Technical Basis Document No. 7: In-Package Environment and Waste Form Degradation and Solubility Revision 1 

July 2004 

1. INTRODUCTION 
This technical basis document summarizes the technical basis and conceptual understanding of 
the chemical environment within breached waste packages after water has come into contact 
with the waste form, the subsequent degradation of the waste forms, and the mobilization of 
radionuclides from degraded waste forms. 
This document is one in a series of technical basis documents that are being prepared for the 
different components relevant to predicting the likely postclosure performance of the Yucca 
Mountain repository system. The relationship of in-package chemistry, waste form degradation, 
and radionuclide solubility to the other repository system components is illustrated in Figure 1-1. 
To predict the chemical environment within a breached waste package, the degradation of the 
waste form, and the mobilization of radionuclides, the models discussed in this technical basis 
document rely on input from other models shown in Figure 1-1. Specifically, models for water 
seepage into the drifts and for waste package and drip shield corrosion are used to predict the 
evolution of the chemical environment within breached waste packages, the degradation of the 
waste forms, and the mobilization of radionuclides from degraded waste forms. Output from the 
models discussed in this document provides the source term for modeling radionuclide transport 
1-1 July 2004 No. 7: In-Package Environment Revision 1 
as dissolved species and as associated with colloids through the different components of the 
engineered barrier system (EBS) (waste package, corrosion products, and invert) and subsequent 
migration through the unsaturated zone, the saturated zone, and discharge to the biosphere. The 
technical understanding of the waste package environment, waste form degradation, and 
radionuclide mobilization is based on 13 reports written to support the license application (LA): 
 Radionuclide Screening (BSC 2002a) 
 Clad Degradation  FEPs Screening Arguments (BSC 2004a) 
 Pitting Model for Zirconium-Alloyed Cladding at YMP (BSC 2003a) 
 Clad Degradation  Summary and Abstraction for LA (BSC 2003b) 
 Seismic Consequence Abstraction (BSC 2003c) 
 Initial Radionuclide Inventories (BSC 2003d) 
 CSNF Waste Form Degradation: Summary Abstraction (BSC 2003e) 
 In-Package Chemistry Abstraction (BSC 2004b) 
 DSNF and Other Waste Form Degradation Abstraction (BSC 2003f) 
 Defense HLW Glass Degradation Model (BSC 2003g) 
 Dissolved Concentration Limits of Radioactive Elements (BSC 2004c) 
 Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: Abstraction 
and Summary (BSC 2003h). 
 Igneous Intrusion Impacts on Waste Package and Waste Form (BSC 2004d). 
This technical basis document uses the information provided in these 13 reports to present a 
coherent perspective of the impact that water ingress into waste packages has on the chemical 
environment within the waste packages, the degradation of the waste forms, and the mobilization 
of radionuclides. In addition, this technical basis document provides the context for responses to 
Key Technical Issue (KTI) agreements (and associated general (GEN) items) and Additional 
Information Needed (AIN) requests made between the U.S. Nuclear Regulatory Commission 
(NRC) and the U.S. Department of Energy (DOE) related to in-package chemistry and 
commercial spent nuclear fuel (SNF) cladding. Technical responses to the following KTI 
agreements and AIN requests are provided as appendices to this document: 
 Appendix AIn-Package Chemistry Environment (Response to CLST 3.02 AIN-1, 
ENFE 3.03, TSPAI 3.14, and GEN 1.01 (Comments 116 and 126)) 
 Appendix BEffects of Radiolysis and Engineered Materials on In-Package Chemistry 
(Response to CLST 3.03 AIN-1 and CLST 3.04 AIN-1) 
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 Appendix CDemonstration of the Adequacy of the In-Package Chemistry Model 
Results (Response to ENFE 3.04 and CLST 3.05) 
 Appendix DLocalized Corrosion and Stress Corrosion Cracking in Cladding 
(Response to CLST 3.06 AIN-1, CLST 3.07, CLST 3.08 AIN-1, CLST 3.09 AIN-1, and 
GEN 1.01 (Comment 124)) 
 Appendix ETotal System Performance Assessment Implementation of In-Package 
Chemistry (Response to TSPAI 3.08). 
1.1 UNDERSTANDING OF IN-PACKAGE CHEMISTRY, WASTE FORM 
DEGRADATION, AND RADIONUCLIDE MOBILIZATION 
As the starting point for all potential radionuclide releases to the biosphere, the source term for 
radionuclides within the EBS is an important submodel in the total system performance 
assessment (TSPA) for LA. For radionuclides to be transported through the EBS, the waste form 
must first degrade and the radionuclides mobilized. The degradation of the waste form and the 
mobilization of radionuclides within the waste package are herein jointly called the waste form 
degradation model. The function of the waste form degradation model is to estimate the 
availability and mobilization of radionuclides that are used as input by the EBS transport model. 
Although radioactive waste can degrade by oxidation without water, radionuclides cannot be 
transported in the absence of water, and water must be present for the chemistry in the package 
to be modeled. Therefore, the models presented in this document assume that water will be 
present in the waste package. In some cases (e.g., in-package chemistry), the quantity of water 
assumed to be present in the waste package is dictated by the needs of the submodel and may not 
be consistent with the quantity of water assumed to be available in other related models. 
The waste form degradation model consists of eight components, shown in Figure 1-2. Five of 
those components determine the degradation rate of waste forms: (1) in-package chemistry; 
(2) degradation of commercial SNF cladding, (3) degradation of commercial SNF, 
(4) degradation of U.S. Department of Energy SNF, and (5) degradation of high-level radioactive 
waste (HLW) glass. The other three components determine the mobilization of radionuclides 
from the waste form into the waste package corrosion products: radionuclide inventory, 
radionuclide solubility, and colloid formation and stability. 
Understanding the source term for radionuclides within the EBS begins with the model of the 
average radionuclide inventory for three generic waste forms: commercial SNF, DOE SNF, and 
HLW encapsulated in borosilicate glass. A fourth waste form, naval SNF, is represented by 
commercial SNF. The three waste forms are placed into two waste package configurations: a 
commercial SNF waste package with various numbers of assemblies of SNF, and a codisposal 
waste package with typically one canister of DOE SNF and five canisters of HLW glass. Of the 
more than 100 radionuclides that will be potentially present in the initial inventory, 
28 radionuclides (from 14 elements) are identified as being important to the estimate of expected 
dose to the reasonable maximally exposed individual (RMEI) during the 10,000-year regulatory 
period. 
July 2004 1-3 No. 7: In-Package Environment Revision 1 
Figure 1-2. Components of the Waste Form Degradation Model 
The evolution of water chemistry inside a breached waste package is controlled by the waste 
package corrosion rate, waste package type (commercial SNF versus codisposed), water inflow 
rate, cladding failure fraction, waste-form corrosion rate, and temperature. Water inflow rate 
will depend on whether seepage water drips onto the waste package. For nondripping 
conditions, water will enter a breached waste package as water vapor, whereas under dripping 
conditions, water will flow through a breached waste package. The pH of water within a waste 
package will be strongly influenced by the presence of corrosion products, primarily goethite. 
This phase will provide sufficient surface area for complexation reactions to buffer the pH below 
8.1. 
For commercial SNF, radionuclides can be released only from those fuel rods on which the 
cladding has failed. The rate of release of radionuclides from the fuel matrix is modeled as a 
function of four variables: temperature, pH, total carbonate concentration, and oxygen fugacity 
(or partial pressure). In addition, a small fraction of radionuclides resides in the grain boundaries 
of the fuel pellets and the gap between the fuel pellet and cladding of commercial SNF. 
Radionuclides (specifically 90Sr, 137Cs, 99Tc, and 129I) residing in the gap or grain boundary are 
available for dissolution as soon as water contacts the waste. The presence of a gap fraction and 
July 2004 1-4 No. 7: In-Package Environment Revision 1 
the rapid degradation of the commercial SNF fuel matrix in an oxic environment, as is expected 
to exist in the repository, implies that the release of most radionuclides from a commercial SNF 
waste package is not limited by their availability (i.e., not limited by the waste form degradation 
rate) but by their solubility. The exceptions are 90Sr, 137Cs, 99Tc, and 129I. The solubilities of 
these four radionuclides are quite high under repository conditions; hence, the degradation rate of 
the commercial SNF fuel matrix and the initial inventory of these radionuclides in the gap and 
grain boundary influences their release rate. 
Eleven DOE SNF groups have been used to categorize the several hundred distinct types of DOE 
SNF that will eventually be emplaced in the repository. A degradation rate model consisting of 
waste form degradation and radionuclide availability rapid enough to occur within the first 
TSPA-LA time step is used for all these waste groups, except naval SNF, which is assumed to 
behave as commercial SNF. 
Many well-known processes contribute to HLW glass degradation, although there is some debate 
as to whether the dissolution process at high dissolved silica (H4SiO4) levels is controlled by 
surface reactions or by the diffusion of silica through alteration phases that form on the glass as it 
dissolves, as discussed further in Section 5.3. The glass dissolution process is controlled by the 
temperature and pH of the solution in contact with the glass. Under acidic and alkaline 
conditions, glass degradation rates increase with, respectively, decreasing and increasing pH. 
The degradation rate has an Arrhenius-type dependence on temperature. 
The solubility of seven important elements (plutonium, americium, neptunium, thorium, 
uranium, protactinium, and actinium) depends on the water chemistry inside the waste package. 
The solubility exhibits a typical U-shaped dependence on pH with a minimum at 
approximately neutral to mildly basic pH values, which coincides with long-term in-package 
conditions. For an eighth element, radium, the solubility is defined solely as a function of pH. 
The solubilities of the remaining five important elements (i.e., carbon, cesium, strontium, iodine, 
and technetium) are high under expected repository conditions. Hence, the availability of these 
radionuclides for transport out of the waste form is determined by the fraction of cladding that 
fails, the degradation rate of the waste form, and the gap and grain boundary inventory. 
Two sources of colloids are considered in the colloid model in the waste form: natural colloids in 
seepage groundwater and colloids generated from degradation of HLW glass, which exhibit a 
clay-like behavior. In addition, a third type of colloid, iron oxyhydroxide corrosion products, is 
assumed to form in the waste package as the waste form support structures corrode. Colloids 
formed from DOE HLW glass and naturally occurring groundwater colloids are both represented 
by smectite clay colloids, and can transport reversibly sorbed radionuclides. In addition, HLW 
glass colloids can transport embedded plutonium and americium. The stability of colloid 
suspensions is a function of ionic strength and pH. Dissolved cesium, americium, plutonium, 
protactinium, and thorium are partitioned (reversibly) between the water and groundwater 
colloids. Plutonium and americium embedded in HLW glass colloids are considered to be 
irreversibly sorbed and, thus, are not partitioned between the water and the HLW glass colloids. 
Corrosion product colloids are likely to be made up of some mixture of goethite, ferrihydrite, or 
hematite. Each solid has a high affinity for many radionuclides. Colloid suspensions are stable 
only when the ionic strength is less than 0.05 mol/L, and, at certain pH values, can be unstable at 
values of ionic strength lower than 0.05 mol/L. Colloid suspensions are believed to be stable 
July 2004 1-5 No. 7: In-Package Environment Revision 1 
only for limited periods of time and under limited combinations of physical and chemical 
conditions during the 10,000-year regulatory compliance period. Moreover, in the nominal case 
scenario, when radionuclide transport within the EBS is expected to be diffusion-dominated, 
colloid-facilitated transport of radionuclides is physically impossible because the colloids will be 
too big to diffuse. 
1.2 APPLICABILITY OF WASTE FORM DEGRADATION MODEL IN THE 
SCENARIO CLASSES 
Waste form degradation is highly dependent on the amount of water entering the waste package. 
This dictates the in-package chemistry, which, in turn, influences waste form degradation rates, 
radionuclide solubility, and colloid stability. Water reaches the repository by infiltrating the 
mountain surface, percolating through the matrix and fractures above the repository, and seeping 
into the emplacement drift. Figure 1-3 shows the advective water flux pathways into and within 
the emplacement drifts, including the EBS and, hence, the potential sources of water for waste 
form degradation and radionuclide mobilization. 
It is conservatively assumed that water will be present to dissolve and transport radionuclides 
even during periods of repository conditions that would inhibit such processes (i.e., when the 
waste package and waste form temperatures are higher than the surrounding surfaces and would 
otherwise drive moisture away). Water will be present in the repository environment, and if 
radionuclides are to leave the drift in large quantities, water must interact with fuel elements. To 
establish the limits of the water-borne dose, the broad features of waste form degradation by 
water must be modeled. If water never contacts the waste form, or if water contacts it in lower 
quantities than envisioned, TSPA predictions of dose will have been overestimated and, hence, 
conservative. 
The waste form degradation model summarized in this document is intended to capture the 
expected chemical reactions induced when water enters a failed waste package either through 
advection (flow pathway 4 in Figure 1-3) or by diffusion and to estimate radionuclide release 
from waste packages into the invert either by advection (flow pathway 6 of Figure 1-3) or 
diffusion. The amount of water that enters a package is strongly dependent on the period of 
interest (i.e., whether temperatures in the repository are above the boiling point of water) and 
whether the engineered barriers have been disrupted. These two important aspects are used to 
define the three scenario classes considered in the TSPA-LA model (BSC 2002b, Section 4): 
nominal, seismic, and igneous scenario classes. A fourth scenario class, human intrusion, is a 
stylized analysis (BSC 2002b, Section 4) because of specific simplified assumptions imposed by 
the NRC in 10 CFR Part 63. 
The models presented in this document are used in the nominal and seismic scenario classes. 
The igneous scenario class has two types of disruption: igneous intrusion of a dike into an 
emplacement drift and volcanic eruption through a waste emplacement drift. For waste packages 
that are destroyed by igneous intrusion, all the models discussed in this document are applicable 
and are used in the TSPA-LA model. For both the volcanic eruption scenario and the human 
intrusion calculations, only the radionuclide inventory model (Section 2) is used because neither 
set of calculations involves transport of radionuclides in groundwater. The scenario classes that 
July 2004 1-6 No. 7: In-Package Environment Revision 1 
implement the models presented in this document and their relationship to waste package 
modeling are summarized in Table 1-1 and are discussed next. 
Figure 1-3. Potential Liquid Advective Pathways in the Engineered Barrier System 
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Table 1-1. Models Implemented in the Scenario Classes Included in the Total System Performance 
Assessment for the License Application 
Colloid 
Formation 
and Stability 
X 
Scenario Class 
Nominal scenario and 
waste packages 
unaffected by disruptive 
events in other scenario 
classes 
X Waste packages affected 
by seismic scenario 
Radionuclide 
Solubility 
X 
X 
X 
Waste Form 
Degradation 
X 
X 
X 
Chemistry 
In-Package Cladding 
Failure 
X X 
X X 
X X Waste packages affected 
by igneous intrusion 
Waste packages affected 
by volcanic eruption 
Inventory 
X 
X 
X 
X 
X Waste packages affected 
by human intrusion 
X 
July 2004 
1.2.1 Degradation in the Nominal Scenario Class 
The nominal scenario represents the collection of features, events, and processes (FEPs) most 
likely to occur during the 10,000-year regulatory period, such as climate change and heating of 
the repository (BSC 2002b, Section 4.1). Based on corrosion analysis (BSC 2003i, 
Section 6.7.1), the drip shield and waste package will remain intact for the 10,000-year 
regulatory period for all but a small fraction of the waste packages. As a result, water seeping 
into the emplacement drifts of the repository (flow pathway 1 of Figure 1-3) will be diverted 
away from all but a small fraction of the packages (flow pathway 3 of Figure 1-3). Hence, 
advective radionuclide transport through the vast majority of waste packages (flow pathway 4) 
cannot occur; only diffusive radionuclide transport through the waste package can occur in the 
10,000 years after repository closure. Radionuclide transport by diffusion is possible only for 
dissolved species. In films thinner than the diameter of the colloids, transport is physically 
hindered. In thicker films, the substantially lower diffusion coefficients for colloids relative to 
dissolved species will tend to prevent appreciable colloid transport. 
For the nominal scenario, only a small fraction of the waste packages (e.g., the probability of 
three or more failed waste packages is 0.026 (BSC 2003i, Table 46)) is expected to have small 
manufacturing weld defects near the waste package lid, allowing water vapor to diffuse into the 
package, saturate the contents of the package, and dissolve radionuclides, thereby establishing a 
pathway for diffusive radionuclide transport (Figure 1-4). 
Only the small fraction of fuel rods that arrive at the repository with perforated cladding (about 
0.1%) can lead to the release of radionuclides during the 10,000-year regulatory period. 
Additional cladding failures from other mechanisms (e.g., localized corrosion, static loading of 
rock) during the regulatory period are not anticipated. The cladding that is initially perforated is 
assumed to split along the entire length of the fuel rod. Subsequent degradation of exposed fuel 
is assumed to produce a water-saturated rind through which dissolved radionuclides diffuse, 
thereby leaving the waste matrix and entering into the corrosion products in the waste package. 
1-8 No. 7: In-Package Environment Revision 1 
10,000 years. 
Figure 1-4. Degradation of Waste Form under Nominal Scenario Class 
NOTE: For the nominal scenario, waste form degradation is limited and only diffusive release through 
manufacturing defects occurs because the drip shield and waste package remain intact for the first 
July 2004 
1.2.2 Degradation in the Seismic Disruptive Scenario 
For the seismic disruptive scenario class (BSC 2002b, Section 4.3), the drip shields, waste 
packages, and cladding can be disrupted due to vibratory ground motion, fault displacement, and 
rockfall, as discussed in Seismic Consequence Abstraction (BSC 2003c, Table 31). The most 
likely waste package failure mechanism from a seismic event is accelerated stress corrosion 
cracking in areas that exceed the residual stress threshold for Alloy 22. It is postulated that a 
dense network of stress corrosion cracks would form, and the effective area of these cracks, 
which is much less than the overall affected surface area of the waste package, is the area 
through which flow and transport occur (Figure 1-5). The percentage of cladding that is 
perforated is a function of the peak ground velocity associated with the seismic event. The 
models of how the cladding splits once it is perforated and how radionuclides are transported 
through the rind are the same in the seismic scenario as they are in the nominal scenario. For 
those waste packages associated with undamaged drip shields or not encountering dripping 
water, radionuclide releases will be due to diffusion, similar to conditions in the nominal 
scenario. 
1-9 No. 7: In-Package Environment Revision 1 
Figure 1-5. Degradation of Waste Form under the Seismic Disruptive Scenario 
1.2.3 Degradation in the Igneous Intrusion Scenario 
In the igneous intrusion scenario, a basalt dike is envisioned to intersect the repository (BSC 
2002b, Section 4.2). Damage to waste packages and waste forms is conceptually divided into 
two distinct zones. In Zone 1 (where the emplacement drifts are intersected by the basaltic dike), 
the drip shields, waste packages, and commercial SNF cladding are disrupted and breached from 
the shock wave and heat. As a result, the waste form is distributed throughout the cooled magma 
but is chemically unchanged from the nominal scenario model. The latter is a reasonable 
assumption because the melting temperatures of fuel components are typically higher than the 
expected maximum temperature of the magma. Therefore, the waste degradation rates and 
dissolved concentration of radionuclides in water in the basalt would have the same dependence 
on water chemistry as does waste not affected by the intrusion (BSC 2004d, Section 6.5.1.2). In 
Zone 2, no damage is assumed, and thus, conditions are expected to be the same as in the 
nominal scenario (BSC 2004d, Section 8.1). 
Degradation and dissolution of the waste form in breached waste packages in Zone 1 and 
degradation of the waste in Zone 2 occur once the drift cools (nominally, within roughly 
100 years). In Zone 1, the drift is assumed to be completely filled with basalt. This scenario is 
most reasonably modeled as a uniform percolation of basalt-equilibrated fluid contacting bare 
fuel components (Figure 1-6) (BSC 2004d, Section 6.3). 
1-10 July 2004 No. 7: In-Package Environment Revision 1 
Figure 1-6. Degradation under the Igneous Intrusion Scenario 
1.3 NOTE REGARDING THE STATUS OF SUPPORTING TECHNICAL 
INFORMATION 
This document was prepared using the most current information available at the time of its 
development. This technical basis document and appendices providing KTI agreement and AIN 
request responses prepared using preliminary or draft information reflect the status of Yucca 
Mountain Project scientific and design bases at the time of submittal. In some cases, this 
involved the use of draft analysis model reports and other draft references whose contents may 
change with time. Information that evolves through subsequent revisions of the analysis model 
reports and other references will be reflected in the LA as the approved analyses of record at the 
time of LA submittal. Consequently, the project will not routinely update either this technical 
basis document or its KTI agreement appendices to reflect changes in the supporting references 
prior to submittal of the LA. 
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July 2004 Revision 1 
2. RADIONUCLIDE INVENTORY 
This section summarizes the various waste forms and waste package configurations planned for 
the repository, as well as the screening process used to select the radionuclides included in the 
source term and the quantities of those radionuclides. 
The sources of data are presented, as are the uncertainties, limitations, and confidence in the 
source term model. The information in this section is drawn primarily from Radionuclide 
Screening (BSC 2002a) and Initial Radionuclide Inventories (BSC 2003d). The information 
presented in this section serves as input to the in-package chemistry model (Section 3), the 
commercial SNF cladding degradation model (Section 4), and the waste form degradation 
models (Section 5), as shown in Figure 1-2. 
2.1.1 Waste Packages 
Four types of waste are to be placed in the repository: commercial SNF, naval SNF, DOE SNF 
(not including naval SNF), and high-level radioactive waste (HLW) glass. Three packaging 
schemes will be used for these waste types. One waste package type will contain only 
commercial SNF. The second will contain only naval SNF but will be modeled as a commercial 
SNF waste package. The third waste package type, designated as a codisposal waste package, 
will contain DOE SNF and HLW in the same waste package. Figure 2-1 illustrates how waste in 
these categories will be combined and placed into waste packages. 
Commercial SNF is classified into two broad categories based on the design of the reactor that 
produced the fuel: pressurized water reactor (PWR) or boiling water reactor (BWR). 
Commercial nuclear power plants use a variety of fuels and fuel configurations in their reactor 
cores to generate power. The predominant nuclear fuel is enriched uranium dioxide. Fuel pellets 
are packed into long cylindrical fuel rods that vary in size depending on reactor design, and these 
fuel rods, which are clad in Zircaloy or stainless steel, are bundled into assemblies. The number 
of fuel rods per assembly and the number of assemblies in a reactor core vary, depending on the 
core and reactor design (i.e., PWRs or BWRs). 
DOE SNF consists of more than 250 distinct types of SNF, and, much like commercial SNF, 
radionuclide inventories vary widely depending on the history of the fuel. The large quantity of 
DOE SNF is indicative of the large number and variety of different research reactors within the 
DOE complex. The DOE fuel assemblies have been categorized by the size, shape, composition, 
and condition of the assemblies, and the size and corrosion resistance of the canister into which 
they may be loaded (DOE 2003a). The DOE SNF waste forms will be packaged in stainless 
steel canisters that will be loaded into the waste packages. 
2.1 TYPICAL WASTE PACKAGES, RELEVANT PROCESSES, AND MODELING 
ASSUMPTIONS 
July 2004 2-1 No. 7: In-Package Environment Revision 1 
Source: BSC 2003d, Figure 1. 
Figure 2-1. An Overview of Various Waste Package Types Containing Different Wastes 
The HLW in storage at DOE sites is produced by treatment of waste from weapons production 
and by the reprocessing of SNF (mostly DOE SNF). The technology for immobilization of 
HLW is vitrification in a borosilicate glass. HLW glass will come from four DOE sites and will 
be delivered to the repository in either short (about 10 ft long) or long (about 15 ft long) 
pour-canisters. The Hanford Site will produce long canisters while the Savannah River Site and 
Idaho National Engineering and Environmental Laboratory will produce short canisters. 
Additionally, a small amount of HLW glass has been produced in short canisters at the West 
Valley Demonstration Project in New York State. Because the fuels reprocessed at each of these 
sites differ, the radionuclide inventory of the HLW and resultant glass product will vary among 
the sites. 
Details regarding the design and long-term performance of naval SNF are provided in a separate 
report that has limited distribution for security purposes. Naval SNF is conservatively treated as 
commercial SNF in the TSPA-LA. 
July 2004 2-2 No. 7: In-Package Environment Based on design criteria such as structural integrity, thermal performance, criticality safety, and 
shielding properties, 10 waste packages were designed to accommodate the numerous waste 
types (DOE 2002a, Section 3.1). Five waste package designs are used to dispose of the two 
types of commercial SNF (BWR and PWR), three waste package designs are used to dispose of 
high-level radioactive waste glass from the four different sources (Hanford, West Valley, 
Savannah River, and Idaho National Engineering and Environmental Laboratory) as well as 
DOE SNF, and two waste package designs are used to dispose of naval SNF. Table 2-1 lists the 
contents of each of the 10 different waste packages and Figure 2-2 illustrates these waste 
package designs. 
Table 2-1. Waste Package Designs 
Waste Package 
Design 
21-PWR Absorber 
Plate 
21-PWR Control Rod 
12-PWR Long 
44-BWR 
24-BWR 
5-DHLW/DOE SNF 
Short 
5-DHLW/DOE SNF 
Long 
Naval SNF Short 
Naval SNF Long 
Source: DOE 2002a, Tables 3-2 and 3-3. 
Capacity: 21 commercial pressurized water reactor assemblies 38%/55% 
and an absorber plate for preventing criticality 
Capacity: 21 commercial pressurized water reactor assemblies 1%/1% 
with higher reactivity, requiring additional criticality control that 
is provided by the placement of control rods in all assemblies 
Capacity: 12 commercial pressurized water reactor assemblies 2%/2% 
and an absorber plate for preventing criticality; longer than the 
fuel assemblies placed in the 21-PWR packages. Because of 
its smaller capacity, it may also be used for fuel with higher 
reactivity or thermal output. 
Capacity: 44 commercial boiling water reactor assemblies and 25%/32% 
an absorber plate for preventing criticality. 
Capacity: 24 commercial boiling water reactor assemblies with 
higher reactivity, requiring a thicker absorber plate to prevent 
criticality than that used in the 44-BWR design 
Capacity: 5 short high-level radioactive waste canisters and 1 
short DOE SNF canister. 
Capacity: 5 long high-level radioactive waste canisters and 1 
long DOE SNF canister 
2-MCO/2-DHLW Long Capacity: 2 DOE multicanister overpacks and 2 long high-level 1%/<1% 
radioactive waste canisters. 
Capacity: 1 short naval SNF canister 
Capacity: 1 long naval SNF canister 
Description 
Approximate 
Percentage of Waste 
Packages/Approximate 
Percentage of MTHM 
1%/<1% 
14%/3% 
15%/4% 
2%/<1% 
1%/<1% 
For purposes of modeling in the TSPA-LA, two representative waste package types are 
considered: (1) a representative commercial SNF waste package and (2) a representative 
codisposal waste package designed to hold both DOE SNF and HLW glass. As shown in 
Table 2-1, the number of commercial SNF configurations dominates the number of codisposal 
waste package configurations. Both waste package designs consist of a waste package with an 
outer corrosion resistant layer of Alloy 22 and an inner layer of stainless steel for structural 
strength (BSC 2003i, Section 1). 
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July 2004 2-3 Source: DOE 2002a, Figure 3-5. 
Figure 2-2. Waste Package Configurations for Commercial Spent Nuclear Fuel, U.S. Department of 
Energy Spent Nuclear Fuel, N Reactor Fuel, and High-Level Radioactive Waste Glass 
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July 2004 2-4 Revision 1 
2.1.2 Radionuclide Screening 
Determining the initial radionuclide inventory is the foundation for estimating the radionuclide 
source term for all transport processes in the TSPA-LA model. This determination involves two 
steps. First, a screening process is used to determine those radionuclides with the highest 
contribution to expected dose.1 Those radionuclides shown not to contribute significantly to 
expected dose can be eliminated from further consideration; in practice, this involved separating 
the radionuclides that make up roughly 95% of the dose (see below) from the remainder. 
Second, the initial inventory of radionuclides is estimated for each waste form type and each 
waste package type. 
Radionuclides contained in the waste packages include fission products from reactor operations, 
actinides from neutron capture in uranium and plutonium, and activation products from neutron 
irradiation of structural materials and trace elements. Altogether, these fission products, 
actinides, and activation products include more than 100 radionuclides that may be collectively 
present in the waste packages at the time of repository closure. Many of the radionuclides are 
present in small quantities or have intrinsic physical and chemical properties that limit the 
quantities that can reach the RMEI (e.g., short half-life, low solubility, or strongly sorbing 
characteristics). Such nuclides will not be significant contributors to expected dose and will not 
pose a radiological risk to a RMEI at the point of compliance. The remaining nuclides represent 
only a small set of the total and need to be considered in the evaluation of repository postclosure 
performance. 
Radionuclides with half-lives less than 10 years that are not decay products of other 
radionuclides in the waste inventory will not contribute significantly to the dose in the 
groundwater scenarios (BSC 2002a, Assumption 5.9). As an additional constraint, radionuclides 
that are most important to expected dose were determined using the screening process of the 
National Council on Radiation Protection and Measurement. This screening was based on dose 
calculations, which in turn consider consumption of locally produced vegetables, fish, meat, and 
milk; water consumption; inadvertent ingestion of soil; gardening and shoreline activities; 
inhalation; and exposure to contaminated ground. Two screening factors were calculated for 
each radionuclide, one for scenario classes involving groundwater transport (nominal, seismic, 
and igneous intrusion), and one for the volcanic eruption scenario, which does not involve 
groundwater transport. Screening factors for groundwater transport scenario classes accounted 
for radionuclide sorption and solubility characteristics. The screening factors are described in 
Screening Models for Releases of Radionuclides to Atmosphere, Surface Water, and Ground 
(NCRP 1996) and were adjusted to reflect the local biosphere, as described in Attachments I and 
II of Radionuclide Screening (BSC 2002a). 
Radionuclides were screened by calculating a radionuclide-screening product for each 
radionuclide. The radionuclide-screening product, which is meant to be roughly proportional to 
dose, is obtained by multiplying the screening factor for each radionuclide by the activity of that 
radionuclide in the inventory. These screening products were ranked from largest to smallest, 
1 Some radionuclides that are not directly important to expected dose are still included because (1) regulatory 
requirements mandate their inclusion (40 CFR 197.30, 10 CFR 63.331), or (2) they are parents to radionuclides 
that are important to dose. 
July 2004 2-5 No. 7: In-Package Environment Revision 1 
and were then summed, starting with the largest and adding the next largest until all of the 
screening products of each contributing radionuclide were included in the sum. Radionuclides 
were then screened based on their contribution to the summed radionuclide-screening product. 
The screening was at the 95% level, meaning that ranked radionuclides that contributed up to 
95% of the summed radionuclide-screening product were considered potentially important and 
were retained for analysis (BSC 2002a, Section 5.5). 
The relative importance of individual radionuclides to expected dose was evaluated for several 
waste types, time frames, and release scenarios. Those scenarios that require groundwater 
transport (e.g., nominal, seismic, and igneous intrusion) were evaluated together as groundwater 
scenarios. The eruptive igneous scenario, which does not involve groundwater transport, was 
analyzed separately. Because radionuclide transport mechanisms differ between the scenarios 
that involve groundwater transport and the eruptive igneous scenario, the set of radionuclides 
identified as being important also differs between the groundwater-transport and nogroundwater-
transport scenarios. The effects of inventory abundance, radionuclide longevity, 
element solubility, and element transport affinity were considered. To evaluate the effects of 
inventory abundance, eight waste types were examined (i.e., BWR, PWR, HLW, and DOE SNF 
for both average and bounding waste forms of each type). For the 10,000-year regulatory period, 
the screening times were at 100, 200, 300, 500, 1,000, 2,000, 5,000, and 10,000 years after 
emplacement. These sets of screening times capture the main features of the changing dominant 
radionuclides and their relative activities (BSC 2002a, Section 6.2.3). To evaluate the effects of 
element solubility and transport affinity, the elements were evaluated in three solubility groups 
(highly soluble, moderately soluble, and slightly soluble to nonsoluble) and in three transport 
affinity groups (highly sorbing, moderately sorbing, and slightly sorbing to nonsorbing). The 
isotopes in each group were compared to one another for relative importance. 
The results of the screening process at the 95% level are shown in Table 2-2. The table also 
includes eight radionuclides (241Pu, 245Cm, 228Ra, 235U, 230Th, 232Th, 242Pu, and 236U) that were 
not important to expected dose but that must be included in the inventory because of regulatory 
requirements or because they are parents to radionuclides that are important to expected dose 
(BSC 2003d, Section 6.1). The rationale for inclusion of these eight radionuclides is as follows: 
 The U.S. Environmental Protection Agency and NRC regulations require consideration 
of the combined activity of 226Ra and 228Ra in groundwater (40 CFR 197.30, 
10 CFR 63.331). Therefore, 228Ra must be added to the list of radionuclides. Because 
228Ra is produced by the decay of 236U and 232Th, both 236U and 232Th must be included 
in the inventory. 230Th must be included because it decays into 226Ra. 
 241Pu and 245Cm must be included because they decay to 241Am, which is one of the 
screened-in radionuclides. 
 235U must be included because it decays (via short-lived 231Th) to 231Pa, which is one of 
the screened-in radionuclides. 
 242Pu must be included because it decays into 238U, which is one of the screened-in 
radionuclides. 
July 2004 2-6 No. 7: In-Package Environment As shown in column 2 of Table 2-2, 28 isotopes of 14 elements must be included in the 
TSPA-LA model during the 10,000-year regulatory period for scenario classes involving 
groundwater transport. A subset of these 28 isotopes and 14 elements (i.e., 14 isotopes of eight 
elements, as shown in column 3 of Table 2-2) must be included in 10,000-year TSPA-LA model 
for volcanic eruption. Therefore, 28 isotopes of 14 elements are included in the initial inventory 
for TSPA-LA calculations. 
Table 2-2. Radionuclides Included in Inventory for the 10,000-Year Regulatory Period 
Radionuclide 
227Ac 
241Am 
243Am 
14C 
245Cm 
135Cs 
137Cs 
129I 
237Np 
231Pa 
238Pu 
239Pu 
240Pu 
241Pua 
242Pub 
226Ra 
228Rac 
90Sr 
99 
229 
230 
232
Tc 
Th 
Thd 
The 
232U 
233U 
234U 
235Uf 
236Ue 
238U 
Isotopes 
Groundwater Scenario Classes 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
28 
Volcanic Eruption 
(No Groundwater Transport) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
14 
8 Elements 
Source: BSC 2002a, Table 10; BSC 2003d, Table 16 and Section 6.1. 
NOTE: a Included because precursor to 241Am. 
b Included because precursor to 238U. 
c Included for groundwater protection requirements. 
d Included because precursor to 226Ra. 
e Included because precursor to 228Ra. 
f Included because precursor to 231Pa. 
No. 7: In-Package Environment 
14 
Revision 1 
July 2004 2-7 Revision 1 
2.2 MODEL OF RADIONUCLIDE INVENTORY 
The nominal repository is designed to accommodate a total waste inventory of 70,000 MTHM. 
As designed, approximately 63,000 MTHM of the total inventory is commercial SNF and 
approximately 7,000 MTHM is DOE SNF and HLW. The radionuclide inventory for a 
representative commercial SNF and a representative codisposal waste package was derived by 
averaging the radionuclide inventories in each of the commercial SNF and codisposal waste 
package configurations over the number of waste packages per configuration. 
Approximately 221,000 commercial SNF assemblies will be disposed of in the five proposed 
commercial SNF waste package designs (see first five entries of Table 2-1). These 
configurations are designed to accommodate each assembly based on its criticality safety needs. 
Each assembly, depending on the reactor configuration, initial fuel enrichment, burnup, and the 
age of the waste (time in storage), will have a unique isotopic composition. The total number of 
commercial SNF waste packages is 7,472 (BSC 2003d, Table 17). 
Naval SNF is a robust fuel, more robust than commercial SNF and much more robust than DOE 
SNF. Releases from naval SNF waste packages are expected to be lower than those for 
commercial SNF waste packages (BSC 2001, Section 6.5). As such, naval fuel is not treated as 
DOE SNF in the codisposal waste package. For the TSPA-LA analysis, the 300 naval SNF 
waste packages have been treated as if they were commercial SNF waste packages with respect 
to their waste form degradation mechanisms and effects, as well as their initial radionuclide 
inventory (BSC 2003d, Section 6.2 and Attachment II). Naval SNF will be disposed of in two 
different waste package configurations, as shown in Figure 2-2. 
Approximately 16,000 HLW glass logs and 3,600 DOE SNF canisters will be disposed of in the 
codisposal waste package configurations. The total number of codisposal waste packages is 
3,412 (BSC 2003d, Table 17). 
The result of the radionuclide screening analysis and the initial radionuclide inventory analysis is 
the initial radionuclide inventory (in terms of mass) of those radionuclides determined to be 
important to expected dose for a representative commercial SNF waste package and a 
representative codisposal waste package. The nominal initial radionuclide inventory for each 
waste form is given in Table 2-3; uncertainty in the radionuclide inventory is discussed in 
Section 2.4. 
For certain radionuclides, the activity decreases with time as a result of simple radioactive decay; 
for others, the activity increases with time because of in-growth. For those radionuclides in the 
fission product category, the model abstraction calculates the variation of activity with time in 
accordance with the first order decay law. The inventory of short-lived fission products, such as 
90Sr and 137Cs, decreases substantially over the 10,000-year compliance period. In contrast, the 
inventories of long-lived fission products such as 14C, 99Tc, and 129I decrease only slightly over 
the compliance period. 
2-8 July 2004 No. 7: In-Package Environment Table 2-3. Nominal Initial Radionuclide Inventory for Each Waste Form 
Commercial SNF 
(g per waste package) 
2.50  10-6 
8.28  103 
1.26  103 
1.37  100 
1.77  101 
4.41  103 
5.97  103 
1.75  103 
4.63  103 
9.28  10-3 
1.54  103 
4.37  104 
2.08  104 
2.69  103 
5.34  103 
b 
b 
2.52  103 
7.64  103 
b 
1.54  10-1 
b 
1.03  10-2 
5.83  10-2 
1.77  103 
6.34  104 
3.89  104 
7.92  106 
Radionuclide 
227Ac 
241Am 
243Am 
14Ca 
245Cm 
135Cs 
137Cs 
129I 
237Np 
231Pa 
238Pu 
239Pu 
240Pu 
241Pu 
242Pu 
226Ra 
228Ra 
90Sr 
99Tc 
229Th 
230Th 
232Th 
232U 
233U 
234U 
235U 
236U 
238U 
Source: BSC 2003d, Section 7.1, Table 21. 
NOTE: Year of inventory projection: Commercial SNF, 2033; DOE SNF, 2030; and HLW, 2030. Total number of 
waste packages is 11,184 (7,772 commercial SNF and 3,412 codisposed). 
a 18% of 14C for commercial SNF resides in the hardware outside of the cladding. 
b For these radionuclides, the inventory is an unknown small number. 
For the radionuclides of actinide elements, the model abstraction accounts for the decay 
sequence and calculates the activity of each radionuclide as a function of decay and in-growth. 
Many of the actinide elements in Table 2-3 are daughter products. 
If the nuclear waste remains contained in the waste packages, the decay history of each 
radionuclide can be analytically calculated in accordance with its decay sequence and half-life 
(Figure 2-3). 
No. 7: In-Package Environment 
DOE SNF 
(g per waste package) 
1.2  10-3 
2.15  102 
6.63  100 
1.78  100 
9.11  10-2 
9.59  101 
9.57  101 
3.51  101 
8.02  101 
2.11  100 
1.23  101 
2.18  103 
4.28  102 
2.88  101 
2.97  101 
4.50  10-5 
1.49  10-5 
5.14  101 
1.56  102 
3.19  10-1 
1.16  10-1 
2.14  104 
1.26  100 
5.30  102 
4.66  102 
2.47  104 
1.23  103 
6.74  105 
2-9 
Revision 1 
HLW 
(g per waste package) 
b 
2.07  10-4 
4.07  101 
6.24  10-1 
5.89  10-2 
1.38  102 
3.28  102 
7.89  101 
1.08  102 
1.66  100 
4.24  101 
6.06  102 
5.01  101 
1.32  100 
4.22  100 
2.63  10-5 
6.51  10-6 
1.89  102 
1.10  103 
3.58  10-3 
8.81  10-4 
3.23  104 
4.43  10-4 
2.11  101 
2.53  101 
1.53  103 
6.50  101 
2.57  105 
July 2004 July 2004 
Source: BSC 2002a, Table 2; Figure adapted from CRWMS M&O 2000a, Figure 3.5-6. 
Figure 2-3. Decay Histories for Radionuclides Modeled in Total System Performance Assessment for License Application 
2-10 No. 7: In-Package Environment Revision 1 
2.3 SOURCE OF DATA AND SUPPORTING DOCUMENTS 
A 1995 data submittal from the commercial utilities provided the basic information from which 
the TSPA for the site recommendation (TSPA-SR) analysis inventory for commercial SNF was 
developed. At that time, the utilities supplied historical information about reactor assembly 
discharges up through December 1995, and they provided forecasts for the next five assembly 
discharges from their reactors. With this information, a design basis waste stream was developed 
in 1999 Design Basis Waste Input Report for Commercial Spent Nuclear Fuel (CRWMS 
M&O 1999a), as well as forecasts for assembly discharges over the lifetime of each commercial 
power reactor. This same information is also used for the TSPA-LA analysis to arrive at the 
nominal values in Table 2-4. In addition, 2002 Waste Stream Projections Report (Williams 
2003) was used in conjunction with the 1995 data to develop the uncertainty bounds for the 
nominal commercial SNF inventory values. 2002 Waste Stream Projections Report (Williams 
2003) provides bounding waste stream information to capture probable limits of what might 
occur in the future with regard to fuel selection by utilities. 
Table 2-4. Uncertainty Multipliers for the Initial Radionuclide Inventory for Each Waste Form Type 
Parameter 
Isotopes 
Distribution 
Minimum 
Most Likely 
DOE SNF 
All except 238U 
Triangular 
0.45 
0.62 
2.9 
Commercial SNF 
All except 238U 
Uniform 
0.85 
N/A 
1.4 Maximum 
Source: BSC 2003d, Section 7.1, Table 22. 
NOTE: See Section 2.4 for a discussion on how uncertainty multipliers were developed. 
DOE SNF information was collected from Source Term Estimates for DOE Spent Nuclear Fuels 
(DOE 2003a). This report provided information for the 28 radionuclides of interest and provided 
the basis for the modification to the codisposal waste package configurations by the addition of 
the wide canisters. 
The HLW radionuclide inventory (in curies per canister for each radionuclide) has been 
evaluated for the borosilicate glass to be produced at the four sites that will be supplying HLW to 
the repository: Hanford Site (14,500 canisters), Savannah River Site (5,978 canisters), Idaho 
National Engineering and Environmental Laboratory (1,190 canisters), and West Valley 
Demonstration Project (300 canisters) for a total of approximately 22,000 HLW canisters 
(CRWMS M&O 2000b, Section 5.0). As this total exceeds the approximately 16,000 HLW 
canisters that will be disposed of in the mountain, each sites total radionuclide inventory was 
proportionally assigned in accordance with its total production. 
High-Level Radioactive Waste 
All 
Triangular 
0.70 
1 
1.5 
July 2004 
2.4 UNCERTAINTIES, LIMITATIONS, AND MODEL CONFIDENCE 
An important source of uncertainty in the model of radionuclide inventory stems from the 
assumption that screening radionuclides at the 95% level is appropriate for TSPA-LA (see 
Section 2.1.2). To address this uncertainty, an additional screening was performed at the 99% 
level. At the 99% level, an additional 10 radionuclides were identified as important to dose in the 
2-11 No. 7: In-Package Environment Revision 1 
groundwater scenarios, and an additional nine radionuclides were identified as important to dose 
in the volcanic eruption scenario. Of the 10 marginally important radionuclides identified at the 
99% level for the groundwater scenarios, five are included in TSPA-LA because they are 
precursors to radionuclides identified as important at the 95% screening level (241Pu, 242Pu, 232Th, 
235 
237
U, and 236U); five are not included in the TSPA-LA (36Cl, 244Cm, 63Ni, 210Pb, and 126Sn). Of 
the nine marginally important radionuclides identified as important to dose in the volcanic 
eruption scenario, six are included in the TSPA-LA modeling of the volcanic eruption scenario 
( Np, 231Pa, 242Pu, 126Sn, 99Tc, and 228Th), while three are not included in TSPA-LA modeling 
of the volcanic eruption scenario (225Ac, 244Cm, and 225Ra). 
There are three sources of uncertainty in the radionuclide inventory common to all waste types. 
The first is due to the computational method and nuclear data used in predicting future 
radionuclide inventories (e.g., isotopic neutron cross section or decay half-life). The second 
source of uncertainty is the completeness of records kept for SNF burnup history and HLW batch 
compositions. The third source of uncertainty, which is the most difficult to quantify, is the 
uncertainty about future decisions that may influence the creation, packaging, or shipment of 
waste (BSC 2003d, Section 6.6). An analysis of these sources of uncertainty yielded the 
uncertainty multipliers shown in Table 2-4. 
2.4.1 Commercial Spent Nuclear Fuel 
An analysis of the potential error associated with the computational method and nuclear data, 
which also includes error in burnup history, has provided correction factors (i.e., ratios) to 
represent the minimum and maximum errors of 0.89 and 1.08 for the inventory of commercial 
SNF (BSC 2003d, Section 6.6.1). 
The uncertainty due to heterogeneity of waste in the repository average inventories was 
investigated by comparing average burnups of three 1999 arrival forecasts and four 2002 arrival 
forecasts (BSC 2003d, Section 6.6.1). The minimum and maximum ratios of the projected 
average burnups for these cases over those given in Table 2-4 are 0.95 and 1.3. When multiplied 
by the minimum and maximum correction factors of 0.89 and 1.08 for the computational 
method, a range of 0.85 to about 1.4 is obtained (BSC 2003d, Section 6.6.1). Since all the 
radionuclide inventory, except 238U, is highly burnup-dependent, they should not be sampled 
independently. Therefore, an uncertainty multiplier is chosen, which is sampled and then applied 
to all radionuclide inventories except 238U. In defining the probability distribution of this 
multiplier, the end points are known, but the shape of the uncertainty distribution is not. In the 
absence of further information, a uniform distribution is the distribution of choice because it 
equally weighs all possible values. In the TSPA-LA model, a uniform distribution from 0.85 
to 1.4 for an uncertainty multiplier is applied to the nominal values (provided in Table 2-3) for 
all radionuclides except 238U. The 238U uncertainty is very small and not modeled in TSPA-LA 
(BSC 2003d, Section 6.6.1). 
2.4.2 U.S. Department of Energy Spent Nuclear Fuel 
The fuel information currently available at DOE storage sites is often determined by the records 
requirements and the intended disposition path at the time the fuel was placed into storage. 
These requirements and disposition paths were often unique to each of the sites and evolved over 
2-12 July 2004 No. 7: In-Package Environment Revision 1 
time. As a result, the availability and completeness of the radionuclide inventories and 
associated documentation varies considerably for DOE SNF. Detailed characterization of these 
fuels is not necessary because the conservative source term estimate for these fuels is used for 
repository design, analyses, and licensing activities. 
A conservative estimate of this SNF inventory was developed for each of the DOE SNF storage 
sites. The inventory was generated using calculational techniques described in Methodologies for 
Calculating DOE Spent Nuclear Fuel Source Terms (DOE 2000), which uses relevant 
experimental data and confirmatory studies. Additional studies that demonstrate the validity of 
the model and underlying codes have been performed (DOE 2003a, p. 14). The result of this 
work is a database with over 500 DOE SNF types. Each DOE SNF type has entries that include 
the radionuclide inventory, number of assemblies, and number and type of canisters that will 
contain the DOE SNF. 
The inventory estimates given in Source Term Estimates for DOE Spent Nuclear Fuels (DOE 
2003a) provide both a nominal and a bounding radionuclide inventory estimate for each type of 
DOE SNF. Even though the bounding radionuclide inventory estimates were provided for the 
purpose of assessing preclosure risk associated with handling a worst-case canister, the values 
are used to represent a bounding inventory for assessing postclosure risk as well (BSC 2003d, 
Section 6.6.2). The nominal and bounding inventories per waste package for the weighted 
average of all DOE SNF waste reported by Source Term Estimates for DOE Spent Nuclear Fuels 
(DOE 2003a) were analyzed. 
A range of total DOE SNF inventory (0.62 to 2 times the nominal inventory) was established on 
the following basis: 
 The nominal inventory includes extremely conservative assumptions applied to the small 
percentage of fuel (0.31%) for which little information is available. These assumptions 
result in 38% of the total inventory in 0.31% of the fuel, resulting in a potential 
overestimation of the total curie inventory by about 38% (DOE 2003a, p. 39). 
 The best estimate inventory has a ratio of 0.62 to the nominal inventory (BSC 2003d, 
Section 6.6.2). The best estimate inventory is lower than the nominal through removal 
of the extreme conservatism applied to the small percentage of fuel (0.31%) with little 
information and in effect replacing these assumptions with the approximation that the 
0.31% of the fuel has the same inventory as the average of the remaining fuel (DOE 
2003a). 
 The bounding radionuclide inventory is 1 to 2 times the nominal inventory per waste 
package (BSC 2003d, Section 6.6.2). 
Taking the total DOE SNF inventory, including uncertainty, the inventory was applied across the 
expected number of DOE SNF canisters. 
Because of uncertainty in the loading of fuel into the DOE SNF canisters, the DOE SNF canister 
count range is between 2,500 and 5,000, with a best estimate of 3,607 (DOE 2003a; Luptak 
2003). Applying a factor of 3,607/5,000 (0.72) to the best estimate inventory (0.62 times the 
July 2004 2-13 No. 7: In-Package Environment Revision 1 
nominal) and a factor of 3,607/2,500 (1.44) to the maximum DOE SNF inventory (2 times the 
nominal) and 3,607/5,000 to the reasonable canister inventories results in a per canister inventory 
range of 0.45 to 2.9 times the nominal, with a best estimate of 0.62 times the nominal. This 
range is shown in Table 2-4. 
238 
Like commercial SNF, the uncertainties of the DOE SNF radionuclide inventories are correlated, 
and an uncertainty multiplier is defined to capture the uncertainty for all radionuclides except 
U. The inventory of 238U has much less relative uncertainty than the other radionuclides 
because it is present in the initial fuel and generally changes little during reactor operation. Only 
three points are available to define a probability distribution for the DOE SNF multiplier, a 
minimum value, a most likely value, and a maximum value. In this situation, the choice of a 
triangular distribution is reasonable. Thus the DOE SNF multiplier is defined as a triangular 
distribution, with a minimum of 0.45, most likely value of 0.62, and a maximum of 2.9. It is to 
be applied to the nominal values for DOE SNF grams per waste package in Table 2-3 for all 
isotopes except 238U. 
2.5 SUMMARY AND CONCLUSIONS 
The radionuclide inventory component defines the four waste forms (commercial SNF; naval 
spent fuel (which is modeled as commercial SNF); DOE SNF; and HLW glass) and the two 
representative types of waste packages (commercial SNF waste packages and the codisposal 
waste packages containing both DOE SNF and HLW glass) planned for disposal in the 
repository. In addition, of the more than 100 radionuclides potentially present in the repository, 
28 radionuclides have been identified as being important to dose during the 10,000-year 
regulatory period. The initial inventory of these 28 radionuclides for the three waste forms 
modeled in TSPA-LA is given, as is the uncertainty associated with that inventory. The 
inventory of commercial SNF, which is the most abundant waste type to be disposed of in the 
2.4.3 High-Level Radioactive Waste Glass 
The uncertainty in the repository average inventories was investigated by comparing three 
radionuclide loadings and three canister-filling cases (BSC 2003d, Section 6.6.3). As was the 
case with commercial SNF and DOE SNF, the uncertainties in HLW radionuclide inventories are 
dependent on the same factors for HLW, in this case radionuclide loading per canister. 
Therefore, an uncertainty multiplier is again used to represent the uncertainty in the HLW 
inventory. With the current information, the nominal values reported in Table 2-4 are the most 
likely values, and thus the most likely value for the uncertainty multiplier is 1 (BSC 2003d, 
Section 6.6.3). The minimum number of waste packages is 0.70 times the central value, and the 
maximum number of canisters is 1.3 times the central value. Because of the uncertainty in 
possible new vitrified waste forms at the Savannah River Site with loading up to 50% higher, it 
is prudent to overestimate the upper limit. A maximum loading of 55% was chosen to provide 
margin, which corresponds to a ratio of 1.5 (BSC 2003d, Section 6.6.3). With the most likely 
maximum and minimum defined, a triangular distribution is chosen for the HLW uncertainty 
multiplier for use by the TSPA-LA model. This multiplier is to be applied to the nominal HLW 
inventories shown in Table 2-3 for all isotopes (including 238U). The uncertainty multiplier has a 
triangular distribution with a minimum of 0.70, most likely value of 1, and a maximum of 1.5 as 
shown in Table 2-4. 
2-14 July 2004 No. 7: In-Package Environment Revision 1 
repository, has the least uncertainty while the inventory of DOE SNF, which is the least 
abundant waste type to be disposed of in the repository, has the most uncertainty. Because the 
TSPA-LA model can simulate the degradation and failure of individual waste packages, the 
radionuclide inventories are provided on a grams-per-package basis. 
July 2004 2-15 No. 7: In-Package Environment INTENTIONALLY LEFT BLANK 
2-16 No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
3.1 ASSUMPTIONS 
As discussed in Section 2, two general types of waste packages are considered as representative 
of all waste packages: commercial SNF and codisposal waste packages containing both DOE 
SNF and HLW glass. For purposes of modeling in-package chemistry, the latter is represented 
by a waste package containing two N Reactor SNF canisters and two HLW canisters (BSC 
2004b, Section 6.3.3). N Reactor SNF was selected to represent the DOE SNF in the codisposal 
waste package because examination of the dissolution rate literature for other DOE SNF waste 
forms (DOE 2002b, Section 6) indicates that the degradation rate of N Reactor fuel generally 
exceeds that of the other types of DOE SNF and because N Reactor SNF comprises 
approximately 85% of the total metric tons of heavy metal of DOE SNF (DOE 2002b, 
Appendix D). 
The in-package chemistry model simulates chemical interactions of water with the waste 
package materials and waste forms for both types of waste packages under different physical, 
hydrologic, and chemical conditions. The simulations are performed with the reaction-path code 
EQ3/6 (Wolery 1992; Wolery and Daveler 1992), with the assumption that the water will always 
maintain equilibrium with precipitated secondary mineral phases as waste package components 
slowly dissolve. The outer shell of the waste package (Alloy 22) is assumed to be inert because 
of its extremely slow corrosion rate (CRWMS M&O 2000c, p. 109, Section 6.9.1). The 
calculation also assumes that oxygen is in equilibrium with the ambient atmosphere outside of 
the waste package. Therefore, the fugacity of O2 is set to the atmospheric value, 0.2 atm (BSC 
2004b, Section 6.3.1). Localized reducing conditions may occur as corrosion products limit the 
access of oxygen to metal surfaces. However, lower redox conditions would favor substantially 
lower radionuclide solubilities. 
Two different water ingress models are considered (BSC 2004b, Section 1): the no-drip model, 
which represents scenarios in which advection through the waste package does not occur, and the 
seepage-dripping model, which represents scenarios in which water can flow through the waste 
3. IN-PACKAGE CHEMISTRY 
Two sets of calculations were performed to estimate the chemistry of fluids reacting with SNF: 
one set represents conditions expected under the nominal and seismic scenario classes; the other 
set represents conditions expected under the igneous intrusion scenario. The bulk of this section 
describes the in-package chemistry model for the nominal and seismic scenario classes. 
Section 3.6 presents the in-package chemistry model for the igneous intrusion scenario. 
The in-package chemistry model simulates the chemistry of water as it reacts with waste package 
components and waste forms inside failed waste packages. The primary input parameters for the 
in-package chemistry model include the chemistry of seepage waters, the compositions of waste 
package materials and waste forms, and the corrosion rates of waste package components. The 
outputs of the model (which include pH, ionic strength, fluoride concentration, and total 
carbonate) are used, either directly or indirectly, as inputs to the models that evaluate dissolved 
concentrations of radionuclides, commercial SNF matrix degradation, HLW glass degradation, 
and colloid stability in the TSPA-LA calculations. The material presented in this section is 
primarily drawn from In-Package Chemistry Abstraction (BSC 2004b). 
3-1 July 2004 No. 7: In-Package Environment Revision 1 
package. In the no-drip model, water enters a failed waste package by vapor diffusion and 
subsequently reacts with the package materials and waste forms. The solutes exit the package by 
diffusion only. The evolution and the uncertainty range of in-package chemistry were simulated 
using individual waste package components or the combinations of these components as 
reactants, based on the consideration that water mixing may not fully occur in the waste package 
due to lack of advective flow. In the seepage-dripping model, seepage water drips into a waste 
package and continuously exits the package by advection after reacting with the package 
contents. In this case, the in-package chemistry is simulated by assuming that solutions reacting 
with various waste package components will be well mixed during the percolation inside a waste 
package. 
One of the model boundary conditions is that a continuous water film forms over the waste 
package components. The thickness of the film is fixed to be 1 mm for commercial SNF and 
2 mm for codisposal waste packages in the no-drip model, and 2.5 mm for commercial SNF and 
3.5 mm for codisposal waste packages in the seepage-dripping model. The basis for these 
thicknesses is that they represent the minimum values required by the EQ3/6 code to complete 
the entire suite of simulations. They are also reasonable separation half-distances for the 
situation being modeled; with degradation and collapse of basket materials, the interior of the 
waste form will consist of loosely compacted fuel and basket elements separated by millimeterto 
centimeter-size apertures. 
In the current model, no water evaporation is allowed to occur inside the waste package (BSC 
2004b, Section 6.3.2). This assumption may lead to a lower ionic strength of in-package water. 
As discussed in Section 7, a high ionic strength decreases colloid stability; thus, a lower ionic 
strength could allow more radionuclides to escape from the waste package in colloidal form 
(BSC 2003h, Figures 4 and 7). 
3.2 RELEVANT PROCESSES 
3.2.1 Water Fluxes 
In the no-drip model, it is assumed that water will form a continuous water film on waste 
package components. However, in reality, this may not occur because water condensation can 
only be possible when the waste package temperature is equal to or below the ambient 
temperature. Given the fact that the interior of a waste package is the source of heat and the 
waste package temperature after closure is maintained above 40C during the 10,000-year 
regulatory period (BSC 2004e, Figure 6.5-3), the formation of a water film over waste package 
materials seems unlikely. As a simplifying assumption, this thermal effect is not considered in 
the in-package chemistry model. The diffusive flux of water vapor is calculated to be 0.8 L/yr 
(43.88 mol/yr) per waste package for isothermal conditions (BSC 2004f, Section 6.3.1). The 
no-drip model is simulated in EQ3/6 as a titration (Wolery and Daveler 1992). That is, water 
and the reactants are added to a reaction vessel at their respective kinetic rates or diffusion flux 
for water; products form and may redissolve, but no water exits the system (Wolery and Daveler 
1992, p. 42, Figure 3). 
