be oxidized to U3O8 by pure steam between 500C to 600C 
(Olander et al. 1999 [DIRS 125562]). However, for the 1-atm pressure boundary conditions in 
the repository, the oxidation cannot exceed the UO2+x single-phase region in the UO phase 
diagram (Olander 1986 [DIRS 170078]). Such oxidation, by reaction with high temperature 
water vapor, would only be significant for fuel exposed to hot air as it cools down from 
approximately 1,000C to which it could be exposed in an igneous intrusion scenario. As 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-7 August 2004 
discussed in the following paragraphs, reaction of fuel with water vapor is negligible for the 
nominal scenario temperature conditions and the oxidation of the CSNF matrix will proceed via 
solid-state reactions associated with the dry air oxidation process (CRWMS M&O 2000 
[DIRS 149230]). As temperature decreases, the relative humidity to which the CSNF matrix will 
be exposed will increase and is expected to approach 100 percent when temperature decreases to 
100C and below. Under these conditions, water vapor complicates the oxidation process by 
facilitating formation of water films on the CSNF and formation of hydrated oxidation products. 
Not only are the reaction products different in the presence of water vapor, but the rates and 
mechanisms for reaction differ as well (Aronson 1958 [DIRS 117653], p. 94; Taylor et al. 1989 
[DIRS 125792]). The following discussion summarizes information on the CSNF corrosion 
process in humid air. It specifically addresses the effects of water vapor in low relative humidity 
air on the rate of dry air oxidation. It also addresses whether the CSNF corrosion process, under 
exposure to air with relative humidities approaching 100 percent and temperatures at or below 
the water boiling point, is best characterized as an interaction between the water vapor and 
oxygen in the air that accelerates the solid-state oxidation process, or as an oxidative dissolution 
process in a film of water at the fuel surface. 
Aronson (1958 [DIRS 117653]) studied the oxidation of UO2 in water containing oxygen as a 
function of temperature. UO2 powders were used with a specific surface area of 0.60 m2/g 
(Aronson 1958 [DIRS 117653], p. 94), which is a factor of about three to eight times larger than 
the CSNF powders (approaching the size of typical individual grains) used in the flow through 
dissolution tests (Table 4-3). At 87C, Aronson reports an oxygen-to-metal ratio of about 2.30 
was achieved within 15 days. X-ray analyses of the partially oxidized samples identified 
dehydrated schoepite and additional water content tests confirmed the molecular ratio of water to 
oxide as about 0.8 (Aronson 1958 [DIRS 117653], p. 94). When the powder was exposed to 
degassed water, the sample did not oxidize or hydrate (Aronson 1958 [DIRS 117653], Figure 2, 
p. 94) even at 177C. By comparing the activation energies (approximately 21 kJ/mol to 
42 kJ/mol) of Aronsons tests (Aronson 1958 [DIRS 117653], p. 94) with those for dry-air 
oxidation (approximately 102 kJ/mol) (McEachern and Taylor 1998 [DIRS 113270], Table 3]) 
McEachern and Taylor (1998 [DIRS 113270]) were able to show that the oxidation of UO2 in 
water requires the presence of dissolved oxygen and its mechanism is different from oxidation in 
air or gaseous oxygen. 
The work of Danroc (as reported by Dehaudt 2001 [DIRS 164037], pp. 363 to 365) also shows 
that the hydration of very high surface area (2.5 m2/g to 6 m2/g) unirradiated UO2 powders 
proceeds quickly in the presence of humid air (Dehaudt 2001 [DIRS 164037], Table 3). Within 
two years at 25C and 100 percent relative humidity, a bulk oxygen-to-metal ratio of 2.22 is 
achieved with 18 percent per mole reacted being the hydrated phase schoepite (Dehaudt 2001 
[DIRS 164037], Table 5). The reaction rate increases with temperature; a bulk oxygen-to-metal 
ratio of 2.48 was achieved within 70 days at 100C and 60 percent humidity. When compared to 
dry air oxidation, which is almost negligible for these time frames (days) at the temperatures 
reported here, it is clear that the humid-air corrosion process is much more rapid than the rate of 
the solid-state oxidation process at this low temperature. 
Sunder and Miller (1996 [DIRS 126463]) also studied oxidation of unirradiated UO2 (disks cut 
from unirradiated, sintered Canada deuterium uranium [CANDU] pellets) at 150C with an 
applied gamma radiation field. They confirmed formation of U3O8 within about two years on all 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-8 August 2004 
samples exposed to air or O2 in the applied gamma field. The samples exposed to O2 and 
60 percent relative humidity underwent the most extensive oxidation, up to a factor of 20 higher 
U(VI)/U(IV) ratio, as measured by x-ray photo-electron spectroscopy, than samples in similar 
closed vessels containing only air. Hydrated phases and U3O8 were formed during the oxidation. 
However, virtually no oxidation occurred for samples exposed to 60 percent relative humidity in 
argon. Such a finding is consistent with the fact that UO2 reaction with steam is relatively 
benign in this temperature range when oxygen is absent (Aronson 1958 [DIRS 117653], 
Figure 2, p. 94). 
Woodley et al. (1989 [DIRS 122032], pp. 74 and 87) used thermogravimetric analysis tests to 
measure the oxidation of CSNF samples between 140C to 225C with the nominal dewpoint 
ranging from -70C to +14.5C. This range represents very low relative humidity. This 
variation in moisture content had little measurable effect on the oxidation rate, and, if anything, 
oxidation was somewhat faster in dry air. Einziger et al. (1991 [DIRS 126446], pp. 389 to 391) 
also examined oven dry-bath CSNF oxidation over the temperature range 110C to 175C and at 
nominal dewpoints of -55C or +80C. Again, the effect of water vapor on the oxidation from 
UO2 to U4O9 was found to be minimal; however, oxidation was found to be somewhat faster in 
air with water vapor. These results indicate, at low dew points, the effects of water vapor on dry 
air oxidation of CSNF are small between 140C and 225C. 
Taylor et al. (1989 [DIRS 125792]) studied the oxidation of UO2 in air-steam mixtures at 200C 
and 225C. They report measurable UO3 hydrate formation within 20 days at 200C and 5 days 
at 225C. More importantly, they found that below approximately 50 percent relative humidity, 
the rates and products of UO2 oxidation are the same as for dry-air oxidation. Above 
approximately 50 percent relative humidity, UO3-hydrated phases, primarily dehydrated 
schoepite (UO3  0.8H2O) and schoepite (UO32 H2O), were observed. With water in excess of 
the amount necessary to saturate the test vessels void volume, the specimens were fully wetted 
and much larger crystals of dehydrated schoepite were obtained. The relative humidities in the 
work of Woodley et al. (1989 [DIRS 122032]) and Einziger et al. (1991 [DIRS 126446]) were 
well below the approximately 50 percent relative humidity level (a maximum of 33 percent 
relative humidity according to Taylor et al. 1989 [DIRS 125792], p. 71). This could explain why 
no significant effect was observed. Below this 33 percent relative humidity threshold, there does 
not appear to be sufficient moisture to form a stable water film on the fuel surface necessary to 
provide a medium for uranium to dissolve and reprecipitate as a hydrated uranyl phase. 
However, as reported by Kohli et al. (1985 [DIRS 126191]), the presence of limited moisture 
during fuel oxidation may allow fuel to oxidize to an oxygen-to-metal ratio of about 2.9, as 
opposed to the 2.75 typical for dry-air oxidation (CRWMS M&O 2000 [DIRS 149230]). 
Taylor et al. (1995 [DIRS 125815]) investigated the composition and microstructure of corrosion 
products formed on unirradiated UO2 fuel exposed at 225 C to humid air with a range of relative 
humidities. In dry air (RH < approximately 30 percent), they observed oxidation to U3O7 and 
U3O8 via a solid-state mechanism. At moderate relative humidities (30 to 70 percent), they 
observed surface and grain-boundary corrosion and fine-grained U3O8 and dehydrated schoepite 
crystals sparsely scattered over the surface. These observations were attributed to dissolution 
and precipitation of uranium within a thin (few molecules thick) and perhaps patchy layer of 
water on the fuel surface. At higher relative humidities (extending up to marginally unsaturated 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-9 August 2004 
conditions), a continuous and coarse-grained layer of U3O8 and dehydrated schoepite were found 
overlying a U3O7 layer. These results were attributed to dissolution and precipitation in a thicker 
film of water, which closely resembles a bulk liquid-phase film. When the test atmosphere was 
moisture-saturated (i.e., 100 percent relative humidity), large (tens to hundreds of micrometers in 
length) dehydrated schoepite crystals were observed consistent with growth from bulk solution. 
Defective CANDU SNF (still in cladding) has also been studied to determine the effect of 
moisture on oxidation (Wasywich et al. 1993 [DIRS 125710]; Johnson and Taylor 1998 
[DIRS 127125]). In these tests, SNF elements with small cladding defects were exposed to 
humid air at 150C in five different experimental phases totaling almost 9.5 years. The initial 
approximately 7.75 years were at limited oxygen concentrations. For example, the oxygen 
concentration during the 720-day interval of Phase 4 showed a decrease in oxygen from the 
original 20 percent to less than 0.28 percent (Johnson and Taylor 1998 [DIRS 127125], 
Section 3.1). During Phase 5, it was found that the oxygen concentration dropped by a factor of 
approximately two within 25 days. Thus, the oxygen availability during most of the testing was 
very low and most likely greatly limited the extent of oxidation. Even during Phase 5 when the 
oxygen was replenished, the rate of alteration in regions near the defect was higher than the bulk 
average value. 
In the dry-air experiments, Wasywich et al. (1993 [DIRS 125710]) found behavior comparable to 
other oxidation studies on CANDU SNF. That is, oxidation begins wherever the oxygen had 
direct access to the UO2 surfaces, followed by preferential grain boundary oxidation. The 
U3O7-like phase then grew into the individual grains until conversion was complete. The 
behavior of the samples exposed to a moist-air environment was quite different, with alteration 
occurring throughout the length of the fuel element rather than being localized to the defect 
region. Because of the limited oxygen supply, bulk oxidation of grains was absent in the 
moist-air tests. At the end of the additional 660-day oxidation of Phase 5, where the oxygen 
supply was replenished to maintain full aeration, extensive bulk oxidation of individual grains to 
U4O9/U3O7 and formation of significant amounts of UO3 hydrate occurred (Johnson and 
Taylor 1998 [DIRS 127125], Section 4.4.3.2). 
The effects of humidity on the corrosion of irradiated and unirradiated UO2 fuels are summarized 
in a review of the oxidation of UO2 at temperatures below 400C (McEachern and Taylor 1998 
[DIRS 113270], Section 2.2). Humidity can influence air oxidation in this temperature range by 
forming hydrated-uranyl phases (e.g., by hydrous disproportionation of U3O8), by supporting 
oxidative dissolution in water films that form on the surface at higher relative humidities, by 
enhancing grain-boundary oxidation in SNF, and by supporting radiolytic processes at grain 
boundaries. These authors point out that because gas-phase diffusion is rapid along grain 
boundaries in SNF (Section 6.2.1.1 for discussion of the connected porosity at grain boundaries 
in CSNF), the oxidation of SNF can be considered to proceed simultaneously at grain boundaries 
throughout the fuel when it is exposed to humid air. At higher temperatures (e.g., between 
500C to 600C), fuel in rods that fail during reactor service is known to be oxidized to U3O8 by 
pure steam (Olander et al. 1999 [DIRS 125562]). 
The rate of dry air oxidation from UO2.4 to UO2.75 is about 3 times faster for samples that had 
schoepite present (CRWMS M&O 2000 [DIRS 149230], p. 44). Kansa et al. (1998 
[DIRS 125636]) propose that the hydrated phases on the surface of the fuel essentially act as 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-10 August 2004 
fins to increase the surface area for oxygen absorption that is readily transferred to the 
unreacted fuel surface below the hydrated phase. The effect from these fins decreases as more 
of the fuel oxidizes to U3O8 and cracks or spalls from the surface. This is observed with the 
thermogravimetric analysis tests; after some initial transient following the plateau, the oxidation 
proceeds in a manner similar to that of the other samples that did not have hydrated phase 
present. 
Results from the corrosion of spent UO2 fuel stored at ambient temperatures in dry air for ten 
years, and subsequently stored in a humid air environment for fifteen years, have shown 
oxidation processes along the grain boundaries that weaken the intergranular bonding and cause 
grain-boundary decohesion (Leenaers et al. 2003 [DIRS 168991]). Examination of the fuel 
showed progression of a grain-boundary corrosion front that extended from the periphery to 
about the midradius of the pellets. Analysis of the gas phase in the storage vessel at the end of 
the storage period showed that it was enriched in hydrogen (approximately 40 vol %) and 
depleted in oxygen (approximately 1.7 vol %). These results were interpreted to be due to the 
combined effects of two reactions. One reaction consumes oxygen by direct oxidation of the 
UO2 fuel to produce U4O9. The other produces hydrogen by oxidation of the fuel to U4O9 
through reaction with H2O2 produced by radiolysis; the net reaction involves oxidation of the 
fuel by water and can be written as one mole of water reacting with four moles of UO2 to 
produce one mole of U4O9 and one mole of hydrogen. The larger oxygen depletion observed in 
the humid-air exposure compared to dry-air exposure indicated that the oxidation of the fuel had 
progressed more rapidly in humid air than in dry air. 
Long-term tests have been performed on UO2 and CSNF fragments exposed to humid air and 
periodic dripping of a simulated groundwater composition (Wronkiewicz et al. 1996 
[DIRS 102047]; Thomas 2003 [DIRS 163048]). The CSNF tests were designated high drip 
rate, low drip rate, and vapor tests. Each test used approximately 8 g of fuel in the form of 
fuel pellet fragments removed from the cladding, sieved to remove fuel fines, and contained in a 
Zircaloy-4 fuel holder supported on a ledge in a closed stainless steel test vessel. For the drip 
tests, water from well J-13 was equilibrated with tuff at 90C and periodically injected into the 
test vessels. This water is referred to as EJ-13 water. For the high drip-rate tests, 0.75 mL of 
EJ-13 water was injected twice weekly into the test vessel in a configuration that was designed to 
allow the injected water to drip onto the fuel in the fuel holder, percolate through the fuel 
fragments, drain through the bottom of the fuel holder, and accumulate in the bottom of the test 
vessel. The low drip-rate test design was similar to the high drip-rate test design, but the EJ-13 
water injection rate (a twice-weekly injection of 0.075 mL) was a tenth of the high drip rate. The 
humid-air tests were conducted in the same test configuration as the high drip rate and low driprate 
tests but without any water injection. To expose the fuel to 100 percent relative humidity, 
approximately 5 mL of EJ-13 water was added to the base of the test vessels at the beginning of 
every test interval before the vessel was sealed and brought to the test temperature of 90C 2C. 
Failure scenarios considered in the design of these tests include a scenario in which groundwater 
drips intermittently onto bare fuel fragments in a humid-air environment, reacts with the fuel, 
dissolves soluble components, and forms solid corrosion products. As illustrated in Figure 6-2, 
and discussed further in Section 6.2.2.3, the fuel matrix was found to react in a similar fashion in 
all three of these test types (Finch et al. 1999 [DIRS 127332]). The results in the vapor tests are 
consistent with corrosion of the fuel matrix occurring in a layer of water that condensed onto the 
corroding fuel surface. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-11 August 2004 
The results summarized above indicate that grain-boundary corrosion and decohesion is likely to 
occur rapidly if CSNF pellets are exposed to humid air at temperatures greater than 100C. 
Because some bulk oxidation of the CSNF grains exposed to these conditions may also occur, 
the effects of such oxidation on the oxidative dissolution rate of the matrix are addressed in 
Section 7.1.3. Although the results in Section 7.1.3 show that the dissolution rate per unit area of 
the higher oxides (up to U3O8) are somewhat greater than the UO2 rates, the most important 
effect of oxidation is the very large increase in the specific surface area which causes the 
fractional dissolution rate to increase accordingly. Because of these results, an instantaneous 
radionuclide release rate model is used for any fuel that is exposed to humid air at temperatures 
greater than 100C when it is subsequently contacted by water after the temperature drops below 
100C (Section 8.1). 
In humid air, when the relative humidity approaches 100 percent, the fuel is probably covered by 
a film of condensate (perhaps promoted by dissolution of deliquescent fission product salts) and, 
as a result, CSNF is altered by the oxidative dissolution process under these conditions. The 
information summarized above is consistent with the hypothesis that the corrosion of CSNF 
exposed to humid air (approaching 100 percent relative humidity) at temperatures less than 
100C is due to oxidative dissolution of the fuel matrix in a film of water on the fuel surfaces. 
6.2.2.3 Oxidative Dissolution of CSNF 
This section provides a summary of the pertinent phenomena and chemical reactions associated 
with the oxidative dissolution process when CSNF is exposed to air-saturated groundwater and 
humid air. It specifically addresses pertinent observations as to how CSNF corrodes as a result 
of oxidative dissolution under conditions (limited amounts of groundwater and humid air) 
relevant to the repository and the factors that influence the rate of the process. 
CSNF is a crystalline ceramic material with grains composed of mostly uranium dioxide. Much 
of the following is abstracted from recent reviews of SNF corrosion processes (Shoesmith 2000 
[DIRS 162405]; Dehaudt 2001 [DIRS 164019]; Dehaudt 2001 [DIRS 164037]; Jegou et al. 2001 
[DIRS 162397]; Pelletier 2001 [DIRS 164034]; Piron 2001 [DIRS 162396]). 
Uranium(IV) minerals, primarily uraninite (UO2) in the mined uranium-bearing ores, are 
relatively insoluble in nonoxidizing aqueous solutions; to dissolve, their uranium(IV) must first 
be oxidized to the much more soluble uranium(VI) oxidation state (hence the term oxidative 
dissolution). Oxidative dissolution is promoted by factors that cause oxidation to uranium(VI) 
and enhance dissolution of the oxidized U(VI) species. For example, uranium is extracted from 
natural uranium(IV) ores by oxidative dissolution with acidic iron(III) sulphate solutions or in 
alkaline carbonate solutions with oxygen under pressure. The overall oxidative dissolution 
process involves a coupled series of redox, surface complexation and dissolution, and 
precipitation reactions with the overall reactions depending on the fluid environment. For 
example upon contact with air-saturated condensate (i.e., pure water), UO2 (and CSNF) is 
expected to undergo reactions of the following type to form dehydrated schoepite and 
metaschoepite: 
UO2 (s) +  O2 (aq) + 0.8 H2O (l) = UO3. 0.8H2O (s) 
UO2 (s) +  O2 (aq) + 2 H2O (l) = UO3. 2H2O (s) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-12 August 2004 
Upon contact with groundwater containing silicate and alkali or alkaline-earth cations, the 
overall reactions form uranyl silicates: 
UO2 (s)+  O2 (aq) + Na+(aq) + Si(OH)4 (aq) +  H2O=Na(UO2)(SiO3OH)(H2O)1.5 (s) + H+ 
The redox reactions involved in these overall reactions can be considered a sum of the 
electrochemical half-reactions: 
UO2 . UO2
2+ + 2e- (Eq. 6) 
oxidant + 2e- . reduced species (Eq. 7) 
These two half-reactions are affected by the solid, and particularly surface, properties of the fuel 
and by environmental factors that influence the rates of the oxidation and dissolution reactions 
involved. 
For the slowly dissolving semiconductor oxides, which include UO2, the rate of the oxidative 
dissolution process can be controlled either by: (1) redox reaction charge transfer at the 
dissolving surface, or (2) surface reactions (e.g., surface complexation and dissolution reactions). 
The factors that influence the rates of these reactions are discussed later in this section and in 
Sections 6.4.2.1 and 6.4.2.2. Also, the production or mass transport rates of oxidants to the 
corroding UO2 surface can, under some circumstances, control and limit the rate of the oxidative 
dissolution process. In the following paragraph, the circumstances under which oxidant 
production rates and oxidant mass transport rates can limit the overall rate of oxidative 
dissolution are briefly summarized before discussing the factors that control the rate of the 
chemical reactions involved in the oxidative dissolution process. 
In reducing repository environments, the radiolytic production rate of oxidants and the mass 
transport rate of dissolved species to and away from the corroding CSNF can control the overall 
rate of reaction and can provide the basis for modeling the dissolution rate (Jegou et al. 2001 
[DIRS 162397], Section 8.2.4). For example, if the radiolytically produced oxidants from alpha 
decay were the only source of oxidants, then it would take more than 7,000 years after the CSNF 
is exposed to water to oxidize all of the fuel (Jegou et al. 2001 [DIRS 162397], Figure 88). In 
contrast to the reducing repository environment, air in the hydraulically unsaturated void spaces 
of the repository is likely to maintain an oxygen fugacity expected for humid air under the local 
relative humidity and temperature conditions (i.e., approximately 0.2 atmospheres at ambient 
temperatures and approximately 0.06 atmospheres at 90C and 100 percent relative humidity) 
and provide an unlimited supply of dissolved oxygen to airwater interfaces. Therefore, in the 
repository, the dissolved oxygen concentration boundary condition at the airwater interface is 
controlled and buffered by the oxygen fugacity in the air space. In some configurations (e.g., 
within clad segments of CSNF rods), the rate of diffusion of dissolved oxygen from the airwater 
interface to parts of the corroding fuel surface may be sufficiently slow to limit the rate of the 
oxidative dissolution process (Section 6.4.1.4). The remainder of this section discusses pertinent 
experimental observations on how CSNF corrodes because of oxidative dissolution at 
temperatures less than 100C and the factors that influence the kinetics of the chemical reactions 
involved. 
Experimental evidence indicates that the long-term oxidative dissolution of UO2 (and CSNF) 
under neutral to basic conditions involves oxidation of a thin layer of the UO2 at the surface

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-13 August 2004 
water interface to a higher oxide UO2+x (e.g., U3O7) followed by further oxidation and 
dissolution of this layer (Casas et al. 1994 [DIRS 162386]; Shoesmith et al. 1989 
[DIRS 162402]). Based on the results of x-ray photoelectron spectroscopy analyses, it has been 
shown that the corrosion of unirradiated UO2 fuel in near neutral to alkaline solutions proceeds 
in two stages (Shoesmith et al. 1998 [DIRS 162404]; Christensen and Sunder 2000 
[DIRS 162387]). The first stage is the rapid formation of a surface UO2+x film that achieves a 
limiting composition of UO2.33. The second stage involves the further oxidation of the UO2.33 
film at the filmwater interface to form soluble secondary U(VI) phases (e.g., UO3.xH2O, 
hydrated schoepite). Under acidic and complexing (carbonatebicarbonate) conditions that 
promote rapid dissolution of this layer, the solid surface is less oxidized (Casas et al. 1994 
[DIRS 162386]; Torrero et al. 1997 [DIRS 114439]; Shoesmith 2000 [DIRS 162405]). 
As discussed in Section 6.2.2.2, the more macroscopic scale corrosion phenomena that occur 
upon exposure of CSNF and UO2 to dripping water and humid air have been studied in long-term 
experiments (Thomas 2003 [DIRS 163048]). After approximately five years, the surfaces of all 
fuel fragments in drip and vapor experiments developed yellowish to white crusts, obscuring the 
underlying (black) fuel. Analyses using a scanning electron microscope revealed these crusts to 
be predominantly uranium-bearing phases, including uranyl oxy-hydroxides and uranyl silicates 
(Section 7.3.2 provides further discussion of alteration phase identification and associated 
uncertainties). Similar phases were identified during corrosion studies of unirradiated UO2 
(Wronkiewicz et al. 1996 [DIRS 102047]) and occur at many oxidized uraninite deposits 
(Section 7.3.1). These results indicate that under the range of hydrologic conditions anticipated 
in the repository, the oxidative dissolution process involves oxidation of the fuel matrix to U(VI), 
dissolution of the oxidized U(VI), and precipitation of uranyl alteration phases back onto the 
corroding fuel surface. As illustrated in Figure 6-2, oxidative dissolution in humid air and 
dripping groundwater at 90C occurs via general corrosion at fuel-fragment surfaces. 
Dissolution along fuel-grain boundaries is also evident. Apparent dissolution of fuel along 
defects that intersect grain boundaries has created dissolution pits that are 50 to 200 nm in 
diameter. These dissolution pits penetrate 1 to 2 m into each grain, producing a worm-like 
texture along fuel-grain boundaries (Figure 6-2). Fuel shards smaller than a micrometer are 
common between fuel grains and may contribute to the reactive surface area of fuel exposed to 
groundwater. Similar grain-boundary dissolution and a wormy texture were observed in fuel 
specimens exposed to Na-bicarbonate water in flow-through dissolution experiments (Gray and 
Wilson 1995 [DIRS 100758], Sections 3.3.4, 4.1.4, and 4.1.6, Figures A.46 to A.60). The effects 
of this wormy fuel grain texture on the effective specific surface area of the corroding fuel is 
addressed in Section 6.4.1.5. Outer surfaces of reacted fuel fragments develop a fine-grained 
layer of corrosion products adjacent to the fuel. A more coarsely crystalline layer of corrosion 
products covers the fine-grained layer, the thickness of which varies considerably among 
samples. Corrosion-layer compositions depend strongly on water composition, with uranyl oxyhydroxides 
predominating in humid-air experiments, and alkali and alkaline earth uranyl silicates 
predominating in high drip-rate experiments (Section 7.3.2). 
Figure 6-2 indicates that the SNF matrix dissolves by three modes: (1) dissolution through the 
outer surface of fuel fragments (general corrosion); (2) dissolution along grain boundaries and 
defects associated with grain boundaries (Figure 6-2a); and (3) dissolution of micrometer-sized 
intergranular fuel particles (Figure 6-2b). Dissolution of fuel fragments in samples from all three 
types of unsaturated tests (humid air, low drip rate, and high drip rate) has proceeded through the 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-14 August 2004 
outermost grains, with little or no preferential bulk dissolution of the matrix along adjacent grain 
boundaries (general corrosion). Similar results were reported by Gray and Wilson (1995 
[DIRS 100758], Figure A.57). These results indicate, although the CSNF oxidative dissolution 
process is a general corrosion process with the dissolution occurring mostly at the outermost 
exposed surfaces, the effective specific surface area of the corroding fuel could differ from 
surface area estimates of the starting material (Section 6.4.1.5). 
Source: Finch et al. 1999 [DIRS 127332] 
NOTES: Figure 6-2a. Backscattered-electron image of a polished section through a fuel particle (bright contrast) 
from a high drip rate experiment with a corrosion layer (intermediate contrast) predominantly composed of 
Na-boltwoodite (ATM-103 high drip rate 3.7 years). 
Figure 6-2b. Backscattered-electron image of a polished section showing curvilinear features at fuel-grain 
boundaries, which may result from dissolution along defects. The darkest contrast (black) is epoxy. 
Figure 6-2. Scanning Electron Microscopy Images of Corroded CSNF 
Environmental factors (e.g., temperature, dissolved oxygen concentration, pH, and carbonate 
concentration) that influence the oxidative dissolution of CSNF are discussed extensively in 
recent reviews (Shoesmith 2000 [DIRS 162405]; Jegou et al. 2001 [DIRS 162397], 
Section 8.2.3). The following paragraphs provide an overview of the effects of these 
environmental parameters; additional discussion of the mechanistic effects of these factors is 
provided in Sections 6.4.2.1 and 6.4.2.2. 
The species available for oxidizing CSNF in the repository include dissolved oxygen and water 
or humid-air radiolysis products. Oxidants such as H2O2 and HNO3 will be supplied by 
radiolysis of water or humid air. A wide range of reaction orders with respect to dissolved 
oxygen concentration has been reported and there is evidence that the reaction order depends on 
the dissolved oxygen concentration and temperature (Jegou et al. 2001 [DIRS 162397], 
Section 8.2.3.3.1; Shoesmith 2000 [DIRS 162405], Section 5.4; Oversby 1999 [DIRS 163420]). 
For example, reaction orders ranging between 1 and 0.1 have been reported for the rate of UO2 
corrosion depending on the dissolved oxygen concentration (Jegou et al. 2001 [DIRS 162397], 
p. 476). The fact that the reaction is first order at low dissolved oxygen concentrations, whereas 
the reaction order is lower at high oxygen concentrations, is attributed to adsorption of dissolved 
oxygen being the rate-limiting step in the oxidative dissolution process; a fractional reaction 
order is obtained when the surface absorption sites approach saturation. The influence of /. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-15 August 2004 
radiolysis on the oxidative dissolution rate of UO2 in aerated and oxygenated solutions shows a 
power law dependence of the dissolution rate on the dose rate (Shoesmith 2000 [DIRS 162405], 
Section 5.7.1; Jegou et al. 2001 [DIRS 162397], Section 8.2.3.4). Since the /. fields associated 
with CSNF decay within hundreds of years, corrosion tests on fresh CSNF (less than 30 years 
out of reactor) may overestimate the corrosion of CSNF in the long term. However, authors who 
have compared the corrosion rates of unirradiated UO2 and CSNF have found that there is no 
significant difference under air-saturated groundwater conditions (Serrano et al. 1998 
[DIRS 162399]; Jegou et al. 2001 [DIRS 162397], p. 542; Shoesmith 2000 [DIRS 162405], 
Figure 29). The reaction order for dissolved oxygen is different for SNF and UO2 in the 
Canadian (Tait and Luht 1997 [DIRS 114435]) and U.S. studies (Stout and Leider 1998 
[DIRS 111047], p. 2-225). It is also probably because the effects of radiolytic oxidizing species 
produced when CSNF corrodes mask the effects of the dissolved oxygen (Oversby 1999 
[DIRS 163420], p. 10). 
Carbonate present in groundwaters, including those at Yucca Mountain, is a strong complexing 
agent for the, U(VI) species (Grenthe et al. 1992 [DIRS 101671]; Shoesmith 2000 
[DIRS 162405]) and promotes dissolution of the oxidized uranium. The influence of carbonate 
on the corrosion of UO2 and CSNF is described as a function of concentration (Shoesmith 2000 
[DIRS 162405], Sections 3 and 5; Jegou et al. 2001 [DIRS 162397], pp. 479 to 480) and is 
explicitly modeled in Section 6.4.2.2. Shoesmith (2000 [DIRS 162405], Section 3.1.3) describes 
the influence of different carbonate concentration ranges on the rate of UO2 oxidative dissolution 
as follows. With no carbonate, an oxidized corrosion product layer (e.g., UO2.33) accumulates 
and suppresses the rate of the oxidative dissolution process. With less than 10-3 molar total 
carbonate, the predominant influence of carbonate is to complex UO2
2+, thus reducing alteration 
product buildup. With between 0.001- and 0.1-molar total carbonate, carbonate is kinetically 
involved in the dissolution process via carbonate surface complexes (Section 6.4.2.2). With 
higher carbonate concentrations, formation of UO2CO3 on the surface may inhibit dissolution, 
and carbonate dependency may lessen. It is not surprising, given these various effects of CO3
2-, 
that the reported reaction orders for carbonate in the concentration range of approximately 
2  10-3 M are complex and depend on the redox conditions and temperature under which the 
corrosion occurs (Shoesmith 2000 [DIRS 162405], Section 5.5). The combined effect with 
redox conditions is explained by the fact that carbonate is a strong complexant for UO2
2+ but not 
for U4+ and, as a consequence has a larger effect on the dissolution rate under more oxidizing 
conditions. The combined effect with temperature (a larger effect of carbonate concentration at 
higher temperature) has been attributed to a switch in the rate determining step from the rate of 
surface oxidation to the rate of U(VI) dissolution, which is promoted by carbonate complexation. 
Information on the influence of pH on the oxidative dissolution rates of UO2 and CSNF has been 
summarized in recent reviews (Shoesmith 2000 [DIRS 162405], Sections 3.1.2 and 5.3; 
Jegou et al. 2001 [DIRS 162397], pp. 478 to 479). For basic pH values, there is little, if any, 
effect. For acidic pH values, the dissolution rate increases with decreasing pH and has a power 
law dependence (exponent approximately equal to 0.4) on the hydrogen ion concentration. 
Radiolysis of a moist-air environment has been shown to increase acidity via production of 
HNO3 (Reed and Bowers 1990 [DIRS 113577]). When pure water is irradiated, either by alpha 
particles or by beta particles or gamma rays, there is no pH change. Irradiation of water or air 
systems results in the fixation of nitrogen from the gas phase, the formation of nitric acid 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-16 August 2004 
(probably also in the gas phase), and its condensation into the liquid water phase (Reed and 
Bowers 1990 [DIRS 113577]). 
Information on the effects of temperature on the oxidative dissolution rates of UO2 and CSNF 
has been summarized in recent reviews (Shoesmith 2000 [DIRS 162405], Section 5.6; 
Jegou et al. 2001 [DIRS 162397], pp. 480 to 481). A range of apparent activation energies 
between 20 and approximately 70 kJ/mol have been reported for different conditions with acidic 
and complexing solutions giving values toward the upper end of this range and noncomplexing 
neutral solutions giving values toward the lower end of the range. These different apparent 
activation energies are consistent with the evidence that the formation and accumulation of 
secondary deposits on the corroding surface in noncomplexing solutions can counterbalance the 
effects of temperature and give an apparent activation energy at the lower end of the range cited 
above. In acidic and carbonate solutions that minimize the buildup of secondary deposits and the 
formation of an intermediate UO2.33 layer, higher apparent activation energies are observed 
because these counterbalancing effects are minimized. The activation energies discussed here 
are referred to as apparent activation energies because a number of mechanistic features of the 
oxidative dissolution process change with increasing carbonate concentration over the 
temperature range 20C to 75C (Shoesmith 2000 [DIRS 162405], Section 5.6). 
Other important groundwater species are calcium and silicon ions, which can greatly reduce the 
rate of oxidative dissolution rate by forming a protective layer of stable corrosion products with 
low solubilities (Gray and Wilson 1995 [DIRS 100758], p. 4.13; Shoesmith 2000 
[DIRS 162405], Section 5.9). Electrochemical studies have shown, although these groundwater 
species can form very thin and protective corrosion product films that significantly reduce the 
rate of the oxidative dissolution process, this effect can be counteracted by the effects of 
carbonate (Shoesmith 2000 [DIRS 162405], Sections 3.1.4 and 5.9). 
6.2.3 FEPs Included in the Model 
Based on the discussion in Section 6.2.2, this model report includes consideration of features of 
the CSNF, its disposal environment, and the events and processes that are expected to influence 
the rate of radionuclide release (DTN: MO0407SEPFEPLA.000 [DIRS 170760]). These FEPS, 
along with their disposition into the Total System Performance Assessment for the License 
Application (TSPA-LA) model, are summarized in Table 6-1. Other screened out CSNF FEPs 
and the basis for their screening decisions are described in Miscellaneous Waste-Form FEPs 
(BSC 2004 [DIRS 163116]). 
Table 6-1. Included FEPs for This Model Report and Their Disposition in the TSPA-LA Model 
FEP 
Number FEP Name Section Where Disposition is Described 
2.1.02.02.0A CSNF degradation (alteration, 
dissolution, and radionuclide 
release) 
Sections 6.2.2 and 6.4.1. 
2.1.02.07.0A Radionuclide release from gap 
and grain boundaries 
Sections 6.2.1 and 6.3. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-17 August 2004 
6.3 MODELING THE INSTANTANEOUS RELEASE FRACTIONS 
6.3.1 The Gap and Grain-Boundary Inventory Data Input and Uncertainties 
The input data for modeling the instantaneous radionuclide release fractions (fi) are the gap and 
grain-boundary inventory fraction data described in Section 4.1.1. Section 6.2.1.1 provides a 
general discussion of the factors that influence the distribution of fission products in irradiated 
CSNF. The following paragraphs provide a discussion of the available data that are pertinent for 
assessing instantaneous release in irradiated UO2 fuels and that provide a basis for assessing the 
extent to which the available data represent the CSNF population. 
