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Sandia National Laboratories

Carlsbad Programs Group

Waste Isolation Pilot Plant

Analysis Package ~or Salado Transport Calculations: Compliance R~certification Application

Revision 0

Author: Thomas S. Lowry (61 15) 11<3 -{) Print Date

Technical MZ~ Review: James Garner (6821) ) -/)- () 3 Print Signature Date

I

Management Review: David Kessel (6821)

Print Signature Date •

QA Review: Mario Chavez (6820)

Print

()Rr ~ ll/1310.3

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Table of Contents TABLE OF CONTENTS .................................................................•............................... 2

TABLE OF FIGURES ...................................................................................................... 4

LIST OF TABLES ............................................................................................................ 6

INTRODUC.TION ....•.................................•....•..•....••.•.....................•.................•.....•........ 7

1.1. BACKGROUND ...................................................................................................... 7 1.2. PURPOSE ................................... ········ ......... .. ........................................................ 7 1.3. OUTLINE ...... .............. .. ................ ......... ........... ................ ....... ............................. 7

2. METHODOLOGY ................................................................................................... 8

2.1. MODELING SCENARIOS ........................................................................................ 8 2.2. UNCERTAINTY ............................................................................ ......................... 9 2.3. SOFTWARE ........................................................................................................... 9

2.3.1. Nuts ................ ......... .. .. ......... .... .. .. ............................ ..... ... .......... .............. ... 9 2.3.2. Other Codes ................................................... ... ................ .. ........................ 9 2.3.3. Data Flow ........................................................................ .... ........ ............. 10

2.4. TYPEOFMODELRUNS ....................................................................................... 10 2.4. I. Screening Runs ................... .. ........ ... .......................................................... 11 2.4.2. Isotope (ISO) Runs ................................................................ .. .................. 13 2.4.3. Time Intrusion (TI) fluns ............ ... ... .. .... .. ........ .. ... ..... .... ............. .. .. ... ....... 13

2.5. MODELING GRID ..................................................................... -........................... 14 2.6. COMPUTATIONAL ENVIRONMENT ...................... : ............................................... 14

3. OTHER DATA AND ASSUMPTlONS ................................................................ lS

4. RESULTS ··························:················································································ 17 4.1. SUMMARY OF POTENTIAL PATHW~YS .......................................................... ..... 17 4.2. s~~EN t~G R~~· . ;' ........ r. ..... :·' ·····: ........ _ ............................................................ 18 4.3. ISO R UNS ............... .......................................................... .... ........... ..... .. ............ 18

4.3.1. Undisturbed Scenario- S1 ....................................................................... 18 4.3.2. 350 Year El Intrusion- S2 ........... ~ ........................................................... 19 4.3.3. 1000 Year El Intrusion - S3 .......... ..................................................... .' ..... 21 4.3.4. 350 Year E2 Intrusion - S4 ....................................................................... 22 4.3.5. JOVO year E-2 Intrusion .. - S5 .': ................................................................... 22

4.4. TJ RUNS .............................. : ........................................ ....... .. ............................. 23 ~

5. SENSITIVITY ANALYSIS .................................................................................... 24

5.1. STEPWISEREGRESSION ..... ................................................................................. 27

6. SUMMARY ........................................................................................................... 29

APPENDIX A: PLOTTING SCRIPT FOR TIME-SERIES ACTIVITY PLOTS .. ......................................................................................................•.... 41

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APPENDIX 8: POST-PROCESSING CODE FOR SUMMARIZE SCREENING RUNS, SCREEN_BL.FOR ............................................................................................. 46

APPENDIX C: SAMPLE NUTS SCREENING INPUT FILE FOR REPLICATE . I, SCENARI"O 1. . .......................................................................................................... 48

APPENDIX 0: SAMPLE ALGEBRA SCREENING INPUT FILE FOR REPLICATE l, SCENARIO l ..................................................................................... 50

APPENDIX E: SAMPLE SUMMARIZE SCREENING INPUT FILE FOR REPLICATE 1, SCENARIO 1. ,_ .................................................................................. 56

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APPENDIX F: ALGEBRA INPUT FILE FOR ANALYSIS OF R1S1V082 ....... 57

APPENDIX G: SUMMARIZE INPUT FILE FOR ANALYSIS OFR1S1V082. 61

APPENDIX H: TI TIME SERIES PLOTS .... ~ ........................................................ 62

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Table of Figures

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FIGURE 1-RELATIONSHIP AND DATA FLOW FOR MAJOR CODES USED IN THE NUTS

ANAL YSJS ........ ............ .. .............................. ................. ......... ..... .... •........................... 11

FIGURE 2- COMPUTA TlONAL GRID USED FOR THE BRAG FLO AND NUTS RUNS FOR THE

SALADO FLOW AND TRANSPORT CALCULATIONS. MB REFERS TO THE MARKER BEDS,

ORZ IS THE DISTURBED ROCK ZONE SURROUNDING THE REPOSITORY AREA, SHFTU IS

THE UPPER SHAFT AREA, SHFTL IS THE LOWER SHAFT AREA. THE GREEN AREA

WITHIN THEDRZ IS THE REPOSITORY AREA. THE GRAYED CELLS (LIGHT COLORED)

EXTENDING FROM THE SURFACE TO THE CASTILE BRINE RESERVOIR SHOWS THE

LOCATION OF THE BOREHOLE ..................................................................................... 15

FIGURE 3 - NORMALIZED CUMULATIVE RELEASE (EPA UNITS) TO THE CULEBRA FROM

THE BOREHOLE FOR EACH ISOTOPE AND THE TOTAL FOR SCENARIO 2: BOREHOLE

INTRUSION INTO THE CASTILE BRINE POCKET AT 350 YEARS ..................................... 20

FIGURE 4 - NORMALIZED CUMULATIVE RELEASE (EPA UNITS) TO THE CULEBRA FROM


INTRUSION INTO THE CASTILE BRINE POCKET AT 1000 YEARS ................................... 2 1

F IGURE 5 - NORMALJZED CUMULATIVE RELEASE (EPA UNITS) TO THE CULEBRA FROM


INTRUSION INTO THE REPOSITORY AT 350 YEARS ............ ................................ ........... 22

FIGURE 6- NORMALIZED CUMULATIVE RELEASE (EPA UNITS) TO THE CULEBRA FROM


INTRUSION INTO THE REPOSITORY AT 1000 YEARS ... ...... ...... ............ ......... .......... ...... . 23

fiGURE 7- CUMULATIVEACTJVITY IN EPA UNITS AFTER 10,000 YEARS VERSUS INTRUSION

TIME. EACH DATA POINT REPRESENTS THE MAXIMUM CUMULATIVE ACTIVITY ACROSS

ALL REPUCATEIVECTOR COMBINATIONS .................................................................... 25

FIGURE 8- PERCENTAGE OF SCREENED-IN VECTORS SHOWING ZERO RELEASE VERSUS

INTRUSION TIME. FOR THEEl INTRUSION AT I 00 AND 350 YEARS, THERE ARE 185

TOTAL SCREENED IN VECTORS; FOR 1000 PLUS YEARS, 157 VECTORS. FOR THE E2

INTRUSION, 37 VECTORS WERE SCREENED-IN FOR THE I 00 AND 350 YEAR. INTRUSION

AND 38 SCREENED-IN FOR THE BALANCE Of INTRUSION TfMES ... ............................... 26

FIGURE 9- NUTS OUTPUT VERSUS REGRESSION MODEL RESULTS FOR AN El INTRUSION AT

350 AND 5000 YEARS AND AN £2 INTRUSION AT 350 YEARS .... ................................. 29

FIGURE 10 - ACTIVITY UP BOREHOLE IN EPA UNITS FOR Rl S2 .................................. ...... 33

FIGURE 11 - ACTIVITY UP BOREHOLE IN EPA UNITS FOR R2S2 ..................... ..... ....... ....... 33

FIGURE 12- ACTIVITY UP BOREHOLE IN EPA UNITS FOR R3S2 ...................... .... .............. 34

FIGURE 13- ACTIVITY UP BOREHOLE IN EPA UNITS FOR R I S3 ........................................ 34

FIGURE 14- ACTIVITY UP BOREHOLE IN EPA UNITS FOR R2S3 ........................................ 35

fiGURE 15 - ACTIVITY UP BOREHOLE IN EPA UNITS FOR R3S3 ........................................ 35

FIGURE 16- ACTIVITY UP BOREHOLE IN EPA UNITS FOR Rl S4 ........................................ 36

FIGURE 17- ACTIVITY UP BOREHOLE IN EPA UNITS FOR R2S4 .................................. ...... 36

FIGURE J 8- ACTIVITY UP BOREHOLE IN EPA UNITS FOR R3S4 ........................................ 37

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FIGURE 19- ACTIVITY UP BOREHOLE IN EPA UNITS FOR Rl S5 ........................................ 37

FIGURE 20 - ACTIVITY UP BOREHOLE IN EPA UNITS FOR R2S5 ........................................ 38

FIGURE 21 - ACTIVITY UP BOREHOLE IN EPA U NITS FOR R3S5 . ................................. ...... 38

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List of Tables

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TABLE 1 - LIST OF THE MAJOR CODES USED IN THE CRA P A .............................. , ......... , ... 1 0

TABLE 2 -SCREENED-IN VECTORS FOR EACH SCENAR IO/REPLICATE COMBINATION ........... 12 TABLE 3- LIBRARY LOCATIONS, FILE NAMES, AND DESCRIPTION FOR THE SALADO

FORMATION FLOW ANp TRANSPORT SIMULATlONS FOR NUTS, ALGEBRA, AND

SUMMARIZE ............................................. ....... ......... ............. ............................ ..... 1 6

TABLE 4- DJSTANCE FROM THE REPOSITORY IN METERS ALONG MB] 39S FOR THE POINT

WHERE THE INTEGRATED ACTIVITY IS LESS THAN IXI0"7 EPA UNITS ........................ 19 TABLE 5 - MAXIM UM NORMALIZED RELEASE (EPA UNITS) OF EACH ISOTOPE AT THE

BOREHOLE/CULEBRA INTERFACE A TEACH TIME INTERVAL OF INTRUSION. NOTE THAT

THE TOTAL ACTIVITY ALSO INCLUDES A SMALL CONTRIBUTION FROM 238Pu, WH ICH IS

NOT LISTED FOR REASONS DISCUSSED ABOVE ............................................................ 24

TABLE 6- PER~ENTAGE OF TOTAL ACTIVITY AFTER 10,000 YEARS FOR EACH ISOTOPE.

THE PERCENT AGES FOR THE E] - 1 00 AND 3 50 YEAR SCENARIOS DO NOT ADD TO 1 00% DUE TO A SMALL CONTR IBUTION FROM 238P U .... ....................................................... .. 25

TABLE 7- fTLE NAMES FOR INCLUSION IN THE REGRESSION ANALYSIS, LOCATED IN CMS

LIBRARY LIBCRA 1 SUM. 'R.x' INDICATES ALL THREE REPLICATE FILES ARE USED

FOR THE REGRESSION . ................................................. ............... .............. .................. 28

TABLE 8- STEPWISE REGRESSION ANALYSIS ON RANK-TRANSFORMED DATA FOR SELECTED

SCENARIOS ACROSS ALL REPLICATES ............................................................. ...... ...... 28

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Introduction

1.1. Background

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The Waste Isolation Pilot Plant (WlPP) is located in southeastern New Mexico and has been developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal oftransuranic (TRU) waste. Containment ofTRU waste at U1e WIPP is regulated by the U.S. Environmental Protection Agency (EPA) according to the regulations set forth in Title 40 of the Code of Federal Regulations (CFR), Parts 19 1 and 1 94. The DOE demonstrates compliance with the containment requirements in the regulations by means of a performance assessment (PA), which estimates releases from the repository for the regulatory period of 1 0,000 years after closure.

ln October 1996, DOE submitted the Compliance Certification Application (CCA) to the EPA, which included the results of extensive PA analyses and modeling. After an extensive review, in May 1998 the EPA certified that the WIPP met the criteria in the regulations and was approved for disposal oftransuranic waste. The first shipment of waste arrived at the site in March 1999.