In the seepage-dripping model, seepage drips onto the upper surface of the waste package and 
penetrates the waste package through an opening. The solution then flows through the waste 
July 2004 3-2 No. 7: In-Package Environment Revision 1 
package, forming a film on the interior waste package components, reacting, mixing, and 
transporting the dissolved material out of the waste package. There is no accumulation of water 
in the waste package. The water flux through the waste package is varied from 0.15 to 15.0 L/yr, 
representing low to moderate seepage rates (BSC 2003j, Figure 6.8-3). Use of a lower water flux 
increases the contact time of water with waste package components and thus allows the chemical 
reactions to progress further. For sensitivity analyses, a few simulations were performed with 
flux rates as high as 1,000 L/yr. In the seepage-dripping model, the solid-centered flow-through 
option in EQ6 is used. This option simulates a single-cell batch reactor. Seepage water gflowsh 
into the cell at a specified rate while reactants are added to the cell at rates dictated by the 
degradation rates of their source materials. Water exits the cell at the same rate as it enters. 
3.2.2 Kinetic Degradation of Package Components 
The rate at which waste package components degrade, together with the rate of water influx, 
determine the evolution of in-package water chemistry. The degradation rate of commercial 
SNF is formulated as a function of pH and dissolved carbonate concentration, while the rate of 
HLW dissolution is described as a function of pH only, based on transition state theory and 
experimental measurements (see Section 5). For the corrosion of the codisposed N Reactor SNF 
and the metal alloys in the waste package, constant reaction rates are used. The reaction rates 
also depend on the surface areas of waste package components exposed, which have been 
estimated based on the configurations and dimensions of waste packages (BSC 2004b, Sections 
6.5.2 to 6.5.4). The surface areas are assumed to remain constant during degradation. This 
assumption is reasonable because the surface area would decrease as the quantity of a remaining 
reactant decreases while, on the other hand, it could also increase due to grain disaggregation. 
For the no-drip model, the calculations have been performed for the following percentages of 
surface area exposure: 10% for commercial SNF, 100% for HLW glass, and 50% for N Reactor 
SNF. For the seepage-dripping model, the following percentages are considered: 1%, 10%, and 
100% for commercial SNF, and 100% for both HLW and N Reactor SNF. These values were 
chosen only to parameterize the impact of coverage on effluent chemistry. The time-dependent 
percentage of commercial SNF surface area exposed as cladding splits (discussed in Section 4) 
and the surface areas of commercial SNF and high-level radioactive waste used in the waste 
form degradation models (discussed in Section 5) are not input to the in-package chemistry 
model when the model is implemented in TSPA-LA. 
(Eq. 3-1) 
(Eq. 3-2) 
3.2.3 Potential Generation of Alkaline or Acid Waters 
The waste package components could have a significant impact on the predicted in-package pH. 
The dissolution of high-level radioactive waste glass, which contains high concentrations of 
sodium and potassium, could generate an alkaline condition by releasing alkalis (Na+, K+, or 
Ca2+) into solution: 
Glass.Na+ + H+  Na+ + Glass.H+ 
In contrast, the Carbon Steel Type A516, which contains elemental sulfur, is a strong acid 
generator: 
Metal.S + H2O + 3/2O2  SO4
2. + 2H+ + Metal+ 
3-3 July 2004 No. 7: In-Package Environment Revision 1 
Similarly, the Stainless Steel Type 304L, also containing elemental sulfur, could also potentially 
generate an acid solution. However, its effect is expected to be much smaller than Carbon Steel 
Type A516, because of its very slow corrosion rate. The pH resulting from these two reactions 
will be altered by other chemical reactions such as surface complexation on corrosion products, 
leading to a near neutral pH range. 
3.2.4 Surface Complexation 
Steel corrosion, which is rapid relative to fuel oxidation, will result in a large accumulation of 
ferric (hydr)oxide corrosion products inside the waste package. It is estimated that 10 to 
40 moles of ferric (hydr)oxides (ferrihydrite, goethite, or hematite) are potentially able to form 
per liter of void space due to the degradation of low carbon and stainless steel (BSC 2004b, 
Section 6.3.3). Roughly half appears in the first 100 years after package failure due to the 
degradation of Carbon Steel Type A516. From 0.2% to 90% of precipitated iron(III) is able to 
react with solutions. This is equivalent to 0.02 to 40 moles of pH-buffering sites available per 
liter of water. Such a large buffering capacity will be a dominant factor controlling in-package 
chemistry. Given the fact that Carbon Steel Type A516 contains only trace amounts of sulfur 
(approximately 0.035 wt %) (Table 3-1), the proton release from reaction (Equation 3-2) will be 
overwhelmed by surface complexation on the corrosion products. Since the isoelectric point of 
ferric (hydr)oxides is approximately pH 7 to 8 (Davis and Kent 1990, Table 4, p. 210), the 
surfaces of these solids will acquire protons at pH less than 7 and lose them to solution at pH 
greater than 8, in effect limiting the pH range to near neutral. 
July 2004 3-4 No. 7: In-Package Environment Sulfur 
Silicon 
Nickel 
Cobalt 
Iron 
Boron 
Zinc 
Element 
Carbon 
Manganese 
Phosphorus 
Chromium 
Molybdenum 
Nitrogen 
Copper 
Magnesium 
Titanium 
Aluminum 
Total 
Source: BSC 2004b, Table 8. 
100.00 
NOTE: a A nickel alloy interspersed with gadolinium (UNS N06464) is recommended to replace Neutronit as the 
absorber material. The effects of this change on in-package chemistry should be negligible because 
deleting the Neutronit does not significantly change the quantity of available stainless steel; the low 
corrosion rates of the nickel alloy relative to stainless steel will result in slightly higher in-package pH 
(i.e., less aggressive), and gadolinium will form phosphates that are fairly insoluble (Loros and Williams 
2004). 
100.00 
3.2.5 Precipitation and Dissolution of Uranium Minerals 
The uranium mineral, schoepite, together with iron corrosion products, dominates the 
composition of the alteration products from 500 years and beyond (BSC 2004b, Section 6.8.2). 
Whether schoepite dissolution produces or consumes hydrogen ions depends on the pH of the 
solution: 
(Eq. 3-3) 
(Eq. 3-4) 
UO32H2O + 2H+  UO2
+2 + 3H2O for pH  5.5 
These two reactions constitute a negative feedback that resists pH excursions to high or low pH, 
and ultimately constrains pH values near the schoepite solubility minimum pH 6.5 to 7, 
No. 7: In-Package Environment 
Table 3-1. Composition of Steel and Aluminum Alloys 
Carbon 
Steel Type 
A516 
(wt %) 
0.28 
1.045 
0.035 
0.035 
0.29 
. 
. 
. 
. 
. 
98.3 
. 
. 
. 
. 
. 
. 
Neutronita 
(wt %) 
0.04 
. 
. 
. 
. 
18.5 
13 
0.2 
2.2 
. 
64.82 
1.245 
. 
. 
. 
. 
. 
100.00 
UO32H2O + 3HCO3 
.  UO2(CO3)3 
.4 + H+ + 3H2O for pH > 7-7.5 
SS31600 
(wt %) 
0.02 
2.00 
0.045 
0.03 
0.75 
17.00 
12.00 
. 
2.50 
0.08 
65.58 
. 
. 
. 
. 
. 
. 
Al-6061 
(wt %) 
. 
0.15 
. 
. 
0.60 
0.195 
. 
. 
. 
. 
0.7 
. 
0.25 
0.275 
1.0 
0.15 
96.68 
100.00 100.00 
3-5 
Revision 1 
Stainless 
Steel Type 
304L 
(wt %) 
0.03 
2.00 
0.05 
0.03 
0.75 
19.00 
10.00 
. 
. 
0.10 
68.05 
. 
. 
. 
. 
. 
. 
Al-1100 
(wt %) 
. 
. 
. 
. 
0.45 
. 
. 
. 
. 
. 
0.50 
. 
. 
0.05 
. 
. 
99.00 
100.00 
July 2004 Revision 1 
depending on the ambient carbon dioxide fugacity. The carbon dioxide fugacity determines the 
total concentration of aqueous carbonate species (see Section 3.5.3). 
3.2.6 Effect of Radiolysis 
When radiation passes through a material, some energy is deposited in the medium and chemical 
reactions can occur from the local deposition of energy (radiolysis). When gamma and fast 
neutron energy pass through moist air, nitric acid is produced. Hydrogen peroxide is generated 
from the radiolysis of water. Thus, radiolysis could potentially influence the results of the 
in-package chemistry model. However, EQ3/6 simulations show that the amount of acid 
produced from radiolysis has an insignificant effect on in-package chemistry (BSC 2004b, 
Attachment III). 
3.2.7 Effect of Temperature 
Most EQ3/6 simulations were run at either 25C or 50C. However, a few simulations were run 
at higher temperatures to investigate the effect of temperature on pH and ionic strength (BSC 
2004b, Section 6.7.5). The results show that as temperature increases, pH tends to decrease and 
ionic strength remains largely unaffected. The reason for the decrease in pH is related to the 
increased dissociation of the water molecule as temperature rises. The effect of temperature was 
included in the pH abstraction used in TSPA-LA, as described in Section 3.5.1. 
3.3 SOURCE OF DATA 
The primary input parameters for the in-package chemistry model include the chemistry of 
seepage waters, the compositions of waste package materials and waste forms, and the corrosion 
rates of waste package components (BSC 2004b, Section 4). For reference, some of these 
parameters are listed in Tables 3-1 through 3-4. Table 3-4 gives experimental corrosion rates of 
the metal alloys in the waste package. The corrosion rate data in Table 3-4 were collected under 
a variety of conditions and provide the basis for the metal alloy corrosion rates used in the inpackage 
chemistry abstraction and shown in the last column of Table 3-4. 
July 2004 3-6 No. 7: In-Package Environment Table 3-2. Input Water Composition for Model Calculation 
Units 
mg/L 
mg/L 
mg/L 
mg/L 
mg/L 
mg/L 
mg/L 
mg/L 
mg/L 
mg/L 
mg/L 
pH 
Source: BSC 2004b, Table 2. 
Parameter 
Ca 
Mg 
Na 
K 
Si 
SiO2 
NO3 
HCO3 
Cl 
F 
SO4 
pH 
NOTE: 
c 
aSample: ECRB-SYS-CS1000/7.3-7.7/UC; DTN: GS020408312272.003. 
bSample: ECRB-SYS-CS2000/16.3-16.5/UC; DTN: GS020408312272.003. 
DTN: MO0006J13WTRCM.000. 
d Harrar et al. 1990, pp. 49. 
Calcium Pore Water 
ECRB-SYS-CS1000/ 
7.3-7.7/UCa 
94 
18.1 
39 
7.6 
N/A 
42 
2.6 
397 
21 
3.4 
36 
7.6 
No. 7: In-Package Environment 
Sodium Pore Water 
ECRB-SYS-CS2000/ 
16.3-16.5/UCb 
81 
3.3 
120 
6.1 
N/A 
42 
0.41 
362 
24 
6 
31 
7.4 
3-7 
Revision 1 
Well J-13c 
13.0 
2.01 
45.8 
5.04 
28.5 
N/A 
8.78 
Calculated from 
charge balance 
7.14 
2.18 
18.4 
7d 
July 2004 Table 3-3. Chemical Composition of Commercial Spent Nuclear Fuel, N Reactor Fuel, and High-Level 
Radioactive Waste Glass 
Element 
Uranium 
Neptunium 
Plutonium 
Zirconium 
Molybdenum 
Technetium 
Ruthenium 
Cesium 
Barium 
Gadolinium 
Oxygen 
Aluminum 
Sulfur 
Calcium 
Phosphorus 
Silicon 
Boron 
Fluorine 
Iron 
Potassium 
Magnesium 
Sodium 
Source: BSC 2004b, Tables 10 and 11. 
No. 7: In-Package Environment 
Commercial SNF 
(mol/100 g) 
0.3617 
0.0009 
0.0027 
0.0005 
0.0009 
0.0008 
0.0020 
0.0013 
0.0010 
0.0035 
0.7385 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
N Reactor Fuel 
(mol/100 g) 
0.42 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
3-8 
Revision 1 
High-Level 
Radioactive Waste 
Glass 
(mol/100 g) 
0.00782 
0 
0 
0 
0 
0 
0 
0 
0.00108 
0 
2.70 
0.0863 
0.00401 
0.0162 
0.000489 
0.776 
0.291 
0.00166 
0.172 
0.0751 
0.0333 
0.577 
July 2004 Metal 
Carbon Steel 
Type A516 
(times less than 
0.53 years) 
Carbon Steel 
Type A516 
(times greater 
than 1.0 years) 
Neutronit 
Aluminum Alloy 
Stainless Steel 
Type 316 
Stainless Steel 
Type 304L 
Source: BSC 2004b, Table 9; BSC 2004g, Table 7-4. 
Min. 
78.71 
58.08 
50.25 
7.39 
65.77 
29.53 
6.77 
3.69 
0.001 
0.025 
1.810 
0.40 
0.12 
0.037 
0.001 
1.588 
0.660 
NOTE:  = standard deviation; SDW = simulated dilute water; SCW = simulated concentrated water. See note in 
Table 3-1. 
Rate Used in the 
In-Package 
Chemistry Model 
(m/yr) 
72 
0.1 
3.0 
0.1 
0.1 
3.3.1 Initial Water Compositions 
For the seepage-dripping model, three water compositions have been used for the initial 
conditions (BSC 2004b, Table 2), as shown in Table 3-2: (1) a calcium-dominated pore water, 
(2) a sodium-dominated pore water, and (3) a J-13 well water. The calcium- and 
sodium-dominated pore water compositions were used because they were obtained from core 
samples near the repository. The decision to use these water compositions was based on several 
lines of reasoning. The J-13 well water composition was used for comparison purposes (i.e., to 
maintain continuity between the current work and past in-package chemistry analyses). The 
calcium and sodium pore water compositions, corresponding to pore waters W5 and W4, 
respectively in Drift-Scale Coupled Processes (DST and THC Seepage) Models (BSC 2004e), 
are used because they were obtained from core samples proximal to the repository. These two 
pore waters represent possible in situ water compositions in the unsaturated repository horizon. 
Pore water compositions can also be perturbed thermally during the postclosure time period. 
However, a sensitivity analysis has shown that this perturbation is negligible (BSC 2004b, 
No. 7: In-Package Environment 
Table 3-4. Metal Alloy Corrosion Rates 
0.0014 14.787 0.7362 
Median Mean 
130.70 101.95 102.71 
81.14 77.05 
68.08 62.77 
12.78 12.42 
77.43 74.56 
51.80 48.70 
10.61 10.83 
6.84 6.75 
0.004 0.003 
0.206 0.203 
11.060 10.190 7.38 
12.95 9.50 
9.69 4.76 
0.0083 0.0136 0.003 
0.248 0.229 
1.939 
0.214 0.1285 
11.441 11.134 5.08 
5.816 2.03 
Freshwater (29.5C) 0.0007 0.0475 
Max. 
130.02 
104.20 
22.06 
106.93 
88.68 
14.36 
9.35 
0.011 
0.330 
29.220 
36.93 
110.91 
0.51 
1.570 
39.147 
15.900 
Condition 
SDW (60C) 
SDW (90C) 
SCW (60C) 
SCW (90C) 
SDW (60C) 
SDW (90C) 
SCW (90C) 
SCW (90C) 
Freshwater (29.5C) 
Freshwater 
(50C to 100C) 
Saltwater (26.7C) 
Freshwater 
Saltwater 
Freshwater 
(50C to 100C) 
Saltwater (26.7C) 
Freshwater 
(25C to 100C) 
Saltwater (26.7C) 
Saltwater (90C) 
Corrosion rate (m/yr) 
3-9 
Revision 1 
 
12.37 
15.13 
14.17 
3.77 
8.83 
12.99 
2.02 
1.25 
0.004 
0.088 
10.84 
15.34 
0.146 
3.346 
0.298 
5.953 
July 2004 Revision 1 
Figure 14). For the no-drip model, the initial water is assumed to be pure water in equilibrium 
with the ambient fugacities of CO2 and O2 (BSC 2004b, Section 6.3). 
3.3.2 Compositions of Waste Package Components 
The chemical composition of each component is listed in Table 3-3. Table 3-3 summarizes the 
composition of commercial SNF, N Reactor fuel, and HLW glass. As discussed in Section 2, the 
commercial SNF is an oxide fuel while the N Reactor SNF is a uranium metal fuel. 
3.3.3 Corrosion Rates 
Metal corrosion rates are used to estimate the mean lifetime of the waste form internals and 
allow prediction of the time needed for structural collapse to occur. Metal corrosion proceeds 
initially by the direct oxidation of reduced constituents (iron, aluminum, chromium, etc.) to form 
a coating of metal hydroxides that can slow the further access of water and oxygen to the 
dissolving metal surface. Consequently, absolute rates of corrosion depend on both the oxidation 
step and the stability and chemical reactivity of the corrosion product layer. Solution chemistries 
that solubilize the latter, for example, can accelerate corrosion. Corrosion rates are important to 
the in-package chemistry model because they allow some predictions to be made of the rate of 
metal hydroxide accumulation, which is important for pH control, radionuclide sorption, and 
potential colloid formation. The corrosion rates of metal alloys are given in Table 3-4. These 
rates will encompass various aqueous parameters such as temperature (up to 100C), water type 
(i.e., fresh versus saline), and pH. The corrosion rates of the different waste forms can be found 
in Section 5 of this document. Note that the corrosion rate of Carbon Steel Type A516 is about 
3 orders of magnitude higher than that of Stainless Steel Type 304L. Therefore, even though 
both materials contain a similar sulfur content, their impacts on in-package pH will be quite 
different, as discussed in Section 3.2.3. 
3.4 EVOLUTION OF IN-PACKAGE CHEMISTRY 
The temporal evolution of in-package chemistry is calculated by assigning kinetic rates to waste 
package component degradation. Note that the starting time for the in-package chemistry model 
refers to the time at which waste packages are breached and sufficient water becomes available 
for chemical reactions. As discussed in Section 3.2, the in-package chemistry is controlled not 
only by waste and waste package corrosion and the associated mineral precipitation or 
dissolution, but also by surface complexation on corrosion products. The EQ3/6 code does not 
model interfacial chemical processes such as surface complexation. Thus, in-package conditions 
have been modeled in two steps. First, EQ3/6 calculations were made simulating the evolution 
of these conditions without considering the effects of surface complexation reactions for 
20,000 years following the start of reactions in the package (which is significantly beyond the 
10,000-year regulatory period). Second, the effects of surface complexation reactions on the 
in-package pH are modeled. The surface sites on which these reactions take place will be 
available for at least several hundred years, as long as a significant amount of steel degradation is 
occurring. During this period, surface complexation rather than the reactions modeled by EQ3/6 
will likely control the in-package pH (BSC 2004b, Section 6.3.3). 
3-10 July 2004 No. 7: In-Package Environment Revision 1 
The pHs predicted inside the waste form tend to be near neutral indicating that the pH-buffering 
ability of corrosion product surface complexation and schoepite dissolution will limit pH 
excursions. The isoelectric point of ferric corrosion products and the solubility minimum of 
schoepite are both near neutral. Equilibration of acidic or alkaline fluids with ferric corrosion 
products and schoepite consequently limits pHs to between approximately 4.5 and 8. Increasing 
carbon dioxide levels causes a slight shift of pHs to lower values while favoring the formation of 
carbonate-containing surface complexes at the corrosion product-solution interface and dissolved 
uranyl-carbonate species. The pH abstractions capture the net effect of these processes (BSC 
2004b, Section 6.8.2). 
3.4.1.1 No-Drip Model 
Figure 3-1 displays the EQ3/6 simulation results for individual commercial SNF components at 
25C (BSC 2004b, Figure 1). The figure provides information on how each waste package 
component contributes to the in-package pH and shows the upper and lower pH limits for a 
commercial SNF package without considering the effects of surface complexation reactions. 
Note that the horizontal axis is in terms of moles of material dissolved independent of time. The 
low pH resulting from Carbon Steel Type A516 corrosion is due to the combination of oxidation 
of elemental sulfur, the large quantity of Carbon Steel Type A516, and its high corrosion rate 
relative to the other waste package components. Both the Al-6061 and the commercial SNF 
have neutral to slightly basic pH profiles, while the S31600 and Neutronit (borated Carbon Steel 
Type 316) have slightly acidic pH profiles. 
The simulations have also been performed for multicomponent ensembles and resulted in a 
similar pH range (BSC 2004b, Figure 2). The simulations show that if it were not for the surface 
complexation reactions, the early-time pH (less than 100 years) will be controlled by Carbon 
Steel Type A516 dissolution, and for 100 to 500 or 1,000 years the pH would be controlled by 
Al-6061 dissolution. At times greater than 500 to 1,000 years, when surface complexation 
reactions may no longer be effective, the pH will be controlled by equilibrium with the corrosion 
products. 
3.4.1 Commercial Spent Nuclear Fuel Waste Packages 
3-11 July 2004 No. 7: In-Package Environment Revision 1 
Source: DTN: MO0403SPAIPCHM.004, CSNF_singlereact_NDM.xls. 
3.4.1.2 Seepage-Dripping Model 
The evolution of in-package chemistry for the median water flux and 10% fuel exposure at 25C 
predicted by EQ3/6 is shown in Figure 3-2. Although nominal temperatures in the repository 
will exceed 40C for the first 10,000 years, 25C runs were used initially to identify the relevant 
chemical processes and trends as thermodynamic and kinetic data are better known at the slightly 
lower temperature. It can be seen that the compositions of seepage water have virtually no effect 
on the pH evolution. The solution pH will be quickly overwhelmed by waste package corrosion 
over the first 10 years. Low pH values modeled without considering the effects of surface 
complexation reactions result from the relatively fast corrosion of a large quantity of Carbon 
Steel Type A516 corrosion (Figure 3-2). As shown in the figure, however, this pH will be 
buffered by the corrosion of other waste package components after a few hundred years. The 
commercial SNF is predicted to completely degrade over approximately 5,000 years. 
Figure 3-1. pH Calculated as a Function of Moles of Reactant Dissolved in Kilograms of Water for 
Commercial Spent Nuclear Fuel Single Component Degradation under No-Drip Conditions 
July 2004 3-12 No. 7: In-Package Environment Revision 1 
NOTE: C22C25B = Ca-pore water, C22J25B = J-13 pore water, and C22N25B = Na-pore water. 
Source: DTN: MO0403SPAIPCHM.004, CSNF_SDM_25_rev03.xls. 
3.4.2 Codisposal Waste Packages 
3.4.2.1 No-Drip Model 
Figure 3-3 displays the 25C pH profiles for individual codisposal waste package components as 
calculated by EQ3/6. Again, note that the horizontal axis is in terms of moles of material 
dissolved independent of time. The codisposal waste package displays a much wider variation in 
pH compared to the commercial SNF package (Figure 3-1). This is because the HLW generates 
high pH conditions due to its high concentrations of sodium and potassium. The Al-1100, 
S31600, and N Reactor fall in the middle of the pH range at approximately 5.7 to 6.2. The 
corrosion of Carbon Steel Type A516 and Stainless Steel Type 304L corrosion leads to pH 
values of 1.5 to 2, as shown in Figure 3-3. However, over the first few hundred years this low 
pH will be buffered by surface complexation reactions of the corrosion products (BSC 2004b, 
Section 6.8.2). Furthermore, over longer time periods, results of the codisposal waste package 
multicomponent ensemble runs show that this low pH will be buffered by either HLW glass or 
N Reactor fuel, since the TSPA-LA model does not take cladding credit for N Reactor fuel or the 
glass pour canisters (BSC 2004b, Section 6.6.2.1, Figure 9). 
Figure 3-2. Evolution of pH and Reactants for Commercial Spent Nuclear Fuel Waste Packages under 
1.5-L/yr Seepage-Dripping Conditions and 10% Fuel Exposure at 25C 
July 2004 3-13 No. 7: In-Package Environment Revision 1 
Source: DTN: MO0403SPAIPCHM.004, CD_singlereact_NDM.xls. 
3.4.2.2 Seepage-Dripping Model 
Figure 3-4 displays the 25C evolution of pH and reactants for codisposal waste packages under 
seepage conditions. The pH profiles converge early in the simulations indicating that the model 
pH response is insensitive to starting water composition, which is similar to the pH profile 
behavior of the commercial SNF seepage-dripping model (Figure 3-2). The figure shows that 
the N Reactor fuel dissolves in just a few years. The rapid dissolution of the N Reactor fuel 
diminishes its contribution to the overall in-package chemistry. However, the resulting large 
quantity of schoepite may have the capacity to influence the in-package chemistry for an 
extended duration. The minimum pH for codisposal waste packages is approximately one unit 
higher that for commercial SNF under the same chemical and physical conditions (Figure 3-2). 
Another important feature of the pH profiles in Figure 3-4 is the absence of a period of sustained 
high pH (greater than 9) that might be expected from the dissolution of the HLW glass. The 
codisposed no-drip models, single reactant and ensemble, both predicted that high pH conditions 
are possible in the absence of an acid-producing reactant (i.e., steel alloy) (Figure 3-3). 
However, in the seepage-dripping model, seepage is allowed to stream through the waste 
package reacting with waste package components, and it is unlikely that seepage, which has 
reacted with HLW glass could exit a waste package without contacting a steel component along 
its flow path. 
Figure 3-3. pH Calculated as a Function of Moles of Reactant Dissolved in Kilograms of Water for 
Codisposed Single Component Degradation under No-Drip Conditions 
July 2004 3-14 No. 7: In-Package Environment Revision 1 
Source: DTN: MO0403SPAIPCHM.004, CDNR_SDM_25_rev03.xls. 
Seepage-Dripping Conditions and 10% Fuel Exposure at 25C 
Figure 3-4. Evolution of pH and Reactants for Codisposal Waste Packages under 1.5-L/yr 
(Eq. 3-6) 
(Eq. 3-7) 
(Eq. 3-8) 
July 2004 
3.4.3 pH Regulated by Surface Complexation 
As discussed in Section 3.2.4 (also see BSC 2004b, Section 6.3), a large quantity of Fe 
oxyhydroxides formed from steel corrosion will be available for surface complexation reactions. 
The surface protonation and deprotonation reactions as described by (Dzombak and Morel 1990; 
Appelo et al. 2002) are: 
+ .>Fe-O-H + H+ K1 = [>Fe-O-H][H+]/[>Fe-O-H2
+] = 10.7.29 (Eq. 3-5) >Fe-OH2 
>Fe-OH .>Fe-O. + H+ K2 = [>Fe-O.][H+]/[>Fe-O-H] = 10.8.93 
where >Fe denotes sites exposed at the FeOOH-solution interface. Sorption of bicarbonate ions 
may also occur: 
>Fe-O-H + H+ + CO3 
2. .>Fe-HCO3 
. + H2O 
KCO3 = [>Fe-HCO3 
.]aH2O/[>Fe-OH][H+][ CO3
2.] = 1012.78 
3-15 No. 7: In-Package Environment Revision 1 
By accounting for the surface complexation effect, the Carbon Steel Type A516 dissolution 
reaction can be written as: 
Fe1.76Mn0.019C0.0233Si0.0103P0.00113S0.00109 + mH2O + fO2  
0.00109SO4
2. + 0.00113(H3PO4 + H2PO4 
. + HPO4
2.) + 0.0103H4SiO4 + 
2CO3 + HCO3 
.) + 0.019MnO2 + (1.76 . X) FeOOH + 0.0233(H 
X(>Fe-O-H + >Fe-O-H2
+ + >Fe-O. + >Fe-O-CO3 
.) + nH+. (Eq. 3-9) 
where X is the amount of Fe in FeOOH (moles) that is exposed to solution, mH2O is moles of 
water, and fO2 is fugacity of oxygen. 
Given a value of X = 0.176 (10% of Fe sites, a reasonable value for goethite) for dissolution of 
1 mol of Carbon Steel Type A516 into a near neutral solution: 
4 
(Eq. 3-10) 
n = 2[SO 2.] + [H2PO4 
.] + 2[HPO4
2.] + [HCO3 
.] + 
[>Fe-O.] + [>Fe-O-HCO3 
.] C [>Fe-O-H2
+]. 
Similarly, charge balance for a solution in contact with carbon dioxide gas at 0.001 atm and into 
which 1 mol of Carbon Steel Type A516 were dissolved would be: 
(Eq. 3-11) 
(H+) + (>Fe-O-H2
+) = 2[SO4
2.] + [H2PO4 
.] + 2[HPO4
2.] + 
(OH.) + (HCO3 
.) + (>Fe-O.) +(>Fe-O-HCO3 
.). 
The two phosphate terms can be manipulated with the equilibrium constant for their conversion 
to give: 
[H2PO4 
.] = 0.00113/(1 + 10.7.2/[H+]) and [HPO4
2.] = 
0.00113(1 C 1/(1 + 10.7.2/[H+])). (Eq. 3-12) 
Substituting these expressions, carbonate equilibrium constants, the surface equilibria above, and 
rearranging to solve for pH: 
(Eq. 3-13) 
(H+) = 0.00218 + 0.00113/(1 + 10.7.2/[H+]) + 
0.00226(1 C 1/(1 + 10.7.2/[H+])) + 0.176[10.8.93/(H+) + 
pCO210.5.28/(H+) C 107.29(H+)] + 10.14/(H+) + 10.7.82 fCO2/(H+) 
The equation above can be solved for pH as a function of fixed fugacity of CO2. For the relevant 
fugacity of CO2, the calculation shows that the surface complexation on the corrosion products 
of Carbon Steel Type A516 can sufficiently neutralize the acidic solution generated by sulfur 
oxidation and that the solutions pH will be buffered at 7.3 to 8.1. This will occur immediately 
after onset of A516 corrosion (that is within the first several decades after waste package 
breach). The effect of increasing carbon dioxide levels in the waste form is to lower the ambient 
pH. 
3.4.4 Upper pH Limit 
As discussed in Section 3.2.5, schoepite together with iron corrosion products dominates the 
composition of the alteration products from time periods of 500 years and beyond. Schoepite 
July 2004 3-16 No. 7: In-Package Environment dissolves at pH values below about 5.5 to consume protons and at pH values above about 7 to 
produce protons (Equations 3-1 to 3-2). These two reactions constitute a negative feedback that 
will constrain pH at 5.5 to 7.5. As Figure 3-3 shows, HLW glass reaction by itself could lead to 
pH values from 8 to 10. For such high pH values to develop, it would require an unlikely 
scenario in which water reacts with the glass without also reacting with schoepite inside the 
waste package. Thus, a HLW glass only, no steel, no fuel scenario that produces high pH 
values is highly unlikely to occur. The most reasonable high-end pH for waste packages 
containing HLW glass is therefore 6.5 to 7. 
3.5 IMPLEMENTATION FOR NOMINAL AND SEISMIC SCENARIOS 
3.5.1 Abstraction of pH 
3.5.1.1 Commercial Spent Nuclear Fuel Waste Package pH Abstraction 
During the period when surface complexation is implemented (0 to 600 years) and based on the 
results of the surface complexation analyses, the pH for a commercial SNF waste package for 
both the nondripping and the dripping models is constrained by the ranges defined as a function 
of the fugacity of carbon dioxide gas, as shown in Table 3-5. 
The minimum pH value, pH 4.5, was set based on a simulation at 90C, indicating that at times 
greater than 600 years the pH stabilizes around 4.5. This simulation is shown in Figure 3-5 and 
represents a minimum pH based on the EQ6 results that do not include surface complexation to 
account for the possibility of localized areas of low pH due to the potential of heterogeneous 
flow through the waste package internals. The maximum pH values for the 0-to-600-year period 
are calculated from Equation 3-13. 
Table 3-5. Commercial Spent Nuclear Fuel Lookup Table of pH as a Function of Carbon Dioxide 
Fugacity 
Period (years) 
0 to 600 
minimum pH 
4.5 
4.5 
4.5 
4.5 
4.5 
4.5 
4.5 
4.5 
4.5 
log(fCO2) 
-5.0 
-4.5 
-4.0 
-3.5 
-3.0 
-2.5 
-2.0 
-1.5 
N/A 600 to 20,000 
Source: BSC 2004b, Table 66; DTN: MO0404SPAIPCHM.005. 
3-17 No. 7: In-Package Environment 
Revision 1 
maximum pH 
8.1 
8.1 
8.0 
7.9 
7.7 
7.5 
7.3 
7.0 
7.0 
July 2004 Revision 1 
Source: BSC 2004b, Figure 24. 
NOTE: C22C25 = 1.5-L/yr seepage dripping conditions, 10% fuel exposure, and calcium pore water at 25C 
Figure 3-5. Effect of Increasing Temperature on the pH Profile for Commercial Spent Nuclear Fuel Waste 
Package 
During the period from 600 to 20,000 years, the minimum pH is set by the high temperature 
results shown in Figure 3-5 and the maximum by the 50-L/yr results shown in Figure 3-6. These 
values provide the maximum plausible range of pH. 
For implementation in TSPA, the 0- to 600-year pH distributions are set using the minimum and 
maximum pH values (Table 3-5) for each log(fCO2). Values for the latter are taken from the 
EBS physical and chemical environment model (BSC 2004h). Each distribution is uniform, and 
pHs for intermediate values of log(fCO2) are linearly interpolated between nearest neighbor 
values. No uncertainty term is added to the sampled value. The abstracted pH values are 
independent of both time and temperature, and are not correlated to the pH in codisposal waste 
packages. 
For the period from 600 to 20,000 years the pH should be uniformly sampled between pH 4.5 
and pH 7 with no additional uncertainty term added. There is no correlation between this pH 
abstraction and that for codisposal waste packages. 
July 2004 3-18 No. 7: In-Package Environment Revision 1 
Source: BSC 2004c, Figure 20. 
NOTE: Cases run using calciumpore water, 10% fuel exposure, at 25C. 
2 
3.5.1.2 Codisposal Waste Package Abstractions 
The abstraction for pH inside a failed codisposal waste package, for both the dripping and 
nondripping models, is given in Table 3-6, and the basis for the minimum and maximum values 
are as follows. When the water flux is less than 150 L/yr per waste package, the maximum pH 
inside a codisposal waste package will not exceed 7 independent of temperature and CO 
fugacity due to a variety of buffering reactions, for example, schoepite dissolution (BSC 2004b, 
Section 6.8.2). For water flux values greater than 150 L/yr per waste package, the maximum pH 
is 8 (BSC 2004b, Figure 22). The lower pH bound, pH = 4.5, was set by the high temperature 
analysis for both flux conditions (BSC 2004b, Figure 25). 
For the purposes of implementation in TSPA, a uniform sampling of the ranges given in 
Table 3-6 with no additional uncertainty will capture the proper pH behavior for nondripping and 
seepage dripping scenarios in a codisposal waste package. 
Table 3-6. Codisposal pH Abstraction 
Period (years) 
0 to 20,000 
Maximum 
pH 
7.0 
Minimum 
pH 
4.5 
Flux Condition 
(L/yr per waste package) 
Q<150 
8.0 4.5 
Source: BSC 2004b, Table 67. 
Figure 3-6. Effect of Water Flux on pH for a Commercial Spent Nuclear Fuel Waste Package 
July 2004 
Q>150 
3-19 No. 7: In-Package Environment Revision 1 
3.5.2 Ionic Strength Abstraction 
The ionic strength results predicted using the EQ6 model were based on layer thickness values 
adjusted to avoid exceeding the ionic strength limit of the aqueous model. Thus, the ionic 
strength boundary condition was set by the model itself. Ionic strength data are used by the 
colloid submodel to determine if the colloid stability threshold, 0.05 mol/kg ionic strength, has 
been exceeded. This threshold value is much below the upper limits of the aqueous model (not 
less than 1 mol/kg) so that use of the higher EQ6 calculated ionic strengths is acceptable. 
The ionic strengths predicted by EQ6 may be high because the underlying calculations do not 
account for removal of dissolved salts from solution by corrosion product sorption. However, 
the inability to predict the identities and abundances (hence site densities) of the corrosion 
products over long periods of time prevents estimation of the magnitude of this effect. For 
example, complete conversion of corrosion products to low site density hematite over time 
would minimize any change in the ionic strengths predicted by EQ6. The possibility of ionic 
strength lowering by sorption reactions is dealt with by setting the uncertainty range to favor 
lower ionic strengths. 
(Eq. 3-14) 
3.5.2.1 
where 
Ionic Strength for Commercial Spent Nuclear Fuel Waste Packages 
The ionic strength distribution for commercial SNF no-drip conditions is constrained from the 
ensemble calculations (BSC 2004b, Figure 28). In the TSPA-LA model, it is sampled from 
0.085 to 0.75 mol/kg with a loguniform probability distribution function. A loguniform 
distribution is used to envelop calculated ionic strengths that themselves vary by orders of 
magnitude. The uncertainty in ionic strength described below should be added to the sampled 
value. 
For a seepage-dripping condition, the ionic strength is calculated as 
y = aeb log f 
y = ionic strength (mol/L) 
a = constant (unitless) 
b = constant (unitless) 
f = seepage flux (L/yr). 
The values of a and b used in Equation 3-14 were calculated from a curve-fitting procedure and 
are shown in Table 3-7, along with the appropriate temperature, flux limits, and regression 
coefficient for the curve fit. Values of ionic strength are either interpolated or linearly 
extrapolated from the 25C and 50C values to other temperatures from 25C to 99C (the limits 
of the in-package chemistry abstraction). As the seepage rate increases, the ionic strength inside 
the package will approach the values of initial waters. If the flux is less than the minimum, then 
the ionic strength should be evaluated using the lower flux limit. Likewise, if the flux is greater 
than the upper limit, then the ionic strength should be evaluated using the upper limit value. 
3-20 July 2004 No. 7: In-Package Environment Table 3-7. Commercial Spent Nuclear Fuel Seepage Dripping Model Ionic Strength Abstraction 
b (unitless) 
-2.1652 
a (unitless) 
0.4755 
Temperature (C) 
25 
-2.2424 0.4472 50 
Flux Limits Per Waste Package 
(L/yr) 
Lower: 0.20 
Upper: 185 
Lower: 0.16 
Upper: 145 
Source: BSC 2004b, Tables 70 to 73. 
3.5.2.2 Ionic Strength for Codisposal Waste Packages 
The ionic strength distribution for codisposal waste packages under no-drip conditions is 
constrained from the ensemble calculations (BSC 2004b, Figure 32). In the TSPA-LA model, it 
is sampled from 0.0032 to 0.75 mol/kg with a cumulative probability distribution shown in 
Table 3-8. The distribution in Table 3-8 is derived from the distribution of ionic strengths 
calculated in the reaction path runs. 
Table 3-8. Codisposal Waste Package No-Drip Model Ionic Strength Cumulative Distribution 
Time Period after Waste Package Breach 
(years) 
0 to 20,000 
Ionic Strength 
(mol/kg) 
3.2  10-3 
0.05 
0.75 
Source: BSC 2004b, Table 74. DTN: MO0404SPAIPCHM.005. 
For dripping conditions, the ionic strength is a function of seepage rate as shown in 
Equation 3-14, as it is for commercial SNF waste packages. The values of a and b used in 
Equation 3-14 were calculated from a curve-fitting procedure and are shown in Table 3-9, along 
with the appropriate temperature, flux limits, and regression coefficient for the curve fit. Values 
of ionic strength are either interpolated or extrapolated to other temperatures. For example, 
while 90C values are not shown, they can be readily extrapolated from the 25C and 50C 
values. As the seepage rate increases, the ionic strength inside the package will approach the 
values of initial waters. If the flux is less than the minimum, then the ionic strength should be 
evaluated using the lower flux limit. Likewise, if the flux is greater than the upper limit, then the 
ionic strength should be evaluated using the upper limit value. 
Table 3-9. Codisposal Waste Package Seepage Dripping Model Ionic Strength Abstraction 
b 
-2.1306 
a 
0.5201 
Temperature (C) 
25 
0.4324 50 
Flux Limits Per Waste Package 
(L/yr) 
Lower: 0.25 
Upper: 180 
Lower: 0.18 
Upper: 180 
Source: BSC 2004b, Tables 75 through 78. 
-2.1369 
3-21 No. 7: In-Package Environment 
Revision 1 
R2 
0.9979 
0.9971 
Probability 
0.0 
0.13 
1.0 
R2 
0.9993 
0.9993 
July 2004 Revision 1 
3.5.2.3 
(Eq. 3-15) 
Uncertainty in Ionic Strength 
The variability of ionic strength is illustrated in Figure 3-7. For both the commercial SNF and 
codisposal waste package seepage-dripping models and no-dripping models at temperatures 
ranging from 25C to 99C (i.e., the temperature boundaries of the in-package chemistry 
abstraction) a uniformly sampled uncertainty value from the range of -0.8 to 0.4 multiplied by 
the sampled value is added to the sampled value. This range accounts for uncertainties in carbon 
dioxide levels as well as the possibility of lower ionic strengths as a result of surface 
complexation effects. Mathematically, the uncertainty for dripping and nondripping cases is 
implemented as follows: 
Ionic strength = ISsampled + (Uncertaintysampled  ISsampled) 
Source: DTN: MO0403SPAIPCHM.004. 
Figure 3-7. Commercial Spent Nuclear Fuel Ionic Strength Profile with Error Bars 
NOTE: C22C25 represents commercial SNF, 1.5-L/yr water flux, calciumpore water, 10% fuel exposure, at 25C. 
July 2004 
3.5.3 Abstraction of Carbonate and Fluoride Concentrations 
Total carbonate is used in the kinetic rate expression for the dissolution of commercial SNF; 
therefore, abstracted values are needed for the TSPA-LA model. In a system where the fugacity 
3-22 No. 7: In-Package Environment Revision 1 
of carbon dioxide (fCO2) is constant over the modeled period and the pH and temperature are 
known, the total carbonate can be calculated using the equilibrium mass action expressions: 
Total C = fCO (Eq. 3-16) 2 (10K 
1 + 10(pH + K 
1 
+ K 
2
) + 10(2pH + K 
1 
+ K 
2 
+ K 
3
)) 
where 
K1 = log K for CO2(g) . CO2(aq); 
K2 = log K for CO2(aq) . H+ + HCO3 
.; and 
K3 = log K for HCO3 
. . H+ + CO3 
2.. 
The values of log K for the above reactions are shown in Table 3-10. 
Table 3-10. Log K Temperature Interpolation Functions for Use in the Total Carbonate Abstraction 
Log K Log K expressiona 
Log K 1 = 7 ~ 10.5T2 . 0.0159T . 1.1023 K 1 
K 2 Log K 2 = 5 ~ 10.7T3 . 0.0002T2 + 0.0132T . 6.5804 
R2 
0.9992 
1.0 
0.9977 K 3 Log K 3 = .8 ~ 10.5T2 + 0.0128T . 10.618 
Source: BSC 2004b, Table 86. DTN: MO0404SPAIPCHM.005. 
NOTE: aT = temperature in degrees Celsius 
The effect of fluoride concentration in the in-package solution is not a direct input parameter in 
TSPA-LA. However, actinide element solubilities are sensitive to fluoride concentrations so the 
ranges of fluoride concentrations were necessary in developing the dissolved concentration 
model (Section 6.2, below). Commercial SNF does not contain fluoride; therefore, the sole 
source of fluoride in commercial SNF waste packages is from the initial seepage waters, which is 
typically low. In contrast, the HLW contains a trace amount of fluoride, which will contribute 
the dissolved fluoride concentration. Based on the EQ3/6 simulations, the upper limit of fluoride 
concentration for codisposal waste packages can be as high as 0.011 mol/kg. 
3.6 IMPLEMENTATION FOR IGNEOUS INTRUSION SCENARIO: 
POSTINTRUSIVE CHEMICAL ENVIRONMENT 
For an igneous intrusion scenario, waste packages in a drift intersected by intrusion will be 
completely damaged and encapsulated by basaltic magma, which will cool and solidify, forming 
a fractured basaltic rock. It is assumed that the postintrusive chemical environment will be 
dominated by the interaction of seepage water percolating through the rock fractures and the 
basaltic rock matrix. In this sense, the chemical environment discussed in this section is no 
longer in-package chemistry per se. 
The chemical interaction of seepage with the basalt is simulated with EQ3/6 for typical seepage 
water and basaltic rock compositions and flow rates (BSC 2004d, Section 6.5.3). The simulation 
results are summarized in Table 3-11. 
July 2004 3-23 No. 7: In-Package Environment Table 3-11. Value of pH and Ionic Strength for Use in the Igneous Intrusion Scenario 
log(fCO2) 
.2 bar 
.2.5 bar 
.3 bar 
.3.5 bar 
Time Period* 
x  25 years 
25 > x  250 years 
250 > x  2500 years 
2500 > x  20000 years 
x  25 years 
25 > x  250 years 
250 > x  2500 years 
2500 > x  20000 years 
x  25 years 
25 > x  250 years 
250 > x  2500 years 
2500 > x  20000 years 
x  25 years 
25 > x  250 years 
250 > x  2500 years 
2500 > x  20000 years 
x  25 years 
25 > x  250 years 
250 > x  2500 years 
2500 > x  20000 years 
.4 bar 
Source: BSC 2004d, Table 8-2. 
NOTE: Time period represents time after re-establishment of seepage flow. Values archived in output 
DTN: MO0402SPAHWCIG.002. 
Maximum and minimum pHs were taken from individual EQ3/6 runs. It can be seen from Table 
3-11 that the pH of the reacted seepage waters will be maintained at a neutral to mildly basic 
range and the ionic strength will remain less than 0.2 mol/kg. EQ3/6 simulations also show 
either no changes or decrease in dissolved fluoride concentration during seepageCbasalt 
interactions. Therefore, for the TSPA implementation, the fluoride concentration, if needed, 
should be set to the initial seepage water concentration. 
3.7 UNCERTAINTIES, LIMITATIONS, AND MODEL CONFIDENCE 
When water comes into contact with the materials inside a waste form, the reactions that ensue 
will be the same as, or very similar to, ones that occur naturally in soils and in other man-made 
materials. Reduced metals, metal oxides, and glass will dissolve and secondary corrosion 
products will form, all in the presence of ambient oxygen and carbon dioxide. In soils, the 
balance between proton production and consumption maintains the pHs to roughly neutral 
values. Only in highly unlikely scenarios do soil pHs fall below 4 (in essence, when high levels 
of reduced sulfur are present). Soil pHs exceed 8 to 9 only when the soil fluids are out of contact 
with the atmosphere and low carbon dioxide fugacities prevail. The in-package chemistry model 
faithfully reproduces these trends and limits. 
3-24 No. 7: In-Package Environment 
pH 
Minimum 
7.6 
7.8 
7.8 
7.7 
8.1 
8.3 
8.3 
8.2 
8.6 
8.8 
8.8 
8.7 
9.0 
9.2 
9.2 
9.1 
9.4 
9.6 
9.6 
9.6 
Maximum 
7.9 
8.1 
8.1 
8.1 
8.4 
8.6 
8.6 
8.6 
8.9 
9.0 
9.1 
9.1 
9.4 
9.5 
9.6 
9.5 
9.7 
9.9 
9.9 
9.9 
Revision 1 
Ionic Strength (mol/kg) 
Maximum 
1.87  10.2 
3.59  10.2 
4.00  10.2 
3.58  10.2 
2.09  10.2 
4.33  10.2 
5.00  10.2 
4.22  10.2 
2.43  10.2 
5.00  10.2 
5.00  10.2 
5.00  10.2 
3.00  10.2 
5.00  10.2 
5.00  10.2 
5.00  10.2 
3.42  10.2 
5.00  10.2 
5.00  10.2 
5.00  10.2 
Minimum 
9.64  10.3 
1.23  10.2 
1.22  10.2 
1.15  10.2 
9.23  10.3 
1.31  10.2 
1.29  10.2 
1.18  10.2 
9.14  10.3 
1.40  10.2 
1.37  10.2 
1.19  10.2 
9.44  10.3 
1.67  10.2 
1.62  10.2 
1.30  10.2 
1.02  10.3 
1.97  10.2 
1.89  10.2 
1.43  10.2 
July 2004 Revision 1 
The in-package chemistry model combines two approaches to model uncertainty: (1) application 
of a factorial design approach to account for known large potential variations in model input 
(reactant combinations, water flux, fuel exposure, temperature, and seepage composition); and 
(2) sensitivity analysis of lesser known, not as well defined input variations (carbon dioxide 
fugacity, Carbon Steel Type A516 sulfur content, corrosion rates, extreme temperatures and flux 
values). Where the first approach provided the functional basis of the in-package chemistry 
model abstraction, the second approach provided the uncertainty ranges of the abstracted 
parameters. Thus, the information to be used in TSPA-LA directly incorporates uncertainty 
exterior to the in-package environment and interior to the in-package environment in a form that 
can be readily implemented in TSPA-LA (i.e., model uncertainty is propagated through the 
abstractions). Thus, the only restrictions on the subsequent use of the in-package chemistry 
model abstraction in TSPA-LA are that all of the abstractions must be applied within the stated 
limits (flux, temperature, fCO2, and fuel exposure) as specified in In-Package Chemistry 
Abstraction (BSC 2004b). 
The in-package chemistry model was validated in two parts. The general output of EQ3/6 was 
validated by comparison with field observations (BSC 2004b, Section 7). Specifically, the 
buffering capacity of the in-package environment was considered quantitatively and shown to be 
reasonable relative to similar natural cases. The reactions that cause long-term shifts from 
neutral pH in natural systems (e.g., acid generation from metal sulfide oxidation, alkalinity 
production by base silicate dissolution in the absence of atmospheric carbon dioxide) were 
shown to be absent in the in-package environment. The modeling of corrosion product surface 
chemistry was validated through expert review, which concurs that the surface effect approach is 
reasonable (BSC 2004b, Section 7). 
3.8 SUMMARY 
Based on the modeling results presented in In-Package Chemistry Abstraction (BSC 2004b), 
in-package chemistry is insensitive to initial water composition, fCO2, decreased corrosion rate 
of the waste package alloys, and minor modifications to waste package design configuration. 
The major controlling factors include waste package type (commercial SNF versus codisposal 
waste package), water flux, fuel exposure (cladding failure) and temperature. The EQ3/6 
calculations establish the range of in-package chemical compositions as dictated by dissolution 
and reprecipitation of solids. Effluent chemistries will be strongly affected by the presence of 
the surfaces of corrosion products (primarily iron oxides and hydroxides). Corrected for the 
effect of surface complexation on corrosion products, the in-package pH ranges from 4.5 to 8.1 
for the nominal and seismic scenario classes. For the igneous intrusion scenario class, the pH is 
neutral to slightly basic (7.6 to 9.9). The ionic strength inside a waste package increases with 
time and can reach a value above 1 mol/L. Increasing temperature and carbon dioxide levels 
would favor decreased pH and vice versa. Higher temperatures cause a general shift in acid-base 
equilibria towards lower pHs. Carbon dioxide levels greater than ambient lower pH as well 
(both inside the waste form and in a basalt-dominated fluid). 
July 2004 3-25 No. 7: In-Package Environment INTENTIONALLY LEFT BLANK 
3-26 No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
4. COMMERCIAL SPENT NUCLEAR FUEL CLADDING DEGRADATION 
Most commercial SNF is encased in Zircaloy cladding. Therefore, degradation of commercial 
SNF considers degradation of the Zircaloy cladding, which can fail either before or after 
emplacement at the repository. The cladding degradation model is implemented in all three 
scenario classes, although in the igneous intrusion modeling case of the igneous scenario, the 
cladding in the impacted drifts is assumed to provide no protection for the fuel. Hence, the only 
aspect of the cladding degradation model implemented in the igneous intrusion modeling case is 
calculating the surface area available for radionuclide diffusion from the waste form to the 
corrosion products in the waste package. The fuel rod cladding contained in drifts that are not 
affected by an igneous intrusion event are treated as those in the nominal scenario. A small 
fraction (1.04%) of fuel rods is encased in stainless steel cladding rather than Zircaloy cladding. 
The cladding model for these fuel rods is bounding in that the cladding is assumed to split along 
the entire length of the fuel rod when the waste package fails. 
The relevant processes and assumptions supporting the cladding degradation model are presented 
below, followed by the cladding degradation model abstraction. A discussion of the sources of 
data, uncertainty, and model confidence building activities concludes this section. The primary 
sources of information for this section are Clad DegradationSummary and Abstraction for LA 
(BSC 2003b), Clad DegradationFEPs Screening Arguments (BSC 2004a), and Seismic 
Consequence Abstraction (BSC 2003c). 
July 2004 
4.1 RELEVANT PROCESSES AND ASSUMPTIONS 
Zircaloy cladding degradation due to general corrosion, localized corrosion (radiolysis-enhanced, 
pitting, crevice, and fluoride-enhanced), the presence of dissolved silica, creep rupture, internal 
pressurization, mechanical impact, diffusion-controlled cavity growth, rockfall, electrochemical 
effects, chemical effects, thermal expansion or stress of in-package components, stress corrosion 
cracking, and hydride embrittlement is not expected for repository conditions (BSC 2004a, 
Table 1-1). Some Zircaloy cladding will degrade before disposal, and those fuel rods that have 
degraded cladding will split when contacted by water as a result of the increased volume of 
corrosion products. Because naval SNF is considered to be commercial SNF for modeling 
purposes, naval SNF cladding is assumed to be affected by the same processes (BSC 2004a, 
Table 1-1). 
The cladding degradation model consists of two phases: cladding failure (perforation) and the 
subsequent splitting of the cladding along the length of the fuel rod. The first phase, cladding 
perforation, is the formation of small cracks or holes in the cladding from various sources. In the 
absence of the cladding degradation mechanisms given above that are not expected for repository 
conditions, cladding perforations can occur (BSC 2003b, Section 5.2): 
1. During reactor operation and subsequent storage (including other operations before 
being received at the repository) 
2. In the repository by mechanical action resulting from seismic events that can occur at 
any time (i.e., the seismic scenario class) 
4-1 No. 7: In-Package Environment Revision 1 
3. In the repository from static loading of rock overburden once the drip shield and waste 
package have failed after the 10,000-year regulatory period. 
In the nominal scenario, only the small fraction of Zircaloy cladding with preexisting defects 
(cause 1, above) is assumed to be perforated during the 10,000-year regulatory period. All 
stainless steel cladding is assumed to be perforated when the waste package fails. Perforation of 
cladding from seismic events (cause 2, above) is modeled exclusively in the seismic scenario 
class. Perforation of cladding from rockfall (cause 3, above) is part of the nominal scenario class 
but occurs after the 10,000-year regulatory period. 
Once the cladding has been perforated and the waste package containing the perforated cladding 
has failed, the cladding is assumed to immediately split along the length of the fuel rod, exposing 
the fuel to corrosion. This is the second phase of cladding degradation and is shown in 
Figure 4-1. Corrosion of the fuel occurs primarily along the cladding-fuel interface and along 
fractures within the fuel matrix. In this phase, corrosion of the fuel forms secondary mineral 
phases composed of higher oxides of UO2 (commonly called rind), causing the cladding split to 
widen further and creating a diffusion pathway for radionuclides and water. Perforation and 
splitting of cladding affects the diffusion of radionuclides from the rind into the corrosion 
products in the waste package. 
Figure 4-1. Fuel Rod Showing Cladding, Fractured Fuel, and Cladding Split 
4-2 July 2004 No. 7: In-Package Environment Revision 1 
A small percentage (1.04%) of commercial SNF has stainless steel cladding instead of Zircaloy 
cladding. Stainless steel cladding was used in early core designs but is no longer used, so the 
quantity of stainless-steel-clad fuel is known and is unchanging. Stainless-steel-clad fuel rods 
are assumed to be perforated upon emplacement in the repository, making them immediately 
available for splitting lengthwise when the waste package fails (BSC 2003b, Section 6.2.2). It is 
assumed in this model that there is a finite probability that any delivery of fuel rods to the Yucca 
Mountain site containing stainless-steel-clad fuel rods will be loaded into two consecutive waste 
packages. This assumption is necessary because it addresses an absence of direct confirming 
evidence of how the waste packages are to be loaded. The basis of this assumption is that the 
stainless-steel-clad fuel is loaded into waste packages as it is received at the repository facilities 
to simplify surface facility operations. The model addresses the probability that for any 
stainless-steel-clad fuel delivery, the waste package being loaded could be near full and the 
remaining stainless-steel-clad fuel assemblies would then be loaded into a new waste package, 
increasing the number of waste packages containing stainless-steel-clad fuel assemblies. The 
shipments are not large enough to sequentially load three waste packages for a given delivery 
(BSC 2003b, Section 5.1). 
Naval SNF cladding is modeled as commercial SNF cladding. Naval SNF has been found to be 
more robust than commercial SNF. Using a naval SNF source term developed by the Naval 
Nuclear Propulsion Program, the DOE estimated that the dose resulting from naval SNF is about 
four orders of magnitude less than that from an equivalent amount of commercial SNF 
(BSC 2001, Section 6). A planned classified Naval Nuclear Propulsion Program Addendum to 
the LA will provide details of this naval SNF source term. Therefore, using commercial SNF as 
a surrogate for naval SNF will overestimate radionuclide releases from naval SNF. 
4.2 MODEL FOR DEGRADATION OF CLADDING 
The model for degradation of commercial SNF cladding is presented in four parts: 
 Fraction of cladding that is defective prior to placement in the repository 
 Number of waste packages containing stainless steel cladding 
 Mechanical failure of cladding after placement in the repository 
 The area available for diffusion and volume of water available for UO2 corrosion after 
the cladding has split. 
The justification for the cladding degradation model used in TSPA-LA is based in large part on 
the exclusion of a number of processes considered unlikely to occur in the repository or to have 
an insignificant impact on repository performance. Some of the processes excluded from the 
cladding degradation model are general corrosion of cladding, localized corrosion from 
radiolysis, localized corrosion in pits and in crevices, creep rupture of cladding, stress corrosion 
cracking, and fluoride-enhanced localized corrosion of cladding. The basis for excluding these 
processes is given in Clad DegradationFEPs Screening Arguments (BSC 2004a, Section 6). 
4-3 July 2004 No. 7: In-Package Environment Revision 1 
4.2.1 Fraction of Cladding Perforated Prior to Placement in the Repository 
The percent of fuel rods with cladding that has perforations prior to placement in the repository 
is given by a loguniform distribution with a range of 0.01% to 1% and a median value of 0.1. 
The loguniform distribution was selected because it gives equal weight to each decade of the full 
distribution. This failure rate is based on an analysis of reactor historical data by S. Cohen & 
Associates (1999, p. 2-31). It also includes defects from wet and dry storage at reactor sites and 
from handling of the waste packages. The as-received perforated fuel rods are available to 
undergo clad splitting and fuel-pellet corrosion once the waste package fails. 