As discussed in Section 6.2.1.1, some fission-produced radionuclides migrate from the UO2 
matrix of light water reactor CSNF at the high fuel temperatures of reactor operation and deposit 
onto the cooler grain boundaries and fuelcladding gap surfaces. Cesium and iodine, in addition 
to the fission gases, are the most conspicuous elements in this category. If water enters fuel rods 
with breached cladding in the repository, these soluble radionuclides can dissolve quickly. The 
fraction of the inventory that is available for such rapid dissolution is therefore modeled as an 
instantaneous release fraction. 
Laboratory measurements of light water reactor CSNF show that the gap and the grain-boundary 
inventories of 99Tc and 90Sr are near the detection limits of the methods used (i.e., less than 
0.2 percent of the total inventories of these elements) (Gray et al. 1992 [DIRS 162393]). 
However, some of the 99Tc resides at the grain boundaries in the form of relatively insoluble 
metallic particles and may not be detected by these tests (Gray et al. 1992 [DIRS 162393]). 
Fission gas release fractions have been used as a basis for comparison to release of other fission 
product elements from the fuel matrix because the gasses are chemically inert and are quickly 
released from the gap and grain boundary upon breach of the cladding. For 137Cs, the combined 
gap and grain-boundary inventories are approximately one third of the fission gas release 
fractions (Gray 1999 [DIRS 121407]). For the same fuels, the available data (Gray 1999 
[DIRS 121407], Figure 1) indicate that the grain-boundary inventories of cesium-135 and 137Cs 
are generally less than about 1 percent of the total inventories of these nuclides and that the gap 
inventories are equal to roughly one fourth of the percentage of fission gas release for a given 
fuel type. 
The first column of Table 4-1 shows that the gap inventory (GI) and grain-boundary inventory 
(GBI) input data were obtained from CSNF with a broad range of fission gas release fractions. 
As pointed out in Section 6.2.1.1, the fraction of the fission product inventory that is located in 
the gap and at the grain boundaries in CSNF is related to the percent fission gas release. The 
mechanism by which fission gasses and other fission product elements migrate out of the fuel 
grains and accumulate at grain boundaries and in the gap regions of fuel rods (Dehaudt 2001 
[DIRS 164019], Section 5.2.9; Pelletier 2001 [DIRS 164034], Section 5.4) provide a basis for 
assessing the extent to which the data in Table 4-1 is likely to represent the CSNF population in 
general. This mechanism involves thermal and athermal diffusion from the fuel grains to the 
grain boundaries, fission gas bubble accumulation at the grain boundaries, coalescence of the 
fission gas bubbles to form an interconnected porosity network at the grain boundaries, and 
migration of mobile fission products along the grain-boundaries by grain-boundary diffusion and 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-18 August 2004 
percolation through the interconnected porosity network. The relative rates of diffusion of the 
fission product elements are controlled by their diffusion coefficients. The relative rates of 
percolation through the connected porosity created by the fission gas bubbles is controlled by the 
difference between the gas pressure in the pores and the gap region as well as the permeability of 
the fuel. In the CSNF fluorite structure, fission product atom diffusion occurs by a substitution 
mechanism involving vacancies in the cation and anion lattices with the mobility through the 
oxygen anion lattice being greater than through the cation lattice. On this basis, the relative 
release rates of the fission product elements has been correlated with their electronegativities 
giving the following order for the relative release rates in high-temperature annealing 
experiments: iodine>xenon>cesium>technetium (Pelletier 2001 [DIRS 164034], 
Section 5.4.6.2). When the fission gas accumulation provides an interconnected porosity 
network allowing percolation of the fission products to the gap region of the fuel rods, the 
migration rate is controlled by the fission products vapor pressure and hence by the axial 
temperature profile in the rods. Even though the diffusion rate of iodine probably exceeds that of 
the fission gasses in the CSNF matrix, its overall release is generally close to or bounded by the 
fission gas release. This is because the fission yield of cesium is much greater than that of iodine 
and iodine combines with cesium to form CsI, which then controls iodine migration as CsI vapor 
after it reaches the grain boundaries (Dehaudt 2001 [DIRS 164019], Section 5.2.9.6). 
As pointed out in Section 6.2.1.1, the fraction of the fission product inventory that is located in 
the gap and at the grain boundaries in CSNF is related to the percent fission gas release. Because 
the fission gas release depends on fuel type, burnup and linear power density (Jegou et al. 2001 
[DIRS 162397], Section 8.2.2) it is useful to compare CANDU and light water reactor fuels in 
considering the effects of these parameters. The Canadian studies allow a comparison of the gap 
and grain-boundary inventory results for CANDU and light water reactor fuels to see whether 
their characteristics can be explained by differences in power levels and burnups. 
Light water reactor CSNF generally operates at lower power but is irradiated to higher burnups 
than CANDU fuel (Jegou et al. 2001 [DIRS 162397], p. 453). The linear power of U.S. light 
water reactor SNF is typically 20 to 30 kW/m (Gray 1999 [DIRS 121407]), which is a little 
lower than even the low power CANDU fuels. Lower power levels generally mean lower fuel 
temperatures; pellet centerline temperatures in operating CANDU reactors are in the range 
1,000C to 1,700C while the centerline fuel pellet temperatures in pressurized water reactors are 
typically in the range 800C to 1,000C. The resulting smaller radial temperature gradient in the 
fuel rods during light water reactor operation reduces the flux of 129I, 137Cs, and fission gases 
diffusing out of the matrix into the grain boundaries and gap. However, CANDU SNFs have 
burnups of about 10 MWd/kgU, which is considerably lower than the light water reactor fuel 
burnups. Lower burnup means that less 129I, 137Cs, and fission gases were generated in the first 
place (Gray 1999 [DIRS 121407]). For CANDU fuels, the radionuclide migration to the gap and 
grain boundaries is lessened by a smaller concentration gradient rather than the smaller 
temperature gradient of the light water reactor CSNF and also by the fact that the lower fission 
gas inventory reduces accumulation, growth and coalescence of fission gas bubbles to form 
connected porosity at the grain boundaries. The combined effects of local fuel temperature and 
burnup on the behavior of fission gas bubbles in pressurized water reactor fuels is described in 
some detail by Dehaudt (2001 [DIRS 164019], Section 5.2.9.2). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-19 August 2004 
Stroes-Gascoyne (1996 [DIRS 113639]) measured gap and grain-boundary inventories of 137Cs, 
129I, 90Sr, 99Tc, and 14C in 15 used (spent) CANDU fuel elements. There was a good correlation 
between the combined gap and grain-boundary inventories of 137Cs and 129I, indicating that these 
fission products exhibit similar behavior in CANDU fuel and light water reactor fuel. Results 
were divided into groups consisting of ten low-power (less than 42 kW/m) and five high-power 
(greater than 42 kW/m) CANDU fuels. This partition was necessary because of wide differences 
in the 129I and 137Cs gap and grain-boundary inventories of these two fuel groups. Correlation 
between combined gap and grain-boundary inventories of 137Cs and 129I with calculated fission 
gas release of CANDU fuels could be confirmed only for lower-power fuels (less than 
42 kW/m). Combined gap and grain-boundary inventories of 90Sr were higher than expected and 
showed no correlation with calculated fission-gas release. No values for the combined gap and 
grain-boundary inventories of 99Tc were obtained because 99Tc in SNF samples is very insoluble 
and requires oxidation prior to dissolution. Combined gap and grain-boundary inventories of 14C 
were independent of fuel power or burnup. For eight out of the nine low-power CANDU fuels, 
almost all of the combined gap and grain-boundary inventories of 129I and 137Cs remained in the 
grain boundaries (Stroes-Gascoyne 1996 [DIRS 113639]). In contrast, a much greater proportion 
of the 129I and 137Cs migrated out of the grain boundaries into the gap in light water reactor fuels 
(Gray 1999 [DIRS 121407]). This difference between the CANDU and light water reactor fuels 
suggests that the grain boundaries in the CANDU fuels are tighter and have less connected 
porosity than CSNF. 
As discussed in Section 6.2.1.1, the CSNF fission gas release fraction bounds the release fraction 
of other radionuclides. However, Jegou et al. (2001 [DIRS 162397], Section 8.2.2.4) conclude, 
based on their review of the available data, that there is no obvious function that describes the 
correlation between the radionuclide inventory available for instantaneous release and the 
percent fission gas release. An alternative to correlating the gap and grain-boundary inventory to 
the fission gas release is to use an empirical distribution function based on the available data 
tabulated in Table 4-1 for each radioelement. This approach is adopted here because it is based 
on data from fuels spanning a wide range of percent fission gas release and because it provides 
results that generally overestimate instantaneous release fractions compiled based on an 
extensive review of CSNF data (Jegou et al. 2001 [DIRS 162397], Section 8.2.2.4). Using this 
approach, any correlation between the instantaneous fractional release data for cesium, 
technetium, strontium, and iodine is lost when the distribution functions are sampled separately. 
However, as pointed out above, there is no obvious correlation between the fractional release 
data for these elements. Even if there is some unknown correlation, it is not significant because 
the instantaneous fractional releases are very small except for iodine and cesium. Table 6-2 
shows the input data from Table 4-1 with the average and range of the data for each element 
rounded to the second decimal point. The Combined GI and GBI Average is obtained by 
adding the average values for the GI and GBI columns and rounding to the second decimal point. 
This approach gives equal weight to the results obtained from each sample and, since most of the 
results are from fuel samples that have high FGR values, is expected to give averages that are 
conservatively biased. The lower and upper ends of the range for the combined GI and GBI are 
obtained by adding the minimum and maximum values, respectively, in the GI and GBI data 
sets. This approach maximizes the range. Negative entries are not included in establishing the 
range minima because the gap and grain-boundary inventory fractions are bounded by 0 percent 
(Note: the negative entries in Table 4-1 are a consequence of measurement uncertainties and 
indicates that these inventory fractions are near zero). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-20 August 2004 
Although the measurement uncertainties are not tabulated for the data in Table 6-2, the overall 
uncertainty in estimating the instantaneous fractional releases includes the experimental 
measurement uncertainties discussed in Section 4.1.1 and potential systematic bias introduced by 
the experimental approach and aleatory uncertainty due to variability in such factors as the linear 
power history, burnup, and fuel grain size of the CSNF inventory (Jegou et al. 2001 
[DIRS 162397], Section 8.2.2). Systematic bias in the experimental approach could be caused 
by the fact that, because some of the technetium inventory is associated with insoluble metal 
particles, its full gap and grain-boundary inventory may not be detected (Gray 1999 
[DIRS 121407]). Comparison of the gap and grain-boundary inventories to the instantaneous 
release fractions from rod segments (Table 7-2) indicates that the potential systematic bias in the 
gap and grain-boundary inventory data does not have a significant effect on the instantaneous 
release of technetium. 
Uncertainties in the extent to which the data in Table 6-2 are likely to represent the gap and 
grain-boundary inventories for the CSNF to be disposed of in the repository can be assessed by 
comparing the average fission gas release for the data in Table 6-2 to data available on the 
fission gas release from CSNF in general. The fission gas release rates from pressurized water 
reactor fuel are generally in the range 1 to 4 percent (Dehaudt 2001 [DIRS 164019], 
Section 5.2.9.4). Also, data for fuel irradiated to a burnup of 60 MWd/kgU show 3 percent 
fission gas release and 10 percent of the fission gas inventory at the grain boundaries. By 
comparison, the average fission gas release for the data set shown Table 6-2 is 9.8 percent and 
the range is 0.59 to 18 percent. Because, as discussed above, the fission gas release bounds the 
release of other fission products, the comparison of the fission gas release data in Table 6-2 to 
the fission gas release data for CSNF in general indicates that the Table 6-2 data set are a 
somewhat conservative subpopulation to represent instantaneous radionuclide release from the 
overall CSNF population. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-21 August 2004 
Table 6-2. Summary of the Gap and Grain-Boundary Inventory Data 
137Cs 99Tc 90Sr 129I FGR 
(%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) 
1.1 1.18E+00 1.98E-01 -2.30E-02 5.04E-02 3.97E-02 2.14E-02 
7.85 7.21E-01 1.17E+00 3.44E-05 -7.75E-02 8.70E-03 2.38E-02 
7.85 8.02E-01 3.67E-01 -1.85E-02 4.76E-02 3.68E-02 5.75E-02 
7.85 2.23E+00 1.52E+00 
7.85 1.55E+00 3.28E+00 
7.85 6.24E-01 5.15E+00 
0.59 2.07E-01 1.88E-01 6.63E-02 1.97E-02 8.00E-02 1.68E-02 
0.59 2.78E-01 1.15E-01 
0.59 7.37E-01 2.09E+00 
7.4 1.80E+00 9.37E-02 
7.4 6.96E-01 7.98E+00 
7.4 6.48E-01 7.53E+00 
11.2 2.57E+00 1.00E+00 1.51E-02 1.54E-02 2.30E-02 1.34E-01 
11.2 2.32E+00 8.22E-03 1.22E-02 
11.2 3.21E+00 1.44E+00 
11.2 3.00E+00 1.08E+00 
11.2 7.64E-01 7.48E+00 
11.2 8.14E-01 9.13E+00 
18 4.25E+00 9.34E-01 5.42E-02 1.33E-01 9.70E-02 8.65E-02 
18 5.05E+00 2.55E-02 4.08E-02 
18 7.46E+00 1.82E+01 
18 1.05E+01 1.23E+01 
18 1.10E+00 8.32E+00 
18 1.23E+00 8.54E+00 
Average: 2.95 0.75 0.01 0.04 0.03 0.07 4.23 7.03 
Maximum: 10.50 1.23 0.05 0.13 0.10 0.13 18.20 9.13 
Minimum: 0.21 0.19 -0.02 -0.08 0.01 0.02 0.02 2.09 
Combined 
GI and GBI 
Average 
3.69 0.05 0.10 11.26 
Range - 
Combined 
GI and GBI 
0.40 to 11.73 0a to 0.19 0.03 to 0.23 2.11 to 27.33 
DTN: MO0407SEPGGBID.000 [DIRS 170619] is the source of the numbers except for the last five rows ; the numbers 
in the last five rows are calculated in the Appendix I, CSNF MR REV2.xls, sheet A13 
NOTES: FGR = fission gas release; GBI = grain-boundary inventory; GI = gap inventory 
a zero is used as the lower end of the range here because a negative value is nonphysical 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-22 August 2004 
6.3.2 The Instantaneous Release Fraction Model 
The mode and range for the probability distributions used in modeling the instantaneous release 
fractions were based on data in the superceded DTN LL000107951021.107 [DIRS 135012]. 
Upon review of the data in the superseded DTN LL000107951021.107 [DIRS 135012], it was 
discovered that some of the fractional releases were reported as percentage release when in fact 
they were fractional releases. The data involved were therefore reanalyzed and the corrected 
data are in Table 4-1. In the course of correcting the data, changes were also made in the data 
analysis methods to improve the accuracy of the results (DTN: MO0407SEPGGBID.000 
[DIRS 170619], ReadMe file). Thus, many numbers in this new table are different from the 
previous values, but typically by only a few percent. One row of data was removed because that 
testing had been performed as part of a different program and is not qualified for use as direct 
input in this report. Table 6-3 shows a comparison of the average and the range for gap and 
grain-boundary inventories in the DTN: MO0407SEPGGBID.000 [DIRS 170619] data set and 
the data from the superseded DTN: LL000107951021.107 [DIRS 135012]. 
Table 6-3. Comparison of Input Gap and Grain-Boundary Inventory Data Distribution Characteristics to 
the Corresponding Distribution Characteristics of the Superceded Data 
Part A: 
137Cs 99Tc 90Sr 129I FGR 
(%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) 
Average: 2.95 0.75 0.01 0.04 0.03 0.07 4.23 7.03 
Maximum: 10.50 1.23 0.05 0.13 0.10 0.13 18.20 9.13 
Minimum: 0.21 0.19 -0.02 -0.08 0.01 0.02 0.02 2.09 
Combined 
GI and GBI 
Average 
3.70 0.05 0.10 11.26 
Range - 
Combined 
GI and GBI 
0.40 to 11.73 0 to 0.18 0.03 to 0.23 2.11 to 27.33 
Part B: 
137Cs 99Tc 90Sr 129I FGR 
(%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) GI (%) GBI (%) 
Average: 2.90 0.73 0.03 0.03 0.03 0.06 4.10 7.14 
Maximum: 9.90 1.16 0.14 0.12 0.12 0.13 17.40 9.35 
Minimum: 0.21 0.18 0.00 0.00 0.00 0.02 0.03 2.01 
Combined 
GI and GBI 
Average 
3.63 0.06 0.09 11.24 
Range - 
Combined 
GI and GBI 
0.39 to 11.06 0.00 to 0.26 0.02 to 0.25 2.04 to 26.75 
Source: Appendix I, CSNF MR REV2.xls, sheet A13 
NOTES: FGR = fission gas release; GBI = grain-boundary inventory; GI = gap inventory 
Based on the discussion in Section 6.3.1, the instantaneous fractional releases for cesium, iodine, 
technetium, and strontium were modeled in Revision 01 of this report (BSC 2003 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-23 August 2004 
[DIRS 163824] as triangular distributions with the apex of the triangular probability distribution 
function located at the average release fractions shown in Table 6-3 and spanning the range 
shown for the DTN: LL000107951021.107 [DIRS 135012] data in this table for each element. 
Comparison of the average fractional release data and ranges for the 
DTN: LL000107951021.107 [DIRS 135012] data to the corresponding corrected data (the 
DTN: MO0407SEPGGBID.000 [DIRS 170619] data in Table 6-3) shows that the changes to the 
corrected values (DTN: MO0407SEPGGBID.000 [DIRS 170619]) are negligible considering the 
measurement uncertainties discussed in Section 4.1.1 and other uncertainties discussed at the end 
of Section 6.1.3. These differences are also small compared to the conservative bias discussed in 
Section 6.1.3. On this basis, the probability distributions described above are not changed in the 
current revision of this report. The triangular probability distribution is fully defined when the 
minimum, mode, and maximum values are defined because the area is equal to 1. The 
corresponding cumulative distribution function (CDF) is obtained by integrating the area under 
the triangular distribution. 
6.4 MODELING THE CSNF MATRIX FRACTIONAL RELEASE RATES 
This section describes models that have been developed for the oxidative matrix dissolution rate 
of CSNF and UO2. Section 6.4.1 describes the models for CSNF matrix degradation under 
alkaline and acidic conditions, Section 6.4.2 describes the alternative models developed 
internationally, and Section 6.4.3 describes the CSNF barrier capability. 
6.4.1 The CSNF Matrix Degradation Models 
The general modeling approach for the specific dissolution rate of the CSNF matrix is to identify 
a suitable mathematical form for the matrix dissolution rate and to determine appropriate values 
for adjustable parameters in that expression by regression analyses using the input data in 
Section 4.1.2. 
The following describes forms of the mathematical expressions developed to describe the 
kinetics of mineral dissolution in aqueous solutions and considers, including the goodness of 
fit to the input data set, to select the models to be used in TSPA-LA. The technical basis for the 
plausible mathematical forms that apply to the CSNF oxidative dissolution process is discussed 
in Waste Form Characteristics Report (Stout and Leider 1998 [DIRS 111047], Section 3.4.2). 
This discussion indicates that the chemical kinetic rate law, which has the general mathematical 
form of simple rate laws given in textbooks (e.g., Stumm and Morgan 1981 [DIRS 100829], 
p. 90, for dependence on the concentrations of reactants and products; and p. 95, for the 
temperature dependence) is useful as a first approximation in evaluating rate constants and 
reaction orders from given sets of experimental data: 
Rate = k[A]a[B]b[C]c...exp(Ea/RT) (Eq. 8) 
where 
k, a, b, c, and Ea are model parameters; R is the gas constant; and 
[A], [B], [C], and T are relevant independent variables; the bracket notation denotes 
solution molar concentrations of the relevant variables (e.g., hydrogen ion, total 
carbonate, and dissolved oxygen) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-24 August 2004 
This expression has the same mathematical form as the general expression developed for the 
rates of heterogeneous reactions between minerals and aqueous solutions (Aagaard and 
Helgeson 1982 [DIRS 101530], p. 237; Lasaga et al. 1994 [DIRS 106466], Eq. 8, p. 2,367) when 
the solution feedback or dissolution affinity term is omitted. More recent emerging 
understanding of mineral dissolution in general, based on the role of energetically reactive 
surface sites (e.g., dislocation defects) shows how these surface features interact with the extent 
of solution undersaturation in initiating dissolution step waves (Lasaga et al. 1994 
[DIRS 106466]; Lasaga and Luttge 2003 [DIRS 168087]). This understanding indicates that, 
while the dissolution rate decreases monotonically as the extent of solution undersaturation 
decreases, the rate may have a very nonlinear dependence on the extent of solution 
undersaturation. Because the data sets used here in the regression analyses are obtained from 
single-pass flow through tests in which the dissolved uranium concentration is kept low, the 
omission of terms that describe the solution feedback effects is appropriate for determining 
values for the parameters that express the dependence of the rate on temperature and solution 
chemistry factors (i.e., for determining a, b, c, and Ea in Equation 8). 
The initial approach is to use the rate law (Equation 8), with parameter values (for the parameters 
a, b, c, and Ea), evaluated by numerical regression analysis over the input set of experimental 
data from single-pass flow-through tests. For implementation in the regression analyses, 
Equation 8 was converted to a linear mathematical form by taking logarithms of each side; 
negative logarithms of the water-chemistry variables are used to be consistent with the standard 
definition of pH: -log10[H+]. This gives the following model form: 
Log10 Rd = a0 + a1  IT + a2  pCO3 + a3  pO2 + a4  pH (Eq. 9) 
where 
Rd is the rate of matrix degradation in mg/m2/day 
a0, a1, a2, a3, and a4 are model parameters 
and IT, pCO3, pO2, and pH are the independent model variables. IT is equal to the 
inverse of the absolute temperature, pCO3 is the negative log of the total carbonate molar 
concentration, pO2 is the negative log of the oxygen fugacity in atmospheres, and pH is 
the negative log of the hydrogen ion molar concentration 
More recent work expresses the general rate laws for heterogeneous reactions in terms of the 
activities of adsorbed reactants on mineral surfaces (Lasaga et al. 1994 [DIRS 106466], 
Equations 11 and 12, p. 2,367). Waste Form Characteristics Report (Stout and Leider 1998 
[DIRS 111047], Section 3.4.2.2) describes how chemisorption of reactants may lead to more 
complex mathematical model forms when the rates are expressed in terms of the solution 
concentrations as independent variables. These more complex models include some of these 
independent variables included in cross-product and quadratic terms. Such nonlinear models 
were examined (Section 6.4.1.1) to see if they could significantly improve the regression models 
Rsqr values (i.e., to see if such regression models could account for a significantly greater fraction 
of the data variation). 
The CSNF degradation rate model was developed in two steps. First, the input flow-through 
data (Tables 4-2, 4-3, and 4-4) were analyzed to determine the values for parameters that 
describe the dependence of the specific dissolution rate of the matrix on the independent 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-25 August 2004 
variables [H+], [CO3], fO2, and T (oxygen fugacity (fO2) rather than dissolved oxygen fugacity 
was used as the independent modeling variable because it is the experimental variable used in 
Tables 4-2, 4-3, and 4-4). Because the dissolved oxygen concentration varies with temperature 
at constant fugacity, the use of oxygen concentration as the independent modeling variable 
results in some convolution of the effects of oxygen fugacity and temperature. The model for the 
specific dissolution rate is developed as a piecewise continuous function for the basic and acidic 
pH regimes. Second, the specific dissolution rate is scaled to repository-relevant degraded fuel 
states (i.e., pellet fragments and rod segments) (Section 6.2.2.1). 
6.4.1.1 Input Data and Uncertainties for Developing CSNF Matrix Degradation Models 
The input data that were used to develop the base-case model for the specific dissolution rate of 
the CSNF matrix are the CSNF and UO2 single-pass flow-through test data described in 
Section 4.1.2. 
Flow-Through Test Results 
Single-pass flow-through dissolution studies were performed to examine the effects of 
temperature and important water chemistry variables on the forward dissolution rates of the UO2 
matrix in unirradiated UO2 and CSNF. The UO2 data are pertinent because the dissolution 
behavior of unirradiated UO2 is similar to the behavior of CSNF after the 137Cs and 90Sr have 
decayed and the associated radiolysis effects are no longer present (Shoesmith 2000 
[DIRS 162405]). Details of the flow-through tests were reported by Stout and Leider (1998 
[DIRS 111047]). Tests were selected to examine systematically the effects of temperature (25C 
to 75C), dissolved oxygen (0.002 to 0.2 atmospheres overpressure), pH (8 to 10), and carbonate 
concentrations (2  10-4 to 2  10-2 molar) on UO2 and SNF dissolution (Stout and Leider 1998 
[DIRS 111047], p. 2-221). The results for the CSNF tests are in Table 4-2 and the UO2 results 
are given in Table 4-3. Results for a series of single-pass flow-through tests conducted over the 
pH range extending from 2.02 to 7.29 are shown in Table 4-4. As described in Section 4.1.2, 
these test results were obtained under carbonate-free conditions; test Y6-A7B provides data for 
the dissolution rate under carbonate-free alkaline conditions. 
The forward reaction includes UO2 oxidation and dissolution reaction steps. The forward 
dissolution rates of UO2 and CSNF were determined by using a single-pass flow-through method 
(Stout and Leider 1998 [DIRS 111047], p. 2-220). The single-pass flow-through technique 
allows the flow rates and specimen size to be controlled so that the UO2 dissolves under 
controlled conditions far from solution saturation (no precipitation of dissolved products). Under 
such conditions, the steady-state dissolution rates are directly proportional to the effective 
surface area of the test specimen. The input data shown in Tables 4-2, 4-3, and 4-4 are 
normalized based on the estimated effective surface area of the test material (i.e., the data are 
expressed in the form of dissolution rate per unit area). 
Uncertainties associated with the use of the single-pass flow-through measurements on CSNF 
and UO2 to assess the specific dissolution rate of aged CSNF (i.e., CSNF after the /. radiolysis 
effects associated with decay of 137Cs and 90Sr are gone) and its parametric dependence on the 
environmental factors identified above include: 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-26 August 2004 
 Measurement uncertainties (standard deviations for the measurement uncertainties are 
included in Table 4-2) 
 Uncertainties due to uncontrolled experimental factors 
 Uncertainties associated with use of test data obtained on fresh CSNF and UO2 to assess 
the dissolution rate of the full range of aged fuel in the repository. 
The following discussion addresses the extent to which the pertinent sources of uncertainty are 
likely to be properly quantified in the parameter uncertainty estimates based on regression 
analyses of the input data from the single-pass flow-through tests. 
A fairly recent comparison and evaluation of worldwide specific dissolution rate data (including 
single-pass flow-through data) for UO2, SIMFUEL, and CSNF noted that the data have a wide 
range (up to a factor of 100 for the SIMFUEL data) for nominally similar conditions 
(Oversby 1999 [DIRS 163420], p. 31). This range is a useful indicator of the combined 
uncertainty associated with uncontrolled experimental factors in the measurements and the 
variability in the test materials and procedures used by different investigators. The uncertainty 
and variability in the data was attributed to various sources including effects of sample surface 
state (specifically oxidation to UO2+x) and differences in test procedures. The results of the 
interlaboratory comparison described in Section 4.1.2 provide an indication of the variability 
associated with implementation of the single-pass flow-through test procedure on the same test 
material at different laboratories. The insensitivity of the CSNF flow-through data to dissolved 
oxygen concentration at 25C is noted (Gray and Wilson 1995 [DIRS 100758], p. 4.3). This 
indicates that the oxidative dissolution rate at lower temperatures is sufficiently slow such that 
the results may be controlled by the initial surface state of the sample rather than the steady state 
surface appropriate for the test conditions involved. This component of the uncertainty would 
cause the data to be biased toward higher dissolution rates. The potential effects of insoluble 
elemental components on the dissolution rate of a complex material such as urananite were 
discussed by Grandstaff (1976 [DIRS 113255]). Similarly, the very insoluble fission product 
and actinide elements in CSNF are probably not completely dissolved under the single-pass 
flow-through conditions and may influence the measured uranium dissolution rate as they 
accumulate at the dissolving CSNF surface. These uncertainty contributions, combined with the 
effects of other uncontrolled experimental factors, contribute to the variability noted by Oversby 
(1999 [DIRS 163420]) and to the conclusion that data variability causes parameter values that 
express the dependence of the specific dissolution rate on environmental factors to be poorly 
constrained (Oversby 1999 [DIRS 163420], Abstract). 
Uncertainty associated with using data from fresh CSNF to assess the oxidative dissolution of 
aged CSNF (i.e., CSNF after decay of the 137Cs and 90Sr content) is due to the uncertain effects 
of radiolytic oxidants produced by the /. decay. As pointed out in Section 6.2.2.3, the 
magnitude of the effects of /. radiolysis on the oxidative dissolution rate of CSNF in aerated 
water is uncertain. However, it is important to note that the effects of /. radiolysis will cause 
the modeled dissolution rate for aged CSNF to be biased on the high side. 
In considering the effects of the uncertainties discussed above on modeling, it is useful to 
categorize them as uncertainties that cause variability in the data and uncertainties that may 
cause the data to be systematically biased, either conservatively or nonconservatively. The 
former category is addressed by propagating the uncertainties indicated by the data variability 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-27 August 2004 
into estimated uncertainties in the modeling parameters; the latter is addressed by assessing 
indications of systematic modeling bias through the model validation (Section 7). 
6.4.1.2 Model for the Specific Dissolution Rate under Alkaline Conditions 
This section discusses the modeling of the specific oxidative dissolution rate of CSNF under 
alkaline conditions. It summarizes the rate model functional form, presents the regression 
analysis of several forms of the model, and presents the recommended alkaline model. The 
approach adopted here for the alkaline conditions model uses experimental input data from 
flow-through dissolution tests for a set of specific CSNF types (approved testing material) and 
for unirradiated UO2 over a range of controlled water chemistries and temperatures (Tables 4-2 
and 4-3). Regression analyses of these data are used to evaluate empirical parameters in the rate 
law that expresses the corrosion rate dependence on the important experimental factors. 
Regression Analysis of Alkaline Data 
As discussed in Section 6.2.2.3 the principal environmental factors that influence the rate of 
oxidative dissolution of CSNF and UO2 are temperature, pH, and carbonate and dissolved 
oxygen concentrations. Based on the discussion of mathematical forms appropriate for 
expressing the reaction rate kinetics the intrinsic CSNF dissolution model can be expressed in the 
following form: 
LgDR = a0 + a1  IT + a2  pCO3 + a3  pO2 + a4  pH (Eq. 10) 
where 
LgDR, the dependent variable, is equal to Log10 Rd; 
a0, a1, a2, a3 and a4 are model parameters; and 
IT, pCO3, pO2, and pH are the independent model variables. 
IT is equal to one over the absolute temperature; pCO3 is the negative log of the total carbonate 
molar concentration; pO2 is the negative log of the oxygen fugacity; and pH is the negative log 
of the hydrogen ion concentration. The negative logarithms of the water chemistry variables are 
used to be consistent with the standard definition of pH (i.e., -log10 [H+]). 
Values for the model parameters a0, a1, a2, a3, and a4 and associated uncertainty estimates were 
obtained by regression analyses using the data sets in Tables 4-2 and 4-3 together and separately. 
The Microsoft Excel spreadsheets used to calculate the values of the dependent and independent 
variables from the input data in Tables 4-2 and 4-3 are attached (Appendix I, CSNF MR 
REV2.xls, sheets A1 and A2). Tables 6-4, 6-5, 6-7, and 6-8 show results from the regression 
analyses with some statistics for the models goodness-of-fit to the experimental data. The 
model forms that were used in the regression analyses are shown in the notes at the bottom of 
each of these tables. For example, Table 6-4 shows the regression results obtained when 
temperature, carbonate, and oxygen fugacity were used as the independent variables and the 
corresponding form of Equation 10 was regressed to the combined data in Tables 4-2 and 4-3. 
Equation 10, with the parameter values determined from this regression, is referred to as 
Model 1. Likewise, Table 6-5 shows the regression results obtained when temperature, 
carbonate, oxygen fugacity, and pH values were used as the independent variables and the 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-28 August 2004 
corresponding form of Equation 10 was regressed to the combined data in Tables 4-2 and 4-3. 
Equation 10, with the parameter values determined from this regression, is referred to as 
Model 2. Because, as discussed in Section 6.4.1, adsorption of reactants can lead to nonlinear 
models involving the independent variables in cross-product and quadratic terms, nonlinear 
multivariate regression analyses were also performed to determine if such nonlinear models 
would give a better fit to the data. Table 6-6 shows the regression analysis results obtained for a 
nonlinear model that includes cross-product terms between total carbonate and temperature and 
between oxygen fugacity and temperature. 
Table 6-4. Linear Regression: Combined CSNF and UO2 Data, Model 1 
Model Term Parameter Standard Error Significance 
Constant 4.705 0.601 <0.001 
IT -1,093.826 186.829 <0.001 
pCO3 -0.102 0.0471 0.034 
pO2 -0.338 0.0506 <0.001 
Source: CSNF MR REV2.xls, Spreadsheets A1 and A2 contain the derived values for the model variables that 
were used for this regression analysis; SigmaPlot regression file CSNF-UO2.JNB is in Appendix I and the 
Mathcad regression results are in Appendix II. 
NOTES: LgDR = 4.705 - (1,093.826  IT) - (0.102  pCO3) - (0.338  pO2) 
N = 67; R = 0.749; Rsqr = 0.561; Adj R sqr = 0.540 
Standard Error of Estimate = 0.347 
Table 6-5. Linear Regression: Combined CSNF and UO2 Data, Model 2 
Model Term Parameter Standard Error Significance 
Constant 4.224 0.758 <0.001 
IT -1,091.611 186.712 <0.001 
pCO3 -0.0933 0.0479 0.056 
pO2 -0.345 0.0509 <0.001 
pH 0.0523 0.0502 0.301 
Source: CSNF MR REV2.xls, Spreadsheets A1 and A2 contain the derived values for the model variables that were 
used for this regression analysis; SigmaPlot regression file CSNF-UO2.JNB is in Appendix I 
NOTES: LgDR = 4.224 - (1,091.611  IT) - (0.0933  pCO3) - (0.345  pO2) + (0.0523  pH) 
N = 67; R = 0.754; R sqr = 0.568; Adj R sqr = 0.541; Standard Error of Estimate = 0.347 
Table 6-6. Nonlinear Regression Model: Combined CSNF and UO2 Data 
Model Term Parameter Standard Error Significance 
a0 5.825 1.866 0.003 
a1 -1,444.2 594.65 0.018 
a2 -1.891 0.672 0.007 
a3 0.259 0.596 0.665 
a5 -119.08 191.15 0.536 
a6 498.7 214.79 0.024 
Source: CSNF MR REV2.xls, Spreadsheets A1 and A2 contain the derived values for the model variables that were 
used for this regression analysis; SigmaPlot regression file CSNF-UO2.JNB is in Appendix I 
NOTES: LgDR = 5.825 - (1,444.2  IT) - (1.891  pO2) + (0.259  pCO3) - (119.08  pCO3  IT) + (498.7  pO2  IT) 
R sqr = 0.598 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-29 August 2004 
Table 6-7. Linear Regression Model: CSNF Data 
Model Term Parameter Standard Error Significance 
Constant 3.424 0.508 <0.001 
IT -776.683 157.894 <0.001 
pO2 -0.279 0.0441 <0.001 
pCO3 -0.0378 0.0395 0.344 
Source: CSNF MR REV2.xls, Spreadsheet A2 contains the derived values for the model variables that were used 
for this regression analysis; SigmaPlot regression file CSNF-UO2.JNB is in Appendix I 
NOTES: LgDR = 3.424 - (776.683  IT) - (0.279  pO2) - (0.0378  pCO3) 
N = 45; R = 0.769; Rsqr = 0.592; Adj Rsqr = 0.562 
Standard Error of Estimate = 0.244 
Table 6-8. Linear Regression Model: UO2 Data 
Model Term Parameter Standard Error Significance 
Constant 7.618 1.313 <0.001 
IT -1,777.525 410.042 <0.001 
pO2 -0.473 0.108 <0.001 
pCO3 -0.286 0.103 0.013 
Source: CSNF MR REV2.xls, Spreadsheet A1 contains the derived values for the model variables that were used for 
this regression analysis; SigmaPlot regression file CSNF-UO2.JNB is in Appendix I 
NOTES: LgDR = 7.618 - (1,777.525  IT) - (0.473  pO2) - (0.286  pCO3) 
N = 22; R = 0.854; Rsqr = 0.729; Adj Rsqr = 0.684 
Standard Error of Estimate = 0.411 
The standard error given in Tables 6-4, 6-5, 6-6, 6-7, and 6-8 provides a measure of the 
uncertainty of the coefficient estimate in the same units as the estimate. The fourth column 
provides statistics related to the test of the hypothesis that the coefficient being estimated is zero. 