The results of the PA conducted for the CCA were subsequently summarized in a Sandia National Laboratories (SNL) report (Helton, et al, 1998) and in refereed journal articles (Helton and Marietta, 2000).

The DOE is required to submit an application for re-certif1cation every five years after the initial receipt of waste. The re-certification applications take into account any information or conditions that have changed since the original certification decision. Accordingly, the DOE is conducting a new PAin support ofthe Compliance Recertification Application (CR.A).

1.2. Purpose

This analysis package describes the transport calculations that are part of the "Salado Flow and Transport Calculations for the Compliance Recertification Application of the Waste Isolation Pilot Plant" as described in Analysis Plan AP-99 (Stein, 2003a). Specifically, it covers the calculations to determine the mobilization and subsequent migration of radioisotopes throughout the repository, shaft system, Salado formation, and possible human intrusion boreholes. Much of the background information for the CRA is based on the original Compliance Certification Application (CCA). An excellent description ofthe CCA PA models can be found in Helton et at. ( 1998) and the CCA version of the tasks described here for the CRA are found in Stockman et al. ( 1 996). This document presents only the changes and updates in the CRA that differ from the CCA and thus the reader is advised to review Helton et al. (1998) and Stockman et al. (1996) prior to reading this document.

1.3. Outline

The rest of this document is organized as follows: Section 2 presents the methodology of the analysis covering the computational aspects such as software, modeling grid, and computational environment, as well as conceptual aspects such as modeling scenarios and

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uncertainty. Section 3 presents the major assumptions and data inclusions that are separate from the methodology but are in integral part of the analysis. Section 4 presents the quantitative aspects of the results. Section 5 covers sensitivity analysis and provides a djscussion of the results that are more qualitative in nature. A complete summary is presented in Section 6. ·The reader is reminded that this report covers only those items that are different from those used in the CCA calculations. However, where it is appropriate, some detail of the CCA calculations, as well as pertinent background information, is presented to provide a better context to the current analyses.

2. Methodology

2.1. Modeling Scenarios The repository is excavated from bedded salt approximately 650 m below the land surface in the Salado formation. It is connected to the surface by four shafts, which will be sealed after waste emplacement is complete. The geologic formations directly above and below the Salado are the Rustler and Castile formations, respectively. The Rustler is ofhigher transmissivity then the surrounding formations with the Rustler sub-unit, the Culebra Dolomite Member, being the most transmissive. The Castile formation lies directly below the repository and contains areas of pressurized brine. It is not known if any of these pressurized pockets are located underneath the repository itself.

To represent possible future states of the repository and to predjct possible releases through the Salado, six modeling scenarios are defmed. Five of these scenarios are modeled using the code NUTS (described below) and the sixth scenario is modeled using the code PANEL (Garner, 2003). PANEL is not discussed in this document. The reasoning and methodology surrounding the choice of these scenarios is contained in the above referenced documents and will not be described here. The six scenarios are:

• 81: Undisturbed performance (no human intervention or intrusion into the repository during lO,OOO years)

• 82: An intrusion borehole at 350 years that penetrates both the repository and an underlying pressurized brine reservoir in the Castile formation (E 1-type intt;tsion)

• S3: An intrusion borehole at I 000 years that penetrates both the repository and an underlying pressurized brine reservoir in the Castile formatjon (El-type intrusion)

• 84: An intrusion borehole at 350 years that penetrates the repository but does not encounter a pressurized -brine reservoir (E2-type intrusion)

• SS: An intrusion borehole at" lOOO years that penetrates the repository but does not encounter a pressurized brine reservoir (E2-type intrusion)

• S6: A multiple intrusion scenario, which includes an E2 intrusion followed by and E 1 intrusion at a later date

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2.2. Uncertainty

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To address the uncertainty in many of the input parameters used in performance assessment calculations, I 00 sets of Latin Hypercube sampled parameters (each unique set is called a vector) are defined. · LHS is a method of Monte Carlo sampling that selects parameters values based on individual probability density functions for each parameter. Each group of I 00 vectors is called a replicate. Three replicates (Rl, R2, and R3) are run in a full PA calculation. This totals to 1500 NUTS simulations; five scenarios of 100 vectors for three replicates. As will be discussed below, screening runs are used to substantially reduce this number. In order to provide an accurate method for assessing the uncertainty in the re·sults, the code CCDFGF is used to create complimentary cumulative distribution functions (CCDFs) that define the probability of exceeding normalized cumulative radionuclide releases. CCDFs are one of the measures used by the EPA to determine compliance. A complete discussion-on the uncertainty involved with the CRA PA modeling can be found in (Hansen, 2003).

2.3. Software

2.3.1. Nuts

The overall transport and decay of radionuclides for scenarios S I toSS are calculated using the computer code NUTS (N1Iclide Iransport futstem), version 2.05a. NUTS is a five-point finite difference code designed to model multi-dimensional, multi-component, and radioactive-contaminant transport in single-porosity (SP), dual-porosity (DP), and/or dual-permeability (DPM) porous media, including parent/daughter first-order decay. Any flow of brine up the shafts, borehole(s), and out the marker beds (see below) is calculated using the code BRAGFLO (Stein and Zelinsld, 2003) and these results are required prior to running NUTS. NUTS requires the BRAGFLO ASCII input fi le containing the grid specifications, initialization parameters, and material maps as well as the BRAGFLO post-processed binary fi les (CDB) that describes the flow-field. The COB files are the source for brine fluxes at the celJ interfaces, porosity, saturation, pressure, and the geometric information. In addition, NUTS uses a COB file that contains the 'effective solubilities', ' lumped inventory' (see below) source terms created by PANEL, and atomic weights and half-lives of the modeled isotopes. NUTS also uses its own input file that contains the run parameters and the isotope decay data .

2.3.2. Other Codes . '

The codes that perform the modeling calculations are BRAGFLO (calculation ofbrine and gas flow), NUTS (radionuclide transport, scenarios S I to S5), and PANEL (radionuclide transport, scenario S6). Uncertainty in the input parameters is included through the use of the Latin hypercube sampler code, LHS. The rest of the codes are used as data manipulation and/or visualization. More information for each code in can be found in the respective design document and/or user manual. A listing of the codes is shown in Table 1.

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For the NUTS calculations, ALGEBRA is run on the output to calculate the integrated fluxes up the borehole, up the shafts, or out through the marker beds. SUMMARIZE is then used Lo provide a summary of the fluxes. The output from SUMMARIZE is used as input to a plotting/visualization program to view the results. Here, Microsoft Excel is used to plot the breakthrough curves for each replicate/vector/isotope combination. A visual basic macro (Appendix A) is used to automate the plotting process.

Table 1 - List of tbe major codes used in the CRA PA.

Code Version Code Function

ALGEBRACDB 2.35 Data processor

BLOTCDB 1.37 Plotting

BRAG FLO 5.00 Brine and gas flow

GENMESH 6.08 Grid generation ·

ICSET 2.22 Sets initial conditions

LHS 2.41 Latin hypercube sampler -

MATSET 9.10 Sets material parameters

NUTS 2.05a Salado transport

PANEL 4.02 Salado transport

POSTBRAG . 4.00 BRAGFLO postprocessor

POSTLHS 4.07 LHS postprocessor

PREBRAG 7.00 BRAOFLO preprocessor

PRELHS 2.30 LHS preprocessor

SPLAT 1.02 Plotting

SUMMARIZE 2.20 Data interpolation

2.3.3. Data Flow

As mentioned above, NUTS requires input from BRAGFLO, and its own input file. The NUTS binary output file is used by ALGEBRA, which in tum produces output used by SUMMARIZE. The output from SUMMARIZE is imported into Microsoft Excel and plotted using the macro in Appendix A. The flow chart in Figure 1 illustrates the relationship between the major codes· use~ in this analysis.

2.4. Type of Model Runs

Three types of model runs are performed using NUTS; screening runs, isotope runs (ISO), and tjme intrusion runs (TI). This section describes each type of run.

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2.4.1. Screening Runs

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Full transport calculations are computationally intensive and can consume large amounts of computer time. The NUTS screening runs are used to filter out those scenario-vector combinations that have no potential to release radionuclides to the Culebra and/or across

the land w~thdrawal boundary (L WB), increasing computational efficiency. A screening

BRAGFLO Input File

BRAG FLO

Downstream Programs .,.__

(CCDFGF)

Output

Visualization (MS Excel)

NUTS ALGEBRA Input File

Concentration Field Output

ALGEBRA

ummarized& Formatted

Fluxes

SUMMARIZE

SUMMARIZE Input File

Integrated Fluxes

Figure I - Relationship and data now for major codes used in the NUTS analysis.

run calculates the transport over 10,000 years of a temporally continuous conservative tracer (constant concentration, Dirichlet boundary condition) with an initial concentration of 1 kglm3 in all waste disposal areas. A vector is considered 'screened-in' if the cumulative tracer mass-flux that enters the "accessible environment" (outside the LWB or to the Culebra via the borehole and/or shaft) exceeds 10·7 kg. The magnitude of the initia l condition and the screening cutoff concentration are considered conservative. The development and assumptions concerning these two values can be found in Stockman et aJ. (1996). Vectors that are not screened-in are not included in the ISO or TI calculations, where specific isotopes and more complicated chemistry are modeled. For this analysis replicates one, two, and three (Rl , R2, and RJ) screened-in I 34, 146, and 135 vectors out of the possible 500 (each), respectively. A list of the screened-in vectors is shown in Error! Reference source not found .. A FORTRAN program called SCREEN_BL.FOR is used to post-process the output from the SUMMARIZE screening runs and to list which runs violate the screening criteria and where the breach occurs (borehole, markerbeds, shaft). Jt also totals the number of 'screened in' vectors for each scenario. SCREEN_BL.FOR is reproduced in Appendix B.

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Table 2- Screened-in vectors for eacb sceoario{replicate combination.

Replicate Scenario Vector Number

SI 82

2, 3, 4,.6, 7, 8, 9, 10, 11, 17, 20, 21 , 23, 25, 28, 30, 31,-32,

S2 33,37,38, 42, 43, 44, 45, 46, 47, 48,54,56,57, 58,60,62, 63,66,67,69, 71, 73, 75, 76, 77, 78, 79,80,81,82,83, 84, 86,88, 90,91, 93,94, 95,96, 97, 98

R1 2, 3, 4, 6, 7, 8, 9, 10, 11 , 17, 20, 21, 23, 25, 28, 30, 31, 32, 33, S3 37,43,44,45,46,47,54,56,57,58,60,63,66, 73, 76, 77,

78, 79, 80,81, 82, 84,86,88, 90, 91,94,95, 96, 97,98

S4 7, 9, 17, 20, 23, 31 , 46, 66, 84, 91 ,97

S5 7,9, 17,20,22, 23,31, 46,66, 84,91,97

Sl No vectors screened-in

1, 4,5, 7, 8,9, 10, 14, 15, 17, 18, 19,21 , 22,23, 24, 25,26,

S2 30,34,35,37,38,39,41,42,43,44, 45,46,47,50,54,56, 57, 58, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 74, 76, 77, 80, 82,83, 84,85,86,87,89, 90,91, 94, 96, 97,98,99, 100

R2 I, 4, 5, 7, 9, 10, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 30,

S3 34,35,37,39,41, 42, 43, 44, 45,46, 47, 50,57, 58, 61, 62, 63,64, 65,67, 68,69, 70, 71, 76, 77, 82, 83,84,87,89,90, 94, 97, 98, 99, 100

S4· 1,9, 10,22, 24,26, 63,64, 77,83,84,89,99, 100

S5 1' 9, 10, 22, 24, 26, 63, 64, 83, 84, 89, 99, 100

Sl No vectors screened-in

1, 2, 5, 7, 9,' 13, 14, 1 5~ 16, ·17, 21, 22, 23, 24, 26, 28, 29, 30, . S2. 31,32, 33,34,35,39, 41, 43, 45, 46,48, 49, 50, 51,52,53,

54,55,57,60,63,64,66,67,69, 70, 72, 74, 75, 77,80,81, 82,83,86,88,90,91, 94, 96,97,98, 100 .