S. Cohen & Associates (1999, p. 2-31) concluded that damage of fuel during shipment, if any, 
will be minor. The rate of cladding perforation resulting from damage during handling at the 
repository surface facility is considered to be similar to that from damage during reactor 
operation and was estimated to be 0.0003% (S. Cohen & Associates 1999, p. 7-1) to 0.0005% 
(CRWMS M&O 2001a, Section 6.4.1). 
(Eq. 4-1) 
4.2.2 Number of Waste Packages Containing Stainless-Steel Cladding 
The fraction of fuel rods that have stainless steel cladding is 1.04% of the total commercial SNF 
inventory. Fuel with stainless steel cladding is placed into waste packages as it arrives at the 
repository. Assuming that deliveries of fuel with stainless steel cladding can be placed into 
either a single waste package or two consecutive waste packages results in 3.5% to 7% 
(uniformly distributed) of the waste packages containing stainless-steel-clad fuel rods (see 
Section 4.3). The percentage of stainless-steel-clad rods in a waste package is calculated in 
Equation 4-1 as: 
% SS - clad rods in waste package = (100%  1.04%) 
clad fuel rods containing SS - % of waste packages 
Thus, waste packages containing stainless-steel-clad fuel rods will contain 15% to 30% 
stainless-steel-clad fuel rods, which are modeled as perforated and available for instantaneous 
splitting when the waste package fails. 
4.2.3 Mechanical Failure of Cladding after Placement in the Repository 
Once the fuel has been emplaced in the repository, intact cladding can be perforated as a result of 
seismic events that are modeled exclusively in the seismic scenario class (vibratory ground 
motion, fault displacement, and rockfall induced by ground motion) or as a result of the static 
load of a rock overburden. These two causes of mechanical failure are discussed below. 
Seismic EventsThe seismic events representing the seismic scenario class can occur at any 
time, and the effects of seismic activity on cladding integrity are developed and analyzed in 
Seismic Consequence Abstraction (BSC 2003c). Seismic events can damage fuel cladding in 
two ways: damage to the cladding from end-to-end impacts between adjacent waste packages 
and damage to the cladding from fault displacement. The end-to-end impact between two 
adjacent waste packages accounts for 87% of the mean damage to the waste package at the 10-6 
per year ground motion level and 92% of the mean damage to the waste package at the 10-7 per 
4-4 July 2004 No. 7: In-Package Environment Revision 1 
year ground motion level (BSC 2003c, Section 6.5.1.2). These results imply that the end-to-end 
impact of adjacent waste packages produces much more severe forces and accelerations than the 
side-on impact between a waste package and the emplacement pallet or drip shield. 
In terms of peak ground velocity, the abstraction for damage to the cladding during a seismic 
event is a simple lookup table with a linear interpolation between the two points in Table 4-1, 
which shows that cladding is assumed to be 100% damaged if the peak ground velocity exceeds 
1.067 m/s. The peak ground velocity values of 0.55 and 1.067 m/s correspond to mean annual 
exceedance frequencies of seismic events of 5  10-5 per year and 10-5 per year, respectively. 
There is no uncertainty in this abstraction because it represents a bounding estimate for cladding 
response at all values of peak ground velocity. 
Table 4-1. Abstraction for Damage to the Cladding from Vibratory Ground Motion 
Damage to Cladding 
(%) 
0 
Peak Ground Velocity Value 
(m/s) 
0.55 
100 1.067 
(Eq. 4-2) 
Source: BSC 2003c, Table 18. 
Static Load of Rock OverburdenCladding perforation from the static load of rock overburden 
is modeled in two steps (BSC 2003b, Section 6.2.3): 
1. Cladding perforation begins when both the fraction of drip shield patch openings 
(i.e., square areas of corrosion) and the fraction of waste package patch openings 
exceed the sampled uncertainty (0.2 to 0.5, uniform distribution).2 Failure of the drip 
shield and the waste package from corrosion is not expected during the 10,000-year 
regulatory period and thus is not included in the cladding degradation model for this 
time period (BSC 2003b, Section 5.2). 
2. After perforation commences, the fraction of cladding with perforations will depend 
linearly on only the fraction of waste package patches open (not the sum of waste 
package and drip shield patch fractions) until all the rods are perforated when an 
additional 50% of the waste package patches are open. 
After there is no waste package or drip shield protection, a rubble bed of rocks from the collapse 
of the drift will mechanically load the bare fuel rods, which will perforate the cladding (CRWMS 
M&O 1999b, Section 6.2). Because there is epistemic uncertainty concerning how the waste 
package and drip shield will fail and when the weight of the rocks will be loaded onto the fuel, 
the equation describing the percent of rods with perforations in a waste package incorporates this 
uncertainty: 
% rods perforated = 200  (PF  X) 
2 For the purpose of modeling spatial variability in waste package degradation, the surface of the waste package is 
divided into patches, each of which has an area of about 232 cm2. The term failure means initial breach of the 
drip shield or waste package (BSC 2003i, Section 6.3.2 and Section 1). 
4-5 July 2004 No. 7: In-Package Environment Revision 1 
where 
PF = waste package patch fraction, in range X  PF  X+0.5 
X = random number, uniformly distributed in range 0.2  X  0.5 
A submodel in the TSPA-LA model predicts when patches are corroded through, giving PF of 
Equation 4-2. 
4.2.4 Split Cladding Geometry 
Fuel rods whose cladding contained preexisting defects or has been damaged mechanically 
during a seismic event or from rock overburden are assumed to be instantly split (cladding splits 
down the length of the fuel rod) once the waste package has failed, leaving the fuel pellets 
exposed to the waste package internal environment and aqueous solutions (see Section 3). This 
assumption bounds the results of experimental observations, and has no uncertainty associated 
with it. It is based on fuel degradation tests performed at Argonne National Laboratory with two 
intentionally perforated fuel segments in steam at 175C (Cunnane 2003). These test conditions 
are more severe than the repository conditions, where waste package surface temperatures are in 
the range of 105C to 120C at 500 years and the air is saturated with humidity but is not entirely 
steam. Both of the fuel segments split in less than two years, an extremely short time in terms of 
the postclosure repository time scale. As a result, in the TSPA-LA model the cladding instantly 
splits once it has been perforated and once the waste package has failed, leaving the fuel pellets 
exposed for corrosion. 
After the cladding has split, the cladding still surrounds the fuel pellets and holds them into the 
original fuel geometry. Intact cladding is an engineered feature that prevents water from 
contacting and interacting with the fuel pellets. After the cladding has been perforated, it may 
still reduce radionuclide mobilization by limiting the exposed surface area of the fuel fragments 
and limiting the amount of water that can contact the pellet. The cladding degradation model 
abstraction considers the effect of cladding in reducing the exposed surface area through which 
radionuclides may be transported, but does not take credit for limiting the degree of corrosion or 
the amount of water that can contact the fuel (BSC 2003b, Section 6.1.1). 
After the cladding splits, UO2 in the fuel pellets along the complete length of the rod is available 
to corrode. The width of the split increases with time as the UO2 corrodes to a secondary 
product, commonly called rind, which has an increased volume. The radionuclides diffuse 
through the split into the surrounding environment, as limited by the cladding. 
The volume rind multiplier is the ratio of the rind volume to the UO2 volume and accounts for 
the increase in volume from the formation of rind, which is schoepite (UO3.2H2O). The volume 
rind multiplier is related to the porosity of schoepite, which is uncertain and assumed to be 
uniformly distributed between 0.05 and 0.3. Because of uncertainty in the porosity of schoepite, 
the value of the volume rind multiplier ranges from 2.8 to 3.8, with a mean value of 3.3, as given 
by Equation 4-3: 
(Eq. 4-3) 
1 . por sch UO2 D 
VRM = MWsch  
DUO2  1 
MW 
4-6 July 2004 No. 7: In-Package Environment Revision 1 
where 
VRM = volume rind multiplier (unitless) 
MWsch = molecular weight of schoepite (322.1 g/mol) 
Dsch = density of schoepite (4.9 g/cm3) 
MWUO2 = molecular weight of UO2 (270 g/mol) 
2 DUO = density of UO2 (10.97 g/cm3) and 
por = porosity in schoepite (unitless). 
The mass of the rind is increased from the UO2 mass by the ratio of the molecular weights of the 
two minerals since water and oxygen are absorbed to form schoepite. Uncertainty in porosity is 
propagated through the model to uncertainty in diffusion parameters. 
Both the corrosion rate of fuel (Section 5) and the amount of fuel corroded are calculated using 
the temperature and chemistry from the in-package chemistry model (Section 3). 
The average distance for radionuclide diffusion from the rod to adjacent metallic corrosion 
products in the waste package is half of the rod diameter. The cross-sectional area for 
radionuclide diffusion from the rind in the failed rods to adjacent metallic corrosion products in 
the waste package is the product of rod length, split opening, and number of failed rods in a 
waste package. 
For the igneous intrusion modeling case of the igneous scenario class, the cladding is destroyed 
by the intruding magma, and the radionuclides can diffuse through any place on the exposed fuel 
rod; thus, the average diffusion distance is one-fourth of the rind and UO2 diameter. The 
diffusion area is the surface area of the rind and unreacted UO2, although the split opening is 
now equivalent to the fuel and rind circumference. 
After the waste package has failed and the relative humidity in the drift approaches 100%, water 
vapor will condense on the fuel and collect in the pores of the rind. The volume of water in the 
rind is used to calculate the mass of solubility-limited radionuclides released from the waste 
form, as discussed in Sections 5 and 6. The split continues to widen until all of the UO2 in the 
rod corrodes, reaching a steady-state geometry. The time for complete corrosion of the rod 
depends on the waste package temperature and chemistry but generally takes less than 
2,000 years (BSC 2003e, Section 6.4.3). Diffusion through the cladding split continues until all 
of the radionuclides are released. 
follows. From Stainless Steel in Waste Package for TSPA-SR (DTN: No. 7: In-Package 
4.3 SOURCES OF DATA AND PARAMETERS 
There are many sources for data on cladding failure during reactor operation. Table 4-2 gives 
failure rates reported by various sources. Fuel reliability is reported every three years in the 
American Nuclear Society and International Atomic Energy Agency-sponsored International 
Topical Meeting on Light Water Reactor Fuel Performance. The Electric Power Research 
Institute also maintains data on fuel reliability during reactor operation as part of their Robust 
Fuels Program. 
The model of the number of waste packages containing stainless-steel cladding was developed as 
4-7 July 2004 No. 7: In-Package Environment Revision 1 
Environment 4-8 June 2004 SN0001T0810599.008), 3.49% of the commercial SNF waste 
packages are modeled to contain 29.9% stainless-steel-clad fuel rods and 70.1% Zircaloy-clad 
fuel rods. The product of these two numbers is 1.04% and represents the total inventory of the 
commercial SNF with stainless steel cladding. However, Stainless Steel in Waste Package for 
TSPA-SR did not consider uncertainty associated with loading stainless-steel-clad fuel into waste 
packages. For the TSPA-LA model, uncertainty in loading of stainless steel-clad fuel into waste 
packages is accounted for by assuming that deliveries to the repository are always timed so that 
all deliveries require partial loading into a new waste package. With this assumption, the 
number of waste packages containing stainless-steel-clad fuel assemblies doubles to 7% and the 
stainless-steel-clad fuel rod content of these waste packages halves to 15%, yielding the 
(rounded) values given in Section 4.2.2. 
Period Fuel 
Table 4-2. Comparison of Fuel Reliability from Various Sources 
1983 
Reference 
Andrews and Matzie 1985, p. 1-3 GE-8  8 
PWR-French Dehon et al. 1985, p. 2-24 
Failure Rate 
(%) 
0.007 
0.001 to 0.01 
0.005 
1979 to 1984 
1984 
To 1997 Sasaki and Kuwabara 1997, pp. 13, 14 
Andrews and Matzie 1985, p. 2-42, Table 2 
EPRI 1997, p. 4-1 
EPRI 1997, p. 4-2 
Sanders et al. 1992, p. I-36 
0.01 
0.002 
0.011 
0.02 to 0.07 
0.006 to 0.03 
0.10 to 0.73 
To 11/1984 
Through 1984 
After 1984 
To 1986 
BWR-Japan 
PWR-Japan 
PWR-CE 
All 
All 
BWR 
PWR 0.07 to 0.48 
0.26 PWR-Westinghouse McDonald and Kaiser 1985, p. 2-5 1 core, debris damage 
after steam generator 
Manaktala 1993, pp. 3-2 and 3-3, Figure 3-1 0.01 to 2+ 
Ravier et al. 1997, p. 34, Figure 4 
Edsinger 2000, p. 162 
0 to 0.055 
0.0005 
0.00058 
0 rod failures 
replacement 
All 1969 to 1976 
PWR-Mark B-B&W 1986 to 1996 
BWR 2000 
BWR, GE Fuel 1995 to 1999 
PWR, Mitsubishi Fuel 1992 to 1999 
Potts 2000, p. 502, Figure 1 
Doi et al. 2000, p. 443 
NOTE: Failure rates are on a rod basis unless noted as assembly-based. The assembly value represents the 
July 2004 
percentage of assemblies that contain at least one failed rod. GE = General Electric. 
The model of mechanical failure of the cladding during a seismic event is based on a study by 
Chun et al. (1987) that calculated g-loads for axial buckling and for yielding due to side drops. 
This data was used in Section 6.7 of Seismic Consequence Abstraction (BSC 2003c) to develop 
the abstraction of waste package perforation as a function of peak ground velocity. 
The model of the split cladding geometry is based on simple geometric principles and constants 
(e.g., fuel pellet diameter, rod length, number of rods in an assembly) and physical constants 
(e.g., molecular weight and density of schoepite and UO2). The porosity of schoepite is also 
used to calculate the split geometry as the pellet corrodes, and ultimately the amount of water 
used in the TSPA calculation. The porosity is uncertain with a range 0.05 to 0.3, uniformly 
distributed (DTN: LL010902212241.026). 
4-8 No. 7: In-Package Environment Revision 1 
4.4 UNCERTAINTIES, LIMITATIONS, AND MODEL CONFIDENCE 
4.4.1 Uncertainties 
The uncertainty in the fraction of fuel that is perforated as-received at the repository is based on 
observed fuel reliability from reactor operations and conservative predictions for other activities 
after removal from the reactor (BSC 2003b, Section 6.2.1). The fraction of commercial SNF that 
is clad in stainless steel is fixed and known, and no uncertainty in that fraction was considered. 
The input-loading pattern for the stainless-steel-clad fuel into waste packages is treated with 
uncertainty as described in Section 4.2.2. 
Uncertainty in rind porosity and UO2 corrosion rates, and chemical and temperature 
environments inside the waste package are treated in the TSPA calculations and produce an 
uncertainty in the rind geometry and diffusion of radionuclides from the fuel rind (BSC 2003b), 
Section 6.2.4). Since a bounding model (instantaneous splitting) is used, no uncertainty is used in 
the TSPA-LA model for clad splitting. 
4.4.2 Limitations 
The cladding degradation model has constraints and limitations (BSC 2003b, Section 1): 
 The model is based on commercial water reactor fuel with Zircaloy cladding. 
Commercial gas-cooled reactor fuel from Fort St. Vrain is included in the inventory of 
DOE SNF and is, thus, not considered in the cladding degradation model.3 (See 
Section 5.2.) Naval SNF is included in the cladding degradation model. 
 Fuel reliability from reactor operation is determined for both PWRs and BWRs. 
 The model is also limited to fuel exposed to normal operations and anticipated 
operational occurrences (i.e., events anticipated to occur within a reactor lifetime) and is 
not applicable to fuel exposed to severe accidents. 
 The model applies to a repository design with the waste package surface temperature 
always less than 350C. For higher waste package temperatures, cladding creep needs to 
be considered. 
4.4.3 Confidence-Building 
This section discusses the activities performed to generate confidence in the model during 
development. Confidence in the cladding degradation model is accomplished by corroborating 
model predictions with results of alternative mathematical models and (or) experimental data 
developed independently and available in the published literature and industry reports. This is 
done independently for each part of the cladding degradation model. 
Confidence in Model of Fraction of Cladding Perforated Prior to Placement in the 
RepositoryModel validation of the as-received cladding perforations is accomplished by 
3 DOE SNF is not considered in the cladding degradation model because it has no cladding. 
July 2004 4-9 No. 7: In-Package Environment Revision 1 
validating model predictions with results of an alternative mathematical model and experimental 
data developed independently and available in the published literature and industry reports 
(BSC 2003b), Section 7.4.1). The alternative mathematical model (CRWMS M&O 2001a, p. 65) 
independently produced very similar results (median 0.095%; range 0.0155% to 1.29%) for the 
rod perforation rate for all causes. This is close to the model presented above (median 0.1%; 
range 0.01% to 1%), and builds confidence in the model of as-received cladding perforation. 
There also is justification of the use of technical information (as-received perforation rates) from 
S. Cohen and Associates (1999). 
Confidence in the Model of Stainless Steel Clad FuelAssuming that all of the stainless steel 
clad fuel rods are perforated when placed in the waste packages is an upper limit. S. Cohen & 
Associates (1999, p. 2-22) estimated that only 0.005% to 1.4% of the stainless-steel-clad fuel 
rods actually had perforations. This reliability is close to that of the Zircaloy cladding and much 
less than the 100% perforation rate used in this model, thereby substantially overestimating the 
availability of UO2 for corrosion. 
The fraction of stainless steel cladding is well known, and since no current fuel manufacturer is 
using stainless steel, the quantity is fixed. An earlier alternative conceptual model (CRWMS 
M&O 1998a, Section 6.3.1.1) had the stainless-steel-clad fuel distribution mixed equally into all 
the waste packages. This is unlikely since the current surface design facility calls for loading 
waste packages as the fuel arrives on the site, and blending the stainless-steel-clad fuel is not 
planned. Assuming that a shipment of stainless-steel-clad fuel rods could be placed in two 
consecutive waste packages increases the percentage of waste packages containing stainless steel 
clad fuel from 3.5% to 7%, with a content ranging from 30% to 15%, respectively. The fraction 
of stainless-steel-clad fuel in a waste package represents a conservation-of-mass calculation and 
needs no validation. 
Confidence in the Model of Mechanical Failure of Cladding in the RepositoryCommercial 
SNF that was emplaced in the repository with intact cladding can be perforated only by 
mechanical failure, either from seismic events or from static loading of rocks once the drip shield 
and waste package have degraded. The model for cladding perforation from seismically induced 
ground motion is bounding and does not require further confidence building. 
Confidence in the model for cladding perforation from static loading of rocks once the drip 
shield and waste package have degraded is obtained by comparison with experimental data. 
Wilmot (1981, Table VII) recommends the use of 71-g accelerations for the failure threshold for 
fuel rods experiencing side impacts and references an experimental threshold of 122 g for SNF. 
He notes that, in drop tests, rods were bent with end impacts of 38 g but did not fail. He 
references experimental thresholds for end impacts of 234 g. Fischer et al. (1987, Figure 8-3) 
suggested that 10% of the rods might fail with a 40-g end impact, and 100% might fail with a 
100-g end impact. Witte et al. (1989, Table 3) report that the acceleration needed to perforate 
rods from side impact varies from 63 g to 211 g, depending on the fuel design. Sanders et al. 
(1992, Attachment III) present detailed structural analysis of various assemblies under impacts 
and give the probability of rod perforation from 9-m drops of transportation casks (Sanders et al. 
1992, Attachment III, Table III-10). All these references show the robustness of spent fuel rods, 
thereby building confidence in the model that the rods break only from a significant amount of 
rock overburden. 
July 2004 4-10 No. 7: In-Package Environment Revision 1 
The TSPA-LA model contains a submodel that predicts when square areas of the waste package 
surface will corrode through. It is intuitive that the waste package will offer some protection 
when the first patch opens and very little after the last one opens. The present model has the 
perforation of cladding start when 20% to 50% of the patches are open, which is reasonable. 
However, in the nominal case the waste package and drip shield will not fail from corrosion until 
after the 10,000-year regulatory period (BSC 2003b, Section 5.2). In the igneous case, all 
cladding is assumed to fail. 
Confidence in the Split Cladding ModelThe cladding instantaneously splits when both waste 
package and cladding perforations exist, leaving bare fuel pellets to react with the in-package 
environment. Model validation of the cladding splitting model is accomplished by a 
corroborating model. The cladding splitting model has predictions that bound results of 
experimental data developed independently and available in the published literature and industry 
reports (BSC 2003b, Section 7.4.3). 
The rind model describes the release of radionuclides through the split opening along the 
cladding and accounts for the reduced radionuclide release compared to completely exposed fuel 
by simulating diffusion through a growing split. In this model, diffusion through a growing width 
of the split is modeled. The split diffusion surface area is treated as increasing with time as the 
split continues to open. Model validation of the rind model is accomplished by corroborating 
model predictions with results of experimental data developed independently and available in the 
published literature and industry reports (BSC 2003b, Section 7.4.3). 
4.5 CONCLUSIONS 
Most commercial SNF will be disposed of with Zircaloy cladding. Naval SNF is modeled in the 
TSPA-LA as Zircaloy-clad commercial SNF. At some time after emplacement, waste packages 
breach and the cladding on commercial SNF will be exposed to water or water vapor. Stainless 
steel cladding is assumed to split immediately upon exposure to water, while Zircaloy cladding is 
assumed to split if it had perforations prior to disposal, if it has been damaged by rockfall 
(following corrosion of the drip shield and waste package), or if it has been damaged by seismic 
events (for the seismic scenario class). The amount of UO2 in any water inside the package 
influences the water chemistry to some extent. The percent of fuel exposed as a result of 
cladding failure was included in in-package chemistry calculations, as described in Section 3. 
For the nominal scenario, waste packages are expected to remain intact for at least 10,000 years 
except for those packages with cracks from manufacturing defects. Hence, only the fraction of 
fuel rods with cladding perforated prior to disposal can release radionuclides from those waste 
packages that have manufacturing defects during the 10,000-year regulatory period. That is, 
commercial SNF placed into the repository with unperforated cladding will remain intact for the 
10,000-year regulatory period. Because the waste package and drip shield will not corrode 
significantly for more than 10,000 years, damage from rockfall following corrosion of the drip 
shield and waste package will not occur during the 10,000-year regulatory period. 
For the seismic scenario, cladding is susceptible to mechanical failure from seismic events, 
depending on the nature and probability of the event, which can increase the number of 
perforated fuel rods. For the igneous intrusion scenario, the cladding is assumed to fail, allowing 
radionuclides to diffuse through the entire surface area of the failed fuel rod. 
July 2004 4-11 No. 7: In-Package Environment Revision 1 
Once the cladding is perforated and water comes into contact with the underlying fuel, oxidation 
of the latter results in the formation of a fuel alteration product assemblage. This leads to further 
splitting of clad, followed by further fuel exposure, and so on until all the fuel is available for 
reaction. The cladding model assumes rapid splitting along the fuel rod. Degradation of the 
exposed fuel produces a rind whose volume, cross-sectional area for diffusion, diffusion 
distance, and width of split are estimated by the commercial SNF cladding degradation 
component. These parameters are used by the engineered barrier system transport submodel to 
determine the rate of diffusion of radionuclides into the corrosion products in the interior of the 
waste package. 
July 2004 4-12 No. 7: In-Package Environment Revision 1 
5. WASTE FORM DEGRADATION MODELS 
This section discusses the degradation processes for commercial SNF, DOE SNF and HLW 
glass, and presents a degradation model for each waste form. Commercial SNF will be disposed 
of in one of five different waste packages, as shown in Figure 2-2. DOE SNF (with the exception 
of naval SNF) and HLW will be disposed of together in codisposal waste packages, as discussed 
in Section 2. Relevant processes and modeling assumptions; data and supporting documents; 
and uncertainties, limitations, and model confidence regarding the waste form degradation 
models are discussed in this section. The primary sources of information for this section are 
CSNF Waste Form Degradation: Summary Abstraction (BSC 2003e), DSNF and Other Waste 
Form Degradation Abstraction (BSC 2003f) and Defense HLW Glass Degradation Model 
(BSC 2003g). 
The degradation models for commercial SNF, DOE SNF and HLW glass calculate rates of 
radionuclide release from the waste form using input from other models (e.g., pH from 
in-package chemistry model discussed in Section 3). The output from these degradation models 
is used in other models (e.g., EBS transport) to estimate rates of radionuclide transport away 
from the waste form. The commercial SNF degradation model is discussed first, followed by the 
DOE SNF degradation model, and the HLW glass degradation model. 
5.1.1 Degradation Processes 
The following discussion of the degradation processes that are expected to affect commercial 
SNF in the repository is summarized from CSNF Waste Form Degradation: Summary 
Abstraction (BSC 2003e). The availability of individual radionuclides for dissolution, once the 
waste package and fuel cladding are breached, is limited by the structure, microstructure, and 
physiochemical properties of the irradiated fuel, as well as the distribution of fission products in 
the fuel rods. Most radionuclides, and essentially all of the rare earth and actinide radionuclides, 
July 2004 
5.1 COMMERCIAL SPENT NUCLEAR FUEL DEGRADATION 
PWR and BWR spent fuel assemblies will be placed in waste packages, with 12, 21, 24, or 
44 assemblies per package, as indicated in Table 2-1. Such a packaging arrangement results in 
7,472 packages containing PWR and BWR spent fuel assemblies. In addition, 300 packages of 
naval SNF are treated as if they were commercial SNF packages (see Section 5.2), yielding a 
total of 7,772 commercial SNF waste packages for the TSPA-LA model. 
Once these waste packages are breached, the spent fuel will be exposed to either water or water 
vapor, and the commercial SNF rods with breached cladding will begin to degrade. The 
following model provides the commercial SNF degradation rate under conditions expected in the 
repository after the waste package and fuel cladding have been breached; degradation of 
cladding was discussed in Section 4. 
When combined with the output from other models giving temperature, pH, total carbonate 
concentration and oxygen fugacity, this degradation model is used to calculate radionuclide 
release rates from commercial SNF in TSPA-LA calculations. The model is used to estimate 
radionuclide release rates from commercial SNF in scenarios that involve degradation of 
commercial SNF as a result of contact with water (see Section 1.2). 
5-1 No. 7: In-Package Environment Revision 1 
are retained in the fluorite structure of the fuel matrix. Transition metals are partly partitioned 
into metallic phases embedded in fuel grains and at the fuel grain boundaries. This part of the 
radionuclide inventory (present either as a solid solution in the fuel matrix or embedded as 
discrete phases in the fuel grains) is not available for dissolution until the fuel matrix is dissolved 
or otherwise altered, and is referred to as the matrix inventory. On the other hand, some fission 
gasses and more volatile radionuclides (i.e., cesium, strontium, iodine, and technetium) migrate 
out of the matrix during in-reactor operations. These accumulate in locations collectively 
referred to as the gap region (i.e., the interface between the pellets and the cladding, the rod 
plenum regions, and pellet fracture surfaces), while some accumulate at grain boundaries. The 
radionuclide inventory that is not a part of the matrix is called the gap and grain boundary 
inventory, and can be released rapidly once the cladding is breached (BSC 2003e). 
Once the waste package and cladding have been breached, oxidation and degradation of the 
commercial SNF matrix will proceed. If the temperature exceeds approximately 230C to 
250C, the degradation will proceed via solid-state reactions (CRWMS M&O 2000d). As the 
temperature decreases below this range, the relative humidity to which the commercial SNF 
matrix is exposed increases. The presence of water vapor complicates the oxidation process by 
facilitating formation of hydrated oxidation products (see Figure 5-1). 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, p. 94; Taylor et al. 1989); McEachern and Taylor 1998, 
Section 2.2). While high-temperature steam does not oxidize UO2, humidity can influence air 
oxidation 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 spent fuel, and by supporting radiolytic 
processes at grain boundaries. Because gas phase diffusion is rapid along the connected porosity 
at grain boundaries in spent fuel the oxidation of spent fuel can be considered to proceed 
simultaneously at grain boundaries throughout the fuel when it is exposed to humid air. This 
grain-boundary corrosion and associated decohesion of the fuel grains is likely to occur rapidly if 
commercial SNF pellets are exposed to humid air at temperatures greater than 100C. The 
effects on the fuel are some bulk oxidation of the commercial SNF fuel grains and a very large 
increase in the specific surface area of the fuel as it disaggregates into grain-sized powders. 
The commercial SNF corrosion process in humid air at temperatures less than 100C is best 
characterized as an oxidative dissolution process in a film of water at the fuel surface, rather than 
an interaction between water vapor and oxygen in the air that accelerates the solid-state oxidation 
process. Thus, the commercial SNF degradation model discussed below assumes that oxidation 
followed by dissolution of commercial SNF in water or humid air at temperatures below 100C 
is the degradation process relevant to repository conditions (BSC 2003e, Section 6.2.2). 
July 2004 5-2 No. 7: In-Package Environment Revision 1 
Source: BSC 2003e, Figure 6.2-2. 
NOTE: (a) 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). (b) Backscattered-electron image of a section showing 
curvilinear features at fuel-grain boundaries, which may result from dissolution along defects. The darkest 
contrast (black) is epoxy. 
5.1.2 Commercial Spent Nuclear Fuel Degradation Model 
For modeling purposes, the radionuclide inventory in commercial SNF rods is divided into two 
fractions: (1) the combined gap and grain boundary inventory and (2) the matrix inventory. 
Separate models are developed for estimating radionuclide releases from the gap and grain 
boundary and from the matrix because the processes that control these releases are different. 
5.1.2.1 
where 
i 
Figure 5-1. Scanning Electron Microscopy Images of Corroded Commercial SNF Fuel 
(Eq. 5-1) 
July 2004 
Model for Release of Gap and Grain Boundary Inventory 
The gap inventory is available for immediate dissolution when the cladding is breached. 
Therefore, release of the gap inventory is instantaneous upon contact of the breached fuel with 
water or water vapor. There is some uncertainty regarding the applicable degradation and release 
mechanisms for the grain boundary inventory. However, the grain boundary inventory is 
combined with the gap inventory in estimating the instantaneous release fraction. 
The inventory available for instantaneous release of the gap and grain boundary inventory is 
expressed as a fraction of the total inventory of radionuclide i in the fuel (BSC 2003e, Section 1): 
Ii
G = fi  Ii 
I G = gap and grain boundary inventory of radionuclide i available for instantaneous 
release (g) 
fi = instantaneous release fraction 
Ii = total inventory of radionuclide i in the fuel (g) 
5-3 No. 7: In-Package Environment The total inventory of each radionuclide i in the fuel (Ii) is given in Table 2-3; uncertainty in 
these inventories is given in Table 2-4. Instantaneous release fractions of these radionuclides are 
also uncertain; empirical probability distributions for instantaneous release fractions of 137Cs, 
129I, 99Tc, and 90Sr are shown in Table 5-1 and were derived from measured gap and grain 
boundary inventories (Gray et al. 1992; DTN: LL000107951021.107). 
137Cs 
(%) (%) 
99Tc 
(%) 
2.04 0.39 
0.26 26.75 11.06 
Source: BSC 2003e, Table 8.1-1. 
NOTE: a Rounded up from 0.06. 
b Changed from zero to provide a nonzero minimum. 
Table 5-1 shows that most of the inventory of fission gasses and volatile radionuclides remains 
in the matrix. This is especially true for 99Tc and 90Sr, where, at most, 0.26% and 0.25% of the 
inventory is in the gap and grain boundary, respectively. The radionuclide with the highest 
fraction of inventory in the gap and grain boundary is 129I, where a maximum of 26.75% of the 
inventory can reside in the gap and grain boundary. 
5.1.2.2 
0.01b 
dissolution 
Table 5-1. Triangular Probability Distribution Functions of Instantaneous Release Fraction 
129I 
11.24 3.63 0.10a Apex 
Minimum 
Maximum 
90Sr 
(%) 
0.09 
0.02 
0.25 
July 2004 
Model of Radionuclide Release from Matrix 
The matrix radionuclide inventory cannot be released into the water until the matrix degrades. 
The key assumption in the matrix degradation model is that it results from oxidative dissolution 
of the fuel matrix in a film of water on the fuel surfaces (BSC 2003e, Section 6.2.2). The 
process of oxidative dissolution begins with the oxidation of uranium in the +4 oxidation state, 
uranium(IV), to uranium in the +6 oxidation state, uranium(VI), followed by the dissolution of 
uranium(VI) in the film of water. The likely electrochemical reaction sequence for this process 
is: 
bulk oxidation surface oxidation 
UO2  UO2+x  UO2.33  {UO2
2+}surface  [UO2
2+]bulk  [UO2 
2+]soln  Secondary Phases 
Three types of dissolution measurements have been made; flow-through, static (batch), and drip 
tests. The purpose of the flow-through dissolution studies was to examine the systematic effects 
of temperature and water chemistry on the forward dissolution rates of the UO2 matrix in both 
unirradiated UO2 and spent fuel. The forward reaction rate entails UO2 corrosion/oxidation and 
detachment of uranium(VI) from the mineral lattice. The intrinsic dissolution rates of UO2+x and 
spent fuel were determined by using a single-pass flow-through reactor that allows UO2 
dissolution to be measured far from solution saturation (no precipitation of dissolved products). 
Note that the forward dissolution rate bounds the release rate, the latter being the material loss 
rate from the fuel plus alteration products. 
5-4 No. 7: In-Package Environment 
Revision 1 Another key assumption is that the fractional rate of radionuclide release from the commercial 
SNF matrix is equal to the fractional rate of oxidative dissolution of the matrix. The rate of 
radionuclide release from the matrix is expressed as (BSC 2003e, Section 1): 
Ri = Ii
M  F 
where 
Ri = rate of release of radionuclide i from the matrix (g/day) 
Ii
M = matrix inventory of radionuclide i (g) 
F = fractional dissolution rate of the fuel (per day) 
The radionuclide release rate given in Equation 5-2 is used in the TSPA-LA model to provide the 
mass of a particular radionuclide available for dissolution into the volume of water available for 
radionuclide dissolution in the rind during a particular period of time (e.g., during a time step in a 
simulation). The concentration of a given radionuclide in water exiting the waste form is equal to 
the dissolved concentration of that radionuclide plus the concentration of that radionuclide 
attached to or embedded in colloids. The dissolved concentration of a given radionuclide is equal 
to its elemental solubility limit if one exists (see Section 6), as apportioned among the various 
isotopes of that element, if sufficient quantities of the radionuclide are available for dissolution. 
If sufficient quantities are not available, then the concentration in solution is equal to the mass 
available from waste form degradation divided by the water volume (i.e., the volume of the rind 
times the porosity of the rind). The dissolved concentration of a radionuclide that does not have 
an elemental solubility limit is equal to the mass released from the waste form during a given 
time step divided by the water volume. Commercial SNF is not a source of colloids although 
radionuclides released from the commercial SNF can attach to colloids generated elsewhere, as 
discussed in Section 7. 
The following model for radionuclide release from the fuel matrix calculates F; the matrix 
inventory of radionuclides (Ii
M) is given by subtracting the gap and grain inventory calculated 
above from the commercial SNF inventory given in Table 2-3. The equation describing the 
model for radionuclide release from the fuel matrix is given as a piecewise-continuous function 
over the acidic and basic pH regimes (BSC 2003e, Sections 1 and 6.4.1.2): 
log(F) = log(A) + a0 + a1  IT + a2  pCO3 + a3  pO2 + a4  pH 
where 
F = fractional dissolution rate of the fuel (1/day) 
a0, a1, a2, a3, and a4= model parameters determined from experimental data 
A = effective specific surface area of the fuel (m2/mg) 
IT = inverse temperature (K-1) 
pCO3 = negative log of the total carbonate molar concentration 
pO2 = negative log of the oxygen fugacity 
pH = negative log of the hydrogen ion molar concentration 
The degradation of commercial SNF is thus a function of temperature, pH, pCO3, and pO2 and is 
valid for ranges of experimental conditions under which data were collected. The burnup of the 
5-5 No. 7: In-Package Environment 
Revision 1 
(Eq. 5-2) 
(Eq. 5-3) 
July 2004 fuels used for the experiments ranged from 15 to 65 MWd/kgU for burnup, and the experimental 
conditions from 2 to 10 for pH, 0.002 to 0.2 atm for oxygen fugacity, 2 10-4 to 2  10-2 mol/L 
for carbonate/bicarbonate concentrations, and 23C to 90C for temperatures (BSC 2003e, 
Section 1). Results of tests conducted on commercial SNF (summarized in Section 5.1.3) were 
used to determine the values of the model parameters (A, a0, a1, a2, a3, and a4) for both alkaline 
conditions and acidic conditions (BSC 2003e, Section 6.4.1), reflecting the functional 
dependence of the commercial SNF degradation rate on these environmental factors. The 
parameter values for both alkaline and acidic conditions are given in Tables 5-2 and 5-3, 
respectively. 
Table 5-2. Parameter Values and Associated Characteristic Values of the Uncertainty Distributions for 
the Alkaline Conditions Model 
Model Parameter 
log(A) 
a0 
a 1 
a 2 
a 3 
a 
Parameter Value 
-6.50 
4.705 
-1093.826 
-0.102 
-0.338 
Source: BSC 2003e, Table 8.1-2. 
NOTE: Estimated standard errors from regression analyses performed using Excel. 
Table 5-3. Parameter Values and Associated Characteristic Values of the Uncertainty Distributions for 
the Acidic Conditions Model 
Model Parameter 
log(A) 
a 0 
a 1 
a 3 
a 4 
a NOTE: 
b 
Parameter Value 
-6.50 
6.60 
-1093.826 
-0.338 
-0.340 
Source: BSC 2003e, Table 8.1-3. 
Estimated standard errors. 
Values for these estimated standard errors were taken from the alkaline conditions 
model and have no associated standard errors in the context of the acidic 
conditions analysis. 
Uncertainty Distribution Characteristic 
Values 
Triangular distribution: Minimum = -7.3 
Apex = -6.5 
Maximum = -5.4 
0.601a 
186.829a 
0.0471a 
0.0506a 
Uncertainty Distribution Characteristic 
Values 
Triangular distribution: 
Minimum = -7.3 
Apex = -6.5 
Maximum = -5.4 
0.67a 
NAb 
NAb 
0.110a 
No. 7: In-Package Environment 
Revision 1 
July 2004 5-6 Revision 1 
The acidic-conditions model is used when the pH is less than 6.8, and the alkaline-conditions 
model is used when the pH is equal to or greater than 6.8. Note that pH is not included in the 
model under alkaline conditions (i.e., a4 = 0 in the alkaline-conditions model). The regression 
analyses indicated that pH was not a significant factor in the dissolution rate in a pH range of 8 
to 10; therefore, it was removed from the alkaline-conditions model. In addition, carbonate 
concentrations are very low under acidic conditions (see Section 3). Therefore, total carbonate 
concentration was removed from the acidic-conditions model (i.e., a2 = 0 in the acidic-conditions 
model). 
The commercial SNF degradation rates and model dependencies on temperature and pO2 are 
shown in Figure 5-2 for a fixed pCO3 of 2.7. pH and temperature dependencies of the model are 
illustrated in Figure 5-3, which shows how long it takes for the commercial SNF matrix to 
degrade completely once the cladding is breached. For all curves in Figure 5-3, the fO2 is 0.2 
atm, the total carbonate concentration is 2  10-2 mol/L, and the value of log(A) is -6.50. As 
shown in this figure, for these nominal values, it will take between 90 and 1,800 years for the 
commercial SNF matrix to degrade completely. Under the acidic conditions expected in waste 
packages containing commercial SNF (see Chapter 3), the degradation rate is most sensitive to 
pH. Above a pH of 6.8, where the alkaline model parameter values are used, the degradation rate 
is most sensitive to oxygen fugacity and temperature (BSC 2003e, Section 6.4.1.2). 
Source: BSC 2003e, Figure 6.4-1. 
Figure 5-2. Base Case Model Plotted against Commercial SNF Degradation Data and UO2 Data 
July 2004 5-7 No. 7: In-Package Environment Revision 1 
Source: Equation 5-3 and Tables 5-2 and 5-3. 
NOTE: fO2= 0.2 atm, [CO2]= 2  10-2 mol/L, and log(A) = -6.50 
Figure 5-3. Time after Cladding Breach until Commercial Spent Nuclear Fuel Matrix Degradation 
Complete as a Function of pH 
July 2004 
5.1.3 Tests Conducted on Commercial Spent Nuclear Fuel 
Several different tests were conducted on commercial SNF by various researchers to provide 
data that form the basis for the commercial SNF degradation model (Gray et al. 1992; Gray and 
Wilson 1995; Stout and Leider 1998; CRWMS M&O 2000e; Thomas 2003; Goldberg 2003). 
These tests are summarized next and are described in more detail in CSNF Waste Form 
Degradation: Summary Abstraction (BSC 2003e, Sections 4.1 and 6.3). 
Gray et al. (1992) measured the gap and grain boundary inventories of cesium, strontium, iodine, 
and technetium (DTN: LL000107951021.107). Tests were run on light water reactor spent fuel, 
and results were given in terms of percentage of total inventory of these four radionuclides in the 
gap region and percentage of total inventory in the grain boundaries. These data were used to 
develop the model for release of the gap and grain boundary inventory. 
Commercial SNF, in the form of grain-sized powders, was tested in single-pass, flow-through 
dissolution studies to provide the parametric dependence of the matrix dissolution rate over a 
range of conditions that span anticipated conditions at the repository (DTNs: 
MO0302PNLDUFTD.000; MO0304PNLLPHDD.000; Gray and Wilson 1995; Stout and Leider 
1998). In these studies, the principal test response was the specific oxidative dissolution rate of 
the matrix. The data set spans a wide range of dissolution conditions, with pH ranging from 2 to 
10, the pO2 ranging from 0.002 to 0.2 atm, the temperature ranging from 23C to 78C, and 
carbonate/bicarbonate concentrations ranging from 2  10-4 to 2  10-2 mol/L. 
5-8 No. 7: In-Package Environment Revision 1 
In addition to commercial SNF, unirradiated UO2 was also tested in these flow-through 
dissolution studies under alkaline conditions (DTN: LL030300112241.027). The UO2 data are 
pertinent because the dissolution behavior of unirradiated UO2 may be similar to the behavior of 
commercial SNF after the 137Cs and 90Sr have decayed and the associated radiolysis effects are 
no longer present (BSC 2003e, Section 6.4.1.1). The data are suitable for determining the 
functional dependence of the oxidative dissolution rate of the commercial SNF matrix on 
environmental factors. 
Another set of tests examined fractional radionuclide release rates from fuel fragments from 
which cladding had been removed (DTNs: LL991001251021.090 and MO0301ANLSF001.451; 
CRWMS M&O 2000e; Thomas 2003). These fragments were exposed to one of two unsaturated 
test conditions, either the high drip rate or the low drip rate, with water that had been 
equilibrated with tuff at 90C (i.e., EJ-13 water). All tests were run at 90C, and some tests have 
been run for more than eight years and are still underway. In these tests, the sources of oxygen 
for oxidative dissolution were as follows: air was dissolved in the water and introduced into the 
test vessel; air was present in the vessel void volume; and air was injected into the vessel as part 
of each twice-weekly injection cycle. These tests were designed to emulate a scenario in which 
dripping water in a failed waste package reacts with the fuel, dissolves soluble components and 
forms solid corrosion products. The data produced by these tests were used to estimate the 
effective surface area scaling factor. 
In addition, five tests were conducted on short segments of fuel rods (1.4 to 3.7 inches long) 
(DTN: MO0301ANLSF001.450). In the tests, conducted at 90C, EJ-13 water (evaporated J-13 
water) was allowed to percolate under a low-hydraulic head through the fuel rod segment, with 
the effluent used to determine radionuclide release. The test configuration, matrix, and 
methodology are described in the data report (Goldberg 2003), together with the data. The tests 
began in February 1999 and are still underway. The data produced by these tests were also used 
to estimate the effective surface area scaling factor. The drip tests provide an effective means for 
identifying the reaction paths that may be relevant to long-term corrosion of spent fuel. They 
also broaden the conceptual understanding of the general controls over fuel degradation. One 
primary conclusion of the drip tests is that the secondary phase assemblages seen in the 
laboratory are broadly similar to the ones seen at natural analogs (see Section 5.1.4.3). 
In summary, the following components of the commercial SNF degradation model are based on 
the following tests. 
 Gap and grain boundary inventory is based on laboratory measurements of light water 
reactor SNF gap inventory and grain boundary inventory (Gray et al. 1992). 
 The parametric dependence of the matrix dissolution rate on environmental conditions is 
based on single-pass, flow-through dissolution studies performed on commercial SNF in 
the form of grain-sized powders (Gray and Wilson 1995; Stout and Leider 1998) and on 
unirradiated UO2 (DTN: LL030300112241.027). 
 The effective surface area scaling factor is based on unsaturated drip tests run on fuel 
fragments from which cladding had been removed (CRWMS M&O 2000e, Thomas 
July 2004 5-9 No. 7: In-Package Environment Revision 1 
2003) and on rod segment tests through which water percolated under a low hydraulic 
head (Goldberg 2003). 
5.1.4 Uncertainties, Limitations, and Model Confidence 
The commercial SNF degradation model is valid for the pH, pO2, and pCO3 ranges expected in 
the waste package (see Section 3) and for temperatures between 23C and 90C (BSC 2003e, 
Sections 1 and 4.1). The model is also valid for exposure to humid air and exposure to dripping 
water. Uncertainties associated with the model and model confidence building are summarized 
next. 
5.1.4.1 Uncertainty in Release of Gap and Grain Boundary Inventory 
The gap and grain boundary inventory of 137Cs, 129I, 99Tc, and 90Sr is uncertain, thus, the 
instantaneous release fractions for these radionuclides are uncertain parameters. The uncertainty 
is believed to be mostly aleatory and associated with factors such as linear power history, grain 
size and burnup that influence fission product migration during in-reactor operation, thereby 
affecting the gap and grain-boundary inventories of the above isotopes. The available data, 
shown in Table 5-4, were analyzed to determine ranges, and plausible probability distribution 
functions in these ranges, for the gap and grain-boundary inventory fractions (fi). A triangular 
probability distribution function is used to represent uncertainty in the instantaneous release 
fractions. This distribution function spans the available data set, as shown in Table 5-1. 
5.1.4.2 Uncertainty in Release of Matrix Inventory 
Uncertainties in the mathematical form of the matrix degradation model and its validity over the 
extended time domain of application were addressed by examining model consistency with the 
current understanding of the oxidative dissolution mechanism that degrades the fuel under the 
range of plausible repository conditions. Comparison of the model with mechanistically based 
alternative models provided confidence that this empirically based modeling approach is 
appropriate for use in long-term predictions of commercial SNF matrix degradation. 
Graphically, Figure 5-2 gives an idea of the uncertainty associated with the model. 
Uncertainties in parameter values (i.e., a0, a1, a2, a3, and a4) are expressed as standard errors 
obtained from regression analyses and summarized in Tables 5-2 and 5-3. Propagating the 
standard errors in each of these terms through the model results in a standard error in Ri (from 
Equation 5-2) of plus or minus 6.4 (i.e., a factor of 6.4) for the alkaline-conditions model, and of 
plus or minus 4.0 (i.e., a factor of 4.0) for the acidic-conditions model. Uncertainty in the 
effective specific surface area of the fuel (A) is expressed as a triangular distribution, as shown in 
Tables 5-2 and 5-3. 
In addition, uncertainties in the parameter values are correlated with each other, as defined in the 
covariance matrix given in Attachment II of CSNF Waste Form Degradation: Summary 
Abstraction (BSC 2003e). The Cholesky decomposition of the covariance matrix is used to 
generate uncertain regression coefficients, which are used in TSPA-LA to produce uncertain 
values of the parameters a0, a1, a2, a3, and a4. 
5-10 July 2004 No. 7: In-Package Environment Source: BSC 2003e, Table 6.3-1. DTN: LL000107951021.107. 
GBI(%) 
5.00  10-2 
7.70  10-2 
5.40  10-2 
6.60  10-2 
-0.190 
0.011 
0.115 
0.03 
0.12 
0.00 
FGR(%) 
1.1 
7.85 
7.85 
7.85 
7.85 
7.85 
0.59 
0.59 
0.59 
7.4 
7.4 
7.4 
7.4 
11 
11 
11 
11 
11 
11 
18 
18 
18 
18 
18 
18 
Average 
Maximum 
Minimum 
Notes: FGR = fission gas release; GBI = grain-boundary inventory; GI = gap inventory. 
There are several sources of uncertainty in the data used to develop the matrix degradation 
model: measurement uncertainties, uncertainties in the test conditions, and uncertainties in the 
extent to which the test conditions and test configuration are relevant to in-service scenarios 
likely to occur in the repository. CSNF Waste Form Degradation: Summary Abstraction (BSC 
2003e, Section 6.4.1.4) discusses these uncertainties and how they affect the matrix degradation 
model. Uncertainties that can be quantified are addressed by incorporating them into the 
uncertainty distribution of effective specific surface area (Tables 5-2 and 5-3). Uncertainties 
associated with the extent to which the input data may cause the model to be systematically 
biased are addressed by comparison against other models. Such a comparison is discussed in 
Section 5.1.4.3. 
The degradation rates for commercial SNF may be biased high because all tests have been 
performed on fresh fuel (i.e., fuel out of the reactor for less than 20 years), although the 
magnitude of this bias is uncertain (BSC 2003e, Section 6.4.1.1). As reported by Tait and Luht 
5-11 No. 7: In-Package Environment 
Table 5-4. Summary of the Gap and Grain Boundary Inventory Data 
Cesium 
GBI(%) 
0.2 
1.16 
0.39 
0.62 
0.l8 
0.74 
0.56 
0.74 
0.72 
1 
0.77 
0.83 
0.88 
1.05 
1.15 
0.73 
1.16 
0.18 
GI(%) 
1.2 
0.72 
0.85 
2.23 
1.55 
0.21 
0.28 
1.92 
3.25 
2.49 
2.32 
3.25 
3.04 
4.11 
4.84 
7.11 
9.9 
2.90 
9.90 
0.21 
Technetium 
GI(%) 
-2.3  10-4 
0.00 
-1.90  10-4 
0.139 
0.015 
8.2  10-3 
0.0527 
0.0245 
0.03 
0.14 
0.00 
Strontium 
GBI(%) 
2.00  10-2 
2.00  10-2 
6.30  10-2 
7.80  10-2 
0.035 
0.13 
0.069 
0.06 
0.13 
0.02 
GI(%) 
4.10  10-4 
8.70  10-5 
3.90  10-4 
2.00  10-4 
0.116 
0.0229 
1.22  10-2 
0.0939 
0.0391 
0.03 
0.12 
0.00 
Revision 1 
Iodine 
GI(%) 
1.55 
3.34 
0.03 
0.12 
0.1 
1.48 
1.1 
17.4 
11.8 
4.10 
17.40 
0.03 
GBI(%) 
5.3 
2.01 
8.5 
8.87 
7.65 
9.35 
7.35 
8.1 
7.14 
9.35 
2.01 
July 2004 Revision 1 
(1997, Figure 16 and p. 23), the associated - and -radiation fields for this fuel may increase the 
dissolution rate in air-saturated water. 
5.1.4.3 Model Confidence 
Building confidence in the commercial SNF degradation model involves presenting technical 
evidence that the model predicts the commercial SNF degradation rate and the associated 
radionuclide release rate at an adequate level of confidence. To build confidence in the model, it 
was compared to data published in referred journals and industrial literature, alternative 
mathematical models, and natural analogs. These comparisons are summarized next. 
Fractional radionuclide release rates estimated by the commercial SNF degradation model were 
compared to multiple sets of published literature on the fractional release rates of radionuclides 
measured under air-saturated dissolution conditions (Wilson 1990a; Forsyth 1997; Tait and Luht 
1997; de Pablo et al. 1999; Torrero et al. 1997). To facilitate comparison of calculated 
radionuclide release rates and measured radionuclide release rates, an error metric was used. The 
error metric equals log(Fc/Fm) where Fc is the calculated fractional release rate (or calculated 
dissolution rate per unit area) and Fm is the measured fractional release rate (or measured 
dissolution rate per unit area). The absence of systematic modeling bias is indicated by values of 
the error metric centered at zero, with most values of the error metric expected to be between .1 
and 1. The average error metric is 0.17, indicating a systematic modeling bias that slightly 
overestimates radionuclide release rates, although the mix of positive and negative values of the 
error metric indicates that this bias is slight. These results show overall good agreement between 
the experimental data and the model-calculated results. 
Two alternative conceptual models for the dissolution of commercial SNF under alkaline 
conditions have been developed: the electrochemical model (Shoesmith 2000; Johnson et al. 
1996; Grambow et al. 2000; Dehaudt 2001a; Dehaudt 2001b; Jegou et al. 2001; Pelletier 2001; 
Piron 2001) and the surface complexation model (de Pablo et al. 1999). The alkaline model was 
compared to these alternative models by evaluating the dissolution rates calculated by each 
model for a set of nominal repository conditions: a fO2 of 0.2 atm, a total carbonate concentration 
of 2.00 ~ 10.3 mol/L, and a temperature of 30C. The details of this comparison are given in 
CSNF Waste Form Degradation: Summary Abstraction (BSC 2003e, Section 7.2), and the 
results are shown in Table 5-5. 
Table 5-5. Comparison of Commercial Spent Nuclear Fuel Dissolution Model to Alternative Models 
3.8 
2.4 
Model 
Alkaline Commercial SNF Dissolution Model 
Surface Complexation Model 
Electrochemical Model 
Source: BSC 2003e, Table 7.2-1. 
The results in Table 5-5 show good agreement between the commercial SNF dissolution 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 is 
Calculated Dissolution Rate (mg/m2.day) 
July 2004 
12.1 
5-12 No. 7: In-Package Environment Revision 1 
well within the standard error range of the alkaline-conditions model (plus or minus 6.4 times the 
calculated dissolution rate). 
To establish confidence that experimental commercial SNF alteration pathways effectively 
represent likely natural processes, it is useful to compare the alteration mineralogy observed in 
experiments with natural occurrences of altered uranium oxides. Uranium dioxide occurs in 
nature as the mineral uraninite, which is structurally similar to commercial SNF. Nopal I, a 
uranium mining site at Pena Blanca, Mexico, contains substantial quantities of uraninite and is 
arguably one of the best natural analogs for commercial SNF degradation in Yucca Mountain, as 
it possesses geologic, geochemical, and hydrogeologic characteristics most similar to those at 
Yucca Mountain (Murphy 1995). The volcanic (tuffaceous) host rock at Nopal I, the youngest of 
the studied sites, has been relatively oxidizing for tens of thousands of years, though uraninite, 
containing U4+, was originally formed several million years ago. The uranium minerals found at 
Nopal I are listed in Table 5-6 (Pearcy et al. 1994). 
Table 5-6. Paragenesis of Uranium Minerals at Nopal I 
Nominal Chemical Formula 
UO2+x 
U4+(U6+O2)5(OH)14.3H20 
UO3.2H20 
UO3.nH2O(n< 2) 
Ca(UO2)6O4(OH)6.8H2O 
Ba(UO2)6O4(OH)6.nH2O(n=4-8) 
K(UO2)(AsO4) .4H2O 
(UO2)2SiO4.2H2O 
K2(UO2)2Si6O15.4H2O 
KH(UO2)SiO4.1.5H2O 
6+ 
Mineral 
Oxide 
Uraninite 
Oxyhydroxides 
Lanthinite 
Schoepite/ 
Dehydrated Schoepite 
Becquerelite 
Billietite(?)/ 
Abernathyite(?) 
Silicates 
Soddyite 
Weeksite 
Boltwoodite 
Uranophane 
-Uranophane 
Source: Pearcy et al. 1994. 
In general, uraninite has been oxidized and hydrated at Nopal I. Silicate carried by groundwater 
has occasionally combined with oxidized U to form soddyite. Silicate in combination with alkali 
ions (e.g., calcium and sodium) form various alkaline uranyl silicate hydrates, such as Naboltwoodite 
and -uranophane. -uranophane dominates alteration mineralogy at long times. 
In the vapor tests a relatively simple combination of uranyl oxy-hydroxide alteration phases is 
observed, dehydrated schoepite (UO2)O0.25-x(OH)1.5+2x (0  x  0.15) and metaschoepite (a minor 
phase is Cs-Ba-Mo-uranate, which incorporates two fission products, cesium and molybdenum). 
The most abundant elements in J-13 water are Na and Si, and, not surprisingly, the most 
abundant alteration products in the high-drip rate tests are Na- and Si-bearing U6+ phases. Other 
U phases are also present, including metaschoepite and -uranophane. The higher drip-rate 
tests exhibit two uranophane-group silicates, -uranophane [Ca(UO2)2(SiO3OH)2(H2O)5] and 
Ca(UO2)2Si2O7.6H2O 
5-13 July 2004 No. 7: In-Package Environment Revision 1 
Na-boltwoodite (Na,K)(UO2)(SiO3OH)(H2O). Samples from low-drip-rate tests include 
metaschoepite, an unidentified Na-uranyl oxy-hydroxide tentatively identified as 
Na-compreignacite, and soddyite (Again, Cs-Ba-Mo-uranate is a minor constituent). In one 
sample, soddyite appears to replace Na-compreignacite. Also, a few isolated crystals of 
Na-boltwoodite were first detected in one low-drip-rate test. The groundwater at Nopal I is richer 
in calcium than J-13 (Pearcy et al. 1994) but poorer in sodium and potassium. This composition 
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. A summary of the corrosion products 
found in the course of the commercial SNF alteration experiments is shown in Table 5-7. A 
comparison of Tables 5-6 and 5-7 shows that the phase assemblage observed at Nopal I is similar 
to that derived experimentally in the commercial SNF degradation tests. This similarity builds 
confidence in the commercial SNF degradation model. 
Table 5-7. Summary of UO2 Alteration Phases Observed in Commercial Spent Nuclear Fuel 
Degradation Tests 
Formula 
UO32H2O 
UO3(0.8-1.0H2O) 
(Na,K)2[(UO2)6O4(OH)6]8H2O 
Ca[(UO2)6O4(OH)6]8H2O 
(UO2)2SiO42H2O 
2)2(SiO3OH)(H2O)4 
Uranyl Oxide Hydrates 
Schoepite (meta-schoepite) 
Dehydrated Schoepite 
Compreignacite 
Becquerelite 
Uranyl Silicate Hydrate 
Soddyite 
Uranyl Alkaline Silicate Hydrates 
-Uranophane 
Boltwoodite 
Na-Boltwoodite 
Sklodowskite 
Non-Uranyl Phases 
Palygorskite 
Ca(UO2)2 (SiO3OH)2 (H2O)5 
K2(UO2)(SiO3OH)(H2O) 
(Na,K)(UO2)(SiO3OH)(H2O) 
Mg(UO
0.12-0.66)5(Si,Al0.12-0.66)8O20 (Mg,Al 
(OH)54H2O 
FeOxOHy 
TiOx 
SiO2 
Fe-Oxides 
Ti-Oxides 
Amorphous Silica 
Source: Stout and Leider 1998, Table 2.1.3.5-10, p. 2-237. 