A high significance value indicates the coefficient is zero and the term can be dropped from the 
model. Conversely, the closer the significance value is to zero, the more important the term. 
The significance statistic in Tables 6-4, 6-5, and 6-7 indicates that carbonate concentration has 
little effect. However, as shown in Table 6-8, it does have a bigger effect (indicated by a smaller 
value of the significance statistic) when the regression analysis is performed using the UO2 data 
alone. The footnotes to the tables also provide some statistics to help assess the fit. First, the 
number of cases or testing data sets (N) is given. The Rsqr and Adj Rsqr are numbers that 
indicate how well the fitted model can account for the variability in the measured values. The 
results in Table 6-5 show that pH is not a big contributor to dissolution rate for the combined 
CSNF and UO2 data set under alkaline conditions. It was removed in the regression to produce 
the model shown in Table 6-4 with little change in the Rsqr and Adj Rsqr values. Temperature 
and oxygen fugacity have the biggest effect on dissolution as indicated by their low significance 
values in Tables 6-4 and 6-5. Based on these results, the model shown in Table 6-4 was selected 
as the base-case model for alkaline conditions. The comparison between the base-case model 
and the CSNF and UO2 data set is shown in Figure 6-3. The covariance matrix corresponding to 
the regression analysis in Table 6-4 is provided in the Mathcad analysis documented in 
Appendix II. This covariance matrix is required for the TSPA-LA models to implement 
uncertainty in the regression coefficients, in a way that accounts for the correlation between 
parameters and is consistent with the range of the dissolution rates and other data used to obtain 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-30 August 2004 
the regressed values. The Mathcad sheet also calculates the Cholesky decomposition of the 
covariance matrix (Appendix II). The Cholesky decomposition facilitates implementation of 
regression coefficient uncertainty in the TSPA-LA model file. 
Figure 6-3. Comparison of the Base-Case Model (pCO3 = 2.7) to the Input CSNF and UO2 Data 
For comparison to the base-case model (i.e., the model shown in Table 6-4), Table 6-6 shows a 
nonlinear model that includes cross product terms between temperature and oxygen fugacity and 
between temperature and total carbonate concentration. This regression analysis shows a weak 
interaction between temperature and oxygen fugacity. However, this nonlinear model provides 
only a slightly better fit to the data than the linear base-case model. This is shown by the 
comparing the Rsqr values for Tables 6-4 and 6-6, which indicate that other effects (i.e., effects 
other than chemisorption) are likely to be important in explaining the parts of the data variance 
that is not explained by the simpler linear models. Other effects that may influence the rate of 
the oxidative dissolution process include solution feedback, preferential dissolution at reactive 
surface sites, and mass transport of reactants and reaction products through surface alteration and 
insoluble residue layers. Because the slightly improved Rsqr values are achieved at the expense 
of ease of comparison of regression parameter values with literature data (literature data 
commonly expresses the dependence on the independent variables using the same mathematical 
form for the oxidative dissolution rate as that used in the base-case mode), more complex 
nonlinear models (Table 6-6) are not adopted for use in TSPA-LA. 
Table 6-7 shows the regression analysis of the CSNF data alone (i.e., the data in Table 4-2) and 
Table 6-8 shows the results of regression analysis of the UO2 data alone (i.e., the data in 
Table 4-3). The results in these tables indicate that the UO2 data depends more strongly on 
temperature, oxygen fugacity, and carbonate concentration than the CSNF data. This point is 
illustrated in Figure 6-4. The Rsqr values for these regressions show that the simple linear model 
can explain a significantly larger fraction of the variability in the UO2 data than in the CSNF 
data. This result is consistent with the expectation that factors peculiar to CSNF (e.g., formation 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-31 August 2004 
of insoluble residue layers on the corroding surface) may influence the CSNF corrosion rate and 
this uncontrolled experimental factor may account for some of the variability in the CSNF data 
set shown in Table 4-2. Also, as pointed out in Section 6.2.2.3, there is some evidence that 
different reaction steps may control the rate of the oxidative dissolution process in different 
regions of the experimental factor space over which the regression analyses were performed. 
Because of this, no single model can explain the variability over the experimental factor space. 
Also, the fact that worldwide data on the oxidative dissolution rates of UO2 and CSNF exhibit 
broad variability attributed to uncontrolled experimental factors (Section 6.4.1.1) may account 
for some of the data variability not accounted for by the regression models. 
Figure 6-4. Comparison of Models (pCO3 = 2.7) Obtained when the CSNF Data (Red) and the UO2 
Data (Blue) are Regressed Separately 
6.4.1.3 Model for the Specific Dissolution Rate Under Acidic Conditions 
The model for CSNF dissolution under acidic conditions was developed using the data in 
Table 4-4 and the model mathematical form shown in Section 6.4.1.2, Equation 10. 
The data set in Table 4-4 shows the pH dependence of the dissolution rate at an average 
temperature of about 26C, oxygen partial pressure of 0.2 atmospheres, and approximately zero 
carbonate concentrations (the test solutions were sparged with carbonate-free air). Comparison 
of the dissolution rate data for test Y6-A7B in Table 4-4 to the dissolution rate data for runs 
number 30 and 27 in Table 4-2 shows that the effect of total carbonate molar concentration 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-32 August 2004 
variation over the range of 0 to 2  10-2 is negligible in neutral to slightly alkaline solutions. 
However, because this result is based on one data set conditions, a value of 2  10-4 molar is to 
be used to conservatively model CSNF degradation under alkaline conditions when the inpackage 
chemistry model calculates a concentration less than 2  10-4 molar. The total carbonate 
concentrations will decrease rapidly as the pH decreases in the acidic regime (BSC 2003 
[DIRS 161962], Figure 39). Also, as described in Section 6.2.2.3, the U(VI) complexation and 
dissolution steps prompted by carbonate ions are unlikely to control the reaction rate under acidic 
conditions. The carbonate dependence term is, therefore, not included in the acid-side model. 
The same temperature and oxygen terms, with their associated uncertainties, were assumed 
(Assumption 5.3) in the acid model as were used in the alkaline model. Justification for this 
modeling assumption is given below. 
The Equation 11 expression was regressed to dissolution rate data, from Table 4-4, using the 
alkaline-side values for the parameters a1 and a3: 
LgDR = a0  1 + a1  IT + a3  pO2 + a4  pH (Eq. 11) 
This regression analysis is documented in Appendix II. The following equation (with 
Rsqr = 0.703) was obtained: 
Adjusted LgDR = 6.600.446 - (0.3400.110  pH) (Eq. 12) 
The resulting acid-side model has the form shown in Equation 11 where the parameter values are 
as follows: ao = 6.600.446, a1 = -1093.826, a3 = -0.338, and a4 = -0.3400.110. Because the 
uncertainties determined for the temperature and oxygen fugacity dependence are assumed to be 
the same as those determined for alkaline conditions (Assumption 5.3), a1 = -1093.826 186.829 
and a3 = -0.338 0.0506. 
The basis for using the same temperature and oxygen fugacity dependence under alkaline and 
acidic conditions is that the same redox reaction steps are likely to be involved under acidic and 
alkaline conditions. As discussed in Section 6.2.2.3, the effects of low pH are to promote 
dissolution of the partially oxidized UO2.33 surface layer, which is also the effect of carbonate 
under alkaline conditions. This indicates that the rate controlling steps under acidic conditions 
are probably similar to those in alkaline carbonate solutions when the dissolution of the UO2.33 
surface layer does not control the overall reaction rate. Also, the oxygen dependence for the rate 
of oxidative dissolution of UO2 under acidic conditions (Torrero et al. 1997 [DIRS 114439]; 
Shoesmith 2000 [DIRS 162405], Section 3.1.2), which shows an oxygen concentration exponent 
value of 0.31, is similar to oxygen concentration exponent value of 0.338 in the base-case model 
for alkaline conditions (Section 6.4.1.2). This also indicates that the processes controlling the 
oxidative dissolution rate under acidic conditions are similar to those controlling the rate under 
alkaline carbonate conditions. 
To determine the pH domains over which the models for alkaline and acidic conditions should be 
applied, the pH at which the alkaline and acidic conditions models give the same value of the 
specific dissolution rate was determined. The pH at which the acid-side model gives the same 
value for the log of the specific dissolution rate as the alkaline-side model is dependent on the 
carbonate concentration. This results in an inconsequential discontinuity if a specific pH is used 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-33 August 2004 
to identify the boundary between the acid and alkaline model domains. Because the total 
carbonate concentration has been calculated to be approximately 1  10-4 molar at near-neutral 
conditions in CSNF waste packages (BSC 2003 [DIRS 161962], Figure 39), the pH boundary 
between the domains of application of the acid and alkaline models was calculated using this 
carbonate concentration. At a total carbonate concentration of 1  10-4 molar, the acid-side and 
alkaline-side models are equal at a pH of 6.8. The acid-side model is therefore used for pH less 
than 6.8; the alkaline side model is used for pH = 6.8. The Microsoft Excel spreadsheet used for 
the acid-side model calculations is attached (Appendix I, CSNF MR REV2.xls, Spreadsheet A3). 
6.4.1.4 Input Data and Uncertainties for Scaling the Specific Dissolution Rate 
As discussed in Section 1, the fractional radionuclide release rate from the CSNF matrix is 
obtained by multiplying the dissolution rate per unit area of the matrix by the specific surface 
area of the corroding fuel. The input data in Section 4.1.3 on the geometric surface area of 
CSNF was used to estimate the CSNF specific surface area results presented in Table 6-9. 
Table 6-9. Summary of Geometric-Specific Surface Area Estimates 
Fuel/(Section) Data Source 
Specific Surface 
Area (cm2/g) 
HBR/ATM-101(C5C-I) Table 4-5 2.42a 
TP/(F6-2B) Table 4-5 2.23a 
ATM-101 (N9C-C&F) Table 4-6 2.22 
ATM-101 (N9C-I&K) Table 4-6 2.15 
ATM-101 (N9C-O&M) Table 4-6 2.00 
NOTE: a Average of input data in Table 4-5 
The specific surface area estimates for the HBR and TP fuels in Table 6-9 (data rows one and 
two) were obtained by averaging the estimates in Table 4-5 for each fuel type. The estimates for 
the ATM-101 fuel (in the third, fourth, and fifth data rows) were obtained by dividing the 
corresponding total surface area of the fuel fragments per millimeter length of fuel (i.e., the input 
data in the sixth column of Table 4-6) by the fuel mass per millimeter length of fuel. The latter 
was obtained by multiplying the volume of fuel per millimeter length (pL(diameter)2/4, where L, 
the length, is equal to one millimeter) by the fuel density; the input data for the fuel diameter and 
density are given in Table 4-6. These geometric-specific surface area data are likely to represent 
the geometric-specific surface area of CSNF in general because, as discussed in Section 6.2.1.1 
and in Appendix V, the extent of CSNF pellet cracking is similar to that observed in these fuels 
and because the specific geometric surface area is not sensitive to the extent or cracking 
(Appendix V). 
Section 6.2.2.1 discussed the likely evolution of the state of breached CSNF rods. The corrosion 
rate of the fuel in the repository will depend on the state of the fuel rods and the water contact 
scenarios because these will control the rate at which dissolved oxygen is transported to the 
corroding fuel surfaces. The plausible states of corroding fuel include fuel pellet fragments and 
short segments of fuel (Section 6.2.2.1). Data available for the fractional release rate of 
radionuclides from flow-through tests on fuel fragments (Gray and Wilson 1995 [DIRS 100758], 
Section 4.1.1, p 4.2) and from short fuel rod segments (DTN: MO0301ANLSF001.450 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-34 August 2004 
[DIRS 162384]) are used to estimate upper and lower bounds on the uncertainty range for the 
specific surface area of fuel rods in these degraded states. 
The basis for using the specific surface area derived from flow-through tests on fuel fragments 
(Gray and Wilson 1995 [DIRS 100758], Section 4.1.1) to establish the high end of the 
uncertainty range for the specific surface area parameter is discussed in Section 6.4.1.5. The rod 
segment data (DTN: MO0301ANLSF001.450 [DIRS 162384]) are used to estimate the lower 
end of the uncertainty distribution range because the short length of the fuel rod segments used in 
the tests and the flow-through configuration are likely to conservatively bound the radionuclide 
release for fuel rod segments immersed or exposed to dripping groundwater or humid air, or 
both, in the repository. By using a test configuration in which water percolates through the test 
segment, these tests reasonably represent or overestimate the rate of dissolved oxygen supply to 
the corroding fuel when it is in the form of short fuel-rod segments. The conservative bias in the 
test results is due to the use of short fuel rod segments in the tests (longer pieces would have 
more oxygen depletion) and use of a flow-through configuration. 
6.4.1.5 Scaling the Specific Dissolution Rate 
The dependent variable for the models discussed in Sections 6.4.1.2 and 6.4.1.3 is the specific 
dissolution rate (i.e., the dissolution rate per unit area) of the CSNF matrix. To apply these 
models to calculating the fractional corrosion rate of the matrix, the specific dissolution rate must 
be multiplied by an estimate of the effective specific surface area (i.e., the effective surface area 
per unit mass) of the corroding fuel. The effective surface area is a scaling factor. The term 
effective surface area is used because it accounts not only for the specific geometric surface 
area of CSNF in plausible degraded states, but also for other factors that can influence the rate of 
oxidative dissolution of CSNF in the plausible degraded states (e.g., dissolved oxygen depletion 
and effects of alteration phases on the rate of mass transport of oxidants to, and reaction products 
away from, the corroding fuel surfaces). 
When the model discussed in Section 6.4.1.2 is multiplied by an estimate of the effective specific 
surface area of the fuel (A), the fractional corrosion rate is given by the following equation: 
Log(F) = Log(A) + 4.705 - (1093.826  IT) - (0.102  pCO3) - (0.338  pO2) (Eq. 13) 
where 
F = Fractional dissolution rate of the fuel (d-1), 
A = Estimate of the fuel effective specific surface area (m2/mg). 
Two approaches are available for estimating the effective specific surface area of the corroding 
fuel: (1) use estimates of the specific surface area of CSNF based on characterization of the 
geometric-specific surface area of the cracked fragments in irradiated fuel pellets, and (2) use the 
radionuclide fractional release rate data presented in Section 6.4.1.4, as summarized in 
Table 6-10, to estimate (F) and then solve the above equation for (A) using the fuel rod segment 
test conditions (T = 90C, pCO3 = 2  10-3 molar, and pO2 = 0.2 atmospheres) (Goldberg 2003 
[DIRS 162410], Tables 6, 7, 8, and 9). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-35 August 2004 
The first of these two approaches is adopted to provide a best estimate of the effective surface 
area parameter A. The second is used with the rod segment data to estimate the lower end of the 
uncertainty distribution range for A. The upper end of the uncertainty distribution range is based 
on the input-specific surface area of fuel fragments estimated from SPFT data (Section 4.1.3). 
Section 6.2.2.1 provides the basis for using fuel fragments and short fuel rod segments to 
represent plausible degraded states of fuel rods with breached cladding. Because the cladding in 
degraded fuel rods limits water and dissolved oxygen access to the fuel, the effective specific 
surface area of the fuel in fuel rod segments is lower than that of fuel exposed in the form of fuel 
pellet fragments. The SPFTs on fuel fragments maximize the effective specific surface area of 
the fuel fragments by minimizing deposition of alteration products on the corroding fuel and 
promoting effective mass transport of dissolved oxygen to the corroding fuel surfaces. As 
described in Section 4.1.3.2, the rod segment tests were conducted on short segments of fuel rods 
in a configuration in which water percolates through the rod segment and facilitates mass 
transport of dissolved oxygen to the corroding. The fractional radionuclide release rate results 
obtained from these tests are expected, therefore, to overestimate the fractional release rates from 
fuel rod segments in the repository. Use of data from these tests to estimate the effective specific 
surface area of the corroding fuel in fuel rod segments will give a conservative (i.e., biased 
toward the high side) estimate of the lower bound on the effective specific surface area 
parameter. 
The best estimate for the specific surface area of CSNF is obtained by averaging the data in the 
third column of Table 6-9. This gives a specific surface area of 2.20 cm2/g (i.e., 2.20  10-4 m2/g 
or 2.20  10-7 m2/mg). The corresponding value for the base ten log of the specific surface area 
is 6.66 when A is expressed in m2/mg and the log is rounded to three significant figures; the 
value is -6.7 when rounded to two significant figures. 
Table 6-10. Average Radionuclide Release Rates (d-1) from Rod Segment Tests 
Fuel 137Cs 129I 90Sr 99Tc 
ATM-103, 1.5 7.09E-7 8.94E-6 7.01E-7 4.01E-7 
ATM-103, 2.6 8.01E-7 1.52E-5 1.05E-6 5.17E-7 
ATM-103, 3.7 7.96E-7 1.37E-5 2.82E-7 6.31E-7 
Average ATM-103 7.69E-7 1.26E-5 6.78E-7 5.16E-7 
ATM-106, 1.4 1.95E-6 1.03E-5 9.09E-7 1.93E-7 
ATM-109, 3.2 5.21E-7 5.07E-6 NDa 2.60E-8 
Fuel Type Averageb 1.08E-6 9.32E-6 7.94E-7 2.45E-7 
Source: Appendix I, CSNF MR REV2.xls, Spreadsheet A6 
NOTE: a Concentration not determined in test solution. 
b Calculated by averaging the results for the ATM-103, ATM-106, and ATM-109 in rows 5, 6, and 7; the 
ATM-103 results in rows 2, 3, and 4 are used to calculate the ATM-103 average results in row 5. 
The overall average fractional release rate (F), as calculated from the fuel rod segment data in 
Table 6-10, is used to estimate the effective specific surface area (A) based on the following 
equation: 
Log(A) = Log(F) - 4. 705 +1093.826(IT) + 0.102(pCO3) + 0.338(pO2) (Eq. 14) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-36 August 2004 
Because the 129I data in Table 6-10 are likely to contain release contributions from the grain 
boundaries, these data were not included in calculating the overall fractional release rate of 
7.0610-7 d-1. Substituting this value for F in Equation 14 and solving for log(A) gives the value 
of -7.33 (i.e., an effective specific surface area of the corroding fuel in the tests of 
4.65  10-5 m2/g). The Microsoft Excel spreadsheet used to perform these calculations is 
attached (Appendix I, CSNF MR REV2.xls, Spreadsheet A8). 
As discussed in Appendix IV, estimates of the specific geometric surface areas of CSNF 
fragments are generally in agreement and consistent with the 2.2  10-4 m2/g best-estimate value 
given above. However, the initial geometric-specific surface area may differ from the specific 
surface area of the reacting fuel since it does not take into account open porosity in the fuel 
fragments, roughness on the outside surface of the fragments, and opening of grain boundaries 
(Section 6.2.2.3). The effects of oxidative dissolution reactions on the specific surface area of 
CSNF were examined by Gray and Wilson (1995 [DIRS 100758], Sections 4.1.1 and 4.2.3). 
These authors estimated the specific surface area of the corroding fuel fragments to be about 
3.9  10-3 m2/g (Gray and Wilson 1995 [DIRS 100758], Section 4.1.1, p 4.2. 
The effective specific surface area for the fuel rod segment tests is about a factor of five lower 
than the geometric-specific surface area. However, the effective specific surface area estimated 
for fuel fragments in flow-through tests (i.e., 3.9  10-3 m2/g) is about a factor of five higher than 
that estimated for fuel fragments in the drip tests (see corroborating section below). This result 
is reasonable because flow-through tests minimize the accumulation of surface alteration layers 
and maximize mass transport of dissolved oxygen to the grain-boundary surfaces. This would 
maximize the effective surface area of the fragments under the single-pass flow-through 
conditions and is not expected in the repository. The 3.9  10-3 m2/g effective specific area 
estimate is, therefore, used as the upper end of the plausible range. Additional arguments, 
presented in the next section, corroborate use of the effective specific surface area from flowthrough 
tests conducted on CSNF fragments for the upper end of the effective specific surface 
area range. 
Based on the above discussion, the effective specific surface area of the corroding fuel is treated 
as an uncertain parameter with the uncertainty represented by a distribution spanning 3.9  10-3 
to 4.7  10-5 m2/g. The upper limit is based on Gray and Wilsons (1995 [DIRS 100758], 
Sections 4.1.1 and 4.2.3) estimate of specific surface area of corroding fuel (3.9  10-3 m2/g). 
The best estimate is based on using the average specific surface area (2.2  10-4 m2/g) calculated 
in Table 6-9 from the input geometric surface area data. The lower end of the uncertainty range 
is based on the average of the rod segment tests, 4.7  10-5 m2/g (DTN: MO0301ANLSF001.450 
[DIRS 162384]). Rounding to two significant figures, the recommendation for the distribution 
of log(A) is triangular with a low of -7.3, apex of -6.7, and max of -5.4 (where A is in units of 
m2/mg). 
6.4.1.6 Corroborating Data for Estimating CSNF Specific Surface Areas 
As discussed in Section 6.2.2.3, the corroding fuel fragments show a total surface area 
(Figure 6-2) that may be significantly different from the geometric surface area. Preferential 
corrosion at grain boundaries, and effects of the wormy texture that has been observed in 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-37 August 2004 
corroded SNF samples could increase the specific surface area while protective effects of the fuel 
alteration phases that precipitate onto the corroding CSNF surface could reduce the effective 
specific surface area (Section 6.2.2.3). Because the effective specific surface of CSNF may 
evolve as the fuel rods degrade and the fuel corrodes (Section 6.2.2.1), it is important to 
corroborate use of the geometric-specific surface area estimates to represent the effective specific 
surface area of corroding CSNF. 
Fractional radionuclide release rate data from the unsaturated fuel fragment tests described in 
Section 6.2.2.2 are shown in Table 6-11. These data were obtained from six long-term tests 
conducted on CSNF pellet fragments exposed to a variety of hydraulically unsaturated test 
conditions (Thomas 2003 [DIRS 163048]). Four tests were begun in September 1992; one high 
drip-rate test and one low drip-rate test on each of two fuels (approved testing material ATM-103 
and ATM-106). The remaining two tests, begun in late 1998, are high drip-rate tests, conducted 
on high-burnup fuels (designated ATM-109A and ATM-109C). As described in Section 6.2.2.2, 
the test configuration was designed to allow the injected water to drip onto the fuel fragments 
and to drain through holes in the bottom of the fuel holder (Thomas 2003 [DIRS 163048], p. 16). 
This test configuration exposed the fuel fragments to transient water flow associated with each 
injection and maintained exposure of the fuel to conditions of 100 percent relative humidity 
between injections. 
Table 6-11 provides the average fractional release rates data for the soluble radionuclides, 137Cs, 
97Mo, 99Tc, and 90Sr from these test data. Data through the approximately 4.8-year sampling are 
associated with DTN: LL991001251021.090 [DIRS 129285]. These data are summarized from 
data package submission (CRWMS M&O 2000 [DIRS 131861]). The interval fractional release 
rate data for these four elements for the subsequent sampling periods through approximately 
8.7 years are associated with DTN: MO0301ANLSF001.451 [DIRS 162383]. The source of 
these data is the data package submission (Thomas 2003 [DIRS 163048]). The Microsoft Excel 
spreadsheets used to calculate the average fractional release results shown in Table 6-11 from the 
input data sources are included in Appendix I, CSNF MR REV2.xls, Spreadsheets A4 and A5 
and summarized in Spreadsheet A7. 
The data compiled in Table 6-11 show that the average fractional release rates of the individual 
radionuclides vary considerably and the radionuclide average for the high drip-rate tests is higher 
than that for the low drip-rate tests. The fractional radionuclide release rate data in Table 6-11 
were used to calculate the effective specific surface area of corroding CSNF (i.e., the modeling 
parameter A) (Appendix I, CSNF MR REV2.xls, Spreadsheet A8). The uncertainties associated 
with the use of these input data for this purpose are described below. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-38 August 2004 
Table 6-11. Average Fractional Release Rates (d-1) in the Fuel Fragment Tests 
Test (fuel/type) Data Period* 90Sr 137Cs 99Tc 97Mo Radionuclide 
Average 
ATM-103 HDR 1.3 to 8.7 years 2.85E-07 3.08E-06 1.29E-05 1.12E-05 6.87E-06 
ATM-106 HDR 1.3 to 8.7 years 7.66E-07 5.33E-06 1.49E-05 9.44E-06 7.60E-06 
ATM-109A HDR 1 to 3 years 7.21E-08 2.33E-06 4.66E-05 8.82E-05 3.43E-05 
ATM-109C HDR 1 to 3 years 2.39E-07 3.00E-06 4.12E-07 1.16E-06 1.20E-06 
HDR Average 3.41E-07 3.43E-06 1.87E-05 2.75E-05 1.25E-05 
ATM-103 LDR 1.6 to 5.7 years 3.37E-06 6.94E-07 1.16E-06 4.98E-08 1.32E-06 
ATM-106 LDR 1.6 to 7.3 years 2.50E-07 4.22E-07 4.62E-06 3.00E-07 1.40E-06 
LDR Average 1.81E-06 5.58E-07 2.89E-06 1.75E-07 1.36E-06 
Source: Calculated in Appendix I CSNF MR REV2.XLS, Sheets A4 and A5, and summarized in Sheet A7 
NOTES: HDR = High Drip Rate (unsaturated testing); LDR = Low Drip Rate (unsaturated testing) 
*duration indicates elapsed time-in-test included among sampling data. 
The uncertainties in the test conditions for the unsaturated fuel fragment tests are described 
elsewhere (Thomas 2003 [DIRS 163048], pp. 50 and 51). The uncertainties are associated with 
the test design (principally how the injected water contacts the fuel in the fuel holder and 
uncertainties in the dissolved oxygen concentration in this water) and with planned and 
unplanned operational occurrences (principally due to reconfiguration of the experimental setup, 
removal of corroded fuel samples, loss of water from the test vessels, and inadvertent dropping 
of pieces of fuel into the leachate). 
Estimates of the oxygen partial pressure (fugacity) in the unsaturated fuel fragment tests are 
described in Unsaturated Testing of Bare Spent UO2 Fuel Fragments (Thomas 2003 
[DIRS 163048], p. 51). At the outset of each test interval, the oxygen partial pressure was 
estimated to be approximately 0.24 atmospheres and decreases rapidly between 0.05 and 
0.04 atmospheres and persisted throughout most of the test cycle. Although the uncertainties in 
these estimates of the oxygen partial pressure in the tests are not known, the available estimates 
indicate that the oxygen partial pressure may be somewhat lower than the approximate 
0.06 atmospheres oxygen partial pressure expected in the repository for 100 percent relative 
humidity air at 90C and significantly lower than 0.2 atmospheres oxygen partial pressure 
expected at ambient temperatures. 
The effects of other planned and unplanned operational occurrences (specifically, 
reconfiguration of the experimental set up, removal of corroded fuel samples, and inadvertent 
dropping of pieces of fuel into the leachate) on the fractional release of soluble radionuclides are 
discussed in Unsaturated Testing of Bare Spent UO2 Fuel Fragments (Thomas 2003 
[DIRS 163048], pp. 40 to 44). The data show these types of occurrences correlate with spikes in 
the fractional release data in the subsequent sampling. The effects of the disturbance then 
subside over time with the observed release fractions returning to approximately the same values 
that were observed prior to the disturbance. Because the data include these spikes, this source of 
uncertainty in the measured fractional release rates is likely to cause them to be unrealistically 
high and therefore somewhat conservative. The following discussion uses the overall average 
fractional release rate from the HDR and LDR test results in Table 6-11 (6.93  10-6 d-1) to 
estimate the effective specific surface area using Equation 14. Solving this equation for log(A) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-39 August 2004 
gives the value of -6.34 (i.e., an effective specific surface area of 4.56  10-4 m2/g of the 
corroding fuel in the tests). The Microsoft Excel spreadsheet used to perform these calculations 
is attached (Appendix I, CSNF MR REV2.xls, Spreadsheet A8). 
This result is about a factor of two higher than the best estimate for the specific surface area 
provided above in Section 6.4.1.5 indicating that the grain-boundary penetration and the wormy 
texture of the corroding CSNF do not greatly increase the effective specific surface area of 
corroding CSNF. In addition, the long-term fractional radionuclide release data in 
DTNs: MO0301ANLSF001.451 [DIRS 162383] and LL991001251021.090 [DIRS 129285] 
show a generally decreasing trend with time indicating, despite the development of grainboundary 
penetration and the wormy texture in the grains, these effects do not result in an 
increase in the effective specific surface area of the corroding fuel. These results are consistent 
with the observations discussed in Section 6.2.2.3, which indicate that the general corrosion 
mode accounts for most of the matrix dissolution. Much of the grain-boundary area does not 
contribute effectively to the oxidative dissolution process, probably due to dissolved oxygen 
depletion in the water that penetrates the grain-boundary openings. This evidence indicates that 
effective surface areas much greater than the fragments geometric surface areas are not likely in 
the repository. In particular, the 0.1m2/g specific surface area for individual fuel grains from 
Gray and Wilson (1995 [DIRS 100758], Section 4.1.1) is ruled out. 
6.4.1.7 Summary of Model Input Data Uncertainties 
Table 6-12 summarizes model input data and associated types of uncertainty. 
6.4.2 Alternative Conceptual Models 
Direct disposal of spent UO2 nuclear fuel is being considered in geologic disposal programs 
being conducted by several countries other than the United States (Grambow et al. 2000 [DIRS 
162391]; Johnson et al. 1996 [DIRS 162372]; Dehaudt 2001 [DIRS 164019]; Dehaudt 2001 
[DIRS 164037]; Jegou et al. 2001 [DIRS 162397]; Pelletier 2001 [DIRS 164034]; Piron 2001 
[DIRS 162396]). The models that have been developed in these programs to predict the longterm 
performance of the SNF are reviewed here to identify possible alternative conceptual 
models for CSNF corrosion in the repository. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-40 August 2004 
Table 6-12. Summary of CSNF Waste Form Degradation Analysis Inputs 
Input Name Input Description 
Input Source 
(DTN) 
Value or 
Distribution 
Type of 
Uncertainty 
Gap/Grain 
Boundary 
Inventory 
Data for the fraction (%) of the 
inventory of 137Cs, 129I, 99Tc, and 
90Sr in the Gap and Grain- 
Boundary Regions of CSNF 
MO0407SEPGGBID.000 
[DIRS 170619] 
Table 6-3 Mostly 
Aleatory 
(discussion in 
Section 6.3.1) 
Average 
Radionuclide 
Release 
Rates 
Data for fractional release rates of 
137Cs, 129I, 99Tc, and 90Sr from 
CSNF rod segment tests 
MO0301ANLSF001.450 
[DIRS 162384] 
Table 6-10 Aleatory and 
Epistemic 
(discussion in 
Section 
6.4.1.4) 
Flowthrough 
Test 
Data 
Data for the specific dissolution 
rate of CSNF and UO2 as a 
function of pH, T, [O2], and [HCO3
-] 
MO0302PNLDUFTD.000 
[DIRS 162385] 
MO0407SEPUDISR.000 
[DIRS 170618] 
MO0304PNLLPHDD.000 
[DIRS 163441] 
Section 6.4.1 Epistemic 
(discussion in 
Section 
6.4.1.1) 
Surface 
areas for 
HBR and TP 
fuels 
Estimated geometric-specific 
surface areas (cm2/g) and 
estimated effective specific surface 
area of corroding CSNF 
Wilson 1990 [DIRS 100793], 
Appendix E 
Barner 1985 [DIRS 109194], 
Tables 4.6 and 4.1 
Gray and Wilson 1995 
[DIRS 100758], Section 
4.1.1, p 4.2 
Table 6-9 
And input 
value of 
3.9  10-3 
(m2/g) for the 
effective 
specific 
surface area of 
corroding fuel 
Aleatory 
(discussed in 
Section 
6.4.1.4) 
The long-term exposure scenarios for spent UO2 nuclear fuel in repository development 
programs outside the United States generally involve contact by reducing groundwater. As a 
consequence, many of the models that have been developed internationally are based on 
modeling how the production, depletion and mass transport processes of oxidizing agents (e.g., 
dissolved O2 and radiolysis products including H2O2 and radicals) limit their availability to 
support oxidative dissolution of the fuel. These modeling approaches are not applicable to 
Yucca Mountain because the rate of oxidative dissolution of CSNF in the repository will be 
limited by the kinetics of the oxidative dissolution process rather than by the availability of 
oxidizing agents. This is based on the fact that dissolved oxygen will be the principal oxidizing 
agent in the repository and an ample supply of atmospheric oxygen is expected to maintain the 
oxygen fugacity at or near 0.2 atmospheres in the repository void spaces (Section 6.2.2.3). 
Under these conditions, the rate of oxidative dissolution will be limited by the rate of the 
chemical reactions involved in the oxidative dissolution process. Exposure to humid air and airsaturated 
groundwater conditions have been considered in several international programs for 
evaluating fuel storage and possible exposure of the fuel in containers with early-life failures to 
oxidizing groundwaters. 
The available experimental evidence from international studies shows that the overall oxidative 
dissolution process for UO2 involves a complex set of adsorption, redox, surface complexation, 
and desorption reaction steps. Two important modeling approaches have been developed based 
on the kinetics of the redox reactions (electrochemical model) and the rates of surface 
complexation reactions (surface complexation model). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-41 August 2004 
6.4.2.1 Electrochemical Model 
The electrochemical approach for modeling the rate of the oxidative dissolution of unirradiated 
UO2 and SNF is based on modeling the rates of the redox reactions involved in the fuel oxidation 
and dissolution (Shoesmith 2000 [DIRS 162405]; Johnson et al. 1996 [DIRS 162372]; 
Grambow et al. 2000 [DIRS 162391]; Dehaudt 2001 [DIRS 164019]; Dehaudt 2001 
[DIRS 164037]; Jegou et al. 2001 [DIRS 162397]; Pelletier 2001 [DIRS 164034]; Piron 2001 
[DIRS 162396]). For these reactions, the key variable is the corrosion potential and, as discussed 
below, is determined largely by the concentrations of oxidizing agents (e.g., dissolved O2 and 
radiolysis products including H2O2 and radicals) at the fuelsolution interface. 
An overall understanding of how the state of the corroding UO2 fuel surfaces varies with the 
surface potential (a measure of the strength of the oxidizing conditions) and other water 
chemistry conditions were developed (Shoesmith 2000 [DIRS 162405]). For the surface 
potential regimes (greater than -100 mV saturated calomel electrode) of interest for the 
repository, the surface state can be described as follows: 
1. In the potential (E) range -100 mV < E <+300 mV, saturated calomel electrode 
oxidation, dissolution, and accumulation of corrosion products on the surface occur 
with the balance between these processes depending on the local pH and solution 
composition as follows: 
A. In noncomplexing neutral and alkaline solutions, a layer of UO2.33 forms at the 
surface and inhibits the fuel dissolution rate 
B. In solutions with pH = 5, an observable surface UO2.33 layer does not form and 
dissolution is rapid 
C. In neutral and alkaline solutions containing sufficient carbonatebicarbonate 
(=10-3 mol/L), a surface layer of UO2.33 does not form and dissolution is 
accelerated 
D. Calcium- and silicon-containing groundwaters enhance the formation of 
protective surface layers; low pH overrides this effect. 