R3 1, 5, 13, 14, 15, 16, 17, 21, 22, 23, 24, 26, 28, 29, 30, 31, 32,

S3 33,34,35,39,40,43,45,46,48,49, 50,51,52,53,54,55, 57,60,63,64,66,69, 70, 72, 74, 75,80, 81,82,83,86,88, 90,97,100

S4 22,26, 28,31,54,55,57, 64, 72,74,83, 88

S5 22,26,28,31,54, 55,57, 64, 72, 74,88

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2.4.2. Isotope (ISO) Runs

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Because NUTS is a computationally intensive code that requires long run times, and because decreasing the number of isotopes it mobilizes can substantially decrease its run time, the possible isotopes and decay chains were examined to determine the minimum number of isotopes required to describe the compliance behavior of the WTPP (Stockman et at., 1996). Isotopes having similar decay behaviors and transport characteristics are

combined in ways that would introduce little or no loss of release information in terms of normalized "EPA units". Combinations of similar isotopes are referred to as ' lumped isotopes' . EPA un its are a measure of normalized releases, nR, calculated as:

nR = L Q1 ( 1 X 1 06 curries)

L, C (l )

where Q; is the 1 0,000-year cumulative release in curries of radionuclide i, L1 is the release limit for radionuclide i (as specified by CFR 40, Part 194), and Cis the total transuranic inventory in the WIPP in curries. All values of EPA units quoted and used in this report are calculated by the ALGEBRA code and are contained in the appropriate ALGEBRA output files stored in the CMS (see Table 3, Page _16, below). The final conclusion of the decay chain analysis is that five "lumped'' isotopes are modeled based on the following simplified decay chains:

241Am

nsPu ~ 234u ~ 230ry-h

239Pu

These are the same isotopes modeled for the CCA, justified by the fact that the available isotopes in the waste packages have not changed between the CCA and the CRA. A complete justification for the use of these five isotopes can be found in Stockman et al. ( 1996).

For this analysis, only 241 Am, 239Pu, 234U, and 230Th are examined individually in the output since the half-life of238Pu is 87.7 years and will have decayed to negligible amounts in the time frames of interest. Total activity releases presented below do include the contribution from 238Pu. The ISO runs consist of modeling each isotope for each scenario and calculating the time-integrated flux laterally across the LWB and vertically to the Culebra (via boreholes or shaft) in EPA units. The NUTS ISO runs consist of the undisturbed scenario (S 1) as well as the 350 and 1000-year intrusion scenarios (S2-S5) as calculated by BRAGFLO.

2.4.3. Time Intrusion (TI) Runs

The NUTS TI runs are for simulating intrusion times that are earlier and later than the 350 and I 000 year intrusions modeled in the ISO runs. This is done by shifting the initial conditions from the BRAGFLO runs to the appropriate time. For instance, the BRAG FLO results for E 1 and E2 at 350 years are used as input to NUTS at both I 00 and at 350 years. For the 100 year intrusion, the flow pattern used in NUTS subsequent to the intrusion is assumed to be the same as the flow pattern predicted by BRAG FLO

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subsequent to an intrusion at 350 years. Transport calculations are also done with intrusion times of 1000, 3000, 5000, 7000, and 9000 yrs. For times greater then 1 000 years, 'shifted intrusion-time' calculations are performed that assume an undisturbed scenario until the time of intrusion and then the 1 000-year intrusion flow-field after the intrusion.

This approach is justified by the fact that previous BRAGFLO simulations for intrusion times greater then I 000 years have shown that undisturbed conditions reach steady-state prior to the intrusion time. In addition, repository perfonnance is most sensitive to gaspressure reiief and brine inflow (from the high-pressure brine pocket and/or marker beds) that occurs at or soon after intrusion. However, it is not sensitive to the kinds of changes (e.g. fracturing) that occur prior to intrusion (Stockman et al., 1996; Stein and Zelinski, 2003). Thus, the flow-field after intrusion is much more dependent on an intrusion event occurring rather than the conditions before the intrusion.

2.5. Modeling Grid

The grid used for the NUTS calculations is the same as used for the BRAGFLO calculations. The extent of the modeling domain is 46,630 m in the horizontal (x) direction by 940 m in the vertical (y) direction, which is the same as for the CCA calculations. The domain is discretized into 68 X 33 (x,y) non-uniform grid cells with higher resolution in the repository area and lower resolution towards the edges of the modeling domain. The grid is more refined and includes a more detailed representation of the panel closures and waste regions than was used for the CCA calculations. The changes made to the grid were accepted by the SaJado Flow Peer Review panel in February 2003 (Caporuscio et al., 2003). The grid is illustrated in Figure 2. A full · description of the grid can be found in Stein and Zelinski (2003).

2.6. Computational Environment

Calculations for the Salado Flow and Transport simulations are performed on ES-40, ES-45, and/or 8400 Compaq ALPHA computers running Open VMS Version 7.3-l (WIPP · PA, 2003a-c). Each j ob is executed using scripts, with run-time input files and output files residing in an access controlled environment on the cluster. The runs utilized the WIPP PA Software Configuration Management System (SCMS) to assure control of the various PA codes and associated fi les. The SCMS in tum is implemented by the Compaq Corporation Code Management System (CMS - W1PP PA, 2003d).

This section documents the input and output files used for NUTS, ALGEBRA, and SUMMARIZE for the Salado Flow and Transport simulations as well as the library locations within the CMS where the fmal versions are archived.

There are three sets of input files for all three programs; one each for the screening, ISO, and TI runs. Each set of input files are numbered according to the run type, replicate, scenario, and vector numbers. The CMS libraries, the included files, and the file descriptions are shown in Table 3. A sample input file for each program is listed in Appendix C, D, and E.

nformation Only

OIIU600 4).)000

111 .. 000 .. ..uooo ,..__ U IIXIII

16.0000 ... .,.. ,..,.. kDIO J4.1lOII ,. 7:1~

J.l.1)00 J.l.nllll UIOO 4.JJOII OJOII 0%100 Ul76 IJ105 1.1101 IJlOI -----o.&!GQ

Ullllll 6I.1ZOa

"noo .. .,.. ll.l4Sl

llUkl

)-. 2

~

r-t I

H· I "'"" )'04'1 .. ""- '

I

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CRA BRAGFLO Grid

IJ %1 :'£

.... - -· !jll

I~ \!~. ~ '-:-r->I IIOC t._T •

'nlrl itl_-tt-1 l± l ·StledO

~ ~~~ ~I • • ! ~ i ~~·~ l l mmm ~' o! !~~~-! l !a~ rfi ~~ i'"'ll !!' ;; 1~ ;m mm l mH

Figure 2 - Computational grid used for the BRAG FLO and NUTS runs for the Salado Plow and transport calculations. MB refers to the marker beds, DRZ is the disturbed rock zone surrounding the repository area, SHFTU is the upper shaft area, SHFTL is the lower shaft area. The green area within the DRZ is tbe repository area. The grayed cells (light colored) extending from the surface to the Castile Brine Reservoir shows the location of the borehole.

3. Other Data and Assumptions This section looks at the major data inputs, modeling factors, and modeling assumptions for the NUTS CRA P A model. It is not intended to be a complete documentation of the inputs and workings of NUTS, but rather to give the reader a sufficient level of understanding to interpret and understand the results. For more detail, the reader is again referred to Stockman et al. ( 1996).

NUTS is designed to model mobilization and decay of the selected isotopes. For mobilization, the code requires the isotope inventory and element solubility. The isotope inventory is apponioned using volume or areal fral:tiuns, to the computational cell(s) of NUTS. This approach is equivalent to assuming a homogeneous waste inventory.

Radionuclide release from the repository to the CuJebra depends on both the amount of brine flow, the solubility limit, and the amount of radionuclide available for transport. Radionuclides are assumed to exist in five states that can be transported from the repository by flowing groundwater (Helton et al., 1998): dissolved, humic colloids,

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Table 3- Library locations, file names, and description for the Salado Formation Flow and Transport simulations for NUTS, ALGEBRA, and SUMMARJZE.

CMS Library FileName* Description '

!'IUT_CRAI_SCN_Rx_Sy.fNP NUTS screening runs input

NUT_CRAI_ISO_ Rx_Sy.INP NUTS ISO runs input

NUT_CRAI_fNTz_Rx_Sy.INP .. I NUTS Tl runs input

LIBCRAI_NUT ALG_NUT_CRAI_SCN_Rx_Sy.fNP

ALGEBRA scn.-cning . runs input

ALG_NUT_CRAI_POST_Sy.INP ALGEBRA ISO runs input

NUT_CRA t_SCN_Rx.OUT . Screening output from

.. SCREEN_BL

NUT_CRAI_SCN_Rx_Sy_ Vnnn.CDB NUTS screening runs binal)' output

NUT_CRAI_ISO_ Rx_Sy_Vnnn.CDB NUTS ISO runs binal)' output•••

.NUT_CRAI_SCN_RxSyVnnn_STEPI .LOG NUTS sc~ening run log

LIBCRAI_NUTRxSy NUT_ISO_CRA I_R:tSyVnnn_STEPI .LOG NUTS ISO run log .. •

. ALG_NUT_CRA I_SCN_Rx_Sy_ Vnnn.CDB

ALGEBRA screening runs binal)' output

NUT_CRAI_INTz_ Rx_Sy_ Vnnn.CDB•• NUTS Tl runs binary output•••

NUT_CRAI_INTz_RxSyVnn~_STEP1_2.LOG .. NUTS Tl run log•• •

ALG_NUT_CRAI_POST_ISO_Rx_Sy_Vnnn.CDB ALGEBRA ISO runs binary output • • •

LIBCRAI_ ALG

ALG_NUT_CRAI_POST_INTz_Rx_Sy_ Vnnn.CDB"" ALGEBRA Tl runs

- bin at)' output .. •

SUM_NUT_ CRA l_SCN_Rx_Sy.fNP SUMMARIZE screening runs Input

SUM_NUT_CRAl_SCN_Rx_Sy.DA T SUMMARIZE . screening runs output

- SUMMARIZEJSO SUM_NUT_CRA I_Rx_Sy.INP

runs input-• UBCR.AI_SUM

SUM_NUT_CRAl_R.x_Sy.TBL SUMMARIZE 150 runs output•••

SUM_NUT_ CRAl_Rx_Sy_Tz.INP•• SUMMARIZE Tl runs input•••

SUM_ NUT_CRAI_Rx_Sy_Tz.TBLu SUMMARIZE Tl runs output•_.