5-14 July 2004 No. 7: In-Package Environment Revision 1 
5.2 U.S. DEPARTMENT OF ENERGY SPENT NUCLEAR FUEL DEGRADATION 
DOE SNF will be placed in codisposal waste packages together with HLW, as discussed in 
Section 2. Eventually the waste packages will be breached, the DOE SNF will be exposed to 
water or water vapor, and the DOE SNF waste forms will begin to degrade. The following 
model provides the DOE SNF degradation rate as input to TSPA-LA for all applicable scenario 
classes (see Section 1.2). 
More than 250 distinct types of DOE SNF may eventually be stored in the repository. Because it 
is not practical to model the degradation of each individual type of DOE SNF, 11 general groups 
were established to represent the entire inventory for TSPA-LA and TSPA-SR. The DOE and 
the National Spent Nuclear Fuel Program, centered at the Idaho National Engineering and 
Environmental Laboratory, collaborated to identify the groups, as documented in DOE Spent 
Nuclear Fuel Information in Support of TSPA-SR (DOE 2002b), which contains postclosure 
performance characteristics of the groups and suggests models for waste-form dissolution. That 
report was subsequently augmented by Review of DOE Spent Nuclear Fuel Release Rate Test 
Results (DOE 2003b), which summarizes available release-rate test data for DOE SNF. 
The 11 DOE SNF groups identified in the National Spent Nuclear Fuel Program documents, 
along with a typical type of SNF for each group, are: 
1. Zircaloy-clad naval SNF (represented by surface ship/submarine assemblies) 
2. Plutonium/uranium alloy (represented by Fermi 1 SNF) 
3. Plutonium/uranium carbide (represented by Fast Flux Test Facility-Test Fuel 
Assembly SNF) 
4. Mixed oxide and plutonium oxide (represented by Fast Flux Test 
Facility-Demonstration Fuel Assembly and Fast Flux Test FacilityTest 
Demonstration Fuel Assembly SNF) 
5. Thorium/uranium carbide (Fort St. Vrain SNF) 
6. Thorium/uranium oxide (Shippingport light-water breeder reactor SNF) 
7. Uranium metal (N Reactor SNF) 
8. Uranium oxide (Three Mile Island-2 core debris) 
9. Aluminum-based SNF (foreign research reactor SNF) 
10. Miscellaneous fuel 
11. Uranium-zirconium hydride (training research isotopes-General Atomics SNF) 
In TSPA-SR, a 12th DOE SNF group, the immobilized plutonium waste form, was evaluated. 
This waste form was projected to consist of disks of a plutonium-containing titanium-based 
July 2004 5-15 No. 7: In-Package Environment Revision 1 
ceramic enclosed in stainless steel cans encased in a borosilicate high-level radioactive waste 
glass matrix (CRWMS M&O 1998b). However, there is no current plan to dispose of this waste 
form in the repository, thus, it is not considered further in this report. 
The degradation model for all DOE SNF groups (except naval SNF) is not a function of 
in-package chemistry and is considered to be very conservative: instantaneous degradation or 
dissolution of the waste form and complete release of radionuclides upon exposure to water (i.e., 
if a codisposal waste package is breached at the beginning of or during a time step, all 
radionuclides in the DOE SNF in that waste package (see Section 2) are available for dissolution 
into the available water during that time step). The dissolved concentration of each radionuclide 
is determined by its elemental solubility limit if one exists (see Section 6) and the 
availability/stability of radioactive colloids (see Section 7). The dissolved concentration of a 
radionuclide that does not have an elemental solubility limit is equal to the mass released from 
the waste form during a given time step divided by the water volume available in the codisposal 
waste package. DOE SNF is not a source of colloids, although radionuclides released from the 
DOE SNF can attach to colloids generated elsewhere, as discussed in Section 7. This model is 
used in the TSPA-LA model for all applicable scenarios because other models developed for 
DOE SNF degradation (BSC 2003f, Section 7.2) are not currently fully validated. 
The degradation of naval SNF is modeled using the degradation model for commercial SNF 
given in Section 5.1 and the radionuclide inventory for commercial SNF given in Section 2. 
Using a naval SNF source term developed by the Naval Nuclear Propulsion Program, the DOE 
estimated that the dose resulting from naval SNF is about four orders of magnitude less than that 
from an equivalent amount of commercial SNF (BSC 2001, Section 6). A planned classified 
Naval Nuclear Propulsion Program Addendum to the LA will provide details of this naval SNF 
source term. Therefore, using commercial SNF as a surrogate for naval SNF will overestimate 
radionuclide releases from naval SNF. 
5.3.1 Degradation Processes 
The words degradation and dissolution are not interchangeable while discussing degradation 
of HLW glass. Dissolution is the process of a solid material entering solution, while degradation 
may also include transformation of a solid material to another solid phase. The glass degradation 
model accounts for both dissolution and solid-to-solid reactions that can occur when glass is 
exposed to humid air or dripping water or is immersed in water. 
Most researchers agree that the dissolution rate of a borosilicate glass is controlled by the 
solution concentration of orthosilicic acid that is produced when glass logs contact water 
(e.g., Advocat et al. 1999; Bourcier 1994; Grambow et al. 1986; Knauss et al. 1990; McGrail 
5.3 HIGH-LEVEL RADIOACTIVE WASTE GLASS DEGRADATION 
High-level radioactive waste immobilized in glass logs and encapsulated in stainless steel 
canisters will be placed in codisposal waste packages together with DOE SNF. Eventually the 
waste packages will be breached, the glass will be exposed to water or water vapor, and the glass 
waste forms will begin to degrade. The following model provides the source term for 
radionuclides released as the glass degrades as a result of contacting water (see Section 1.2). 
5-16 July 2004 No. 7: In-Package Environment Revision 1 
et al. 1998). Dissolution occurs through a hydrolysis reaction to break a silicon oxygen bond at 
the glass surface and release a unit of orthosilicic acid. The dissolution reaction is written as: 
(Eq. 5-4a) glass  Si-O-Si(OH)3 + H2O  glass  Si-OH + H4SiO4(aq) 
where the last term is orthosilicic acid and the reverse reaction as: 
(Eq. 5-4b) glass  Si-OH + H4SiO4(aq)  glass  Si-O-Si(OH)3 + H2O 
The net dissolution rate is the difference between the rates of the forward (dissolution) reaction 
in Equation 5-4a and the reverse (condensation) reaction in Equation 5-4b. The net dissolution 
rate depends on the activity (concentration) of orthosilicic acid and decreases as that activity 
increases. The glass cannot reach true equilibrium with the solution because glass is 
thermodynamically unstable (i.e., the net rate cannot decrease to zero). For the purposes of 
modeling glass dissolution, the solution is considered to have attained an apparent saturation 
limit when the net dissolution rate becomes immeasurably low (Grambow 1985). Glass 
degradation is experimentally observed to occur in three steps: Stage 1, early dissolution 
unaffected by low concentrations of orthosilicic acid; Stage II, dissolution decreases in the 
presence of orthosilicic acid; and Stage III, dissolution accelerates to rates approaching Stage I. 
Two different conceptual models have been proposed to describe glass degradation under 
apparent saturation conditions. One assumes that diffusion of orthosilicic acid through the 
chemically and physically altered layer of glass that forms when glass degrades (i.e., rind) 
controls the glass degradation rate, while the other assumes that solution chemistry provides the 
primary control on dissolution rates. The conceptual model underlying the glass dissolution 
model presented here is based on the assumption that the glass dissolution rate is determined by 
the temperature and chemistry (i.e., pH) of the aqueous solution in contact with the glass. Thus, 
limiting effects of water transport to the reacting glass surface or transport of species released 
from the glass are not explicitly modeled. However, because experimentally measured 
dissolution and degradation rates are used to determine model parameter values, any transport 
limitations in the experimental system are included implicitly in the rate expression. A 
discussion of the alternative conceptual model (i.e., glass degradation as a diffusion-limited 
process) is provided in Defense HLW Glass Degradation Model (BSC 2003g, Section 6.4). 
While transport through the alteration rind of species released from the glass is not modeled as 
part of the glass degradation rate, radionuclides that have been released from the glass must still 
diffuse through the rind to reach the surface of the rind so that they can be transported away from 
the waste form (see Figure 5-4). Release of a specific element into solution depends on its 
solubility and rate of diffusion through the rind. The role of the alteration layer as a diffusion 
barrier is discussed in Attachment IV of Defense HLW Glass Degradation Model (BSC 2003g). 
The model presented below calculates the volume of the rind, the volume of water in the rind, 
and thickness of the rind; a separate model is used to calculate the transport of radionuclides 
released from glass based on their diffusion coefficients, solubility limits, sorption behavior, etc. 
for the TSPA. 
July 2004 5-17 No. 7: In-Package Environment Revision 1 
Source: BSC 2003g, Figure IV-1. 
Figure 5-4. Canister Cross Section Showing Conceptual Model of Degradation of HLW Glass Logs 
(Eq. 5-5) 
July 2004 
5.3.2 High-Level Radioactive Waste Glass Degradation Model 
The release rate of radionuclides resulting from the degradation of HLW glass is calculated as 
the product of three terms (BSC 2003g, Section 6.5). These are: 
Ri = rateG  S  Ii 
where 
Ri = release rate of radionuclide i from HLW glass (g i/day) 
rateG = specific degradation rate of the glass (g glass/(m2 day)) 
S = surface area of glass contacted by water (m2) 
Ii = inventory of radionuclide i in the glass (g i/g glass). 
The radionuclide release rate given in Equation 5-5 is used in the TSPA-LA model to estimate 
the mass of a particular radionuclide i available for dissolution into water in the codisposed 
package during a particular period of time (e.g., during a time step). The concentration of a 
given radionuclide i in water exiting the waste form is equal to the dissolved concentration of 
that radionuclide in solution plus the concentration of that radionuclide attached to or embedded 
in colloids. The dissolved concentration of a given radionuclide in solution is equal to its 
elemental solubility limit if one exists (see Section 6), as apportioned among the various isotopes 
of that element, if sufficient quantities of the radionuclide are available for dissolution. If 
sufficient quantities are not available, then the concentration in solution is equal to the mass 
available divided by the water volume. The dissolved concentration of a radionuclide that does 
not have an elemental solubility limit is equal to the mass released from the waste form during a 
5-18 No. 7: In-Package Environment Revision 1 
given time step divided by the water volume available in the codisposed package. The water 
volume available for dissolution is equal to the volume of water contained in the glass alteration 
layer (rind). HLW glass is considered to be a source of colloids; radionuclides can be embedded 
in these HLW colloids as the colloids form or can attach to colloids generated by other sources. 
Colloid generation in the waste form is discussed in Section 7. Spallation of radionuclidebearing 
colloidal sized glass shards has been observed in drip tests. When these colloids move 
into waters containing dissolved radionuclides, they can be expected to sorb and subsequently 
carry additional radionuclides in the colloidal state. 
Mathematical expressions for the specific degradation rate of the glass and the surface area of the 
glass contacted by water are given in Equations 5-6 and 5-7, respectively (BSC 2003g). The 
inventory of radionuclides in HLW glass logs is presented in Table 2-3. 
The specific degradation rate of the glass is given as (BSC 2003g, Section 6.5.2): 
(Eq. 5-6) rateG = kE ~ 10ŁEpH ~ exp(.Ea/RT) 
where 
kE = glass degradation rate coefficient (g glass/(m2 day)) 
 = the pH dependence coefficient 
pH = .log[H+] 
Ea = the effective activation energy (kJ/mol) 
R =the gas constant (8.314 J/mol.K) 
T = the temperature (K) 
The value of kE accounts for the effects of glass composition, including heterogeneity of the 
waste inventory, as well as the effects of solution composition (except pH). While solution 
composition will be considered below, the assumption of little compositional impact on 
dissolution must be considered further. The mean plus or minus one standard deviation of the 
values of log(kE) calculated for the nine glasses having different compositions is log(kE) = 5.14 
}0.17 g/(m2.d). The mean and standard deviation for a subset of reference HLW glasses 
(Hanford-D, WV ref 6, SRL 51S, SRL 165U, SRL 202U, and SRL 131U) is log(kE) = 5.07 
}0.13 g/(m2.d). The percent relative standard deviation is small in both cases (100 ~ 0.17/5.14 = 
3.31% and 100 ~ 0.13/5.07 = 2.56%, respectively). This suggests that compositional differences 
within the waste inventory will have little effect on overall glass degradation rates. 
The model requires specification of three parameter values (kE, , and Ea) and is a function of pH 
and temperature. Values of the three parameters were determined as described in Defense HLW 
Glass Degradation Model (BSC 2003g, Section 6.5.3). 
Figure 5-5 shows the functional form of the rate law and rates measured at 70C and 90C. 
July 2004 5-19 No. 7: In-Package Environment Revision 1 
Source: BSC 2003g, Figure 6.5-2. 
NOTE: The filled symbols represent results from a series of measurements of the effect of iron on degradation 
rates. 
(Eq. 5-7) 
where 
July 2004 
Figure 5-5. HLW Glass Degradation Rates at 70C and 90C 
The equation used to calculate the glass surface area contacted by water as glass dissolves during 
each time step accounts for an increase in surface area from cracking and loss in surface area due 
to dissolution (BSC 2003g, Section 6.5.6): 
S = fexposure ~ Ssp ~ (M0 . M) 
S = surface area of glass log available for reaction in the current time step (m2) 
fexposure = exposure factor (dimensionless) 
Ssp = specific surface area of a glass log (m2/kg) 
M0 = initial mass of glass log (kg) 
M = total mass of glass degraded in all previous time steps (kg) 
The surface area is greater than the geometric surface area of the glass cylinder because waste 
logs crack from handling and from thermal and mechanical stresses generated as glass cools in 
the pour canisters. These cracks result in surfaces deep in the log that may be open to water or 
humid air. To account for the increase in surface area resulting from cracking and the 
uncertainties associated with cracking, the geometric surface area of the glass log (as determined 
by the dimensions of a pour canister and its fill height) is multiplied by a water exposure factor, 
fexposure. The latter value is assumed to have a triangular distribution with a maximum at 17, a 
minimum at 4, and a most probable value of 4, as well. 
5-20 No. 7: In-Package Environment Revision 1 
Three key simplifications were made in developing this model. The first is that variability in 
orthosilicic acid activity resulting from variations in the glass degradation rate and the exposure 
conditions over time can be accounted for by using a range of rate coefficient values (kEs) (BSC 
2003g, Section 6.3.2). Minimum values of the rate coefficients represent exposure to small 
volumes of water in which high orthosilicic acid activities are expected; maximum values 
represent exposure to large volumes of water maintaining low orthosilicic acid activities. 
Because only small volumes of water are expected to contact waste glass, the minimum values of 
the rate coefficient are the most expected values. 
The second key simplification is that degradation rates of all waste glasses increase as the pH 
decreases from neutral to lower values (i.e., the acidic leg) and as the pH increases from neutral 
to higher values (i.e., the alkaline leg). This simplification is based on tests with several different 
waste glasses (BSC 2003g, Section 6.3.2). To model this, parameters for Equation 5-6 were 
developed for both the acidic leg and the alkaline leg. The higher of the two rates calculated (i.e., 
the rate calculated using the parameters for the acidic leg and the rate calculated using the 
parameters for the alkaline leg) is used as the degradation rate of the glass. Again, this 
pH-dependent behavior is shown in Figure 5-5. 
The third simplification is that all glass compositions have the same dependence on pH and 
temperature (BSC 2003g, Section 6.3.2). As discussed above, the effect of composition on the 
dissolution rate at fixed pH and temperature has been found to be small, so all glasses are 
modeled to have the same pH and temperature dependencies for the acidic and alkaline legs. 
Degradation rates for most silicates tend to decrease over time. Glass degradation rates derived 
from long-term field measurements likewise tend to be much lower than rates measured over the 
space of weeks to months in laboratories. The glass degradation model is based largely on 
laboratory measurements. To adequately reflect the generally lower field rates, the most 
probable degradation rates are weighted toward the lowest laboratory rates. 
A description of the alteration rind as a diffusion barrier is given in Attachment IV of Defense 
HLW Glass Degradation Model (BSC 2003g), including equations to calculate the volume, 
thickness, and water content of the rind, in terms of the mass of HLW glass that degrades in a 
canister. The volume of the alteration rind is calculated as: 
(Eq. 5-8) VL = 3.7 ~ 10.4 M 
where 
VL = volume of the alteration rind (m3). 
The volume of pore water in the alteration rind is calculated as the product of the volume of the 
alteration rind and the porosity of the rind, which is 0.17, yielding (see discussion in Defense 
HLW Glass Degradation Model (BSC 2003g, Section 6.7 and Attachment II)): 
(Eq. 5-9) VW = 6.3 ~ 10.5 M 
where 
VW = volume of pore water in the alteration rind (m3). 
July 2004 5-21 No. 7: In-Package Environment The equation used to calculate the thickness of the rind through which radionuclides must diffuse 
(see Figure 5-4) after being dissolved is given as (BSC 2003g, Attachment II): 
where 
R = thickness of the rind (m). T 
The parameter values to be used in the glass degradation model are shown in Table 5-8. These 
parameter values were calculated from glass dissolution test data as described in Section 6.5 of 
Defense HLW Glass Degradation Model (BSC 2003g) and as summarized below in 
Section 5.3.3. In addition, the results of vapor hydration tests indicated that a relative humidity 
of at least 44% is required for dissolution to occur. Therefore, the glass degradation rate is 
applied only when the relative humidity is above 44%. 
Parameter 
Name 
acidic 
alkaline 
Ea_acidic 
Ea_alkaline 
KE_acidic 
KE_alkaline 
fexposure 
Ssp 
M0 
 
Source: BSC 2003g, Table 8.1-1. 
The glass degradation model dependencies on pH, temperature, and glass degradation rate 
coefficient are shown in Figure 5-6, which gives the percentage of glass remaining in a HLW 
glass log after 10,000 years of degradation. For all curves in Figure 5-6, the glass exposure 
factor used was the most probable value (i.e., 4). As this figure shows, the degradation rate is 
highly dependent on pH and the glass degradation rate coefficient (kE), particularly at low pH 
values. Above a pH of about seven, most of the glass log is expected to be still intact after 
10,000 years of degradation, regardless of the temperature or degradation rate coefficient. 
No. 7: In-Package Environment 
TR = 0.30 . [0.090 . (3.0~10.5) ~ M]1/2 
Parameter Description 
pH coefficient for acidic 
solutions 
pH coefficient for alkaline 
solutions 
Table 5-8. Glass Degradation Rate Parameters 
.0.49 
0.49 
31 kJ/mol Temperature coefficient for 
acidic solutions 
69 kJ/mol Temperature coefficient for 
alkaline solutions 
Glass degradation rate 
coefficient for acidic solutions 
Minimum: 8.41 ~ 103 g/(m2 day) 
Maximum: 1.15 ~ 107 g/(m2 day) 
Most probable: 8.41 ~ 103 g/(m2 day) 
Glass degradation rate 
coefficient for alkaline 
solutions 
Minimum: 2.82 ~ 101 g/(m2 day) 
Maximum: 3.47 ~ 104 g/(m2 day) 
Most probable: 2.82 ~ 101 g/(m2 day) 
Glass exposure factor 
Minimum: 4 
Maximum: 17 
Most probable: 4 
2.70 ~ 10.3 m2/kg Specific surface area of a 
glass log 
Initial mass of a glass log 
Porosity of glass rind 
2,710 kg 
0.17 
5-22 
Revision 1 
(Eq. 5-10) 
Value Distribution 
Single value 
Single value 
Single value 
Single value 
Triangular 
Triangular 
Triangular 
Single value 
Single value 
Single value 
July 2004 Revision 1 
Source: Equations 5-6 and 5-7, and Table 5-8. 
Figure 5-6. Percentage of Glass Log Degraded After 10,000 Years of Degradation with fexposure = 4 
5.3.3 Tests Conducted on High-Level Radioactive Waste Glass 
Several different tests on HLW glass were conducted by Argonne National Laboratory to 
provide data that form the basis for the glass degradation model (Ebert 2000, 2003a, 2003b, 
2003c). These tests are summarized next. 
One set of tests involved immersing a monolithic glass sample of known geometric surface area 
in a test solution within a sealed vessel for a predetermined duration (DTNs: 
MO0306ANLGIM01.525 and MO0306ANLGIM02.525; Ebert 2003a). At the end of the test, the 
solution was analyzed for dissolved glass components to measure the extent of glass dissolution. 
These measured concentrations were used to calculate the normalized boron mass, which was in 
turn used to calculate the forward glass dissolution rate as a function of pH and temperature. 
Tests were conducted at specific solution pH values (measured at room temperature) and at 
temperatures of 70C, 90C, and 90C with added iron compounds. Thus, the collected data 
provide a measure of dissolution rates of glass immersed in water at different pHs, temperatures, 
and with and without iron corrosion products (see Figure 5-6). These tests and the data collected 
are described in more detail in Section 6.5.4 and Attachment I of Defense HLW Glass 
Degradation Model (BSC 2003g). 
A second set of tests consisted of vapor hydration tests conducted specifically to measure the 
degradation rates of three glasses at 70C, 90C, 125C, 150C, 175C, and 200C in humid air 
(DTN: MO0306ANLGVH01.526; Ebert 2003b). The vapor hydration tests were conducted by 
suspending two monolithic glass samples in a sealed stainless steel vessel with a small amount of 
demineralized water, then heating at an elevated temperature for durations between a few hours 
and a few years. The extent of corrosion was quantified using the thickness of an alteration layer 
5-23 July 2004 No. 7: In-Package Environment Revision 1 
that formed on the sample surfaces. Tests were conducted at several different values of relative 
humidity by varying the amount of water that was added, although the relative humidity was not 
measured directly. Tests were conducted with three glasses that were made at Argonne National 
Laboratory to be representative of Defense Waste Processing Facility waste glasses. At the end 
of the test, the vessel was opened and the pH of the water at the bottom of the vessel was 
measured. Thus, the collected data provide a measure of dissolution rates of glass exposed to 
water vapor at different pHs and temperatures. The data were used to calculate the near-neutral 
minimum glass degradation rate coefficient used in the model under alkaline conditions. These 
tests and the data collected are described in more detail in Section 6.5.5.3 and Attachment III of 
Defense HLW Glass Degradation Model (BSC 2003g). 
A third set of tests examined the dissolution of two different glasses when periodically contacted 
by small amounts of a tuff groundwater dripping onto the glass (DTN: 
MO0301ANLGNN01.527; Ebert 2003a). The tests were conducted for more than 13 years at 
90C, and the injected tuff groundwater had a pH of about eight. About every six months, water 
that had first collected on the glass sample and corroded the sample, then dripped off the sample 
into the bottom of the test vessel and was removed and analyzed for boron. The amount of boron 
released was used as a measure of the extent of glass degradation. The data were used to 
calculate the near-neutral minimum glass degradation rate coefficient used in the model under 
acidic conditions. These tests and the data collected are described in more detail in Section 
6.5.5.4 of Defense HLW Glass Degradation Model (BSC 2003g). 
Another set of tests examined the effect of glass composition on the dissolution rate (DTN: 
MO0308ANLGPC01.528; Ebert 2000). Nine different glasses having compositions spanning the 
likely ranges of waste forms from Defense Waste Processing Facility, the West Valley 
Demonstration Project, and Hanford were subjected to Materials Characterization CenterTest 
Method 1 (MCC-1) tests at 90C for durations between 1 and 15 days. These nine HLW glasses 
were also subjected to a 7-day product consistency test at 90C. While both test methods involve 
immersion of a sample in water, the MCC-1 uses a large volume of water (to achieve a dilute 
solution) and the product consistency test uses a small volume of water (to achieve a 
concentrated solution). The pH was allowed to drift in both tests and was measured at the 
conclusion of the test. For both tests, the measured boron concentration was used to determine 
the glass dissolution rate. The data from the MCC-1 tests were used to calculate the maximum 
glass degradation rate coefficient under acidic conditions, while the data from the 7-day product 
consistency tests were used to calculate the maximum glass degradation rate coefficient under 
alkaline conditions. These tests and the data collected are described in more detail in Sections 
6.5.4.2 and 6.5.4.5 of Defense HLW Glass Degradation Model (BSC 2003g). 
Thus, the data used to develop the HLW glass degradation model were collected under 
conditions similar to the three water-contact modes anticipated in the monitored geologic 
repository (see discussion in Section 1): contact with humid air, contact with dripping water and 
immersion. In addition, the data were collected for a range of HLW glass compositions that meet 
the chemical durability requirement stipulated in Waste Acceptance System Requirements 
Document (DOE 2002c) and Waste Acceptance Product Specifications for Vitrified High-Level 
Waste Forms (DOE 1996). Applicability of the glass degradation model to HLW forms other 
than those compliant with this requirement must be determined separately. 
July 2004 5-24 No. 7: In-Package Environment Revision 1 
5.3.4 Uncertainties, Limitations, and Model Confidence 
The HLW glass degradation model is valid for the pH ranges expected in the waste package (see 
Section 3) and for the temperatures expected in the waste package (see Section 6). The model is 
also valid for exposure to humid air, exposure to dripping water, and immersion. Uncertainties 
associated with the model and model confidence building are discussed in the following section, 
which is summarized from Defense HLW Glass Degradation Model (BSC 2003g, Section 6.8). 
5.3.4.1 Uncertainty in Glass Degradation Model 
Uncertainty in the glass degradation model is represented by a range of values and distributions 
assigned to the degradation rate coefficients (kE) for contact by acidic and alkaline solutions. 
Because the coefficients are based on results of laboratory tests conducted over a range of water 
contact modes (humid air, dripping water and immersion), glass compositions, and chemical 
affinities, the range accounts for uncertainty in the water contact mode, the glass composition, 
and the chemical affinity term. For acidic and alkaline solutions, the distributions of kE assume 
the most likely environmental condition will be small amounts of condensed water vapor 
forming thin films on exposed glass, and the least likely environmental condition will be 
immersion in a large volume of water. 
In laboratory tests, the release of boron is used to represent the glass degradation rate, thereby 
representing the release rates of all radionuclides, because boron is the most rapidly released 
structural element of borosilicate waste glass. Tests have shown that technetium, which bonds 
weakly to the glass, is released nearly stoichiometrically with boron (some tests show that 
technetium is released faster than boron, while other tests show that technetium is released 
slower than boron), while other radionuclides (e.g., plutonium and americium) are released much 
slower than boron. A radionuclide-specific factor could be included in the model to scale the 
release of individual radionuclides to the release of boron. However, such a scaling factor was 
not used because data were not available to reliably quantify the relative releases of all 
radionuclides. Using the release rate of boron to represent the release rate of all radionuclides is 
reasonable. 
Uncertainty in the surface area is represented by a range of values and distributions assigned to 
the exposure factor, fexposure. The area of glass that is accessible to water and the area that is 
contacted by water in a breached waste package are the most difficult model parameter values to 
measure. The maximum value of 17 represents a high estimate of the increase in surface area 
based on measurements of thermal cracking and impact cracking, combined with a high estimate 
of the fractions of glass logs that will be affected. A factor of 12 (Smith and Baxter 1981) is used 
to account for thermal cracking for all canisters, and a factor of 40 (Smith and Ross 1975) is used 
to account for impact cracking of 1% of the canisters (i.e., (40  12  1%) + (12  99%)). The 
minimum value of four represents a subjective estimate of the accessibility of water to tight 
cracks within the glass log and the reduced reactivity of glass within tight cracks due to transport 
limitations, both for water entering cracks and radionuclides exiting cracks. This minimum 
value is rounded down from 4.25, which is obtained by starting with the maximum value of 17, 
and then assuming that 0.5 of the glass surfaces formed by fractures are freely accessible to 
water and that glass within cracks has one-half the reactivity of glass at free surfaces. Estimates 
of water accessibility and glass reactivity are based on qualitative experimental evidence. 
5-25 July 2004 No. 7: In-Package Environment Revision 1 
Assignment of the distribution of values is based on experimental evidence that the dissolution of 
large samples with cracking representative of full-sized glass logs increases by less than a factor 
of four, compared to laboratory-sized samples without cracks. In addition, over time, the 
accumulation of alteration products is likely to decrease water accessibility. 
The constant values used for the specific surface area of a glass log and the initial mass are based 
on the expected number of long and short canisters, and the estimated geometric surface area and 
mass of the glass logs. These values are intended to reflect uncertainty in whether a long or short 
waste package is breached in a simulation. 
5.3.4.2 Model Confidence 
The glass degradation model realistically estimates the glass degradation rate for waste glasses 
immersed in water, exposed to humid air, or contacted by dripping water. Building confidence in 
the model involved showing that the glass degradation rate equation and its associated range of 
parameter values adequately represent waste glass degradation and radionuclide release. This 
proof was obtained several ways, as discussed in Defense HLW Glass Degradation Model (BSC 
2003g, Section 7) and summarized next. First, the general algebraic form of the proposed model 
is widely accepted and used in the literature of waste glass corrosion, and has also been applied 
to describe the alteration of basalt, a natural analog for waste glass, on the seabed. This wide 
acceptance substantially validates the basic algebraic form of the model, namely, a forward 
reaction rate, a dependence on the solution pH, an Arrhenius temperature dependence, and a term 
representing the feedback effects of solutes (BSC 2003g, Section 7.1). 
Figure 5-7 shows the general consistency of the approach by demonstrating that long-term 
degradation rates (Stage III) can be adequately predicted by the model. In this figure, filled 
circles represent maximum rates calculated by the model and open circles represent minimum 
calculated rates. The vertical lines show the range of rates calculated with the range of values of 
kE for that pH and 90C. A diagonal line is drawn to indicate where the calculated and measured 
rates are equal. The maximum rates for all pH values lie above the diagonal line, which 
indicates that the model provides an upper bound estimate of the long-term rates for these 
glasses, and the minimum rates for all pH values lie below the diagonal line. This provides 
confidence that the upper limit of the base case model bounds the maximum degradation rate of a 
disposed waste glass. 
Second, literature data were examined to build confidence in the model of exposed surface area, 
namely in the exposure factor, fexposure. As shown in Table 5-9, the exposure factor was compared 
with results of fracturing tests conducted on full-scale samples of nonradioactive glass. The 
fracture ratios for the published tests were generally within the range of 4 to 17 that is used in the 
glass degradation model, providing confidence in the range of surface areas calculated in the 
TSPA-LA glass degradation model. 
July 2004 5-26 No. 7: In-Package Environment Revision 1 
Sample 1 
Table 5-9. Measured Surface Areas for Full-Scale Glass Logs of R7T7 Glass 
15 4 Ratio of measured surface area to geometric surface area 7 2 
as determined by 2-D tomography 
Ratio of measured surface area to geometric surface area 3 2 37 24a 
as determined by Soxhlet Method 
Notes: a Results not representative of a canistered waste glass because of excessive fracturing. 
Source: BSC 2003g, Table 7.2-1. 
Source: BSC 2003g, Figure 7.1-1. 
Sample 2 
July 2004 
Figure 5-7. Comparison of Stage III and Model Output Rates for a Variety of Glasses 
Third, the TSPA-LA glass degradation model was compared with basalts, which are used as 
natural analogs for waste glasses. The dissolution rates calculated using the model presented 
above with the minimum and maximum values of kE were compared directly with the dissolution 
rate of basalts recovered from the seabed. The dissolution rates of several basalt samples were 
calculated based on the thickness of the layer of palagonite that forms as an alteration phase and 
the age of the basalt (Grambow et al 1986, pp. 268 to 269, Table 2, Figure 3). The dissolution 
rates for basalts covered in sediment and exposed to Si-saturated seawater at about 3C are about 
0.1  10-6 m per thousand years, which is equivalent to 6  10-7g/(m2 day). The typical pH 
range for seawater is seven to nine. The respective minimum and maximum dissolution rates 
calculated using the model presented above at 3C are 6.62  10-9 and 1.16  10-5 g/(m2 d) at pH 
7, and 6.33  10-8 and 1.11  10-4 g/(m2 d) at pH 9 (BSC 2003g, Attachment II, Table II-5). 
Thus, the rate expression given previously bounds the long-term dissolution rate of basalts, 
providing confidence in the model. 
5-27 No. 7: In-Package Environment Revision 1 
Finally, the glass degradation model parameters that account for pH and temperature 
dependencies ( and Ea) were compared with pH and temperature dependencies that were either 
determined previously for the dissolution rates of other alkali borosilicate glasses or determined 
from published test results. The comparison indicated that the pH and temperature dependence 
parameter values used in the TSPA-LA glass degradation model are either representative of a 
wide range of borosilicate waste glass compositions or provide an upper bound, thus building 
confidence in these values. 
5.4 SUMMARY AND CONCLUSIONS 
Once a commercial SNF waste package and some fraction of the cladding contained therein have 
failed, and the exposed fuel has been contacted by water, radionuclides in the gap and grain 
boundary (137Cs, 129I, 99Tc, and 90Sr) are released. Radionuclides in the fuel matrix are released 
more slowly as the matrix degrades. The degradation rate of the matrix is a function of the pH, 
pO2, and pCO3 in the waste package, the temperature in the package, and the surface area of the 
fuel. As the matrix degrades, radionuclides dissolve in and diffuse through the water in the rind 
formed by the dissolution of UO2, making them available for transport away from the waste 
form. Degrading commercial SNF is not considered a source of colloids. 
Once DOE SNF has been contacted by water, it is modeled to degrade in one time step, thereby 
making all radionuclides available for dissolution and transport away from the waste form. The 
HLW encapsulated in glass is released more slowly, with a release rate determined by the pH of 
water in the codisposed package, the temperature in the package, the surface area of the glass 
log, and sampled values of the degradation rate constant. As the glass degrades, radionuclides 
dissolve in and diffuse through the water in the alteration rind formed by the dissolution of glass, 
making them available for transport away from the waste form. Degrading HLW glass is a 
source of colloids, some of which contain embedded radionuclides and some of which have 
attached radionuclides, while DOE SNF is not considered a source of colloids. 
July 2004 5-28 No. 7: In-Package Environment Revision 1 
6. DISSOLVED RADIONUCLIDE CONCENTRATIONS 
This section summarizes an analysis of dissolved concentration limits (also referred to as 
solubility limits) of radioactive elements identified to be important under relevant repository 
conditions. When water comes into contact with spent fuel and waste glass, radionuclides will 
dissolve into solution up to a solubility limit that will depend on the particular radionuclide, 
solution chemistry, and the secondary phases likely to form in equilibrium with the fluid. For 
some radionuclides (e.g., 99Tc in oxidizing solutions) there are no secondary phases likely to 
limit dissolved technetium levels appreciably. For others, such as uranium and many of the 
actinides, dissolved levels are controlled by secondary phases that tend to grow rapidly from 
natural waters. Estimating the dissolved concentrations of radionuclides in contact with solids 
containing them requires a thermodynamic database, a numerical means for calculating solution 
concentrations, a broad picture of the likely range in fluid chemistry (e.g., pH and fCO2) and 
temperature, and an explicit choice of the likely solubility-limiting phases for the individual 
radionuclides. 
Estimated dissolved concentrations as a function of solution chemistry are provided to TSPA-LA 
as lookup tables, distributions, or single values and, except for uranium, are applicable to all 
scenario classes that involve groundwater transport of radionuclides. There are two uranium 
solubility models: one for commercial SNF waste packages in the nominal and seismic scenarios 
and the other for commercial SNF waste packages for the igneous intrusion scenario and for the 
invert and for CDSP waste packages in all scenarios. The scenario-specific nature of the 
uranium solubility model accounts for the likely impact of dissolved silica on the identity of 
U-containing solids in glass-containing waste packages and in basalt. 
The information in this section is drawn mainly from Radionuclide Screening (BSC 2002a), 
Dissolved Concentration Limits of Radioactive Elements (BSC 2004c), and Drift-Scale Coupled 
Processes (DST and THC Seepage) Models (BSC 2004e). 
6.1 RELEVANT PROCESSES AND MODELING ASSUMPTIONS 
July 2004 
6.1.1 Physical and Chemical Environment of the Engineered Barrier System 
The general physical and chemical environment in the waste package is important for 
understanding dissolved radionuclide concentrations. In particular, temperature, pH, ionic 
strength, fCO2, and water chemistry are important factors in calculating dissolved radionuclide 
concentrations in the waste package. 
The peak temperature of a waste package varies from 115C to 180C, as shown in Figure 6-1. 
To estimate the effects of changing temperature on solubilities, solubility calculations were 
carried out at 100C for a range of pH values at a single fCO2 value. In all cases, the solubilities 
at 100C were found to be lower than those at 25C. Therefore, solubilities given in this report 
are for 25C. Because of the lower solubilities at higher temperatures, this is a conservative 
approach (BSC 2004c, Section 6.3.3.3). 
6-1 No. 7: In-Package Environment Revision 1 
Source: BSC 2004i, Figure 6.3-2 
Figure 6-1. The Complementary Cumulative Distribution Function (CCDF) for Peak Temperature on the 
Drift Wall and on the Waste Packages for Lower-Bound, Mean, and Upper-Bound Infiltration 
Flux Cases. 
Solution pH and ionic strength are calculated by the in-package chemistry model, as presented in 
Section 3, and are used to calculate dissolved radionuclide concentrations, as shown below. The 
fugacity of carbon dioxide (fCO2) is calculated by the model presented in Engineered Barrier 
System: Physical and Chemical Environment Model (BSC 2004h) and is also used to calculate 
dissolved radionuclide concentrations, as shown below. 
Uncertainties in water chemistry were examined, and it was determined that solubilities of the 
actinide elements are sensitive to the fluoride content of the water because of actinide 
ion-fluoride solution complexes (BSC 2004c, Section 6.3.3.2). The effects of different fluoride 
concentrations are treated as a source of uncertainty in the actinide solubility calculations, as 
described below. 
6.1.2 Selection of Radionuclides 
Selection of an appropriate set of radionuclides for TSPA-LA is documented in Radionuclide 
Screening (BSC 2002a), and summarized in Section 2.2.2. As shown in Table 2-2, and described 
in Section 2.1.2. Fourteen elements have been identified to be important to total dose 
July 2004 6-2 No. 7: In-Package Environment Revision 1 
calculations: actinium, americium, carbon, cesium, curium,4 iodine, neptunium, plutonium, 
protactinium, radium, strontium, technetium, thorium, and uranium (BSC 2004c, Section 6.3.1). 
The solubility limits for these elements are discussed below. 
6.1.3 Solubility Limit and Controlling Solid Phase 
The term solubility used in repository performance assessments generally refers to elemental 
solubility. The selection of an appropriate set of controlling solid phases is crucial for evaluating 
dissolved radionuclide concentration limits. In the context of thermodynamics, the most stable 
solid is selected as the controlling phase because it ultimately replaces less stable phases. 
However, due to kinetic effects, the thermodynamically most stable solid may not appear under 
expected repository conditions. Therefore, the selection of the controlling solid purely from 
thermodynamic considerations may be not reliable. To obtain a reasonable but conservative 
concentration limit, a less stable solid phase may be chosen. Laboratory experiments and 
observations of natural systems provide the basis for such choices, which are shown in Table 6-1 
(BSC 2004c, Section 6.3.2). 
6.1.4 Water Chemistry and Aqueous Speciation 
Table 6-2 gives the composition of the base-case water used as a starting point to develop the 
solubility models. The water composition given in this table does not represent the only 
conditions under which the solubility models are applicable. Water of this composition has been 
used as the benchmark for the repository for many years and, for that reason, was used instead of 
the Ca or Na-type waters listed in Section 3. A detailed rationale for using this water has been 
thoroughly discussed by a project committee (Harrar et al. 1990). In addition, a sensitivity 
analysis to evaluate the effect of various cation and anion concentrations on radionuclide 
solubilities was carried out as described in Section 6.4.2.5 of Dissolved Concentration Limits of 
Radioactive Elements (BSC 2004c). Because all of the solubility-affecting solution chemistry 
parameters (e.g., fluoride, carbon dioxide and proton activities) are varied by orders of 
magnitude from that of J-13, the solubility expressions themselves are largely independent of the 
choice of J-13 as the starting composition. 
The dissolved concentration limits of radionuclides were calculated for a range of pH and fCO2 
values in water having the composition of J-13 water, modified by the addition of Na+ or SO4 as 
required for solution electroneutrality. The calculations were made using the speciationsolubility 
code EQ3NR (Wolery 1992) and the thermodynamic database Data0.ymp.R2 
(Steinborn et al. 2003) with updates described in Section 4.1 of Dissolved Concentration Limits 
of Radioactive Elements (BSC 2004c). Activity coefficients of aqueous species are estimated 
using the b-dot equation (Wolery 1992). The dominant species for each element under various 
modeled conditions are tabulated in Table 6-1. These species account for more than 95% of total 
dissolved concentration. The principal properties of the matrix water to which the solubilities 
show sensitivity are pH, fCO2, and fluoride concentration. The effects of varying pH and fCO2 
are directly considered, and values of calculated radionuclide concentrations are presented in 
4 Curium is important only because 245Cm decays through 241Pu to 241Am, which is an important radionuclide in 
terms of human dose. Rather than model curium explicitly in TSPA-LA, the inventory of 241Am will be increased 
appropriately to account for the production of 241Am by the decay of 245Cm. Therefore, a model for curium 
solubility is not included in this section. 
6-3 July 2004 No. 7: In-Package Environment lookup tables for a range of pH and fCO2 values. The effects of different fluoride concentrations 
are uncertain and are addressed through the use of uncertainty coefficients, as discussed below. 
Table 6-1. Controlling Solid Phases and Dominant Aqueous Species in Solubility Modeling 
Controlling 
Solid(s) 
PuO2 (hyd, aged): 
hydrated and aged 
amorphous PuO2 
Np2O5 and 
NaNpO2CO3 
Schoepite, 
Na-boltwoodite, 
and Na4UO2(CO3)3 
ThO2(am) 
AmOHCO3 
RaSO4 
Element(s) 
Plutonium 
Neptunium 
Uranium 
Thorium 
Americium 
Actinium 
Protactinium 
Radium 
Technetium, 
Carbon, Iodine, 
Cesium, and 
Strontium 
Source: BSC 2004c, Section 6. 
Dominant Aqueous Species 
PuO2SO4, PuO2F+, PuO2 
2+ , 
PuO2CO3, PuO2 
+, PuO2(CO3)- 
and PuO2(CO3)3 
4- 
NpO2 
+, NpO2SO4, 
NpO2(SO4)2 
2-, NpO2(CO3)2 
2- , 
NpO2CO3 
-, and NpO2(CO3)3 
4- 
UO2(CO3)3 
4-, UO2SO4, 
(UO2)2(OH)2 
2+, UO2F+ , 
UO2CO3, (UO2)2CO3(OH)3 
- , 
UO2(CO3)2 
2-, UO2F2, 
UO2OH+, UO2HPO4, 
UO2PO4 
-, UO2(OH)3 
-, and 
HUO4 
- 
Th(SO4)2, Th(OH)3CO3 
- , 
Th(OH)4, Th(CO3)5 
6- & ThF3 
+ 
Am(CO3)3 
3-, Am(CO3)2- , 
AmCO3 
+, Am(OH)2 
+, AmOH2 
+ , 
AmSO4 
+, Am(SO4)2 
- and 
Am3+ 
Comments 
Np2O5 is the phase identified in 
laboratory solubility experiments. See 
Section 6.4.3 
Americium is used as a chemical analog 
to actinium. Actinium solubility is 
assumed to be 106 times americium 
solubility (BSC 2004c, Section 6.10.4). 
Based on considerations of chemical 
analogy, protactinium solubility should 
range from below that of thorium to 
below that of neptunium(V). 
Solubility is modeled as a piecewise 
function of pH for pH < 9.75. For pH 
> 9.75, the concentration of radium is 
assumed to be not limited by solubility. 
Not limited by solubility; limited by 
inventory and dissolution rate. 
No. 7: In-Package Environment 6-4 
Revision 1 
July 2004 Revision 1 
Table 6-2. Chemical Composition of Reference Water (from Well J-13) 
Concentration (mg/L)a 
45.8 
5.04 
13.0 
2.01 
28.5 (60.97) 
Uncertainty 
2.29 
0.61 
0.99 
2.01 
1.85 
0.61 
0.29 
1.03 
1.03 
7.14 
2.18 
8.78 
18.4 
Component 
Na+ 
K+ 
Ca2+ 
Mg2+ 
Si (SiO2(aq)) 
CI- 
F- 
NO3 
- 
SO4 
2- 
Source: BSC 2004c, Table 4-2. 
NOTE: aDTN: MO0006J13WTRCM.000 contains recommended mean values of major 
6.1.5 Redox Reactions 
The design of the repository is such that it is under atmospheric conditions throughout. Thus, 
oxidizing conditions are assumed, and all solubilities were calculated at a redox potential 
corresponding to an fO2 of 0.2 bars, the atmospheric value, with the exception of plutonium 
solubility. For plutonium, the solubility model using a redox potential calculated from fO2 = 0.2 
is inconsistent with experimentally determined plutonium solubilities. Therefore, for plutonium, 
solubilities were calculated with a modified Eh-pH relationship that provides a better fit of 
calculated solubilities to experimentally determined solubilities (BSC 2004c, Section 6.5). 
6.2 SOLUBILITY MODEL 
The solubility model has three different forms: 
 Lookup tables as a function of pH and fCO2 along with associated uncertainty terms 
 A constant for specified pH intervals 
 No solubility limit. 
These three different forms of the model and the elements to which each form is applied are 
discussed below. 
6.2.1 Lookup Tables 
Results of the calculations for the solubilities of plutonium, neptunium, uranium, thorium, 
americium, actinium, and protactinium (without uncertainty) are shown in Figures 6-2 
through 6-9 and are tabulated in Tables 6.5-3, 6.6-2, 6.7-3, 6.7-5, 6.7-6, 6.8-2, 6.9-2, 6.10-2, and 
6.11-3 of Dissolved Concentration Limits of Radioactive Elements (BSC 2004c). The solubilities 
of these actinide elements increase with pH and fCO2 under alkaline conditions, while under acid 
conditions they increase with decreasing pH values. This U-shape (or V-shape) curve is 
typical for actinides. Minimum solubilities are attained at neutral to mildly basic pH values, 
constituents in well J-13 water. In the technical data management system database, the 
term abundance was used instead of concentration. 
July 2004 6-5 No. 7: In-Package Environment generally within the range of pH predicted by the in-package chemistry model (Section 3). The 
solubility of actinides increases with fCO2. This dependence on fCO2 can cause solubility to 
vary by several orders of magnitude for a given pH. 
Source: BSC 2004c, Table 6.5-3. 
Figure 6-2. Solubility of Plutonium Modeled as a Function of fCO2 and pH 
6-6 No. 7: In-Package Environment 
Revision 1 
July 2004 Source: BSC 2004c, Table 6.6-3. 
Figure 6-3. Solubility of Neptunium Modeled as a Function of fCO2 and pH 
Source: BSC 2004c, Figure 6.7-1. 
NOTE: Schoepite is the controlling mineral under all conditions of pH and fCO2. 
Figure 6-4. Uranium Solubility in Commercial SNF Packages Breached under Nominal and Seismic 
Scenarios 
No. 7: In-Package Environment 
Revision 1 
July 2004 6-7 Revision 1 
Source: BSC 2004c, Figure 6.7-2. 
NOTE: Shaded areas are boundaries between pH  fCO2 regions controlled by indicated minerals. 
Figure 6-5. Uranium Solubility in Commercial SNF Packages Breached by an Igneous Event, 
Codisposal Waste Packages under Any Breach Scenario, and Waters in the Invert Modeled 
as a Function of pH and fCO2 
Source: BSC 2004c, Figure 6.8-1. 
Figure 6-6. Solubility of Thorium Modeled as a Function of fCO2 and pH 
July 2004 6-8 No. 7: In-Package Environment Revision 1 
Source: BSC 2004c, Figure 6.9-1. 
Figure 6-7. Solubility of Americium Modeled as a Function of fCO2 and pH 
Source: BSC 2004c, Table 6.10-2. 
Figure 6-8. Solubility of Actinium Modeled as a Function of fCO2 and pH 
July 2004 6-9 No. 7: In-Package Environment Revision 1 
Figure 6-9. Solubility of Protactinium Modeled as a Function of fCO2 and pH 
Source: BSC 2004c Table 6.11-3 
The solubility model for uranium requires some discussion because three lookup tables (BSC 
2004c, Tables 6.7-3, 6.7-5, and 6.7-6) are used, rather than one. This reflects the fact that the 
conditions for mineral phase formation will be different. In the nominal and seismic scenarios, 
very little silica will exist in solution and will prevent the formation of otherwise insoluble 
uranium silicates. Instead, schoepite will likely form. The first lookup table provides uranium 
concentrations calculated assuming schoepite control of solubility in commercial SNF waste 
packages for the nominal and seismic scenario classes. Glass-containing packages will generate 
Si-rich solutions, as will interaction of groundwater with basalt in the igneous scenario. In Sirich 
fluids, dissolved uranium levels will be controlled by either schoepite or Na-boltwoodite, a 
sodium uranyl silicate. The second two tables provide uranium concentrations in codisposal 
waste packages for all scenario classes, in commercial SNF waste packages for the igneous 
intrusion scenario, and in the invert. The second table gives concentrations for low and 
circum-neutral pH values and Si-rich fluids. The third table provides concentrations at 
circum-neutral to high pH values for Si-rich fluids. These correspond to Na-boltwoodite or to 
Na4UO2(CO3)3 solubility, although for part of the table it is uncertain whether schoepite or 
Na-boltwoodite is controlling uranium solubility. 
Calculated uranium concentrations are summarized in Figures 6-4 and 6-5. At the lower pH 
values, schoepite is the least soluble phase. At pH values around 7, there is an inflection in the 
concentration curves where the solubility curves of Na-boltwoodite cross those of schoepite so 
that the controlling mineral phase changes. With increasing pH values, the uranium 
concentrations increase steeply. The pH values corresponding to this increase vary with fCO2, 
being lowest at the highest fCO2 values. This is due to the increasing carbonate content of the 
July 2004 6-10 No. 7: In-Package Environment Revision 1 
water with increasing pH and fCO2, which leads to the formation of high concentrations of 
uranyl carbonate solution complexes. When the carbonate content reaches sufficiently high 
values, the uranyl carbonate solid, Na4UO2(CO3)3, becomes stable, thus limiting further increases 
in the uranium concentration. 
The concentrations used in TSPA-LA consist of the solubilities calculated by EQ3NR, which are 
found in a lookup table (the values of which were shown in Figures 6-2 through 6-9), plus 
uncertainty terms. The concentration (except for plutonium and uranium) is expressed as (BSC 
2004c, Section 6): 
(Eq. 6-1) f log(RN ) = S ( pH , log CO 2) + 1 + 2 
i 
where 
1 
i 
2 
RN = solubility limit of the element used in TSPA-LA (mg/L) 
S = logarithmic value of the solubility calculated with the speciation code, EQ3NR 
(tabulated in lookup tables and shown in figures above) 
= uncertainty associated with the equilibrium constant 
= uncertainty associated with the effect of the dissolved F. concentration 
= either the commercial SNF or codisposal waste package. i 
For plutonium and uranium, the concentration is expressed as (BSC 2004c, Sections 6.5.8.5 and 
6.7.6): 
(Eq. 6-2) log(RN ) = S (pH , log f CO 2) + 1 + 2 
i * N (pH) 
where 
2 
 
N(pH) = normalized pH dependence of fluoride uncertainty factor i
. 
Uncertainty associated with the equilibrium constant, K, of the reaction between controlling 
solids and aqueous species is captured by the uncertainty term 1. This uncertainty is derived 
from uncertainties in the standard state Gibbs free energy of formation (.fG0) of the species 
involved in the dissolution reaction. Uncertainties in .fG0 values are taken from various sources 
(BSC 2004c, Section 6.3.3.1) and correspond to the 95% confidence interval, which is taken to 
represent two standard deviations (2) in a normal distribution. Uncertainty in log K is calculated 
from the expression .fG0
products . .fG0
reactants = .RTln K. This expression is a simple algebraic 
sum, so uncertainties in the .fG0 terms can be combined by the root-mean-square procedure to 
obtain 2logK. This calculation is performed for all dominant aqueous species of a given 
element, and the largest value of 2logK is used to determine 1 as the standard deviation of a 
normal distribution. These values are shown as the 1 uncertainty term in Table 6-3 for 
plutonium, neptunium, uranium, thorium, americium, actinium, and protactinium. 
July 2004 6-11 No. 7: In-Package Environment No. 7: In-Package Environment 
Table 6-3. Uncertainties Associated with Lookup Table Solubility Model 
Uncertainty Term 
 CDSP . INV 
 CSNF 
 
N(pH) Radionuclide 2 2 1 
Normal distribution: BSC 2004c, Table Triangular distribution: Triangular distribution: 
a = b = 0 
c = 2.15 
6.5-4. a = b = 0 
c = 4.91 ~ 10 .1 
 = 0 
 = 1.0 
Normal distribution: 
Plutonium 
Triangular distribution: Triangular distribution: 
 = 0 
 = 0.8 
a =b = 0 
c = 0.474 
a = b = 0 
c = 1.59 ~ 10 .2 
Neptunium N/A 
Uranium BSC 2004c, Table Normal distribution: Triangular distribution: 6.7-10. (Commercial SNF waste 
packages, nominal and 
seismic scenario classes) 
 = 0 
 = 0.5 
a = b = 0 
c = 1.03 
N/A 
Normal distribution: BSC 2004c, Table Triangular distribution: Uranium Triangular distribution: 
 = 0 
 = 0.6 (Na4 UO2 (CO3 ) 3 ) 
 = 0.5 (schoepite and Naa 
= b = 0 
c = 2.40 (schoepite) 
6.7-12. a = b = 0 
c = 1.65 (schoepite) 
(codisposal waste packages 
for all scenario classes and 
commercial SNF waste c = 1.17 (Na-boltwoodite) c = 0.368 (Na-boltwoodite) 
boltwoodite) packages for igneous 
intrusion modeling case) 
c = 0 (Na4 UO2 (CO3 ) 3 ) c = 0 (Na4 UO2 (CO3 ) 3 ) 
Normal distribution: Triangular distribution: Triangular distribution: 
a = b = 0 
c = 5.72 
 = 0 
 = 0.7 
Thorium a = b = 0 
c = 3.10 
N/A 
Triangular distribution: Normal distribution: Triangular distribution: 
a = b = 0 
c = 5.91 ~ 10 .2 
a = b =0 
c = 1.46 
 = 0 
 = 1.0 
Normal distribution: 
Americium N/A 
6-12 July 2004 
Triangular distribution: Triangular distribution: 
 = 0 
 = 2.0 
a = b = 0 
c = 1.46 
a = b = 0 
c = 0.04 
Actinium N/A 
Triangular distribution: Triangular distribution: Uniform distribution: 
a = b = 0 
c = 5.79 
a = b = 0 
c = 3.10 
Protactinium N/A  = -1.02 
 = 3.04 
Source: BSC 2004c, Tables 6.5-5, 6.6-4, 6.7-11, 6.8-4, 6.9-5, 6.10-3, and 6.11-4; Section 6.7.6. 
NOTE: Models and associated uncertainties for protactinium and actinium are based on their corresponding chemical analogs (neptunium 
and thorium for protactinium and americium for actinium). Revision 1 
Uncertainty associated with the effects of F. concentration is captured by the uncertainty term 2 
i 
where i refers to either the commercial SNF waste package or the codisposal waste package and 
the invert. The only source of fluoride for commercial SNF waste packages is the groundwater, 
whereas HLW glass releases fluoride as it dissolves. To evaluate the effects of fluoride content 
on solubilities, calculations for a range of pH values at a single fCO2 value were performed at 
higher fluoride contents for each type of waste: 10 times the base-case (shown in Table 6-2) for 
commercial SNF and 200 times the base-case for codisposal waste packages and the invert 
(except for uranium). To evaluate the effects of fluoride content on uranium solubilities in 
codisposal waste packages and in the invert, calculations were performed with fluoride contents 
that were 96 times the base-case values (for the no-dripping case) and 27 times the base-case 
values (for dripping cases). In general, increased fluoride concentration is associated with 
increased actinide solubility, as illustrated for uranium in Figure 6-10. Table 6-3 gives the 
distributions of the uncertainty term 2 
i for the seven actinide elements. 
Source: BSC 2004c, Figure 6.7-3. 
Na-Boltwoodite at log(fCO2) = .3.0 
As Figure 6-10 shows, fluoride increases uranium (and plutonium) solubilities; the maximum 
impact at 25C is around pH 6. Fluoride can appear inside the waste form inflow from 
groundwater or by dissolution out of HLW glass. No-drip models with commercial SNF will 
have no fluoride. Codisposed, seepage and igneous intrusion models will all involve fluids 
containing some fluoride. Absolute levels in the waste form are uncertain and must be modeled 
as such. Fluoride impacts on solubility are bounded as a function of pH and scenario as shown 
Figure 6-10. Effect of Scenario-Dependent Fluoride Concentration on the Solubilities of Schoepite and 
July 2004 6-13 No. 7: In-Package Environment Revision 1 
in Figure 6-10. For plutonium and uranium, this variation has been captured explicitly by 
expressing the fluoride uncertainty as a function of pH (Equation 6-2). The maximum fluoride 
uncertainties are shown in Table 6-3 (parameter c). The factors modulating these maximum 
uncertainties as functions of pH are given in Tables 6.5-4 (for plutonium) and Tables 6.7-10 and 
1.2 
No Limit 
6.2.3 No Solubility Limit 
For technetium, carbon, iodine, cesium, and strontium, it is assumed that no solubilitycontrolling 
solid exists. Therefore, solubility is undefined for these five elements. Release of 
these five elements should be controlled by the dissolution rate of waste forms rather than by 
solubility. 
6.3 SOURCE OF DATA 
Direct inputs for solubility calculations include the thermodynamic database data0.ymp.R2 
(Steinborn et al. 2003) with updates described in Section 4.1 of Dissolved Concentration Limits 
of Radioactive Elements (BSC 2004c) and representative J-13 water chemistry. Water chemistry 
data are shown in Table 6-2. The thermodynamic database was built from the experimental 
6.7-11 (for uranium) of Dissolved Concentration Limits of Radioactive Elements (BSC 2004c). 
Under some conditions, the concentrations of certain radionuclides cannot be established by the 
above solubility calculations. This occurs for americium and actinium at low pH values 
(Figures 6-7 and 6-8), for plutonium, thorium, neptunium, and protactinium at high pH and fCO2 
values (Figures 6-2, 6-3, 6-6, and 6-9), and for all radionuclides at ionic strengths greater than 
three. In principle, concentrations are also limited by the rate of radionuclide release from the 
waste form and the available water volume. In some TSPA-LA realizations, calculated 
concentrations are higher than physically possible. In these cases, a solubility cap equal to the 
density of the solid metal is imposed. 