2. In the potential range above +300 mV, saturated calomel electrode rapid dissolution 
occurs and the associated hydrolysis of the released uranyl ions can lead to local pH 
decrease at the corroding surface. 
Under neutral and basic conditions, the initial rate of surface oxidation is greater than the rate of 
dissolution of the oxidized surface layer resulting in the growth of the surface layer until a steady 
state is established in which the rate of growth matches the rate of dissolution. Under acidic and 
complexing conditions, the rate of dissolution is greater than the rate of oxidation resulting in the 
dissolution of the oxidized surface layer as it is formed. 
When exposed to aqueous solutions, the potential of corroding UO2 fuel will float to the potential 
(referred to as the corrosion potential Ecorr), at which the anodic dissolution current is equal to the 
cathodic current at the corroding fuelwater interface. Because the corrosion rate is related to 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-42 August 2004 
the anodic dissolution current using Faradays Law, the corrosion rate can be determined from 
the corrosion current (Icorr) as follows: 
F 
w 
n 
A I 
t 
W corr = (Eq. 15) 
where 
W = Mass loss (g) 
Aw = Molecular weight of UO2 
n = Number of electrons involved 
F = Faraday constant 
t = Duration of the corrosion period 
The anodic corrosion current density IA (i.e., the corrosion current per unit area; Icorr = A IA 
where A is the corroding surface area) is given by the following equation (Shoesmith and 
Sunder 1991 [DIRS 113366], p. 9): 
IA = kUO2 N [X]m[Y]P exp(bE) (Eq. 16) 
where 
kUO2 = heterogeneous rate constant for oxidative dissolution 
N = parameter that accounts for the UO2 fuel reactivity 
[X] and [Y] = concentrations of species X and Y (e.g., H+ and HCO3
-) 
m and p = parameters that express the reaction orders for species X and Y 
E = the potential 
b = parameter that expresses the dependence of the rate on potential (referred 
to as the Tafel line slope, which is the slope of log IA versus E plot) 
Similar expressions can be developed for the cathodic current densities associated with reduction 
of various oxidants (e.g., dissolved O2 and radiolysis products including H2O2 and radicals) 
(Shoesmith and Sunder 1991 [DIRS 113366], Section 3.3). Since the anodic and cathodic 
current densities are equal at E = Ecorr, the expressions for the anodic and cathodic current 
densities could, in principle, be combined to give an expression for the corrosion potential and 
the corresponding corrosion current (dissolution rate) as a function of the parameters that control 
dissolution. However, because the expressions for the anodic and cathodic current densities are 
incompletely developed, a semi-empirical modeling approach has been developed based on the 
log IA versus E relationships (Tafel plots) for anodic dissolution in repository-relevant solutions 
(Shoesmith 2000 [DIRS 162405]; Shoesmith and Sunder 1991 [DIRS 113366]; Shoesmith and 
Sunder 1992 [DIRS 113368]). The approach is based on the following three steps: 
1. Measure the anodic corrosion current as a function of applied potential to determine 
the Tafel lines (i.e., the log of the current density versus applied potential) for 
repository-relevant water chemistry conditions 
2. Measure the corrosion potential (Ecorr) for repository relevant water chemistry 
conditions 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-43 August 2004 
3. Extrapolate the Tafel lines (determined in Step 1) to the corrosion potentials 
(determined in Step 2) to obtain the current density (and, hence, the corrosion rate) 
corresponding to the corrosion potentials measured in Step 2. 
The following information is necessary to implement this approach: 
1. Equations for the anodic dissolution Tafel lines under repository-relevant water 
chemistry conditions 
2. Corrosion potential (Ecorr) for repository relevant water chemistry conditions. 
Extensive studies have been conducted in Canada (summarized in Shoesmith 2000 
[DIRS 162405]) and in Europe (Grambow et al. 2000 [DIRS 162391]; Christensen and 
Sunder 2000 [DIRS 162387]) to determine the relationships between anodic corrosion current 
and applied potential for UO2 and SNF under a broad range of water chemistry conditions. As 
discussed by Shoesmith (2000 [DIRS 162405]), the log currentpotential relationships are 
sensitive to the carbonatebicarbonate concentration. Carbonate concentration affects the shape 
of log currentpotential relationships (and the Tafel line slopes) indicating that the mechanistic 
effects of carbonate are complex (Shoesmith 2000 [DIRS 162405], Figure 12). Even low 
carbonate concentrations (5 mmol l-1) increase the corrosion current density for a given applied 
potential by more than two orders of magnitude when compared to noncomplexing solutions. 
Other groundwater species are known to increase or decrease the corrosion rate at a given 
potential depending on how they influence the dissolution of the UO2+x surface layers 
(Shoesmith 2000 [DIRS 162405]). The corrosion rate increases dramatically with increasing 
hydrogen ion concentration [H+] (decreasing pH) because the proton-mediated transfer of U(VI) 
species to solution is rapid. It is sufficiently rapid to prevent incorporation of O2- into the UO2 
and the consequent formation of a detectable UO2+x surface layer (Shoesmith 2000 
[DIRS 162405], Section 3.1.2). On the other hand, calcium and silicon ions can decrease the 
corrosion rate by forming surface layers resistant to dissolution. When carbonate is present, it 
counterbalances the effects of Ca2+ and Si4+ (Shoesmith 2000 [DIRS 162405]). Despite the 
effects discussed above, it has been shown that a single log corrosion ratepotential relationship 
can be used for a variety of water chemistries (Grambow et al. 2000 [DIRS 162391], 
Figure I.6-32). The relationship between the corrosion rate and the corrosion potential for 
noncomplexing solutions is given by the following expression (Christensen and Sunder 2000 
[DIRS 162387] Equation 6): 
) 16 4 . 4 ( 10 78 corr E CR + -  = (Eq. 17) 
where 
CR = corrosion rate (g cm-2 day-1) 
Ecorr = steady-state corrosion potential (volts saturated calomel electrode) 
The effects of carbonatebicarbonate concentrations on this relationship can be estimated 
approximately from the available data (Shoesmith 2000 [DIRS 162405], Figure 12). The steady 
state corrosion potential in air-saturated aqueous solutions is not sensitive to nonoxidizing 
aqueous species such as [HCO3
-] (Shoesmith et al. 1989 [DIRS 162402]). However, it is 
influenced by the concentrations of oxidizing species (e.g., [O2], [H2O2], and [radiolytic 
radicals]) that contribute to the cathodic reaction current (Shoesmith 2000 [DIRS 162405]; 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-44 August 2004 
Christensen and Sunder 2000 [DIRS 162387]). The corrosion potentials appropriate for the 
repository conditions can be estimated approximately from the available literature data. The 
most direct approach is to use measured corrosion potential (Ecorr) data for air-saturated water 
exposed to radiation field conditions (alpha, beta, and gamma) relevant to fuel corrosion in the 
repository (Shoesmith 2000 [DIRS 162405]; Christensen and Sunder 2000 [DIRS 162387]). An 
electrogeochemical modeling approach for estimating Ecorr has been described by Grambow et al. 
(2000 [DIRS 162391], pp. 193 and 194). This approach involves calculating the location of the 
corrosion potential between the equilibrium potential for UO2 dissolution and Eh of air-saturated 
water. Because the anodic and cathodic corrosion current-versus-potential relationships within 
this range are not known for Yucca Mountain conditions, the corrosion potential cannot be 
calculated precisely. However, published dissolution rate expressions based on the 
electrochemical modeling approach outlined above can be used. For example, the bicarbonate 
dependence of the oxidative dissolution rate at 25C in air-saturated water can be expressed as 
(Shoesmith et al. 1998 [DIRS 162404], Figure 2; Shoesmith 2000 [DIRS 162405], Figure 30): 
DR = 128  [HCO3
-]0.38 (Eq. 18) 
where 
DR = dissolution rate (mg/m2d) 
[HCO3
-] = Bicarbonate concentration (mol/L) 
This expression is used to estimate the CSNF oxidative dissolution rate based on the 
electrochemical model in Section 7.2. 
6.4.2.2 Surface Complexation Model 
A surface complexation model has been developed to predict the dissolution rates of unirradiated 
UO2 and SNF based on a three-step bicarbonate-promoted oxidative dissolution mechanism (de 
Pablo et al. 1999 [DIRS 162388]). The three steps are: 1) initial oxidation of the UO2 surface; 
2) binding of bicarbonate ions at the U(VI) sites of the oxidized layer; and 3) detachment of the 
U(VI) carbonate surface complex. The reactions and associated rate constants for these steps 
are found in Equations 19 through 20. Note that the notation used in the paper by de Pablo et al. 
(1999 [DIRS 162388]) is used here to show the surface reactions: 
Step 1. Oxidation of the solid surface: 
3 
k 
k 2 2 UO O 2 
1 UO 1
1 
> . + > 
- 
(Eq. 19) 
Step 2. Complexation of U(VI) by the HCO3
-: 
> UO3 + -
3 HCO .. . 2 k > UO3 -
3 HCO (Eq. 20) 
Step 3. Dissolution (desorption) of the carbonate species created (considered to be rapid): 
> UO3 -
3 HCO aq 
k U(VI) 3 .. . (Eq. 21) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-45 August 2004 
This three-step mechanism has led to the following equation for the rate of dissolution 
(de Pablo et al. 1999 [DIRS 162388]): 
] [HCO k ] [O k k 
] ][HCO [O } UO { k k r 
3 2 2 1 1 
3 2 tot 2 2 1 
- 
- 
- 
+ +
> = (Eq. 22) 
where 
r = Dissolution rate (mol m-2 s-1), 
k1, k-1, and k2 = Rate constants for the first two reaction steps 
{>UO2} = Density of surface sites (10-6 mol m-2) 
[O2] = Dissolved oxygen concentration (mol/L) 
[HCO3
-] = Bicarbonate concentration (mol/L) 
The above model has four parameters (k1, k-1, k2, and {>UO2}) and two independent variables. 
The value of the {>UO2} parameter (10-6 mol m-2) is based on the average site density in various 
oxides. Values for the model parameters k1, k-1, and k2 are determined by regression analysis of 
a set of UO2 dissolution rate data spanning a matrix of test conditions. The values are 
temperature dependent and the values determined at 10C, 25C, 45C, and 60C are tabulated in 
Table 4 of The Oxidative Dissolution Mechanism of Uranium Dioxide. I. The Effect of 
Temperature in Hydrogen Carbonate Medium (de Pablo et al. 1999 [DIRS 162388]). 
Because this model is semi-empirical, its application is limited to the range of the experimental 
data from which the model parameters are derived (i.e., fO2 = 0.2 atmospheres; pH > 7; 
10 < T(C) < 60; 0.1 mmol/L< [HCO3
-] < 50 mmol/L). Also, because the database from which 
the model parameters are derived is limited to tests conducted with unirradiated UO2, it is not 
appropriate to incorporate this model into the TSPA-LA model. It will be used for validation of 
the base-case model in accordance with Section 5.4.1 of AP-SIII.10Q, Models. 
6.4.2.3 Alternative Conceptual Model Screening 
The electrochemical and the surface complexation models are screened out for the TSPA-LA 
model. This is because the data that would support their application can only be estimated 
approximately for repository conditions. However, these models provide mechanistic insight 
and, in turn, provide confidence in long-term predictions. By comparing them to the base-case 
model, they provide a basis for discussing the validity of the base-case models use for long-term 
extrapolations (Section 7 addresses model validation in accordance with AP-SIII.10Q, 
Section 5.4.1 c) 2) corroboration of results with alternative mathematical models). The 
alternative conceptual models discussed above are more mechanistically based than the empirical 
approach used in the base-case model for CSNF corrosion in the repository. However, because 
the base-case approach spans the range of conditions expected, it provides the best available 
approach to estimating the rate of CSNF corrosion. Comparison of the base-case model with the 
mechanistically based alternative models provides confidence that the empirically based 
modeling approach is indeed appropriate for use in long-term predictions. Such comparisons of 
these models with some of the data used in development of the Yucca Mountain model have 
already been published (Shoesmith 2000 [DIRS 162405]; Shoesmith et al. 1998 [DIRS 162404]; 
de Pablo et al. 1999 [DIRS 162388]). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 6-46 August 2004 
The alternative conceptual models considered above are summarized in Table 6-13. 
Table 6-13. Alternative Conceptual Models Considered 
Alternative 
Conceptual Models Key Assumptions Screening Assessment and Basis 
Electrochemical The anodic Tafel lines can be 
extrapolated to the corrosion potential. 
The long-term corrosion behavior of SNF 
is similar to that of unirradiated UO2. 
Differences between the corrosion 
behavior of SNF and unirradiated UO2 are 
due to water radiolysis. 
Do not incorporate into the TSPA-LA 
model; data necessary to apply the model 
can only be estimated approximately. 
Use for base-case model validation, 
particularly for validation of long-term 
extrapolation. 
Surface Complexation 
Model 
The overall rate of CSNF corrosion is 
controlled by the rate of surface 
complexation reactions. 
The long-term corrosion behavior of spent 
is similar to that of unirradiated UO2. 
Do not incorporate into the TSPA-LA 
model; data necessary to apply the model 
can only be estimated approximately from 
open literature. 
Use for base-case model validation, 
particularly for validation of long-term 
extrapolation. 
6.4.3 Description of Barrier Capability 
The barrier function of the CSNF waste form is to limit mobilization of radionuclides. The 
capability of the CSNF to perform this barrier function is indicated by the degradation rates and 
the range of time to complete degradation. 
The model developed here shows that the degradation rate of CSNF with breached cladding will 
depend on the temperature, oxygen fugacity, dissolved carbonate, and pH conditions to which it 
is exposed in the repository. The expected time trajectories of each of these independent 
variables will be considered in the TSPA-LA model estimates of the CSNF degradation rates and 
lifetimes in the repository. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-1 August 2004 
7. VALIDATION 
The purpose of the CSNF waste form degradation modeling effort is to develop models that can 
be used to calculate the rate of release of radionuclides from CSNF after the cladding is breached 
in the repository. The developed models include an instantaneous release fraction model (for 
modeling release of the combined gap and grain-boundary inventories) and models for the matrix 
inventory release under alkaline and acidic conditions. Validation of these models involves 
presenting technical evidence that these models predict the CSNF degradation rate and the 
associated radionuclide release rates at an adequate level of confidence. 
Sensitivity analyses (BSC 2003 [DIRS 168796]) indicate that estimated dose at the regulatory 
compliance point is not sensitive to the CSNF model. Consequently, as stated in Table 2-1 of 
Technical Work Plan for: Regulatory Integration Modeling and Analysis of the Waste Form and 
Waste Package (BSC 2004 [DIRS 169944]), Level 1 model validation is specified for the CSNF 
models. 
Confidence-Building During Model Development to Establish Scientific Basis and 
Accuracy for Intended UseSection 2.2.1 of Technical Work Plan for: Regulatory Integration 
Modeling and Analyses of the Waste Form and Waste Package (BSC 2004 [DIRS 169944]) 
specifies the following steps for Confidence Building During Model Development: The model 
will contain documentation of decisions and activities implemented during the model 
development process to build confidence and verify a reasonable and credible technical approach 
using scientific and engineering principles. The development of the model should be documented 
in accordance with the requirements of Section 5.3.2(b) of AP-SIII.10Q. The development of the 
CSNF waste form degradation model was conducted according to the following criteria: 
(1) Selection of input parameters and/or input data, and a discussion of how the 
selection process builds confidence in the model. (AP-SIII.10Q 5.3.2(b)(1); 
AP-2.27Q, Attachment 3, Level I (a)) 
The bases for selecting the input data used to determine and develop the CSNF waste form 
model are documented Section 4.1. Additional bases and discussions regarding the selection and 
appropriateness of the data in Tables 4-5 and 4-6 are documented in Appendix V. Model 
assumptions have been described in Section 5. Detailed discussion about model concepts can be 
found in Section 6.2.1 and 6.2.2. Thus, this requirement can be considered satisfied. 
(2) Description of calibration activities, and/or initial boundary condition runs, 
and/or run convergences, simulation conditions set up to span the range of 
intended use and avoid inconsistent outputs, and a discussion of how the activity 
or activities build confidence in the model. Inclusion of a discussion of impacts of 
any non-convergence runs. ((AP-SIII.10Q 5.3.2(b)(2); AP-2.27Q, Attachment 3, 
Level I (e)). 
Detailed discussion of the CSNF initial state (microstructure, physicochemical condition, and 
distribution of fission products and actinide elements) is discussed in 6.2.1.1. Model 
formulations for the CSNF degradation and radionuclide release rates under alkaline and acidic 
conditions are discussed in Sections 6.3.1, 6.3.2, 6.4.1.2, and 6.4.1.3. The CSNF degradation and 
radionuclide release modes span the range of intended use conditions for each of the factors (i.e., 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-2 August 2004 
temperature, pH, dissolved oxygen concentration, and carbonate concentration) that influence the 
rates of the important CSNF degradation and radionuclide release processes. Thus, this 
requirement can also be considered satisfied. 
(3) Discussion of the impacts of uncertainties to the model results including how 
the model results represent the range of possible outcomes consistent with 
important uncertainties. ((AP-SIII.10Q 5.3.2(b)(3); AP-2.27Q, Attachment 3, 
Level 1 (d) and (f)). 
Uncertainties associated with the data used to determine the models parameter values are 
discussed in Sections 6.3.1 and 6.4.1.1. Regression analyses are documented in Sections 6.4.1.2 
and 6.4.1.3, which discuss alkaline and acidic conditions to address the range of possible 
outcomes consistent with important uncertainties. Additional discussion of uncertainties related 
to scaling the model is in Section 6.4.1.4. A summary discussion on uncertainties and their 
impact is given in Section 8.2. 
(4) Formulation of defensible assumptions and simplifications. (AP-2.27Q, 
Attachment 3, Level I (b)). 
Discussion of assumptions and simplifications and their rationale are provided in Section 5 and 
Section 6.2. 
(5) Consistency with physical principles, such as conservation of mass, energy, 
and momentum. (AP-2.27Q, Attachment 3, Level I (c)). 
Sections 6.2.2.2 and 6.2.2.3 provide a summary of the pertinent physical phenomena and 
chemical reactions associated with the oxidation and oxidative dissolution process that occur 
when CSNF is exposed to air-saturated groundwater and humid air. The content of these 
sections specifically addresses pertinent observations on how CSNF corrodes as a result of 
oxidative dissolution under humid air and infiltrating groundwater conditions relevant to the 
repository and the factors that influence the process rate. 
Confidence-Building After Model Development to Support the Scientific Basis of the 
ModelThe specific validation activities to be applied to the CSNF model after model 
development are identified in the technical work plan (BSC 2004 [DIRS 169944], Table 2-1). 
(1) Corroboration of model results with data acquired from the laboratory, field 
experiments, analog studies, or other relevant observations, not previously used 
to develop or calibrate the model. (AP-SIII.10Q, Section 5.3.2). 
(3) Corroboration with information published in refereed journals or literature 
(AP-SIII.10Q, Section 5.3.2). 
Confidence in the accuracy of the models is addressed by evaluating the consistency of the 
models with data from laboratory tests not used to develop the models and data published in 
refereed journals or industrial literature (Section 7.1). Section 7.1.2 establishes confidence in the 
developed instantaneous radionuclide release model by comparing the model to available project 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-3 August 2004 
data not used in model development. Section 7.1.3 establishes confidence in the developed 
models for radionuclide release from the CSNF matrix under alkaline and acidic conditions by: 
 Comparing model-calculated radionuclide fractional release rates with experimental 
rates published in peer-reviewed or industrial literature for alkaline and acidic conditions 
 Comparing model-calculated specific dissolution rate of the UO2 fuel matrix to 
experimental rates in peer-reviewed or industrial literature for alkaline and acidic 
conditions. 
In addition, comparison of experimental data with natural analogue studies is addressed in 
Section 7.3 to provide confidence that the results observed in laboratory tests are likely to occur 
in an actual repository setting. 
2) Corroboration of results with alternative mathematical models (AP-SIII.10Q, 
Section 5.3.2). 
Section 7.2 describes the comparison of the developed model for the specific dissolution rate of 
the CSNF matrix with alternative mathematical models. In this section, confidence in the model 
is established by comparing the specific dissolution rates calculated by the developed model 
applicable to alkaline conditions to the rates calculated by the alternative mathematical models 
for a set of nominal exposure conditions. This section also establishes confidence in the model 
by comparing the model parameters to the reaction order, (i.e., exponents of a power law 
dependence) reported in the literature. 
The criteria specified in Table 2-1 of Technical Work Plan for: Regulatory Integration Modeling 
and Analysis of the Waste Form and Waste Package (BSC 2004 [DIRS 169944]) for evaluating 
consistency between the CSNF models and the experimental data or modeling results with which 
they are to be compared in each of these validation activities are corroborating data must match 
qualitatively and must be bounded by model predictions. As discussed in Section 2.2.5 of 
Technical Work Plan for: Regulatory Integration Modeling and Analysis of the Waste Form and 
Waste Package (BSC 2004 [DIRS 169944]), corroboration for Level 1 validation is indicated by 
agreement within one to two orders of magnitude. 
Sections 7.1.2 and 7.2 use an error metric (EM) to compare the CSNF matrix models to the 
validation data and to alternative mathematical models. This is the ratio of the base 10 log of the 
model-calculated rate to the base 10 log of the rates in the data sets used for validation. The 
range of values for EM expected to result from such comparisons can be assessed by considering 
estimates of the standard errors in the modeling and the data sets to which the models are 
compared. As shown in the footnote to Table 6-4, the standard error of estimate (i.e., an 
estimate of the modeling error) from the regression analysis of the alkaline-side data is 0.347. 
The corresponding 2s error is 0.694 or 0.7 when rounded to one significant figure. Only 
approximate estimates are available for the errors in the validation data. As discussed in 
Section 6.4.1.1, the range of variability in the nominally comparable worldwide data is 10 
(i.e., an approximate order of magnitude). The corresponding range for the base-ten log of the 
dissolution rate data is 0 1. Propagating these errors (using the square root of the sum of the 
squares error propagation rule) to estimate the error in EM, results in 1.2 as the expected range 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-4 August 2004 
of values for EM. Hence, agreement between the model and the validation data is indicated by a 
value of the EM in the range 01.2. Positive values for the EM within this range indicate a 
conservative modeling bias. 
The data and alternative model sources used for validation are listed in Table 7-1. 
Table 7-1. Data and Alternative Model Sources Used for Validation 
Data/Alternative Model Data Source Validation Use 
Instantaneous fractional release rate data 
from rod segment tests (DTN: 
MO0301ANLSF001.450 [DIRS 162384]) 
Goldberg 2003 
[DIRS 162410] 
Validate instantaneous fractional release model 
for gap and grain-boundary inventories. 
CSNF radionuclide fractional release rate 
data in Table 7-2, Table 7-3, and Table 
7-5 (alkaline conditions) and Table 7-6 
Wilson 1990 
[DIRS 100793], Test 
Series 3 
CSNF radionuclide fractional release rate 
data in Table 7-7  alkaline conditions 
Forsyth 1997 
[DIRS 123134] 
Validate base-case model for radionuclide 
fractional release rate  alkaline conditions 
CSNF radionuclide fractional release rate 
data in Table 7-8  acidic conditions 
Forsyth 1997 
[DIRS 123134] 
Validate base-case model for radionuclide 
fractional release rate  acidic conditions 
Exponent for CSNF corrosion rate 
dependence on pH under acidic 
conditions 
Rllin et al. 2001 
[DIRS 162398] 
Comparison of the acid-side models pH 
dependence to literature data 
UO2 dissolution rate per unit area data in 
Table 7-9  alkaline conditions 
Tait and Luht 1997 
[DIRS 114435] 
UO2 dissolution rate per unit area data in 
Table 7-10  alkaline conditions 
de Pablo et al. 1999 
[DIRS 162388] 
Validate base-case model for the specific rate of 
oxidative dissolution of the fuel matrix  alkaline 
conditions 
UO2 dissolution rate per unit area data in 
Table 7-11  acidic conditions 
Torrero et al.1997 
[DIRS 114439] 
Validate base-case model for the specific rate of 
oxidative dissolution of the fuel matrix  acidic 
conditions 
Alternative electrochemical model  
Eq. 20 
Shoesmith 2000 
[DIRS 162405] 
Alternative surface complexation model  
Eq. 24 
de Pablo et al. 1999 
[DIRS 162388] 
Validate base-case model for the specific rate of 
oxidative dissolution of the fuel matrix  alkaline 
conditions 
7.1 COMPARISON OF BASE-CASE MODEL TO EXPERIMENTAL DATA 
7.1.1 Comparison of the Instantaneous Release Model to CSNF Instantaneous Release 
Data 
Instantaneous fractional release rates of 137Cs, 129I, 90Sr, and 99Tc from CSNF rod segment tests 
are shown in Table 7-2. The CSNF rod segment tests are described in Section 4.1.3. The 
instantaneous release fraction data from fuel rod segments are compared to the instantaneous 
fractional release model for the gap and grain-boundary inventories in Table 7-3. Note that 
although the fractional release rate data from these tests were used to estimate the lower end of 
the range for the effective specific surface area of corroding CSNF (Section 6.4.1.5), the 
instantaneous fractional release data were not used for model development and are, therefore, 
appropriate for use in validation. The first three rows in the table show the instantaneous release 
fraction data (Goldberg 2003 [DIRS 162410], Tables 6, 7, 8, and 9). For comparison, the 
models average release fractions and the ranges used for the uncertainty distributions are shown 
in rows four and five of Table 7-3. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-5 August 2004 
The data summarized in Table 7-3 show that the instantaneous release fractions measured in the 
fuel rod segment tests are within the instantaneous release models uncertainty range for cesium 
and technetium and lower than the models range for iodine and strontium. Table 7-2 is the 
source for the data in the first three rows of Table 7-3. A qualitative comparison of the models 
uncertainty distribution range in Table 7-3 to worldwide data on instantaneous release fractions 
(Jegou et al. 2001 [DIRS 162397], Section 8.2.2.4 and Table 27) also shows that the model gives 
generally conservative results. These worldwide data for light water reactor fuel are summarized 
in Table 7-4. In this table, the tcumulative(d) column shows the test time in days. Examination of 
these results shows that they are generally consistent with the applicable validation criterion that 
the model must match the validation data qualitatively. A conspicuous exception is that some of 
the 90Sr and 99Tc data from the longer-term results shown in Table 7-4 are significantly higher 
than the upper end of the models distribution range. This is probably because these 
experimental results are influenced by corrosion of the fuel matrix. In general, this comparison 
indicates that the model provides a reasonably conservative representation of the instantaneous 
release fractions when compared to the available instantaneous release fraction data. 
Table 7-2. Instantaneous Fractional Release - Rod Segment Tests 
Fuel 137Cs 129I 90Sr 99Tc 
ATM-103, 1.5 4.81E-3 6.002E-3 4.01E-5 7.40E-5 
ATM-103, 2.6 4.68E-3 1.34E-3 9.16E-5 2.21E-5 
ATM-103, 3.7 5.22E-3 7.02E-3 3.89E-5 2.83E-5 
Average ATM-103 4.84E-3 4.79E-3 5.69E-5 4.15E-5 
ATM-106, 1.4 7.18E-2 1.18E-2 1.51E-5 1.68E-4 
ATM-109, 3.2 4.36E-2 1.52E-2 ND 4.95E-6 
Source: DTN: MO0301ANLSF001.450 [DIRS 162384] 
NOTE: ND = Concentration not determined in test solution; ATM = Approved Testing Material 
Table 7-3. Comparison of Instantaneous Release Fraction Data from Rod Segment Tests to Model 
Distribution Ranges for the Gap and Grain-Boundary Inventories 
Release Fraction (%) 
Fuel Type 137Cs 129I 90Sr 99Tc 
ATM-103a 0.48 0.48 0.006 0.004 
ATM-106a 7.18 1.18 0.002 0.017 
ATM-109a 4.36 1.52 ND 0.0005 
Models Average Release Fraction 
(from Table 6-3) 
3.63 11.24 0.09 0.06 
Models Uncertainty Distribution 
Range (from Table 6-3) 
0.39 to 11.06 2.04 to 26.75 0.02 to 0.25 0.00 to 0.26 
NOTE: a Table 7-2 is the source of the data in these rows. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-6 August 2004 
Table 7-4. Literature Data - Combined Gap and Grain-Boundary Inventories 
Percent of Inventory in Fuel Burnup, 
GWd/t FG (%) tcumulative (d) 137Cs (%) 129I (%) 90Sr (%) 99Tc (%) 
3 0.13 <5.41 9.74  10-3 <4.08 x 10-2 
22 0.14 
62 0.27  3.23  10-2 <0.17 
3 0.48 <3.09 1.70  10-2 2.87  10-2 
37 0.23 
62 0.62  2.97  10-2 <0.1 
3 1.51 <2.41 6.96  10-2 <1.93  10-2 
47 0.41 
62 2.22  0.14 <7.76  10-2 
3 0.33 <0.09 8.72  10-3 <4.03  10-2 
62.8 2.59 
62 0.93  3.48  10-2 <0.22 
2 0.37 <0.26 4.98  10-3  
62.8 2.59 
23 1.69 <2.54 1.07  10-2  
2 0.93  0.8 0.12 
47 0.41 
97 3.5  2.8 0.54 
1 0.53  2.70  10-2 <5.3 x 10-2 
62.8 2.59 
140 0.83  0.13 0.24 
30  315 0.3 0.03 6  10-3  
50  315 0.3 to 1 0.1 6 x 10-3  
31 1.40 to 1.71  0.0843 to 0.311 <6.68  10-3 to 6.89  10-3 
76 1.97 to 2.18  0.11 to 0.335 <6.89  10-3 to <1.34  10-2 50.4  
513 to 750 2.85 to 3.06  0.212 to 0.382 <8.04  10-3 to 4.06  10-2 
30 to 32 0.95 to 1.06  0.0211 to 0.0362 <6.51  10-3 to <7.02  10-3 
71 to 74 1.15 to 1.63  0.0241 to 0.0436 <6.59  10-3 to <7.02  10-3 50.4  
511 to 752 2.59 to 3.89  0.0456 to 0.0599 <6.51  10-3 to <4.70  10-2 
63 3.48  0.25 1.27  10-4 
50.4  
173 3.66  0.27 <3.65  10-4 
33 1.22  0.28 6.12 
76 1.34  0.3 >6.12 50.4  
516 2.55  0.66 >6.12 
0.014 2.71  6.43  10-2 <2.59  10-3 
40 2.85  9.16  10-2  50.4  
449 to 657 4.73 to 6.71  0.89 to 1.23 0.12 to 0.22 
Source: Jegou et al. 2001 [DIRS 162397], Table 27; the CANDU and MOX data in this source are not used 
here because they are not directly applicable. 
7.1.2 Validation of CSNF Matrix Radionuclide Release Models for Alkaline and Acidic 
Conditions 
Two sets of published literature data on the fractional release rates of radionuclides measured 
under air-saturated dissolution conditions are used in this assessment. First, the developed model 
for alkaline conditions is compared to the Nevada Nuclear Waste Storage Investigations 
(NNWSI) Series 3 batch test data (Wilson 1990 [DIRS 100793]). Then the developed models 
for alkaline and acidic conditions are compared to the extensive CSNF test results published by 
the Swedish SNF corrosion program (Forsyth 1997 [DIRS 123134]; Rllin et al. 2001 
[DIRS 162398]). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-7 August 2004 
To facilitate comparison of the fractional release rate models with experimental data, an error 
metric is used. The error metric is equal to the log of the ratio of the calculated to measured 
values for the fractional release and matrix dissolution rates. Stated mathematically, the error 
metric is equal to log10(Fc / Fm), where Fc is the calculated fractional release rate and Fm is the 
measured fractional release rate. The absence of systematic modeling bias is indicated by values 
of the error metric centered at zero. An approximate order of magnitude agreement between the 
model and the data is indicated when most of the value of the error metric are expected to be in 
the range 0  1. The Microsoft Excel spreadsheets showing the validation calculations are 
attached (Appendix I, CSNF MR REV2.XLS, sheets A9 to A12). 
7.1.2.1 Comparison with Batch (Semi-Static) Test Data 
Batch or semi-static dissolution tests were performed over a decade ago on SNF samples 
(Wilson 1990 [DIRS 100793]). The tests involved placing a few grams of SNF in various 
configurations in less than a liter of synthetic J-13 groundwater and periodically sampling and 
analyzing the solution for various radionuclides. The results include data on the fraction of 
radionuclide released per unit time. 
Data from the Series 3 semi-static leaching tests by Wilson (1990 [DIRS 100793]) provide a 
basis for comparing modeling fractional radionuclide release rates to experimental data from 
batch immersion tests. The Series 1 tests described (Stout and Leider 1998 [DIRS 111047], 
p. 2-213) were the first of three series of batch or semi-static tests conducted to characterize 
potential radionuclide release from, and behavior of, spent fuel stored under YMP conditions. In 
the Series 1 tests, specimens prepared from Turkey Point Reactor Unit 3 fuel were tested in 
deionized distilled water in unsealed fused silica vessels under ambient hot cell air and 
temperature conditions. Four specimen configurations were tested: (1) intact fuel rod segments 
with water-tight end fittings; (2) fuel rod segments containing small (approximately 200-m 
diameter) laser-drilled holes through the cladding and with water-tight end fittings; (3) fuel rod 
segments with a machined slit through the cladding and water-tight end fittings; and (4) bare-fuel 
particles removed from the cladding plus the cladding hulls. A semi-static test procedure was 
developed in which periodic solution samples were taken with the sample volume replenished 
with fresh deionized water. A test cycle was used to refer to a testing period, where samples 
are taken at its conclusion, the test vessels are stripped and cleaned or replaced, and the next 
testing period is initiated. Series 1 and 2 tests were similar except (1) Series 2 tests were run in 
reference J-13 well water, (2) each of the four specimen configurations was duplicated using the 
Turkey Point Reactor and H. B. Robinson Reactor pressurized water reactor CSNF types, and (3) 
a vessel and specimen rinse procedure was added to the cycle termination procedures. 
The Series 3 tests (Wilson 1990 [DIRS 100793]) were run for three cycles. For this test series, 
the tests were run in sealed vessels made of Stainless Steel Types 304 and 304L; the tests used 
the same four-specimen configurations used in Series 1 and Series 2. Five specimens (one for 
each of the four configurations using H. B. Robinson reactor fuel, plus an additional bare fuel 
specimen using Turkey Point reactor fuel) were tested at 85C, and a sixth specimen 
(H. B. Robinson bare fuel) was run at 25C. 
The Series 3 tests were chosen for this analysis because they were performed in stainless steel 
containers and with J-13 well water. The previous Series 1 and 2 tests were conducted in silica 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-8 August 2004 
vessels from which leached silica components could affect the results. The bare fuel tests were 
chosen because they offered full exposure of the fuel to water. The final test period, Cycle 3, 
was selected because the originally unwashed fuel samples had been exposed to two separate 
batches of J-13 well water for almost a year. Easily removed radionuclides that had segregated 
to the surface or left in unremoved fines would have likely dissolved by the time Cycle 3 had 
started. Cycle 3, therefore, would most likely represent radionuclide release from the fuel 
matrix. The unfiltered sample-concentration data for the very soluble radionuclides, 90Sr, 99Tc, 
129I, and 137Cs, in the bare fuel tests were used and taken respectively from Tables A.2 through 
A.4 provided by Wilson (1990 [DIRS 100793], Series 3) for the HBR/BF-25, TP/BF-85, and 
HBR/BF-85 samples. 