'File nami ng convention: Rx refers to the replicate number (x= I I 2. or 3), Sy rofers to the scenario number (y= I to S), Tz refer.s to the intrusion l ime (z=IOO, 3000, 5000, 7000, or 9000), Vnnn refers to the vector number (nnn = 001,002, ... , 099, 100)

• ' TJ tiles are only applicable to scenarios 2 to 5

•••These files are generated only for vectors that are 'screened-in·

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microbial colloids, mineral fragment colloids, and actinide intrinsic colloids. The concentration in each of these states is a function of one or more sampled variables. Element solubility is defmed to be the maximum concentration that the brine can hold both suspended on colloids and dissolved in the brine. Stockman et al. (1996) and Gamer (2003) provide a full discussion on effective solubility related to the radionuclide transport calculations.· Mobilization is assumed to be instantaneous at the solubility limit (up to the inventory limit) meaning the concentration in the brine and on the colloids is a lways at equilibrium. However, the isotope inventory changes with time due to decay and ingrowth, so the equilibrium is not steady-state.

The k,ey processes modeled with NUTS are advective transport, decay, precipitation, solubility 1imits, and interior sources, all in a single porosity, continuous matrix. No dispersion is modeled. The initial condition for each run is to assume no contamina6on present within the model domain, with the exception of the source tenn in the waste panel area.

4. Results This section presents the results from the NUTS simulations for transport of the four radioisotopes within the Salado fonnation for scenarios SI to S5 as discussed above. As mentioned, there are five isotopes that are modeled but since 238Pu is only a small fraction of the inventory within the repository and due to its short haJf-life (87.7 years) it is not included in the final analysis.

4.1. Summary of Potential Pathways

To provide a more complete picture of the results a conceptualization of the physical processes is given here. A complete analysis of the BRAGFLO results is given by Stein and Zelinski (2003).

When brine enters the disposal region, gas is generated by anoxic corrosion of iron and biodegradation of organic materials, and radioisotopes are released into the brine from the waste. If sufficient quantiti es of gas are generated, pressures in the djsposal region will increase, reducing brine flow into the repository. Brine containing dissolved radioisotopes may be expelled from the repository if pressure in the repository exceeds the brine pressure in the immediately surrounding fonna1ion. Brine saturation in the waste has to exceed residual brine saturation in order for brine to be expelled from the repository.

Three potential pathways for migration of radioisotopes in dissolved brine are considered in this analysis. The first, and as is shown below, the most important pathway is a human intrusion into and possibly through the repository. Under this scenario, brine may be released up the borehole toward the Culebra Dolomite member of the Rustler formation. Once in the Culebra, contaminated brine may then move toward the subsurface land withdrawal boundary. Direct brine releases to the surface are modeled and analyzed separately (Stein> 2003b). In the second pathway, brine may migrate through or around the panel seals through the disturbed rock zone (DRZ) surrounding the repository to the shaft and then upward toward the Culebra. In the third pathway, brine may migrate from

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the repository through the DRZ and then laterally toward the subsurface land withdrawal boundary within the anhydrite inter-beds (marker beds 138 and 139, Figure 2).

The dynamics of the brine movement are complex and highly dependent <>n the BRAGLO input parameters. Initially, brine may flow into the repository from anyone of the migration pathways mentioned.above. If sufficient brine enters the repository the radioisotopes become mobilized in both solute and colloidal sorbed forms. Once the radioisotopes are mobilized, transport away from the repository can only occur if the head potential within the repository exceeds that outside the repository and if brine saturation in the waste exceeds residual brine satUration. Brine may be consumed by the oxidation of ferrous material in the waste containers and the waste itself, which has an effect on the solubility of the radioisotopes.

4.2. Screening Runs The intent of the screening runs was to reduce the total number of 'complete' model simulations necessary by eliminating model vectors that have no possibility of transporting radionuclides beyond the repository boundary. To do this, a conservative tracer is modeled and the cumulative mass is monitored at several key points in the modeling domain. The key points are the borehole/Culebra intersection, the shaft/Culebra intersection, and the marker beds at the L WB. Vectors that show a cumulative mass tracer of I 0'7 kg or more at those key points are deemed 'screened-in' and are then passed through to perform the full radionuclide transport simulations. The full process of the screening runs as well as the results are presented above in section 2.4.1.

4.3. ISO Runs The ISO runs are for 350 and I 000-year El intrusions (scenarios S2 and S3), 350 and I 000-year E2 intrusions (scenarios S4 and S5), as well as the undisturbed case (scenario S1). .

4.3.1. Undisturbed Scenario- S1

For replicates Rl , R2, and R3, there are 1, 0, and 0 screened-in runs, respectively. The screenings detected the conservative contaminant at the L WB through the Marker Bed 139 South, with cumulative mass of 1.025x I O.o7 kg for Rl S I v082. However, for the ISO case, the activity for the isotopes drops considerably with a total integrated flux of 2.89xi0-15 EPA units. ·

The distance from the repository to where the integrated release becomes less then 1 X 1 0'7

EPA units can be calculated. Not surprisingly, 239Pu penetrates the MB 139S the furthest, moving 984.34 m from the repository boundary·. AU of the isotopes move through the marker bed a minimum of 677.48 m. As a comparison, the totaJ distance from the repository to the L WB is 2400 m. The travel distance for each isotope for Rl S I v082 is shown in Table 4.

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Table 4- Distance from the repository in meters along MBJ39S for the point where tbe integrated actlviry is less than JxJ 0'7 EPA units.

Vector# Am-241 Pu-239 U-234 Th-230

R1Siv082 677.48 984.34 984.34 677.48

The small integrated activities at the L WB are almost certainly due to numerical dispersion due to the coarse grid between the repository and the L WB. Numerical dispersion in finite difference codes such as NUTS is due to the truncation of the Taylor series expansion that occurs when developing the algebraic representation ofthe initial governing equations. The net effect is an increase in the dispersion coefficient that is proportional to the grid cell size as well as the groundwater velocity. For the timedependent, one-dimensional, advection-dispersion equation, the 'numerical dispersion coefficient', Dnd, is given by (de Marsily, 1986):

D =ulu nd 2 (2)

where u is the groundwater pore velocity and L1x is the cell size. Dnd will change depending on the formulation and dimensions of the problem, but will always be linked to the velocity and cell size. It should be mentioned that there are higher order terms that have been ignored in this example; the most notable is a 'source/sink' term that is equivalent to half the time step multiplied by the second order time derivative of the concentration. This term can impart oscillations in the solution if the time step is too large but does not add to the artificial spreading of the plume. Continuing, if we assume the lx10'7 EPA units represents the advective front ofthe plume, the maximum average pore velocity through the marker bed is 0.098 rn/yr e39pu moved 984.34 m in 10,000 yrs). For an average cell size of78 m (cell size ranges from 2.0 m close to the repository to 363.23 mat the 984.34 m mark), this translates to a numerical dispersion coefficient of 3.84 m2/yr, or an equivalent dispervisity of about 38 m, which is enough to account for the slight predicted activities at the LWB. To further evaluate this slight release out the marker bed for vector R 1 S 1 v082, an ALGEBRA run was made to convert the ISO results to concentrations in curies per liter (see Appendix F: and Appendix G: for the input files). These concentrations were then extracted with SUMMARIZE and compared with the concentrations from the CCA (DOE, 2004). The concentrations from R lSI v082 are at least five orders of magnitude lower than the concentrations evaluated for undisturbed case of the CCA.

4.3.2. 350 Year E1 Intrusion- S2

Scenario S2 models an El intrusion, which penetrates the repository and the lower Castile formation, at 350 years. The S2 scenario is highly influenced by the conditions within the brine pocket. The timing of the 350-year intrusion allows for brine inflow into the repository, but is not long enough to have secondary processes, such as gas production, displace the brine. Consequently, S2 shows the highest number of screened in vectors as well as the highest outward fluxes ofbrine and radioisotopes.

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- The only pathway to show any contaminant movement at the L WB was up the borehole. No activity was shown through the marker beds or up the shaft at the Culebra interface. Thus, the analysis concentrates on the total activity in EPA units at the point where the borehole intersects the Culebra. The time series plots for each isotope, vector, and replicate are shown in Figure I 0, Figure 11, and Figure 12. Most vectors result in littJe or no release due to limited brine flow. Most of the release occurs over a relatively_ short period of time, shortly after the borehole intrusion, and then continues at a reduced rate or stops entirely. The maximum activity for any isotope occurred in R1S2v007, which showed an integrated activity of~ 1.63 EPA units for 241 Am. For 239Pu, vector R3S2v028 showed the maximum with an integrated activity of5.24 EPA units. 234U and 230Th were comparatively minor with maximums for each of.0.0066 and 0.00098 EPA units in vectors R2S2v009 and R2S2v007, respectively. On the average, 241Arn and 239Pu account for 68.1% and 31.4% of the total activity respectively with 234U and 230Th comprising the remaining 0.5%. The average percentage is calculated by summing the percent contribution of each isotope for each vector, and then dividing by the number of vectors. Mathematically, this is: ·

:IEPA150,

i=l EPAr. -___ ...;..' - = P (3)

NSI

where EPA,so; is the 10,000 year activity for a particular isotope, EPAr; is the 10,000 year

total activity (including 238Pu), Ns1 is the number of screened in vectors, Pis the average percentage of the total, and the i summation indicates the values are summed across all screened-in vectors. The releases in EPA units for each isotope and the total release are shown in Figure 3.

E1 at 350 years (R1, R2, R3)

Am-241 . x a " XX XX X X x• . .C.:.C)()t()( )OC ,._..-,c..,...____ X

Pu,239 X ll ll '< .... ll XX*" X-lO<JO< ---~--->OUOC X

U·234 ..._____ • X

Th-230 ""'~---------"

Total • · ••• • • • •• s • a • ax a: • ._.. • __ _,_ -----

1.E-08 l .E-07 1.E.{)6 1 E-05 l.E-04 1.E-03 1.E.{)2 l .E-01 1.E+OO 1.E>{)1 1.E+02

EPA Units

Figure 3 - Normalized Cumulative Release (EPA Units) to the Culebra from the borehole for each isotope and the total for Scenario 2: Borehole intrusion into the Castile brine pocket at 350 years.

0

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The time difference between S2 and S3 allows more time for chemicaJ and biological activity to either consume brine or produce gas, both of which reduce the amount of brine in the repository at the time of intrusion. This in tum reduces the capacity for nuclide transport. However, like scenario S2, the results of S3 are highly influenced by the conditions in the pressurized Castile brine pocket and thus we see that S3 has similar characteristics to the S2 scenario, although the number of screened vectors and the maximum activities are s lightly less.

Like S2, the only pathway with any activity is through the borehole. No activity was shown in the marker beds or the shaft for any of the vectors. The time series plots for each isotope, vector, and replicate are shown in Fi~re 13, Figure 14, and Figure 15. As can be seen in the figures, and like the S2 scenario, the releases for the S3 takes place over a short period oftime. The maximum activity for any isotope occurred in ~IS3v091, which showed an integrated activity of 4.84 EPA units for 241 Am. For 239pu, vector R3S3v028 showed the maximum with an integrated activity of4.94 EPA units. 234U and 230Th were comparatively minor with maximums for each of0.00591 and 0.00086 showing in vector R2S3v009. On the average, 241Am and 239Pu account for 48.24% and 50.87% of the total activity respectively with 234U and 230Th comprising the remaining 0.89%. The releases in EPA units for each isotope and the total release are shown in Figure 4.

E1 at 1000 years (R1, R2, R3

Atll·241 W )I( X X )a.;JC .JOCCXX xx • Xlia~- --~-----

Pu·239

----------k>< "·----------

Total aa:a• s. aJDl a •••-- •• .. ..,.,. __ •• ______ _

I.E-$ 1.E·OT l .E-06 I.E-05 l.f.().4 1.E·03 1.E.02 1.E.01 1 Eo()() 1 E~1 I.E•02

EPA Units

Figure 4- Normalized Cumulative Release (EPA Units) to the Culebra from the borehole for each isotope and the total for Scenario 3: Borehole intrusion into the Castile brine pocket at 1000 years.