6.2.2 Constant Solubility for Specified pH Intervals 
The solubility of one element, radium, is constant for specified pH intervals. The solubility limit 
of radium is based on the solubility of pure RaSO4 and is modeled as a piecewise function of pH 
(BSC 2004c, Table 6.12-1). For slightly alkaline (J-13 water) and acidified Yucca Mountain 
waters, the calculated radium solubility ranges from 9.1  10-3 to 1.9  10-2 mg/L. Thus, a 
constant solubility of 2.0  10-2 mg/L is recommended for radium for pH 7.75 or less. Under 
more alkaline conditions, pH from 7.75 to 9.75, the calculated solubility ranges from 7.1  10-2 
to 1.2 mg/L. For this pH range a constant value of 1.2 mg/L is recommended. Above a pH of 
9.75, there is no solubility limit. The solubility model for radium is summarized in Table 6-4. 
Table 6-4. Radium Solubility Values 
Radium Solubility (mg/L) pH Range 
2  10-2 3.0 to 7.75 
7.75 to 9.75 
> 9.75 
July 2004 
Source: BSC 2004c, Table 6.12-1. 
6-14 No. 7: In-Package Environment measurements and theoretical analysis by a large number of international experts. The database 
specifically includes the most recent actinide solubility measurements and parameterizations of 
the Organization for Economic Cooperation and DevelopmentNuclear Energy Agency (OECDNEA) 
and the Swiss radionuclide solubility effort (e.g., Grenthe et al. 1992; Silva et al. 1995; 
Lemire 2001; Hummel et al. 2002). As an example, the equilibrium constants for the dissolution 
and precipitation of solubility-controlling mineral phases from the database data0.ymp.R2 in 
DTN: MO0302SPATHDYN.000 are listed in Table 6-5. 
Table 6-5. Equilibrium Constants for the Dissolution and Precipitation of Solubility-Controlling Mineral 
Phases 
Controlling Solid 
PuO2(hyd,aged) 
Np2O5 
UO32H2O 
(schoepite) 
ThO2(am) 
AmOHCO3 
NaNpO2CO3 
Na-boltwoodite 
Na4UO2(CO3)3 
Reaction 
PuO2(hyd,aged) + 4H+ = Pu4+ + 2H2O 
Np2O5 + 2H+ = 2NpO2 
+ + H2O 
UO32H2O + 2H+ = UO2 
2+ + 3H2O 
Source: DTN: MO0302SPATHDYN.000: database data0.ymp.R2. 
Log K 
-1.9803 
3.7031 
4.8443 
ThO2(am) + 4H+ = Th4+ + 2H2O 7.3100 
AmOHCO3 + 2H+ = Am3+ + HCO3 
- + H2O 3.1520 
NaNpO2CO3 + H+ = Na+ + HCO3 
- + NpO2 
+ -1.3265 
Na-boltwoodite + 3H+ = Na+ + SiO2 + UO2 
2+ + 4H2O 5.82 
Na4UO2(CO3)3 + 3H+ = 4Na+ + 3HCO3 
- + UO2 
2+ 4.0504 
6.4 UNCERTAINTIES, LIMITATIONS, AND MODEL CONFIDENCE 
6.4.1 Model Limitations 
The solubility models presented above are valid only within certain ranges of pH, fCO2, 
temperature, F- concentration, and ionic strength. These limits are summarized in Table 6-6. It 
should be noted, however, that dissolved concentration limits could not be determined for all 
elements for all values of pH and fCO2, as demonstrated in Figures 6-2 through 6-9. 
Variable 
pH 
log(fCO2) 
Temperature 
F- concentration 
Table 6-6. Valid Range of the Solubility Models 
Value or Range 
Given by individual solubility table 
-5.0 to -1.5 bars 
25C to 100C 
1 to 10 times base-case value for commercial SNF waste packages 
1 to 200 times base-case value for codisposal waste packages and the invert 
For uranium: 
1 to 96 times base-case value for no-dripping commercial SNF waste packages and invert 
1 to 27 times base-case value for dripping commercial SNF waste package and invert 
Less than 1 mol/kg. With additional uncertainty in solubility value, less than 3 mol/kg. Ionic strength 
Source: BSC 2004c, Table 8-5. 
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Revision 1 
July 2004 6-15 Revision 1 
6.4.2 Model Uncertainty 
Uncertainty from various sources has been addressed in developing the models presented above. 
The primary sources of uncertainty are (1) uncertainty in log K of the solubility-controlling 
phase, (2) uncertainty associated with temperature variations, and (3) uncertainty associated with 
variations in fluoride concentrations. For elements with solubility limits that are taken from 
lookup tables, these uncertainties (except for uncertainties associated with temperature 
variations) are addressed through the use of uncertainty terms added to the solubility value taken 
from the lookup table (Equations 6-1 and 6-2, and Table 6-3). The uncertainty associated with 
temperature variations is addressed by performing calculations at 25C. Because actinides have 
lower solubilities at 100C than at 25C, performing calculations at the lower temperature results 
in higher solubilities. For radium, uncertainty is not addressed, nor is it addressed for 
technetium, iodine, cesium, strontium, or carbon because solubility limits are not provided for 
these five elements. 
6.4.3 Model Confidence 
Developing solubility models involves: 
1. A thermodynamic database and a modeling tool 
2. The specification of environmental conditions 
3. Construction of a conceptual model 
4. The calculation of solubility limits using a geochemical modeling tool based on the 
conceptual model. 
The thermodynamic database was composed of data developed to support general geochemical 
use of the EQ3/EQ6 package, augmented by data principally on actinides from the critical 
reviews carried out within the Chemical Thermodynamic Database project of the Organization 
for Economic Cooperation and DevelopmentNuclear Energy Agency (Grenthe et al. 1992; 
Lemire 2001; Silva et al. 1995). The specific database used received additional technical review 
and approval for project use (Steinborn et al. 2003; DTN: MO0302SPATHDYN.000) and so 
requires no additional confidence building. The geochemical modeling was carried out using the 
EQ3NR (Wolery 1992) code from the EQ3/EQ6 package. This group of codes is widely used 
and accepted for general geochemical modeling and in support of nuclear waste activities and 
required no additional confidence building for its application here. It has received approval for 
project use. Neither the code nor the database was used outside its range of validity. Thus, 
aspects 1 and 4 above are exempted from further model confidence building. Aspect 2 consists of 
inputs to this report; thus, no model confidence building is necessary. Therefore, model 
confidence building focuses on whether the conceptual model (i.e., the selection of solubilitycontrolling 
mechanism) is appropriate. 
The selection of solubility-controlling solids was based on laboratory and/or field observations 
that were determined to be applicable to the expected range of conditions. Specifically, potential 
solubility-forming solids for specific radionuclides were eliminated from consideration if they 
were not observed to form in the laboratory or the field under the chemical conditions being 
July 2004 6-16 No. 7: In-Package Environment Revision 1 
considered. In the absence of experimental or field evidence of appearance and solubility control 
the conservative approach of selecting less crystalline, more hydrated phases was followed. This 
results in prediction of higher solubilities relative to those resulting from control by more 
crystalline, less hydrated phases. The end result is that the outputs of the solubility model are 
either directly supported by experimental or field observations or, where observation is lacking, 
conservative. 
In addition, the plutonium and neptunium solubility models were corroborated with laboratory 
measurements or relevant observations not previously used to develop the model. These 
corroborative activities are discussed below. 
PlutoniumFigure 6-11 presents the model of plutonium solubility at log(fCO2) = -3.5 bars, as 
well as several sets of experimental data. Experiments conducted by Rai (1984) and Rai et al. 
(2001) were open to air, while experiments conducted by Nitsche et al. (1993a, 1993b) and by 
Efurd et al. (1998) were conducted in an argon/CO2 atmosphere. Other conditions were 
comparable to the modeled conditions. Four different types of solutions were used in the 
experiments conducted by Rai et al. (2001): 
 0.403 mol/L NaCl solution 
 0.408 mol/L NaClO4 solution 
 4.36 mol/L NaCl solution 
 4.92 mol/L NaClO4 solution. 
July 2004 6-17 No. 7: In-Package Environment Revision 1 
Figure 6-11. Comparison of Experimental Data with Predictions of Plutonium Solubility Model 
Source: BSC 2004c, Figure 7-1. 
Since the thermodynamic database used in this report is invalid for high ionic strength solutions, 
only the results of the lower ionic strength solutions reported by Rai et al. (2001) are shown in 
Figure 6-11. The other two data sets are plutonium concentrations measured in SNF leaching 
experiments by Wilson (1990a, 1990b). 
Most of the data points from these seven experiments fall within the uncertainty range of the 
model. The good match between the model results and experimental results, which were not 
used in the model development, indicates that this proposed plutonium solubility model is 
representative of measured solubilities given the assumed controlling solid phase and assumed 
Eh conditions, and it enhances confidence in the plutonium solubility model. 
NeptuniumFigure 6-12 presents Np2O5 solubility at 25C and at log(fCO2) = -3.5 bars. The 
inputs for this model were derived from the thermodynamic analysis of experimental data 
reported in Lemire (2001) and elsewhere (see discussion in BSC 2004c, Section 6.6). To show 
that the model is conservative the Np2O5 solubility model predictions can be compared against 
July 2004 6-18 No. 7: In-Package Environment Revision 1 
actual fuel leaching experiments. Figure 6-12 also presents measured neptunium concentrations 
in several SNF corrosion experiments. These experiments were conducted at Pacific Northwest 
National Laboratory (Wilson 1990a, 1990b) and at Argonne National Laboratory (Finn et al. 
1994; CRWMS M&O 2000e; CRWMS M&O 2000f; DTN: LL991001251021.090). This 
comparison shows that the Np2O5 solubility model is conservative and thus is adequate for use in 
the TSPA-LA model. An alternative conceptual model using the high temperature phase NpO2 
as the controlling phase led to solubilities only about 1 order of magnitude lower than those from 
the Np2O5 model (BSC 2004c, Figure 6.6-2). The fact that the measured neptunium 
concentrations in spent fuel corrosion experiments are 4 to 6 orders of magnitude lower than the 
concentrations modeled using Np2O5 and 3 to 5 orders of magnitude lower than the 
concentrations modeled using NpO2 indicates that neptunium may be controlled by different 
mechanisms than by pure phase solubility. Another alternative conceptual model for neptunium 
dissolved concentrations based on incorporation of neptunium into secondary uranium phases 
has also been investigated (BSC 2004c, Section 6.6). This model is based on incorporation of 
neptunium into secondary uranium phases, such as schoepite. However, this alternative model is 
not used in TSPA because the experimental data upon which the inputs to the model are based 
have been questioned (Adkins 2003). 
Source: BSC 2004c, Figure 7-2. 
Figure 6-12. Comparison of Np2O5 Neptunium Solubility Model with Pacific Northwest National 
Laboratory and Argonne National Laboratory Measurements 
UraniumThe uranium solubility model is based on calculations of the solubility of several 
uranium-bearing solids: schoepite (UO32H2O), the controlling mineral at low to moderate pH 
and fCO2 values; Na-boltwoodite (NaUO2SiO3OH1.5H2O), the controlling solid at moderate to 
high pH and fCO2 values; and the solid Na4UO2(CO3)3, the controlling solid at high pH and fCO2 
July 2004 6-19 No. 7: In-Package Environment Revision 1 
values. In these calculations, dissolved silica concentration was fixed by saturation with the silica 
phase chalcedony. Evidence supporting the selection of these uranium- and silicon-controlling 
phases was presented in Section 5.1.4.3 and is summarized below. A more complete discussion 
can be found in Section 6.7 of Dissolved Concentration Limits of Radioactive Elements 
(BSC 2004c). 
The selection of the solids used to model uranium concentrations was based on laboratory studies 
(Wronkiewicz et al. 1997; Pearcy et al. 1994) describe a natural analog study of uraninite 
alteration in the Nopal I deposit at Pea Blanca, Mexico, an environment similar in most respects 
to that of Yucca Mountain. The paragenesis of alteration products in the natural analog study is 
entirely consistent with that of the laboratory study; the differences that are evident are related to 
the chemistry of the alteration water in the two situations. These studies are described in more 
detail in Section 5.1.4.3, where they also provide confidence in the commercial SNF dissolution 
model. 
The alteration paragenesis found in both the laboratory and natural analog study begins with 
uranyl-oxide hydrate minerals (principally of the schoepite group) and passes to alkali and 
alkaline earth uranyl silicates hydrates. In the natural analog, the principal silicate alteration 
product is uranophane. Weeksite and boltwoodite are also found, but they occur farther from the 
uraninite deposit itself and tend to form over earlier-formed phases. Pearcy et al. (1994, p. 726) 
conclude that the specific uranyl silicate formed in a given area depended on the local 
geochemical conditions rather than on the broad evolution of the oxidizing system. In the 
laboratory study, uranophane is also an important secondary silicate but it is more evident that 
the final silicate phase is Na-boltwoodite (Wronkiewicz et al. 1997, Section 4.2.1 and Figure 7). 
Considering the phases for which data are available and the principal secondary phases observed 
in the laboratory study, schoepite and Na-boltwoodite were selected as the controlling phases for 
the uranium solubility model. Although uranophane is the most common uranyl silicate found in 
the natural analog study, calcium must be present in the degrading water for it to form. The 
groundwater at Pea Blanca has a considerably higher calcium and lower sodium content than 
J-13 water or the water used for the laboratory degradation experiments (Pearcy et al. 1994, 
Table 5). Furthermore, conditions of high pH and high fCO2 that can occur in the drift should 
lead to high dissolved carbonate contents and, consequently, to low calcium concentrations 
because of the precipitation of carbonate minerals such as calcite. Thus, the natural analog study 
validates the selection of schoepite and Na-boltwoodite. 
Schoepite saturation leads to the lowest uranium concentrations at low pH values. With 
increasing pH, Na-boltwoodite becomes the controlling phase. At still higher fCO2 and pH 
values, the carbonate content of the solution increases strongly. The uranyl ion forms a series of 
strong complexes with the carbonate ion so increasing carbonate contents lead to increasing 
uranium concentrations. When the dissolved carbonate reaches a high enough concentration, 
modeling shows that the solid Na4UO2(CO3)3 will form, limiting further increase in dissolved 
uranium. This phase is not observed in the laboratory studies or natural analog site because the 
pH and fCO2 conditions in these studies are too low for this solid to form. 
ThoriumThe minimum thorium concentration modeled is 6.36  10-4 mg/L (2.7  10-9 mol/L) 
at an fCO2 of 10-5 bars and a pH of 6.25. At this pH and low fCO2, the impact of F-, SO4
2-, and 
July 2004 6-20 No. 7: In-Package Environment Revision 1 
CO3
2- complexes is minimal and the hydroxyl complex Th(OH)4(aq) dominates. This solubility 
should therefore represent the experimental solubility of thorium dioxide in pure water at 
moderate to high pH values. Hummel et al. (2002, Section 5.21) have developed an internally 
consistent set of thermodynamic data for thorium. As part of their reported work they summarize 
the results of a number of studies of thorium solubility in pure water. From these data, they show 
that at pH values above 6 the solubility is 10-8.50.6 mol/L (Hummel et al. 2002, Figure 5.21.1). 
The minimum solubility modeled in this report (2.7  10-9) equals 10-8.6, close to the mean and 
well within the uncertainty of the measured values. The above comparison demonstrates that the 
thorium solubility model compares favorably with test data; thus, it is adequate for use in TSPALA. 
AmericiumAmOHCO3 was chosen as the controlling solid phase in all calculations of 
americium solubility. The choice of this mineral is based on the studies by Nitsche et al. (1993a; 
1993b, p. 1494), which identify AmOHCO3 as the solid phase precipitated from water 
corresponding to the water used in these calculations at a pH range from 5.9 to 8.4 and 
temperatures from 25C to 90C. This is the most likely controlling phase under the range of 
environmental variables of interest to this analysis. Thus, the model of americium solubility is 
consistent with experimental data generated for the Yucca Mountain Project and is adequate for 
use in TSPA-LA. 
ActiniumActinium is commonly considered to be directly analogous to americium (e.g., Berner 
2002, p. 37). Only one set of actinium solubility measurements is known (Baes and Mesmer 
1986, Table 7.2) and was used to develop the actinium solubility model in which actinium 
concentrations are 106 times those of americium. Thus, no direct validation is possible. 
ProtactiniumThermodynamic data for protactinium are not available in the database, based on 
their similar positions in the periodic table, so protactinium was assumed to be an analog of 
neptunium for the purpose of solubility calculations. Protactinium solubility is, thus, taken to be 
equal to neptunium solubility. Experiments on protactinium behavior in solutions of several 
types at a range of pH values are described by Berry et al. (1989). The protactinium behavior 
was dominated by sorption, but the authors were able to develop a solubility limit of 10-10 mol/L 
(2.3  10-5 mg/L) at high pH values in waters typical of those emanating from cements. This is 4 
orders of magnitude lower than the lowest neptunium solubility, indicating that the solubility 
model developed for protactinium is conservative. 
RadiumThe radium solubility given is based on the solubility of pure RaSO4. Field studies 
have shown that radium concentrations in some natural waters are orders of magnitude below 
levels corresponding to RaSO4 saturation. Radium concentrations more probably correspond to 
the solubilities of radium in solid solutions of more common sulfate solids such as SrSO4 or 
BaSO4 (Langmuir and Riese 1985). A radium concentration based on pure RaSO4 solubility is 
therefore a conservative value and adequate for TSPA use. 
Technetium, Carbon, Iodine, Cesium, and StrontiumThese are more soluble radionuclides 
and their dissolved concentrations are determined from their inventory and waste degradation 
rates (BSC 2004c, Sections 6.14 to 6.18). Therefore, no solubility models are used for these 
elements in TSPA-LA and confidence building of the solubility models is not necessary. 
July 2004 6-21 No. 7: In-Package Environment Revision 1 
6.5 SUMMARY AND CONCLUSIONS 
A total of 14 radionuclides have been identified important to total dose calculations: plutonium, 
neptunium, uranium, thorium, americium, curium,5 actinium, protactinium, radium, technetium, 
carbon, iodine, cesium, and strontium. The solubility-controlling solid phases for plutonium, 
neptunium, uranium, thorium, and americium have been carefully selected; concentration limits 
of these radionuclides are calculated as functions of pH and CO2 fugacity and fed to TSPA-LA 
as lookup tables. Uncertainties associated with predicted limits are captured in two stochastic 
terms, representing uncertainties in the thermodynamic data and the effect of dissolved fluoride, 
respectively. All solubilities used in TSPA-LA are calculated at 25C, because solubilities of 
relevant radionuclides decrease with temperature. 
Concentration limits and the associated uncertainties of actinium and protactinium are estimated 
from their chemical analogs (americium for actinium, and neptunium and thorium for 
protactinium). The solubility of radium is chosen as a piecewise function of pH. The 
concentrations of other more soluble radionuclides (technetium, carbon, iodine, cesium, and 
strontium) are limited by their inventory and waste dissolution rates. 
The implementation of the solubility limits of radionuclides in the waste form degradation model 
is shown in Figure 6-13. The radionuclide mass dissolved in the rind water, whether limited by a 
solubility limit, by inventory, or by the waste degradation rate, is used as input to the engineered 
barrier system flow and transport models to calculate radionuclide transport into the waste 
package corrosion products. 
5 Curium is important only because 245Cm decays through 241Pu to 241Am, which is an important radionuclide in 
terms of human dose. Rather than model curium explicitly in TSPA-LA, the inventory of 241Am will be increased 
appropriately to account for the production of 241Am by the decay of 245Cm. Therefore, a model for curium 
solubility is not included in this section. 
July 2004 6-22 No. 7: In-Package Environment Revision 1 
Figure 6-13. Implementation of Solubility Component in Waste Form Degradation Model in TSPA 
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7. COLLOIDAL RADIONUCLIDE AVAILABILITY 
Colloids are very fine particles ranging in size from 1 nm to 10 m in characteristic dimension. 
Colloids have been of concern because of the possibility that radionuclides attached to colloids 
may be transported faster than radionuclides dissolved in water. The primary source of 
information used in this section is Waste Form and In-Drift Colloids-Associated Radionuclide 
Concentrations: Abstraction and Summary (BSC 2003h). 
This section discusses the formation, stability, and concentration of radioactive colloids in the 
waste package once water has contacted the waste and the waste has started to degrade. The 
discussion below describes processes occurring in the waste form that affect colloids; tests that 
have been performed on colloidal systems; the waste form colloid model; and uncertainties, 
limitations, and confidence in the model. 
7.1 RELEVANT PROCESSES AND ASSUMPTIONS 
The relevant processes and assumptions included in the model of colloids in the waste form are 
summarized below, organized by the major concepts considered: colloid formation and 
occurrence, colloid stability and concentration, and attachment of radionuclides to colloids. 
Some processes included in modeling colloid-facilitated transport in other parts of the repository 
are not included in the model of colloid generation in the waste form: colloid filtration, colloid 
sorption at the air-water interface, microbial effects, and the effects of elevated temperature. 
Reasons for not including these processes in the model of colloid generation in the waste form 
are given in Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: 
Abstraction and Summary (BSC 2003h, Sections 5.6 to 5.9). 
7.1.1 Colloid Formation and Occurrence 
Radioactive colloids (i.e., colloids that carry radionuclides) have been classified as either Type I 
colloids or Type II colloids. Type I colloids are also called true colloids, intrinsic colloids, real 
colloids, Eigenkolloide, and primary colloids, and are formed from the hydrolysis and 
polymerization of actinide ions dissolved in solution. Type I colloids are not likely to form in the 
waste form, and are not likely to be stable if they were to form, as discussed in Section 6.3.1 of 
Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: Abstraction and 
Summary (BSC 2003h). Therefore, they are not included in the model of colloids in the waste 
form. Type II colloids are formed when radionuclides adsorb onto colloids suspended in the 
water, and are the type of colloids included in the waste form colloid model. 
Three sources of Type II colloids are included in the waste package colloid model: natural 
colloids, corrosion-produced colloids, and waste form colloids. Natural (or groundwater) 
colloids occur in seepage water entering the waste package and can be organic (microbes or 
humic substances) or inorganic (mineral fragments). As discussed in Section 6.3.1 of Waste 
Form and In-Drift Colloids-Associated Radionuclide Concentrations: Abstraction and Summary 
(BSC 2003h), organic colloids are not important at the repository; therefore, only inorganic 
natural colloids are considered, and these are modeled as smectite. 
Corrosion-produced colloids are derived from the corrosion of waste package materials, and are 
modeled as iron oxyhydroxides. Under oxic (i.e., with oxygen) repository conditions, iron 
July 2004 7-1 No. 7: In-Package Environment Revision 1 
oxyhydroxide colloids most likely consist of three species: goethite, hematite, and ferrihydrite 
(or hydrous ferric oxide) (Langmuir 1997, pp. 436 and 437). In addition to colloid-sized 
particles, the corrosion of waste package materials will produce iron oxyhydroxides that can 
form an immobile phase and large-scale particles that will settle out. Radionuclides can adsorb to 
the colloid-sized particle, the immobile phase, and the settled out particles. 
Waste form colloids are formed from the corrosion of DOE HLW glass and are also modeled as 
smectite because experiments have shown that the degradation products of HLW glass are 
predominantly smectite clay (BSC 2003h, Section 4.1). Commercial SNF and DOE SNF are not 
considered to be sources of waste form colloids, as summarized in Section 7.3.1 and explained in 
Section 6.3.1.2 of Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: 
Abstraction and Summary (BSC 2003h). Colloids produced from glass waste forms contain 
measurable and irreversibly attached plutonium and americium. The radionuclides are modeled 
as embedded within the colloid, such that the properties of the smectite colloid govern the 
colloidal properties of the radionuclide-bearing phases. 
7.1.2 Colloid Stability and Concentration 
For radionuclide-bearing colloids to affect repository performance, the colloidal system must be 
stable during the transport period and must be able to transport significant quantities of 
radionuclides. The stability of colloidal suspensions against coagulation (also called 
agglomeration or flocculation) is controlled by electrostatic and chemical processes at colloid 
surfaces and by the attractive and repulsive forces between colloids. The attractive force is 
inversely proportional to the distance between the colloids and colloid size (Guzy et al. 1983). 
That is, the closer the colloids are to each other and the smaller they are, the higher the strength 
of the attractive force, and the more likely the colloids are to coagulate and become unstable. 
There are several factors that affect the strength of the repulsive force: the zeta potential (a 
measure of the surface charge), colloid size, ionic strength, pH, and temperature. The repulsive 
force is inversely proportional to the square root of temperature and to the square root of the 
ionic strength (Guzy et al. 1983, Equations 47 and 48). Thus, higher ionic strengths and higher 
temperatures lead to weaker repulsive forces between colloids, causing the colloids to coagulate 
and the colloidal suspensions to become unstable. 
Ionic strength is one of the key factors in modeling the generation and stability of colloids inside 
the waste package. Initially, temperatures in the waste package will be well in excess of 100C, 
which will cause dilute species in the water to become concentrated by evaporation, resulting in 
a high-ionic strength solution (greater than about 0.05 mol/L) that, in turn, will tend to 
destabilize colloid suspensions. Another key factor in colloid stability is pH. The pH at which the 
net surface charge of a colloid is zero is called the zero point of charge. Colloids become 
unstable and flocculate near the zero point of charge because of the reduced repulsive forces 
between colloids. Examples of the interaction between ionic strength and pH are given in 
Figures 7-1 and 7-2. Note that in these figures, the colloids are unstable above an ionic strength 
of 0.05 mol/L, regardless of the pH. 
July 2004 7-2 No. 7: In-Package Environment Source: BSC 2003h, Figure 4. 
NOTE: zpc = zero point of charge. 
Figure 7-1. Experimental Determination of Montmorillonite (a Variety of Smectite) Stability as a 
Function of pH and Ionic Strength 
Source: BSC 2003h, Figure 7. 
NOTE: zpc = zero point of charge. 
Figure 7-2. Iron Oxyhydroxide Colloid Stability as a Function of pH and Ionic Strength 
The ionic strength of solutions in the waste package is modeled in TSPA-LA, as described in In- 
Package Chemistry Abstraction (BSC 2004b) and summarized in Section 3, and is a complex 
function of waste form, flux of water through the waste package and temperature. The ionic 
No. 7: In-Package Environment 
Revision 1 
July 2004 7-3 Revision 1 
strengthcolloid stability relations are applied to the nominal, seismic, and igneous intrusion 
scenarios. 
Formation of stable colloid suspensions during much of the regulatory period is not likely 
because of the ionic strength of the solution in the waste package is likely to exceed 0.05 mol/L 
(e.g., see Sections 3.5.2.1 and 3.5.2.2). It is postulated, therefore, that colloid suspensions will 
be stable only for limited periods and limited combinations of physical and chemical conditions 
during the regulatory compliance period. 
Colloid concentrations are related to the ionic strength and were obtained from a variety of 
experiments and tests, as described below in Section 7.3. 
7.1.3 Radionuclide Sorption onto Colloids and Stationary Corrosion Products 
Colloids become important to performance assessment only when radionuclides are attached to 
them. Three types of radionuclide attachment to colloids are modeled in the waste form: 
 Embedded plutonium and americium from the formation of colloids from HLW glass 
 Irreversible sorption of plutonium and americium on iron oxyhydroxide corrosion 
product colloids 
 Reversible sorption of plutonium, americium, thorium, protactinium, and cesium onto 
HLW glass, groundwater colloids, and oxyhydroxide colloids. 
These five radionuclides are included in the colloid model because they (1) have isotopes with 
long half-lives, (2) have low solubilities, (3) can attach to colloids, (4) represent a major portion 
of the inventory, and (5) have large biological dose conversion factors. The screening process by 
which these radionuclides were chosen is discussed in more detail in Section 6.3.3 of Waste 
Form and In-Drift Colloids-Associated Radionuclide Concentrations: Abstraction and Summary 
(BSC 2003h). 
Plutonium and americium isotopes embedded in HLW glass degradation colloids are an intrinsic 
part of the colloid and are not in equilibrium with the aqueous system. These embedded 
radionuclides are, in effect, irreversibly attached to the colloid. Therefore, it is not necessary to 
model the attachment/detachment of these radionuclides (BSC 2003h, Section 6.3.3.3). 
Plutonium and americium attach irreversibly to iron oxyhydroxide colloids and attach reversibly 
to all three kinds of colloids. The irreversibly sorbed fraction of these elements is modeled as a 
function of specific surface area, site density, mass of corrosion colloids, dissolved concentration 
of plutonium and americium, and other parameters internal to the TSPA-LA model. As a result, 
90% to 99% of the plutonium and americium is irreversibly attached to iron oxyhydroxide 
colloids (BSC 2003h, Section 6.3.3.2). 
Reversible attachment of the five elements on the three colloid types is modeled using the linear 
isotherm approach (Langmuir 1997), which results in radionuclide-specific distribution 
coefficients (Kds) describing the partitioning of that radionuclide between the solute and the solid 
to which it is attached. Because both natural colloids and HLW glass colloids are modeled as 
7-4 July 2004 No. 7: In-Package Environment Revision 1 
smectite, Kds are needed for each element for smectite and iron oxyhydroxide colloids (BSC 
2003h, Table 10). 
To accurately represent both irreversible and reversible sorption of plutonium and americium, 
the upper bound of the Kd range for the reversibly sorbed fraction is reduced by a factor of 100. 
This results in solid/fluid partitioning that is consistent with partitioning in the unsaturated and 
saturated zone (BSC 2003h, Section 6.3.3.2). 
Iron oxyhydroxide corrosion products form immobile phases, as well as colloids and the colloid 
model includes attachment of radionuclides (irreversibly for plutonium and americium, and 
reversibly for all five radionuclides) onto this immobile phase. A significant fraction of 
plutonium and americium attaches to immobile iron oxyhydroxide phases, which is more 
realistic than not allowing these elements to attach to immobile phases, but which also decreases 
the quantities of plutonium and americium available for transport. 
7.2.1 Colloid Stability and Concentration 
In the first step, colloid stability and concentration are determined based on ionic strength and 
pH (as provided by the in-package chemistry model). The experimental data shown in Figure 7-1 
were used to establish a relationship between ionic strength and pH to determine the stability of 
smectite colloids (BSC 2003h, Section 6.3.2.2) as follows. 
 If the ionic strength exceeds pH/200, the smectite colloid suspension is not stable 
 If the ionic strength is less than pH/200, the smectite colloid suspension is stable. 
The pH/200 criterion represents a fit to experimental data that is discussed further in Section 7.3. 
If the smectite colloid suspension is stable, the concentration of groundwater colloids is sampled 
from the probability distribution function shown in Table 7-1. If the colloid suspension is not 
stable, the groundwater colloid concentration is not zero, but is set to a very small number, 
1  10-6 mg/L. 
The concentration of iron oxyhydroxides is based on experimental data discussed further in 
Section 7.3 and is given as a uniform distribution with a minimum concentration of 0.05 mg/L 
and a maximum concentration of 50 mg/L, for an ionic strength less than 0.05 mol/L (BSC 
2003h, Section 6.3.2.3). This probability distribution is sampled to obtain the colloid 
concentration. For ionic strengths greater than 0.05 mol/L, the iron oxyhydroxide concentration 
is 10-3 mg/L, a small nonzero number (BSC 2003h, Section 6.3.2.3). 
7.2 MODEL OF COLLOIDS IN THE WASTE PACKAGE 
The model of radioactive colloid generation in the waste form consists of executing two steps 
(BSC 2003h, Section 6.5.1) at each time step in the calculations: calculation of colloid stability 
and concentration, and calculation of radionuclide attachment. The two steps are presented 
below, along with the equations and parameters needed to execute the steps. 
7-5 July 2004 No. 7: In-Package Environment Revision 1 
Table 7-1. Probability Distribution Function for Groundwater Colloid Concentrations Based on Ionic 
Strength of the Water 
Groundwater Colloid Concentration (mg/L) 
0.001 to 0.1 
Ionic Strength 
< 0.05 mol/L 
0.1 to 1.0 
1.0 to 10 
10 to 50 
50 to 200 
 0.05 mol/L 
Source: BSC 2003h, Table 5. 
The concentration of waste form colloids is a function of ionic strength and is given in terms of 
the concentration of plutonium-bearing colloids determined by fitting experimental data (BSC 
2003h, Section 6.3.2.4), 
[plutonium colloid] = .2.5  10.6  I + 1.25  10.7, for 0.01 < I < 0.05 (Eq. 7-1) 
where 
[plutonium colloid] = concentration of plutonium-bearing waste form colloids (mol/L) 
I = ionic strength (mol/L). 
At ionic strengths less than 0.01 mol/L, the plutonium-bearing concentration of colloids is 
10.7 mol/L, while at ionic strengths greater than 0.05 mol/L, the plutonium-bearing colloid 
concentration is stipulated to be 10.11 mol/L. The concentration of americium-bearing colloids is 
calculated by multiplying the plutonium-bearing concentration by the ratio of americium to 
plutonium in the radionuclide inventory of HLW glass at each TSPA-LA time step (BSC 2003h, 
Section 6.3.2.4). The concentration of colloids is obtained from the experimentally determined 
relationship that 1 ppm of colloids is equivalent to 2  10.8 mol/L of plutonium-bearing colloids 
(BSC 2003h, Section 6.3.1.1). 
7.2.2 Radionuclide Attachment 
Once colloid concentrations have been estimated, the available dissolved inventory of cesium, 
americium, plutonium, protactinium and thorium is partitioned between the solute and the 
colloids. For reversible sorption processes, a linear isotherm model is used to determine 
effective reversibility of the radionuclides on colloids (BSC 2003h, Section 6.5.1): 
CRN,adsorbed = CRN,dissolved  Kd, RN  Mcolloid  10.6 
where 
CRN, adsorbed = concentration of radionuclide reversibly attached to colloids (mg/L) 
CRN, dissolved = concentration of dissolved radionuclide (mg/L) 
Kd,RN = distribution coefficient for radionuclides on colloids (mL/g) 
Mcolloid = colloid concentration (mg/L) 
10.6 = correction factor to account for various units 
Probability of Occurrence 
50 
25 
15 
8 
2 
100 
(Eq. 7-2) 
July 2004 
1  10.6 
7-6 No. 7: In-Package Environment The concentration of dissolved radionuclides is given by the model described in Section 6, while 
the colloid concentrations are given above. The probability distributions for the distribution 
Kd Value Interval 
Probabilities 
0 
0.04 
0.08 
0.25 
0.2 
0.35 
0.08 
0 
0 
0.15 
0.2 
0.5 
0.15 
0 
0 
0.07 
0.1 
0.23 
0.2 
0.32 
0.08 
0 
0 
0.15 
0.2 
0.55 
0.10 
0 
0 
0.2 
0.25 
0.5 
0.05 
0 
0 
0.13 
0.22 
0.55 
0.1 
0 
coefficients used in Equation 7-2 are given in Table 7-2. 
Table 7-2. Kd Values (mL/g) Used for Reversible Radionuclide Sorption on Colloids 
Radionuclide 
Pu 
Am, Th, Pa 
Cs 
Source: BSC 2003h, Table 10 
Colloid 
Smectite 
Iron Oxyhydroxide 
Smectite 
Iron Oxyhydroxide 
Smectite 
Iron Oxyhydroxide 
Kd Value Intervals 
(mL/g) 
<1  103 
1  103 to 5  103 
5  103 to 1  104 
1  104 to 5  104 
5  104 to 1  105 
1  105 to 5  105 
5  105 to 1  106 
> 1  106 
<1  104 
1  104 to 5  104 
5  104 to 1  105 
1  105 to 5  105 
5  105 to 1  106 
> 1  106 
<1  104 
1  104 to 5  104 
5  104 to 1  105 
1  105 to 5  105 
5  105 to 1  106 
1  106 to 5  106 
5  106 to 1  107 
> 1  107 
<1  105 
1  105 to 5  105 
5  105 to 1  106 
1  106 to 5  106 
5  106 to 1  107 
> 1  107 
<1  102 
1  102 to 5  102 
5  102 to 1  103 
1  103 to 5  103 
5  103 to 1  104 
> 1  104 
<1  101 
1  101 to 5  101 
5  101 to 1  102 
1  102 to 5  102 
5  102 to 1  103 
> 1  103 
No. 7: In-Package Environment 7-7 
Revision 1 
July 2004 Revision 1 
The distribution of plutonium and americium irreversibly attached to mobile and immobile iron 
oxyhydroxide colloids is calculated by a kinetic model that is described in Attachment II of EBS 
Radionuclide Transport Abstraction (BSC 2004f). It is a function of the specific surface area of 
iron oxyhydroxide colloids and corrosion products, the mass of iron oxyhydroxide colloids and 
corrosion products, the porosity of the iron oxyhydroxide matrix, and the reaction rate of the 
irreversible attachment reaction. 
The distribution of plutonium and americium in embedded HLW glass colloids does not need to 
be calculated because it was calculated as a part of determining the colloid concentration (see 
Equation 7-1). 
7.2.3 Output to Model of Corrosion Products in Waste Package 
The waste form colloid source term model provides eight outputs to the model of the invert. 
They are: 
 Mass concentration of waste form (HLW glass, modeled as smectite) colloids. This is 
calculated from stability requirements and Equation 7-1, as scaled by the relationship of 
1 ppm colloids = 2  10-8 mol/L plutonium colloids. 
 Mass concentration of iron oxyhydroxide colloids. This is calculated from stability 
requirements and sampled from iron oxyhydroxide colloid concentration distribution 
 Mass concentration of groundwater colloids. This is calculated from stability 
requirements and sampled from the distribution shown in Table 7-1. 
 Radionuclide (americium and plutonium) mass concentration embedded in waste form 
colloids. This is calculated from stability requirements and Equation 7-1. 
 Radionuclide mass concentration reversibly attached to waste form colloids. This is 
calculated from stability requirements, Equation 7-1 as scaled by the relationship of 
1 ppm colloids = 2  10-8 mol/L plutonium colloids, Equation 7-2, and Kd values given 
in Table 7-2. 
 Radionuclide (americium and plutonium) mass concentration irreversibly attached to 
iron oxyhydroxide colloids. This is calculated from stability requirements, sampled iron 
oxyhydroxide colloid concentrations, and kinetic equations (BSC 2004f, Attachment II). 
 Radionuclide mass concentration reversibly attached to iron oxyhydroxide colloids. 
This is calculated from stability requirements, sampled iron oxyhydroxide colloid 
concentrations, Equation 7-2, and Kd values given in Table 7-2. 
 Radionuclide mass concentration reversibly attached to groundwater colloids. This is 
calculated from stability requirements, groundwater colloid concentrations sampled from 
the distribution given in Table 7-1, Equation 7-2, and Kd values sampled from the 
distributions given in Table 7-2. 
7-8 July 2004 No. 7: In-Package Environment Revision 1 
7.3 SOURCES OF DATA AND EXPERIMENTAL PROGRAM 
The data from many experiments and tests have been used in developing the colloid model. 
Results from tests and experiments on waste forms, tests to determine colloid concentrations, 
studies of colloid stability, and experiments to determine radionuclide sorption properties are 
discussed below. 
Generation 
Long-term corrosion testing of commercial SNF and DOE SNF under hydrologically 
unsaturated, oxidizing conditions have been performed at Argonne National Laboratory and 
Pacific Northwest National Laboratory to examine release of dissolved and colloid-associated 
radionuclides (Mertz et al. 2003, DTN: MO0306ANLSF001.459). This testing is described in 
some detail in Section 6.3.1.2 of Waste Form and In-Drift Colloids-Associated Radionuclide 
Concentrations: Abstraction and Summary (BSC 2003h). Testing was designed to simulate a 
variety of repository-relevant water-exposure conditions for several waste forms spanning a 
range of fuel burnups and compositions. Results from the unsaturated testing of commercial SNF 
indicated some spallation of alteration products containing low concentrations of uranium-based 
colloids as well as low concentrations of actinides. Data from DOE SNF tests are still 
preliminary but show rapid disintegration of uranium metal into particles, many of colloid size. 
Most of the colloids were uranium oxyhydroxides; the data suggest that over time some smectite 
colloids with associated plutonium formed. In separate tests designed to investigate the stability 
of schoepite colloids, it was observed that they became less stable over a period of a few months, 
and that they appeared to dissolve when introduced to well J-13 water. 
Colloids formed from commercial SNF and DOE SNF degradation are not included in the 
colloid model and abstraction based on the Argonne National Laboratory and Pacific Northwest 
National Laboratory results. Additional information obtained at Argonne National Laboratory on 
the characterization of uranium-phase colloid suspensions, and results from natural analog 
studies (BSC 2003h, Section 6.3.1.2) substantiate this modeling approach. 
Tests on HLW in borosilicate glass have been conducted at Argonne National Laboratory in 
which EJ-13 water or deionized water drips onto or immerses HLW glass waste forms (CRWMS 
M&O 2001b). Waste forms tested include Savannah River Laboratory and West Valley 
Demonstration Project glasses. Transmission electron microscopy and filtration were used to 
characterize colloids produced during the tests. The unsaturated drip tests are representative of 
drip conditions that may occur in a geologic repository with canisters exposed to groundwater 
and air. These tests allow the spallation of fragments (including colloids) to be investigated. 
Saturated tests appear to be suitable for simulating colloid formation from glass reaction (Buck 
and Bates 1999), where similar colloids (smectite clay and rhabdophane colloids) are formed in 
both unsaturated and saturated tests. 
7.3.1 Tests on Commercial Spent Nuclear Fuel, U.S. Department of Energy Spent 
Nuclear Fuel, and High-Level Radioactive Waste Glass to Examine Colloid 
July 2004 7-9 No. 7: In-Package Environment Revision 1 
7.3.2 Tests to Determine Colloid Concentrations 
Corrosion colloid concentration values are estimated based on experiments performed at the 
University of Nevada, Las Vegas. In these experiments, scaled-down miniature waste packages 
were exposed to Well J-13 groundwater in either a bathtub mode or a flow-through mode. 
Corrosion products released from the miniature waste packages were characterized by x-ray 
diffraction (DTN: MO0302UCC034JC.003), and the quantity of corrosion products transported 
out of the waste packages was measured by microfiltration (DTN: MO0212UCC034JC.002). 
Recently acquired data from the Yucca Mountain area and the Nevada Test Site by Los Alamos 
National Laboratory show a wide range in natural (groundwater) colloid concentrations over a 
relatively narrow range of ionic strength. The colloid concentration data, as well as the 
probability distribution used in TSPA-LA, were collected from nine different sources, as 
described in Section 6.3.2.5 of Waste Form and In-Drift Colloids-Associated Radionuclide 
Concentrations: Abstraction and Summary (BSC 2003h). Literature data (Degueldre et al. 2000) 
and groundwater sampling at the Idaho National Engineering and Environmental Laboratory 
(BSC 2003h, Section 6.3.2.5) provided additional technical information that corroborated 
site-specific data. 
The saturated tests on HLW glass performed at Argonne National Laboratory described above 
were also used to estimate the concentration of plutonium-bearing colloids resulting from the 
degradation of HLW glass (CRWMS M&O 2001b). Colloid concentration was observed to 
decrease as the ionic strength increased, and flocculation was observed at an ionic strength of 
approximately 0.05 mol/L. 
7.3.3 Tests to Examine Colloid Stability 
The DOE HLW tests conducted at Argonne National Laboratory resulted in measured pH 
ranging between approximately 9 and 11.5 (Buck and Bates 1999), which is part of the range at 
which smectite colloids exhibit the highest surface charge and, hence, are most stable. Tombacz 
et al. (1990) investigated the stability of smectite (referred to as montmorillonite by Tombacz et 
al.) suspensions as a function of pH and ionic strength in a NaCl solution. It was found that 
suspensions became unstable and flocculated at pH 2, 4, and 8 in 0.01 mol/L, 0.025 mol/L, and 
0.04 mol/L NaCl solutions, respectively (see Figure 7-1). The abstracted relationship between 
ionic strength and smectite colloid stability as a function of pH (up to pH 10 and ionic strength of 
0.05 mol/L) for use in TSPA-LA model is discussed in Section 7.2 (i.e., colloid suspension is 
stable if ionic strength is less than pH/200). 
The stability of iron oxyhydroxide colloids has been investigated (Liang and Morgan 1990) with 
the results used to abstract the stability region as a function of pH and ionic strength. At and 
near the zero point of charge, colloid suspensions are not stable, even at low ionic strengths. For 
iron oxyhydroxide colloids, the zero point of charge ranges from about pH 5.5 to about 8.5, 
depending on colloid mineralogy and water chemistry. Colloids are stable at higher and lower 
pH values, similar to other mineral colloids, if the ionic strength is less than 0.05 mol/L. The 
result is a U-shaped stability curve (Liang and Morgan 1990). 
July 2004 7-10 No. 7: In-Package Environment Revision 1 
7.3.4 Radionuclide Sorption 
The Kd values used to model reversible radionuclide sorption onto colloids were obtained from 
various data sources and measurements including those from National Cooperative for the 
Disposal of Radioactive Waste, the U.S. Environmental Protection Agency, and Yucca 
Mountain-specific projects. These sources are discussed in Section 6.3.3.1 of Waste Form and 
In-Drift Colloids-Associated Radionuclide Concentrations: Abstraction and Summary (BSC 
2003h). Based on these sources, professional judgment was used to develop the uncertainty 
distributions given in Table 7-2. 
Sources of Yucca Mountain Projectspecific sorption data from Los Alamos National 
Laboratory research provide valuable information on the sorption of plutonium onto colloidal 
dispersions of hematite, goethite, montmorillonite, and silica (Lu et al. 1998; Lu et al. 2000). 
Data on americium were generated for all but goethite. Sorption was measured as a function of 
time up to 4 or 10 days. Desorption was measured after 150 days because desorption tends to be 
much slower in these mineral-sorbate systems. 
Two compendia of sorption data were considered in the development of Kd values. One is the 
compendium developed by the National Cooperative for the Disposal of Radioactive Waste 
(NAGRA, Switzerland) in which a set of Kd values was compiled (Stenhouse 1995). The second 
compendium was developed by the U.S. Environmental Protection Agency (EPA 1999). In that 
work, information on radionuclides including strontium, cesium, thorium, uranium, and 
plutonium is compiled. Predicted ranges of Kd values are presented for soils in shallow 
subsurface environments. The group of radionuclides covered does not provide analog elements 
for americium(III), protactinium(V), or neptunium(V). 
In developing Kd values a modeling analysis was performed (Honeyman and Ranville 2002) that 
incorporated Yucca Mountainvicinity groundwater chemistry and colloid size and concentration 
data (Kingston and Whitbeck 1991). Kd values were calculated by accounting for electrostatic 
forces and chemical reactions occurring at the surface of the colloids. Colloid concentrations of 
up to about 100 mg/L were included. In the system analyzed, approximate Kd value ranges for 
americium, neptunium, plutonium, thorium, and uranium were provided. 
Plutonium and americium sorb strongly to iron oxyhydroxides, based on observations that 
plutonium binds strongly to soils, leaving very little, if any, soluble plutonium available for 
groundwater transport or plant uptake. The estimated exchangeable plutonium, which is the 
quantity of plutonium that may desorb after initial sorption, is less than 1% (Coughtrey et al. 
1985; Litaor and Ibrahim 1996; Bunzl et al. 1995). Exchangeable americium ranges from 1.5% 
to 15% (Bunzl et al. 1995). Transport of colloidal plutonium over hundreds of meters was 
observed at the Nevada Test Site (Kersting et al. 1999). The laboratory experiments of plutonium 
sorption onto iron oxides cited above have shown that only approximately 1% of the initially 
sorbed plutonium can be desorbed into solution, even after months of time have elapsed (Lu et 
al. 2000), which is broadly consistent with field observations. 
July 2004 7-11 No. 7: In-Package Environment Revision 1 
7.4 UNCERTAINTIES, LIMITATIONS, AND MODEL CONFIDENCE 
The model for radioactive colloid generation, stability, and concentration in the waste form is 
valid for the ionic strengths and pH ranges expected in the waste package (see Section 3). 
Uncertainties associated with the model and model confidence building are discussed below. 
7.4.1 Uncertainty in Colloid Model 
There are several sources of uncertainty in the colloid model, mostly resulting from incomplete 
knowledge of the system and its properties (epistemic uncertainty): temporal scaling, spatial 
scaling, physical and chemical conditions of data acquisition, waste form colloid concentrations, 
groundwater colloid concentrations, colloid stability conditions, and colloid sorption parameters. 
The discussion of these uncertainties presented below is based on Waste Form and In-Drift 
Colloids-Associated Radionuclide Concentrations: Abstraction and Summary (BSC 2003h, 
Section 6.6.2). Many of the uncertainties in the model are treated by using parameter ranges and 
distributions rather than a single deterministic value in the model. These uncertainties are 
propagated through to the model results by using Monte Carlo simulation techniques. 
Temporal ScalingData gathered over days to a few years, or perhaps decades, were used to 
develop a model of what is anticipated to occur over thousands of years. For fundamental 
parameters, such as radionuclide half-life, this extrapolation does not introduce uncertainty. For 
other parameters, such as those influenced by kinetic considerations, such an extrapolation can 
introduce significant uncertainty. This uncertainty is best accounted for by developing and using 
scientifically sound assumptions. 
Spatial ScalingData collected in the laboratory on processes occurring on a small scale 
(centimeters) were used to develop a model of what is anticipated to occur on a much larger 
scale (kilometers). This uncertainty was accounted for by corroborating data obtained from 
small-scale processes with empirical field observations, to the extent possible. For example, 
empirical field observations of uranium mines and deposits showed low concentrations of 
uranium-bearing colloids and showed that uranium leaches from colloids as they migrate 
downgradient (BSC 2003h, Section 6.3.1.2.4). 
Physical and Chemical Conditions of Data AcquisitionData may have been collected under 
physical or chemical conditions that are not directly relevant to repository conditions (e.g., pH). 
In such cases, the experimental conditions and the expected repository conditions were compared 
in detail to determine the applicability of the data. For example, data on generation of waste 
form colloids was obtained at pHs ranging from 9 to 11.5. However, the pH in the codisposal 
waste package is expected to range from 4.5 to 8.1 under the nominal and seismic scenarios and 
from 7.6 to 9.9 in the igneous intrusion scenario. The experimental data were found to be 
applicable because smectite is stable in the pH range expected in the codisposal waste package 
(BSC 2003h, Sections 6.3.2.4 and 7.1). 
Waste Form ColloidsPotentially significant uncertainty comes from basing modeled glass 
waste form colloid concentrations, as well as associated plutonium concentrations, on two data 
sets generated from glass-waste-form degradation testing. Additional uncertainty is introduced 
by using assumptions (spherical form, single density, etc.) for converting particle concentrations 
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to mass concentrations. Colloid and plutonium mass concentrations are predicted on the basis of 
these data, and ionic strength and pH obtained from in-package chemistry calculations. 
Iron Oxyhydroxide ColloidsThree studies reported groundwater colloid concentrations 
dominated by iron oxyhydroxide colloids, and the colloid concentrations reported varied greatly 
(BSC 2003h, Section 6.3.1.3). As a result, the values and ranges developed for the corrosion 
colloid source-term parameters have a large degree of uncertainty. To reduce uncertainty and 
increase the defensibility of the developed parameters, the distribution of iron oxyhydroxide 
colloid concentrations was based on quantities of iron oxyhydroxide colloids observed in 
scaled-down miniature waste package degradation experiments by an ongoing DOE-funded 
research project at the University of Nevada, Las Vegas. The use of appropriately scaled-down 
waste package configurations (i.e., surface area to total waste package volume scaled-down 
proportionally to approximately 1/70 the size of actual repository waste packages) and the use of 
water of similar chemical composition to the groundwater found at Yucca Mountain render the 
experiments directly relevant to conditions anticipated in the repository. Additionally, bounding 
values have been established for corrosion product colloid parameters recommended for use in 
the model in order to propagate the uncertainty associated with these parameters throughout the 
TSPA-LA. 
Groundwater ColloidsUncertainties associated with the determination of colloid concentration 
in seepage or groundwater result from differences in field-sampling techniques (including 
differences in pumping rates at each well during extraction of the water samples; unknown 
factors affecting the quantities of particles suspended in water samples such as additives 
introduced to the wells during the drilling process), and errors inherent to the laboratory methods 
used to measure the quantities of colloids suspended in the water samples (e.g., filter ripening, 
and interference and detection limitations for dynamic light-scattering measurement techniques). 
In general, sampling perturbations may result in overestimation of colloid concentrations. To 
account for this uncertainty, a cumulative distribution function was developed based on 
groundwater samples extracted in the vicinity of Yucca Mountain and Idaho National 
Engineering and Environmental Laboratory (BSC 2003h, Table 5). 
Uncertainty in Colloid StabilityUncertainty in the stability of smectite colloids as a function 
of pH and ionic strength is associated mostly with the extrapolation of laboratory data reported in 
literature or project-supported experimental work solution chemistry in the repository 
environment over the regulatory compliance period. Establishing bounding values and ranges of 
parameter values accommodate much of this uncertainty, which is propagated in TSPA-LA 
model by stochastic sampling of distributions. 
Uncertainty in Sorption Partition CoefficientsThere is uncertainty associated with the 
development of sorption partition coefficients (Kd values) that describe the degree of sorption of 
specific radionuclides to colloids in TSPA-LA model. Values reported in literature are primarily 
the result of experimental work that establishes Kd values for contaminants sorbed onto rocks, 
soils and other minerals; literature specific to colloid-size minerals is not readily available. For 
this reason, the Kd value parameters established for smectite colloids largely rely on limited 
experimental work conducted at the Los Alamos National Laboratory (Lu et al. 1998; Lu et al. 
2000). Corroborative data reported in the literature were evaluated to augment this work 
(BSC 2003h, Section 6.3.3.1, Table 10). 
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7.4.2 Model Confidence Building 
To build confidence in the model, various aspects of the model were compared to data published 
in refereed journals or literature, and to data from natural analog sites, when possible. A 
summary of these comparisons is presented below, arranged by waste form, colloid 
concentration, and radionuclide attachment modeling. A more detailed discussion is in Section 7 
of Waste Form and In-Drift Colloids-Associated Radionuclide Concentrations: Abstraction and 
Summary (BSC 2003h). 
Waste FormsThe assumption that commercial SNF and DOE SNF are not a source of colloids 
has been corroborated by the observation of few colloids in the vicinity of uranium mines and 
deposits. HLW glass data were collected at pH values ranging from 9 to 11.5, but the pH range 
anticipated in the repository is from 4.5 to 8.1 for the nominal and seismic scenarios and from 
7.6 to 9.9 for the igneous intrusion scenario. The applicability of the data from the tests to 
repository conditions was evaluated by considering the physicochemical properties of smectite 
colloids and similarities between test conditions and the probable microenvironment at the 
degrading glass surface. Uncertainty in plutonium concentration was accounted for by using a 
bounding value. 
Colloid ConcentrationsSmectite colloid concentrations obtained from HLW glass testing 
appear to conform to accepted scientific principles with respect to colloid suspensions, and they 
are reasonable with respect to observations of colloids of similar mineralogy in the literature. 
Groundwater colloid concentrations used in the model are site-specific, have been bounded 
realistically, are internally consistent, and are similar to colloid concentration data from disparate 
geologic terrain around the world. Iron oxyhydroxide colloid concentrations could not be 
compared with literature values because of the lack of peer-reviewed publications that present 
colloid concentrations as a function of aqueous chemistry (BSC 2003h, Section 7.2). However, 
they are reasonable and realistic, have been corroborated by laboratory-scale waste package 
corrosion tests, and perhaps overpredict colloid concentrations. 
Radionuclide Attachment ModelingThe assumption that plutonium is embedded in HLW 
glass colloids is based on the results of HLW glass tests performed at Argonne National 
Laboratory. These are one-of-a-kind data and so cannot be compared to results in refereed 
journals, nor are there any analog sites. Some model confidence can be built, however, by noting 
that repeated sample analyses produced similar results. Furthermore, this assumption increases 
the potential mobility of plutonium. The Kd values chosen to model reversible attachment of 
radionuclides to colloids have a strong scientific basis because of good agreement between 
literature-based and observation-based values. The assumption that plutonium is irreversibly 
attached to iron oxyhydroxides has been corroborated in laboratory experiments, which show 
almost complete retention of plutonium onto iron oxyhydroxide colloids for up to 6 months and 
in field observations over periods up to 50 years. 
7.5 SUMMARY AND CONCLUSIONS 
Colloids come from the degradation of HLW glass and associated components in the repository 
or from water seeping into the waste package. The TSPA-LA model of colloids in the waste 
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package includes colloids from both HLW glass (smectite), steel (iron oxyhydroxides), and 
seepage water (smectite). Commercial SNF and DOE SNF are not sources of colloids. 
Colloid stability and concentrations are determined in the TSPA-LA model and are based on 
ionic strength and pH. If chemical conditions are favorable for sustaining a colloidal suspension, 
colloid concentrations are estimated from equations or sampled from uncertainty distributions. If 
chemical conditions lead to an unstable colloidal suspension, colloid concentrations are assigned 
a very small (nonzero) value. 
Two types of colloid attachment, reversible (using a linear isotherm) and irreversible, are 
modeled. Plutonium and americium are (irreversibly) embedded in HLW glass colloids and can 
also attach irreversibly to iron oxyhydroxide colloids and stationary iron oxyhydroxide corrosion 
products in the waste package. The reversible attachment of five elements is modeled: 
plutonium, americium, thorium, protactinium, and cesium. These five elements sorb reversibly 
onto the three different colloid types. 
The model provides colloid mass concentrations (mass concentrations of HLW glass, iron 
oxyhydroxide, and natural colloids) and radionuclide mass concentrations (mass concentrations 
of embedded plutonium and americium; of plutonium and americium irreversibly attached to 
iron oxyhydroxide colloids; and of plutonium, americium, thorium, protactinium, and cesium 
reversibly attached to HLW glass, iron oxyhydroxide, and natural colloids) to the model of the 
invert. These concentrations are used to model radionuclide movement through the rest of the 
repository system. 
As long as chemical conditions are favorable to colloid stability, stable colloid suspensions may 
form, and colloids may facilitate movement of radionuclides through the repository. However, 
colloid suspensions are not highly likely to be stable in the waste package because the chemical 
conditions are not highly likely to be favorable. 
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July 2004 Revision 1 
8. SUMMARY AND CONCLUSIONS 
As the starting point for all potential radionuclide releases to the biosphere, the source term for 
radionuclides within the engineered barrier system is an important component model for the 
TSPA-LA. This document deals with an aspect of the source term component  the degradation 
of the waste form and the mobilization of radionuclides within the waste package  herein 
called the waste form degradation model. The function of the waste form degradation model is 
to estimate the availability and mobilization of radionuclides that are used as input by the 
engineered barrier system transport model. The engineered barrier system transport model 
discussed in Technical Basis Document No. 9: Engineered Barrier System Radionuclide 
Transport provides the radionuclide source term to the unsaturated zone transport model 
July 2004 
discussed in Technical Basis Document No. 10: Unsaturated Zone Transport. 