Sample-specific data are at the top in the analysis Tables 7-5, 7-6, and 7-7. These data include 
experimental vessel volume, fuel specific area and isotope inventories, and sample weight, as 
well as tables referenced by Wilson (1990 [DIRS 100793]). Isotope specific data in the analysis 
tables are organized by isotope at the bottom. For each isotope, the sample time and volume, and 
sample isotope concentration are listed. The fraction released, fraction released per day, and 
equivalent specific dissolution rates are calculated values using Equations 23 through 25. 
Fr = (Ci VV)/(Ii ms 1,000,000) (Eq. 23) 
Frd = Fr/ts (Eq. 24) 
Rd = [Frd 1,000]/AS (Eq. 25) 
where 
(AS) = Sample specific surface area (m2/g) 
(ts) = Sampling time (days) 
(Ci) = Sample isotope concentration (pCi/mL) 
(Fr) = Fraction released (#) 
(Frd) = Fraction released per day (d-1) 
(Ii) = Radionuclide inventory (Ci/g) 
(ms) = Sample mass (g) 
(Rd) = Equivalent UO2 dissolution rate (mgU/(m2  d)) 
(FWi) = Chemical formula weight (g) 
(VV) = Vessel volume (mL) 
The following is a detail of a calculation using the above equations. The calculation is taken 
from Table 7-5 for test HBR/BF-25 with 137Cs at a sampling time of 97 days. 
Fr = (1.92  106  250)  (6.37  104  83.66  1,000,000) = 9.01  10-5 (Eq. 26) 
The factor of 1,000,000 accounts for microcurie to picocurie conversion. 
Frd = 9.01  10-5  97 = 9.29  10-7 d-1 (Eq. 27) 
Rd = (9.29  10-4  1,000)  (2.4  10-4  3) = 1.29 (Eq. 28) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-9 August 2004 
The factor of 1,000 accounts for gram-to-milligram conversion. The factor of three in the 
surface area is the roughness factor by Gray and Wilson (1995 [DIRS 100758], p. 2.7). 
The NNWSI fractional release rate data from the last sampling period are summarized in 
Table 7-8. The fifth row in this table shows the calculated release rates for the test conditions 
using the base-case model for alkaline conditions. Comparisons of the model-calculated results 
to the experimental data show that the agreement is good and well within an order of magnitude. 
Specifically, the calculated error metric values in the last row of Table 7-6 are all positive 
(indicating that the model-calculated fractional release rates may be somewhat conservatively 
biased). The error metric values are all much less than 1.0, indicating better than order of 
magnitude agreement, which satisfies the applicable validation criterion. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-10 August 2004 
Table 7-5. Calculations of Equivalent Intrinsic Dissolution Rates of H. B. Robinson Fuel at 25C 
Table A.2: Table 2.1: Table 2.3: 
Vessel volume (mL) [Figure 2.1] = 250 
mL 
Cycle 3 Sample Weight (g) = 83.66 RN Inventories (Ci/g) a (HB Robinson 
ORIGEN-2) 
Specific Surface Area (Geometric) = 2.4010-4 m2/g [Table E.3, Col. 2] 137Cs 6.37E+04 
Specific Surface Area (x3 Roughness) = 7.2010-4 m2/g 99Tc 10.5 
90Sr 4.17E+04 
129I 0.0265 
137Cs 99Tc 
Dissolution Rate (Cesium) 
Dissolution Rate 
(Technetium) 
Time 
(Days) 
Sampling 
Volume 
(mL) pCi/mL 
Fraction 
Released FracRel/d 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) pCi/mL 
Fraction 
Released FracRel/d 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) 
0 
20 25 1.19E+06 5.58E-05 2.79E-06 1.16E+01 3.88E+00 6.76E+01 1.92E-05 9.62E-07 4.01E+00 1.34E+00 
55 25 1.58E+06 7.41E-05 1.35E-06 5.62E+00 1.87E+00 1.26E+02 3.59E-05 6.52E-07 2.72E+00 9.06E-01 
97 250 1.92E+06 9.01E-05 9.29E-07 3.87E+00 1.29E+00 2.34E+02 6.66E-05 6.87E-07 2.86E+00 9.54E-01 
90Sr 129I 
Dissolution Rate (Strontium) 
Dissolution Rate 
(Iodine) 
Time 
(Days) 
Sampling 
Volume 
(mL) pCi/mL 
Fraction 
Released FracRel/d 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) pCi/mL 
Fraction 
Released FracRel/d 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) 
0 
20 25 4.73E+05 3.39E-05 1.69E-06 7.06E+00 2.35E+00 
55 25 6.53E+05 4.68E-05 8.51E-07 3.55E+00 1.18E+00 6.04E-02 6.81E-06 1.24E-07 5.16E-01 1.72E-01 
97 250 7.84E+05 5.62E-05 5.79E-07 2.41E+00 8.04E-01 8.26E-02 9.31E-06 9.60E-08 4.00E-01 1.33E-01 
Source: Wilson 1990 [DIRS 100793] 
NOTE: a 1,000,000 pCi = 1 Ci 
SA = surface area 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-11 August 2004 
Table 7-6. Calculations of Equivalent Intrinsic Dissolution Rates of H. B. Robinson Fuel at 85C 
Table A.4 Table 2.1: Table 2.3 
Vessel volume (mL) [Figure 
2.1]= 
250 mL Cycle 3 Sample Weight 
(g) 
RN Inventories (Ci/g)a (H. B. Robinson 
ORIGEN-2) 
Specific Surface Area 
(Geometric) = 
2.40E-04 m2/g [Table E.3, Col. 2] HBR/BF-85 78.67 137Cs 6.37E+04 
Specific Surface Area (x3 
Roughness) = 
7.20E-04 m2/g 99Tc 10.5 
90Sr 4.17E+04 
129I 0.0265 
137Cs 99Tc 
Dissolution Rate Dissolution Rate 
Time 
(Days) 
Sampling 
Volume 
(mL) pCi/mL 
Fraction 
Released 
FracRel/ 
day 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) pCi/mL 
Fraction 
Released FracRel/ day 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) 
0 
20 25 5.86E+05 2.92E-05 1.46E-06 6.09E+00 2.03E+00 2.34E+02 7.08E-05 3.54E-06 1.48E+01 4.92E+00 
55 25 1.25E+06 6.24E-05 1.13E-06 4.72E+00 1.57E+00 5.41E+02 1.64E-04 2.98E-06 1.24E+01 4.13E+00 
97 250 2.09E+06 1.04E-04 1.07E-06 4.48E+00 1.49E+00 9.91E+02 3.00E-04 3.09E-06 1.29E+01 4.29E+00 
90Sr 129I 
Dissolution Rate Dissolution Rate 
Time 
(Days) 
Sampling 
Volume 
(mL) pCi/mL 
Fraction 
Released 
FracRel/ 
day 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) pCi/mL 
Fraction 
Released FracRel/d 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) 
0 
20 25 5.59E+05 4.26E-05 2.13E-06 8.87E+00 2.96E+00 
55 25 5.72E+05 4.36E-05 7.93E-07 3.30E+00 1.10E+00 8.15E-01 9.77E-05 1.78E-06 7.40E+00 2.47E+00 
97 250 6.13E+05 4.67E-05 4.82E-07 2.01E+00 6.69E-01 1.24E+00 1.49E-04 1.53E-06 6.39E+00 2.13E+00 
Source: Wilson 1990 [DIRS 100793] 
NOTE: a 1,000,000 pCi = 1 Ci 
SA = surface area 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-12 August 2004 
Table 7-7. Calculations of Equivalent Intrinsic Dissolution Rates of Turkey Point Fuel at 85C 
Table A.3 Table 2.1 Table 2.3 RN Inventories (Ci/g)a 
Vessel volume (mL) [Figure 2.1]= 250 mL Cycle 3 Sample Weight (g) Turkey Point ORIGEN-2 
Turkey 
Point /BF-85 
83.64 137Cs 6.04E+04 Specific Surface Area (Geometric) = 2.21E-04 m2/g [Table E.3, Col. 2] 
99Tc 9.74 
Specific Surface Area (x3 Roughness) = 6.63E-04 m2/g 90Sr 4.03E+04 
129I 0.0242 
137Cs 99Tc 
Dissolution Rate Dissolution Rate 
Time 
(Days) 
Sampling 
Volume 
(mL) pCi/mL 
Fraction 
Released 
FracRel/ 
day 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) pCi/mL 
Fraction 
Released FracRel/ day 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) 
0 
20 25 5.36E+05 2.65E-05 1.33E-06 6.00E+00 2.00E+00 1.49E+02 4.57E-05 2.29E-06 1.03E+01 3.45E+00 
55 25 1.16E+06 5.74E-05 1.04E-06 4.72E+00 1.57E+00 3.24E+02 9.94E-05 1.81E-06 8.18E+00 2.73E+00 
97 250 1.70E+06 8.41E-05 8.67E-07 3.92E+00 1.31E+00 5.41E+02 1.66E-04 1.71E-06 7.74E+00 2.58E+00 
90Sr 129I 
Dissolution Rate Dissolution Rate 
Time 
(Days) 
Sampling 
Volume 
(mL) pCi/mL 
Fraction 
Released 
FracRel/ 
day 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) pCi/mL 
Fraction 
Released FracRel/ day 
Geo. SA 
mgU/(m2d) 
Rough SA 
mgU/(m2d) 
0 
20 25 4.41E+05 3.27E-05 1.64E-06 7.40E+00 2.47E+00 
55 25 5.63E+05 4.18E-05 7.59E-07 3.44E+00 1.15E+00 5.95E-01 7.35E-05 1.34E-06 6.05E+00 2.02E+00 
97 250 6.26E+05 4.64E-05 4.79E-07 2.17E+00 7.22E-01 9.15E-01 1.13E-04 1.17E-06 5.27E+00 1.76E+00 
Source: Wilson 1990 [DIRS 100793] 
NOTE: a 1,000,000 pCi = 1 Ci 
SA = surface area 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-13 August 2004 
Table 7-8. Summary of the NNWSI Series 3 Fractional Release Rate (d-1) Results for Last Sampling 
Period 
Isotope H. B. Robinson Fuel 
(25C) 
H. B. Robinson Fuel 
(85C) 
Turkey Point Fuel 
(85C) 
90Sr 5.79E-07 4.82E-07 4.79E-07 
137Cs 9.29E-07 1.07E-06 8.67E-07 
99Tc 6.87E-07 3.09E-06 1.71E-06 
Average 7.32E-07 1.55E-06 1.02E-06 
Base-Case Model Calculated 
Fractional Release Rates (d-1) 7.32E-07 3.02E-06 3.02E-06 
Error Metric 0.0 0.3 0.5 
Source: Appendix I, CSNF MR REV2.xls. Spreadsheet A9 
7.1.2.2 Comparison to the Swedish CSNF Data 
An extensive series of experimental results on CSNF radionuclide fractional release rates have 
been reported under oxidizing conditions (Forsyth 1997 [DIRS 123134], Tables 6-11 and 6-12). 
The results for alkaline conditions are summarized in Table 7-9. The sixth row of numbers in 
this table shows the model-calculated fractional release rates for the test conditions involved. 
When these calculated results are compared to the average of the measured fractional release 
rates (d-1) for strontium, cesium, molybdenum, and technetium, the agreement is seen to be 
excellent and well within an order of magnitude. The values for the error metric, as shown in the 
last row of Table 7-9, are all close to zero, which indicates a very good agreement between the 
model and these experimental data. The applicable validation criteria are satisfied with little 
indication of any systematic modeling bias. 
Table 7-9. Average Fractional Release Rates (d-1) Measured in the Series 11 Tests on Fuel Rod 
Segments with Different Burnups and in the Series 3 Tests on Fuel Fragments 
Rod Segmentsa Fragmentsb 
Element 
Burnup Range 
(27 to 30 
MWd/kgU) 
Burnup Range 
(35 to 46 
MWd/kgU) 
Burnup Range 
(46 to 49 
MWd/kgU) 
Burnup (42 
MWd/kgU) 
Strontium 2.07E-07 3.19E-07 3.12E-07 2.03E-06 
Cesium 5.61E-07 6.69E-07 7.90E-07 3.53E-06 
Molybdenum 1.85E-06 2.25E-06 1.28E-06 3.82E-06 
Technetium 1.61E-06 1.80E-06 1.35E-06 2.56E-06 
Average 1.06E-06 1.26E-06 9.33E-07 2.99E-06 
Base-Case Model Calculated 
Fractional Release Rates (d-1) 8.42E-07 8.42E-07 8.42E-07 8.42E-07 
Error Metric -0.1 -0.2 0.0 -0.5 
Source: Appendix I, CSNF MR REV2.xls., Spreadsheet A9 
NOTE: a Forsyth 1997 [DIRS 123134], Tables 6-11 
b Data shown here are the average fractional release rates for the 3.23 and 3.24 tests (Forsyth 1997 [DIRS 
123134], Tables 6-12). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-14 August 2004 
Table 6-7 of The SKB Spent Fuel Corrosion Program: An Evaluation of Results from the 
Experimental Programme Performed in the Studsvik Hot Cell Laboratory (Forsyth 1997 [DIRS 
123134]) also contains test data on the fractional radionuclide release rates (d-1) measured under 
acidic conditions. These data are summarized in Table 7-10 and compared to the developed 
model for acidic conditions; the model-calculated results are shown in the eighth column of this 
table. The calculated error metric results, shown in the ninth column, show the acid-side model 
may have a small conservative bias (indicated by generally positive error metric values) and 
reproduces the experimental results within an order of magnitude as indicated by values for the 
error metric in the 0 1 range. 
A recent article (Rllin et al. 2001 [DIRS 162398]) provides single-pass flow-through data on 
the oxidative dissolution rate of CSNF under acidic conditions. The results (Rllin et al. 2001 
[DIRS 162398], Figure 9) show dissolution rates comparable to those calculated by the 
developed model for acidic conditions. 
Table 7-10. Fractional Release Rates (d-1) Measured Under Acidic Conditions in the SKB 
Series 3 and 11 Tests 
Experiment 
Number pH 90Sr 137Cs 97Mo 99Tc Average Fi Calc 
Error 
Metric 
3.2.5 2.3 1.37E-05 1.56E-05 1.47E-05 1.88E-05 0.1 
3.5.8.91 3.65 8.47E-06 9.08E-06 8.78E-06 6.52E-06 -0.1 
3.3.9 4.2 8.26E-07 1.56E-06 1.19E-06 4.24E-06 0.6 
3.2.9 4.25 1.43E-06 2.68E-06 2.06E-06 4.08E-06 0.3 
3.4.8.91 4.3 1.32E-05 1.70E-05 1.51E-05 3.92E-06 -0.6 
3.3.5 4.75 3.38E-06 5.62E-06 4.50E-06 2.76E-06 -0.2 
3.3.10 5.1 1.77E-06 3.03E-06 2.40E-06 2.10E-06 -0.1 
3.2.10 6.6 6.04E-07 2.63E-06 1.62E-06 6.48E-07 -0.4 
11.14.4 3.64 3.02E-06 6.13E-06 3.63E-07 6.29E-07 2.54E-06 6.58E-06 0.4 
11.7.4 3.8 1.20E-06 1.66E-06 2.91E-07 5.33E-07 9.21E-07 5.80E-06 0.8 
3.1.9 3.85 1.71E-06 3.08E-06 2.40E-06 5.58E-06 0.4 
3.1.5 3.9 4.10E-06 6.24E-06 5.17E-06 5.36E-06 0.0 
3.1.10 5.2 3.21E-06 3.76E-06 3.49E-06 1.94E-06 -0.3 
Source: Appendix I, CSNF MR REV2.xls. Spreadsheet A9 
NOTE: Results for the exposure terms of approximately twenty days to acidic conditions are not included because 
they are more likely to be influenced by the fuel-exposure conditions prior to exposure to acidic conditions. 
The above comparisons show that the model-calculated fractional release rates are consistent, 
within an order of magnitude, with experimentally measured fractional release rates in the 
available literature for acidic and alkaline conditions. Thus, the applicable validation criterion is 
satisfied and the required level of confidence is indicated for acidic and basic conditions. 
7.1.2.3 Comparison with UO2 Data 
Grambow (1989 [DIRS 113233]) and McKenzie (1992 [DIRS 131639]) provide reviews of the 
literature prior to 1992. There are three more-recent reports of particular interest for model 
validation. Tait and Luht (1997 [DIRS 114435]) summarizing UO2 and CANDU SNF 
flow-through dissolution studies performed over an extended period of time at Atomic Energy of 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-15 August 2004 
Canada Limited, Whiteshell Laboratories is represented in Table 7-11. de Pablo et al. (1999 
[DIRS 162388]) summarized flow-through oxidative dissolution studies of UO2 in aqueous 
solutions as a function of temperature and carbonate concentration (Table 7-12). Torrero et al. 
(1997 [DIRS 114439]) measured uranium dioxide dissolution at various dissolved oxygen 
concentrations and pH at room temperature (Table 7-13). These results can be compared to the 
developed models for the dissolution rate per unit area under alkaline and acidic conditions. To 
facilitate this comparison, the error metric in this case is defined as log10(DRcalculated/DRmeasured), 
where DR represents the specific dissolution rate (i.e., the dissolution rate per unit area). 
As shown by the error metric results in column 7 of Table 7-11, the developed model for alkaline 
conditions predicts the various Tait and Luht (1997 [DIRS 114435]) results well over a range of 
temperature, oxygen, and water-chemistry conditions, providing a good confirmation of the 
model. The error metric values in Table 7-11 (all of which are less than 1.0) and the mix of 
positive and negative error metric values indicate little, if any, systematic modeling bias. This 
indicates model predictions are consistent with these experimental data within an approximate 
order of magnitude and applicable validation criteria are satisfied. 
Table 7-11. Comparison of Data from Tait and Luht (1997 [DIRS 114435]) with the Alkaline Model 
Sample Water 
T 
(C) 
From 
Tait and Luht 
(1997 [DIRS 
114435]) 
DRm 
(mgU/m2d) 
DRc 
(mgU/m2d) 
Error 
Metric 
UO2 SCb 25 Figure 4 1.37 3.5 0.4 
UO2 SCb 25 Figure 9 1.4 3.5 0.4 
UO2 (CANDU) SCb 35 Figure 13 10.2 4.6 -0.3 
UO2 (CANDU) SCb 50 Figure 13 22 6.7 -0.5 
UO2 (CANDU) SCb 50 Figure 13 25.6 6.7 -0.6 
UO2 (CANDU) SCb 75 Figure 13 102 11.7 -0.9 
Used (CANDU) Fuela SCb 25 Figure 17 13 3.5 -0.6 
Used (CANDU) Fuela SCb 25 Figure 19 4 3.5 -0.1 
Used (CANDU)Fuela SCb 35 Figure 21 20 4.6 -0.6 
Used (CANDU) Fuela SCb 75 Figure 21 45 11.7 -0.6 
Source: Appendix I, CSNF MR REV2.xls. Spreadsheet A10 
NOTE: Experimental data in column DRm are approximate; they were from figures in the source document. 
CANDU = Canada Deuterium Uranium; DRm = measured dissolution rate, in mg/(m2d); DRc = calculated 
dissolution rate, in mg/(m2d) 
a Used Fuel Burnup =10 MWd/kgU 
b SC = Aerated 0.01M NaHCO3/0.1 M NaCl (pH.9.3) 
Table 7-12 summarizes flow-through oxidative dissolution studies of UO2 in aqueous solutions 
as a function of temperature and carbonate concentration (de Pablo et al. 1999 [DIRS 162388], 
Tables 1 and 2). In these tests, leaching solutions were in contact with air concentration 
(de Pablo et al. 1999 [DIRS 162388], p 3,098) (i.e., the oxygen fugacity was 0.2 atm). The data 
obtained at 10C are not included in Table 7-12 because this temperature is outside the 
repository-relevant temperature range. These results were obtained using a continuous 
flow-through reactor with the flow rates adjusted to minimize the possibility of secondary 
reactions (including precipitation). The error metric results shown in column 7 show that there is 
generally good agreement (i.e., consistent within an approximate order of magnitude) between 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-16 August 2004 
the experimental data and the model-calculated results and that the applicable validation criteria 
are satisfied. 
Table 7-12. Experimental UO2 Dissolution Rates as a Function of Temperature and Bicarbonate 
Concentration 
Temperature 
(C) 
[HCO3
-] 
(mmol/L) 
pH 
(0.1) pO2 
Dissolution Rate 
(mg m-2 d-1) 
Calculated 
Dissolution 
Rate (mg m-2 d-1) 
Error 
Metric 
25 0.1 8.1 0.7 2.10E-01 2.5E+00 1.1 
25 0.1 8.1 0.7 2.22E-01 2.5E+00 1.0 
25 0.1 8.1 0.7 2.36E-01 2.5E+00 1.0 
25 0.1 8.1 0.7 2.36E-01 2.5E+00 1.0 
25 0.1 8.1 0.7 2.38E-01 2.5E+00 1.0 
25 1 8.3 0.7 9.26E-01 3.1E+00 0.5 
25 1 8.3 0.7 9.33E-01 3.1E+00 0.5 
25 1 8.3 0.7 9.33E-01 3.1E+00 0.5 
25 1 8.3 0.7 9.52E-01 3.1E+00 0.5 
25 10 8.3 0.7 6.88E+00 3.9E+00 -0.2 
25 10 8.3 0.7 7.30E+00 3.9E+00 -0.3 
25 10 8.3 0.7 7.60E+00 3.9E+00 -0.3 
25 10 8.3 0.7 7.65E+00 3.9E+00 -0.3 
45 0.1 7.8 0.7 3.15E-01 4.2E+00 1.1 
45 0.1 7.8 0.7 3.41E-01 4.2E+00 1.1 
45 0.1 7.8 0.7 3.36E-01 4.2E+00 1.1 
45 1 8.1 0.7 1.88E+00 5.3E+00 0.5 
45 1 8.1 0.7 1.97E+00 5.3E+00 0.4 
45 1 8.1 0.7 1.99E+00 5.3E+00 0.4 
45 1 8.1 0.7 2.01E+00 5.3E+00 0.4 
45 1 8.1 0.7 1.99E+00 5.3E+00 0.4 
45 1 8.1 0.7 1.84E+00 5.3E+00 0.5 
45 1 8.1 0.7 2.05E+00 5.3E+00 0.4 
45 1 8.1 0.7 1.85E+00 5.3E+00 0.5 
45 10 8.2 0.7 1.86E+01 6.7E+00 -0.4 
45 10 8.2 0.7 2.02E+01 6.7E+00 -0.5 
45 10 8.2 0.7 1.93E+01 6.7E+00 -0.5 
45 10 8.2 0.7 1.91E+01 6.7E+00 -0.5 
45 10 8.2 0.7 1.94E+01 6.7E+00 -0.5 
45 10 8.2 0.7 1.92E+01 6.7E+00 -0.5 
60 0.1 7.6 0.7 6.63E-01 6.0E+00 1.0 
60 0.1 7.6 0.7 6.00E-01 6.0E+00 1.0 
60 0.1 7.6 0.7 5.93E-01 6.0E+00 1.0 
60 1 8.0 0.7 2.92E+00 7.6E+00 0.4 
60 1 8.0 0.7 2.80E+00 7.6E+00 0.4 
60 1 8.0 0.7 2.68E+00 7.6E+00 0.5 
60 1 8.0 0.7 2.66E+00 7.6E+00 0.5 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-17 August 2004 
Table 7-12. Experimental UO2 Dissolution Rates as a Function of Temperature and Bicarbonate 
Concentration (Continued) 
Temperature 
(C) 
[HCO3
-] 
(mmol/L) 
pH 
(0.1) pO2 
Dissolution Rate 
(mg m-2 d-1) 
Calculated 
Dissolution 
Rate (mg m-2 d-1) 
Error 
Metric 
60 1 8.0 0.7 2.75E+00 7.6E+00 0.4 
60 1 8.0 0.7 2.73E+00 7.6E+00 0.4 
60 10 8.1 0.7 6.63E+01 9.6E+00 -0.8 
60 10 8.1 0.7 6.70E+01 9.6E+00 -0.8 
60 10 8.1 0.7 6.67E+01 9.6E+00 -0.8 
60 10 8.1 0.7 6.72E+01 9.6E+00 -0.8 
60 10 8.1 0.7 6.79E+01 9.6E+00 -0.9 
60 10 8.1 0.7 6.65E+01 9.6E+00 -0.8 
Source: Appendix I, CSNF MR REV2.xls. Spreadsheet A11 
Table 7-13 compares published acid-side UO2 data (Torrero et al. 1997 [DIRS 114439]) to the 
developed model for acidic conditions. The error metric results in the sixth column show the 
developed acid side model often overpredicts the experimental UO2 data by more than an order 
of magnitude (error metric is greater than 1), indicating a potentially conservative modeling bias. 
This systematic discrepancy between the acid-side model and these data is explained, in part, by 
the fact that the sample surface areas were measured by the BET method, which is known to 
overestimate the effective surface area for oxidative dissolution of UO2 and CSNF (Gray and 
Wilson 1995 [DIRS 100758], Section 2.1.3). Dissolution rates by Torrero et al. (1997 
[DIRS 114439]) under acidic conditions were low in general when compared to other work 
(Grambow 1989 [DIRS 113233]). 
Table 7-13. Measured UO2 Flow-Through Dissolution Data with Variation of Oxygen and pH 
pHa T(C)a 
O2 Pressure 
(atmospheres)a 
Dissolution Rate 
(mgUO2 d-1 m-2) 
Calculated Dissolution 
Rate (mgUO2 d-1 m-2) 
Error 
Metric 
3.3 25 0.05 1.80E+00 23.4 1.1 
3.6 25 0.05 1.25E+00 18.5 1.2 
4.4 25 0.05 7.09E-01 9.9 1.1 
4.8 25 0.05 5.51E-01 7.2 1.1 
5.2 25 0.05 4.00E-01 5.3 1.1 
5.6 25 0.05 2.77E-01 3.9 1.1 
6.6 25 0.05 1.30E-01 1.8 1.1 
3.2 25 0.21 3.44E+00 41.1 1.1 
3.5 25 0.21 2.88E+00 32.5 1.1 
4.1 25 0.21 1.93E+00 20.3 1.0 
4.7 25 0.21 9.83E-01 12.7 1.1 
5.2 25 0.21 5.38E-01 8.6 1.2 
6.2 25 0.21 3.45E-01 3.9 1.1 
6.5 25 0.21 2.46E-01 3.1 1.1 
6.6 25 0.21 1.77E-01 2.9 1.2 
Source: Appendix I, CSNF MR REV2.xls. Spreadsheet A12 
a from Torrero et al. 1997 [DIRS 114439], Table 1 
NOTE: Surface Area (BET) = 1.13E-02 m2/g 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-18 August 2004 
The above comparisons show that the model-calculated specific dissolution rates of UO2 are 
consistent, within an approximate order of magnitude, with experimentally measured specific 
dissolution rates in the available literature for alkaline and acidic conditions. Thus, the 
applicable validation criterion is satisfied and the required level of confidence is indicated. 
7.1.3 Comparison with UO2+x Data 
Based on the discussion in Section 6.2.2.2, it is likely that CSNF will corrode by the oxidative 
dissolution process when exposed to humid air at temperatures less than 100C. However, the 
possibility of oxidation of CSNF in the repository environment before it is contacted by water 
cannot be excluded (Section 6.2.2.2). Hence, dissolution studies of the higher oxides are 
discussed in this section. 
The dependence of UO2+x dissolution kinetics on pH, temperature, time, and carbon dioxide 
carbonatebicarbonate concentrations was investigated (Stout and Leider 1998 [DIRS 111047], 
p. 2-223). All tests in the higher oxide test series were run at 0.2 atmospheres oxygen partial 
pressure or 8 ppm dissolved oxygen. The flow-through tests were carried out in basic buffer 
solutions (pH of 8 to 10). Tests were done at three temperatures (25C, 50C, and 75C), three 
carbonatebicarbonate concentrations (2  10-4 to 2  10-2 mol/L), and three pHs (8, 9, 10) for 
the two compounds U3O8 and UO3xH2O. Stout and Leider (1998 [DIRS 111047], p. 2-224, 
Table 2.1.3.5-5, Part 1) list the uranium dissolution rates for the three oxidesUO2, U3O8, and 
UO3xH2Omeasured under atmospheric oxygen conditions and show the oxide state has, by 
far, the strongest effect on the uranium dissolution rate. The rate increase from UO2 to U3O8 is 
small, but it increases dramatically from U3O8 to UO3xH2O. Increasing carbonate 
concentrations increase dissolution rates of U3O8 and UO3xH2O, as shown previously with UO2. 
An increase in U3O8 dissolution rate with increasing temperature is seen as well. A similar 
temperature effect occurs on UO3xH2O. For example, raising the temperature to 75C from 
room temperature increases the dissolution rate by a factor of two to four for the two higher 
oxides. Similar to the UO2 results, alkaline pH does not have a significant role in changing the 
dissolution rate of the higher oxides. 
Stout and Leider (1998 [DIRS 111047], p. 2-224) data indicate with higher oxides, unlike UO2, 
carbonate concentration affects the dissolution rate to a greater extent than does temperature. 
The enhancement is particularly strong at the highest carbonate concentration. Shoesmith (2000 
[DIRS 162405], Section 5.2) states this is consistent with the results seen in a dissolution study 
of soddyite, another fully oxidized uranium oxide. This strong dependence of U(VI) uraniumoxide 
dissolution on carbonate indicates that concentrated carbonate might prevent deposition of 
corrosion products during SNF dissolution. 
Results were also reported from flow-through dissolution tests with oxidized specimens of SNF 
and unirradiated U3O7 and U3O8 (Stout and Leider 1998 [DIRS 111047], p. 2-225). Dissolution 
rates of CSNF samples oxidized to U4O9+x were measured for three CSNF types: ATM-104, 
ATM-105, and ATM-106. The surface area normalized dissolution rate of oxidized fuel grains 
was little or no higher than unoxidized (UO2) grains for ATM-105. Oxidized ATM-106 fuel 
grains dissolved faster than unoxidized grains, but the difference was a factor of only about five. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-19 August 2004 
Oxidation has the potential to change SNF intrinsic dissolution rates by increasing the effective 
surface area. The specific dissolution rates (i.e., dissolution rates per unit area) of ATM-104, 
ATM-105, and ATM-106 (data obtained using grain specimens) (Stout and Leider 1998 
[DIRS 111047], p. 2-226) were not significantly affected by oxidation to U4O9+x. The data 
suggest that oxidation up to the U4O9+x stage does not have a large effect on specific dissolution 
rates (the largest increase was less than a factor of 6). However, data for some of the particle 
specimens suggest this degree of oxidation may markedly increase fractional dissolution rates of 
relatively intact fuel rods by opening the grain boundaries, thereby increasing the effective 
surface area available for contact by water. From a disposal viewpoint, this is the more 
important consideration (Stout and Leider 1998 [DIRS 111047], pp. 2-226 and 2-227). When 
ATM-106 fuel was oxidized to U3O8, its surface area-normalized dissolution rate was up to 10 
times faster than unoxidized ATM-106 fuel grains and about twice as fast as ATM-106 fuel 
grains oxidized to U4O9+x (Gray and Wilson 1995 [DIRS 100758], Section 3.4.4 and Figure 68). 
A more important effect of oxidation to U3O8 was the very large increase in surface area 
compared to the particles used to prepare the U3O8. This resulted in a fractional dissolution rate 
(rate per unit specimen weight) of U3O8 equal to 150 times that of the unoxidized particles. 
At O2 overpressure of 0.2 atmospheres, the intrinsic dissolution rate of unirradiated U3O7 
[approximately 3 mgU/(m2d)] was similar to UO2 [approximately 2.5 mgU/(m2d)] and the 
intrinsic dissolution rate of unirradiated U3O8 [approximately 10-15 mgU/(m2d)] was about four 
to six times that of UO2. At an O2 overpressure of 0.003 atmospheres, the intrinsic dissolution 
rate of the U3O7 was two to three times that of UO2 [0.5-1 mgU/(m2d)]. These estimates are 
based on a single test on each oxide at each condition) (Stout and Leider 1998 [DIRS 111047], 
p. 2-227). 
In summary, for each supplemental test conducted with oxidized SNF, unirradiated U3O7, or 
U3O8, the intrinsic dissolution rate of the oxidized material was only moderately higher than the 
unoxidized (UO2) material. The largest difference was a factor of 10 with CSNF U3O8. 
However, because of the potentially large increases in the specific surface area associated with 
oxidation of CSNF to higher oxides, an instantaneous radionuclide release rate model is used for 
any fuel that is exposed to humid air at temperatures greater than 100C. 
7.2 COMPARISON OF THE DEVELOPED MODEL TO ALTERNATIVE MODELS 
This section addresses validation by comparing the developed model for the specific dissolution 
rate of the CSNF matrix for alkaline conditions to the rates calculated using the electrochemical 
and surface complexation models described in Sections 6.4.2.1 and 6.4.2.2. 
As described in Section 6.4.2, alternative models were developed for the specific dissolution rate 
(i.e., the dissolution rate per unit area) of CSNF under alkaline conditions. The base-case 
alkaline model was compared to the alternative electrochemical (Equation 17) and surface 
complexation (Equation 22) models by evaluating the dissolution rates calculated by each model 
for a set of nominal repository conditions. The parameter values in the surface complexation 
model were taken from the paper by de Pablo et al. (1999 [DIRS 162388], Table 4) and the total 
density of surface sites (i.e., {>UO2}tot = 10-6 mol m-2). The nominal conditions used in this 
comparison are: oxygen fugacity = 0.2 atmospheres, total carbonate concentration equal to 
2.00  10-3 mol/L, and T = 30C. The results are shown in Table 7-14. Although the 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-20 August 2004 
Equation 20 form of the electrochemical model applies at 25C, this difference from the nominal 
conditions is not significant. 
Table 7-14. Comparison of the Base-Case Model to Alternative Models 
Model Calculated Dissolution Rate (mg/m2 /d) 
Base-Case 3.8 
Surface Complexation 2.4 
Electrochemical 12.1 
The results in Table 7-14 show excellent agreement between the base-case model and the surface 
complexation model. The dissolution rates calculated by the electrochemical model are about a 
factor of five larger than the rates calculated by the other two models. This result suggests that 
either the base-case and surface complexation models have a nonconservative bias or the 
electrochemical model is conservatively biased. The latter interpretation is more likely. As 
discussed in Section 6.2.2.3, the oxidative dissolution process consists of redox reaction steps 
[that convert U(IV) to U(VI)] followed by U(VI) dissolution steps promoted by carbonate 
complexation. In any such reaction process involving a sequence of steps, the slower steps will 
control the overall rate of the process. The electrochemical model is based on the rate of the 
redox reaction steps; and is therefore expected to overestimate the overall reaction rate for any 
reaction conditions where slower surface complexation, dissolution, or mass transfer steps, or 
both, influence the overall reaction rate. The likely conservative bias in the electrochemical 
model is apparent from the comparison with of the model with other data sources in the source 
documents for Equation 18 (Section 6.4.2.1). These comparisons between the base-case model 
and the surface complexation and electrochemical models, for a set of nominal repository 
conditions, show an adequate level of confidence is indicated for the developed model for the 
specific dissolution rate of the CSNF matrix under alkaline conditions and the applicable 
validation criteria are satisfied. 
A recent article (Rllin et al. 2001 [DIRS 162398]) provides single-pass flow-through data on 
the oxidative dissolution rate of CSNF under acidic conditions. The results (Rllin et al. 2001 
[DIRS 162398], Figure 9) show dissolution rates comparable to those calculated by the 
developed model for acidic conditions. 