The difference in the activity distribution between the S2 and S3 scenarios is attributed to the time lag for the S3 intrusion. At early times, the release tends to be dominated by Am-241 , with an additional contribution from 238Pu at very early times (not shown).

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With increasing time, 241Am is lost due to decay, and the release is dominated by 239Pu due to its long half-life.


For most vectors associated with an E2 intrusion, BRAGFLO predicts nonzero or very little brine flow from the repository to the Culebra. All other pathways are zero or minimal. Figure 16, Figure 17, and Figure 18 show the time-series plots for each vector across all replicates. Since the E2 releases are not dominated by a physical process like the brine pocket pressure release associated with an E 1 intrusion, the plots appear more 'disorganized' in that the standard deviation of the release times is much higher. This also creates a difference in the distribution of the contribution to the total activity. On the average, 241Am contributes 33.16% to the total and 239Pu contributes 66.50%. Like the S3 scenario, this is due to the longer time periods associated with the transport process in the El intrusiop scenarios. The maximum activities for 241 Am, 239Pu, 234U, and 230Th are 2.58, 0.172, 0.000149, and 0.0000328 EPA units, respectively. The releases in EPA units for each isotope and the total release are shown in Figure 5:

E2 at 350 years (R1 , R2, R3)

.. XXX X ,._ X )It JIOC • •• - - X JOIC )I. • a X Jl

Pu·239 "

U·234 .. .. .. - --- • ,.,._._ " )I X

Th·230 • • • x •--"" ""

Tol.-' • a a_,. • ... -=-::.:•••• • •

I E-08 I E..07 l .E-06 1 E..OS I E.()4 1.£.()3 I E..02 I E..OI 1 E+OO 1 E~l I E+OZ

EPA Units

FigureS - Normalized Cumulative Release (EPA Units) to the Culebra from the borehole for e2ch isotope and the total for Scenario 4: Borehole intrusion into the repository at 350 years.

4.3.5. 1000 year E2 Intrusion - S5

Like the 350 year E2 intrusion; the I 000 year intrusion shows very few vectors with significant amounts ofradjonuclide releases. Over the 1000 years before the intrusion, gas pressure builds up and brine is consumed through chemical and biological processes. This in tum reduces the brine movement through the repository in comparison to the earlier intrusion times.

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Figure 19, Figure 20, and Figure 21 show the time-series plots for each vector across a1l replicates. Again, the plots appear more 'disor~anized' then the El intrusion plots. For 85, 241Am contributes 17.92% to the totaJ and 2 9Pu contributes 81.69%. The 1000 year · intrusion time allows for more decay of the 241 Am and shift to a higher percentage of the total for 239Pu. The maximum activities for 241 Am, 239Pu, 234U, and 230Th are 0.074, 0.141, 0.000138, and 0.00003 EPA units, respectively. The releases in EPA units for each isotope and the total release are shown in Figure 6.

E2 at 1000 years (R1, R2, Rl)

Pu-2.39 )c

U-234 IOC XX*X . ,._,. ~---)(XX

Th-230 " x *""" --- """"

Tot91 a • a:.:a.s.- .-: -- - s

t .E-06 1.E-07 I.E~ 1.1:-{)5 LE-04 1.E-03 1.E..02 t .E-<11 I.E.OO 1.E+01 1,E+02

EPA Units

Figure 6- Normalized Cumulative Release (EPA Units) to the Culebra from the borehole for each isotope and the total for Scenario S: Borehole intrusion into the repository at J 000 years.

4.4. Tl Runs The Tl runs show the same pattern in terms of nuclide transport as do the ISO runs in that the El intrusions associated with early time intrusions show the highest activities, while the E2 intrusions associated with late time intrusions show the lowest. The reasons behind this are the same as discussed above. Table 5 shows the maximum normalized release in EPA units for each scenario/intrusion time combination. Note that the total activity also includes a small contribution from 238Pu, which is not listed for reasons discussed above. The time series plots for each isotope at each time/vector/replicate combination are presented in Appendix H. Combining the ISO and TI runs into one figure, the domination 241 Am and 239Pu in relation to the total activity is clearly seen (Figure 7). The decay of241 Arn is evident in the straight line decrease in activity over time on the logarithmic y-axis. The differential between the El intrusions (solid lines) and the E2 intrusions (dashed lines) can also be seen

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As the time of intrusion increases, the number of vectors showing a zero release also increases. The increase in zero release vectors with increasing intrusion time is approximately linear, as illustrated· in Figure 8. Extrapolating forwards, this indicates that for an El intrusion, approximately 12.9% ofthe screened in vectors will show zero release for intrusions at J o',ooo years. For the E2 intrusion, no vectors will show a release if an intrusion occurs after about 9440 years.

Table 5 - Maximum normalized release (EPA units) of each isotope at the Borehole/Culebra interface at each time interval of intrusion. Note that tbe total activity also includes a small contribution from 138Pu, which is not listed for reasons discussed above.

Am-241 Pu-239 U-2.34 Th-230 Total

EJ - 100 yrs 2.80£+01 5.34£+00 6.64£-03 9.82£-04 2.90£+01

3000 yrs 5.42£-01 4.12£+00 5.52E-03 9.19E-04 4.15£+00

5000 yrs 4.80£-02 3.29£+00 5.01£-03 9.55E-04 3.29£+00

7000 yrs 2.96E-03 2.46£+00 4.33E-03 9.64£-04 2.46£+00

9000 yrs 7.28£-04 1.33E+OO 2.80£-03 1.05£-03 1.33£+00

E2- 100 yrs 2.93£+00 1.79£-01 l.55E-04 3.39E-05 2.99£+00

3000 yrs 5. 10£-03 8.93£-02 9.65£-05 2.11£-05 9. 1JE-02

5000 yrs 4.53£-04 4.03E-02 5.50£-05 1.20£-05 4.05£-02

7000 yrs 2.16E-05 2.72£-02 1.45£-05 3. 13£-06 2.72£-02

9000 yrs 3.02£-07 1.19E-02 1.30£-06 2.04£-07 1.19£-02

241Am shows the highest maximum activity (28.0 EPA units) of aJl the isotopes across all ISO and Tl runs for a 100 year intrusion and is the isotope with the highest release for 45 ofthe 185 screened in vectors for the 100 year intrusion time across all replicates. For a 350 year intrusion, 241 Am is highest in 14 vectors. For intrusion times greater then 350 years, 239Pu shows the highest activity across all replicates. In terms of the average percentage of total activity, 239Pu provides the highest contribution for all intrusion times with the exception ofan E1 intrusion at 100 1,ears, where 241 Am is the highest. As mentioned above, this is due to the decay of 41Am combined with the long half-life of 239Pu. For those vectors where brine outflow from the repository was large at smalltime, 241Am is the dorhlnant isotope. This trend is listed numerically in Table 6. ·

5. Sensiti~ity Analysis Sensitivity analysis allows for determining which input parameters have the most influence on the output of a model. . Here we present the results of several types of analysis that help determine the most influential parameters. By understanding which parameters are most intJuential to the model output, one can determine the main source of uncertainty as well as where to concentrate future efforts to help reduce that uncertainty.

A good tool for examining the relationship between model inputs and model outputs is regression analysis. In this approach, a model of the form

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Table 6- Percentage of total activity after 10,000 years for each isotope. The percentages for the El-100 and 350 year scenarios do not add to 100% due to a small contribution from 238Pu.

Am-241 Pu-239 U-234 Th-230

Et- JOO yrs 68.60% 30.64% 0.29% 0. 17%

350 yrs 68.09% 31.39% 0.26% 0.15%

1000 yrs 48.24% 50.87% 0.24% 0.65%

3000 yrs 9.49% 89.92% 0.48% 0. 11%

SOOO yrs 1.61% 97.45% 0.73% 0.21%

7000 yrs 0.46% 98.23% 1.06% 0.26%

9000 yrs 0.25% 98.42% 1.17% 0.16%

E2-JOO yrs 34.63% 65.01% 0.28% 0.05%

JSO yrs 33.16% 66.50% 0.29% 0.05%

1000 yrs 17.92% 81.69% 0.34% 0.05%

3000 yrs 4.25% 95.34% 0.36% 0.06%

5000 yrs 1.00% 98.66% 0.28% 0.06%

7000 yrs 0.21 % 99.48% 0.28% 0.03%

9000 yrs 0.05% 99.76% 0.17% 0.02%

Maximum Activity vs. Time of Intrusion 1.E+02

1.E+01 • •

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Figure 7 - Cumulative activity in EPA units after 10,000 years versus intrusion time. Each data point represents the maximum cumulative activity across all r eplicate/vector combinations.

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Percentage of Screened in Vectors Showing Zero Release vs Intrusion Time

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----===::: 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time (years)

Figure 8 - Percentage of screened-in vectors showing zero release versus intrusion time. For the El intrusion at 100 and 350 years, there are 185 total screened in vectors; ror 1000 plus years, J 57 vecters. For the E2 intrusion, 37 vectors were screened-in for the 100 and 350 year intrusion and 38 screened-in for the balance of intrusion times.

n

y=b0 +LbJxJ (4) j=l

is developed where y is the model output parameter (dependent variable), xi are the model input parameters (independent variables), bj are the coefficients that must be determined, and n is the number of independent variables included in the modeL The coefficient bo is the dependent variable intercept. For cases where the independent variables are uncorrelated, the coefficients, bj, can be used to indicate the importance ofthe independent variables with respect to the dependent variable.

When many independent variables are involved, as is the case here, the inclusion <>fall of them is not necessarily needed nor desired. Typically~ only a relatively few number of independent variables are needed to produce a satisfactory regression model. In addition, if some of the independent variables are correlated, this can render the regression coefficients unstable and result in the inability to determine the most sensitive parameters. Here, the variable pairs [BPPRM, BPCOMP], [HALPRM, HALCOMP), and [ANHPRM, ANHCOMP] show a -0.732, -.987, and-{).937 correlation, respectively. These three pairs refer to the permeability and compressibility of the Castile, Salado (Halite), and Anhydrite material.

' To circum~ent these issues, three steps are taken in this regression analysis. First is the use of stepwise regression rather then multiple regression described by equation 4. With stepwise regression, a sequ ence of regression models is constructed; with each model adding the single input variable that has the, largest impact on the uncertainty of the

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output (i.e. the variable that has the hjghest correlation with the output). Thus, the first regression model will be of the form:

y=b0 +b1x1

where x 1 is the input variable that has the highest correlation with y. The second regression model is:

(S)

(6)

where x2 is the input variable the accounts for the balance of the uncertainty in y not explained by x,. Construction of the regression model con6nues in this manner until a user defined point. of dimirushing return is reached, usually determined by the use of the t-test. An explanation of the stepwise regression and the independent variable selection criteria as applied to the CCA is given in Helton et al. (1998).

The second step in determining parameter sensitivity is to perform a rank transformation of the input and output parameters prior to conducting the stepwise regression. A rank transformation ranks each value of a parameter based on its magnitude in relation to the other instances of that parameter. Thus, for each parameter, the vector with the lowest value is given a value of ' I ', the next rughest vector a value of ' 2', and so on. This is done across all replicate/vector combinations. The benefit of performing the rank transformation is it helps reduce any non-linearity effects that may e)l.ist between the independent variables and the dependent variables. It also helps with the inclusion of •flag-type' parameters. Flag-type parameters are parameters whose value has no physical basis or are not directly utilized as model input. The input parameter, ANHBCVGP. which determines the relative permeability model for the South Marker Bed 139, is an example of this.