8.2 RADIONUCLIDE INVENTORY 
An important input to the waste form degradation model is the average radionuclide inventory 
(expressed as grams of each radionuclide per package) for three generic waste forms: 
commercial SNF, DOE SNF and HLW encapsulated in borosilicate glass. A fourth waste form, 
naval SNF, is conservatively represented by commercial SNF. The three waste forms are placed 
into two waste package configurations: a commercial SNF waste package with various numbers 
of assemblies of SNF, and a codisposal waste package with typically one canister of DOE SNF 
and five canisters of HLW. As described in Section 2, 28 radionuclides (14 elements) were 
identified (BSC 2002a, Section 6.3.1) as being important to evaluating expected dose within the 
biosphere during the 10,000-year regulatory period. Of these 14 elements, curium was decayed 
and added into the inventory of americium in the TSPA-LA model. 
8.3 IN-PACKAGE CHEMISTRY 
The in-package chemistry component models the evolution of water chemistry inside the 
breached waste package for the nominal scenario class when only water vapor enters the waste 
8.1 COMPONENTS OF WASTE FORM DEGRADATION MODEL 
The waste form degradation model consists of eight components. Five of those components 
determine the degradation rate of waste forms: in-package chemistry (Section 3); degradation of 
commercial SNF cladding (Section 4); and degradation of commercial SNF fuel pellets, DOE 
SNF, and HLW vitrified in borosilicate glass (Section 5). 
Of the remaining three componentsradionuclide inventory (Section 2), dissolved concentration 
of radionuclides (Section 6), and concentration of radionuclides attached to colloids 
(Section 7)the latter two determine the mobilization of radionuclides from the degraded waste 
form into the waste package corrosion products. 
All eight components are used in the nominal and seismic scenario classes and the igneous 
intrusion modeling case of the volcanic scenario class (Table 1-1). Only the radionuclide 
inventory component is used to model radionuclide release in those emplacement drifts of the 
repository that are disrupted by a volcanic eruption For the human intrusion analysis, only the 
radionuclide inventory component is used. 
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package, in the igneous intrusion scenario when bare fuel comes into contact with basaltequilibrated 
groundwater, and for the seismic scenario class when dripping water enters several 
waste packages. Factors that control the in-package chemistry component are the waste package 
corrosion rate, waste package type (commercial SNF versus codisposed), water inflow rate, 
cladding failure fraction, waste-form corrosion rate, and temperature. The water inflow rate is 
modeled under two different situations: water vapor inflow and variable dripping water inflow. 
In the latter situation, in-package chemistry is a direct function of the dripping water rate and 
fraction of failed cladding on commercial SNF. In-package chemistry is insensitive to initial 
water composition, decreased corrosion rate of the waste package alloys and minor modifications 
to waste package design. 
Primary outputs from the in-package chemistry component include time-dependent pH, ionic 
strength, total carbonate, and fluoride. Of these outputs, pH and ionic strength are the most 
important. Effluent chemistries will be strongly affected by the presence of the surfaces of 
corrosion products, primarily iron oxides and hydroxides. Corrected for the effect of surface 
complexation on corrosion products, the in-package pH ranges from 4.5 to 8.1 for the nominal 
and seismic scenario classes. For the igneous intrusion scenario class, the pH is neutral to 
slightly basic (7.6 to 9.9). The ionic strength inside a waste package increases with time and can 
reach a value above 1 mol/L. 
This water chemistry information is used by five waste form degradation and radionuclide 
mobilization models: commercial SNF matrix degradation, HLW degradation, dissolved 
concentration of radionuclides, HLW colloidal concentration, and corrosion products and 
groundwater colloidal concentration. Some of these models are very sensitive to the pH in the 
waste package, particularly low values of pH that can lead to higher waste form degradation rates 
and higher radionuclide solubilities. 
8.4 COMMERCIAL SPENT NUCLEAR FUEL CLADDING DEGRADATION 
COMPONENT 
For the nominal scenario class, only the small fraction of fuel rods that arrive at the repository 
with perforated cladding (about 0.1%) can lead to the release of radionuclides during the 
10,000-year regulatory period. Additional failures during the regulatory period from other 
mechanisms (e.g., hydride embrittlement, localized corrosion) are not anticipated. Once 
cladding is perforated, the cladding is assumed to rapidly split along the entire length of the fuel 
rod. Subsequent degradation of exposed fuel is assumed to produce a rind, the volume of which 
is estimated by the commercial SNF cladding degradation component. Radionuclides dissolved 
in available water must diffuse through the rind to escape from the waste matrix. 
However, for the seismic scenario class, the cladding is susceptible to mechanical failure from 
seismic events, depending on their nature and probability, which can increase the number of 
failed fuel rods. Once the cladding is perforated, the progression of clad splitting, fuel 
dissolution, and radionuclide transport proceeds as in the nominal scenario. 
In the igneous intrusion scenario, cladding is assumed to be destroyed, exposing the fuel pellets 
to water. 
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8.5 WASTE FORM DEGRADATION 
8.5.1 Commercial Spent Nuclear Fuel Matrix Degradation 
The radionuclide inventory in commercial SNF rods is divided into two fractions. The largest 
fraction of the inventory resides in the fuel matrix. A small fraction of the inventory resides in 
the grain boundaries of the fuel pellets and the gap between the fuel pellet and cladding. 
Radionuclides (specifically 90Sr, 137Cs, 99Tc, and 129I) residing in the gap or grain boundary are 
available for dissolution as soon as water contacts the waste. Uncertainty in the gap and grain 
boundary fractions is represented by triangular probability distributions. 
The release of radionuclides from the fuel matrix is based on a statistical fit of experimental data 
using four variables: temperature, pH, total carbonate concentration and oxygen fugacity. Based 
on the experimental data, two models were developed, one for alkaline conditions and one for 
acidic conditions. The specific degradation rate is lowest at values of pH above 6.8. The 
fractional release rate of any radionuclide in the fuel matrix is obtained by multiplying the 
specific degradation rates of the fuel matrix by the estimated effective specific surface area of the 
fuel and the matrix inventory of that radionuclide. The uncertainty in specific surface area of the 
fuel has an important effect on radionuclide releases and was estimated from experimental 
fractional release data. 
The presence of a gap fraction and the rapid degradation of the commercial SNF fuel matrix in 
an oxic environment, as expected to exist in the repository, implies that the release of most 
radionuclides from the waste package is not limited by their availability (i.e., not limited by the 
degradation rate) but by their solubility or by adsorption onto corrosion products. However, the 
solubilities of 99Tc and 129I are quite high in an oxic environment and are not readily adsorbed; 
hence, the degradation rate of the commercial SNF fuel matrix influences the release rate of 
these two radionuclides. 
8.5.2 U.S. Department of Energy Spent Nuclear Fuel Degradation Component 
Eleven DOE SNF groups have been used to categorize several hundred distinct types of DOE 
SNF that will eventually be emplaced in the repository. Although degradation models were 
developed for each DOE SNF group, their use is not recommended because they are based on 
currently unqualified data. Instead, a degradation rate model consisting of waste form 
degradation and radionuclide dissolution within the first time step in the TSPA model following 
waste package breach is applied for all waste groups except naval SNF. Naval SNF cladding is 
modeled as commercial SNF cladding. Naval SNF has been found to be more robust than 
commercial SNF. Using the naval SNF source term developed by the Naval Nuclear Propulsion 
Program, the DOE estimates that the dose resulting from naval SNF is significantly less than that 
from an equivalent amount of commercial SNF. A planned classified Naval Nuclear Propulsion 
Program Technical Support Document to the LA will provide details of this naval SNF source 
term. Therefore, using commercial SNF as a surrogate for naval SNF will overestimate 
radionuclide releases from naval SNF. 
Because radionuclides from DOE SNF are considered to be available for transport once the 
waste form is contacted by water, the dissolved concentration of a particular radionuclide is 
8-3 July 2004 No. 7: In-Package Environment Revision 1 
controlled either by radionuclide solubility (for those radionuclides with solubility limits) or by 
the waste form degradation rate (for those radionuclides that do not have solubility limits). 
8.5.3 High-Level Radioactive Waste Degradation Component 
The dissolution of HLW glass into water has been studied for many years, producing an 
extensive database of experimental results. Many processes contribute to glass degradation and 
these are well known, although there is some debate as to whether the rate of dissolution at high 
dissolved silica concentrations is controlled by surface reactions or by the diffusion of silica 
through alteration phases that form on the glass as it dissolves. 
Glass degradation is controlled by the temperature and pH of the solution in contact with the 
glass. The degradation rate equation has an Arrhenius-type dependence on temperature, and 
uncertainty in the degradation rate is accounted for by an uncertain degradation rate coefficient. 
Experimental data obtained from experiments in which HLW glasses were exposed to water 
vapor and dripping water, and were immersed in water, were used to develop the degradation 
rates of the HLW glass logs. Because specific glass dissolution rates are lowest at a near-neutral 
pH and increase as the pH decreases from 7 or increases from 7, model parameters were 
developed for both acidic and alkaline conditions. The mass of a particular radionuclide released 
from the HLW glass per time is calculated by multiplying the specific glass degradation rate by 
the effective specific surface area of the glass log and the inventory of that radionuclide. 
The specific surface area of the glass contacted by water changes as the glass dissolves and 
increases as a result of cracking. These surface-area effects are addressed by recalculating the 
amount of glass remaining after each time step, and by multiplying the surface area by an 
empirically determined uncertain exposure factor to account for cracking. The uncertainty in this 
exposure factor is described by a triangular probability distribution. 
8.6 DISSOLVED RADIONUCLIDE CONCENTRATION COMPONENT 
The dissolved radionuclide concentration component determines the availability of specific 
radionuclide species in the waste package. For all scenario classes involving transport of 
colloidal and dissolved radionuclides by groundwater, the solubility of seven important elements 
transported (plutonium, americium, neptunium, thorium, uranium, protactinium and actinium) 
depends on the water chemistry inside the waste package (specifically, pH and fugacity of 
carbon dioxide). The solubility of these actinides exhibits a typical U-shaped dependence on pH 
with a minimum at approximately neutral to mildly basic pH values, which coincides with 
long-term in-package conditions. For example, compare the solubility minima in Figures 6-2 to 
6-10 with the pH range of 4.5 to 8.1 expected in the waste form. Error terms are added to the 
solubility value calculated by EQ3NR and used to characterize (1) the uncertainty in 
thermodynamic data, and (2) the uncertainty in the fluoride concentration. The latter term was 
added because the fluoride content of in-package water will vary depending on the source of the 
water (condensate or seepage) and the extent of dissolution of fluoride-containing HLW glass. 
The relationship of solubilities to pH and fCO2 was based on numerous thermodynamic 
equilibrium calculations at 25C. Actinide solubilities decrease with increasing temperature so 
the use of 25C values for higher temperatures is conservative. The range of applicability for the 
functional relationships are an ionic strength less than 3 mol/L, a log(fCO2) between -5 and 
8-4 July 2004 No. 7: In-Package Environment Revision 1 
-1.5, and a pH between 2 and 11, depending on the element. The latter pH envelope is much 
larger than the expected pH range, 4.5 to 8.1 and allows functional dependencies in solubility to 
be highlighted. 
For an eighth element, radium, the solubility is defined solely as a function of pH without any 
error terms. The solubilities of the remaining five important elements transported (i.e., carbon, 
cesium, strontium, iodine, and technetium) depend mostly on the oxygen state of the repository. 
Over the long-term, the repository is expected to be fairly oxic (i.e., in equilibrium with 
atmospheric oxygen); as a result, these radionuclides are highly soluble. Hence, the availability 
of these radionuclides for transport out of the waste form is determined by the fraction of 
cladding that fails and the degradation rate of the waste form. 
8.8 CONCLUSIONS 
The models described in this technical basis document provide the mass of radionuclides, both as 
dissolved species and associated with colloids, in water that leaves the waste form. Therefore, 
these models provide the source term for all subsequent radionuclide transport analyses within 
TSPA-LA calculations. The estimation of total dose depends on the quantity and rate of 
radionuclides released from the waste form, regardless of other processes that affect radionuclide 
transport from the source to the receptor. 
8.7 COLLOID CONCENTRATION COMPONENT 
The colloid model estimates the concentration and stability of colloids available for transporting 
radionuclides. Three sources of colloids are considered in the colloid model in the waste form: 
natural colloids in seepage groundwater, colloids generated from degradation of HLW glass, and 
colloids formed from corrosion products. Colloids formed from DOE HLW glass and naturally 
occurring groundwater colloids are represented by smectite clay colloids, and can transport 
reversibly sorbed radionuclides. In addition, HLW glass colloids can transport embedded 
plutonium and americium. Colloids formed from corrosion products are iron oxyhydroxide 
minerals such as goethite, hematite, and ferrihydrite. 
The availability of colloids is evaluated in two steps executed at each time step. First, the 
groundwater colloid mass is sampled from a probability distribution function and the HLW glass 
colloid mass is determined as a function of ionic strength. The stability of these colloidal masses 
is determined as a function of ionic strength and pH. If the colloids are determined to be 
unstable, a low (but nonzero) value of colloid concentration is used. Second, dissolved cesium, 
americium, plutonium, protactinium, and thorium are partitioned (reversibly) between fluid, 
mobile colloids (both smectite and iron oxyhydroxide), and fixed corrosion products. Plutonium 
and americium embedded in HLW glass colloids are considered to be irreversibly sorbed and, 
thus, are not partitioned between the water and the HLW glass colloids. In addition, dissolved 
americium and plutonium are irreversibly partitioned between fluid, mobile colloids, and 
immobile corrosion products. 
Colloid suspensions are stable only when the ionic strength is less than 0.05 mol/L, and, at 
certain pH values, can be unstable at values of ionic strength lower than 0.05 mol/L. 
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9. REFERENCES 
9-1 
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the Spent-Fuel Contribution to Transport Cask Containment Requirements. SAND90-2406. 
Albuquerque, New Mexico: Sandia National Laboratories. ACC: MOV.19960802.0116. 
Sasaki, S. and Kuwabara, S. 1997. Utility Perspective on Commercial Light-Water Reactor Fuel 
in Japan. Proceedings of the 1997 International Topical Meeting on Light Water Reactor Fuel 
Performance, Portland, Oregon, March 26, 1997. Pages 1120. La Grange Park, Illinois: 
American Nuclear Society. TIC: 232556. 
Shoesmith, D.W. 2000. Fuel Corrosion Processes under Waste Disposal Conditions. Journal 
of Nuclear Materials, 282, (1), 131. Amsterdam, The Netherlands: North-Holland. TIC: 
254043. 
Silva, R.J.; Bidoglio, G.; Rand, M.H.; Robouch, P.B.; Wanner, H.; and Puigdomenech, I. 1995. 
Chemical Thermodynamics of Americium. Chemical Thermodynamics, 2. Amsterdam, The 
Netherlands: Elsevier. TIC: 237106. 
Smith, P.K. and Baxter, C.A. 1981. Fracture During Cooling of Cast Borosilicate Glass 
Containing Nuclear Wastes. DP-1602. Aiken, South Carolina: E.I. du Pont de Nemours & 
Company, Savannah River Laboratory. TIC: 238536. 
Smith, T.H. and Ross, W.A. 1975. Impact Testing of Vitreous Simulated High-Level Waste in 
Canisters. BNWL-1903. Richland, Washington: Battelle Pacific Northwest Laboratories. TIC: 
238924. 
Steinborn, T.L.; Wolery, T.J.; Alcorn, S.R.; Arthur, S.E.; Bernot, P.A.; Brady, P.V.; Chen, Y.; 
Domski, P.S.; Jolley, D.M.; Metcalf, R.C.; and Thomas, E. 2003. Data Qualification: Update 
and Revision of the Geochemical Thermodynamic Database, Data0.ymp. TDR-EBS-MD- 
000022 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20030331.0003. 
Stenhouse, M.J. 1995. Sorption Databases for Crystalline, Marl and Bentonite for Performance 
Assessment. NAGRA Technical Report 93-06. Wettingen, Switzerland: National Cooperative for 
the Disposal of Radioactive Waste. TIC: 247885. 
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. 
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. 
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, 
(12), 7075. Amsterdam, The Netherlands: Elsevier. TIC: 246601. 
July 2004 9-11 No. 7: In-Package Environment Revision 1 
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. 
Tombacz, E.; Abraham, I.; Gilde, M.; and Szanto, F. 1990. The pH-Dependent Colloidal 
Stability of Aqueous Montmorillonite Suspensions. Colloids and Surfaces, 49, 7180. 
Amsterdam, The Netherlands: Elsevier. TIC: 246046. 
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), 261267. New York, New York: John Wiley & Sons. TIC: 246160. 
Williams, N.H. 2003. Contract No. DE-AC-28-01RW12101Revision to Report No. 
TDR-CRW-SE-000022, WBS 9.1.2. Letter from N.H. Williams (BSC) to B. Hamilton-Ray 
(DOE/ORD), April 7, 2003, NHW:jmt0331036688, with enclosure. ACC: 
MOL.20030520.0367. 
Wilmot, E.L. 1981. Transportation Accident Scenarios for Commercial Spent Fuel. SAND802124. 
Albuquerque, New Mexico: Sandia National Laboratories. ACC: HQO.19871023.0215. 
Wilson, C.N. 1990a. Results from NNWSI Series 2 Bare Fuel Dissolution Tests. PNL-7169. 
Richland, Washington: Pacific Northwest Laboratory. ACC: NNA.19900814.0048. 
Wilson, C.N. 1990b. Results from NNWSI Series 3 Spent Fuel Dissolution Tests. PNL-7170. 
Richland, Washington: Pacific Northwest Laboratory. ACC: NNA.19900329.0142. 
Witte, M.C.; Chun, R.C.; and Schwartz, M.W. 1989. Dynamic Impact Effects on Spent Fuel 
Assemblies. 9th International Symposium on the Packaging and Transportation of Radioactive 
Materials, Washington, D.C., June 1116, 1989. 1, 186194. Oak Ridge, Tennessee: Oak Ridge 
National Laboratory. TIC: 240741. 
Wolery, T.J. 1992. EQ3NR, A Computer Program for Geochemical Aqueous Speciation- 
Solubility Calculations: Theoretical Manual, Users Guide, and Related Documentation 
(Version 7.0). UCRL-MA-110662 PT III. Livermore, California: Lawrence Livermore National 
Laboratory. ACC: MOL.19980717.0626. 
Wolery, T.J. and Daveler, S.A. 1992. EQ6, A Computer Program for Reaction Path Modeling of 
Aqueous Geochemical Systems: Theoretical Manual, Users Guide, and Related Documentation 
(Version 7.0). UCRL-MA-110662 PT IV. Livermore, California: Lawrence Livermore National 
Laboratory. TIC: 205002. 
Wronkiewicz, D.J.; Bates, J.K.; Buck, E.C.; Hoh, J.C.; Emery, J.W.; and Wang, L.M. 1997. 
Radiation Effects in Moist-Air Systems and the Influence of Radiolytic Product Formation on 
Nuclear Waste Glass Corrosion. ANL-97/15. Argonne, Illinois: Argonne National Laboratory. 
TIC: 234821. 
July 2004 9-12 No. 7: In-Package Environment Revision 1 
9.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
10 CFR 63. Energy: Disposal of High-Level Radioactive Wastes in a Geologic Repository at 
Yucca Mountain, Nevada. Readily available. 
40 CFR 197. Protection of Environment: Public Health and Environmental Radiation Protection 
Standards for Yucca Mountain, Nevada. Readily available. 
9.3 DATA, LISTED BY DATA TRACKING NUMBER 
GS020408312272.003. Collection and Analysis of Pore Water Samples for the Period from April 
2001 to February 2002. Submittal date: 04/24/2002. 
LL000107951021.107. Radionuclide Inventory in Spent Fuel. Submittal date: 01/12/2000. 
LL010902212241.026. CSNF Alteration Phase Porosity Estimates. Submittal date: 09/21/2001. 
LL030300112241.027. Uranium Dissolution Rates from Uranium Dioxide. Submittal date: 
03/03/2003. 
LL991001251021.090. DraftCSNF Waste Form Degradation: Unsaturated Drip Tests 
G2020 Analysis and Modeling Report. Submittal date: 10/04/1999. 
MO0006J13WTRCM.000. Recommended Mean Values of Major Constituents in J-13 Well 
Water. Submittal date: 06/07/2000. 
MO0212UCC034JC.002. Mass Transport of Solids in Effluent Solution During Miniature Waste 
Package Corrosion. Submittal date: 12/09/2002. 
MO0301ANLGNN01.527. Glass Unsaturated (Drip) Test Results (BSC Test Plan: 
SITP-02-WF-002): Boron Release. Submittal date: 01/15/2003. 
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. 
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. 
MO0302PNLDUFTD.000. Flowthrough Dissolution Data. Submittal date: 02/28/2003. 
MO0302SPATHDYN.000. Thermodynamic Data Input Files - Data0.YMP.R2. Submittal date: 
02/05/2003. 
MO0302UCC034JC.003. Graphical X-Ray Diffractometer Data and Mineral Analysis of 
Filtered Solids from Effluent Solution During Miniature Waste Package Corrosion. Submittal 
date: 02/10/2003. 
MO0304PNLLPHDD.000. Low pH Dissolution Data. Submittal date: 04/09/2003. 
9-13 July 2004 No. 7: In-Package Environment Revision 1 
MO0306ANLGIM01.525. Glass Immersion Test Results (BSC Test Plan: SITP-02-WF-002); 
Concentrations and pH. Submittal date: 06/12/2003. 
MO0306ANLGIM02.525. Glass Immersion Test Results (BSC Test Plan: SITP-02-WF-002); 
Normalized Mass Losses. Submittal date: 06/12/2003. 
MO0306ANLGVH01.526. Glass Vapor Hydration Test Results (BSC Test Plan: SITP-02-WF- 
002). Submittal date: 06/12/2003. 
MO0306ANLSF001.459. Colloids Generated from Irradiated N Reactor Fuel, Data Report. 
Submittal date: 06/04/2003. 
MO0308ANLGPC01.528. Immersion Tests for Glass Composition Dependence. Submittal 
date: 08/15/2003. 
MO0402SPAHWCIG.002. Heat and Water Chemistry Output from Igneous Intrusion. 
Submittal date: 02/23/2004. 
MO0403SPAIPCHM.004. Abstractions of In-Package Chemistry for TSPA-LA. Submittal 
date: 03/15/2004. 
MO0404SPAIPCHM.005. Abstractions of In-Package Chemistry for TSPA-LA. Submittal 
date: 04/29/2004. 
SN0001T0810599.008. Stainless Steel in Waste Packages for TSPA-SR (Total System 
Performance Assessment-Site Recommendation). Submittal date: 01/18/2000. 
July 2004 9-14 No. 7: In-Package Environment APPENDIX A 
IN-PACKAGE CHEMISTRY ENVIRONMENT 
(RESPONSE TO CLST 3.02 AIN-1, ENFE 3.03, TSPAI 3.14, 
AND GEN 1.01 (COMMENTS 116 AND 126)) 
No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
Note Regarding the Status of Supporting Technical Information 
This document was prepared using the most current information available at the time of its development. This 
Technical Basis Document and its appendices providing Key Technical Issue Agreement responses that were 
prepared using preliminary or draft information reflect the status of the Yucca Mountain Projects scientific 
and design bases at the time of submittal. In some cases this involved the use of draft Analysis and Model 
Reports (AMRs) and other draft references whose contents may change with time. Information that evolves 
through subsequent revisions of the AMRs and other references will be reflected in the License Application 
(LA) as the approved analyses of record at the time of LA submittal. Consequently, the Project will not 
routinely update either this Technical Basis Document or its Key Technical Issue Agreement appendices to 
reflect changes in the supporting references prior to submittal of the LA. 
July 2004 No. 7: In-Package Environment Revision 1 
APPENDIX A 
IN-PACKAGE CHEMISTRY ENVIRONMENT 
(RESPONSE TO CLST 3.02 AIN-1, ENFE 3.03, TSPAI 3.14, 
AND GEN 1.01 (COMMENTS 116 AND 126)) 
This appendix provides a response to Key Technical Issue (KTI) agreements Container Life and 
Source Term (CLST) 3.02 Additional Information Needed (AIN)-1, Evolution of the Near Field 
Environment (ENFE) 3.03, Total System Performance Assessment and Integration (TSPAI) 
3.14, and General Agreement (GEN) 1.01 (Comments 116 and 126). These agreements relate to 
the in-package chemistry environment. 
A.1 KEY TECHNICAL ISSUE AGREEMENTS 
CLST 3.02 
In the revision to the Summary of In-Package Chemistry for Waste Forms, 
AMR, address specific NRC questions regarding radiolysis, incoming water, 
localized corrosion, corrosion products, transient effects, and a sensitivity study 
on differing dissolution rates of components. DOE stated that these specific 
questions are currently being addressed in the revision of the Summary of 
In-Package Chemistry for Waste Forms AMR, ANL-EBS-MD-000050 and 
related AMRs and calculations. To be available in January 2001. 
1 This documents title and number differ from that identified in the original agreement for KTI CLST 3.02. 
A.1.1 CLST 3.02 AIN-1, ENFE 3.03, TSPAI 3.14, and GEN 1.01 (Comments 116 and 126) 
Agreement CLST 3.02 was reached during the U.S. Nuclear Regulatory Commission 
(NRC)/U.S. Department of Energy (DOE) Technical Exchange and Management Meeting on 
CLST held September 12 and 13, 2000, in Las Vegas, Nevada (Schlueter 2000). Subissues 1, 2, 
3, 4, and 6 were discussed at that meeting. 
In-Package Chemistry for Waste Forms (BSC 2001a),1 which addresses agreement CLST 3.02, 
was provided to the NRC in March 2001 (Brocoum 2001). After reviewing the document, the 
NRC determined that additional information was needed to close the agreement, resulting in 
CLST 3.02 AIN-1. 
Agreement ENFE 3.03 was reached during the NRC/DOE Technical Exchange and Management 
Meeting on Evolution of the Near Field Environment held January 9 through 12, 2001, in 
Pleasanton, California (Reamer 2001). ENFE KTI subissues 1, 2, 3, and 4 were discussed at that 
meeting. 
Agreement TSPAI 3.14 was reached during the NRC/DOE Technical Exchange and 
Management Meeting on TSPAI held August 6 through 10, 2001, in Las Vegas, Nevada 
(Reamer and Gil 2001a). 
The wording of these agreements and AIN is as follows: 
July 2004 A-1 No. 7: In-Package Environment CLST 3.02 AIN-1 
1. 
2. 
3. 
4. 
5. 
6. 
Provide the technical basis for the corrosion rates and the likely corrosion 
mechanisms of internal waste package components. 
Provide the technical justification and appropriate ranges for the model 
abstraction parameters for each internal component of the waste package. 
Provide the results of sensitivity analyses using current values of the 
parameters included in the model abstractions. 
Provide the technical basis for the in-package water chemistry, taking into 
account the formation of locally aggressive environments in crevices and 
tight spaces inside the waste package that can enhance the degradation of 
waste forms and the solubility of radionuclides. 
Provide documentation which validates the use of EQ3/6 to predict the 
formation of secondary phases resulting from high level waste glass 
dissolution. 
Provide the technical bases for ignoring the role of halides in the 
dissolution process. 
ENFE 3.03 
Provide analyses to verify that the bulk-scale chemical processes dominate the 
in-package chemical environment. The DOE will provide analyses justifying the 
use of bulk chemistry as opposed to local chemistry for solubility and waste form 
degradation models. These analyses will be documented in an update to the 
Miscellaneous Waste-Form FEPs AMR (ANL-WIS-MD-000009) or in an update 
to the Summary of In-Package Chemistry for Waste Forms AMR 
(ANL-EBS-MD-000050), expected to be available in FY 02. 
TSPAI 3.142 
DOE should account for the full range of environmental conditions for the 
in-package chemistry model (ENG4.1.1). 
DOE will update the in-package chemistry model to account for scenarios and 
their associated uncertainties required by TSPA. This will be documented in the 
In-Package Chemistry AMR (ANL-EBS-MD-000056) expected to be available to 
NRC in FY 2003. 
GEN 1.01 was reached during the NRC/DOE Technical Exchange and Management Meeting on 
Range of Thermal Operating Temperatures held September 18 and 19, 2001. At that meeting, 
the NRC provided additional comments that relate to agreements CLST 3.02 and TSPAI 3.14. 
2 The content of ENG4.1.1 (Cornell 2001) is adequately represented within the text of TSPAI 3.14. 
No. 7: In-Package Environment 
Revision 1 
July 2004 A-2 Revision 1 
These resulted in GEN 1.01, Comments 116 and 126. The DOE provided an initial response to 
those comments at the meeting (Reamer and Gil 2001b). Specific pages and chapter cited as part 
of GEN 1.01, Comments 116 and 126, are from FY01 Supplemental Science and Performance 
Analyses, Volume 1: Scientific Bases and Analyses (BSC 2001b). 
GEN 1.01 (Comment 116) 
Page 10-22: What does the calculation that shows the rate of water consumption 
is 27 times greater than the diffusional inflow suggest about the chemical 
conditions inside the package? Are the simulations for in-package chemistry 
assuming a bathtub model appropriate? 
GEN 1.01 (Comment 126) 
In Chapter 9, the assessment of in-package chemistry did not consider the spatial 
heterogeneity. Provide the rationales for the homogeneity. 
A.1.2 Related Key Technical Issue Agreements 
Per the DOE request to the NRC (Ziegler 2002), KTI agreements CLST 4.02 and CLST 4.03 
were subsumed into CLST 3.02 and CLST 3.03 (Schlueter 2002) because the wording and 
response for agreements CLST 4.02 and CLST 4.03 were the same as those for agreements 
CLST 3.02 and CLST 3.03, respectively. 
A.2 RELEVANCE TO REPOSITORY PERFORMANCE 
The chemical reactions that occur inside a breached waste package exert strong controls over the 
nature and extent of radionuclide transport from the waste form into the engineered barrier 
system and ultimately into the geosphere. Because they collectively determine both the chemical 
speciation and abundance of mobile radionuclides in effluents, the reactions that occur during 
waste form degradation are critical to predictions of repository performance. The waste form 
degradation model incorporates corrosion rates of waste form materials (steel, fuel, and glass) 
and assumed compositional ranges of incoming fluids as inputs and calculates the formation of 
secondary phases and changes in fluid composition. 
The in-package chemistry model must account for the full ranges of environmental conditions 
that may be encountered within the waste package. Local processes within the waste package 
could produce high or low pH values, which could enable cladding corrosion, increased waste 
reaction, and accelerated radionuclide mobilization. For example, widely ranging pH values can 
affect the solubility of radionuclide-bearing phases and hence, the quantities of radionuclides 
released and made available for transport. Other potentially important parameters include 
seepage water chemistry, temperature, and corrosion rates. Model inputs must be adequately 
modeled in isolation, and a physically realistic accounting of water balances inside the degrading 
waste form must be made. It must also be demonstrated that the complete ensemble of reactions 
is treatable and that the model sufficiently addresses all likely environmental conditions. The 
waste form degradation model must adequately predict the chemical reactivity of the waste form 
under the variety of conditions expected at Yucca Mountain. If these conditions are not met, the 
A-3 July 2004 No. 7: In-Package Environment Revision 1 
chemical form of radionuclides might potentially be incorrectly predicted (e.g., dissolved versus 
colloidal) and the actual mobile mass of the radionuclide might be incorrectly estimated. 
A.3 RESPONSE 
A.3.1 CLST 3.02 AIN-1 
The inputs used in the in-package chemistry model cover a realistic range of processes and 
parameters. In-Package Chemistry Abstraction (BSC 2004a) includes a suite of reaction path 
calculations in which input fluid compositions, waste form materials, temperatures, and fluid 
flux rates are considered. The abstraction procedure is adequate and appropriate on the basis of 
sensitivity testing and validation of the model. The technical basis for the model is summarized 
in Section 3 of this technical basis document and has been outlined in considerable detail in In- 
Package Chemistry Abstraction (BSC 2004a). Confidence has been established that the inpackage 
chemistry model adequately captures the appropriate material properties and the range 
of conditions required to assess repository performance. 
Corrosion rates and mechanisms for alloys used in the in-package chemistry model were derived 
from project-specific degradation experiments and industry handbook data (BSC 2004a). The 
specific values used were chosen according to the temperature range considered in the inpackage 
chemistry model (25C to 90C), which was within the range of temperatures used in 
the experiments (BSC 2002). The values used are also the same as those used in the 
geochemical study and sensitivity analysis performed to analyze the corrosion of 21-PWR 
commercial spent nuclear fuel (SNF) waste packages (BSC 2002). 
Abstraction parameters used in the analysis of waste package internal components are listed in 
Table A-1 and described in the context of the abstraction calculations in In-Package Chemistry 
Abstraction (BSC 2004a, Section 6.5). To provide portable inputs to TSPA, the results of 
reaction path calculations are abstracted into reasonably simple parameterizations that effectively 
envelop actual results. The abstraction procedure is described in In-Package Chemistry 
Abstraction (BSC 2004a, Chapter 8). 
July 2004 A-4 No. 7: In-Package Environment Table A-1. Input References for the Waste Package Components used in In-Package Chemistry 
Abstraction 
Material/Property 
21-PWR commercial 
SNF waste package 
geometric information 
Codisposal waste 
package geometric 
information 
Periodic table of the 
elements and mass list 
of radioactive isotopes 
Stainless Steel Type 
316 
Stainless Steel Type 
304L 
Carbon Steel Type 
A516 
Aluminum Alloy 6061 
Aluminum Alloy 1100 
Neutronita 
High-Level Radioactive 
Waste Glass 
N Reactor Fuel 
Commercial SNF 
Goethite 
Source: BSC 2004a, Table 7. 
Waste Package 
Component 
Dimensions 
Waste Package 
Component 
Dimensions 
Atomic Weight of 
the Elements and 
Isotopes 
Corrosion rate 
Density 
Composition 
Corrosion rate 
Density 
Composition 
Corrosion rate 
Density 
Composition 
Corrosion rate 
Density 
Composition 
Corrosion rate 
Density 
Composition 
Corrosion rate 
Density 
Composition 
Density 
Composition 
Composition 
Isotopic Inventory 
Surface properties 
Input 
NOTE: PWR = pressurized water reactor. 
a A nickel alloy interspersed with gadolinium (ASTM designation UNS N06464) is recommended to 
replace Neutronit as the absorber material. The effects of this change on in-package chemistry should 
be negligible because deleting the Neutronit does not significantly change the quantity of available 
stainless steel; the low corrosion rates of the nickel alloy relative to stainless steel will result in slightly 
higher in-package pH (i.e., less aggressive pH), and gadolinium will form phosphates that are fairly 
insoluble (Loros and Williams 2004). 
Reference 
DTN: MO0401SPAMCRAE.000 
ASTM G 1-90 1999, Table XI, p. 7 
ASME 1998, Section II, SA-240, Table 1, p. 366, for 
composition of 316; 
ASM International 1987, p. 931, for C and N content of 316 
DTN: MO0401SPAMCRAE.000 
ASTM G 1-90 1999, Table XI, p. 7 
ASTM A 240/A 240M-99b 2000, Table 1, p. 2 
DTN: MO0401SPAMCRAE.000 
ASTM A 20/A 20M-95a 1995, p. 21 
ASME 1998, Section II, SA-516/SA-516M, Table 1, p. 925 
ASM International 1987, Table 12, p. 603 
ASTM G 1-90 1999, Table X1.1 
ASME 1998, Section II, SB-209, Table 1, p. 236 
ASM International 1987, Table 12, p. 603 
ASTM G 1-90 1999, Table XI, p. 7 
ASTM B 209-96 1996, Table 1, p. 7 
DTN: MO0401SPAMCRAE.000 
DTN: MO0109RIB00049.001; 
For B content ASTM A 88789 2000, Table 1, S30463, S30464, 
and S30466 
BSC 2003; 
Stout and Leider 1994, Table 6.4 
BSC 2003; 
Baxter 1988, Table 10 
BSC 2003; 
DOE 2000, Table 3-1 
BSC 2003; 
BSC 2001c, Attachment III Disk 1 of 9, ATT III/ LPM1/ 
uniform_profile/ 3.5/ ft71-case10.N04 
Langmuir 1997, p. 345, Table 10.2 
No. 7: In-Package Environment 
Revision 1 
BSC 2004b, Table 1a; 
BSC 2003; 
Punatar 2001, p. 25 
BSC 2003; 
DOE 2000, Section 3 
Audi and Wapstra 1995; 
Parrington et al. 1996 
July 2004 A-5 Revision 1 
Sensitivity analyses were performed (BSC 2004a, Section 6.7) to (1) determine the uncertainty 
range for ionic strength; (2) expand the temperature and CO2 partial pressure ranges; and 
(3) demonstrate how inputs such as the waste package design configuration (including sulfur 
content), chromium, and seepage composition affect the model output. The results of the 
sensitivity analyses are described in Section A.4.1. 
The possibility of corrosion occurring in crevices and tight spaces, accompanied by potentially 
enhanced waste package degradation and radionuclide solubilities, was addressed in the inpackage 
chemistry model, in part, by calculating the chemistry resulting from corrosion of 
individual waste package internal components. The resulting heterogeneous processes were not 
directly considered in the in-package chemistry model because the waste form components are 
initially spread uniformly throughout the waste package and degradation will tend to make the 
mix of components more homogeneous. Since a fluid capable of transporting radionuclides from 
the waste form would have to either move or be continuous over distances greater than about 1 
m, and pH would be buffered by waste package components and waste forms to the extent that 
pH extremes would not occur, chemical transport would minimize the effects of spatial 
heterogeneity, if there was any heterogeneity initially. 
The EQ3/6 model is appropriate for modeling glass degradation. Basaltic glass is considered to 
be an accurate natural analog for high-level radioactive waste glass (Grambow et al. 1986). 
EQ3/6 runs predict that high-level radioactive waste glass degradation will result in the 
formation of a host of secondary minerals typical of altered natural glass. Thus, it is reasonable 
to expect EQ3/6 models of high-level radioactive waste glass alteration to roughly mimic natural 
alteration of basaltic glass. 
The role of metal halides on waste package and fuel degradation has been evaluated using ferric 
chloride and excluded from further consideration in the total system performance assessment for 
the license application (TSPA-LA) model because of the low likelihood that conditions 
necessary for metal halides to impact corrosion exist in the Yucca Mountain in-package 
environment. Specifically, as pH levels below 4.5 are not expected inside breached waste 
packages, it is highly unlikely that such low pH conditions will occur. 
A.3.2 ENFE 3.03 and GEN 1.01 (Comment 126) 
It is impossible to anticipate the exact paths by which water molecules will move through 
degraded waste forms. Consequently, there is no absolute certainty regarding which waste form 
components a given volume of water will interact with. However, for heterogeneous chemical 
processes to persist and leave a waste package without coming into contact with other fluids or 
solids, a fluid capable of transporting radionuclides from the waste form would have to either 
move or be continuous over distances greater than about 1 m. Accordingly, there is ample 
indication that spatial heterogeneity will not significantly alter the overall course of waste form 
degradation. The calculated pH is the crucial feature of the in-package chemistry model because 
it is used as input in the fuel and glass degradation models, in calculations of radionuclide 
solubility and retardation, and in the estimation of colloid stability. The reactants that would 
tend to buffer very high or low pH values (corrosion product surfaces, uranyl hydroxide solids, 
and stainless steels) are more abundant and tend to react more quickly than those components 
that might cause local pH extremes (e.g., high-level radioactive waste glass). 
A-6 July 2004 No. 7: In-Package Environment Revision 1 
The dissolution of high-level radioactive waste glass is the only factor likely to produce extreme 
pH values. However, only in the unlikely scenario of a fluid contacting the glass inside the 
package could extreme pH levels persist. Subsequent contact with degrading metals generally 
results in near-neutral pH fluids. Deprotonation of corrosion product surfaces would likewise 
drive pH levels back toward neutral. Dissolution of schoepite, an important component of 
codisposal packages, will also keep pH values near neutral. Consequently, these chemical 
principles strongly support the conclusion that spatial heterogeneity has a negligible impact. 
A.3.3 TSPAI 3.14 
Model inputs have been treated both separately and in ensembles of reactions in such a way that 
the model sufficiently reproduces all likely environmental conditions expected at Yucca 
Mountain. Analyzed scenarios include the effects of intrusion of magma into the waste form 
(BSC 2004c), seismic damage, water accumulation in the waste form through seepage, water 
accumulation through vapor condensation, and heterogeneously distributed reactions of water 
with waste form components (BSC 2004a). A range of fluid compositions have also been 
considered that bound the compositions of fluids likely to prevail inside the repository over long 
periods of time. Input fluid compositions have been shown to have little effect on output fluid 
compositions (Section 3.4 of this technical basis document). 
A.3.4 GEN 1.01 (Comment 116) 
In previous model descriptions, the model required more water for the described reactions than 
was available (BSC 2004a). The water supplied by diffusion was estimated to be 1/27 of that 
required by the bathtub model. The two unsaturated models currently adopted (condensation of 
water films from the vapor phase and water film development from dripping into the waste 
package) resolve that inconsistency. 
The information in this report is responsive to agreements ENFE 3.03, TSPAI 3.14, and GEN 
1.01 (Comments 116 and 126) made between the DOE and NRC and responsive to AIN request 
CLST 3.02 AIN-1. The report contains the information that DOE considers necessary for NRC 
review for closure of these agreements. 
A.4 BASIS FOR THE RESPONSE 
A.4.1 CLST 3.02 AIN-1 
As described in Section A.3.1, corrosion rates and mechanisms for alloys used in the in-package 
chemistry model were derived from project-specific degradation experiments and industry 
handbook data (BSC 2004a; Table 3-4 of this technical basis document). The specific values 
used were chosen according to the applicable temperature range of the in-package chemistry 
model (from 25C to approximately 100C), which was within the range of temperatures used in 
the experiments (BSC 2002). The values used are also the same as those used in the 
geochemical study and sensitivity analysis that was performed to analyze the corrosion of 21- 
PWR commercial SNF waste packages (BSC 2002). Total System Performance Assessment 
(TSPA) Model for Site Recommendation (CRWMS M&O 2000) demonstrated that corrosion 
rates are not sensitive parameters. 
July 2004 A-7 No. 7: In-Package Environment Revision 1 
Abstraction parameters used in the analysis of waste package internal components are listed in 
Table A-1 and described in the context of the abstraction calculations in In-Package Chemistry 
Abstraction (BSC 2004a, Section 6.5). The parameters were taken primarily from handbook data 
and DOE technical product output and include waste and alloy corrosion rates; waste package 
component dimensions; physical properties and chemical compositions; water compositions and 
diffusion fluxes; and corrosion product properties. The reactions of waste package components 
with water were calculated for single components and for combinations of components in order 
to capture the situation most likely to prevail, as well as to assess potential extreme chemical 
conditions from the less likely situation in which single components are allowed to degrade in 
isolation. 
Sensitivity testing (or abstraction model validation) using current values of parameters included 
in the model abstractions is described in In-Package Chemistry Abstraction (BSC 2004a, 
Section 6.7 and Attachment II). These tests were performed to (1) determine the uncertainty 
range for ionic strength; (2) expand the temperature and CO2 partial pressure ranges; and 
(3) demonstrate how inputs such as the waste package design configuration (including sulfur 
content), chromium, and seepage composition affect the model output. 
Figures A-1, A-2, and A-3 illustrate the behavior of specific processes and their responses to the 
varying of certain parameters. These processes are themselves parts of a complex ensemble of 
processes that occur simultaneously within the waste package. For example, surface 
complexation of steel corrosion products, which is discussed in Section A.4.2, has a profound 
moderating effect on pH. This process was not included in the sensitivity runs described here. 
As a result, pH resulting from the corrosion of steel appears in the figures to reach lower values 
than are expected or considered possible, given the overall chemistry that is expected to develop 
within the waste package. 
The following conclusions were drawn. First, incoming water compositions have little effect on 
in-package pH and ionic strength (see Appendix B, Figures B-1 and B-2). Second, the oxidation 
state of chromium has little effect on overall pH history. Figure A-1 illustrates that the effects on 
pH and ionic strength of chromium(III) and chromium(VI) are small. Figure A-2 shows that 
CO2 partial pressure has little effect on pH and ionic strength for both commercial SNF and 
codisposed waste, although sulfur (which causes the degradation of Carbon Steel Type A516) 
has a large influence on pH but a lesser effect on ionic strength. 
July 2004 A-8 No. 7: In-Package Environment Source: BSC 2004a, Figure 16. 
Figure A-1. 
No. 7: In-Package Environment 
Revision 1 
pH and Ionic Strength Outputs from a Model Run Showing the Effects of Chromium(III) 
and Chromium(VI) 
July 2004 A-9 Revision 1 
Source: BSC 2004a, Figure 17. 
NOTE: C22C25 represents commercial SNF, 1.5 L/yr water flux, calciumpore water, 10% fuel exposure, at 25C. 
Figure A-2. Effect of pCO2 and Sulfur Content of Carbon Steel Type A516 on pH of Commercial 
Spent Nuclear Fuel 
The quantity of sulfur contained in the A516 steel had a strong influence on the system pH 
during the period when A516 was degrading (Figure A-2). Increasing the sulfur content tended 
to lower the pH, and decreasing the sulfur tended to moderate the pH compared to the base case. 
Replacing Carbon Steel Type A516 with Type S31600 resulted in more moderate early pH and 
similar late pH. Increased temperature results in lowering the pH for commercial spent nuclear 
fuel and codisposed waste. This effect is due to the increased dissociation of the water molecule 
as temperature increases. 
Increased water flux results in more rapid stabilization of the chemistry (Figure A-3) close to the 
initial incoming water composition. Figure A-3 shows that as the water flux was increased, the 
simulation would end at progressively earlier times; this was caused by the lack of change in the 
chemistry, which terminates the computer run. This result is a product of the reactants 
dissolution being kinetically controlled and the lack of significant reaction of the water and 
reactants. Similarly, increasing the corrosion rates of the metal alloy components has the same 
impact on the chemistry as decreasing the water flux (i.e., the contact time of the water with the 
alloy determines the degree of reaction) (see Section 6.6.5 of this technical basis document). 
July 2004 A-10 No. 7: In-Package Environment Revision 1 
Source: BSC 2004a, Figure 20. 
NOTE: Cases run using calciumpore water, 10% fuel exposure, at 25C. 
Figure A-3. Effect of Water Flux on pH for a Commercial Spent Nuclear Fuel Waste Package 
The uncertainty range for ionic strength is discussed in Section 3.5.2.3 of this technical basis 
document and illustrated in Figure A-4. This range accounts for uncertainties in carbon dioxide 
partial pressures, as well as for the possibility of lower ionic strengths resulting from surface 
complexation effects. 
July 2004 A-11 No. 7: In-Package Environment Revision 1 
Source: DTN: MO0403SPAIPCHM.004. 
NOTE: C22C25 represents commercial SNF, 1.5 L/yr water flux, calciumpore water, 10% fuel exposure, at 25C. 
July 2004 
Figure A-4. Commercial Spent Nuclear Fuel Ionic Strength Profile with Error Bars 
It is reasonable to speculate that if fluids were to accumulate in crevices and tight spaces inside 
the waste package, low pH conditions could develop and, in the extreme case, result in cladding 
failure. The potential for these low-volume fluids to significantly reduce the pH is small because 
the presence of abundant ferric (hydr)oxide corrosion products will constrain pH levels near 
neutral by sorbing protons from solution. 
High-level radioactive waste glass degradation is predicted in EQ3/6 runs to result in the 
formation of a number of secondary minerals, particularly chalcedony, sodium-nontronite (a 
clay), borax (Na2[B4O5(OH)4]:8H2O), nahcolite (NaHCO3), microcline (KAlSi3O8), calcite, and 
saponite (a clay). Basaltic glass is considered to be an accurate natural analog for high-level 
radioactive waste glass (Grambow et al. 1986). Except for the presence of boron and the 
subsequent formation of secondary borate phases, it is reasonable to expect EQ3/6 models of 
high-level radioactive waste glass alteration to roughly mimic natural alteration of basaltic glass 
if the comparison is valid. The fact that chalcedony, clays, calcite, and microcline are routinely 
observed in the weathering of basalt glass in natural environments suggests that EQ3/6 is a valid 
tool for modeling the formation of secondary phases during high-level radioactive waste glass 
alteration. 
A-12 No. 7: In-Package Environment Revision 1 
Ferric chloride (and other halides) are unlikely to impact corrosion processes within the inpackage 
environment. In the drip scenario, seepage water enters the waste package. This water 
contains dissolved species, including chloride and bicarbonate ions. Ferric chloride (and 
fluoride) may form and, with nitric acid from radiolysis, tend to lower pH. However, the 
bicarbonate ion tends to raise pH, and ferric hydroxide forms from waste package corrosion 
above about pH 3. Dissolution of such metal oxide and hydroxide minerals (primarily iron) tends 
to drive pH upward. Thus, a set of competing chemical reactions occurring in the waste package 
tends to maintain pH at moderate values and prohibit deviations to extremely low pH. As pH 
values below 4.5 are not expected inside breached waste packages, it is highly unlikely that the 
low pH conditions needed for ferric chloride (or other halide-associated) impacts will ever occur. 
A.4.2 ENFE 3.03 and GEN 1.01 (Comment 126) 
Schoepite, which with iron corrosion products dominates the composition of the alteration 
products from time periods of 500 years and beyond, dissolves at acid pH to consume protons 
and at above neutral pH to produce protons. These two reactions constitute a negative feedback 
that resists pH deviations to high or low pH and ultimately limits pH values near the schoepite 
solubility minimum of pH 6.5 to 7 (BSC 2004a, Section 6.8), which shifts to lower pH as the 
ambient carbon dioxide pressure increases (BSC 2004a, Figure 6.7-1). 
4 
Consideration of the steels and glass in isolation from the schoepite points to another mechanism 
by which alkalinity increases from high-level radioactive waste glass dissolution are resisted: 
chromium oxidizes to CrO 2- above pH 6.5, but forms HCrO4 
- below pH 6.5. For this reason, 
ambient pH has an important effect on proton production by steel degradation. Specifically, at a 
pH less than or equal to 6.5, steel degradation either consumes protons or produces relatively 
small amounts of them, while at a pH greater than 6.5, degradation of both Stainless Steel Types 
304L and 316 produces relatively large amounts of protons. This mechanism constrains pH 
values close to 6.5 and counters proton consumption by glass degradation. 
The fact that proton consumption prevails at pH values greater than or equal to 6.5, but proton 
production amplifies above pH 6.5 indicates that long-term pH values will converge toward 6.5 
as long as sufficient steel and high-level radioactive waste glass exist in the package. Note that 
there is more than twice as much Stainless Steel Type 316 as high-level radioactive waste glass 
and roughly 20% more Stainless Steel Type 304L than high-level radioactive waste glass. 
The revised In-Package Chemistry Abstraction (BSC 2004a, Sections 6.6.1.1 and 6.6.2.1) 
demonstrates that the reactions of the individual waste form components (steels, glass, fuel) 
produce fluids with pH values that are close to neutral. The exceptions are Carbon Steel Type 
A516 and Stainless Steel Type 304L steel degradation, which both produce protons, and highlevel 
radioactive waste glass, which generates alkalinity. Explicit consideration of the pHbuffering 
of corrosion products indicates that Carbon Steel Type A516 degradation will actually 
produce near neutral fluids (BSC 2004a, Section 6.8). Stainless Steel Type 304L degradation is 
also unlikely to result in acid pH solutions because it dissolves very slowly (BSC 2004a, 
Section 6.5.1.1), so proton production is likely to be diluted by incoming fluids. High-level 
radioactive waste glass dissolution can produce locally high pH levels because of its high base 
cation content. However, there is far more steel than high-level radioactive waste glass in 
codisposal packages, and large amounts of schoepite are estimated to form as well. Dissolution 
A-13 July 2004 No. 7: In-Package Environment Revision 1 
of either steel or schoepite consumes alkalinity and would tend to make the persistence of high 
pH levels in the waste form unlikely. In particular, relatively rapid dissolution of schoepite 
would tend to drive pH levels towards its solubility minimum, near neutral pH. 
pH is the critical feature of concern to the in-package chemistry model and is controlled by an 
ensemble of processes that occur throughout the waste package. Reactants that tend to buffer pH 
levels (i.e., corrosion product surfaces, uranyl hydroxide solids, and stainless steels) are more 
abundant and tend to be faster reacting than components such as high-level radioactive waste 
glass that might cause local pH extremes. The presence of multiple processes capable of 
providing a negative-feedback control over high pH is discussed in Section 3.4 of this technical 
basis document and in In-Package Chemistry Abstraction (BSC 2004a, Section 6.8). In 
summary, the dissolution of high-level radioactive waste glass is the only way to generate locally 
extreme pHs. Because high-level radioactive waste glass is present in minor amounts (and 
dissolves more slowly) relative to other pH-buffering reactants, the potential for long-term 
persistence of extreme pH levels is considered remote. 
A.4.3 TSPAI 3.14 
The updated In-Package Chemistry Abstraction (BSC 2004a) considers wide ranges in input 
fluid compositions and ambient gas (oxygen and carbon dioxide) partial pressures; the presence 
and absence of cladding; the effects of an igneous intrusion (BSC 2004c); degradation in the 
presence of water condensate and seepage; and wide ranges in the fluxes of both condensate and 
seepage. A pH range of 4.5 to 8.1 is found to adequately account for the full range of in-package 
conditions. 
Higher fluid fluxes tend to result in fluid pH values close to the original input values, which tend 
to be close to neutral. Low fluid fluxes (condensate or seepage) allow the accumulation of salts 
and, as expected, result in higher ionic strengths; pH values close to neutral share this tendency. 
Sensitivity calculations have considered the impacts of higher and lower ambient carbon dioxide 
and oxygen levels. None of the conditions affects the output pH significantly. This is true for 
carbon dioxide primarily because the kinetics of the reactions controlling proton production and 
consumption are not strong functions of carbon dioxide levels. High carbon dioxide levels might 
result from biomass production on the drift floor. Redox conditions lower than ambient might 
occur as a result of the shielding of fuel by corrosion product accumulation. While lowered redox 
states would affect solubilities, they do not affect pH materially. 
As emphasized elsewhere in this document, a number of pH-buffering solid-solution reactions 
tend to anchor local and bulk pH close to neutral. These reactions include schoepite dissolution; 
steel degradation; and proton uptake, release, or both, by corrosion products. Ambient cycling of 
oxygen through the mountain sets the upper oxygen partial pressure and the nominal carbon 
dioxide levels. The assemblage of reactions has the effect of driving output chemistries toward a 
common endpoint despite the large variation in possible initial environmental conditions. 
A.4.4 GEN 1.01 (Comment 116) 
In previous model descriptions, the model required more water for the described reactions than 
was available (BSC 2004a). The water supplied by diffusion was estimated to be 1/27 of that 
July 2004 A-14 No. 7: In-Package Environment Revision 1 
required by the bathtub model. The two unsaturated models currently adopted (condensation of 
water films from the vapor phase and water film development from dripping into the waste 
package) resolve that inconsistency. Both the no-drip and the seepage dripping model have 
separate process models and separate abstraction models, thus allowing implementation of 
nondripping (water vapor condensation) and dripping (seepage dripping model) models in the 
TSPA-LA. For both of these models, the chemical reaction of the waste package components 
with a liquid was simulated as a function of time. 
A.5 REFERENCES 
A.5.1 Documents Cited 
ASME (American Society of Mechanical Engineers) 1998. 1998 ASME Boiler and Pressure 
Vessel Code. 1998 Edition with 1999 and 2000 Addenda. New York, New York: American 
Society of Mechanical Engineers. TIC: 247429. 
ASM International 1987. Corrosion. Volume 13 of Metals Handbook. 9th Edition. Metals 
Park, Ohio: ASM International. TIC: 209807. 
Audi, G. and Wapstra, A.H. 1995. Atomic Mass Adjustment, Mass List for Analysis. Upton, 
New York: Brookhaven National Laboratory, National Nuclear Data Center. TIC: 242718. 
Baxter, R.G. 1988. Defense Waste Processing Facility Wasteform and Canister Description. 
DP-1606, Rev. 2. Aiken, South Carolina: E.I. du Pont de Nemours & Company, Savannah River 
Plant. TIC: 8704. 
Brocoum, S. 2001. Transmittal of Analysis and Model Reports (AMR) and other Items 
Addressing Key Technical Issue (KTI) Technical Exchanges. Letter from S.J. Brocoum (DOE) 
to C.W. Reamer (NRC), March 30, 2001, with enclosure. ACC: MOL.20010529.0119. 
BSC (Bechtel SAIC Company) 2001a. In-Package Chemistry for Waste Forms. ANL-EBSMD-
000056 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20010322.0490. 
BSC 2001b. FY01 Supplemental Science and Performance Analyses, Volume 1: Scientific Bases 
and Analyses. TDR-MGR-MD-000007 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20010712.0062. 
BSC 2001c. PWR Assembly End-Effect Reactivity Evaluation. CAL-UDC-NU-000006 REV 00. 
Las Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20010412.0158. 
BSC 2002. Geochemistry Model Abstraction and Sensitivity Studies for the 21 PWR CSNF 
Waste Packages. MDL-DSU-MD-000001 REV 00. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: MOL.20021107.0154. 
BSC 2003. Repository Design Project, Repository/PA IED Emplacement Drift Committed 
Materials 1 of 2. 800-IED-WIS0-00301-000-00A. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: ENG.20030627.0003. 
July 2004 A-15 No. 7: In-Package Environment Revision 1 
BSC 2004a. In-Package Chemistry Abstraction. ANL-EBS-MD-000037 REV 03B. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20040609.0094. 
BSC 2004b. D&E/PA/C IED Typical Waste Package Components Assembly. 800-IED-WIS0- 
00202-000-00B. Las Vegas, Nevada: Bechtel SAIC Company. ACC: ENG.20040202.0010. 
BSC 2004c. Igneous Intrusion Impacts on Waste Packages and Waste Forms. MDL-EBS-GS- 
000002 REV 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20040421.0002. 
Cornell, V. 2001. Analysis of Resolution Status Key Technical Issue: Total System Performance 
Assessment and Integration Subissue 3: Model Abstraction. Slide presentation at U.S. Nuclear 
Regulatory Commission/U.S. Department of Energy Technical Exchange and Management 
Meeting on Total System Performance Assessment and Integration, August 6 through 10, 2001. 
ACC: MOL.20010921.0129. 
CRWMS M&O (Civilian Radioactive Waste Management System Management and Operating 
Contractor) 2000. Total System Performance Assessment (TSPA) Model for Site 
Recommendation. MDL-WIS-PA-000002 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001226.0003. 
DOE (U.S. Department of Energy) 2000. N Reactor (U-Metal) Fuel Characteristics for 
Disposal Criticality Analysis. DOE/SNF/REP-056, Rev. 0. Washington, D.C.: U.S. Department 
of Energy, Office of Environmental Management. TIC: 247956. 