In comparing the developed models to alternative models, it is instructive to compare the 
developed models parameter values to the corresponding coefficients (partial reaction orders 
and activation energy) in the published literature. As described by Jegou et al. (2001 
[DIRS 162397], Section 8.2.2.3) the rate law commonly accepted for homogenous reaction 
kinetics has the same mathematical form as the base-case model. It is, therefore, appropriate to 
compare the base-case models parameters with the activation energies and the partial reaction 
orders that have been reported in the literature for expressing the rate dependence on 
temperature, dissolved oxygen concentration, total carbonate, and pH. Shoesmith (2000 
[DIRS 162405], Section 6) pointed out that there is evidence that different mechanisms control 
the rate for different regions of the data domain used in the base case model regression. Hence, 
only approximate agreement between the base-case model parameters and the partial reaction 
orders reported in the literature can be expected. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-21 August 2004 
As shown by Shoesmith et al. (1998 [DIRS 162404], Figure 1), the coefficient for the dissolved 
oxygen concentration dependence of the SNF dissolution rate is 0.39, which compares well to 
the oxygen fugacity dependence of 0.338 in the base-case model. However, values from 1 to 0.1 
for the partial order dependence on dissolved oxygen has been reported (Jegou et al. 2001 
[DIRS 162397], Section 8.2.3.3; Shoesmith 2000 [DIRS 162405], Sections 5.4 and 6.2). Also, 
there is evidence that the partial order for oxygen dependence depends on temperature; it is very 
low at 25C and increases as temperature increases (Shoesmith 2000 [DIRS 162405], 
Section 6.4; Jegou et al. 2001 [DIRS 162397], Section 8.2.3.3). Literature data show a weaker 
dependence of CSNF on dissolved oxygen concentration compared to UO2 (Shoesmith 2000 
[DIRS 162405], Section 5.4). This is probably due to the effects of radiolytic oxidants, which 
become more significant as the dissolved oxygen concentration is reduced and, as a result, cause 
a smaller apparent dependence on the dissolved oxygen concentration. 
The dependence on carbonate concentration in the base-case model (coefficient of 0.102) is 
significantly weaker than the coefficient for carbonate dependence in the electrochemical model 
(i.e., 0.38). However, it is worth noting that the UO2 carbonate dependence (total carbonate 
parameter value = 0.473 in Table 6-8) agrees more closely with the literature value. This is 
consistent with the fact that the literature values for the partial order dependence on total 
carbonate are based on UO2. 
The temperature dependence in the base-case model corresponds to an activation energy of 
21 kJ/mol. This is at the low end of the range (20 to 60 kJ/mol.) reported by others for UO2 
(Jegou et al. 2001 [DIRS 162397], p. 481). Note that the value indicated for the UO2-only model 
(Table 6-8) is 34 kJ/mol. The effects of temperature are known to be complex in carbonate 
solutions; the activation energies reported are lower for low carbonate concentrations that give 
apparent activation energies close to the value obtained for the base-case model (Shoesmith 2000 
[DIRS 162405], Section 6.5). As discussed in Section 6.2.2.3, this is consistent with the 
interpretation that diffusion through surface alteration layers influences the rate of the oxidative 
dissolution process under conditions where these layers are formed. Under higher carbonate or 
acidic conditions that promote rapid dissolution of U(VI) alteration layers, these mass transport 
effects do not influence the rate and activation energies near the upper end of the range are 
observed (Shoesmith 2000 [DIRS 162405], Section 5.6). As discussed in Section 7.1.2, 
Table 7.1-5 results provide evidence that the model for the CSNF matrix radionuclide release 
rate is valid for temperatures up to 85C. Uncertainty associated with extending the models 
domain of application to 100C can be assessed by examining how uncertainty in the applicable 
activation energy could influence the modeled rate. Using the models activation energy of 
21 kJ/mol the rate increases by approximately 20 percent between 85C and 100C. If an 
activation energy of 60 kJ/mol is used (i.e., the upper end of the activation energy range), the rate 
increases by a factor of about 2.2 between 85C and 100C. However, if a more likely activation 
energy of 40 kJ/mol (i.e., the middle of the activation energy range) is used, the rate increases by 
approximately 70 percent between 85C and 100C. These results indicate that extension of the 
models temperature domain to 100C introduces, at most, an uncertainty factor of two. 
Extension of the model domain of application to 100C is justified because the uncertainty 
associated with this extension is small compared to the overall modeling uncertainties. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-22 August 2004 
The lack of pH dependence of the rate under alkaline conditions is consistent with published data 
(Shoesmith 2000 [DIRS 162405], Section 6.3). For acidic conditions, Grambow (1989 
[DIRS 113233]) calculated room-temperature acidic dissolution rates for boiling water reactor 
SNF types with data reported by Forsyth et al. (1986 [DIRS 120955]). Grambow (1989 
[DIRS 113233], p. 11) in his Figure 2 plot of the converted Forsyth et al. (1986 [DIRS 120955]) 
acidic SNF dissolution rate data, gives a slope of about -0.5. In his review of the dependence of 
UO2 corrosion rate in the pH regime 3<pH<6.7, Shoesmith (2000 [DIRS 162405], Section 3.1.2) 
reports a power law dependence on pH with an exponent of 0.37. A recent article 
(Rllin et al. 2001 [DIRS 162398]) provides single-pass flow-through data on the oxidative 
dissolution rate of CSNF under acidic conditions. The results (Rllin et al. 2001 [DIRS 162398], 
Figure 9) show a coefficient of 0.6 for dependence on pH. Although this is a higher dependence 
on pH than the 0.34 coefficient (i.e., the value for the a4 parameter determined in Section 6.4.1.3) 
in the base-case acid model the dissolution rates presented are comparable to those calculated by 
the base-case acid model. Although the base-case model parameters are generally consistent 
with literature data, this consistency constitutes weak validation because of the wide range of 
activation energies and reaction orders reported in the literature. 
7.3 NATURAL ANALOGUES 
7.3.1 Studies of Natural Analogue Sites 
This section on natural analogues has been included to provide a qualitative overview of the 
uranium mineral phases seen at natural uranium-bearing sites around the world and to provide a 
comparison with corrosion products seen in laboratory tests. It does not provide data that can be 
used directly for validating the CSNF models. However, confidence can be gained in using the 
base-case model for modeling fuel corrosion over extended time periods by showing that 
secondary phase development in multiyear laboratory tests is similar to the alteration phase 
paragenesis determined at the uranium-bearing natural analogue sites. Similarities in the 
alteration phases observed in laboratory experiments to those observed in very long-term 
alteration of uraninite provides evidence that the overall alteration processes observed in the 
laboratory are similar to those likely to control alteration in the long term. 
CSNF consists of uranium dioxide (UO2) having a cubic fluorite crystalline structure. Uranium 
dioxide occurs in nature as the mineral uraninite, also exhibiting a fluorite structure. Numerous 
geologic sites contain uraninite, and studies of natural uraninite alteration cover a wide range of 
geologic conditions. Of the several extensively studied sites, only Nopal I, the uranium mining 
site at Pena Blanca, Mexico, has geologic, geochemical, and hydrogeologic characteristics 
similar to those at Yucca Mountain (Murphy 1995 [DIRS 100469]). The volcanic (tuffaceous) 
host rock at Nopal I, the youngest of the studied sites, has been exposed to oxygen for tens of 
thousands of years. Uraninite, containing U(IV), was originally formed several million years 
ago. Pearcy and Murphy (1991 [DIRS 130197]) discuss in some detail other natural analogue 
sites around the world. The oxidizing sites discussed are Koongarra in Australia, Pocos de 
Caldas in Brazil, the Shinkolobwe mine in the Congo, and the Krunkelbach mine in Germany. 
The other sites are either somewhat reducing or hydrologically saturated, or the mineralogy of 
the uraninite alteration is significantly affected by the presence of chemical elements not found 
in underground repositories or their environs (e.g., lead, phosphorus, or vanadium). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-23 August 2004 
The process of uranium mineral formation and subsequent uranium transport at Nopal I have 
been extensively studied. Because the sites are geologically similar, it is anticipated that the 
uranium compound alteration and transport processes will be comparable to those that would 
occur at the repository. 
The data in this section provide the basis for comparing the mineral phases and paragenesis seen 
at Nopal I, the natural geologic site, and the chronological progression of CSNF and UO2 
corrosion products seen in laboratory tests. 
The uranium minerals found at Nopal I are listed in Table 7-15 with a qualitative illustration of 
their relative time sequence of formation and relative abundance. The compounds found are 
limited compared to other sites because of the simple chemistry of the Pena Blanca system. 
Table 7-15. Paragenesis of Uranium Minerals at Nopal I 
Mineral Group Mineral Time Nominal Chemical Formula 
Oxide Uraninite         UO2+x 
Ianthinite . U4+(U6+O2)5(OH)143H20 
Schoepite/Dehydrated 
Schoepite 
.      UO32H20 
UO3nH2O(n< 2) 
Becquerelite .   Ca(UO2)6O4(OH)68H2O 
Oxyhydroxides 
Billietite(?)/Abernathyite(?)        Ba(UO2)6O4(OH)6nH2O(n=4-8) 
K(UO2)(AsO4) 4H2O 
Soddyite .- (UO2)2SiO42H2O 
Weeksite and Boltwoodite           K2(UO2)2Si6O154H2O 
KH(UO2)SiO41.5H2O 
Silicates 
Uranophane: -Uranophane ------------- Ca(UO2)2Si2O76H2O 
Source: Pearcy et al. 1994 [DIRS 100486] 
NOTES:        minor 
.      abundant, then minor 
...- abundant 
------ very abundant 
? indicates tentative identification 
7.3.2 Comparison of Laboratory Corrosion Products to Nopal Minerals 
Combined optical, scanning-electron microscope, energy dispersive x-ray spectroscope, and 
x-ray diffraction examinations of samples taken from unsaturated tests performed on the two 
CSNF types (ATM-103 and ATM-106) indicate that the time-dependent evolution of the 
alteration phases is strongly dependent on the rate at which the EJ-13 water contacts the spent 
UO2 fuel (Stout and Leider 1998 [DIRS 111047], p. 2-250). The three tests (high drip rate, low 
drip rate, and vapor) show several similarities, including corroded grain boundaries, dissolution 
of fuel grains, and precipitation of alteration phases (Figure 6-2). 
Analyses of the alteration phases were done by sampling the alteration phases and by analyzing 
the collected samples using a combination of methods, including x-ray diffraction, transmission 
electron microscopy, scanning electron microscopy, and light microscopy. Only a small fraction 
of the total volume of corrosion products that have formed on the surfaces of UO2 and SNF 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-24 August 2004 
fragments have been removed and analyzed. The extent to which these samples are 
representative of the entire alteration phase assemblage is, therefore, uncertain. Of the analysis 
methods used, only transmission electron microscopy and x-ray diffraction provide positive 
identification of a crystal structure by diffraction. The transmission electron microscope 
provides additional information on chemical composition using x-ray energy dispersive 
spectrometry and electron energy loss spectrometry, plus magnified images that provide crystal 
domain size, shape, etc. The scanning electron microscope provides information on elemental 
composition using x-ray energy dispersive spectrometry, plus morphological information such as 
crystal shape and growth habit. Light microscopy, as used in these studies, provides only 
information about crystal growth patterns, some crystal shape, and color (for instance, many 
uranium (VI) phases are characteristically bright yellow). The composition of the alterationphase 
assemblage remains uncertain; the techniques used and the extent of the analyses are not 
sufficient to ensure all of the phases formed have been identified and to establish a complete 
understanding of the phase paragenesis in these tests. 
The available alteration phase identification results indicate that the alteration-phase assemblage 
depends strongly on water flux and its composition, with uranyl oxy-hydroxides predominating 
in vapor tests and alkali and alkaline earth uranyl silicates predominating in high drip-rate tests. 
Low drip-rate tests exhibit a complex assemblage of corrosion products, including phases 
identified in vapor and high drip-rate tests (Stout and Leider 1998 [DIRS 111047], 
Section 2.1.3.5). 
The vapor tests display the simplest assemblage of alteration products. Samples from vapor tests 
display a relatively simple combination of uranyl oxy-hydroxide alteration phases dominated by 
dehydrated schoepite (UO2)O0.25-x(OH)1.5+2x (0 = x = 0.15) and metaschoepite. This assemblage 
is readily explained by the lack of added cations in the vapor and condensate that contacts the 
fuel surface. The only cations (except H+) available for the precipitation of solids come from the 
dissolution of fuel. A CsBaMouranate phase, which incorporates two fission products, 
cesium and molybdenum, was identified (Buck et al. 1997 [DIRS 112904]). The precipitation of 
dehydrated schoepite and metaschoepite in these tests indicates that the film of water that forms 
on the fuel surface is sufficiently corrosive to dissolve the fuel and form a thin corrosion rind of 
alteration products. Such a water film is likely present in the drip tests, as well as during those 
intervals that EJ-13 water is not being dripped onto the fuel. It seems likely that the corrosion 
processes important in the vapor tests remain important in the drip tests. Dehydrated schoepite 
or metaschoepite, or both, may also form in the drip tests between water injections. If these 
phases are present when contacted by EJ-13 water, they may be at least as susceptible to 
dissolution or replacement, or both, as the unoxidized fuel. 
The drip tests display more chemically complex alteration phases, owing to the interaction of the 
fuel with EJ-13 water (rather than water vapor and condensate only). The most abundant 
elements in EJ-13 water are Na and Si, and the most abundant alteration products in the high 
drip-rate tests are Na- and Si-bearing U(VI) phases. Other U(VI) phases are also present, 
including metaschoepite and -uranophane, indicating the importance of additional minor phases 
and elements to the overall corrosion process. 
Fuel samples exposed to the higher drip-rates yield two uranophane-group silicates, 
-uranophane [Ca(UO2)2(SiO3OH)2(H2O)5] and Na-boltwoodite (Na,K)(UO2)(SiO3OH)(H2O), 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-25 August 2004 
compared to the complex alteration-phase assemblage seen in the low drip-rate tests. The simple 
phase assemblage in the high drip-rate tests reflects higher overall reaction progress for the spent 
fuel in these tests. Samples from low drip-rate tests possess a much more complex assemblage 
of U(VI) phases than observed in samples from either vapor or high drip-rate tests. This 
complexity may reflect the limited influx of EJ-13 water, which contributes Si, Na, Ca, and other 
cations. Common corrosion products from low-drip-rate tests include metaschoepite, an 
unidentified Na-uranyl oxy-hydroxide tentatively identified as Na-compreignacite, and 
soddyite, a CsBaMouranate phase that commonly occurs adjacent to dissolving fuel grains. 
In one sample from the ATM-103 low-drip-rate test at the 5.2-year interval, soddyite appears to 
replace Na-compreignacite. Also, a few isolated crystals of Na-boltwoodite were first detected 
in the ATM-103 low-drip-rate test at the 4.1-year interval. They were later abundant at the 
5.2-year interval but less abundant than soddyite. These observations provide limited direct 
evidence for the replacement of uranyl oxy-hydroxides by uranyl silicates. 
In Wilsons (1990 [DIRS 100793]) Series 3 tests using J-13 well water, the uranium silicate, 
soddyite, and calcium uranium silicates, -uranophane (haiweeite, minor), were found using 
x-ray diffraction and scanning electron microscopy (Stout and Leider 1998 [DIRS 111047], 
p. 2-261). 
The sequence of uraninite alteration at Nopal I is similar to that of CSNF and UO2 in the 
laboratory tests. Uraninite is already partially oxidized (Pearcy et al. 1994 [DIRS 100486]). 
Spent fuel and UO2 must first undergo that first surface oxidation to approach uraninite. The 
corrosion products observed in laboratory CSNF and UO2 tests conform to the mineral phases 
seen at Nopal I. The general sequence is oxidation of the solid surface followed by hydration, 
thereby forming uranyl-oxide hydrates. Silicate in the groundwater is incorporated as soddyite. 
The silicate, in combination with alkali ions (e.g., calcium and sodium), forms various alkaline 
uranyl silicate hydrates, such as Na-boltwoodite and -uranophane. The exact sequence and 
timing of formation depends significantly on local chemical environment, water flows, and time 
in the laboratory tests and at the Nopal I site. Simultaneous precipitation is indicated in 
laboratory and field tests. Some alteration phases, such as sklodowskite and compreignacite, are 
found in the laboratory tests but not at Nopal I. This may simply be a result of the small number 
of samples in all studies. Also, some phases, such as ianthanite, seen at the Nopal I site have not 
been reported in the laboratory tests. The fact that ianthanite was not observed does not preclude 
its possible presence in the tests. Ianthanite is an interesting phase, containing a mixture of 
U(VI) and U(IV) sites. The conditions under which it forms, thus, may reflect local redox 
conditions present in the natural system at Nopal, but not reproduced in the drip tests. 
The groundwater at Nopal I is richer in calcium than J-13 well water (Pearcy et al. 1994 
[DIRS 100486]), but poorer in sodium and potassium. This could explain the dominance of 
-uranophane at the natural site, as well as the limited soddyite and weeksite occurrence. There 
is substantial calcite at Yucca Mountain. In time, this may make repository-alteration products 
conform more to the Nopal I sequence than that seen in the laboratory, which produces 
-uranophane at long times. 
In summary, the natural analog data builds confidence in using the base-case models for 
modeling fuel corrosion over extended time periods by showing that secondary-phase 
development in multiyear laboratory tests is similar to the alteration-phase paragenesis 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 7-26 August 2004 
determined at the uranium-bearing natural analogue sites. Similarities in the alteration phases 
observed in laboratory experiments to those observed in very long-term alteration of uraninite 
provides evidence that the overall alteration processes observed in the laboratory are similar to 
those likely to control alteration in the long term. Hence, the laboratory processes that provide 
the basis for the base-case models are likely to be relevant for describing CSNF degradation into 
the distant future. 
7.4 VALIDATION SUMMARY 
The CSNF model has been validated by applying acceptance criteria based on an evaluation of 
the models relative importance to the potential performance of the repository system. All 
validation requirements defined in Section 2 of Technical Work Plan for: Regulatory Integration 
Modeling and Analysis of the Waste Form and Waste Package (BSC 2004 [DIRS 169944]) have 
been fulfilled, including corroboration of model results with experimental data, publications of 
refereed journals, and corroboration with alternative conceptual models. Activities required for 
confidence-building during model development have been satisfied. The model development 
activities and postdevelopment validation activities described establish the scientific basis for the 
CSNF models. The model validation exercises described in Sections 7.1 and 7.2 indicate the 
models developed for alkaline and acidic conditions are consistent with the data used for model 
validation and with the alternative conceptual models, although a somewhat conservative 
modeling bias is indicated. However, the conservative modeling bias may be larger than 
indicated by the validation because the CSNF data used for validation also include the radiolytic 
effects of short-lived  and . radiation and because the test configurations may cause the 
effective surface area in the tests to be unrealistically high. Also, long-term weathering rates of 
minerals in general, as measured in laboratory tests, greatly exceed rates observed under natural 
weathering conditions (White and Brantley 2003 [DIRS 168088]). This general observation has 
been attributed to factors such as progressive depletion of the more energetically reactive sites, 
weathering conditions that approach equilibrium (i.e., minimize solution undersaturation), and 
effects of secondary-phase precipitates and alteration residues on the rate of mass transport to 
and from the corroding surfaces. These effects will also decrease the long-term oxidative 
dissolution rate of CSNF in the repository, but the extent of the conservative bias introduced by 
their omission is uncertain. 
Based on this, the CSNF models used in this model report are sufficiently accurate and adequate 
for their intended purpose. The level of confidence required by the models relative importance 
to the performance of the repository system has been met. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-1 August 2004 
8. CONCLUSIONS 
8.1 DEVELOPED OUTPUT - THE MODEL PARAMETER VALUES 
The developed outputs from this model report are models for the instantaneous release fractions 
(fi) and for the matrix fractional release rates (Fi) under acidic and basic conditions. The output 
also includes the model parameter values and the associated distributions that capture the 
assessed uncertainty in these parameters. 
This report developed models, associated model parameter values, and uncertainty distributions 
for release of: 
 Gap and grain-boundary inventory fractions of cesium, iodine, technetium, and 
strontium 
 Fuel matrix inventory under basic and acidic conditions. An instantaneous radionuclide 
release rate model is to be used for any fuel that is exposed to humid air at temperatures 
greater than 100C and is subsequently contacted by water. 
These outputs are described below. 
Model for Release of Gap and Grain-Boundary Inventory 
The release of the gap and grain-boundary inventories of cesium, iodine, technetium, and 
strontium is modeled as an instantaneous release of the fraction (fi) of the total inventory of each 
of these elements estimated to be in the gap and grain-boundary regions. The instantaneous 
fractional releases for 137Cs, 129I, 99Tc, and 90Sr are modeled as triangular distributions with the 
apex of the triangular probability distribution function located at the average release fractions 
shown in Table 8-1 and spanning the range shown in the last row of this table for each element. 
Table 8-1. Characteristic Values of the Triangular Probability Distribution Functions for Instantaneous 
Fractional Releases of Cesium, Iodine, Technetium, and Strontium 
137Cs 129I 99Tc 90Sr 
Average Release 
Fraction (%) 3.63 11.24 0.10 a 0.09 
Range (%) 0.39 to 11.06 2.04 to 26.75 0.01b to 0.26 0.02 to 0.25 
NOTE: a This value was rounded up from Table 6-2. This roundup is conservative and reasonable 
based on the measurement uncertainties in these very small fractions (Section 4.1.1) 
b This value was changed to provide a nonzero minimum. This is reasonable because it is 
consistent with the measurement results (Section 4.1.1) and because a zero value is not 
physically plausible; some diffusion out of the fuel grains is expected. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-2 August 2004 
Model for Release of Fuel Matrix Inventory - Alkaline Conditions 
The radionuclide release rate from the CSNF matrix under alkaline conditions (pH = 6.8, Section 
6.4.1.3) is modeled as the fractional matrix dissolution rate (d-1) with the following mathematical 
form: 
Log (F) = Log (A) + a0 + a1  IT + a2  pCO3 + a3  pO2 
This model has one dependent variable (Log (F)), three independent variables (IT, pCO3, and 
pO2), and five parameters (Log (A), a0, a1, a2, and a3) defined as follows: 
Dependent Variable: 
Log (F) = Base 10 log of the fractional dissolution rate of the fuel (d-1) 
Independent Variables: 
IT = Inverse temperature (K-1) (i.e., IT = 1/T) 
pCO3 = Negative base 10 log of total carbonate (molar) (i.e., pCO3 = - 
log10([HCO3
-] + [CO3
2-])) 
pO2 = Negative base 10 log of oxygen pressure (atmospheres) (i.e., pO2 = 
- log10(O2)) 
Parameters: 
Log (A) = Base 10 log of the fuel effective specific surface area (m2/mg) 
a0, a1, a2, a3 = Regression parameters for the dissolution rate per unit area 
The best-estimate values and the corresponding characteristic values of the uncertainty 
distributions for each of the five model parameters are given in Table 8-2. 
Table 8-2. Parameter Values and Associated Characteristic Values of the Uncertainty Distributions for 
the Alkaline Conditions Model 
Model Parameter Parameter Value 
Uncertainty Distribution 
Characteristic Values 
Log(A) - 6.7 Triangular Distribution a 
a0 4.705 0.601 b 
a1 -1,093.826 186.829 b 
a2 -0.102 0.0471 b 
a3 -0.338 0.0506 b 
NOTES: a Distribution minimum = -7.3; distribution maximum = -5.4; 
distribution apex = -6.7. 
b Estimated standard errors. The uncertainties in the regression 
coefficients are also related through the covariance matrix 
provided in Appendix II. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-3 August 2004 
Model for Release of Fuel Matrix Inventory - Acidic Conditions 
The radionuclide release rate from the CSNF matrix under acidic conditions (pH < 6.8) is 
modeled as the fractional matrix dissolution rate (d-1) in the following mathematical form: 
Log (F) = Log (A) + a0 + a1  IT + a3  pO2 + a4  pH 
This model has one dependent variable (Log (F)), three independent variables (IT, pO2, and pH), 
and five parameters (Log (A), a0, a1, a3, and a4) are defined as follows: 
Dependent Variable: 
Log (F) = Base 10 log of the fractional dissolution rate of the fuel (d-1) 
Independent Variables: 
IT = Inverse temperature (K-1) (i.e., IT = 1/T) 
pO2 = Negative base 10 log of oxygen pressure (atmospheres) 
(i.e., pO2 = - log10(O2)) 
pH = Negative base 10 log of the hydrogen ion concentration (molar) 
Parameters: 
Log (A) = Base 10 log of the fuel effective specific surface area (m2/mg) 
a0, a1, a2, a3 = model parameters 
The model parameter values and the corresponding uncertainties for each of the five model 
parameters are given in Table 8-3. 
Table 8-3. Parameter Values and Associated Characteristic Values of the Uncertainty Distribution for the 
Acidic Conditions Model 
Model Parameter Parameter Value Uncertainty Distribution 
Characteristic Values 
Log(A) - 6.7 Triangular Distribution a 
a0 6.60 0.446 b 
a1 -1,093.826 186.829b 
a3 -0.338 0.0506b 
a4 -0.340 0.110 b 
NOTES: a Distribution minimum = -7.3; distribution maximum = -5.4; 
distribution apex = -6.7. 
b Estimated standard errors. The uncertainties in the 
regression coefficients are also related through the 
covariance matrix provided in Appendix II. Values for 
parameters a1 and a3 have been taken from the analysis for 
the alkaline conditions model and have no associated 
standard errors in the context of the acid conditions analysis; 
the standard errors obtained from the alkaline side 
regression analysis are used. a1 and a3 are to be 
considered as independent parameters (i.e., no covariance). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-4 August 2004 
Model for the Radionuclide Release Rate from Fuel Exposed to Humid Air at 
Temperatures Greater than 100C 
An instantaneous radionuclide release-rate model is to be used for any fuel exposed to humid air 
at temperatures greater than 100C and is subsequently contacted by water after the temperature 
drops below 100C. 
8.2 UNCERTAINTY IN THE DEVELOPED PARAMETER VALUES 
As summarized in Section 8.1, this report developed values for two sets of parameters: 1) 
instantaneous release fractions (fi) for cesium, iodine, technetium, and strontium, and 2) acidic 
and alkaline conditions (A, a0, a1, a2, a3, and a4) used in the CSNF matrix fractional release rate 
models. 
As described in Section 6.3.3, the instantaneous release fractions for cesium, iodine, technetium, 
and strontium are uncertain parameters. The uncertainty is mostly aleatory uncertainty 
associated with factors such as linear power history, grain size, and burnup that influence the gap 
and grain-boundary inventories of these elements. The available data are analyzed (Section 6.3) 
to determine ranges, and plausible probability distribution functions within these ranges, for the 
gap and grain-boundary inventory fractions ( i f ). Uncertainty is represented by a probability 
distribution function spanning the range of the available data set (Section 6.3.3). 
Uncertainties in the developed parameter values for the base-case model for the CSNF matrix 
fractional release rate include: those in the mathematical form of the model that may influence 
the validity of long-term extrapolations, those in the models parameter values derived from 
uncertainties in the empirical data from which these parameters are taken (i.e., epistemic 
uncertainties), and those associated with the variability in the properties of CSNF and the extent 
to which these are adequately represented by the test materials from which the input data 
(Section 4.1) are obtained (i.e., aleatory uncertainties). 
Uncertainties in the mathematical form of the base-case model and in its validity over the 
extended time domain of application are addressed by discussing the models consistency with 
current understanding of the mechanism for the process (referred to as oxidative dissolution) that 
degrade the fuel under the range of plausible exposure conditions. Comparison of the base-case 
model with the mechanistically based alternative models provides confidence that the 
empirically based modeling approach is indeed appropriate for use in long-term predictions. 
As discussed in Section 1, the parameters in the base-case model for the radionuclide fractional 
release rate from the CSNF matrix are: (1) effective specific surface area (i.e., the surface area 
per unit mass) of the fuel (A); and (2) parameters a0, a1, a2, a3, and a4, which express the 
dependence of the CSNF matrix oxidative dissolution rate on environmental factors 
(temperature, dissolved oxygen concentration, carbonatebicarbonate concentration, and pH). 
Uncertainties in a0, a1, a2, a3, and a4 are represented as characteristic values of probability 
distributions associated with the parameter values obtained from the regression analyses 
described in Sections 6.4.1.2 and 6.4.1.3, and summarized in Tables 8-2 and 8-3. The values and 
associated uncertainties for the CSNF-specific surface-area parameter (A) are discussed in 
Section 6.4.1.5. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-5 August 2004 
The developed parameter values and the associated uncertainty distributions are summarized in 
Table 8-4. The model validation exercises described in Sections 7.1 and 7.2 indicate that the 
models developed for alkaline and acidic conditions are consistent with the data used for model 
validation and with the alternative conceptual models, although a somewhat conservative 
modeling bias is indicated. However, the conservative modeling bias may be larger than 
indicated by the validation because the CSNF data used for validation also include the radiolytic 
effects of short-lived  and . radiation and because the test configurations may cause the 
effective surface area in the tests to be unrealistically high. Also, long-term weathering rates of 
minerals in general, as measured in laboratory tests, greatly exceed rates observed under natural 
weathering conditions (White and Brantley 2003 [DIRS 168088]). This general observation has 
been attributed to factors such as progressive depletion of the more energetically reactive sites, 
weathering conditions that approach equilibrium (i.e., minimize solution undersaturation), and 
effects of secondary phase precipitates and alteration residues on the rate of mass transport to 
and from the corroding surfaces. These effects will also decrease the long-term oxidative 
dissolution rate of CSNF in the repository. 
Because the models developed in this report are largely empirical, the domain of application 
should be restricted to the range of qualified experimental data used for the model development 
and validation. The range of the input data spans CSNF with burnup from 15 to 65 MWd/kgU 
and percent fission gas release from 1 percent to 18 percent. It also spans pH values down to 2 
and up to 10.3, temperatures from 25C to 90C, oxygen partial pressure from 0.002 to 
0.2 atmospheres, and carbonatebicarbonate concentrations from 0 to 2  10-2 molar. Based on 
the discussion in Section 6.4.1.3, a value of 2  10-4 molar is to be used for modeling CSNF 
degradation under alkaline conditions when the in-package chemistry model calculates a total 
carbonate molar concentration less than 2  10-4 molar. Because the temperature dependence of 
the rate is adequately described by the Arrhenius form of the temperature dependence used in the 
base-case model and because it is unlikely that the mechanism controlling the rate of the 
oxidative dissolution process will change between 90C and 100C, the upper end of the 
temperature range can be extended to 100C. An instantaneous radionuclide release rate model 
is to be used for any fuel exposed to humid air at temperatures greater than 100C and is 
subsequently contacted by water after the temperature drops below 100C. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-6 August 2004 
Table 8-4. Summary of Output Developed in This Model Report 
Output Uncertainty 
Output Name Output Description 
Sources of 
Uncertainty 
Uncertainty 
Distribution 
Characteristic 
Values 
Instantaneous release 
fractions (fi) 
Probability distribution 
functions for the 
instantaneous release 
fractions for 137Cs, 
129I, 99Tc, and 90Sr 
Principally variability 
(aleatory uncertainty) 
in the input data 
Triangular 
centered at the 
data mean and 
spanning the 
range of the 
input data 
Table 8-1 for average 
and range maximum 
and minimum values 
(F) Fractional 
radionuclide release 
rate from the CSNF 
matrix  alkaline 
conditions; parameters 
(a0, a1, a2, a3) 
Values and standard 
errors for each of 
these parameters 
Uncertainties in the 
input flow-through 
data set (Sections 
4.1.2 and 6.4.1.1) and 
in the form of the 
model 
Normal Standard errors in 
Table 8-2 
Covariance matrix in 
Appendix II 
(F) Fractional 
radionuclide release 
rate from the CSNF 
matrix  acidic 
conditions; parameters 
(a0, a1, a3, a4) 
Values and standard 
errors for each of 
these parameters 
Uncertainties in the 
input flow-through 
data for acidic 
conditions (Sections 
4.1.2 and 6.4.1.1) and 
in the form of acidside 
model 
Normal Standard errors 
Table 8-3 
Covariance matrix in 
Appendix II 
Effective specific 
surface area of 
corroding CSNF (A) 
Probability distribution 
function for the log of 
the estimated specific 
surface area (A) 
Uncertainties in the 
input data set 
(Sections 4.1.3) and 
evolution of (A) as 
CSNF corrodes 
(Section 6.4.1.5) 
Triangular Characteristic values 
of the triangular 
distribution in Tables 
8-2 and 8-3 
NOTE: All data in this table is contained in Output DTN: MO0404ANLSF001.001. 
8.3 YUCCA MOUNTAIN REVIEW PLAN ACCEPTANCE CRITERIA 
Yucca Mountain Review Plan, Final Report (YMRP) (NRC 2003 [DIRS 163274]) contains 
acceptance criteria intended to establish the basis for the review of the material contained in the 
license application. As this model report serves, in part, as the basis for the license application, it 
is important to show how the information contained herein addresses each of the applicable 
YMRP acceptance criteria. 
YMRP acceptance criteria applicable to this report are identified in Technical Work Plan for: 
Regulatory Integration Modeling and Analysis of the Waste Form and Waste Package 
(BSC 2004 [DIRS 169944], Table 3-1). Each applicable criterion is quoted in italics, followed 
by pointers to where within the report the information addressing the criterion can be found. In 
some cases, the criterion is only partially addressed. A demonstration of full compliance 
requires a review of multiple reports. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-7 August 2004 
8.3.1 System Description and Demonstration of Multiple Barriers 
The CSNF waste form meets the definition of a barrier in 10 CFR 63.2 [DIRS 156605]. The 
following Acceptance Criteria can be found in Section 2.2.1.1.3 of Yucca Mountain Review Plan, 
Final Report (NRC 2003, [DIRS 163274]). 
Acceptance Criterion 1Identification of Barriers Is Adequate 
Barriers relied on to achieve compliance with 10 CFR 63.113(b), as 
demonstrated in the total system performance assessment, are adequately 
identified, and are clearly linked to their capability. 
The CSNF barrier functions of limiting radionuclide release are addressed throughout this 
document and, more specifically, in Section 6.4.3. 
Acceptance Criterion 2Description of Barrier Capability to Isolate Waste Is Acceptable 
The capability of the identified barriers to prevent or substantially reduce the rate 
of movement of water or radionuclides from the Yucca Mountain repository to the 
accessible environment, or prevent the release or substantially reduce the release 
rate of radionuclides from the waste is adequately identified and described: 
(1) The information on the time period over which each barrier performs its 
intended function, including any changes during the compliance period, is 
provided; 
(2) The uncertainty associated with barrier capabilities is adequately described; 
(3) The described capabilities are consistent with the results from the total system 
performance assessment; and 
(4) The described capabilities are consistent with the definition of a barrier at 10 
CFR 63.2. 
The capability of CSNF to reduce radionuclide release rate after a hypothetical failure of the 
waste package and fuel rod cladding is identified and described throughout this report; the 
models developed to describe this capability are summarized in Section 8.1. Uncertainties 
associated with the CSNF barriers capabilities to perform its intended function are addressed in 
Sections 8.1 and 8.2. The CSNF models and their associated uncertainties are consistent with the 
models used in TSPA-LA. 
Acceptance Criterion 3Technical Basis for Barrier Capability Is Adequately Presented 
The technical bases are consistent with the technical basis for the performance 
assessment. The technical basis for assertions of barrier capability is 
commensurate with the importance of each barriers capability and the 
associated uncertainties. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-8 August 2004 
The technical bases for the CSNFs capabilities to perform its intended function of limiting 
radionuclide release are addressed throughout this document and, more specifically, in 
Section 6.2. The technical bases are consistent with the corresponding technical bases in 
performance assessment because the CSNF models are used as input to TSPA-LA. 
8.3.2 Degradation of Engineered Barriers 
The following Acceptance Criteria can be found in Section 2.2.1.3.1.3 of Yucca Mountain 
Review Plan, Final Report (NRC 2003, [DIRS 163274]). 
Acceptance Criterion 1System Description and Model Integration are Adequate 
(1) TSPA adequately incorporates important design features, physical phenomena 
and couplings and uses consistent assumptions throughout the degradation of 
engineered barriers abstraction process; 
The appropriate design features and applicable physical phenomena, as well as environmental 
factors and their coupling are addressed in Section 6.2. 