We do not include those independent variables that are highly correlated with other independent variables. As mentioned, there are only three pairs that fall into this category, [BPPRM, BPCOMP], [HALPRM, HALCOMP], and (ANHPRM, ANHCOMP]. For this analysis, the variables BPCOMP, HALCOMP, and ANHCOMP are not included. In addition, the variable BPINTPRS, which is the brine pocket internal pressure, is not included for the E2 intrusion scenarios since the brine pocket is not modeled in these cases.

To help increase the accuracy of the regression, all three replicates are grouped as one. Trus provides a longer record for describing the independent/dependent variable relationsrup. Thus, unless otherwise noted, all stats for this section are based on the entire replicate/vector group. Each scenario is separately modeled.

5.1. Stepwise Regression

The dependent variable used for this regression analysis is the total integrated flux after I 0,000 years in EPA units in the borehole at the Culebra interface. The integrated flux refers to the total integrated activity after the 1 0,000-year modeling time.

Both the El and E2 disturbed scenarios are examined; the El at two different intrusion times of 350 and 5000 years and the E2 at 350 years. The file-names of the flies included in the analysis are given in Table 7. For E2 intrusions greater then 350 years as well as

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for the undisturbed scenario (Sl), there are an insufficient number of data points to provide a meaningful analysis. Two times are chosen for the E 1 intrusion to help determine if the sensitivity ofthe model changes with time of intrusion. The regressed scenarios are referred to as Case 1 (El intrusion at 350 years), Case 2 (El intrusion at 5000 years), and Case 3 (E2 intrusion at 350 years).

Table 7- File names for inclusion in the regression analysis, located in CMS Ubrary LlBCRAJ_SlJM. 'Rx' indicates all three replicate files are used for the regression.

Time EJ E2

350 years SUM NUT CRA I Rx S2.TBL SUM_NUT_CRA1_Rx_S4.TBL - - - - I

5000 years SUM NUT CRAI Rx 53 T5000.TBL I NA - - - - - I

Table 8 lists the most influential dependen't variables, their associated coefficients, and the R2 value indicating the goodness of fit for a four-step regression for each Case. Figure 9 shows the regression model output versus the NUTS output data, providing a visual comparison of how well the regression model fits the transformed data.

Table 8-Stepwise regression analysis on ra':'k-transformed data for selected scenarios across all replicates.

~ Var, ji, Var1 ~ Var1 pj Var1 p. Case J 10.936 BHPERM ' 0.973 BPINTPRS 0.174 WGRCOR -0.144 WMICDFLG -0.209

R2 80.75 83. 11 84.89 86.13

Case2 -2.498 BHPERM 0.924 BP)\'ITB.S 0.124 WGRCOR ·0.082 SHUPRM 0.065

R2 79.23 80.6 81.30 81.7

Case3 4.1 12 BHPERM 0.58 WRGSSAT 0.30 SHUPRM -0.25 HALPRM 0.25

Rl 43.36 50.66 54.65 60.10

The parameter BHPERM has the greatest influence over the amount of release in the borehole at the CuJebra interface for all three cases. BHPERM is the permeability ofthe borehole and shows a strong positive correlation with the total activity. It explains 80.75%,79.23 and 43.36% of the variability in the output variable for Case 1, 2, and 3, respectively. A higher penneability in the borehole leads to a more open pathway for migration ofbrine into the repository and the subsequent migration of radioisotopes out of the repository, so this relationship is expected.

For Cases 1 and 2, the second most influential parameter is BPINTPRS. BPINTPRS is the Castile brine-pocket internal.pressure. An increase in this value results in higher brine flows into the repository when the brine pocket is penetrated in the El intrusions, leading to a higher ability to mobilize contaminants. The third most influential parameter for C3$eS 1 and 2 is WGRCOR. . WGRCOR is the corros.ion rate of steeL Steel in this context refers to the steel in the containers and within the waste. Both Cases show a slight negative correlation because steel corrosion consumes brine and produces gas, resulting in higher pressures and lower brine inflow. Both the increased consumption of brine and the decreased brine inflow results in lower saturations and thus a lower ability to mobilize radioisotopes. WGRCOR becomes less influential with time because the source of steel is consumed resulting in lower gas production and brine consumption. In

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Figure 9 - Nuts output versus regression model results for an Et intrusion at 350 and 5000 years and an E2 intrusion at 350 years.

comparison to BHPERM, BPINTPRS and WGRCOR have rela tively minor influence accounting for approximately 2% each of the total variability.

The second most influential parameter for Case 3 is WRGSSA T. WRGSSA T is the residual gas saturation level in the waste emplacement area. WRGSSAT is able to explain approximately 7.3% of the output variabil ity. The third most influential parameter is SHUPRM. which is the permeability of the upper shaft. There is a slight negative correlation to this parameter since an increase in the shaft permeability will lower pressures in the repository before an intrusion would occur, and result in lower releases. Ove~all, the fit for the E2 scenario accounts for 60.10% of the total variability.

The sensitivity parameters are all BRAGFLO input variables and control the flow of brine into and out of the repository. For that reason, the reader is directed to Stein and Zelinski (2003) for further discussion.

6. Summary This analysis package describes the transport calculations that are part of the "Salado F low and Transport Calculations for the Compliance Recertification Application (CRA)

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of the Waste Isolation Pilot Plant (WIPP)" as described in Analysis Plan AP-99 (Stein, 2003a). Specifically, it covers the caJculations to determine the mobilization and subsequent migration of radioisotopes throughout the repository, shaft system, Salado formation, and possible human intrusion boreholes. These calculations are part of the Performance Assessment (PA) that estimates releases from the repository for the reguJatory period of 10,000 years after closure, which is required for the CRA.

To represent possible future states of the repository and to predict possible releases through the Salado, six modeling scenarios are defined. The six scenarios are:

• 81: Undisturbed performance (no human intervention or intrusion into the repository during 10,000 years)

• 82: An intrusion borehole at 350 years that penetrates both the repository and an underlying pressurized brine reservoir in the Castile formation (E 1-type intrusion)

• 83: An intrusion borehole at 1000 years that penetrates both the repository and an underlying pressurized brine reservoir in the Castile f~rmation (El -type intrusion)

• 84: An intrusion borehole at 350 years that penetrates the repository but does not encounter a pressurized brine reservoir (E2-type intrusion)

• 85: An intrusion borehole at I 000 years that penetrates the repository but does not encounter a pressurized brir).e reservoir (E2-type intrusion)

• 86: A multiple intrusion scenario, which includes an E2 intrusion followed by and El intrusion at a later date

Scenario S6 is addressed in a separate analysis (Garner, 2003) and is not discussed in this report.

To address the uncertainty in many of the input parameters used in the PA calculations, three replicates of 100 sets of Latin Hypercube sampled parameters (each unique set is called a vector) are defined. The analysis is composed of three types of modeling runs: the screening runs, the ISO runs, and the TI runs. The screening runs are used to screen out those vectors that have no possibility of radioactive release to the accessible environment. This is accomplished by performing transport simulations on all vectors using a continuous l kg!m3 source of a conservative tracer as the contaminant and calculating the cumulative mass crossing into the accessible environment. Those vectors that show cumulative mass greater than lx I o·7 kg are 'screened-in' and are used for the more detailed 1SO and Tl runs.

Unlike the screening runs, the ISO and TJ runs (ISO refers to isotope runs and TJ refers to time-intrusion runs) model specific isotopes and decay chains. To help reduce computational overhead the possible isotopes and decay chains were examined to determine the minimum number of isotopes required to describe the compliance behavior of the WIPP (Stockman et aJ., 1996). Isotopes having similar decay behaviors and transport characteristics are combined in ways that introduce little or no loss of release information in terms of normalized "EPA units". EPA units are a relative unit that is proportional to the ratio ofthe cumulative release of an isotope to the release limit for that isotope. Combinations of similar isotopes are referred to as ' lumped isotopes'. This

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analysis models 5 lumped isotopes: 241 Am, 238Pu, 239pu, 234U, and 230Th. ISO runs are performed on scenarios S I through S5 and directly util ize the 350 and I 000 year intrusion time flow-fields from the brine and gas flow code, BRAGFLO (Stein and Zelinski, 2003). The TI runs are used to examine the sensitivity of the results to different borehoJe.intrusion times. Intrusion times of 100, 3000, 5000, 7000, and 9000 years are modeled in the TI runs using 'time-shifted' flow-field inputs from BRAGFLO. Time shifting means applying a flow-field based on one intrusion time to a different intrusion time by extending or shortening the pre-intrusion conditjon as necessary. This prevents the need of re-running the computationally intensive flow model for each intrusion time. Justification for this approach is discussed in section 2.4.3.

The transport and decay of radionuclides are calculated using the computer code NUTS (NUclide !ransport fu'stem), version 2.05a. NUTS is a five-point finite difference code designed to model multi-dimensional, multi-component, and radioactive-contammant transport. The key processes modeled here are advective transport, decay, precipitation, solubility limits, and interior sources, all in a single porosity, continuous matrix. No dispersion is modeled.

Three potential pathways for migration of radioisotopes in dissolved brine are considered in this analysis. The first, and the most important pathway, is a human intrusion into and possibly through the repository. Under this scenario, brine may be released up the borehole toward the Culebra Dolomite member of the Rustler formation. In the second pathway, brine may migrate through or around the panel seals through the disturbed rock zone (DRZ) surrounding the repository to the shaft and then upward toward the Culebra. In the third pathway, brine may migrate from the repository through the DRZ and then laterally toward the subsurface Jand withdrawal boundary within the anhydrite inter-beds (marker beds) of the Salado formation.

The dynamics of the brine movement are complex and highly dependent on the BRAGLO input parameters. Initially, brine may flow into the repository from anyone of the migration pathways mentioned above. If sufficient brine enters the repository the radioisotopes become mobiJjzed in both solute and colloidal sorbed forms. Once the radioisotopes are mobilized, transport away from the repository can only occur if the head potential within the repository exceeds that outside the repository and if brine saturation in the waste exceeds residual brine saturation. Brine may be consumed by the oxidation of ferrous material in the waste containers and the waste itself, which has an effect on the solubility of the radioisotopes.

The screening runs p.roduced 415 'screened-in ' vectors out of a possible 1 500 (three replicates of 100 vectors across 5 scenarios). Results of the ISO and TI runs show that there is effectively no releases out the marker beds or up the shaft. The only release for either of these two pathways was found in scenarioS 1, where only a trivial amount of activity was detected in the marker beds (total activity of 2.89x I o-ts EPA units) and no activity indicated at the shaft/Culebra interface.

The E l intrusions (S2 and S3) produced the rughest releases, with a maximum total activity at the borehole/Culebra intersection of28.26 EPA units occurring with a 100 year intrusion time. For perspective, the average for all vectors at the I 00 year intrusion time is 1.42 EPA units and the median is 0.23 7 EPA units. No releases are predicted in the

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marker beds for the E l intrusions, and a maximum total activity at the shaft/Culebra intersection was calculated to be 8.74x t 0"23 EPA units. As the intrusion time increases, the maximum total release tends to decrease. For example, at an intrusion time of 5000 years, the maximum total activity for an El intrusion is predicted to be 2.89 EPA units.

The E2 intrusions produced somewhat lower maximum total activities at alJ intrusion times than the E 1 intrusion. The maximum total activity at the boreho]e/Culebra intersection for all intrusion times is predicted to be 2.12 EPA units (I 00 year intrusion time). This too declines with increasing intrusion times, with 0.049 EPA units predicted at a 5000 year intrusion time. Like the other scenarios, little or no activity is predicted in the marker beds or at the sbaft/Culebra intersection.