Grambow, B., Jercinovic, M.J., Ewing, R.C., and Byers, C.D. 1986. Weathered Basalt Glass: 
A Natural Analogue for the Effects of Reaction Progress on Nuclear Waste Glass Alteration. 
Scientific Basis for Nuclear Waste Management IX, Symposium held September 9-11, 1985, 
Stockholm, Sweden. Werme, L.O., ed. 50, 263272. Pittsburgh, Pennsylvania: Materials 
Research Society. TIC: 203664. 
Langmuir, D. 1997. Aqueous Environmental Geochemistry. Upper Saddle River, New Jersey: 
Prentice Hall. TIC: 237107. 
Loros, E. and Williams, N.H. 2004. Meeting Minutes, Bechtel SAIC Company, LLC, Technical 
Review Board (TMRB) Meeting, April 22, 2004. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20040622.0297. 
Parrington, J.R.; Knox, H.D.; Breneman, S.L.; Baum, E.M.; and Feiner, F. 1996. Nuclides and 
Isotopes, Chart of the Nuclides. 15th Edition. San Jose, California: General Electric Company 
and KAPL, Inc. TIC: 233705. 
Punatar, M.K. 2001. Summary Report of Commercial Reactor Criticality Data for Crystal River 
Unit 3. TDR-UDC-NU-000001 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20010702.0087. 
July 2004 A-16 No. 7: In-Package Environment Revision 1 
Reamer, C.W. 2001. U.S. Nuclear Regulatory Commission/U.S. Department of Energy 
Technical Exchange and Management Meeting on Evolution of the Near-Field Environment 
(January 912, 2001). Letter from C.W. Reamer (NRC) to S. Brocoum (DOE/YMSCO), 
January 26, 2001, with enclosure. ACC: MOL.20010810.0033. 
Reamer, C.W. and Gil, A.V. 2001a. Summary Highlights of NRC/DOE Technical Exchange and 
Management Meeting on Total System Performance Assessment and Integration. Meeting held 
August 610, 2001. Las Vegas, Nevada. Washington, D.C.: U.S. Nuclear Regulatory 
Commission. ACC: MOL.20010921.0121. 
Reamer, C.W. and Gil, A.V. 2001b. Summary Highlights of NRC/DOE Technical Exchange 
and Management Meeting of Range on Thermal Operating Temperatures, September 1819, 
2001. Washington, D.C.: U.S. Nuclear Regulatory Commission. ACC: MOL.20020107.0162. 
Schlueter, J. 2000. U.S. Nuclear Regulatory Commission/U.S. Department of Energy Technical 
Exchange and Management Meeting on Container Life and Source Term (September 1213, 
2000). Letter from J. Schlueter (NRC) to S. Brocoum (DOE/YMSCO), October 4, 2000, with 
enclosure. ACC: MOL.20010731.0161. 
Schlueter, J. 2002. Container Life and Source Term Agreements. Letter from J. R. Schlueter 
(NRC) to J. D. Ziegler (NRC). July 2, 2002. ACC: MOL.20020819.0277. 
Stout, R.B. and Leider, H.R., eds. 1994. Preliminary Waste Form Characteristics Report, 
Version 1.0. UCRL-ID-108314, Rev. 1. Livermore, California: Lawrence Livermore National 
Laboratory. ACC: MOL.19960314.0440. 
Ziegler, J.D. 2002. Transmittal of Information Addressing Key Technical Issue (KTI) 
Container Life and Source Term (CLST) Agreements 4.02, 4.03, 4.04, And 4.05. Letter from J. 
D. Ziegler (DOE) to J. R. Schlueter (NRC), June 21, 2002. ACC: MOL.20020815.0586. 
A.5.2 Codes, Standards, and Regulations 
ASTM A 20/A 20M-95a. 1995. Standard Specification for General Requirements for Steel 
Plates for Pressure Vessels. West Conshohocken, Pennsylvania: American Society for Testing 
and Materials. TIC: 240026. 
ASTM A 240/A 240M-99b. 2000. Standard Specification for Heat-Resisting Chromium and 
Chromium-Nickel Stainless Steel Plate, Sheet, and Strip for Pressure Vessels. West 
Conshohocken, Pennsylvania: American Society for Testing and Materials. TIC: 248529. 
ASTM A 887-89 (Reapproved 2000). 2000. Standard Specification for Borated Stainless Steel 
Plate, Sheet, and Strip for Nuclear Application. West Conshohocken, Pennsylvania: American 
Society for Testing and Materials. TIC: 249544. 
ASTM B 20996. 1996. Standard Specification for Aluminum and Aluminum-Alloy Sheet and 
Plate. West Conshohocken, Pennsylvania: American Society for Testing and Materials. TIC: 
247078. 
July 2004 A-17 No. 7: In-Package Environment Revision 1 
ASTM G 1-90 (Reapproved 1999). 1999. Standard Practice for Preparing, Cleaning, and 
Evaluating Corrosion Test Specimens. West Conshohocken, Pennsylvania: American Society 
for Testing and Materials. TIC: 238771. 
A.5.3 Data, Listed by Data Tracking Number 
MO0109RIB00049.001. Waste Package Material Properties: Neutron Absorbing Materials. 
Submittal date: 09/17/2001. 
MO0401SPAMCRAE.000. Materials Corrosion Rates in Aqueous Environments. Submittal 
date: 01/16/2004. 
MO0403SPAIPCHM.004. Abstractions of In-Package Chemistry for TSPA-LA. Submittal date: 
03/15/2004. 
No. 7: In-Package Environment July 2004 A-18 APPENDIX B 
EFFECTS OF RADIOLYSIS AND ENGINEERED MATERIALS 
ON IN-PACKAGE CHEMISTRY 
(RESPONSE TO CLST 3.03 AIN-1 AND CLST 3.04 AIN-1) 
No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
Note Regarding the Status of Supporting Technical Information 
This document was prepared using the most current information available at the time of its development. This 
Technical Basis Document and its appendices providing Key Technical Issue Agreement responses that were 
prepared using preliminary or draft information reflect the status of the Yucca Mountain Projects scientific 
and design bases at the time of submittal. In some cases this involved the use of draft Analysis and Model 
Reports (AMRs) and other draft references whose contents may change with time. Information that evolves 
through subsequent revisions of the AMRs and other references will be reflected in the License Application 
(LA) as the approved analyses of record at the time of LA submittal. Consequently, the Project will not 
routinely update either this Technical Basis Document or its Key Technical Issue Agreement appendices to 
reflect changes in the supporting references prior to submittal of the LA. 
July 2004 No. 7: In-Package Environment Revision 1 
APPENDIX B 
EFFECTS OF RADIOLYSIS AND ENGINEERED MATERIALS 
ON IN-PACKAGE CHEMISTRY 
(RESPONSE TO CLST 3.03 AIN-1 AND CLST 3.04 AIN-1) 
This appendix provides a response to Key Technical Issue (KTI) agreements Container Life and 
Source Term (CLST) 3.03 Additional Information Needed (AIN)-1 and CLST 3.04 AIN-1. 
These agreements relate to the effects of radiolysis and engineered materials on in-package 
chemistry. 
B.1 KEY TECHNICAL ISSUE AGREEMENTS 
CLST 3.03 and CLST 4.03 
Provide a more detailed calculation on the in-package chemistry effects of 
radiolysis. DOE stated that the calculations recently performed as discussed at 
the 9/12/00 Technical Exchange and preceding teleconferences are being 
documented. These calculations will be referenced and justified in the revision of 
B.1.1 CLST 3.03 AIN-1 and CLST 3.04 AIN-1 
Agreements CLST 3.03 and CLST 3.04 were reached during the U.S. Nuclear Regulatory 
Commission (NRC)/U.S. Department of Energy (DOE) Technical Exchange and Management 
Meeting on CLST held September 12 and 13, 2000, in Las Vegas, Nevada (Schlueter 2000). 
Subissues 1, 2, 3, 4, and 6 were discussed at that meeting. 
In-Package Chemistry for Waste Forms (BSC 2001), which addresses agreements CLST 3.03 
and CLST 3.04, was provided to the NRC in March 2001 (Brocoum 2001). After reviewing the 
document, the NRC determined that the effect of radiolysis on cladding corrosion was not 
discussed in the DOE analysis. Also, according to the NRC, the report did not discuss the 
applicability of abstractions for incoming water, taking into account information provided in 
Environment on the Surfaces of the Drip Shield and Waste Package Outer Barrier (CRWMS 
M&O 2000). The NRC, therefore, asked for specific additional information to close these 
agreements, resulting in CLST 3.03 AIN-1 and CLST 3.04 AIN-1 (Reamer 2001). 
The NRC AIN request for agreement CLST 3.03 (for commercial spent nuclear fuel (SNF) 
stored in commercial SNF waste packages) was also applicable to CLST 4.03 (for DOE SNF and 
DOE high-level radioactive waste stored in codisposal waste packages). Similarly, the AIN 
request for agreement CLST 3.04 was applicable to CLST 4.04 (for DOE SNF and DOE highlevel 
radioactive waste). Accordingly, this appendix provides responses to the AINs that the 
NRC requested for agreements CLST 3.03, 3.04, 4.03, and 4.04. This approach is consistent 
with the DOE letter request dated June 21, 2002 (Ziegler 2002), and the NRC letter concurrence 
dated July 2, 2002 (Schlueter 2002). 
The wording for agreements CLST 4.03 and CLST 4.04 is identical to the wording for 
agreements CLST 3.03 and CLST 3.04, respectively. The wording of these agreements and 
AINs is as follows: 
B-1 July 2004 No. 7: In-Package Environment Revision 1 
the Summary of In-Package Chemistry for Waste Forms AMR, 
ANL-EBS-MD-000050 and will be available in January 2001. 
CLST 3.04 and CLST 4.04 
Need consistency between abstractions for incoming water and sensitivity studies 
conducted for in-package calculations, in particular, taking into account the 
interaction of engineered materials on the chemistry of water used for input to 
in-package abstractions. DOE stated that the revision of the Summary of 
In-Package Chemistry for Waste Forms AMR, ANL-EBS-MD-000050 will 
discuss the applicability of abstractions for incoming water, taking into account 
the revised Environment on the Surfaces of the Drip Shield and Waste Package 
Outer Barrier AMR. The revision will be available in January 2001. 
CLST 3.03 AIN-1 and CLST 4.03 AIN-1 
Provide additional information on the dose rate calculation as a function of time 
for typical fuels in the waste package and the technical basis for 50 R/hr dose rate. 
CLST 3.04 AIN-1 and CLST 4.04 AIN-1 
Provide documentation, which shows consistency between abstractions for 
incoming water and sensitivity studies conducted for in-package calculations. 
B.1.2 Related Key Technical Issue Agreements 
There are no other KTI agreements directly related to agreements CLST 3.03, 3.04, 4.03, and 
4.04. 
B.2.1 CLST 3.03 AIN-1 
Radiolysis can affect in-package chemistry, particularly with regard to the formation of nitric 
acid (HNO3), which could promote the corrosion of zirconium alloy cladding. Higher cladding 
corrosion rates could lead to higher dissolution rates of commercial SNF and result in higher 
radionuclide release rates from the engineered barrier system. These higher radionuclide release 
rates could, in turn, result in higher doses to the reasonably maximally exposed individual 
(RMEI). The critical question in agreement CLST 3.03 is whether radiolysis (gamma, neutron, 
and alpha) of humid air can produce HNO3 in quantities sufficient to corrode zirconium alloy 
cladding. 
B.2.2 CLST 3.04 AIN-1 
Seepage water entering a damaged waste package may vary in chemical composition. If inpackage 
fluid chemistries resulting from the reaction of seepage water with the waste form and 
waste package components are found to be sensitive to the compositions of the waters that 
initially enter a package, the performance of the repository could be affected. 
B.2 RELEVANCE TO REPOSITORY PERFORMANCE 
B-2 July 2004 No. 7: In-Package Environment Revision 1 
B.3 RESPONSE 
B.3.1 CLST 3.03 AIN-1 
Radiolysis occurs when radiation passes through a material and transfers energy to it, resulting in 
chemical reactions. In aqueous solutions and airwatervapor systems, a large number of 
possible radicals and molecules containing hydrogen and oxygen may form. Two species, HNO3 
and hydrogen peroxide (H2O2), are of particular concern because of their potential to alter the inpackage 
chemistry (BSC 2004a, Attachment III) and promote the corrosion of zirconium alloy 
cladding. The amount of HNO3 and H2O2 produced is directly proportional to the amount of 
energy deposited in the material and thus, is directly proportional to the dose rate. Hence, the 
NRC requested further information on dose rate as a function of time. 
The DOE initial response stated that very high concentrations of HNO3 (14.2 mol/L) are needed 
to promote the corrosion of zirconium alloy cladding and that buildups of such concentrations 
are not likely because: (1) the dose rate after waste package breach is low; (2) some of the nitric 
oxide may escape through waste package breaches, thereby producing less HNO3; and 
(3) dripping water flushes away HNO3 absorbed in the water film during a long period (Reamer 
2001). 
In the drip scenario, seepage water enters the waste package. This water contains dissolved 
species, including chloride and bicarbonate ions. Ferric chloride (and fluoride) may form and, 
with HNO3, tend to control and promote Zircaloy corrosion. However, bicarbonate ion tends to 
raise pH, and ferric hydroxide forms from waste package corrosion above about pH 3. 
Dissolution of such metal oxide and hydroxide minerals (primarily iron) tends to drive pH 
upward. Thus, a set of competing chemical reactions occurring in the waste package tends to 
maintain pH at moderate values and prohibit excursions to extremely low pH. 
The NRC finds the foregoing arguments acceptable, for the most part, but notes that the 
calculation for HNO3 concentration is based on a dose rate of 50 R/hr, a value not previously 
documented by the DOE. Data (presented in Section B.4.1) show that with an assumed package 
failure at 500 years (chosen to provide an arbitrarily high dose rate), the gamma dose rate has 
declined to less than 6 R/hr, about 1 order of magnitude lower than the dose rate used in the 
analysis. 
The NRC also notes that the analysis ignores the radiolysis effect of alpha particles that may 
escape from cladding-perforated fuels. While gamma and neutron radiation decline to 
insignificant levels with respect to radiolytic effects after a few centuries, alpha radiation will 
persist for a few thousand years. However, due to the nature of alpha radiolysis, the chemical 
species produced are less aggressive oxidants than are the products of gamma and neutron 
radiolysis. Furthermore, the dose rate will be lower than the unrealistically high dose rate 
modeled in the sensitivity analysis for the gamma dose rate (see Section B.4.1). On these bases, 
it is concluded that alpha radiation persisting after gamma and neutron radiation decline will not 
promote Zircaloy-cladding corrosion. 
July 2004 B-3 No. 7: In-Package Environment Revision 1 
B.3.2 CLST 3.04 AIN-1 
In-Package Chemistry Abstraction (BSC 2004a) documents that the compositions of fluids 
resulting from in-package chemical reactions are largely insensitive to the composition of the 
seepage entering the package. Three thermally perturbed fluid compositions abstracted in 
Engineered Barrier System: Physical and Chemical Environment Model (BSC 2004b) were used 
as inputs to a sensitivity analysis (BSC 2004a, Section 6.7.1). The three water compositions 
represent different carbon dioxide fugacity (fCO2) and temperature conditions. 
Inputs used in the in-package chemistry abstraction (BSC 2004a, Section 6.5) also included three 
water compositions: calciumpore water; sodiumpore water; and J-13 well water. The 
calcium and sodiumpore water compositions were used because they were obtained from core 
samples taken near the repository site. The J-13 composition was used for comparison purposes. 
The results from the two sets of sensitivity runs show that pH and ionic strength vary little with 
input water chemistry. Comparisons of two pairs of water composition output from the 
sensitivity analysis (BSC 2004a, Section 6.7.1) are presented in Section B.4.2. 
This information in this report is responsive to AIN requests CLST 3.03 AIN-1 and CLST 3.04 
AIN-1. This report contains the information that the DOE considers necessary for NRC review 
for closure of these agreements. 
B.4 BASIS FOR RESPONSE 
B.4.1 CLST 3.03 AIN-1 
Dose RatesGamma dose rates outside pressurized water reactor SNF rods are shown in 
Table B-1. These rates are taken from Gamma and Neutron Radiolysis in the 21-PWR Waste 
Package from Ten to One Million Years (BSC 2002, Tables 15 and 17). Neutron dose rates are 
roughly 4 orders of magnitude lower than gamma dose rates for the time period discussed; they 
are listed in Gamma and Neutron Radiolysis in the 21-PWR Waste Package from Ten to One 
Million Years (BSC 2002). Doses are from the central nine SNF assemblies and were calculated 
using the software code MCNP. The pressurized water reactor SNF assembly used in the 
calculation was a Babcock and Wilcox 15-by-15 assembly, and it was assumed to have 4.0 wt % 
initial U235, 48 GWd/MTU burnup, and 21-year decay time. The waste package containing the 
21-PWR was assumed to fail, resulting in a pressure of 1 atm. The temperature was assumed to 
be 90C with relative humidity between 40% and 90%. The rationale for this assumption is that 
the waste package has failed and been exposed to the atmosphere; the temperature and relative 
humidity ranges correspond to conditions under which nitrate production was measured (BSC 
2002, Section 3.11). The dose rates shown in Table B-1 are for two moist air conditions. Other 
assumptions are documented in Gamma and Neutron Radiolysis in the 21-PWR Waste Package 
from Ten to One Million Years (BSC 2002). 
July 2004 B-4 No. 7: In-Package Environment Table B-1. Gamma Dose Rates outside Pressurized Water Reactor Spent Nuclear Fuel Rods 
Age 
(Years) 
10 
15 
20 
25 
35 
50 
70 
100 
150 
200 
250 
350 
500 
700 
1,000 
1,500 
2,000 
2,500 
3,500 
5,000 
7,000 
10,000 
Dose Rate at Relative 
Humidity 40%, 
Temperature 90C 
(R/hr) 
Source: BSC 2002, Table 15. 
The dose rate of 50 R/hr occurs around 325 years, as shown in Table B-1. The dose rate at 
500 years is only about 6 R/hr. Thus, using a dose rate of 50 R/hr to calculate HNO3 production 
overestimates such production by about 1 order of magnitude and is, therefore, a conservative 
value. 
Alpha RadiolysisBecause alpha activity in SNF decreases more slowly than gamma and 
neutron activity, it is possible that oxidizing conditions and HNO3 formation could persist for 
some time (a few thousand years) after gamma and neutron radiation became ineffective (a few 
centuries). However, due to the nature of alpha radiation, fewer reactive chemical species form 
in response to water radiolysis than do in the case of neutron and gamma radiation, which results 
in chemical conditions significantly less conducive to Zircaloy corrosion. 
For low-linear-energy-transfer radiation, such as gamma radiation, the formation of oxygen- and 
hydrogen-bearing radicals and free electrons (e.g., H3O+(aq), OH- , e-(aq)) is favored over 
molecular species (e.g., H2, H2O2). Alpha radiation is high-linear-energy-transfer radiation and 
has the opposite effect (i.e., formation of more molecular species). The oxidizing effect of 
molecular species is lower than that of radicals (Christensen and Sunder 2000). Because 
Zircaloy is less susceptible to corrosion from H 
7.81  104 
5.75  104 
4.69  104 
4.02  104 
3.04  104 
2.10  104 
1.31  104 
6.51  103 
2.05  103 
6.46  102 
2.07  102 
2.52  101 
5.54 
4.65 
4.29 
4.07 
3.83 
3.75 
3.65 
3.52 
3.39 
3.24 
B-5 
Dose Rate at Relative 
Humidity 90%, 
Temperature 90C 
(R/hr) 
7.82  104 
5.76  104 
4.69  104 
4.02  104 
3.04  104 
2.10  104 
1.31  104 
6.50  103 
2.05  103 
6.46  102 
2.07  102 
2.52  101 
5.54 
4.65 
4.29 
4.07 
3.83 
3.75 
3.65 
3.52 
3.39 
3.24 
2O2 than from HNO3, and because HNO3 does 
No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
not significantly affect Zircaloy degradation at the concentrations anticipated, prolonged alpha 
radiation will likewise have a minimal effect on Zircaloy cladding integrity. 
B.4.2 CLST 3.04 AIN-1 
In-Package Chemistry Abstraction (BSC 2004a) demonstrates that the compositions of fluids 
resulting from in-package chemical reactions are largely insensitive to the composition of the 
seepage entering the package. Three thermally perturbed fluid compositions abstracted in 
Engineered Barrier System: Physical and Chemical Environment Model (BSC 2004b) were used 
as inputs to a sensitivity analysis using the in-package chemistry model (BSC 2004a, Section 
6.7.1). The three water compositions represent different fCO2 and temperature conditions. In the 
in-package chemistry abstraction (BSC 2004a, Section 6.5), inputs also included three different 
water compositions: calciumpore water; sodiumpore water; and J-13 well water. The 
calcium and sodiumpore water compositions were used because they were obtained from core 
samples taken near the repository site. The J-13 water composition was used for comparison 
purposes. 
The EQ6 runs were set up to reflect the increased temperature and fCO2, and both the 
commercial SNF and high-level radioactive waste glass kinetic rate coefficients were 
recalculated at the new temperatures (BSC 2004a, Tables 18 and 19). The EQ6 runs were 
executed at these conditions using both the thermally perturbed seepage compositions and the 
calciumpore water composition for comparative purposes. Perturbed seepage compositions are 
presented in Table B-2; the compositions for sodiumpore water, calciumpore water, and J-13 
well water are presented in Table B-3. Fluids can be formed with compositions outside of the 
ranges considered in this analysis, particularly in the early stages of the repository when 
temperatures are high and evaporative concentration of seepage on the drip shield and waste 
package surface occurs. While evaporative concentration can lead to anomalous fluid 
chemistries, it also results in substantially lower fluid fluxes, which, in turn, limit long-term 
transport of radionuclides. 
July 2004 B-6 No. 7: In-Package Environment Table B-2. Thermally Perturbed Seepage Compositions 
Parameter 
Temperature 
Log(fCO2) 
pH 
HCO3 
- 
Ca2+ 
Cl- 
F- 
K+ 
Mg2+ 
Na+ 
SO4 
2- 
SiO2(aq) 
NO3 
- 
NH3(aq) 
Al3+ 
Source: DTN: MO0303MWDSCMAB.000; 10006: 
CS2000/16.5thc_w4_r.xls, 20013: CS1000/7.3 
mol/kg 1.00  10-12 1.00  10-12 1.00  10-12 
thc6_w5_r.xls, and 51: SD-9/990.4 thc6_w6_r.xls are 
20013 
40.2 
-2.198 
7.94 
6.92  10-3 
5.73  10-4 
5.61  10-4 
6.43  10-4 
2.76  10-4 
8.51  10-5 
7.31  10-3 
3.55  10-4 
1.79  10-3 
3.97  10-5 
1.50  10-9 
Table B-3. Input Water Compositions for In-Package Chemistry 
pointers from the DTN in Engineered Barrier System: 
Physical and Chemical Environment Model (BSC 2004b). 
CalciumPore Water 
ECRB-SYS-CS1000/7.3-7.7/UC 
2.35  10-3 
7.45  10-4 
1.70  10-3 
1.94  10-4 
6.99  10-4 
4.19  10-5 
6.76  10-3 
5.92  10-4 
1.79  10-4 
3.75  10-4 
7.6 
Parameter Units 
Ca 
Mg 
Na 
K 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
Si 
N 
C 
Cl 
F 
S 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
pH pH 
Source: BSC 2004a, Table 2. 
The steady-state chemistries of incoming fluids after reaction with waste form components are 
anchored near neutral. Figures B-1 and B-2 show the sensitivity of pH and ionic strength to 
different incoming water compositions for a commercial SNF waste package and a codisposal 
waste package, respectively. The simulation results are in close agreement even though the 
initial fluid compositions were different, which implies that incoming water composition has 
little influence over the in-package pH and ionic strength. 
No. 7: In-Package Environment 
Units 
C 
 
 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
mol/kg 
10006 
56.2 
-1.950 
7.78 
6.58  10-3 
4.40  10-4 
7.12  10-4 
7.81  10-4 
4.55  10-4 
5.19  10-5 
7.49  10-3 
3.94  10-4 
2.43  10-3 
6.58  10-5 
3.73  10-9 
SodiumPore Water 
ECRB-SYS-CS2000/16.3-16.5/UC 
2.02  10-3 
1.36 x 10-4 
5.22  10-3 
1.56  10-4 
6.99  10-4 
6.61  10-6 
8.62  10-3 
6.77  10-4 
3.16  10-4 
3.23  10-4 
7.4 
B-7 
Input File Names 
51 
91.8 
-2.532 
8.14 
2.04  10-3 
7.24  10-5 
7.34  10-4 
9.77  10-4 
5.02  10-4 
2.54  10-7 
4.27  10-3 
1.18  10-4 
4.15  10-3 
3.10  10-4 
6.09  10-8 
Revision 1 
J-13 Well 
Water 
3.24  10-4 
8.27  10-5 
1.99  10-3 
1.29  10-4 
1.02  10-3 
1.42  10-4 
2.49  10-3 
2.01  10-4 
1.15  10-4 
1.95  10-4 
7 
July 2004 Revision 1 
Source: BSC 2004a, Figure 14. 
Figure B-1. Comparison of Thermally Perturbed Composition 51 (W6) with CalciumPore Water (W5) 
for a Commercial Spent Nuclear Fuel Waste Package 
Source: BSC 2004a, Figure 15. 
Figure B-2 Comparison of Thermally Perturbed Composition 20013 (W5) with SodiumPore Water 
(W4) for a Codisposal Waste Package 
Results indicated that pH and ionic strength vary little with input water chemistry (Sections 3.4 
and 3.7 of this technical basis document) and that phases produced from in-package chemical 
interactions are similar and have similar quantities for all input water chemistries. The 
July 2004 B-8 No. 7: In-Package Environment Revision 1 
comparison could have been made using J-13 water as the starting composition, rather than the 
sodium or calciumpore water, and the results would have been the same. In In-Package 
Chemistry Abstraction (BSC 2004a, Section 6), it was demonstrated that the in-package 
chemistry was virtually the same, irrespective of the starting water chemistry: J-13 water, 
sodiumpore water, and calciumpore water all resulted in virtually the same in-package 
chemistry. Thus, comparing J-13 well water with the thermally perturbed water would have 
resulted in the same in-package chemistry. 
B.5 REFERENCES 
B.5.1 Documents Cited 
Brocoum, S. 2001. Transmittal of Analysis and Model Reports (AMR) and Other Items 
Addressing Key Technical Issue (KTI) Technical Exchanges. Letter from S. Brocoum (DOE) 
to C.W. Reamer (NRC), March 30, 2001, with enclosure. ACC: MOL.20010529.0119. 
BSC (Bechtel SAIC Company) 2001. In-Package Chemistry for Waste Forms. ANL-EBS-MD- 
000056 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20010322.0490. 
BSC 2002. Gamma and Neutron Radiolysis in the 21-PWR Waste Package from Ten to One 
Million Years. 000-00C-DSU0-00700-00-00A. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20021004.0002. 
BSC 2004a. In-Package Chemistry Abstraction. ANL-EBS-MD-000037 REV 03B. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20040609.0094. 
BSC 2004b. Engineered Barrier System: Physical and Chemical Environment Model. ANLEBS-
MD-000033 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
DOC.20040212.0004. 
Christensen, H. and Sunder, S. 2000. Current State of Knowledge of Water Radiolysis Effects 
on Spent Nuclear Fuel Corrosion. Nuclear Technology, 131, (1), 102123. La Grange Park, 
Illinois: American Nuclear Society. TIC: 254040. 
CRWMS M&O (Civilian Radioactive Waste Management System Management and Operating 
Contractor) 2000. Environment on the Surfaces of the Drip Shield and Waste Package Outer 
Barrier. ANL-EBS-MD-000001 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20000328.0590. 
Reamer, C.W. 2001. Container Life and Source Term Key Technical Issue Agreements. 
Letter from C.W. Reamer (NRC) to S.J. Brocoum (DOE/YMSCO), December 21, 2001, 
0111021102, with enclosure. ACC: MOL.20020402.0608. 
Schlueter, J. 2000. U.S. Nuclear Regulatory Commission/U.S. Department of Energy Technical 
Exchange and Management Meeting on Container Life and Source Term (September 1213, 
2000). Letter from J. Schlueter (NRC) to S. Brocoum (DOE/YMSCO), October 4, 2000, with 
enclosure. ACC: MOL.20010731.0161. 
July 2004 B-9 No. 7: In-Package Environment Revision 1 
Schlueter, J. 2002. U.S. Nuclear Regulatory Commission/U.S. Department of Energy Technical 
Exchange and Management Meeting on Container Life and Source Term (September 1213, 
2000). Letter from J. Schlueter (NRC) to J. Ziegler (DOE/YMSCO), July 2, 2002, with 
enclosure. ACC: MOL.20020819.0277. 
Ziegler, J.D. 2002. Transmittal of Information Addressing Key Technical Issue (KTI) Container 
Life and Source Term (CLST) Agreements 4.02, 4.03, 4.04, and 4.05. Letter from J.D. Ziegler 
(DOE) to J.R. Schlueter (NRC), June 21, 2002. ACC: MOL.20020815.0586. 
B.5.2 Data, Listed by Data Tracking Number 
MO0303MWDSCMAB.000. THC Seepage Chemistry Model Abstraction Binning EQ3 Input, 
Pickup and Output Files. Submittal date: 03/06/2003. 
B-10 July 2004 No. 7: In-Package Environment APPENDIX C 
DEMONSTRATION OF THE ADEQUACY OF THE IN-PACKAGE 
CHEMISTRY MODEL RESULTS 
(RESPONSE TO ENFE 3.04 AND CLST 3.05) 
No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
Note Regarding the Status of Supporting Technical Information 
This document was prepared using the most current information available at the time of its development. This 
Technical Basis Document and its appendices providing Key Technical Issue Agreement responses that were 
prepared using preliminary or draft information reflect the status of the Yucca Mountain Projects scientific 
and design bases at the time of submittal. In some cases this involved the use of draft Analysis and Model 
Reports (AMRs) and other draft references whose contents may change with time. Information that evolves 
through subsequent revisions of the AMRs and other references will be reflected in the License Application 
(LA) as the approved analyses of record at the time of LA submittal. Consequently, the Project will not 
routinely update either this Technical Basis Document or its Key Technical Issue Agreement appendices to 
reflect changes in the supporting references prior to submittal of the LA. 
July 2004 No. 7: In-Package Environment Revision 1 
APPENDIX C 
DEMONSTRATION OF THE ADEQUACY OF THE IN-PACKAGE 
CHEMISTRY MODEL RESULTS 
(RESPONSE TO ENFE 3.04 AND CLST 3.05) 
This appendix provides a response to Key Technical Issue (KTI) agreements Container Life and 
Source Term (CLST) 3.05 and Evolution of the Near Field Environment (ENFE) 3.04. These 
agreements relate to demonstrating the adequacy of the in-package chemistry model. 
C.1.1 ENFE 3.04 and CLST 3.05 
Agreement CLST 3.05 was reached during the U.S. Nuclear Regulatory Commission 
(NRC)/U.S. Department of Energy (DOE) Technical Exchange and Management Meeting on 
Container Life and Source Term held September 12 to 13, 2000, in Las Vegas, Nevada 
(Schlueter 2000). Subissues 1, 2, 3, 4, and 6 were discussed at that meeting. 
Agreement ENFE 3.04 was reached during the NRC/DOE Technical Exchange and Management 
Meeting on Evolution of the Near Field Environment held January 9 and 12, 2001, in Pleasanton, 
California (Reamer 2001). ENFE KTI subissues 1, 2, 3, and 4 were discussed at that meeting. 
There have been no specific submittals to the NRC or meetings subsequent to the original 
technical exchange on this subject. 
Wording of the agreements is as follows: 
ENFE 3.04 
Complete validation of in-package chemistry models. Agreement #5 for CLST 
Subissue 3 addresses testing plans. Model validation based on this testing and 
further analysis will be documented in an update to the Summary of In-Package 
Chemistry for Waste Forms AMR (ANL-EBS-MD-000050), expected to be 
available in FY 02. 
CLST 3.05 
Provide the plan for experiments demonstrating in-package chemistry, and take 
into account subsequent NRC comments, if any. DOE stated that the current 
planning provides for the analysis of additional in-package chemistry model 
support. This analysis will determine which parts of the model are amenable to 
additional support by testing, and which parts are more amenable to sensitivity 
analysis, or use of analogs. Based on these results, longer range testing will be 
considered. If testing is determined to be appropriate, test plans will be written in 
FY01 and made available to the NRC. 
C.1 KEY TECHNICAL ISSUE AGREEMENTS 
C-1 July 2004 No. 7: In-Package Environment Revision 1 
C.1.2 Related Key Technical Issue Agreements 
Per the DOE request to the NRC (Ziegler 2002), KTI agreement CLST 4.05 was subsumed into 
CLST 3.05 (Schlueter 2002) because the wording and response for agreement CLST 4.05 was 
the same as agreement CLST 3.05. 
This agreement is related to agreements Evolution of the Near Field Environment (ENFE) 3.03, 
ENFE 3.04, and CLST 3.02 in that they address the in-package chemistry modeling. 
C.3 RESPONSE 
The information in this report is responsive to agreements CLST 3.05 and ENFE 3.04 made 
between the DOE and NRC. The report contains the information that DOE considers necessary 
for NRC review for closure of these agreements. 
C.3.1 ENFE 3.04 
The revised in-package chemistry abstraction has been validated (BSC 2004, Section 7). 
Validation of the in-package chemistry model was accomplished with a combination of an 
independent technical review (surface complexation model) and comparison of the model 
response with analog data presented in peer-reviewed literature (physical chemistry model). 
Validation of the in-package physical chemistry model requires it predict the general features of 
corrosion product assemblages observed in corrosion of metals. It also requires that the broad 
ranges of pH and ionic strength it predicts be consistent with pH values and ionic strengths 
observed in analog natural systems where the same, or similar, mechanisms prevail. The critical 
features of the in-package physical chemistry model are production of large amounts of metal 
(hydr)oxide corrosion products, alkaline waters (pH greater than 8) by interaction of dilute 
solutions with waste form glass components, acidic (pH greater than 3) waters by interaction of 
incoming solutions with waste form metallic components (primarily Carbon Steel Type A516 
and Stainless Steel Type 304) and high-ionic-strength solutions (greater than 1 mol/L) by 
reaction with waste form components over long periods of time. Corrosion products are similar 
to those seen when steels are corroded under oxidizing conditions. Ionic strength and pH values 
have been validated by comparing them with analog chemistries of natural waters, in which pH 
values rarely exceed 8.5 and range below 5 only for very specific reasons (e.g., the presence of 
high concentrations of reduced sulfur or organic acids) that will not be present at Yucca 
Mountain. The surface complexation in-package chemistry model was validated by an 
independent technical reviewer. The model validation provides confidence that the results of 
C.2 RELEVANCE TO REPOSITORY PERFORMANCE 
In-package chemistry will have a significant impact on the degradation of the waste form, which 
in turn impacts the rate at which radionuclides can be mobilized. Mobilized radionuclides will 
form the source term for transport of radionuclides through the engineered barrier system and 
release to the unsaturated zone. Therefore, understanding the in-package chemistry is important 
to repository performance. 
The primary outputs of the calculations are pH and ionic strength; other outputs are fluoride, 
chloride, total carbonate concentration, and Eh. 
C-2 July 2004 No. 7: In-Package Environment both the physical chemistry and surface complexation in-package chemistry models provide 
outputs that are representative of potential waterwaste package chemistries that potentially 
could occur at Yucca Mountain. 
C.3.2 CLST 3.05 
Developing an adequate understanding of in-package chemistry was accomplished through a 
nexus of data analyses, model runs, and model validation. These efforts provide confidence that 
the in-package chemistry model will be representative of the range of chemical conditions likely 
to occur in the water that contacts the internals of the waste package. Development of a robust 
in-package chemistry model was accomplished by consideration of an expanded input set, which 
included both environmental and system parameters. Confidence in the model output was 
bolstered by comparison to natural analogs. As a result of incorporating parameter uncertainty 
(for these system, environmental, and material parameters most sensitive to the in-package 
chemistry evolution) and reasonably constraining the model using natural analogs, additional 
testing to address specific waterin-package material interactions is not necessary. 
C.4 BASIS FOR THE RESPONSE 
Direct Input 
High-Level Radioactive Waste 
C.4.1 ENFE 3.04 
Movement of liquid or vapor phase water through cracks or other openings in the waste package 
is anticipated to trigger a series of coupled, nonlinear chemical processes dominated by 
degradation of fuel and steel components inside the waste package. The interacting processes 
are collectively termed waste package source term processes and must be effectively predicted 
because they largely control the in-package pH, ionic strength, carbonate concentration, Eh, 
fluoride concentration, and chloride concentration. In turn, these parameters are used in other 
models either directly, in the case of pH, carbonate concentration, and ionic strength, or 
indirectly, as is the case of Eh, fluoride, and chloride. Table C-1 lists the models and the inpackage 
parameters where direct inputs are used to calculate a numerical value and indirect 
inputs are qualitatively used to determine if the model is applicable. 
Waste Form Submodel l 
Solubility 
Table C-1. In-Package Chemistry Feed Parameters to Waste Form Submodels 
pH 
pH 
Glass Degradation Rate 
Colloid Concentration 
Cladding 
The pH is the most important output parameter from the in-package chemistry abstraction 
because it has the greatest influence in determining rates of radionuclide release from waste 
packages. Thus, confidence that the in-package chemistry model represents the in-package 
environment translates to confidence that the model output pH covers the potential pH range of 
the actual in-package pH. Confidence that the model output pH range is representative is gained 
Commercial Spent Nuclear Fuel pH, carbonate concentration 
Degradation Rate 
pH 
Ionic strength 
C-3 No. 7: In-Package Environment 
Revision 1 
Indirect Input 
Ionic strength, Eh, fluoride 
pH, chloride 
July 2004 Revision 1 
via extensive model-exercised sensitivity analyses and model validation exercises, including 
comparisons to natural analogs. 
The other output parameters used as direct input to waste form submodels include the total 
carbonate concentration and ionic strength. The carbonate concentration is calculated directly 
from thermodynamic relationships using the pH, temperature, and partial pressure of carbon 
dioxide as inputs. Thus, if the pH is calculated correctly, then the total carbonate concentration 
is calculated correctly as well. Note that temperature and partial pressure of carbon dioxide are 
input feeds to the in-package chemistry model. The ionic strength is used to determine if 
colloids are stable as a suspension in solution. If the ionic strength exceeds a threshold value, 
then the colloids are no longer stable and do not remain in solution, however, if below the 
threshold the ionic strength is used to calculate the colloid concentration. 
The critical features of the in-package chemistry model are production of: (1) alkaline waters 
(pH between 8 and 10) by interaction of dilute solutions with waste form glass components; 
(2) acidic (pH greater than 3) waters by interaction of incoming solutions with waste form 
metallic components (primarily Carbon Steel Type A516 steel and Stainless Steel Type 304); 
(3) high-ionic-strength solutions (greater than 1 mol/L) by prolonged reaction with waste form 
components; and (4) buffering of low-pH fluids to moderate pH by interaction with corrosion 
products (surface complexation reactions with iron oxyhydroxides). 
Validation of the in-package physical chemistry model is accomplished by showing that the 
broad ranges of pH and ionic strength it predicts are consistent with the pH levels and ionic 
strengths observed in natural systems where the same, or similar, mechanisms prevail (BSC 
2004). 
The in-package chemistry model consists of three input elements: 
1. System PropertiesWater distribution and mode of occurrence inside of the waste 
package 
2. Environmental PropertiesTemperature, gas partial pressures, water flux rates 
3. Material PropertiesCompositions, degradation and corrosion rates, masses and 
surfaces areas. 
The combination of properties from these three input elements constitutes the conceptual model 
of the in-package chemistry model. Variability of the properties within each of the input 
elements provides the basis for the range of model response. Thus, if the range of values used 
from each input element is representative of the actual system, then the model output will capture 
the system behavior. Investigation of the sensitivity of variability of the system properties and 
environmental and material properties is incorporated in In-Package Chemistry Abstraction 
(BSC 2004, Sections 6.6 and 6.7). 
The system properties describe the distribution and mode of occurrence of water within a waste 
package. The waste package is modeled as unsaturated; that is, not all of the void space is 
occupied with liquid water, some of it is occupied with gas. Water may enter the waste package 
either as a vapor phase, which presumably condenses and forms a layer of liquid water on the 
July 2004 C-4 No. 7: In-Package Environment Revision 1 
waste package components surfaces, or as seepage, which drips onto the failed waste package 
and streams through the waste package. In the no-drip model, each waste package component 
was examined individually as a single reactant to determine each components contribution to 
chemistry, and ensembles of the various waste package components were examined to determine 
chemical effects of the reactant combinations. In the seepage dripping model, the waste package 
components are considered together, and the resulting chemistry is that of a well-mixed singlecell 
flow-through batch reactor. Due to the geometric complexity of the waste package interior, 
the large degree of uncertainty in explicitly modeling a single pathway in the myriad of water 
flow pathways inside of a failed waste package, and the influence of surface complexation and 
mineral equilibria reactions on system pH, it was determined that the most robust model was the 
model that included all of the waste package components because it was the simplest model that 
retained all of the chemical trends observed in the single component and ensemble model results. 
The environmental properties are those parameters that define the in-drift and in-package 
environment. The temperature range, partial pressure of carbon dioxide, the water flux values, 
and initial water composition used in the model are all sufficiently broad to include a wide range 
of eventual in-drift and in-package environments. The temperature range was varied from 25C 
to 90C (BSC 2004, Section 6.7.5) to determine its influence on pH and ionic strength. The 
partial pressure of carbon dioxide was varied between 10-5 to 10-1.5 bars (BSC 2004, 
Section 6.7.3) to determine if the pH varied with changes in CO2 partial pressure. The total 
carbonate abstraction is cast in terms of pH, temperature, and CO2 partial pressure. The water 
flux rate was varied from 0.15 to 1,000 L/yr (BSC 2004, Section 6.7.4) to determine the rate at 
which the incoming seepage composition was affected by the reactions with the waste package. 
Note that increasing the water flux rate has the same influence on the model results as decreasing 
the corrosion rates of the alloys; the contact time of the water and metal alloy determine the 
degree of reaction that is possible. 
The material properties include the alloy and waste form compositions, the seepage water 
compositions, the alloy and waste form corrosion or degradation rates, and the waste package 
geometric information, which is used to calculate the masses and surface areas of the reactants. 
The composition of the alloys is well-defined handbook information (BSC 2004, Section 4.1.3). 
In the case of Carbon Steel Type A516, the sulfur content, which was identified as the primary 
contributor to the low pH trends observed in the model output, was varied to determine the 
magnitude of its influence on the model response (BSC 2004, Section 6.7.3). The geometry of 
the commercial SNF and codisposal waste packages used in the model report (BSC 2004, 
Section 4.1.3) was based on design information. Additional analyses were performed to 
determine the influence on the model results of replacing the Carbon Steel Type A516 in the 
commercial SNF waste package with Stainless Steel Type 316 (BSC 2004, Section 6.7.6) as may 
be the case for a naval waste package. The impact of seepage water composition on in-package 
chemistry was explored and shown to have only a minor influence on the in-package chemistry 
(BSC 2004, Sections 6.6.1.2, 6.6.2.2, and 6.7.1). 
Aqueous corrosion of metals in industrial settings under oxidizing conditions tends to result in 
the accumulation of iron(III)-containing corrosion products (Stumm and Morgan 1996, pp. 720 
to 724, 799 to 800; Snoeyink and Jenkins 1980, pp. 363, 379 to 380), such as predicted by the inpackage 
chemistry model (BSC 2004, Sections 6.6.1.2 and 6.6.2.2). Oxidation of aluminum 
metal likewise results in the formation of a passivating aluminum oxide surface layer (Snoeyink 
July 2004 C-5 No. 7: In-Package Environment Revision 1 
and Jenkins 1980, pp. 363, 375) similar to that predicted in the in-package chemistry model 
(BSC 2004, Sections 6.6.1.2 and 6.6.2.2). Therefore, the ability of the in-package chemistry 
model to accurately predict mineral assemblages that resulted from similar reactants lends 
confidence that the model is robust. 
Alkaline pH values are routinely observed when glass degrades under laboratory conditions or in 
the field (Berner and Berner 1996), although pH values above 10 are rarely seen in natural 
waters (Langmuir 1997, p. 410). When they do occur, it is generally because the waters have 
interacted with a base-containing silicate (e.g., ultramafic mineral assemblages) not in contact 
with atmospheric CO2 (Hem 1985, p. 64). This scenario is unlike the situation expected to 
prevail at Yucca Mountain, where atmospheric carbon dioxide is expected. Moreover, pH 
excursions above approximately 10 in fluid-mineral systems are also limited by precipitation of 
metal-carbonate minerals, deprotonation of aqueous species (e.g., silica, Al(OH)3(aq)), proton 
loss from mineral surface groups, and, most importantly, by schoepite dissolution. The results in 
In-Package Chemistry Abstraction (BSC 2004, Section 6.8.2) indicate that the maximum pH 
inside codisposal N Reactor waste packages will not exceed 7, independent of temperature and 
CO2 fugacity due to a variety of buffering reactions (e.g., schoepite dissolution). At water flux 
rates greater than 150 L/yr, waste package pH may equal 8 or the input pH. 
A number of predicted reactions prevent any pH excursions to values below approximately 4.5, 
the most important being protonation of iron corrosion products via surface complexation. These 
are discussed in detail in the in-package chemistry model (BSC 2004, Section 6.8). Natural 
waters having pH values below 4.5 generally occur because of the presence of abundant organic 
acids or the oxidation of metal sulfide groups (Langmuir 1997, p. 162), both in the absence of 
effective pH-buffering reactions. Organic acids are not expected at Yucca Mountain, and sulfide 
levels are too low to result in acidic pHs (BSC 2004, Section 6.8). In other words, because 
conditions that favor pH lower than 4.5 (low acid-buffering capacity and high sulfur or organic 
acids or both) are absent in the in-package chemistry model, the lower limit of pH 
(approximately 4.5) estimated by the in-package chemistry model is consistent with natural 
observations. 
The in-package surface chemistry model is an analytic derivation of pH and considers the major 
physical-chemical reactions and their effects on pH and how these interactions with iron 
corrosion products will ultimately influence the effluent pH. This analytical model and its 
results are supported by surface complexation calculations (BSC 2004, Section 6.9) that 
demonstrate the validity of the analytical model. The physical chemistry of the system and the 
surface chemistry are intricately linked and ideally would be combined in a fully coupled inpackage 
chemistry model. However, it was not possible to construct such a model due to the 
necessity of maintaining continuity with the reaction-path geochemical modeling approach, 
which does not couple surface complexation reactions with the physical chemistry of the system. 
The software used in modeling the in-package physical chemistry, EQ3/6 v7.2b (Wolery and 
Daveler 1992), is a reaction-path code which simulates the interaction of water with a set of 
reactants under various user-specified conditions. At this time, EQ3/6 v7.2b does not have the 
facility to simulate the coupled process of the degradation of the reactant metal alloys and the 
simultaneous precipitation of corrosion products and the influence surface complexation 
reactions of the bulk fluid with the corrosion products. Software, such as PHREEQC, version 
2.3 (BSC 2002), which is similar to EQ3/6 and does have the facility to model surface 
July 2004 C-6 No. 7: In-Package Environment Revision 1 
complexation reactions and was used to confirm the results of the analytic surface complexation 
model (BSC 2004, Section 6.9). However, PRHEEQC was not used in a fully coupled mode 
because of its limited capability to perform reaction tracing, an operation at which EQ6 excels. 
Surface complexation describes the electrostatic interactions of charged ions in solution with 
high surface area charged mineral surfaces, such as clays and iron (hydr)oxide corrosion 
products. The immobile charged surfaces of the mineral phases act to retain ions in solution so 
that the bulk solution properties, such as pH, are altered. In the waste package environment 
where the predominant alteration phases are iron corrosion products, which by their nature carry 
an electrical charge and have extremely high surfaces areas (BSC 2004, Table 12), the influence 
of the corrosion products on the solution chemistry is potentially important. The effect of 
surface complexation on the in-package pH was estimated analytically in the in-package 
chemistry model report (BSC 2004) because it was not possible to model the effects directly 
using the reaction-path software. 
In a multireactant system, such as the waste package interior, the evolution of the chemistry is 
most influenced by reactants that react rapidly, contain elements that have the potential to 
perturb the ambient chemical conditions, and are present in large enough quantities to exert a 
chemical change on the system. For these reasons, Carbon Steel Type A516 is considered to be 
of primary importance because of its high corrosion rate (BSC 2004, Table 9) relative to the 
other reactants, the presence of reduced sulfur in its composition which lowers system pH when 
oxidized, and its abundance in the waste package. During the period of rapid corrosion of 
Carbon Steel Type A516, a large quantity of ferric (hydr)oxides will accumulate in the waste 
packages in about 200 years, and the oxidation of sulfur will tend to lower the system pH. 
Occurring simultaneously with Carbon Steel Type A516 corrosion are corrosion and degradation 
of the other waste package metal alloys and SNF, although at much slower rates than the Carbon 
Steel Type A516. While the system chemistry would be dominated by Carbon Steel Type A516 
corrosion, the complexity of the system chemistry would be increased by the 
corrosion/degradation of the other waste package components. The steel alloys would form 
ferric (hydr)oxides and other metal corrosion products (e.g., pyrolusite), the aluminum alloys 
would form gibbsite and clay minerals, and, while the SNF would form primarily schoepite, the 
high-level radioactive waste glass would form clays, nearly all of the degradation products of the 
non-Carbon Steel Type A516 waste package components are phases with surface complexation 
properties. Considering only the reaction of Carbon Steel Type A516 would tend to 
underestimate the number of surface sites available for the surface complexation reactions, as 
well as the nature of the sites (e.g., site density). In an actual degrading waste package, the 
potential for surface complexation and pH buffering would be greater than in the simplified 
system, which only considers Carbon Steel Type A516 corrosion products. 
It is during the 0- to 600-year postclosure period that the analytic surface complexation model 
was used to predict the in-package pH. The reasons for limiting the application of the surface 
complexation model to this period are (1) increasing uncertainty in the ferric (hydr)oxide mineral 
assemblage as a function of time and (2) this relatively brief period of low pH, as predicted by 
the EQ6 physical chemistry model, would be most influenced by the presence of surface 
complexation reactions. By not overapplying the surface complexation model, its output 
underestimates the magnitude of the potential surface complexation reactions that could take 
place in a degrading waste package. For commercial SNF, the pH was calculated as a 
July 2004 C-7 No. 7: In-Package Environment Revision 1 
distribution that was a function of the partial pressure of carbon dioxide. Surface complexation 
tends to mediate pH to a range between approximately pH 4.5 to 8. 
The results presented in In-Package Chemistry Abstraction (BSC 2004, Sections 6.6, 6.7 and 
6.8) illustrate that variations in the model properties influence the pH time trajectories for the 
physical chemistry of the system. The surface complexation model tends to buffer low pH 
values, while mineral equilibria tends to limit alkaline pH values limiting the range between 
approximately 4.5 and 8. The ionic strength uncertainty terms, which were derived based on the 
results of the sensitivity analyses, and water flux rate functionality of ionic strength abstractions 
provide confidence that the ionic strength calculated by the model is representative the of the 
system. Model validation exercises, coupled with extensive sensitivity analyses, provide a high 
level of confidence that the system pH and ionic strength will fall within the model output ranges 
for these parameters. 
C.4.2 CLST 3.05 
Confidence that the model output brackets the range of chemistries from a degrading waste 
package was gained by prudent choice of model inputs combined with a large array of sensitivity 
analyses. The three input elements identified in Section C.4.1 and the properties within each 
comprise the in-package chemistry model. 
The system element comprises the mode of occurrence and distribution of water within the waste 
package. For both the commercial SNF and the codisposal waste packages, the range in output 
physical chemistry was simulated for water vapor condensing and accumulating in the waste 
package and seepage dripping and flowing through the waste package. The effect on the 
chemistry of individual and groups, or ensembles, of reactants was examined to gain 
understanding of the role of water distribution within the waste package. The results of these 
analyses can be found in In-Package Chemistry Abstraction (BSC 2004, Sections 6.6.1 and 
6.6.2). 
The material element includes the waste package design information (used to calculate the 
masses and surface areas of each waste package component), the composition of the waste 
package components and spent nuclear fuel, the composition of the incoming seepage, the 
corrosion rates of the metal alloys, and the degradation rates of the commercial SNF and highlevel 
radioactive waste glass. Variations in these inputs were examined for their influence on the 
model response. The results of these analyses can be found in In-Package Chemistry 
Abstraction (BSC 2004, Section 6.7). 
The environmental element includes the temperature, the partial pressure of carbon dioxide, and 
rate of water flux through the waste package. These parameters were varied over a wide range of 
values to account for a large number of possible waste package environments. The results of 
these analyses can be found in In-Package Chemistry Abstraction (BSC 2004, Section 6.7). 
Based on the results of the sensitivity analyses and the large number of eventual waste package 
conditions that the sensitivity analyses encompass, the need for future testing is unnecessary. 
July 2004 C-8 No. 7: In-Package Environment Revision 1 
Berner, E.K. and Berner, R.A. 1996. Global Environment: Water, Air, and Geochemical 
C.5 REFERENCES 
Cycles. Upper Saddle River, New Jersey: Prentice Hall. TIC: 253965. 
BSC (Bechtel SAIC Company) 2002. Software Code: PHREEQC. V2.3. PC. 10068-2.3-01. 
BSC 2004. In-Package Chemistry Abstraction. ANL-EBS-MD-000037 REV 03B. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20040609.0094. 
Hem, J.D. 1985. Study and Interpretation of the Chemical Characteristics of Natural Water. 
3rd Edition. Geological Survey Water-Supply Paper 2254. Washington, D.C.: U.S. Government 
Printing Office. ACC: NNA.19940427.0181. 
Langmuir, D. 1997. Aqueous Environmental Geochemistry. Upper Saddle River, New Jersey: 
Prentice Hall. TIC: 237107. 
Reamer, C.W. 2001. U.S. Nuclear Regulatory Commission/U.S. Department of Energy 
Technical Exchange and Management Meeting on Evolution of the Near-Field Environment 
(January 912, 2001). Letter from C.W. Reamer (NRC) to S. Brocoum (DOE/YMSCO), 
January 26, 2001, with enclosure. ACC: MOL.20010810.0033. 
Schlueter, J.D. 2000. U.S. Nuclear Regulatory Commission/U.S. Department of Energy 
Technical Exchange and Management Meeting on Container Life and Source Term (September 
12-13, 2000). Letter from J. Schlueter (NRC) to S. Brocoum (DOE/YMSCO), October 4, 2000, 
with enclosure. ACC: MOL.20010731.0161. 
Schlueter, J.D. 2002. U.S. Nuclear Regulatory/U.S. Department of Energy Technical Exchange 
and Management Meeting on Container Life and Source Term (September 12-13, 2000). Letter 
from J. Schlueter (NRC) to J. Ziegler (DOE/YMSCO), July 2, 2002, with enclosure. 
ACC: MOL.20020819.0277. 
Snoeyink, V.L. and Jenkins, D. 1980. Water Chemistry. New York, New York: John Wiley & 
Sons. TIC: 256179. 
Stumm, W. and Morgan, J.J. 1996. Aquatic Chemistry, Chemical Equilibria and Rates in 
Natural Waters. 3rd Edition. New York, New York: John Wiley & Sons. TIC: 246296. 
Wolery, T.J. and Daveler, S.A. 1992. EQ6, A Computer Program for Reaction Path Modeling 
of Aqueous Geochemical Systems: Theoretical Manual, Users Guide, and Related 
Documentation (Version 7.0). UCRL-MA-110662 PT IV. Livermore, California: Lawrence 
Livermore National Laboratory. ACC: MOL.19980701.0459. 
Ziegler, J.D. 2002. Transmittal of Information Addressing Key Technical Issue (KTI) 
Container Life and Source Term (CLST) Agreements 4.02, 4.03, 4.04, AND 4.05. Letter from 
J.D. Ziegler (DOE/YMSCO) to J.R. Schlueter (NRC), June 21, 2002, OL&RC:TCG-1262. 
ACC: MOL.20020815.0586. 
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C-10 No. 7: In-Package Environment 
Revision 1 
July 2004 APPENDIX D 
LOCALIZED CORROSION AND STRESS CORROSION CRACKING IN CLADDING 
(RESPONSE TO CLST 3.06 AIN-1, CLST 3.07, CLST 3.08 AIN-1, 
CLST 3.09 AIN-1, AND GEN 1.01 (COMMENT 124)) 
No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
Note Regarding the Status of Supporting Technical Information 
This document was prepared using the most current information available at the time of its development. This 
Technical Basis Document and its appendices providing Key Technical Issue Agreement responses that were 
prepared using preliminary or draft information reflect the status of the Yucca Mountain Projects scientific 
and design bases at the time of submittal. In some cases this involved the use of draft Analysis and Model 
Reports (AMRs) and other draft references whose contents may change with time. Information that evolves 
through subsequent revisions of the AMRs and other references will be reflected in the License Application 
(LA) as the approved analyses of record at the time of LA submittal. Consequently, the Project will not 
routinely update either this Technical Basis Document or its Key Technical Issue Agreement appendices to 
reflect changes in the supporting references prior to submittal of the LA. 
July 2004 No. 7: In-Package Environment Revision 1 
APPENDIX D 
LOCALIZED CORROSION AND STRESS CORROSION CRACKING IN CLADDING 
(RESPONSE TO CLST 3.06 AIN-1, CLST 3.07, CLST 3.08 AIN-1, 
CLST 3.09 AIN-1, AND GEN 1.01 (COMMENT 124)) 
This appendix provides a response to Key Technical Issue (KTI) agreements Container Life and 
Source Term (CLST) 3.06 Additional Information Needed (AIN)-1, CLST 3.07, CLST 3.08 
AIN-1, CLST 3.09 AIN-1, and General Agreement (GEN) 1.01 (Comment 124). These 
agreements relate to localized corrosion and stress corrosion cracking in cladding. 
D.1 KEY TECHNICAL ISSUE AGREEMENTS 
CLST 3.06 
Provide additional technical basis for the failure rate and how the rate is affected 
by localized corrosion. DOE stated that the technical basis for local corrosion 
conditions will be added to by additional discussion of local chemistry in the 
D.1.1 CLST 3.06 AIN-1, CLST 3.07, CLST 3.08 AIN-1, CLST 3.09 AIN-1, and GEN 1.01 
(Comment 124) 
Agreements CLST 3.06, CLST 3.07, CLST 3.08, and CLST 3.09 were reached during the U.S. 