(2) Abstraction uses assumptions, technical bases, data and models that are 
appropriate and consistent with [those used] in other abstractions. 
Throughout this report, the performed analyses use assumptions, technical bases, input data, and 
models that appropriately reflect the design and contents of the waste form and the humid-air and 
groundwater media that may come in contact with the waste form. The assumptions are 
addressed in Section 5. The data, technical bases, and models are addressed in Sections 4.1 and 
6. This information is used in a manner that is consistent with other abstractions of processes 
associated with the degradation of the CSNF waste form. 
(3) The descriptions of the engineered barriers, design features, degradation 
processes, physical phenomena, and couplings that may affect the degradation of 
the engineered barriers are adequate. 
Detailed descriptions of the relevant CSNF design features and degradation processes 
(Sections 6.2.1 and 6.2.2), and the physical phenomena and their couplings that may affect the 
CSNF degradation (Sections 6.2.2 and 6.2.3) are provided. 
(4) Initial and boundary conditions are propagated consistently throughout the 
abstraction process. 
The CSNF initial (Section 6.2.1) and relevant environmental boundary conditions for the 
performed analyses (Section 6.2) are described and used in developing the CSNF degradation 
and radionuclide release models. 
(5) Sufficient technical basis for the inclusion and exclusion of FEPs are 
provided; 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-9 August 2004 
The features, events, and processes (FEPs) relevant to assessing CSNF degradation and 
radionuclide release are identified and discussed in Section 6.2. The technical bases for the 
included FEPs are described in Sections 6.2.1 and 6.2.2. Section 6.2.3 indicates that two FEPs 
are included and Table 6-1 provides an explanation of this decision. A listing of excluded FEPs, 
including the technical bases for the exclusion decisions, is provided in Miscellaneous Waste 
Form FEPs (BSC 2004 [DIRS 163116]). 
(6)  
Not applicable to this report. 
(7) Guidance in NUREG 1297 and NUREG 1298 [re: Expert Elicitation] are 
followed. 
Not applicable to this report because expert elicitation is not used. 
Acceptance Criterion 2Data Are Sufficient for Model Justification 
(1) Parameters used to evaluate the degradation of EBS are adequately justified; 
The input data and parameters used for the performance of the CSNF waste form degradation 
model come, primarily, from laboratory experiments and tests performed for the project (Section 
4.1), and technical or scientific reports and papers (Sections 6 and 7). 
Justification for the use of specific data is typically provided in the section in which the data are 
initially discussed (Section 4.1) and used (Sections 6.2, 6.3, and 7.1). 
(2) Sufficient data have been collected to establish initial and boundary 
conditions; 
Extensive scientific investigations and experiments have been performed by the project to 
develop the data necessary to support the analyses provided in this report (Sections 4.1 and 6.2). 
All initial and boundary conditions are adequately and appropriately established and justified. 
(3) Data on the degradation of the engineered barriers (e.g.  general and 
localized corrosion, microbially induced corrosion, galvanic interactions, 
hydrogen embrittlement and phase stability) are based on laboratory 
measurements, site-specific field measurements, industrial and/or natural analogs 
and tests designed to replicate anticipated conditions. As appropriate, sensitivity 
or uncertainty analyses are provided and are shown to be adequate. 
Data related to the various potential degradation modes for the CSNF waste form are discussed 
in detail in Section 6.2.2. 
(4) Degradation models for the applicable processes are adequate. For example, 
general and localized corrosion, microbially induced corrosion, galvanic 
interactions, hydrogen embrittlement and phase stability are given appropriate 
consideration and treatment. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-10 August 2004 
The various models related to the potential degradation modes for the CSNF waste form are 
discussed in detail throughout this report. Specifically, the conceptual basis for assessing the 
progression of fuel rod degradation following initial breaching of the cladding is discussed in 
Section 6.2, modeling of the instantaneous release fractions is discussed in Section 6.3, and 
modeling of the CSNF matrix fractional release rates is addressed in Sections 6.4.1 and 6.4.2. 
Acceptance Criterion 3Data Uncertainty is Characterized and Propagated through the 
Model Abstraction 
(1) Models use parameter values, assumed ranges, probability distributions 
and/or bounding assumptions that are technically defensible, reasonably account 
for uncertainties and variabilities, and do not result in under-representation of 
the risk estimate. 
Each of the models developed in this report use parameter values, assumed ranges, probability 
distributions or bounding, or both assumptions that are technically defensible, reasonably 
account for uncertainties and variabilities, and do not result in underrepresentation of the risk 
estimate. In each situation, discussion and consideration of the uncertainties associated with 
specific data are addressed in detail. The effects of data uncertainties on the parameter ranges 
and uncertainty distributions in the instantaneous radionuclide release and matrix release rate 
models are discussed in Sections 6.3 and 6.4, respectively. 
(2) Appropriate parameters, based on techniques that may include laboratory 
experiments, field measurements, and industrial analogs are used. 
The various models addressed in this report use data and parameters that were developed based 
on laboratory experiments (Sections 4.1, 6.3.1, and 6.4.1). Sections 7.1 and 7.3 use peerreviewed 
and industrial literature and relevant natural analog data for model validation. 
(3) Assumed range of values and probability distributions for parameters used in 
conceptual and process-level models are not likely to underestimate the actual 
degradation and failure of engineered barriers. 
In those instances where uncertainties exist regarding the range of values and probability 
distributions, care is taken to choose values anticipated to provide conservative results 
(Sections 6.3 and 6.4). As discussed in Section 8.2, model validation results indicate a 
conservative modeling bias. 
(4)  
Not applicable; no NDE of CSNF waste form materials was involved with the development of 
this model report. 
(5) Where sufficient data do not exist, the definition of parameter values and 
conceptual models is based on appropriate use of other sources, such as expert 
elicitation. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-11 August 2004 
The available data is generally sufficient to support the definition of parameter values and CSNF 
conceptual models. As described in Section 5, appropriate and conservative assumptions are 
used in a few instances where sufficient data are not available. 
Acceptance Criterion 4Model Uncertainty is Characterized and Propagated Through the 
Model Abstraction 
(1) Alternative modeling approaches are considered and are consistent with 
available data and current scientific understanding. 
Alternative modeling approaches consistent with available data and current scientific 
understanding are considered and discussed in Section 6.4.2. Although these alternative models 
are screened out for use in TSPA-LA (Section 6.4.2.3), they are used for model validation in 
Section 7.2. 
(2) Consideration of conceptual model uncertainty is consistent with available 
site characterization data, laboratory experiments,  and the treatment of 
uncertainty does not result in under-estimation of the risk estimate. 
Consideration of uncertainties in the models used in this report is an integral part of the model 
development and validation processes. Conceptual model uncertainty is consistent with the 
information that has been developed through laboratory experiments (Sections 6 and 7). Care to 
ensure the treatment of uncertainty does not result in underestimation of the risk estimate is 
taken. As discussed in Section 8.2, model validation results indicate a somewhat conservative 
modeling bias. 
(3) Alternative modeling approaches, consistent with available data and current 
scientific understanding, are used and the modeling results are evaluated using 
tests that are sensitive to the processes modeled. 
Alternative modeling approaches, consistent with available data and current scientific 
understanding, are considered (Section 6.4.2). In all instances, it is determined that the base case 
model provides a more credible modeling basis than would be achieved by using the alternative 
models. Thus, although due consideration is given to the use of alternative modeling approaches, 
no alternative models are used in TSPA-LA. Model validation includes comparison with the 
alternative models (Section 7.2). 
Acceptance Criterion 5Model Abstraction Output Is Supported By Objective 
Comparisons 
(1) Models implemented in this total system performance assessment abstraction 
provide results consistent with output from detailed process-level models and or 
empirical observations (laboratory and field testing, and/or natural analogs). 
Results of CSNF degradation models implemented in this total system performance assessment 
abstraction are compared with empirical observations (laboratory and natural analogs) as part of 
the model validation (Sections 7.1 and 7.3). 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-12 August 2004 
(2)  
Not applicable; this report includes no numerical models. 
(3) Evidence is sufficient to show that models will not underestimate the actual 
degradation and failure of engineered barriers. 
In those instances where there is doubt about the appropriateness or accuracy of the models or 
data, care is taken to select a conservative approach or conservative data that would result in an 
overestimation of risk. Model validation results indicate that this approach results in a 
somewhat conservative modeling bias (Section 8.2). 
(4) Mathematical degradation models are based on the same environmental 
parameters, material factors, assumptions and approximations shown to be 
appropriate for closely analogous applications. 
The testing of irradiated spent fuel samples for degradation mechanisms and effects has a long 
history that has led to the establishment of well-accepted methodologies and understanding of 
the processes that control the CSNF degradation and radionuclide release processes. As a result, 
models are based on the same environmental parameters, material factors, assumptions, and 
approximations appropriate for closely analogous applications (Section 6.2). 
(5) Accepted and well documented procedures are used to construct and test the 
numerical models that simulate the EB chemical environment and degradation of 
EB; 
The development and testing of the models used to simulate the CSNF degradation and 
radionuclide release, is performed in accordance with previously established well-documented 
Yucca Mountain project procedures based on industry established norms. These procedures also 
establish the appropriate Quality Assurance requirements for such activities and appropriate 
checking, auditing, and other activities performed to ensure the adequacy and appropriateness of 
the models (Section 2). 
(6)  
Not applicable to the scope of this model report. 
8.3.3 Radionuclide Release Rates and Solubility Limits 
The following Acceptance Criteria can be found in Section 2.2.1.3.4.3 of Yucca Mountain 
Review Plan, Final Report (NRC 2003, [DIRS 163274]). 
Acceptance Criterion 1System Description and Model Integration Are Adequate 
(1)  
Not Applicable. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-13 August 2004 
(2) The abstraction of radionuclide release rates and solubility limits uses 
assumptions, technical bases, data, and models that are appropriate and 
consistent with other related U.S. Department of Energy abstractions. For 
example, the assumptions used for this model abstraction are consistent with the 
abstractions of Degradation of Engineered Barriers (Section 2.2.1.3.1); 
Mechanical Disruption of Waste Packages (Section 2.2.1.3.2); Quantity and 
Chemistry of Water Contacting Waste Packages and Waste Forms (Section 
2.2.1.3.3); Climate and Infiltration (Section 2.2.1.3.5); and Flow Paths in the 
Unsaturated Zone (Section 2.2.1.3.6). The descriptions and technical bases 
provide transparent and traceable support for the abstraction of radionuclide 
release rates and solubility limits; 
The CSNF degradation rate abstraction model is developed in Sections 6.3 and 6.4 from inputs in 
Section 4.1. 
(3) The abstraction of radionuclide release rates and solubility limits provides 
sufficient, consistent design information on waste packages and engineered 
barrier systems. For example, inventory calculations and selected radionuclides 
are based on the detailed information provided on the distribution (both spatially 
and by compositional phase) of the radionuclide inventory, within the various 
types of high-level radioactive waste; 
The CSNF degradation model reflects detailed information on distribution of radionuclide 
inventory within the CSNF in Sections 4.1.1, 6.2, and 6.3. 
(4) The U.S. Department of Energy reasonably accounts for the range of 
environmental conditions expected inside breached waste packages and in the 
engineered barrier environment surrounding the waste package. For example, 
the U.S. Department of Energy should provide a description and sufficient 
technical bases for its abstraction of changes in hydrologic properties in the near 
field, caused by coupled thermal-hydrologic-mechanical-chemical processes; 
The CSNF degradation model considers dripping and humid-air conditions within the breached 
waste package. It covers the expected range of pH, temperature, and carbonate concentrations as 
described in Sections 1 and 6. 
(5) The description of process-level conceptual and mathematical models is 
sufficiently complete, with respect to thermal-hydrologic processes affecting 
radionuclide release from the emplacement drifts. For example, if the U.S. 
Department of Energy uncouples coupled processes, the demonstration that 
uncoupled model results bound predictions of fully coupled results is adequate; 
The CSNF degradation model is temperature dependent and coupled to the thermal-hydrologic 
processes within the TSPA-LA model. 
(6) Technical bases for inclusion of any thermal-hydrologic-mechanical-chemical 
couplings and features, events, and processes in the radionuclide release rates 
and solubility limits model abstraction are adequate. For example, technical 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-14 August 2004 
bases may include activities, such as independent modeling, laboratory or field 
data, or sensitivity studies; 
The CSNF degradation model is temperature and chemistry dependent and coupled to these 
processes within the TSPA-LA model. The thermal and chemistry dependence of the model is 
based on laboratory testing described in Section 4 and validated with testing described in 
Section 7. 
(7-8)  
Not Applicable. 
Acceptance Criterion 2Data Are Sufficient for Model Justification 
(1) Geological, hydrological, and geochemical values used in the license 
application are adequately justified. Adequate description of how the data were 
used, interpreted, and appropriately synthesized into the parameters is provided; 
The CSNF degradation model receives hydrologic and geochemical information from other 
models where that information is justified. The CSNF degradation model is designed to cover 
the full range of hydrologic and geochemical conditions to which it is sensitive. 
(2) Sufficient data have been collected on the characteristics of the natural system 
and engineered materials to establish initial and boundary conditions for 
conceptual models and simulations of thermal-hydrologic-chemical coupled 
processes. For example, sufficient data should be provided on design features, 
such as the type, quantity, and reactivity of materials, that may affect 
radionuclide release for this abstraction; 
Extensive data have been collected to develop and validate the semi-empirical model for the 
CSNF degradation model, as described in Sections 4 and 7. 
(3)  
Not Applicable. 
(4) The corrosion and radionuclide release testing program for high-level 
radioactive waste forms intended for disposal provides consistent, sufficient, and 
suitable data for the in-package and in-drift chemistry used in the abstraction of 
radionuclide release rates and solubility limits. For expected environmental 
conditions, the U.S. Department of Energy provides sufficient justification for the 
use of test results, not specifically collected from the Yucca Mountain site, for 
engineered barrier components, such as high-level radioactive waste forms, drip 
shield, and backfill. 
The first sentence in this criterion, which addresses in-package and in-drift chemistry, is not 
applicable to this modeling report. The CSNF degradation model does not directly use any test 
results not specifically collected for the YMP. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-15 August 2004 
Acceptance Criterion 3Data Uncertainty Is Characterized and Propagated Through the 
Model Abstraction 
(1) Models use parameter values, assumed ranges, probability distributions, and 
bounding assumptions that are technically defensible, reasonably account for 
uncertainties and variabilities, and do not result in an under-representation of the 
risk estimate; 
Data uncertainty is described in Sections 4.1, 6.3.1, and 6.3.3. Propagation of this uncertainty is 
discussed in Section 6.4.1. Section 7 discusses how the model is reasonable or conservative, or 
both. 
(2) Parameter values, assumed ranges, probability distributions, and bounding 
assumptions used in the abstractions of radionuclide release rates and solubility 
limits in the total system performance assessment are technically defensible and 
reasonable based on data from the Yucca Mountain region, laboratory tests, and 
natural analogs. For example, parameter values, assumed ranges, probability 
distributions, and bounding assumptions adequately reflect the range of 
environmental conditions expected inside breached waste packages; 
The CSNF degradation model receives hydrologic and geochemical information from other 
models where that information is justified. As stated in Section 1, the CSNF degradation model 
is designed to cover the full range of hydrologic and geochemical conditions to which it is 
sensitive: pH 2 to 10.3, oxygen partial pressure from 0.002 to 0.2 atmospheres, and carbonate 
bicarbonate concentrations from 0 to 2  10-2 molar. The model is based on laboratory data 
collected for the Yucca Mountain Project as described in Section 4.1. 
(3) The U.S. Department of Energy uses reasonable or conservative ranges of 
parameters or functional relations to determine effects of coupled thermalhydrologic-
chemical processes on radionuclide release. These values are 
consistent with the initial and boundary conditions and the assumptions for the 
conceptual models and design concepts for natural and engineered barriers at the 
Yucca Mountain site. If any correlations between the input values exist, they are 
adequately established in the total system performance assessment. For example, 
estimations are based on a thermal loading and ventilation strategy; engineered 
barrier system design (including drift liner, backfill, and drip-shield); and natural 
system masses and fluxes that are consistent with those used in other 
abstractions; 
The CSNF degradation model receives hydrologic and geochemical information from other 
models where that information is justified. As stated in Section 1, the CSNF model is designed 
to cover the full range of hydrologic and geochemical conditions to which it is sensitive: pH 2 to 
10.3, oxygen partial pressure from 0.002 to 0.2 atmospheres, and carbonatebicarbonate 
concentrations from 0 to 2  10-2 molar. 
(4) Uncertainty is adequately represented in parameter development for 
conceptual models, process models, and alternative conceptual models 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-16 August 2004 
considered in developing the abstraction of radionuclide release rates and 
solubility limits, either through sensitivity analyses or use of bounding analyses; 
Uncertainty is addressed in Sections 8.2 and 8.3. 
(5-7)  
Not Applicable. 
(8) The U.S. Department of Energy adequately considers the uncertainties, in the 
characteristics of the natural system and engineered materials, such as the type, 
quantity, and reactivity of material, in establishing initial and boundary 
conditions for conceptual models and simulations of thermal-hydrologic-chemical 
coupled processes that affect radionuclide release; and 
Uncertainty is addressed in Sections 8.2 and 8.3. 
(9)  
Not Applicable. 
Acceptance Criterion 4Model Uncertainty Is Characterized and Propagated Through 
the Model Abstraction 
(1) Alternative modeling approaches of features, events, and processes are 
considered and are consistent with available data and current scientific 
understanding, and the results and limitations are appropriately considered in the 
abstraction; 
Alternative modeling approaches are discussed in Section 6.4.2 and compared to the base-case 
modeling approach in Section 7.2. 
(2) In considering alternative conceptual models for radionuclide release rates 
and solubility limits, the U.S. Department of Energy uses appropriate models, 
tests, and analyses that are sensitive to the processes modeled for both natural 
and engineering systems. Conceptual model uncertainties are adequately defined 
and documented, and effects on conclusions regarding performance are properly 
assessed. For example, in modeling flow and radionuclide release from the drifts, 
the U.S. Department of Energy represents significant discrete features, such as 
fault zones, separately, or demonstrates that their inclusion in the equivalent 
continuum model produces a conservative effect on calculated performance; and 
In the consideration of alternative conceptual models (Section 6.4.2), appropriate models, tests, 
and analyses sensitive to the processes modeled for natural and engineering systems are used. 
Conceptual model uncertainties are adequately defined and documented, and effects on 
conclusions regarding performance are properly assessed. Although alternative conceptual 
models are considered, it is determined that the baseline model is more appropriate and the 
considered alternative conceptual models are not used in TSPA-LA (Section 6.4.2). Comparison 
of the base-case model to the alternative models in Section 7.2 shows good agreement. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-17 August 2004 
(3) Consideration of conceptual model uncertainty is consistent with available 
site characterization data, laboratory experiments, field measurements, natural 
analog information and process-level modeling studies; and the treatment of 
conceptual model uncertainty does not result in an under-representation of the 
risk estimate; and 
The good agreement between the CSNF degradation rate results obtained using different 
conceptual models (Section 7.2) indicates that conceptual model uncertainty does not result in 
underrepresentation of the CSNF contribution to risk estimates. 
(4) The effects of thermal-hydrologic-chemical coupled processes that may occur 
in the natural setting, or from interactions with engineered materials, or their 
alteration products, on radionuclide release, are appropriately considered. 
Appropriate consideration is given to the effects of thermal-hydrologic-chemical coupled 
processes that may occur in the natural setting, or from interactions with engineered materials, or 
their alteration products, on radionuclide release. Related features, events, and processes are 
discussed in Section 6.2.3. Alternative conceptual models are discussed in Section 6.4.2. 
Acceptance Criterion 5Model Abstraction Output Is Supported by Objective 
Comparisons 
(1) The models implemented in this total system performance assessment 
abstraction provide results consistent with output from detailed process-level 
models and/or empirical observations (laboratory and field testings and/or 
natural analogs); 
Comparison with laboratory testing, other process-level models, and natural analogs are 
described in Sections 7.1, 7.2, and 7.3. 
(2)  
Not Applicable. 
(3) The U.S. Department of Energy adopts well-documented procedures that have 
been accepted by the scientific community to construct and test the numerical 
models, used to simulate coupled thermal-hydrologic-chemical effects on 
radionuclide release. For example, the U.S. Department of Energy demonstrates 
that the numerical models used for high-level radioactive waste degradation and 
dissolution, and radionuclide release from the engineered barrier system, are 
adequate representations; include consideration of uncertainties; and are not 
likely to underestimate radiological exposures to the reasonably maximally 
exposed individual and releases of radionuclides into the accessible environment; 
and 
The CSNF degradation model is developed using ASTM C1174-97 [DIRS 105725]. 
(4) ... 
Not Applicable. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 8-18 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-1 August 2004 
9. INPUTS AND REFERENCES 
9.1 DOCUMENTS CITED 
101530 Aagaard, P. and Helgeson, H.C. 1982. Thermodynamic and Kinetic Constraints on 
Reaction Rates Among Minerals and Aqueous Solutions. I. Theoretical 
Considerations. American Journal of Science, 282, (3), 237-285. New Haven, 
Connecticut: Yale University. TIC: 225516. 
117653 Aronson, S. 1958. Oxidation of UO2 in Water Containing Oxygen. In Bettis 
Technical Review, Reactor Metallurgy. 14th Edition. WAPD-BT-10. TID-4500. 
Pittsburgh, Pennsylvania: Westinghouse Electric Corporation, Bettis Atomic Power 
Division. ACC: NNA.19911025.0062. 
109194 Barner, J.O. 1985. Characterization of LWR Spent Fuel MCC-Approved Testing 
MaterialATM-101. PNL-5109, Rev. 1. Richland, Washington: Pacific Northwest 
Laboratory. TIC: 240883. 
163824 BSC (Bechtel SAIC Company) 2003. CSNF Waste Form Degradation: Summary 
Abstraction. ANL-EBS-MD-000015 REV 01. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: DOC.20030708.0004. 
161962 BSC 2003. In-Package Chemistry Abstraction. ANL-EBS-MD-000037 REV 02. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20030723.0003. 
168796 BSC 2003. Risk Information to Support Prioritization of Performance Assessment 
Models. TDR-WIS-PA-000009 REV 01 ICN 01, with errata. Las Vegas, Nevada: 
Bechtel SAIC Company. ACC: MOL.20021017.0045; DOC.20031014.0003. 
163116 BSC 2004. Miscellaneous Waste-Form FEPs. ANL-WIS-MD-000009 REV 01. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20040421.0005. 
169944 BSC 2004. Technical Work Plan for: Regulatory Integration Modeling and Analysis 
of the Waste Form and Waste Package. TWP-WIS-MD-000009 REV 00. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: DOC.20040616.0006. 
112904 Buck, E.C.; Wronkiewicz, D.J.; Finn, P.A.; and Bates, J.K. 1997. A New Uranyl 
Oxide Hydrate Phase Derived from Spent Fuel Alteration. Journal of Nuclear 
Materials, 249, (1), 70-76. Amsterdam, The Netherlands: Elsevier. TIC: 234844. 
166275 Canori, G.F. and Leitner, M.M. 2003. Project Requirements Document. TER-MGRMD-
000001 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: DOC.20031222.0006. 
162386 Casas, I.; Gimnez, J.; Mart, V.; Torrero, M.E.; and de Pablo, J. 1994. Kinetic 
Studies of Unirradiated UO2 Dissolution Under Oxidizing Conditions in Batch and 
Flow Experiments. Radiochimica Acta, 66/77, 23-27. Mnchen, Germany: R. 
Oldenbourg Verlag. TIC: 254041. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-2 August 2004 
162387 Christensen, H. and Sunder, S. 2000. Current State of Knowledge of Water 
Radiolysis Effects on Spent Nuclear Fuel Corrosion. Nuclear Technology, 131, (1), 
102-123. La Grange Park, Illinois: American Nuclear Society. TIC: 254040. 
131861 CRWMS M&O (Civilian Radioactive Waste Management System Management and 
Operations) 2000. Commercial Spent Nuclear Fuel Degradation in Unsaturated Drip 
Tests. Input Transmittal WP-WP-99432.T. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.20000107.0209. 
149230 CRWMS M&O 2000. Clad Degradation - Dry Unzipping. ANL-EBS-MD-000013 
REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20000503.0200. 
162406 Cunnane, J.; Ebert, W.; Goldberg, M.; Finch, R.; and Mertz, C. 2003. Yucca 
Mountain Project Report, Waste Form Testing Work. Argonne, Illinois: Argonne 
National Laboratory, Chemical Technology Division. ACC: MOL.20030630.0418. 
164019 Dehaudt, P. 2001. Physical and Chemical State of the Nuclear Spent Fuel After 
Irridiation. Section 5.2 of Synthesis on the Long Term Behavior of the Spent 
Nuclear Fuel. Poinssot, C., ed. CEA-R-5958(E). Volume I. Paris, France: 
Commissariat  l'nergie Atomique. TIC: 253976. 
164037 Dehaudt, P. 2001. State of the Art of the Oxidation of Spent Nuclear Fuel. 
Section 7.2 of Synthesis on the Long Term Behavior of the Spent Nuclear Fuel. 
Poinssot, C., ed. CEA-R-5958(E). Volume II. Paris, France: Commissariat  
l'nergie Atomique. TIC: 253976. 
162388 de Pablo, J.; Casas, I.; Gimnez, J.; Molera, M.; Rovira, M.; Duro, L.; and Bruno, J. 
1999. The Oxidative Dissolution Mechanism of Uranium Dioxide. I. The Effect of 
Temperature in Hydrogen Carbonate Medium. Geochimica et Cosmochimica Acta, 
63, (19-20), 3097-3103. New York, New York: Pergamon. TIC: 254443. 
171386 DOE (U.S. Department of Energy) 2004. Quality Assurance Requirements and 
Description. DOE/RW-0333P, Rev. 16. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. 
ACC: DOC.20040823.0004. 
126446 Einziger, R.E.; Marschman, S.C.; and Buchanan, H.C. 1991. Spent-Fuel Dry-Bath 
Oxidation Testing. Nuclear Technology, 94, 383-393. Hinsdale, Illinois: American 
Nuclear Society. TIC: 246459. 
127332 Finch, R.J.; Buck, E.C.; Finn, P.A.; and Bates, J.K. 1999. Oxidative Corrosion of 
Spent UO2 Fuel in Vapor and Dripping Groundwater at 90C. Scientific Basis for 
Nuclear Waste Management XXII, Symposium held November 30-December 4, 1998, 
Boston, Massachusetts, U.S.A. Wronkiewicz, D.J. and Lee, J.H., eds. 556, 431-438. 
Warrendale, Pennsylvania: Materials Research Society. TIC: 246426. 
123134 Forsyth, R. 1997. The SKB Spent Fuel Corrosion Program. An Evaluation of Results 
from the Experimental Programme Performed in the Studsvik Hot Cell Laboratory. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-3 August 2004 
SKB TR-97-25. Stockholm, Sweden: Svensk Krnbrnslefrsrjning A.B. 
TIC: 246406. 
120955 Forsyth, R.S.; Werme, L.O.; and Bruno, J. 1986. The Corrosion of Spent UO2 Fuel 
in Synthetic Groundwater. Journal of Nuclear Materials, 138, 1-15. Amsterdam, 
The Netherlands: Elsevier. TIC: 246407. 
162410 Goldberg, M. 2003. Rod Segment Test Data Report, CSNF Degradation Model: Q 
Data Input from ANL Testing. Task Number: PAWC1M5. Argonne, Illinois: 
Argonne National Laboratory, Chemical Engineering Division. 
ACC: MOL.20030627.0221. 
113233 Grambow, B. 1989. Spent Fuel Dissolution and Oxidation: An Evaluation of 
Literature Data. SKB TR-89-13. Stockholm, Sweden: Svensk 
Krnbrnslefrsrjning A.B. TIC: 208579. 
162391 Grambow, B.; Loida, A.; Martinez-Esparza, A.; Diaz-Arocas, P.; de Pablo, J.; Paul, 
J.L.; Marx, G.; Glatz, J.P.; Lemmens, K.; Ollila, K.; and Christensen, H. 2000. 
Long-Term Safety of Radioactive Waste Disposal: Source Term for Performance 
Assessment of Spent Fuel as a Waste Form, Final Report. Forchungszentrum 
Karlsruhe, Technik und Umwelk, FZKA 6420. Karlsruhe, Germany: 
Forschungszentrum Karlsruhe GmbH. TIC: 254058. 
113255 Grandstaff, D.E. 1976. A Kinetic Study of the Dissolution of Uraninite. Economic 
Geology and the Bulletin of The Society of Economic Geologists, 71, (8), 1493-1506. 
El Paso, Texas: Economic Geology Publishing. TIC: 246339. 
121407 Gray, W.J. 1999. Inventories of Iodine-129 and Cesium-137 in the Gaps and Grain 
Boundaries of LWR Spent Fuels. Scientific Basis for Nuclear Waste Management 
XXII, Symposium held November 30-December 4, 1998, Boston, Massachusetts, 
U.S.A. Wronkiewicz, D.J. and Lee, J.H., eds. 556, 487-494. Warrendale, 
Pennsylvania: Materials Research Society. TIC: 246426. 
170109 Gray, W.J.;Steward, S.A.;Tait, J.C.;Shoesmith, D.W. 1994. Interlaboratory 
Comparison of UO2 Dissolution Rates International Conference on High Level 
Radioactive Waste Management (5th : 1994 : Las Vegas, Nev.), UCRL-JC-115356. 
TIC: 210975 
162393 Gray, W.J.; Strachan, D.M.; and Wilson, C.N. 1992. Gap and Grain-Boundary 
Inventories of Cs, Tc and Sr in Spent LWR Fuel. Scientific Basis for Nuclear Waste 
Management XV, Symposium held November 4-7, 1991, Strasbourg, France. 
Sombret, C.G., ed. 257, 353-360. Pittsburgh, Pennsylvania: Materials Research 
Society. TIC: 204618. 
100758 Gray, W.J. and Wilson, C.N. 1995. Spent Fuel Dissolution Studies FY 1991-1994. 
PNL-10450. Richland, Washington: Pacific Northwest Laboratory. 
ACC: MOL.19960802.0035. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-4 August 2004 
101671 Grenthe, I.; Fuger, J.; Konings, R.J.M.; Lemire, R.J.; Muller, A.B.; Nguyen-Trung, 
C.; and Wanner, H. 1992. Chemical Thermodynamics of Uranium. Volume 1 of 
Chemical Thermodynamics. Wanner, H. and Forest, I., eds. Amsterdam, The 
Netherlands: North-Holland Publishing Company. TIC: 224074. 
109206 Guenther, R.J.; Blahnik, D.E.; Campbell, T.K.; Jenquin, U.P.; Mendel, J.E.; and 
Thornhill, C.K. 1988. Characterization of Spent Fuel Approved Testing Material 
ATM-106. PNL-5109-106. Richland, Washington: Pacific Northwest Laboratory. 
ACC: NNA.19911017.0105. 
109205 Guenther, R.J.; Blahnik, D.E.; Campbell, T.K.; Jenquin, U.P.; Mendel, J.E.; Thomas, 
L.E.; and Thornhill, C.K. 1988. Characterization of Spent Fuel Approved Testing 
MaterialATM-103. PNL-5109-103. Richland, Washington: Pacific Northwest 
Laboratory. ACC: NNA.19911017.0104. 
162397 Jegou, C.; Paul, J.L.; and Lucchini, J.F. 2001. State of the Art of the Leaching and 
RN Release from Spent Fuel. Section 8.2 of Synthesis on the Long Term Behavior 
of the Spent Nuclear Fuel. Poinssot, C., ed. CEA-R-5958(E). Voume II. [Paris], 
France: Commissariat  l'nergie Atomique. TIC: 253976. 
162372 Johnson, L.H.; LeNeveu, D.M.; King, F.; Shoesmith, D.W.; Kolar, M.; Oscarson, 
D.W.; Sunder, S.; Onofrei, C.; and Crosthwaite, J.L. 1996. Vault Model. Volume 2 
of The Disposal of Canada's Nuclear Fuel Waste: A Study of Postclosure Safety of 
In-Room Emplacement of Used CANDU Fuel in Copper Containers in Permeable 
Plutonic Rock. AECL-11494-2. Pinawa, Manitoba, Canada: Atomic Energy of 
Canada Limited. TIC: 226733. 
127125 Johnson, L.H. and Taylor, P. 1998. Alteration of Spent CANDU Fuel in Aerated 
Steam at 150C. Draft. Pinawa, Manitoba, Canada: Atomic Energy of Canada 
Limited. ACC: MOL.19981118.0337. 
125636 Kansa, E.J.; Hanson, B.D.; and Stout, R.B. 1998. Grain Size and Burnup 
Dependence of Spent Fuel Oxidation: Geological Repository Impact. UCRL-JC- 
131592. Livermore, California: Lawrence Livermore National Laboratory. 
ACC: MOL.19990420.0128. 
126191 Kohli, R.; Stahl, D.; Pasupathi, V.; Johnson, A.B.; and Gilbert, E.R. 1985. The 
Behavior of Breached Boiling Water Reactor Fuel Rods on Long-Term Exposure to 
Air and Argon at 598 K. Nuclear Technology, 69, 186-197. La Grange Park, 
Illinois: American Nuclear Society. TIC: 245332. 
168087 Lasaga, A.C. and Luttge, A. 2003. A Model for Crystal Dissolution. European 
Journal of Mineralogy, 15, (4), 605-615. Stuttgart, Germany: E. Schweizerbart'sche 
Verlagsbuchhandlung. TIC: 255729. 
106466 Lasaga, A.C.; Soler, J.M.; Ganor, J.; Burch, T.E.; and Nagy, K.L. 1994. Chemical 
Weathering Rate Laws and Global Geochemical Cycles. Geochimica et 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-5 August 2004 
Cosmochimica Acta, 58, (10), 2361-2386. New York, New York: Pergamon. 
TIC: 237441. 
168991 Leenaers, A.; Sannen, L.; Van den Berghe, S.; Verwerft, M. 2003. Oxidation of 
Spent UO2 Fuel Stored in Moist Environment. Journal of Nuclear Material, 317, 
226-233. Amsterdam, The Netherlands: Elsevier. TIC: 256014. 
113270 McEachern, R.J. and Taylor, P. 1998. A Review of the Oxidation of Uranium 
Dioxide at Temperatures Below 400C. Journal of Nuclear Material, 254, 87-121. 
Amsterdam, The Netherlands: Elsevier. TIC: 246427. 
131639 McKenzie, W.F. 1992. UO2 Dissolution Rates: A Review. UCRL-ID-111663. 
Livermore, California: Lawrence Livermore National Laboratory. TIC: 246408. 
100469 Murphy, W.M. 1995. Natural Analogs for Yucca Mountain. Radwaste Magazine, 
2, (6), 44-50. La Grange Park, Illinois: American Nuclear Society. TIC: 237929. 
163274 NRC (U.S. Nuclear Regulatory Commission) 2003. Yucca Mountain Review Plan, 
Final Report. NUREG-1804, Rev. 2. Washington, D.C.: U.S. Nuclear Regulatory 
Commission, Office of Nuclear Material Safety and Safeguards. TIC: 254568. 
125562 Olander, D.R.; Kim, Y.S.; Wang, W-E.; and Yagnik, S.K. 1999. Steam Oxidation of 
Fuel in Defective LWR Rods. Journal of Nuclear Materials, (270), 11-20. New 
York, New York: Elsevier. TIC: 246527. 
170078 Olander, D.R. 1986. Oxidation of UO2 by High-Pressure Steam Nuclear 
Technology, 74, 215-217. La Grange Park, Illinois: American Nuclear Society. 
TIC: 256246. 
163420 Oversby, V.M. 1999. Uranium Dioxide, SIMFUEL, and Spent Fuel Dissolution 
Rates - A Review of Published Data. SKB TR-99-22. Stockholm, Sweden: Svensk 
Krnbrnslefrsrjning A.B. TIC: 246954. 