For all scenarios, the permeability in the borehole is the parameter most strongly correlated with the total activity at 'the borehole/Culebra interface. For the El intrusions, this correlation is quite strong, accounting for over 80% of the output variability at the 350 year intrusion time. However, the correlation becomes Jess pronounced (43.36%) for the E2 intrusions. This is due to the E2 intrusion being more reliant on brine inflow into the repository prior to borehole penetration. With an E I penetration, brine is supplied directly from the Castile brine pocket, meaning other sources of brine are not as important. Scenarios 2 and 3 (EL intrusion) are secondarily sensitive to the brine pocket pressure (BPINTPRS) showing a positive correlation to this parameter. Scenarios 4 and 5 (E2 intrusion) are secondarily sensitive to the residual gas saturation in the waste emplacement area, a lso showing a positive correlation.

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: t £.(1.1

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1£.()8

0 2000 4000 6000 1000 10000

Tlmo (yro)

Figure JS- Activity up borehole in EPA lh!its for R3S3.

nOny

Am·241 Total Activity Up Borehole


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Pu-239 Total Activity Up Borehole Replicate 1, Scenario 4: E2 Intrusion @ 350 YB

IE~2r--------------~~-~--, R~pllcate 1, Scenario 4: E2 Intrus ion @ 350 yrs

1.E•02 ...---=-- -----------------, i I.E-01

~ tE~ ~- ' E.OI ! r.

I.E.02 ! ,g ~ I E.03

! I.E-04 e . 0: 1 E.05

I.E~

0

Thne tynl

U·234 Total Activity Up Borehole Replicate 1, Scenario 4: E2 Intrusion @ 350 yn1

1 E+02

i c :::.

1.£+01

: 1.E+OCI

!:!!.

i 1 E-01

j 1.E..OZ

a. 1.£..()3 :::.

! I.E-04

.! t.E-05 • at

I.E-06

0 2000 4000 6000 8000 10000

i I.E~l c ~ 1.E•OO

~ I.E.01 i 2 1.e.o2 ,g .. 1 E.Ol :>

! ~

1 E-0<1

I E.05

0

TI .... (YB)

Th-230 Total Activity Up Bo111hole Replicate 1, Scenario 4: E2 Intrusion @ 350 yrs

1.E+02 ..,---------------------------------,

~ I.E-0<1

1.E.05

~~ I.E~ ~----~----~:t;:-.:::::::2;::...--.o:;::z===~ 0 eooo 10000

Figure 16- Activity up ~:;e<J.'ilJe in EPA Units for R1S4.

Am-241 Total Activity Up Borehole Replicate 2, Scenario 4: E2 Intrusion @ 350 yrs

1.£ +02

~ :::.

1.£+01

: 1.E+OO !! • 1..E..()1 0 Jl! I.E-02 e ~ t .E-03 a. :::. • t.E-D'I • • .! 1.£..05 0 at

t .E-06 0

nme(y,..)

U·234 Total Activity Up Borahole Replicate 2, Scenario 4: E2 Intrusion @ 350 yrs

IE~?,-------------------~------,

i 1 ~+01

< :> I.E"()!) < ~ 1£..41 I r. 1E.02 t 0 .. ... :>

I.E.03

·! I.E.O• t 0: I.E .OS

I E-06

0 2000 •ooo Gooo 8000 10000

TI..,. ()'R)

Pu-239 Total Activity Up Borehole Replicate 2, Scenario 4: E2 Intrusion @ 350 y,S

1.E'02 ...-----------------'------,

'j 1 E•01 . ; 1.E+oo

~ 1E~1 ~ 0

~ S.E-02

" 1 E-o3 :> • : 1.E-04

: 1.E.OS

0 2000 0000 10000

Th-230 Total Activity Up Borehole Replicate 2, Scenario 4: E2 Intrusion @ 350 yrs

1.E•02 r----------------'~----,

D 1.E•01 c: ; I.E"()!)

~ 1 E.01

1 1,£.02

Q. 1 E-03 ~ . : 1 E-04 ~ ~

Time W") Pigure 17- Activity up borehole in EPA Units for R2S4.

onOny

Am-241 Total Activity Up Borehole Replicate 3, Scenario 4: E21ntruslon @350 yrs

1 £•02

~ 1.£<01

~ l ,E OOO l !!! 1.£..01 ~

! 1.£..02 e

.:! ... ~

I £.(13

: 1.£.(14

! 1,£-05

1.£.(16

2000 •ooo &000

U-234 Total Activity Ulp Borehole Replicate 3, Scenario 4: E2 Intrusion@ 350 yrs

u;.a~

i 1 E•01

c

" IE<OO • .. ~ I.E-01 . 0

"' I.E-02 t & .. 1.£-03 ~ . I 1.E.o4

~ " 1.£·05

I .E-06 2000 •ooo 6000 1000

Jlmo(yral

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Pu-239 Total Aoetlvlty Up Borehole Replicate 3, Scenario 4: E21ntruslon@ 350 yrs

1.£•02 ,..---------------------,

i 1,£•01 c ~ 18-00 < .. !!:!. 1 E..OI i "' I E-42 i ! I E-03

: I E-00 .! • c 1 e-os

1 E-06

10000 0

Tlmo (yro)

Th·230 Total Activity Up Borehole Replicate 3, Scenario <&: E2 Intrusion @ 350 yrs

1£•02~-------------------~

'i 1.£ • 01

~ 1E-OO c ~ I £.01 .t ~ 1,£.02

l .. 1,£.03 ~

i I.E.(Iol . .. " t.E-05 -

1 E-o&

10000 2000 8000 10000

Figure 18- Activity up borehole in EPA Units for R3S4.

Am·241 Total Activity Up Bwehole Pu-239 Total Ac:tlvlty Up Borth9lt

Replicate 1, Scenario 5: E2 Intrusion@ 1000 yrs Repllcate1, Scenario 5: E21ntruslon@ 1000 yrs 1.£<02 I.Eo02

i 1,£>01 f I,Eo01

c ~ 1.£000 ; 1.£>00 c ... ~ !!:!. 1 E-01 1.£.01

i .t 0

t 1.£.(12 I 1.£.02

.:! ! 1 £.(13 ! 1£.03

i • 1.E.o4 : I E-00

.! • . " I £.(15 c 1 E.05

I.E-06 I E-015

0 2000 •ooo 6000 8000 10000 0 2000 •ooo 6000 8000 10000

TI_!Y .. I nmo()'ra)

U-234 Total Activity Up Bonthole Th-230 Total Activity Up Borehole

'Replicate 1, Scenario 5: E21ntruslon@ 1000 yre Rapllcate1, Scenario 5: E21ntruslon@ 1000 yrs 1.E•02 1.£•02

I 1.£<01 ii I .EOOI

u :-oo ~ 1.E<ll0 < c ~ 1.£.(11 ~ 1£.01

i .t 0

1 I £.(12 "' 1 E.02 t .:! .. 1£.(13 ! I E.03

~

= .

~ l .E-00 ~ 1 E-00

! .!

It 1£.(15 c 1.£.05 -1,£.(18 I.E-418

0 2000 •ooo 6000 9000 10000 2000 4000 6000 9000 10000

Tllmo(yra) Tlmolr "'l

Figure 19 - Activity up ';orehoJ<";; in EPA Units for Rl SS

n

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Am-241 Total Activity Up Borehole Replicate 2, Scenario 5: E2 Intrusion @ 1000 )'1$

1[~2~--~----------------------~~--~--,

I 1E~1 :> 1£-00 .. _:

1 E.01 i 1£.02

! I E.o3

i t .E.o.f

! 1£.0!1

TJmo(y"')

U-234 Total Activity Up Borehole Replicate 2, Sce!Mirio 5: E2 Intrusion@ 1000 yrs

1E~zr---~----------------------~~--~~

i l.E.Ot

! I.E-ocl c ~ I.E.OI

I 1 e.oz l ! I £.03 . . IE .o-f . t "' I E.OS

Pu-239 Total Activity Up Borehole Replicate 2, Seen11rio 5: E21ntruslon@ 1000 yrs

1 £~2?---~----------------------~~--~~

f 1 £~1

~ 1Eo00

~ I £.01

~ I E.02

~ I !.03

: 1£-o.f . .! . « I £.05

t .E.o&

0

TJma(y"')

Th·230 Total Activity Up Borehole Replicate 2, Scenario 5: E21ntruslon@ 1000 yrs

JE•~r-------------------------------------,

i 1£•01

! IE~ : !! 1 E.OI t 1:

l t .£.02

... :0

, E.o3

: . .!

I.E.o.f

l I E.o5

tE.OS tE.oa~------~ .................. ~--~~~==~~ 0 2000 4000 &CCO 8000 10000 0 4000 8000 10000

n-11"') Figure 20 - Activity up borehole in EPA Units for R2SS.

Tlme(~rtl

Am-241 Total Activity Up Borehole Replicate 3, Scenario 5: E21nlruslon@ 1000 yrs

1£·~~--~----~----------------~----~--~

i 1 E•OI

~ I.E•OO c .. I! I.E·OI

I 5 I.E.OZ ·

II

! I E.03

i IE .o-f t c IE.05

I E.o&

zooo •ooo 6000 8000 10000

U·234 Total Activity Up Borehole Replicate 3, Scenario 5: E2 Intrusion @ 1000 yrs

1£·~~--~----~----------------~----~--~

I IE•OI

~ I E•OO

~ I E.OI

J I E.OZ

... I E-03 :0 . I I E·OO . "' I.E-05

I.E.OS

0 2000 •ooo 6000 8000 10000

Tlmo (yrt)

Pu-239 Total Activity Up Borehole Replicate 3. Scenario 5: E21ntruslon @ 1000 yrs

1£•02~--~----------------------~----~~

i I .E~!

:0 1,E•OO : ~ u:.-01 .!

I 1 E-02

... :0

1 Eo03

j IE .o-f

« I E-o5

1E-C6

0 zooo •ooo 6000 8000 10000

Tll-230 Total Activity Up Borehole Rtplleatt 3, Scenario 5: E2 Intrusion@ 1000 yrs

1£~2~--~----~----------------~--~~--,

i 1[~1

3 c tE-o<~ .. '!I. l !.01

i 5 1 E.OZ .. ! te.03

! I E.o.t -! « I.E.OS

Tl""' (y,.)

Figure 21 -Activity up ~reholl' :n EPA Units for R3SS.

References

Salado Tmnsport Calculations: Compliance Reccrttlication Application

ERMS #530164

Verston 00

Page J9 of76

Caporuscio, F., Gibbons, J., and Oswald, E. 2003. Waste Isolation Pilot Plant: Salado Flow Conceptual Models Final Peer Review Report. Report prepared for the U.S. Department of Energy, Carlsbad Area Office, Office of Regulatory Compliance. ERMS# 526879. .

De Marsily, G. 1986, Quantitative Hydrogeology: Groundwater Hydrology for Engineers. Academjc Press, San Diego, CA.

DOE/WIPP, 2004, Title 40 CFR part 191, subparts B and C Compliance Recertification Application, 2004-3231.

Garner, J. 2003. "Analysis Package for PANEL: Compliance Recertification A~cation" AP-99. Carlsbad, NM: Sandia National Laboratories. ERMS# ~·_!_.Z•:mt.

Hansen, C. 2003. "Analysis Plan for Calculation ofCCDF's" Compliance Recertification Application" AP-1 02. Carlsbad, NM: Sandia National Laboratories. ERMS# 530149.