Nuclear Regulatory Commission (NRC)/U.S. Department of Energy (DOE) Technical Exchange 
and Management Meeting on CLST held September 12 to 13, 2000, in Las Vegas, Nevada 
(Schlueter 2000). 
The NRC reviewed documents related to these CLST agreements, and, as a result, several AIN 
items were identified. With regard to CLST 3.06, the NRC reviewed In-Package Chemistry for 
Waste Forms (BSC 2001), Clad DegradationSummary and Abstraction (CRWMS M&O 2001), 
and Clad DegradationLocal Corrosion of Zirconium and Its Alloys Under Repository 
Conditions (CRWMS M&O 2000a) and determined that these documents did not satisfy the 
agreement. The NRC asked for specific additional information to close the agreement, resulting 
in CLST 3.06 AIN-1 (Reamer 2001). 
With regard to CLST 3.08, the NRC reviewed the following documents: Initial Cladding 
Condition (CRWMS M&O 2000b), Thermal History of Cladding in a 21 PWR SNF WP Loaded 
with Average Fuel (CRWMS M&O 2000c), Abstraction of NFE Drift Thermodynamic 
Environment and Percolation Flux (CRWMS M&O 2000d), and Multiscale Thermohydrologic 
Model (CRWMS M&O 2000e). As a result of that review, the NRC requested additional 
information to close the agreement, resulting in CLST 3.08 AIN-1 (Reamer 2001). 
With regard to CLST 3.09, the NRC reviewed Clad DegradationSummary and Abstraction 
(CRWMS M&O 2001) and determined that the document did not satisfy the agreement. As a 
result, the NRC requested additional information to close the agreement, resulting in CLST 3.09 
AIN-1 (Reamer 2001). 
The wording of each agreement and AIN is as follows: 
July 2004 D-1 No. 7: In-Package Environment Revision 1 
Summary of In-package Chemistry for Waste Forms revision ANL-EBS-MD- 
000050 which will be available in January 2001. Current Clad Degradation 
Summary Abstraction Section 6.3, ANL-WIS-MD-000007 and Clad Degradation 
- Local Corrosion of Zirconium and its Alloys Under Repository Conditions, 
ANL-EBS-MD-000012 contain the overall technical basis. 
CLST 3.06 AIN-1 
1) Provide the technical basis to define the chemical composition of the inpackage 
water and its evolution with time. 
2) Provide the technical basis for excluding the effect of localized corrosion in the 
form of pitting promoted by chloride. 
3) Provide clarification on whether or not DOE assumes that acidic conditions 
will be present inside the waste packages and the pH range expected if acidic 
conditions prevail. 
4) Provide additional information on the environmental conditions, in terms of 
chemical composition, prevailing inside breached waste packages. 
CLST 3.07 
Provide data to address chloride induced localized corrosion and SCC under the 
environment predicted by in-package chemistry modeling. DOE stated that the 
technical basis for the models used for localized corrosion and SCC will be 
expanded in future revisions of the Clad Degradation Summary Abstraction 
AMR, ANL-WIS-MD-000007, available by LA. 
CLST 3.08 
Provide the documentation on the distribution for cladding temperature and stress 
used for hydride embrittlement. DOE stated that the stresses are documented in 
the Initial Cladding Conditions AMR, ANL-EBS-MD-000048. CAL-UDC-ME- 
000001 contains the waste package internal temperatures. Waste package surface 
temperatures were provided within the TSPA model (ANL-EBS-HS-000003, 
Rev 00, ICN 01 and ANL-EBS-MD-000049). The updated versions of these 
documents will be available in January 2001. 
CLST 3.08 AIN-1 
1) Provide additional information to justify that the temperature is not sufficiently 
high for dissolution of some of the precipitated hydrides. 
2) Provide additional information regarding the accuracy and validity of the stress 
and temperature data. 
July 2004 D-2 No. 7: In-Package Environment Revision 1 
CLST 3.09 
Provide a technical basis for critical stress that is relevant for the environment in 
which external SCC takes place. DOE stated that critical stress from SCC 
experiments under more aggressive conditions will be cited in the Revision of the 
Cladding Degradation Summary Abstraction AMR, ANL-WIS-MD-000007, 
which will be available in January 2001. 
CLST 3.09 AIN-1 
Provide specific information on the critical stress required for the occurrence of 
stress corrosion cracking initiated from the external surface of the fuel cladding in 
the presence of the possible in-package environments. 
The NRC/DOE Technical Exchange and Management Meeting on Range of Thermal Operating 
Temperatures was held September 18 to 19, 2001 (Reamer and Gil 2001). At that meeting, the 
NRC provided additional comments that relate to the CLST Key Technical Issues (KTIs) 
discussed in this appendix, including GEN 1.01 (Comment 124) (Reamer and Gil 2001). 
Agreement GEN 1.01 was reached. Wording of these agreements and of the DOE initial 
response to the general agreement comments is as follows: 
GEN 1.01 (Comment 124) 
In p. 7-74, Ferric Chloride generation is very remote spatially. Provide the basis 
for the hypothesis. 
NRC Clarification: Correct page number is 7-64, Section 7.3.7.1: 
Although useful in ranking a range of alloys, these standard tests utilize 
aggressive environments (in particular, a ferric chloride solution) that are not 
directly relevant to expected waste package surface environments because the 
potential for ferric chloride generation is very remote. 
The NRC expressed a concern that DOE models might neglect the potential 
accelerating effect of ferric chloride on corrosion/degradation of waste package 
materials and fuel cladding materials. NRC requested clarification about DOE's 
intent to evaluate the potential role of ferric chloride in waste package and fuel 
cladding degradation. 
DOE Initial Response to GEN 1.01 (Comment 124) 
Electrochemical corrosion testing is determining the effect that minor constituents 
will have on the waste package corrosion processes. This will include the effects 
of ferric ion in the aqueous test solutions. Activities will also determine the 
extent that minor constituents can concentrate in the aqueous solutions. This 
work is covered under existing KTI agreement CLST 1.1. 
D-3 July 2004 No. 7: In-Package Environment Revision 1 
Per existing KTI agreement CLST 3.7, a ferric-chloride local clad corrosion 
model is being developed that will be documented in a future revision of the Clad 
Degradation Summary Abstraction AMR. 
D.1.2 Related Key Technical Issue Agreements 
There are no other KTI agreements directly related to agreements CLST 3.06, 3.07, 3.08, and 
3.09. 
D.3 RESPONSE 
The information in this report is responsive to agreements CLST 3.07 and GEN 1.01 (Comment 
124) made between the DOE and NRC and responsive to AIN requests CLST 3.06 AIN-1, CLST 
3.08 AIN-1, and CLST 3.09 AIN-1. The report contains the information that the DOE considers 
necessary for NRC review for closure of these agreements. 
D.3.1 CLST 3.06 AIN-1 
Item-1The technical basis for the time-dependent chemical composition of the in-package 
water is documented in In-Package Chemistry Abstraction (BSC 2004a) for water interactions in 
commercial SNF waste packages (BSC 2004a, Section 6.6.1) and codisposal (N Reactor SNF 
and high-level radioactive waste glass) waste packages (BSC 2004a, Section 6.6.2). A summary 
of in-package chemistry evolution is presented in Section 3.4. The in-package chemistry model 
simulates chemical interactions of water with the waste package materials and the waste form. 
The results from the in-package chemistry model include time concentration histories of the 
elements and their aqueous complexes constituting the waste package, waste form, seepage, and 
gas composition. Results also include time histories of pH, Eh, ionic strength, and mineralogical 
phase abundance. The in-package chemistry model abstraction simplifies these results for use in 
the TSPA-LA. 
Item 2Zirconium alloys are susceptible to pitting in a particularly aggressive combination of 
chloride (Cl-) ions, ferric ions (Fe3+), or hydrogen peroxide (H2O2). However, because pH levels 
below pH 4.5 and Fe3+ levels above 1 ppm are not predicted by the in-package chemistry model, 
D.2 RELEVANCE TO REPOSITORY PERFORMANCE 
Intact cladding is a crucial impediment to corrosion of fuel and the export of radionuclides 
because radionuclides cannot be released from spent nuclear fuel (SNF) with intact cladding. 
Clad corrosion is, therefore, potentially an important factor that must be considered in 
calculations of repository performance. Local corrosion of cladding requires low pH values and 
high dissolved concentrations of metal halide complexes. 
Stress corrosion cracking mechanisms can also contribute to cladding failure and accelerate 
radionuclide release. 
For stress corrosion cracking to occur, a cladding manufacturing defect, corrosion pit, or other 
small cladding crack must exist, and stresses and stress intensity factors must be large enough to 
promote stress corrosion cracking. 
D-4 July 2004 No. 7: In-Package Environment Revision 1 
the acid conditions and high Fe3+ levels needed for localized corrosion cannot be achieved 
(dissolved Fe3+ will be limited by ferric hydroxide solubility, which is low at pH 3 and above). 
The mechanisms for pH control are described in Appendix A of this document and treated in 
quantitative detail in Section 3. 
Item 3In-package pH levels are not expected to drop below 4.5. Calculations pointing to the 
absence of low pH conditions are central to the conclusion that localized corrosion will not be an 
important factor inside a breached waste package. As discussed in Section 3.5.1 in this technical 
basis document, analyses in In-Package Chemistry Abstraction (BSC 2004a, Section 6.8) 
indicate that low pH will not occur. 
Item 4A summary of in-package chemistry (pH, Eh, ionic strength, fluoride concentration, 
chloride concentration, and total carbonate concentration) evolution is presented in Section 3.4. 
The technical basis for environmental conditions in the waste packages is found in In-Package 
Chemistry Abstraction (BSC 2004a, Sections 6 and 8). 
D.3.2 CLST 3.07 and GEN 1.01 (Comment 124) 
CLST 3.07The technical basis for the response to this KTI agreement is based on the feature, 
event, and process (FEP) screening argument presented in Clad DegradationFEPs Screening 
Arguments (BSC 2004b, Table 7-1, FEP 2.1.02.16.0A, Section 6.6). Stress corrosion cracking 
results from simultaneous action of a tensile stress and a corrosive environment and requires a 
certain chemical environment and sufficiently high stresses (stress intensity factors at the crack 
tip). Chloride-induced stress corrosion cracking could occur on the outside of the cladding but 
requires that the passive layer of oxides on the zirconium surface be unstable. These are the 
same conditions under which pitting occurs. As demonstrated in FEP 2.1.02.160A, the chemical 
conditions for pitting and for stress corrosion cracking do not exist in the waste package because 
the repassivation potential exceeds the corrosion potential. As a result, the passive layer of 
oxides on the zirconium surface is not unstable. The zirconium pitting model (BSC 2003) 
describes the conditions in which pitting is observed at high concentrations of chlorides at 
extremely low pH (below -0.6), which can lead to the general dissolution of the protective 
zirconium oxide film. Figure D-1 demonstrates the chemical conditions in which pitting would 
occur. No pitting is predicted to occur for any chemical conditions that were associated with 
Zircaloy cladding in the repository. Chemical conditions expected to prevail inside the waste 
package would represent a location in the extreme lower left corner of Figure D-1. Analyses in 
these references concluded that cladding failures caused by chloride-induced stress corrosion 
cracking are not anticipated and can be excluded from consideration in the TSPA-LA. 
D-5 July 2004 No. 7: In-Package Environment Revision 1 
Source: BSC 2003, Figure 7-1. 
D.3.3 CLST 3.08 
Because of a close tie between temperature and stress, CLST 3.08 AIN-1 and CLST 3.08 AIN-2 
will be addressed together. 
NOTE: Data used for this graph are being updated; these updates, however, will not impact conclusions obtained 
herein. 
July 2004 
Figure D-1. Experimental Measurements of Pitting and Pitting Model 
GEN 1.01 (Comment 124)The role of ferric chloride on waste package and fuel degradation 
has been evaluated and excluded from further consideration in the TSPA-LA model because of 
the low likelihood that the conditions for ferric chloride to impact corrosion exist in the inpackage 
environment. A zirconium-pitting model (BSC 2003) was developed to investigate the 
chemical conditions at which pitting occurs. Zirconium alloys are susceptible to pitting in a 
particularly aggressive combination of chloride (Cl-) ions, ferric ions (Fe3+), or hydrogen 
peroxide (H2O2). The model predicts the conditions where pitting is observed. No pitting was 
predicted to occur in the predicted in-package environment. In a sensitivity study in which acid 
production from radiolysis was increased by a factor of 10, no pitting was predicted to occur. 
Results from the application of this model support the TSPA-LA treatment that when the inpackage 
pH is above 3, the cladding will not fail from either pitting or general corrosion (BSC 
2004b, Section 6.6). In a sensitivity study, the pH was artificially suppressed to 2.2, and yet 
pitting was still not predicted to occur. 
D-6 No. 7: In-Package Environment Revision 1 
Stresses and stress intensity factors are too low for crack propagation, and cladding failure by 
delayed hydride cracking is unlikely. Maximum temperatures are too low to dissolve much 
hydrogen, most rods have stresses too low for hydride reorientation, and cladding failure by 
hydride reorientation is unlikely. Lastly, hydrogen axial migration is limited at the 268C 
maximum temperature expected during emplacement. 
Clad DegradationFEPs Screening Arguments (BSC 2004b, Section 6.12) presents the accuracy 
and validity of the cladding stress and temperature data that impact the defensibility of the 
screening arguments. The screening arguments are based on the maximum fuel rod temperature, 
which is estimated for the center rod and maximum fuel loading conditions to be approximately 
268C. Maximum stress intensity factors at 268C are approximately 10% of the critical stress 
intensity factor threshold for crack propagation for moist chloride. At the 268C maximum 
temperature, less than 5% of the fuel rods would be candidates for hydride reorientation. Those 
fuel rods that may be candidates for hydride reorientation also would require that high stress 
exist in order for hydride reorientation to occur. Less than 1% of the fuel rods would have 
stresses sufficiently high to result in some level of reorientation. At the 268C maximum 
temperature, a temperature gradient between the hotter and cooler portions of a fuel rod of at 
least 50C would be required for hydride axial migration to occur. Predicted temperature 
gradients are 19C, which would be insufficient to result in axial migration. 
D.3.4 CLST 3.09 
The information requested supports the screening of FEP 2.1.02.21.0A, stress corrosion cracking 
of cladding. This FEP has been excluded from further consideration in the TSPA-LA because of 
low consequence in the in-package environment. In order for stress corrosion cracking to occur, 
conditions similar to those that would support pitting must be present at a cladding 
manufacturing defect, corrosion pit, or other small cladding crack, and stresses and stress 
intensity factors must be large enough to promote stress corrosion cracking. The chemical 
environment necessary to promote either pitting or stress corrosion cracking will not exist in the 
in-package environment. Even if such chemical conditions were to occur, the stresses and stress 
intensity factors are too low for stress corrosion cracking to occur, as discussed in Section D.4.4. 
Details on the technical basis for the critical stress state in which external stress corrosion 
cracking takes place are found in Clad DegradationFEPs Screening Arguments (BSC 2004b, 
Table 7-1, FEP 2.1.02.16.0A, Section 6.6, and FEP 2.1.02.21.0A, Section 6.11). 
The maximum pressure, stresses, and stress intensity factors occur either at the time of maximum 
temperature or at the end of the regulatory period of 10,000 years, depending on the gas release 
fraction for helium (BSC 2004b, Section 6.11). Conservatively using 100% helium gas release, 
maximum stress intensity factors (KI) are in the range of approximately a mean value of 0.47 to a 
maximum of 2.7 MPa-m0.5. This is less than the critical stress (KISCC) of 28 MPa-m0.5 for moist 
chlorine at 70C (or 24 MPa-m0.5 at 100C) measured by Cox (1990, Figure 20, p. 15), which is 
pertinent to the case of external cracking. Therefore, stress corrosion cracking is not expected. 
July 2004 D-7 No. 7: In-Package Environment Revision 1 
D.4 BASIS FOR THE RESPONSE 
D.4.1 CLST 3.06 AIN-1 
D.4.1.1 CLST 3.06 AIN-1 Item 1 
The temporal evolution of in-package chemistry is described in Section 3.4. 
The technical basis of the in-package chemistry model is an assemblage of mass action and mass 
balance expressions that permit the anticipation of waste form steel and water interaction over 
time. Kinetic rate constants and dependencies capture the broad scale features of irreversible 
processes and an updated thermodynamic database reproduces the characteristics of reversible 
processes. The outputs of the model are reasonable when compared against the observed 
behavior of natural waters. The chemistry of fluids inside the waste package is controlled by the 
breakdown of waste package components. Changing the flow rate changes the output pH and 
ionic strength. Raising the flow rate causes pH and ionic strengths to be closer to that of the 
input fluid. Increasing the fuel exposure (i.e., decreasing clad coverage), also affects model 
outputs by raising fuel degradation, via the increase in the fuel surface area, leading to more 
moderate pH conditions due to buffering by schoepite formation. 
Over the first 100 years when the degraded waste form consists primarily of A516 corrosion 
products and intact fuel, pH will be held to near-neutral by the surface properties of the corrosion 
products, which are the most abundant proton donor/acceptors in the package. Over longer time 
periods, when the degraded waste form will consist of schoepite, dissolving steels, and possibly 
high-level radioactive waste glass, the pH will be anchored near neutral by dissolution reactions 
of schoepite that consume protons at acidic pH values and release them at alkaline pH values. 
Lastly, the in-package chemistry calculations assume that reaction of steel components will 
proceed unhindered by corrosion product buildup until the steels are completely dissolved. It 
also assumes that corrosion product sites will themselves remain available for interaction with 
ambient solutions. In reality, corrosion product buildup will likely prevent access of water and 
oxygen to the fresh metal (as well as fuel and high-level radioactive waste glass) surfaces and 
hinder further reaction. Waste form degradation will then proceed in a stochastic fashion: 
corrosion products forming and stopping degradation, followed by sloughing off of protective 
layers and reinitiation of corrosion. 
D.4.1.2 CLST 3.06 AIN-1 Item 2 
The effect of localized corrosion in the form of pitting has been excluded from the TSPA-LA 
model because its inclusion in the TSPA-LA modeling would have a low consequence. The 
technical basis for excluding the effect of localized corrosion of the cladding in the form of 
pitting promoted by chloride is contained in Clad DegradationFEPs Screening Arguments 
(BSC 2004b, Section 6.6). This conclusion is based on the results from a pitting model for 
zirconium-alloy cladding (BSC 2003) and assumes that the in-package pH will not fall below 
4.5. This model predicts the chemical conditions that cause pitting of zirconium cladding used 
on commercial SNF. Zirconium alloys are susceptible to pitting in a particularly aggressive 
combination of chloride (Cl-) ions, ferric ions (Fe3+), or hydrogen peroxide (H2O2). In order to 
predict cladding failure from chloride pitting, a review of the literature for pitting rates and 
July 2004 D-8 No. 7: In-Package Environment Revision 1 
electrochemical data for various zirconium alloys was conducted. Based on this review of the 
literature, failure criteria were constructed based on an electrochemical definition of pitting as 
the condition at which the corrosion potential exceeds repassivation potential (i.e., Ecorr greater 
than Erp). 
Figure D-1 shows experimental measurements of pitting corrosion and the clad pitting model. 
No pitting is predicted to occur for any chemical conditions that were associated with Zircaloy 
cladding in the repository. Note that Fe3+ levels of 1 ppm (a level never exceeded in the inpackage 
chemistry calculations) would require chloride levels exceeding 10 mol/L (equally 
unlikely even under conditions of high evaporation) for clad pitting to occur. In-package 
chemistry calculations point to maximal levels of Fe3+ to be 1 ppm; chloride levels never exceed 
0.001 mol/L. In other words, the chemical conditions expected to prevail inside the package 
would represent a location in the extreme lower left corner of Figure D-1. 
In sensitivity studies in which acid production from radiolysis increased by a factor of 10 or the 
pH was artificially suppressed to 2.2, the pitting model predicted a corrosion potential less than 
the repassivation potential, and, as a result, no pitting was predicted to occur. Even at the 
minimum pH predicted in the in-package chemistry model, no pitting is predicted to occur (BSC 
2003, Section 6.6.). In-package chemistry predictions are compared with chemical conditions 
necessary for pitting as determined by the pitting model in Figure D-1. The chemistry conditions 
in which pitting has been observed and on which the pitting model is based, are illustrated in 
relation to the model and its uncertainty range in the screening argument for FEP 2.1.02.16.0A 
(BSC 2003, Section 6.6, Figure 6.6-2). 
+ (Eq. D-1) 2 
D.4.1.3 CLST 3.06 AIN-1 Item 3 
Waste package pH is expected to remain between 4.5 and 8.1. The technical basis for this is 
discussed below, in In-Package Chemistry Abstraction (BSC 2004a, Section 6.8), and in 
Section 3.2. Dissolution of Carbon Steel Type A516 has the potential to lower pH due to the 
oxidation of elemental sulfur to sulfuric acid. Degradation of other alloys and fuels results in 
roughly neutral pH. 
The A516 dissolution reaction is: 
Fe1.76Mn0.019C0.0233Si0.0103P0.00113S0.00109 + mH2O + pO2 
 0.00109SO4
2. + 0.00113(H3PO4 + H2PO4 
. + HPO4
2.) + 0.0103H4SiO4 
+ 0.0233(H2CO3 + HCO3 
.) + 0.019MnO2 + (1.76 . X)FeOOH 
+ X(>Fe-O-H + >Fe-O-H + >Fe-O. + >Fe-O-CO3 
.) + nH+ 
where X is the amount of iron in FeOOH (moles) that is exposed to solution, m is moles of water, 
and p is partial pressure. Note that the uncharged species in Equation D-1 are not included in 
Equation D-2 because they do not contribute directly to the pH. However, as weak acids they do 
contribute to the overall buffer capacity of the solution. 
July 2004 D-9 No. 7: In-Package Environment Revision 1 
Given an X value of 0.176 (10% of iron sites, a reasonable value for goethite (BSC 2004a)) for 
dissolution of 1 mol of Carbon Steel Type A516 into a near-neutral solution: 
2.] + [H n = 2[SO4 2PO4 
.] + 2[HPO4
2.] + [HCO3 
.] + [>Fe-O.] + [>Fe-O-HCO3 
.] . [>Fe-O-H2 
+] (Eq. D-2) 
Similarly, charge balance for a solution in contact with carbon dioxide gas at 0.001 atm and into 
which 1 mol of A516 was dissolved would be 
. (H+) + (>Fe-O-H2
+) = 2[SO4
2.] + [H2PO4 ] + 2[HPO4 
2.] 
(Eq. D-3) + (OH.) + (HCO3 
.) + (>Fe-O.) +(>Fe-O-HCO3 
.) 
The equation above can be solved for pH as a function of fixed partial pressure of CO2, given the 
following surface complexation constants (Dzombak and Morel 1990) and other existing 
thermodynamic relations. 
+ >Fe-O-H + H+ K1 = [>Fe-O-H][H+]/[>Fe-OH2
+] = 10.7.29 >Fe-OH2 
>Fe-OH >Fe-O.+ H+ K2 = [>Fe-O.][H+]/[>Fe-OH] = 10.8.93 
>Fe-O-H + H+ + CO3 
2. >Fe-HCO3 
. + H2O 
(Eq. D-4) KCO3 = [>Fe-HCO3 
.]aH2O/[>Fe-OH][H+][ CO3
2.] = 1012.78 
where >Fe denotes sites exposed at the FeOOH.solution interface. Performing the algebraic 
calculation above allows the prediction of the pH values that will result from Carbon Steel Type 
A516 degradation as a function of carbon dioxide levels (Table D-1) during the period from 0 to 
600 years when surface complexation is implemented. Consideration of the corrosion product 
surface chemistry suggests that the pH of fluids in contact with degrading Carbon Steel Type 
A516 will not be acidic but rather will be close to neutral. To summarize, the lack of acidgenerating 
reactions in the waste package will prevent acidic conditions from prevailing. 
Table D-1. Calculated pHs 
pH 
8.1 
8.1 
8.0 
7.9 
7.7 
7.5 
7.3 
7.0 
6.8 
log(fCO2) (atm) 
.5.0 
.4.5 
.4.0 
.3.5 
.3.0 
.2.5 
.2.0 
.1.5 
.1.0 
Source: BSC 2004a, Table 61. 
D-10 July 2004 No. 7: In-Package Environment Revision 1 
D.4.1.4 CLST 3.06 AIN-1 Item 4 
Three scenarios are modeled in TSPA-LA. In the nominal (no volcanoes or earthquakes) 
scenario, water drips or condenses into the waste form where it encounters a large amount of 
steel and some fuel (clad in the case of commercial SNF) or high-level radioactive waste glass. 
The water will be near neutral, oxidizing, and carrying ambient levels of carbon dioxide. 
Corrosion of low carbon steel in the first 3 to 5 decades will cause the formation of large 
quantities of ferric (hydr)oxide corrosion products. Water encountering fuel in commercial SNF 
packages will initiate dissolution of the fuel if the cladding has failed because of manufacturing 
defects or damage during fuel handling activities. In codisposal waste packages, DOE SNF will 
immediately begin to corrode, as will high-level radioactive waste glass. The resulting pH of 
fluids inside the waste form will be held close to neutral by a combination of corrosion product 
surface chemistry and a pH-buffering dissolution of uranium(VI)-hydroxides, stainless steels, 
and glass. Dissolved concentrations of neptunium, plutonium, americium, and uranium will be 
limited to the near-neutral pH solubilities of their respective oxides or other less soluble 
minerals. At early and late time periods, when ionic strengths are calculated to be large, colloids 
will tend to agglomerate inside the waste form. Late-term high ionic strengths occur due to the 
long-term accumulation of dissolved solids. In between, dilute conditions might allow the export 
of radionuclides attached to colloidal hosts. Waste form degradation in the seismic scenario will 
proceed as in the nominal case; however, some fraction of the clad will be destroyed as a result 
of the seismic activity, allowing more commercial SNF to be dissolved. Seismic scenario pH 
levels will be close to neutral and likely buffered by the presence of uranium and other metal 
hydroxide phases. In the third scenario, when a volcano sends basalt into the drift, dispersing the 
fuel into the basalt matrix, the chemistry of basaltwater seepage interaction will control the 
breakdown of fuel and the release of radionuclides. Igneous intrusion scenario pH levels will be 
somewhat above neutral and reflect the influence of base cation leaching from the basalt matrix. 
D.4.2 CLST 3.07 and GEN 1.01 (Comment 124) 
CLST 3.07Stress corrosion cracking (Cox 1973, Abstract; Farina et al. 2002, p. 5; Yau and 
Webster 1987, p. 718) is the propagation of an existing crack on the surface of a metal because 
of the presence of a chemical in the crack tip. Stress corrosion cracking requires a certain 
chemical environment and sufficiently high stresses (stress intensity factors at the crack tip). 
Chloride-induced stress corrosion cracking could occur on the outside of the cladding, with the 
requirement that the passive layer of oxides on the zirconium surface be first chemically 
attacked. These are the same conditions under which pitting occurs. As demonstrated in Pitting 
Model for Zirconium Alloyed Cladding at YMP (BSC 2003, Section 8.2) and in the response to 
CLST 3.06 AIN-2, the chemical conditions for pitting and stress corrosion cracking do not exist 
in the Yucca Mountain environment. See Clad DegradationFEPs Screening Arguments (BSC 
2004b, Section 6.6) for the screening of the pitting corrosion FEP. Consequently, chlorideinduced 
stress corrosion cracking is not expected in the waste package environment. 
Even if the chemical environment for stress corrosion cracking existed, stresses and stress 
intensities are too low for stress corrosion cracking to occur. Stresses and stress intensity factors 
on the cladding are estimated in Clad DegradationFEPs Screening Arguments (BSC 2004b, 
Section 6.11) at three times in the waste package: (1) at initial emplacement time, (2) at the time 
of maximum cladding temperature, and (3) at the regulatory period of 10,000 years. The 
D-11 July 2004 No. 7: In-Package Environment conditions at the time of maximum temperature (10 years from closure, 268C (BSC 2004b, 
Table A-3)) are determined by using the ideal-gas law and the initial temperature conditions. 
The conditions at 10,000 years are also obtained by adjusting for helium production from alpha 
decay and assuming that all of the helium is released from the fuel matrix. The mean 
temperature at 10,000 years for average loading is estimated to be 61C (the maximum 
temperature at 10,000 years for maximum loading is estimated to be 73C, which is used in the 
pressure and stress calculations) (BSC 2004b, Table A-3). The mean pressure at 10,000 years, 
which includes helium production and released fission gas, is estimated to be 13 MPa. Stress 
intensity factors vary from a mean value of 0.47 to a maximum of 2.7 MPa-m0.5, shown in 
Table D-2. This is compared to the threshold stress intensity factor for stress corrosion cracking, 
the value at which crack propagation will start but with a very slow propagation velocity. 
Threshold values from the open literature for various chemical solutions are shown in Table D-3. 
The lowest value from the open literature in Table D-3 is 4 MPa-m0.5. The threshold stress 
intensity factor (KISCC) of 28 MPa-m0.5 for moist chlorine is included in this table and is pertinent 
to the case of external cracking (Cox 1990, Figure 20, p. 15). Since the estimated stress 
intensities are less than each threshold value in Table D-3, chloride-induced cracking is not 
expected. 
Table D-2. Temperature, Pressure, Stress, and Stress Intensity Factors for Three Different Times 
Time 
Parameter 
Time (years) 
Temperature Temperature (C) 
Mean (MPa) Pressure 
95% (MPa) 
98% (MPa) 
Maximum (MPa) 
Mean (MPa) Stress 
95% (MPa) 
98% (MPa) 
Maximum (MPa) 
Stress Intensitya Mean (MPa-m0.5) 
95% (MPa-m0.5) 
98% (MPa-m0.5) 
Properties at Peak 
Temperature 
10 
268 
8.7 
13 
16 
32 
69 
111 
136 
263 
0.47 
1.09 
1.38 
2.73 Maximum (MPa-m0.5) 
Source: BSC 2004b, Table 6.11-1. 
NOTE: a For a sharp crack (Reed-Hill 1973, p. 800). 
Ambient 
Temperature 
Properties 
0 
27 
4.8 
7.3 
8.9 
18 
38 
62 
76 
146 
0.26 
0.61 
0.77 
1.52 
D-12 No. 7: In-Package Environment 
Revision 1 
Properties at Peak 
Temperature, 
Regulatory Limit 
10,000 
73 
13 
16 
18 
28 
107 
137 
154 
233 
0.73 
1.34 
1.56 
2.42 
July 2004 Revision 1 
Table D-3. Threshold Stress Intensity Factors for Zirconium Alloys from the Open Literature 
Solution 
3 mol/L Potassium Iodide (KI) 
Reference 
Cox 1990, Figure 14, p. 12 
Cox 1990, Figure 15, p. 12 1 mol/L KBr + 0.25 mol/L Br2 
Cox 1990, Figure 20, p. 15 
Cox 1990, Figure 20, p. 15 
Cox 1990, Figure 20, p. 15 
Cox 1990, Figure 7a, p. 7 
Cox 1990, Figure 7b, p. 7 
Tasooji et al. 1984, Figure 12, 
KISCC 
12 (100C) to 22 (22C) MPa-m0.5 
12 MPa-m0.5 (22C) 
28 MPa-m0.5 (70C) 
13 MPa-m0.5 (22C) 
5 MPa-m0.5 
20 to 28 MPa-m0.5 (Zirc4) 
13 to 22 MPa-m0.5 (Zirc2) 
4.0 to 15 MPa-m0.5 (Zirc2, value varies 
with texture, 300C) 
July 2004 
Chlorine (Moist) 
Iodine + air 
Nitrate/Iodide Melt 
Hydrogen Gas (6.7 to 86 kPa) 
Hydrogen Gas (1 to 100 kPa) 
Iodine, 0.001 kg/m2 
D-13 
p. 612 
GEN 1.01 (Comment 124)The potential role of ferric chloride in waste package and fuel 
cladding degradation is evaluated in Clad DegradationFEPs Screening Arguments (BSC 
2004b, Sections 6.5 and 6.6). Zirconium and its alloys resist localized corrosion, such as pitting, 
crevice corrosion, and stress corrosion cracking, in all but concentrated acidic metal halide 
solutions that are themselves not predicted to occur in the repository. Temperature has little 
effect on the process. 
A zirconium-pitting model (BSC 2003) was developed to investigate the chemical conditions at 
which pitting occurs. Zirconium alloys are susceptible to pitting in a particularly aggressive 
combination of chloride (Cl-) ions, ferric ions (Fe3+), or hydrogen peroxide (H2O2). The model 
predicts the chemical conditions under which pitting is expected to occur. Details of the analysis 
are documented in Clad DegradationFEPs Screening Arguments (BSC 2004b, Section 6.6). 
Figure D-1 shows the chemical conditions in which pitting would occur and the chemical regions 
predicted. No pitting was predicted to occur in the waste package environment because the 
appropriate chemical conditions do not exist there. In sensitivity studies where acid production 
from radiolysis was increased by a factor of 10 and the pH was artificially suppressed to 2.2, no 
pitting was predicted to occur. 
Results from the application of this model support the TSPA-LA treatment that the cladding will 
not fail from either pitting or general corrosion in the Yucca Mountain environment. As 
predicted from the in-package chemistry analysis (BSC 2004a, Attachment III) and the pitting 
model (BSC 2003, Section 8.2), pitting is not expected to occur. 
The pitting model was generated using data for as-polished samples, but commercial SNF is 
coated with thick oxides (54 m mean, 5% to 95% m range of 5.3 to 112 m) (CRWMS M&O 
2000b, Section 6.4). These oxides will affect the corrosion potential (open circuit potential) 
because of the high electrical resistance of the coating. Zirconium samples with oxides elevate 
the corrosion potential above the as-polished corrosion potential and repassivation potential, but 
no pitting corrosion was observed, even when exposed to 5 mol/L FeCl3 (BSC 2004b, 
Section 6.6). 
As further validation of the pitting model and its results, a review of the literature for pitting 
observations for various zirconium alloys was conducted and conditions under which pitting 
No. 7: In-Package Environment Revision 1 
occurs were documented (BSC 2004b, Section 6.6). A comparison of the chemistry and the 
regions where pitting is observed shows that cladding failure from pitting is unlikely. 
Concentrations of chlorides and ferric ions predicted in the in-package chemistry model are 
many orders of magnitude lower than those necessary for pitting; therefore, cladding failure from 
pitting is not expected. 
D.4.3 CLST 3.08 
Because of the close tie between temperature and stress, CLST 3.08 AIN-1 and CLST 3.08 
AIN-2 will be addressed jointly. 
The information below is taken from Clad DegradationFEPs Screening Arguments (BSC 
2004b, Section 6.1.2) and is addressed as FEP 2.1.02.22.0A, hydride cracking of cladding. 
Three subissues from the FEP screening argument rely on temperature and stress data: delayed 
hydride cracking (of cladding), hydride reorientation (of cladding), and hydride axial migration 
(of cladding). These subissues are relevant to this KTI agreement response and are discussed 
below. 
The FEPs screening arguments for the three subissues above rely on estimates of the maximum 
fuel rod temperatures. The stresses and stress intensities are a function of temperature. 
Table D-4 gives the temperature history for the rod located in the waste package center location 
(hottest location). Both the mean (average) and the maximum rod temperatures are presented. 
July 2004 D-14 No. 7: In-Package Environment Table D-4. Time-Dependence of Temperatures for Center Rod, Average and Maximum Fuel Loading 
(C) 
159 
208 
219 
221 
220 
214 
181 
169 
145 
111 
82 
71 
61 
Time 
(Years) 
0 
5 
10 
15 
20 
30 
90 
130 
290 
950 
3,000 
6,000 
10,000 
Source: BSC 2004b, Appendix A, Table A-3. 
The pressure, stress, and stress intensity factors are presented in Table D-2 for three times. The 
stress intensity factor is a measure of the concentration of stress at the tip of existing cracks. The 
peak temperature occurs at 10 years after closure of the repository. Using 100% helium gas 
release, the mean, 95%, and 99% values of pressure are higher at 10,000 years than at 10 years; 
however, the maximum value is actually higher at 10 years. Table D-2 shows that the maximum 
stress intensity factors are about 2.7 MPa-m0.5. As the table also shows, most rods have 
significantly lower stress intensities. These stress intensities are less than the critical stress 
intensity (KISCC) of 28 MPa-m0.5 for moist chlorine measured by Cox (1990, Figure 20, p. 15). 
Therefore, cracking is not expected. 
Details of how the temperature profiles are generated are given in Clad DegradationFEPs 
Screening Arguments (BSC 2004b, Appendix A). Stress and stress intensity factors of Table D-2 
are used in the screening arguments of the FEPs presented in Clad DegradationFEPs Screening 
Arguments (BSC 2004b, Sections 6.11 and 6.12). 
At the peak temperature of 268C for the center rod of the hottest waste package, the saturation 
limit for hydrogen is about 44 ppm (BSC 2004b, Table 6.12-2). That is, for all the hydrides to 
dissolve at 268C and for hydride reorientation to occur, the initial hydrogen concentration must 
be less than 44 ppm. No more than 5% of the fuel has an average hydrogen concentration of 44 
ppm or less; hence, less than 5% of the fuel could have all its hydrogen go into solution and, 
thus, be a candidate for reorientation. 
Analyses conclude that hydride reorientation is of low likelihood because stresses and 
temperatures are too low for hydride reorientation to occur (BSC 2004b, Section 6.12.4). These 
analyses have shown that most of the fuel and the cladding material will maintain sufficient 
fracture toughness, even if hydride reorientation did occur, so that failure would not be expected. 
At the maximum cladding temperature of 2 68C, the stress would have to be greater than 174 
Temperature, Average Loading, 
Average Waste Package Temperature 
D-15 No. 7: In-Package Environment 
Revision 1 
(C) 
214 
260 
268 
266 
264 
254 
212 
197 
170 
131 
99 
85 
73 
Temperature, Maximum Fuel Loading, 
Maximum Waste Package Temperature 
July 2004 Revision 1 
MPa before reorientation would occur. This corresponds to a stress value of about 94 MPa at 
room temperature. It is estimated (CRWMS M&O 2000b, Figure 26) that only 0.45% of the 
rods could have stresses this high and might undergo some reorientation. This is a small amount 
of the total inventory and supports the screening argument of low consequence. Hydride 
reorientation has been excluded on the basis of low consequence since few, if any, rods may 
undergo reorientation, and such reoriented rods may not fail. 
Hydride axial migration, if it were to occur to a significant extent, could result in increased 
cladding failure. Analyses (BSC 2004b, Section 6.12.5) have shown that little or no hydride 
axial migration is expected because of a lack of large temperature gradients (the driving force for 
axial migration along the rod) in the waste packages and the low peak temperatures (which limit 
the amount of hydrogen in solution). 
Hydrides can form in cooler parts of the rod, such as the end sections, because the hydrogen can 
dissolve into the fuel-cladding metal matrix at a warmer area, diffuse toward the cooler area, and 
condense there. McMinn et al. (2000, Figure 15) give the solubility of hydrogen. The solubility 
varies depending on whether hydrogen is dissolving or precipitating to form hydrides. McMinn 
et al. (2000, Figure 15) show that a minimum of 42C temperature difference between the hot 
location and cold location in the cladding is needed to move the hydrogen. At temperatures 
below 200C, temperature differences of more than 75C are needed to axially move the 
hydrogen. At a peak cladding-center temperature of 268C, at least a difference of about 50C is 
needed to move and precipitate hydrogen. After repository closure, temperature differences of 
19 C are predicted (CRWMS M&O 2000c, Table 6-4). Therefore, little or no axial migration is 
expected based on the difference between condensation and dissolution solubility. 
D.4.4 CLST 3.09 
The chemical environment in the waste package, as discussed in Section D.4.2, is not sufficiently 
aggressive for pitting or stress corrosion cracking on the outer fuel cladding surface to occur. 
Even if an aggressive chemical environment existed in waste packages, stresses and stress 
intensities are too low for stress corrosion cracking to occur. Table D-2 gives the pressure, 
stresses, and stress intensity distributions for cladding at three times and temperatures. Details of 
how the entries in this table were determined can be found in Clad DegradationFEPs Screening 
Arguments (BSC 2004b, Section 6.11). 
During the times of interest (peak temperature or helium pressure) the stress intensity factors 
vary from 0.47 mean value to 2.7 MPa-m0.5 maximum. This is compared to the threshold stress 
intensity factor for stress corrosion cracking, the value at which crack propagation will start, but 
with a very slow propagation velocity. If the stress intensity factor exceeds the threshold, failure 
will occur. Table D-3 gives the threshold values for various chemical solutions and 
temperatures. All values in the table are greater than or equal to 4 MPa m0.5. The threshold 
stress intensity factor (KISCC) of 28 MPa-m0.5 for moist chlorine is included in this table and is 
pertinent to the case of external cracking. The initial source of these stress intensity factors is 
Cox (1990, Figure 20, p. 15). Since the estimated stress intensities are less than the threshold 
values, chloride-induced cracking is not expected. 
July 2004 D-16 No. 7: In-Package Environment Revision 1 
D.5 REFERENCES 
BSC (Bechtel SAIC Company) 2001. In-Package Chemistry for Waste Forms. ANL-EBS-MD- 
000056 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20010322.0490. 
BSC 2003. Pitting Model for Zirconium Alloyed Cladding at YMP. MDL-WIS-MD-000001 
REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20031017.0003. 
BSC 2004a. In-Package Chemistry Abstraction. ANL-EBS-MD-000037 REV 03B. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20040325.0129. 
BSC 2004b. Clad DegradationFEPs Screening Arguments. ANL-WIS-MD-000008 REV 01. 
Las Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20040322.0001. 
Cox, B. 1973. Stress Corrosion Cracking of Zircaloy-2 in Neutral Aqueous Chloride Solutions 
at 25 C. Corrosion, 29, (4), 157166. Houston, Texas: National Association of Corrosion 
Engineers. TIC: 248988. 
Cox, B. 1990. Environmentally-Induced Cracking of Zirconium Alloys  A Review. Journal 
of Nuclear Materials, 170, 123. Amsterdam, The Netherlands: North-Holland. TIC: 234774. 
CRWMS M&O (Civilian Radioactive Waste Management System Management and Operating 
Contractor) 2000a. Clad DegradationLocal Corrosion of Zirconium and Its Alloys Under 
Repository Conditions. ANL-EBS-MD-000012 REV 00. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.20000405.0479. 
CRWMS M&O 2000b. Initial Cladding Condition. ANL-EBS-MD-000048 REV 00 ICN 01. 
Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20001002.0145. 
CRWMS M&O 2000c. Thermal History of Cladding in a 21 PWR SNF WP Loaded with 
Average Fuel. CAL-UDC-ME-000001 REV 00. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.20000216.0105. 
CRWMS M&O 2000d. Abstraction of NFE Drift Thermodynamic Environment and Percolation 
Flux. ANL-EBS-HS-000003 REV 00 ICN 01. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.20001206.0143. 
CRWMS M&O 2000e. Multiscale Thermohydrologic Model. ANL-EBS-MD-000049 REV 00 
ICN 01. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20001208.0062. 
CRWMS M&O 2001. Clad Degradation  Summary and Abstraction. ANL-WIS-MD-000007 
REV 00 ICN 01. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20010214.0229. 
Dzombak, D.A. and Morel, F.M.M. 1990. Surface Complexation Modeling, Hydrous Ferric 
Oxide. New York, New York: John Wiley & Sons. TIC: 224089. 
July 2004 D-17 No. 7: In-Package Environment Revision 1 
Farina, S. B.; Duffo, G.S.; and Galvele, J.R. 2002. Stress Corrosion Cracking of Zirconium 
and Zircaloy-4 in Iodine Containing Solutions. Corrosion/2002, 57th Annual Conference & 
Exposition, April 7-11, 2002, Denver, Colorado. Paper No. 02436. Houston, Texas: NACE 
International. TIC: 253841. 
McMinn, A.; Darby, E.C.; and Schofield, J.S. 2000. The Terminal Solid Solubility of 
Hydrogen in Zirconium Alloys. Zirconium in the Nuclear Industry: Twelfth International 
Symposium, Toronto, Canada, 15-18 June 1998. Sabol, G.P. and Moan, G.D., eds. Pages 173 
195. West Conshohocken, Pennsylvania: American Society for Testing and Materials. 
TIC: 247102. 
Reamer, C.W. 2001. Container Life and Source Term Key Technical Agreements. Letter 
from C.W. Reamer (NRC) to S. Brocoum (DOE/YMSCO), December 21, 2001, with enclosure. 
ACC: MOL.20020402.0608. 
Reamer, C.W. and Gil, A.V. 2001. Summary Highlights of NRC/DOE Technical Exchange and 
Management Meeting of Range on Thermal Operating Temperatures, September 1819, 2001. 
Washington, D.C.: U.S. Nuclear Regulatory Commission. ACC: MOL.2002017.0162. 
Reed-Hill, R.E. 1973. Physical Metallurgy Principles. 2nd Edition. New York, New York: D. 
Van Nostrand Company. TIC: 237154. 
Schlueter, J. 2000. U.S. Nuclear Regulatory Commission/U.S. Department of Energy Technical 
Exchange and Management Meeting on Container Life and Source Term (September 1213, 
2000). Letter from J. Schlueter (NRC) to S. Brocoum (DOE/YMSCO), October 4, 2000, with 
enclosure. ACC: MOL.20010731.0161. 
Tasooji, A.; Einziger, R.E.; and Miller, A.K. 1984. Modeling of Zircaloy Stress-Corrosion 
Cracking: Texture Effects and Dry Storage Spent Fuel Behavior. Zirconium in the Nuclear 
Industry, Sixth International Symposium, Vancouver, British Columbia, June 28- July 1, 1982. 
Franklin, D.G. and Adamson, R.B., eds. ASTM STP 824. Pages 595626. Philadelphia, 
Pennsylvania: American Society for Testing and Materials. TIC: 241417. 
Yau, T.L. and Webster, R.T. 1987. Corrosion of Zirconium and Hafnium. Corrosion, 13, 
707721 of ASM Handbook. Formerly 9th Edition, Metals Handbook. Materials Park, Ohio: 
ASM International. TIC: 240704. 
July 2004 D-18 No. 7: In-Package Environment APPENDIX E 
TOTAL SYSTEM PERFORMANCE ASSESSMENT 
IMPLEMENTATION OF IN-PACKAGE CHEMISTRY 
(RESPONSE TO TSPAI 3.08) 
No. 7: In-Package Environment 
Revision 1 
July 2004 Revision 1 
Note Regarding the Status of Supporting Technical Information 
This document was prepared using the most current information available at the time of its development. This 
Technical Basis Document and its appendices providing Key Technical Issue Agreement responses that were 
prepared using preliminary or draft information reflect the status of the Yucca Mountain Projects scientific 
and design bases at the time of submittal. In some cases this involved the use of draft Analysis and Model 
Reports (AMRs) and other draft references whose contents may change with time. Information that evolves 
through subsequent revisions of the AMRs and other references will be reflected in the License Application 
(LA) as the approved analyses of record at the time of LA submittal. Consequently, the Project will not 
routinely update either this Technical Basis Document or its Key Technical Issue Agreement appendices to 
reflect changes in the supporting references prior to submittal of the LA. 
July 2004 No. 7: In-Package Environment Revision 1 
APPENDIX E 
TOTAL SYSTEM PERFORMANCE ASSESSMENT 
IMPLEMENTATION OF IN-PACKAGE CHEMISTRY 
(RESPONSE TO TSPAI 3.08) 
This appendix provides a response to Key Technical Issue (KTI) agreement Total System 
Performance Assessment and Integration (TSPAI) 3.08. This agreement relates to 
implementation of in-package chemistry in total system performance assessment (TSPA). 
E.1 KEY TECHNICAL ISSUE AGREEMENT 
Agreement TSPAI 3.08 was reached during the U.S. Nuclear Regulatory Commission 
(NRC)/U.S. Department of Energy (DOE) Technical Exchange and Management Meeting on 
TSPAI held August 6 through 10, 2001, in Las Vegas, Nevada (Reamer and Gil 2001a). 
E.1.1 TSPAI 3.08 
The wording of the agreement is as follows: 
TSPAI 3.081 
Provide the technical basis (quantification) for the abstraction of in-package 
chemistry and its implementation into the TSPA which will demonstrate that the 
utilization of the weighted-moving-average methodology will not result in an 
underestimation of risk (ENG3.1.3). 
DOE will provide the technical basis (quantification) for the abstraction of inpackage 
chemistry and its implementation into the TSPA, which will demonstrate 
that the implementation methodology will not result in an underestimation of risk. 
The technical basis will be documented in TSPA-LA and is expected to be 
available in FY 2003. 
General agreement (GEN) 1.01 was reached during the NRC/DOE Technical Exchange and 
Management Meeting on Range of Thermal Operating Temperatures, held September 18 to 19, 
2001, in Las Vegas, Nevada. Associated with that meeting, the DOE provided initial responses 
to GEN 1.01 (Comment 126) that referred in part to TSPAI 3.08 (Reamer and Gil 2001b). 
Comment 126, however, is addressed entirely in Appendix A. 
E.1.2 Related Key Technical Issue Agreements 
There are no other KTI agreements directly related to agreement TSPAI 3.08. 
1 The content of ENG3.1.3 (Cornell 2001) is adequately represented within the text of TSPAI 3.08. 
E-1 July 2004 No. 7: In-Package Environment Revision 1 
E.3 RESPONSE 
The model calculates the time-varying chemical conditions in the waste package for each time 
step of the TSPA-LA calculations (Section 3.5 of this technical basis document). 
Once a waste package fails and degradation of the waste form and package components 
commences, pH values are expected to remain within a range of 4.5 to 8.1 during the first 
600 years after breach and within a range of 4.5 to 7 after 600 years (nominal and seismic 
scenarios; see below) (BSC 2004, Sections 6.6.1.1 and 6.6.2.1). Potentially low pH from the 
corrosion of steel waste package components, particularly during the first 600 years after 
package failure, would be buffered by the corrosion product goethite; potentially high pH from 
the corrosion of high-level radioactive waste glass would be counteracted by corrosion of the 
large amount of steel in the codisposal packages. The difference in the upper end of the pH 
range (pH 8.1 at times less than 600 years after breach, versus pH 7 at times greater than 
600 years after breach) is not significant. 
The information in this report is responsive to agreement TSPAI 3.08 made between DOE and 
NRC. This report contains the information that DOE considers necessary for NRC review for 
closure of this agreement. 
E.4 BASIS FOR THE RESPONSE 
The weighted-moving average methodology for calculating the time-phased failure of waste 
packages will be used in the TSPA-LA in combination with a set of calculations that determine 
the time-varying chemical conditions that will evolve in a waste package once it has failed 
(Section 3.4 of this technical basis document) (BSC 2004, Section 6). The time-varying 
chemical conditions are obtained with the in-package chemistry model (BSC 2004). 
With the weighted-moving average methodology (using the same approach in the TSPA-LA 
model as that used in Total System Performance Assessment (TSPA) Model for Site 
Recommendation (CRWMS M&O 2000), the average waste package failure time is calculated as 
the average duration that each failed waste package within a bin has been breached; it is 
calculated for each percolation flux subregion (bin) and for nondripping and dripping conditions. 
For each bin, a curve is used to represent the fraction of waste packages failed at any given time. 
Due to differences in the numbers of packages in the bins, waste package failures are realized at 
different times in different bin environments. 
E.2 RELEVANCE TO REPOSITORY PERFORMANCE 
The in-package chemistry (water chemistry, pH, ionic strength, Eh, fluoride concentration, 
chloride concentration, and total carbonate concentration) can potentially impact radionuclide 
and colloid concentration and transport, as well as waste package and waste form component 
degradation in TSPA for the license application (TSPA-LA). The calculation of time-dependent 
chemical parameters in combination with the weighted-moving average method of calculating 
waste package failure times could potentially underestimate waste package and waste form 
degradation and radionuclide transport. 
E-2 July 2004 No. 7: In-Package Environment Revision 1 
The in-package chemistry model simulates the chemistry inside of a failed waste package (i.e., 
the chemistry of the fluid that reacts with the waste package components and the waste forms). 
The in-package chemistry model begins calculation of the fluid chemistry resulting from waste 
form and package component degradation in the TSPA-LA model when the first package fails 
and an aqueous phase is present within the waste package (i.e., when the waste package 
temperature falls below 100C). In each time-step of the TSPA-LA calculations, the particular 
chemical condition (e.g., pH, ionic strength, Eh, total carbonate, chloride, or fluoride 
concentration) is abstracted into response surfaces or parameter distributions that are sampled to 
establish the chemical condition for a particular computational realization in the TSPA-LA. 
In the in-package chemistry calculations used in the TSPA for the site recommendation model, 
pH in a breached waste package dropped to low values within about 50 years after breach, 
primarily due to corrosion of sulfur-bearing steel alloys and became more moderate after about 
500 years (CRWMS M&O 2000). Under these circumstances, the weighted-moving average 
methodology for calculating waste package failure time could underestimate the duration of lowpH 
conditions. However, refinements in the in-package chemistry calculations (see below) used 
in TSPA-LA make the effects of time averaging insignificant due to the similar pH ranges prior 
to and after 600 years. The likelihood that a combination of a relatively late-failing package with 
calculated time-varying chemical conditions could reduce releases and underestimate risk is 
small because the pH within the waste package will avoid extremes throughout the time 
analyzed. 
Constant Moderate ChemistrypH values are expected to remain within a range of 4.5 to 8.1 
during the first 600 years after breach and within a range of 4.5 to 7 after 600 years (nominal and 
seismic scenarios). Although time-varying pH is calculated only for commercial SNF waste 
packages, for completeness this discussion describes the chemistry within both commercial SNF 
and codisposal packages. The revised In-Package Chemistry Abstraction (BSC 2004, 
Sections 6.6.1.1 and 6.6.2.1) demonstrates that reaction of the individual waste form components 
(steels, glass, fuel) produces fluids with pH values that are near neutral. The three exceptions are 
Carbon Steel Type A516 and Stainless Steel Type 304L steel degradation, which both produce 
protons (acid conditions), and high-level radioactive waste glass, which generates alkalinity. 
Explicit consideration of the pH-buffering of corrosion products indicates that Carbon Steel Type 
A516 degradation will actually produce near-neutral fluids (BSC 2004, Section 6.8). Stainless 
Steel Type 304L degradation is similarly unlikely to result in acid pH solutions because it 
dissolves very slowly (BSC 2004, Section 6.5.1.1); hence, proton production is likely to be 
diluted by incoming fluids. High-level radioactive waste glass dissolution can produce locally 
high pH values because of its high base cation content. However, there is far more steel than 
high-level radioactive waste glass in codisposal packages, and large amounts of schoepite are 
estimated to form as well. Dissolution of either consumes alkalinity and would tend to make the 
persistence of high pH values unlikely in the waste form. In particular, relatively rapid 
dissolution of schoepite would tend to drive pH values toward its solubility minimum 
near-neutral pH. The presence of multiple processes capable of providing a negative-feedback 
control over high pH is discussed in Section 3.4 of this technical basis document and in 
In-Package Chemistry Abstraction (BSC 2004, Section 6.8). The dissolution of high-level 
radioactive waste glass is the only way to generate locally extreme pHs. Because high-level 
radioactive waste glass is present in minor amounts (and dissolves more slowly) relative to other 
July 2004 E-3 No. 7: In-Package Environment Revision 1 
pH-buffering reactants, the potential for long-term persistence of extreme pHs is considered 
remote. 
The difference in the upper end of the pH range (pH 8.1 at times less than 600 years after breach, 
versus pH 7 at times greater than 600 years after breach) and its effect on waste form degradation 
rates do not adversely affect chemistry, since the degradation rates of commercial SNF at pH 7 
and 8.1 are essentially the same. While codisposal waste packages are not affected by the 
weighted-moving average methodology, it is worth noting that high-level radioactive waste glass 
has a lower degradation rate at the higher pH value and is, therefore, conservative. 
The difference in the upper end of the pH range has a mixed effect on actinide solubilities. 
Neptunium has a lower solubility at pH 8.1 than pH 7 at lower fCO2 values and a higher 
solubility at pH 8.1 at higher fCO2 values (roughly an order of magnitude higher between pH 7 
and 8.1). Protactinium shows a similar pattern. 
Uranium solubility is controlled by one of three phases (schoepite, Na-boltwoodite, or 
NA3UO2(CO3)3, depending on the pH and the fCO2 (BSC 2004, Section 6.7). There is a complex 
functionality of dissolved uranium to both pH and the fCO2 (BSC 2004, Figure 6.7-2). Thus, for 
any of the uranium controlling phases, the trend in the uranium concentration at different pH 
values can be either by increasing or decreasing, depending on fCO2. 
Plutonium solubility is flat between pH 7 and 8.1 at higher fCO2 values and lower at pH 8.1 at 
lower fCO2 values. Americium solubility follows a roughly similar pattern. Thorium solubility 
is higher at pH 8.1 for all fCO2 values. 
In conclusion, the moderate chemistry anticipated in a breached waste package will result in pH 
values that will vary within narrow limits over time. This means that the pH during the first 
600 years after waste package failure will not be greatly different from the pH beyond that 
period. As a result, the use of the weighted-moving average method for calculation of 
progressive waste package failure due to localized corrosion processes is not likely to 
underestimate dose to the reasonably maximally exposed individual. 
E.5 REFERENCES 
BSC (Bechtel SAIC Company) 2004. In-Package Chemistry Abstraction. ANL-EBS-MD- 
000037 REV 03B. Las Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20040609.0094. 
Cornell, V. 2001. Analysis of Resolution Status Key Technical Issue: Total System Performance 
Assessment and Integration Subissue 3: Model Abstraction. Slide presentation at U.S. Nuclear 
Regulatory Commission/U.S. Department of Energy Technical Exchange and Management 
Meeting on Total System Performance Assessment and Integration, August 6 through 10, 2001. 
ACC: MOL.20010921.0129. 
CRWMS M&O (Civilian Radioactive Waste Management System Management and Operating 
Contractor) 2000. Total System Performance Assessment (TSPA) Model for Site 
Recommendation. MDL-WIS-PA-000002 REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001226.0003. 
July 2004 E-4 No. 7: In-Package Environment Revision 1 
Reamer, C.W. and Gil, A.V. 2001a. Summary Highlights of NRC/DOE Technical Exchange 
and Management Meeting on Total System Performance Assessment and Integration. Meeting 
held August 610, 2001, Las Vegas, Nevada. Washington, D.C.: U.S. Nuclear Regulatory 
Commission. ACC: MOL.20010921.0121. 
Reamer, C.W. and Gil, A.V. 2001b. Summary Highlights of NRC/DOE Technical Exchange 
and Management Meeting of Range on Thermal Operating Temperatures, September 1819, 
2001. Washington, D.C.: U.S. Nuclear Regulatory Commission. ACC: MOL.20020107.0162. 
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E-6 No. 7: In-Package Environment 
Revision 1 
July 2004