162412 Pan, Y.-M.; Brossia, C.S.; Cragnolino, G.A.; Jain, V.; Pensado, O.; and Sridhar, N. 
2001. Effects of In-Package Chemistry on the Degradation of Vitrified High-Level 
Radioactive Waste and Spent Nuclear Fuel Cladding. CNWRA 2002-01. San 
Antonio, Texas: Center for Nuclear Waste Regulatory Analyses. TIC: 254197. 
130197 Pearcy, E.C. and Murphy, W.M. 1991. Geochemical Natural Analogs Literature 
Review. CNWR 90-008. San Antonio, Texas: Center for Nuclear Waste Regulatory 
Analyses. TIC: 213164. 
100486 Pearcy, E.C.; Prikryl, J.D.; Murphy, W.M.; and Leslie, B.W. 1994. Alteration of 
Uraninite from the Nopal I Deposit, Pena Blanca District, Chihuahua, Mexico, 
Compared to Degradation of Spent Nuclear Fuel in the Proposed U.S. High-Level 
Nuclear Waste Repository at Yucca Mountain, Nevada. Applied Geochemistry, 9, 
713-732. New York, New York: Elsevier. TIC: 236934. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-6 August 2004 
164034 Pelletier, M. 2001. State of the Art on the Potential Migration of Species. Section 
5.4 of Synthesis on the Long Term Behavior of the Spent Nuclear Fuel. Poinssot, C., 
ed. CEA-R-5958(E). Volume I. [Paris], France: Commissariat  l'nergie 
Atomique. TIC: 253976. 
162396 Piron, J.P. 2001. Presentation of the Key Scientific Issues for the Spent Nuclear 
Fuel Evolution in a Closed System. Section 5.1 of Synthesis on the Long Term 
Behavior of the Spent Nuclear Fuel. Poinssot, C., ed. CEA-R-5958(E). Volume I. 
Paris, France: Commissariat  l'nergie Atomique. TIC: 253976. 
113577 Reed, D.T. and Bowers, D.L. 1990. Alpha Particle-Induced Formation of Nitrate in 
the Cm-Sulfate Aqueous System. Radiochimica Acta, 51, 119-125. Mnchen, 
Germany: R. Oldenbourg Verlag. TIC: 245894. 
162398 Rllin, S.; Spahiu, K.; and Eklund, U.-B. 2001. Determination of Dissolution Rates 
of Spent Fuel in Carbonate Solutions Under Different Redox Conditions with a Flow- 
Through Experiment. Journal of Nuclear Materials, 297, (3), 231-243. Amsterdam, 
The Netherlands: North-Holland. TIC: 254044. 
162399 Serrano, J.A.; Rondinella, V.V.; Glatz, J.P.; Toscano, E.H.; Quiones, J.; Diaz- 
Arocas, P.P.; and Garcia-Serrano, J. 1998. Comparison of the Leaching Behaviour 
of Irradiated Fuel, SIMFUEL, and Non-irradiated UO2 Under Oxic Conditions. 
Radiochimica Acta, 82, (1-4), 33-37. Mnchen, Germany: R. Oldenbourg Verlag. 
TIC: 254195. 
162405 Shoesmith, D.W. 2000. Fuel Corrosion Processes Under Waste Disposal 
Conditions. Journal of Nuclear Materials, 282, (1), 1-31. Amsterdam, The 
Netherlands: North-Holland. TIC: 254043. 
162402 Shoesmith, D.W.; Sunder, S.; Bailey, M.G.; and Wallace, G.J. 1989. The Corrosion 
of Nuclear Fuel (UO2) in Oxygenated Solutions. Corrosion Science, 29, (9), 1115- 
1128. New York, New York: Pergamon Press. TIC: 254042. 
113366 Shoesmith, D.W. and Sunder, S. 1991. An Electrochemistry-Based Model for the 
Dissolution of UO2. SKB TR-91-63. Stockholm, Sweden: Svensk 
Krnbrnslefrsrjning A.B. TIC: 206581. 
113368 Shoesmith, D.W. and Sunder, S. 1992. The Prediction of Nuclear Fuel (UO2) 
Dissolution Rates Under Waste Disposal Conditions. Journal of Nuclear Materials, 
190, 20-35. Amsterdam, The Netherlands: Elsevier. TIC: 246431. 
162404 Shoesmith, D.W.; Sunder, S.; and Tait, J.C. 1998. Validation of an Electrochemical 
Model for the Oxidative Dissolution of Used CANDU Fuel. Journal of Nuclear 
Materials, 257, (2), 89-98. New York, New York: Elsevier. TIC: 243109. 
111047 Stout, R.B. and Leider, H.R. 1998. Waste Form Characteristics Report, CD-ROM 
Version. UCRL-ID-132375. Livermore, California: Lawrence Livermore National 
Laboratory. TIC: 246106. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-7 August 2004 
113639 Stroes-Gascoyne, S. 1996. Measurements of Instant-Release Source Terms for 
137Cs, 90Sr, 99Tc, 129I and 14C in Used CANDU Fuels. Journal of Nuclear Materials, 
238, 264-277. Amsterdam, The Netherlands: Elsevier. TIC: 245893. 
100829 Stumm, W. and Morgan, J.J. 1981. Aquatic Chemistry, An Introduction Emphasizing 
Chemical Equilibria in Natural Waters. 2nd Edition. New York, New York: John 
Wiley & Sons. TIC: 208448. 
126463 Sunder, S. and Miller, N.H. 1996. Oxidation of CANDU Uranium Oxide Fuel by 
Air in Gamma Radiation at 150 degrees C. Journal of Nuclear Materials, 231, 121- 
131. Amsterdam, The Netherlands: Elsevier. TIC: 246429. 
114435 Tait, J.C. and Luht, J.L. 1997. Dissolution Rates of Uranium from Unirradiated UO2 
and Uranium and Radionuclides from Used CANDU Fuel Using the Single-Pass 
Flow-Through Apparatus. 06819-REP-01200-0006 R00. Toronto, Ontario, Canada: 
Ontario Hydro. TIC: 243164. 
125792 Taylor, P.; Wood, D.D.; Duclos, A.M.; and Owen, D.G. 1989. Formation of 
Uranium Trioxide Hydrates on UO2 Fuel in Air-Steam Mixtures Near 200C. 
Journal of Nuclear Materials, 168, (1&2), 70-75. Amsterdam, The Netherlands: 
Elsevier. TIC: 246601. 
125815 Taylor, P.; Wood, D.D.; and Owen, D.G. 1995. Microstructures of Corrosion Films 
on UO2 Fuel Oxidized in AirSteam Mixtures at 225C. Journal of Nuclear 
Materials, 223, (3), 316-320. New York, New York: Elsevier. TIC: 256013. 
163048 Thomas, E. 2003. Transmittal of Unsaturated Testing of Bare Spent UO2 Fuel 
Fragments: Data Report, Argonne National Laboratory. Interoffice memorandum 
from E. Thomas (BSC) to J.C. Cunnane, July 2, 2003, 0702037939, with attachment. 
ACC: MOL.20030702.0116; MOL.20030311.0097. 
114439 Torrero, M.E.; Baraj, E.; De Pablo, J.; Gimenez, J.; and Casas, I. 1997. Kinetics of 
Corrosion and Dissolution of Uranium Dioxide as a Function of pH. International 
Journal of Chemical Kinetics, 29, (4), 261-267. New York, New York: John Wiley 
& Sons. TIC: 246160. 
125710 Wasywich, K.; Hocking, W.H.; Shoesmith, D.W.; and Taylor, P. 1993. Differences 
in Oxidation Behavior of Used CANDU Fuel During Prolonged Storage in Moisture- 
Saturated Air and Dry Air at 150C. Nuclear Technology, 104, (3), 309-329. La 
Grange Park, Illinois: American Nuclear Society. TIC: 246598. 
168088 White, A.F. and Brantley, S.L. 2003. The Effect of Time on the Weathering of 
Silicate Minerals: Why do Weathering Rates Differ in the Laboratory and Field?. 
Chemical Geology, 202, (3-4), 479-506. New York, New York: Elsevier. 
TIC: 255730. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-8 August 2004 
100793 Wilson, C.N. 1990. Results from NNWSI Series 3 Spent Fuel Dissolution Tests. 
PNL-7170. Richland, Washington: Pacific Northwest Laboratory. 
ACC: NNA.19900329.0142. 
122032 Woodley, R.E.; Einzeger, R.E.; and Buchanan, H.C. 1989. Measurement of the 
Oxidation of Spent Fuel Between 140 and 225 Degrees C. Nuclear Technology, 85, 
(1), 74-88. La Grange Park, Illinois: American Nuclear Society. TIC: 243577. 
102047 Wronkiewicz, D.J.; Bates, J.K.; Wolf, S.F.; and Buck, E.C. 1996. Ten-Year Results 
from Unsaturated Drip Tests with UO2 at 90C: Implications for the Corrosion of 
Spent Nuclear Fuel. Journal of Nuclear Materials, 238, (1), 78-95. Amsterdam, 
The Netherlands: North-Holland Publishing Company. TIC: 243361. 
9.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
156605 10 CFR 63. Energy: Disposal of High-Level Radioactive Wastes in a Geologic 
Repository at Yucca Mountain, Nevada. Readily available. 
AP-2.27Q, Rev. 1, ICN 4. Planning for Science Activities. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. 
ACC: DOC.20040610.0006. 
AP-SIII.2Q, Rev. 1, ICN 2. Qualification of Unqualified Data. Washington, D.C.: 
U.S. Department of Energy, Office of Civilian Radioactive Waste Management. 
ACC: DOC.20040127.0008 
AP-SIII.10Q, Rev. 2, ICN 5. Models. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. 
ACC: DOC.20040615.0003. 
105725 ASTM C1174-97. 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. 
LP-SI.11Q-BSC, Rev. 0, ICN 0. Software Management. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. 
ACC: DOC.20040225.0007. 
9.3 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
135012 LL000107951021.107. Radionuclide Inventory in Spent Fuel. Submittal date: 
01/12/2000. 
129285 LL991001251021.090. Draft - CSNF Waste Form Degradation: Unsaturated Drip 
Tests - G2020 Analysis and Modeling Report. Submittal date: 10/04/1999. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-9 August 2004 
162384 MO0301ANLSF001.450. Initial Release Fractions and Fractional Release Rates (Per 
Day) for Rod Segment Tests (BSC Test Plan: SITP-02-WF-001, REV 0). Submittal 
date: 01/15/2003. 
162383 MO0301ANLSF001.451. Summary of Fractional Release Rate Data (Per Day) for 
Fuel Fragment Tests (BSC Test Plan: SITP-02-WF-001, REV 0). Submittal date: 
01/15/2003. 
162385 MO0302PNLDUFTD.000. Flowthrough Dissolution Data. Submittal date: 
02/28/2003. 
163441 MO0304PNLLPHDD.000. Low pH Dissolution Data. Submittal date: 04/09/2003. 
170619 MO0407SEPGGBID.000. Gap and Grain Boundary Inventory Data for Commercial 
Spent Nuclear Fuel 
170618 MO0407SEPUDISR.000. Conversion of Uranium Dissolution Rates from Spent Fuel 
and Uranium Dioxide. 
170760 MO0407SEPFEPLA.000. LA FEP List. Submittal date: 7/20/2004 
9.4 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
MO0404ANLSF001.001. CSNF Radionuclide Release Model. Submittal date: 
04/09/2004. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 9-10 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 I-1 of I-2 August 2004 
APPENDIX I 
MICROSOFT EXCEL SPREADSHEETS AND SIGMA PLOT REGRESSION 
ANALYSES (WITH CD-ROM) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 I-2 of I-2 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-1 of 8 August 2004 
APPENDIX II 
MATHCAD REGRESSION ANALYSIS OF ALKALINE AND ACID DATA 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-2 of 8 August 2004 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-3 of 8 August 2004 
APPENDIX II 
II.1 MATHCAD REGRESSION ANALYSIS OF ALKALINE DATA 
rS 
0.36114 
108.86521 - 
0.00501 -
0.00499 - 
108.86521 - 
34904.98147 
0.24973 -
0.47255 
0.00501 -
0.24973 -
0.00222 
0.00002 
0.00499 -
0.47255 
0.00002 
0.00256 
...... 
.
.. 
. 
= 
rS0 0 , 0.601 = rS 
rYT rY  rbT rXT  rY  - ( ) 
length rY ( ) cols rX ( ) - ( ) 
rXT rX  ( ) 1 - 
 := 
Covariance 
rbT 4.705 1093.826 - 0.102 - 0.338 - ( ) = rb rXT rX  ( ) 1 - 
rXT  rY  := 
Regression Coefficients 
rX 3 < > 
Dat 3 < > 
:= 
pO2 
rX 2 < > 
Dat 2 < > 
:= 
pCO3 
rX 1 < > 
Dat 1 < > 
:= 
IT 
rX 0 < > 
Dat 0 < > 
:= 
Dummy column of 1's - 
used to get constant in model 
log Degradation Rate (LDR) rY Dat 4 < > 
:= 
Alkaline-Side Model 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-4 of 8 August 2004 
Cholesky Decomposition of rS 
L cholesky rS ( ) := 
L 
0.60095 
181.15646 - 
0.00834 -
0.00831 - 
0 
45.68717 
0.03852 - 
0.0226 - 
0
0 
0.02586 
0.03574 - 
0
0
0 
0.02659 
...... 
.
.. 
. 
= 
Standard Error 
StErr 
rS0 0 , 
rS1 1 , 
rS2 2 , 
rS3 3 , 
....... 
.
... 
. 
:= StErr 
0.6009 
186.8287 
0.0471 
0.0506 
...... 
.
.. 
. 
= 
Other Statistics 
SSreg rX rb  mean rY ( ) - ( )2 . := SSreg 9.694 = Sum of Squares regression 
or "Explained SS" 
SStot rY mean rY ( ) - ( )2 . := SStot 17.283 = Sum of Squares total 
Coefficient of determination, R2 
This is the fraction of the total 
squared error that is explained 
by the model. R2 measures the 
relative predictive power of a 
model. 
Rsq 
SSreg 
SStot 
:= Rsq 0.561 = 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-5 of 8 August 2004 
SSres rY rX rb  - ( )2 . := SSres 7.59 = Sum of Squares residual 
se 
SSres 
length rX 1 < > ( ) cols rX ( ) - 
:= se 0.347 = Standard Error of Estimate 
This statistic measures the 
spread around the regression 
line. 
Table II-1. Data for Alkaline-Side Regression Analysis 
Constant IT pCO3 pO2 LDR 
1.00000 0.00310 2.70000 0.70000 0.88000 
1.00000 0.00309 2.70000 0.70000 0.93000 
1.00000 0.00310 2.70000 0.70000 0.80000 
1.00000 0.00337 1.70000 0.70000 0.62000 
1.00000 0.00289 1.70000 0.70000 1.14000 
1.00000 0.00286 1.70000 0.70000 1.33000 
1.00000 0.00287 3.70000 0.70000 1.02000 
1.00000 0.00290 3.70000 0.70000 1.00000 
1.00000 0.00338 3.70000 0.70000 -0.24000 
1.00000 0.00342 3.70000 0.70000 0.03000 
1.00000 0.00337 1.70000 0.70000 0.51000 
1.00000 0.00337 2.70000 0.70000 0.39000 
1.00000 0.00334 3.70000 1.70000 0.33000 
1.00000 0.00285 3.70000 1.70000 0.25000 
1.00000 0.00334 1.70000 1.70000 0.39000 
1.00000 0.00286 1.70000 1.70000 0.55000 
1.00000 0.00337 1.70000 2.70000 0.47000 
1.00000 0.00288 1.70000 2.70000 -0.02000 
1.00000 0.00287 3.70000 2.70000 0.15000 
1.00000 0.00341 3.70000 2.70000 -0.12000 
1.00000 0.00309 1.70000 2.70000 0.08000 
1.00000 0.00339 2.70000 2.70000 0.29000 
1.00000 0.00288 1.70000 1.70000 0.75000 
1.00000 0.00310 2.70000 0.70000 0.82000 
1.00000 0.00335 1.70000 0.70000 0.21000 
1.00000 0.00338 1.70000 0.70000 0.61000 
1.00000 0.00286 1.70000 0.70000 0.97000 
1.00000 0.00338 3.70000 0.70000 0.42000 
1.00000 0.00287 3.70000 0.70000 1.04000 
1.00000 0.00337 1.70000 0.70000 0.56000 
1.00000 0.00336 1.70000 0.70000 0.58000 
1.00000 0.00287 1.70000 0.70000 0.84000 
1.00000 0.00336 3.70000 0.70000 0.45000 
1.00000 0.00288 3.70000 0.70000 0.98000 
1.00000 0.00287 1.70000 2.70000 0.13000 
1.00000 0.00333 3.70000 2.70000 0.31000 
1.00000 0.00288 3.70000 2.70000 0.54000 
1.00000 0.00333 1.70000 0.70000 0.51000 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-6 of 8 August 2004 
Table II-1. Data for Alkaline-Side Regression Analysis (Continued) 
Constant IT pCO3 pO2 LDR 
1.00000 0.00287 1.70000 0.70000 1.08000 
1.00000 0.00333 3.70000 0.70000 0.57000 
1.00000 0.00335 1.70000 0.70000 0.60000 
1.00000 0.00287 1.70000 0.70000 0.75000 
1.00000 0.00334 3.70000 0.70000 0.40000 
1.00000 0.00286 3.70000 0.70000 0.83000 
1.00000 0.00287 1.70000 2.70000 -0.07000 
1.00000 0.00335 1.70000 0.70000 0.38400 
1.00000 0.00287 1.70000 0.70000 1.88900 
1.00000 0.00287 3.70000 0.70000 1.03700 
1.00000 0.00335 3.70000 0.70000 0.40700 
1.00000 0.00335 1.70000 0.70000 0.82700 
1.00000 0.00335 2.70000 0.70000 0.97000 
1.00000 0.00334 3.70000 1.70000 -0.92100 
1.00000 0.00287 3.70000 1.70000 0.96400 
1.00000 0.00334 1.70000 1.70000 0.27200 
1.00000 0.00287 1.70000 1.70000 0.70800 
1.00000 0.00335 1.70000 2.70000 -0.65800 
1.00000 0.00287 1.70000 2.70000 0.74900 
1.00000 0.00287 3.70000 2.70000 -0.29200 
1.00000 0.00334 3.70000 2.70000 -0.63800 
1.00000 0.00309 1.70000 2.70000 0.66300 
1.00000 0.00334 2.70000 2.70000 0.18200 
1.00000 0.00309 2.70000 1.70000 1.09000 
1.00000 0.00309 2.70000 1.70000 0.90100 
1.00000 0.00309 2.70000 1.70000 1.01700 
1.00000 0.00287 3.70000 0.70000 0.81200 
1.00000 0.00287 2.70000 0.70000 1.36700 
1.00000 0.00287 1.70000 0.70000 1.73200 
DTN: MO0302PNLDUFTD.000 [DIRS 162385]; 
MO0407SEPUDISR[DIRS 170618] 
NOTES: Data for IT has been rounded to 3 significant figures. 
Data for LDR, pCO3, and pO2 has been rounded to 2 
significant figures. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-7 of 8 August 2004 
II.2 MATHCAD REGRESSION ANALYSIS OF ACID DATA 
StErr 
0.446 
0.11 
... 
.
. 
= StErr 
rSA0 0 , 
rSA1 1 , 
.... .
. 
:= 
Standard Error 
LA 
0.44575 
0.10478 - 
0 
0.03495 
... 
.
. 
= 
LA cholesky rSA ( ) := 
Cholesky Decomposition of Covariance Matrix 
rSA 
0.19869 
0.0467 - 
0.0467 -
0.0122 
... 
.
. 
= rSA 
rYAT rYA  rbAT rXAT  rYA  - ( ) 
length rYA ( ) cols rXA ( ) - ( ) 
rXAT rXA  ( ) 1 - 
 := 
Covariance Matrix 
rbAT 6.599 0.34 - ( ) = rbA rXAT rXA  ( ) 1 - 
rXAT  rYA  := 
Regression Coefficients 
pH rXA 1 < > 
DatA 1 < > 
:= 
rXA 0 < > 
DatA 0 < > 
:= 
Dummy column of 1's - 
used to get constant in model 
Adjusted LDR rYA DatA 2 < > 
:= 
DatA 
1
1
1
1
1
1 
2.02 
3.01 
3.25 
3.8 
5.07 
5.82 
5.5812 
5.4183 
5.9547 
5.6121 
4.7488 
4.4725 
........ 
.
.... 
. 
:= 
Acid-Side Model 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 II-8 of 8 August 2004 
Other Statistics 
SSregA rXA rbA  mean rYA ( ) - ( )2 . := SSregA 1.13 = Sum of Squares regression 
or "Explained SS" 
SStotA rYA mean rYA ( ) - ( )2 . := SStotA 1.608 = Sum of Squares total 
Coefficient of determination, R2 
This is the fraction of the total 
squared error that is explained 
by the model. R2 measures the 
relative predictive power of a 
model. 
RsqA 
SSregA 
SStotA 
:= RsqA 0.703 = 
SSresA rYA rXA rbA  - ( )2 . := SSresA 0.477 = Sum of Squares residual 
se 
SSresA 
rows rXA ( ) cols rXA ( ) - 
:= se 0.345 = Standard Error of Estimate 
This statistic measures the 
spread around the regression 
line. 
Table II-2. Data for Acid-Side Regression Analysis 
Constant T(deg C) O2 pH IT=1/(T+273.15) DR LDR 
Adjusted 
LDR 
1 27 0.2 2.02 3.3317E-03 50.20 1.70 5.5812 
1 27 0.2 3.01 3.3317E-03 34.50 1.54 5.4183 
1 24 0.2 3.25 3.3653E-03 109.00 2.04 5.9547 
1 27 0.2 3.8 3.3317E-03 53.90 1.73 5.6121 
1 26 0.2 5.07 3.3428E-03 7.18 0.86 4.7488 
1 26 0.2 5.82 3.3428E-03 3.80 0.58 4.4725 
DTN: MO0302PNLDUFTD.000 [DIRS 162385] (Run #66 Data); MO0304PNLLPHDD.000 [DIRS 163441] 
NOTES: Adjusted LDR = LDR + 1093.826IT - 0.338log10(O2) 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 III-1 of 4 August 2004 
APPENDIX III 
FILE INFORMATION FOR ATTACHED CD-ROM 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 III-2 of 4 August 2004 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 III-3 of 4 August 2004 
APPENDIX III - FILE INFORMATION FOR ATTACHED CD-ROM 
III.1 FILE LISTING FOR APPENDIX I 
Volume in drive D is 040831_1427 
Volume Serial Number is 81DD-BB5A 
Directory of D:\ 
08/06/2004 08:24a 101,376 CSNF-UO2.JNB 
08/26/2004 03:31p 212,480 CSNF MR REV02_Attachment I.xls 
2 File(s) 313,856 bytes 
0 Dir(s) 0 bytes free 
III.2 SPREADSHEET INFORMATION FOR APPENDIX I 
The following spreadsheets are contained in Microsoft Excel file CSNF MR REV2.XLS 
Spreadsheet A1: UO2 Input Data and Derived Values for the Model Variables 
Spreadsheet A2: CSNF Input Data and Derived Values for the Model Variables 
Spreadsheet A3: Calculations Supporting the Development of the CSNF Model for Acidic 
Conditions. 
Spreadsheet A4: Calculation of Average Release Rates for the High Drip Rate Tests. 
Spreadsheet A5: Calculation of Average Release Rates for the Low Drip Rate Tests. 
Spreadsheet A6: Calculation of Average Release Rates for the Rod Segment Tests. 
Spreadsheet A7: Summary Fractional Release Rates of Sr-90, Tc-99, Mo-97 and Cs-137 @ 
90C (1/day) 
Spreadsheet A8: Calculation of Effective Surface Area of Corroding CSNF 
Spreadsheet A9: Comparison of Fractional Release Rate Model with Literature Data for 
Alkaline and Acidic Conditions 
Spreadsheet A10: Comparison of Matrix Specific Dissolution Rate Model with Literature UO2 
Data for Alkaline Conditions 
Spreadsheet A11: Comparison of Matrix Specific Dissolution Rate Model with Literature UO2 
Data for Alkaline Conditions 
Spreadsheet A12: Comparison of Matrix Specific Dissolution Rate Model with Literature UO2 
Data for Acidic Conditions 
Spreadsheet A13: Analysis of Gap- and Grain-Boundary Inventory Data 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 III-4 of 4 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 IV-1 of 4 August 2004 
APPENDIX IV 
DATA QUALIFICATION PLAN 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 IV-2 of 4 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 IV-3 of 4 August 2004 
APPENDIX IV - DATA QUALIFICATION PLAN 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 IV-4 of 4 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 V-1 of 6 August 2004 
APPENDIX V 
DATA QUALIFICATION REPORT 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 V-2 of 6 August 2004 
INTENTIONALLY LEFT BLANK 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 V-3 of 6 August 2004 
APPENDIX V - DATA QUALIFICATION REPORT 
V.1 PURPOSE 
The purpose of this data qualification report is to qualify the input data in Tables 4-5 and 4-6 for 
use in estimating the specific geometric surface area of commercial spent nuclear fuel (CSNF) 
pellet fragments within this modeling report. The purpose also includes qualifying the input 
value of 3.9  10-3 (m2/g) for the specific surface area of corroded CSNF fragments based on 
single-pass flow-through test results (Section 4.1.3). 
V.2 QUALIFICATION METHODS 
The input surface area data (Wilson 1990 [DIRS 100793], Appendix E; Barner 1985 
[DIRS 109194], Tables 4.1 and 4.6; Gray and Wilson 1995 [DIRS 100758], Section 4.1.1, p. 
4.2)) are qualified in accordance with AP-SIII.2Q, Qualification of Unqualified Data. The 
qualification process used for these data involves a combination of the following methods (APSIII.
2Q, Attachment 3): 
 Technical Assessment 
 Corroborating Data. 
This qualification process is designed to provide the desired level of confidence and its intended 
use is only for this modeling report. 
This report documents the data qualification task conducted in accordance with the approved 
data qualification plan (Appendix IV). For each of the qualification methods the qualification 
process attributes 1 through 6 and 10 (AP-SIII.2Q, Attachment 4) were used as appropriate. The 
rationale for selecting the technical assessment method is that it can be used to establish 
confidence in the correctness of the unqualified data by evaluating the methods used to plan, 
collect, and analyze the data. The corroboration method was also used because comparisons 
with other unqualified data provide additional confidence. 
V.3 TECHNICAL ASSESSMENT 
V.3.1 Table 4-5 Data 
The pertinent sections of the source document from which the input data in Table 4-5 were 
obtained (i.e., Wilson 1990 [DIRS 100793], Appendix E) were reviewed. As described in the 
source document (Wilson 1990 [DIRS 100793], Acknowledgements) these data were developed 
by the Westinghouse Hanford Company and Pacific Northwest National Laboratory (PNNL) for 
the Yucca Mountain Project (YMP). The work was conducted under Contract No. 
W-7405-ENG-48 and the YMP-approved WHC/PNNL Quality Assurance Programs that met the 
contractual QA requirements. The laboratory activities were completed under the Westinghouse 
Hanford Company QA program, from Feb. 1986 to May 1987, and much of the data evaluation 
and the preparation of the report by Wilson (1990 [DIRS 100793]) were performed under the 
PNNL quality assurance program. This work was transferred from WHC to PNNL in June 1987 
as part of the Hanford site contract consolidation effort. The WHC and PNNL quality assurance 
programs were reviewed and approved by LLNL, which managed the waste package task for 
YMP and treated WHC and PNNL as suppliers. Technical implementing documents, including 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 V-4 of 6 August 2004 
test plans, test procedures, and technical drawings were reviewed and approved by LLNL and 
WHC prior to their implementation, and determined to be technically adequate for the collection 
and analyses of the data. Test Plan for Series 3 NNWSI Spent Fuel Leaching/Dissolution 
Tests, HEDL-7577, January 1986, C. N. Wilson, also included an appendix that identified all 
test instructions, technical procedures, and analytical procedures, including solution and solids 
analyses, and burnup and radiochemical analyses. The initial evaluation of the overall data 
quality has determined that the controls and methods used to plan, collect, and analyze the data 
in question were adequate when compared to generally accepted scientific and engineering 
practices at the time the data was generated. 
V.3.2 Table 4-6 Data 
The pertinent sections of the source document from which the input data in Table 4-5 were 
obtained (i.e., Barner 1985 [DIRS 109194], Section 4.5 and Table 4.1). [Note: Section 4.5 in the 
Barner report is MCC-generated data; the data in Table 4.1 of Barners ATM-101 report was not 
generated by the MCC, but was taken from the fuel vendor data in the Carolina Power and Light 
Company, H.B. Robinson Unit 2 FSAR  Docket 50261-104]. As described in the source 
document (Barner 1985 [DIRS 109194]) these data were developed by the Pacific Northwest 
National Laboratorys Materials Characterization Center (MCC) under the U.S. Department of 
Energy Contract DE-AC06-76LO 1830. The MCC mission included development of 
characterization data for spent fuel to be designated as Approved Testing Materials (ATMs), 
and the characterization data (including those used as input to Table 4-5) were developed using 
characterization plans and technical procedures that were consistent with this mission. The MCC 
data was generated under the controls of a PNL QA program that employed an NQA-1-based 
approach. The MCC QA program was scrutinized by several DOE repository programs (BWIP, 
SRPO, OCRD) as well as other clients. The MCC QA program implemented then was written to 
address contractual QA requirements from BWIP and SRPO, and was reviewed and approved by 
these DOE repository programs. The initial evaluation of the overall data quality has determined 
that the controls and methods used to plan, collect, and analyze the data in question were 
adequate when compared to generally accepted scientific and engineering practices at the time 
the data was generated. 
V.3.3 Effective Specific Surface Area Data for CSNF in SPFTs 
The pertinent sections of the source document (Gray and Wilson 1995 [DIRS 100758], Sections 
4.1.1 and 4.1.6) from which the value of 3.9  10-3(m2/g) for the specific surface area of corroded 
CSNF fragments was obtained. As described in the source document these data were developed 
by the Pacific Northwest National Laboratory (PNNL) for the Yucca Mountain Project (YMP). 
The work was conducted under Contract No. DE-AC06-76RLO 1830 and the YMP-approved 
WHC/PNNL quality assurance programs, which met the contractual QA requirements. The 
PNNL quality assurance program was reviewed and approved by LLNL, which managed the 
waste package task for YMP and treated PNNL as a supplier. Technical implementing 
documents, including test plans, test procedures, and technical drawings were reviewed and 
approved by LLNL and PNNL prior to their implementation, and determined to be technically 
adequate for the collection and analyses of the data. Evaluation of the overall data quality has 
determined that the controls and methods used to plan, collect, and analyze the data in question 
were adequate when compared to generally accepted scientific and engineering practices at the 
time the data was generated. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 V-5 of 6 August 2004 
All three source documents clearly describe the technical approach used to determine the surface 
area data used as inputs in Tables 4-5 and 4-6 and Section 4.1.3 (Section 4.1.3). The technical 
approaches used to obtain the data in Tables 4-5 and 4-6 are consistent with the best available 
practices and are, therefore, acceptable. They involve estimating the geometric surface areas of 
the cracked fuel pellets using fragment weight and fragment or pellet crack dimensions based on 
photographic records. Because these are simple measurement processes conducted by 
technically competent individuals under an adequate and effective quality assurance program in 
effect at the MCC, the results are credible. The estimated value of 3.9  10-3 (m2/g) for the 
specific surface area of corroding CSNF is based on comparison of BET and geometric-specific 
surface areas to estimate the surface roughness contribution to the specific surface area and 
comparison of the fractional dissolution rates measured on grain-sized powders and fuel pellet 
fragment samples of the same fuel to estimate the effects of grain boundary penetration (Gray 
and Wilson 1995 [DIRS 100758]). The result is reasonable and consistent with observation of 
surface roughness and grain-boundary penetration in corroded CSNF (Section 6.2.2.3). Because, 
as discussed in Section 6.4.1.4, the single-pass flow-through method is likely to maximize the 
effective specific surface area of the fragments, it is reasonable to use this value as an upper 
bound for the uncertainty range in the specific surface area parameter. 
V.4 CORROBORATING DATA 
V.4.1 Comparison of Results from Different Data Sources 
As described in Section 4.1.3 two different approaches were used to obtain the data in Tables 4-5 
and 4-6. The fact that similar geometric-specific surface areas were obtained for the ATM-101 
fuel using the different methods employed by Wilson (1990 [DIRS 100793]) and Barner (1985 
[DIRS 109194]) (Table 6-9) corroborates each data set. 
Published data on geometric-specific surface areas of CSNF fragments have been reviewed and 
compiled by Jegou et al. (2001 [DIRS 162397], Table 38). When the CSNF fragment data are 
adjusted to remove the shape factor of 3, the specific surface area results are approximately 
2 to 6 cm2/g with most of the results being approximately 2 cm2/g. The good agreement between 
these literature data and the input data in Tables 4-5 and 4-6 (Table 6-9 for conversion of these 
input data to specific geometric surface area estimates) corroborates these input data. 
The subsection Corroborating Data for Estimating CSNF Specific Surface Areas (Section 
6.4.1.5) shows that the effective surface area, estimated based on the results of long-term drip 
tests, are consistent with use of the value of 3.9  10-3(m2/g) for the specific surface area of 
corroded CSNF fragments of (Gray and Wilson 1995 [DIRS 100758], Sections 4.1.1 and 4.1.6) 
as an upper bound on the specific surface area of corroding CSNF. 
V.4.2 Comparison with Fuel Pellet Cracking Data 
As described in Section 6.2.1.1, literature data (Dehaudt 2001 [DIRS 164019], Section 5.2.7.1) 
show that the radial and transverse cracking of fuel pellets in irradiated CSNF results in creation 
of about 15 to 20 fragments per pellet. The specific surface area expected for such fragmented 
pellets can be estimated as described below. 

CSNF Waste Form Degradation: Summary Abstraction 
ANL-EBS-MD-000015 REV 02 V-6 of 6 August 2004 
Because the fuel fragments result from radial and transverse cracking of the fuel pellets, it is 
reasonable to approximate the fragment geometry for a cracked pellet to form 18 fragments as 
illustrated in Figure V-1. 
Figure V-1. CSNF Pellet with Eighteen Pie-Shaped Fragments Resulting From Radial and Transverse 
Cracking 
Using the expression given above for the specific surface area of these idealized fragments, and 
the as-fabricated pellet dimensions and density data provided at the bottom of Table 4-5, the 
specific surface area of the fragments can be calculated to be about 1.6 cm2/g. 
To assess the sensitivity of the estimated geometric-specific surface area to the fragment shapes 
it is instructive to note that if the pellet were divided into 18 equally sized cubic fragments the 
specific surface area would be approximately 1.32 cm2/g. If the 18 pellet fragments were in the 
form of spheres (which would minimize the specific surface area), the specific surface area 
would be approximately 1.27 cm2/g. Although the literature data (Dehaudt 2001 [DIRS 164019], 
Section 5.2.7.1) do not show a very wide variation in the extent of CSNF cracking, it is worth 
noting the geometric-specific surface area is not very sensitive to the extent of cracking and, 
hence, the number of fuel fragments. Simple geometric arguments show that the specific 
geometric surface area will increase in proportion to the cubic root of the number of fragments 
(e.g., a factor of eight increase in the number of fragments would cause the specific geometric 
surface area to increase by about a factor of two). These estimates, based on the literature data 
for the number of pellet fragments expected in CSNF, and simple assumptions concerning the 
fragment shape, show that the specific geometric surface area is expected to be approximately 
2 cm2/g. This corroborates the data presented in Tables 4-5 and 4-6. 
V.5 CONCLUSION 
The conclusion from this report is that the qualification status of the input data considered here 
should be changed to show that it is qualified for the purposes of this report.