Helton, J .C., Bean, J .E., Berglund, J.W., Davis, F.J., Garner, J .W., Johnson, J.D., MacKinnon, R.J., Miller, J., O'Brien, D.G., Ramsey, J.L., Schreiber, J.D., Shinta, A., Smith, L.N., Stoelzel, D.M., Stockman, C., and Vaughn, P. 1998. Uncertainty and Sensitivity Analysis Results Obtained in the 1996 Performance Assessment for the Waste Isolation Pilot Plant. SAND98-0365. Albuquerque, NM: Sanrua National Laboratories.

Helton, J.C., and Marietta, M.G. 2000. "The 1996 performance assessment for the Waste Isolation Pilot Plant," Reliability Engineering & System Safety. Vol. 69, no. J -3, ) -3.

Stein, J. 2003a. "Analysis Plan for Calculations of Salado Flow and Transport: Compliance Recertification Application." AP-99. Carlsbad, NM: Sandia National Laboratories. ERMS# 526891.

Stein, J.S. 2003b. "Analysis Package for Direct Brine Releases: Compliance Recertification Application." Carlsbad, NM: Sandia National Laboratories. ERMS# 530163.

Stein, J.S., and Zelinski, W. 2003. "Analysis Package for BRAGFLO: Compliance Recertification Apmlication." Carlsbad, NM: Sandia National Laboratories. ERMS#@M~J.

Stockman, C., Shinta, A., and Garner, J . 1996. "Analysis Package for the Salado Transport Calculations (Task 2) of the Performance Assessment Analysis Supporting the Compliance Certification Application." Carlsbad, NM: Sandia National Laboratories. WPO# 4051 5.

WIPP PA (Performance Assessment). 2003a. "Analysis Report for the 8400 Regression Test." Sanrua National Laboratories. Sandja WIPP Central Files Records Package # 525278.

Information Only


ERMS #530164

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WIPP PA (Performance Assessment). 2003b. "Analysis Report for the ES45 Regression Test." Sandia National Laboratories. Sandia WIPP Central Files Records Package # 525278.

WIPP P A (Performance Assessment). 2003c. "Analysis Report for the ES40 Regression Test." Sandia National Laboratories. Sandia WIPP Central Files Records Package # 525278.

WIPP PA (Performance Assessment). 2003d. '!Execution of the Performance Assessment for the Compliance Recertification Application (CRAI)." Sandia National Laboratories. Sandia WIPP Central Files Records Package # 530170

Info ation Only I

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Appendix A: ·plotting script for time-series activity plots. Sub Plot_ Ti()

' Plot_ T i Macro

'Macro recorded by Thomas Lowry

' Keyboard Shortcut: Ctrl+a

Const realization As String= I 'Replicate #

Consl fsTitle As Integer= J 8 'Font size of chart title

Const fsAxis As Integer= I 6 'Font size of axis titles

Const fsLabel As Integer= 16 'Font size of axis labels

Dim scenario As Integer 'Scenario#

Dim iSeries As Integer 'Series counter

Dim intn As String 'Undisturbed, E I or E2 Dim inTime As String 'I ntrusion time

Dim fName As String 'File name

Dim cTitle As S tring 'Chart title

Dim dSheet As String 'Name of data sheet

Dim cSheet As String 'Name of chart sheet

Dim oldSheet Ar:. Integer 'Name of previous sheet

Dim wBook As String 'Name of data file workbook

D im pBook As String 'Name of permanent workbook

Dim nukes 'Array of nuclide names

Dim cSeril!s 'Array of number of vectors

Dim sTime As String 'Intrusion time for scenarios 3 & 5

pBook = "Chart_macro.xls"

cSeries=Array(O, 1,60,47, 13, 13)

nukes= Array("Am-241 ", "Pu-239", "U-234", "Th-230")

oldSheet :::. I

For scenario= I To 5 'Loop through each scenario

If scenario = 2 Or scenario = 4 Then

inTime=350

Else If scenario = 3 Or scenario = 5 Then

inTime = 1000

End I f

If scenario = 2 Or scenario = 3 Then

intn = "E l"

Eisel f scenario = 4 Or scenario= 5 Then

Information Only

yrs"

Salado Transpon Calcula1ions: Compliance Recenificalion Applica1ion

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intn= "£2"

Else

intn = "Undisturbed Case"

End If

For inukes = 0 To 3 'Loop through each nuclide

'Set file names, etc.

fName = "C:\Oocuments and Senings\tslowry\My Documents\W IPP\NUTS\ISO Runs\R" _

& realization & "\r'' & realization & "_s" & scenario & "_" &

nukes(ioukes) & ".txt"

dSheet = ''r" & realization & "_s" & scenario & "_" & nukes(inukes)

cSheet = dSheet & "_ch"

wBook :::: dSheet & " .txt"

I f scenario = I Then

cTitle = nukes(inukes) & " Total Activity Up Borehole" & Chr( I 0) & "Replicate "_

& realization & ",Scenario" & scenario & ": " & intn

Else

cTitle = nukes(inukes) & " Total Activity Up Borehole " & Chr( I 0) & "Replicate 11 _

& realization & ",Scenario II & scenario & ": " & intn & " Intrusion@" & inTime & "

End lf

'Open data file and impon data to new workbook of same name

Workbooks.OpenText fileName:=tName _

, Origin:=xiWindows, StartRow:= l , DataType:=xiFixedWidth, Fieldlnfo:= _:

Array(Array(O, 1), Array(6, 1}, Array(l9, 1), Array(32, 1), Array(45, 1), Array(58, 1),_

Array(? I , 1), Array(84, 1), Array(97, I), Array( 110, 1), Array(J 23, 1), Array(136, 1),_

Array( 149, I), Array( 162, 1 ). Array( 175, 1), Array( 188, 1), Array(20 I, 1), Array(214, 1),_

A.rray(227, 1 ), Array(240, I), Array(253, I), Array(266, I), Array(279, I), Array(292, I),_

Array(305, 1), Array(3 18, 1). Array(331, 1), Array(344, I). Array(357, I), Array(370, 1),_

Array(383, 1), Array(396, 1), Array(409, 1), Array(422, 1), Array(435, I}, Array(448, 1),_

Array(461,1),Array(474, 1),Array(487, 1),Array(500, I).Array(513,1),Array(526, 1),_

Array(539, 1), Array(552, 1), Array(565, 1), Array(578, 1), Array(591, 1), Array(604, 1),_

Array{617, 1), Array(630, 1), Array(643, I), Arr~y(656, 1), Array(669, 1), Array(682, 1),_

Array(695, 1), Array(708, l),Array(721, 1), Array(734, l ),Array(747, 1), Array(760, 1),_

Array(773, 1), Array(786, 1), Array(799, 1), Array(812, 1))

'Add scauer chan

Chans.Add

ActiveChan.ChartType = xiX YScatterSmoothNoMarkers

ActiveChart.SetSourceData Source:=Sheets(dSheet).Range( _

"A I :BII 99"), P1otBy:=x1Columns

ActiveChart.Location Where:=xiLocationAsNewSheet, Name:=cSheet

Information Only

'Set chart and axis tit les

With ActiveChan

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.HasTitle =::True

.chartTitle .Characters.Font.Size = fsTitle

.chartTitle.Characters.Text =:: cTitle

.Axes(xJCategory, xiPrimary).HasTitle =True

.Axes(xiCategory, xiPrimary).AxisTitle.Characters.Font.Size =:: fsAxis

.Axes(xlCategory, xiPrimary).AxisTitle.Cbaracters.Text ="Time (.yrs)"

.Axes(xiYalue, xJPrimary).HasTitle = True

.Axes(xiYalue, xiPrimary).AxisTitle.Characters.Font.Size =:: fsAxis

.Axes(xiYalue, xiPrimary).AxisTitle.Characters.Text = _ "Release Up Borehole (EPA Units)"

End With

'Change scale and format of y-axis

ActiveChart.Axes(x !Value ).Select

With ActiveChart.Axes(xJYalue)

.MinimumScale = 0.00000 I

.MaximumScale = 100

.MinorUnitlsAuto = True

.MajorUnitJsAuto = True

.Crosses= xiCustom

.CrossesAt =:: 0.00000 I

.ReversePiotOrder = False

.Scale Type= xiLogarithmic

.DisplayUnit = xiNone

.TickLabels.NumberFonnat = ''O.E+OO"

.Tick.Labels.Font.Size = fsLabel

End With

'Change scale of x-axis

ActiveChart.Axes(xiCategory).Select

With Acti veChart.Axes( x !Category)

.MinimumScalelsAuto = True

.MaximumScale = 10000

.MinorUnitlsAuto = True

.MajorUnitlsAuto =::True

.Crosses = xiAutomatic

.ReversePiotOrder := False

.Scale Type = xlLinear

.DisplayUnit = xiNone

.Tick.Labels.Font.Size = fsLabel

Information Only

End With

'Delete legend

Acti veChart.Legend.Select

Selection. Delete

'Put border around chart

Acti veChart.PlotArea.Select

With Selection.Border

.Weight= xiThin

.LineStyle = xiContinuous

.Colorlndex = I

End With

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'Change interior color to white (none)

Selection.lnterior.Colorlndex = xlNone

Acti veCbart.CbartArea.Select

'Loop through each data series and tum series black

For iSeries = I To cSeries(scenario)

On Error Resume Next

ActiveChart. SeriesCollection( iSeries ).Select

With Selection.Border

.Colorlndex = I

.Weight = xiThjn

.LineStyle = xlContinuous

End With

With Selection

.MarkerBackgroundColorlndex = x!None

.MarkerForegroundColorlndex = xiNone

.MarkerStyle = xJNone

. Smooth = True

.MarkerSize = 3

.Shadow= False

End With

Next iSeries

'Tum offgridlines

Acti veChart. P lotArea.Se lect

With ActiveChart.Axes(xiCategory)

.HasMajorGridlines = False

.HasM inorGridl ines = False

End With

Information Only

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Appendix B: Post-processing code for SUMMARIZE screening runs, SCREEN_BL.FOR.

c

PROGRAM SCREEN_BL

CHARACTER *1 0, CHARl, CHAR2, DUMMY

OPEN (lO,FILE='SUM_ NUT_CRAl_SCN_Rl_Sl.DAT;l',STATUS= ' OLD',

& BLANK= . NULL I )

OPEN (12,FILE= 'SUM_NUT_CRA1_$CN_Rl_S2.DAT;l ',STATUS='OLO')

OPEN (14,FILE='SUM_NUT_CRA1_SCN_Rl_S3.DAT;l ',STATUS='OLD')

OPEN (16,FILE='SUM_NUT_CRAl_SCN_R1_S4.DAT;l',STATUS='OLD')

OPEN (18,FILE='SUM_NUT_CRAl_SCN_Rl_S5.DAT;l',STATUS= ' OLD')

OPEN (20,FILE= ' NUT_CRAl_SCN.OUT',STATUS= ' NEW')

CHARl : 'Borehol e

CHAR2 = 'Markerbeds'

DO 200 J = 0,8,2

IUNITl ::. 10 + J

IS = 1 + J /2

WRITE (20,30) IS

30 FORMAT (2X, 'SCENARIO Rl_S' ,Il, ':',/).

ICOUNT = 0

DO 100 I= 1,100

IF(IS.eq.l)THEN

READ(IUNITl , *)IVECTORl,TIME,SWASTElC ,SMB39SlC,SMBABSlC,SMB38NlC,

& SMB38SlC,SMB39NlC,SMBABNlC,SCULBRlC,SHUP1C,SURFSHlC,SSHM1C,

& BHUPlC,SURFBHlC,SSALADlC

ELSE

READ(IUNIT1, * )IVECTORl ,TIME,SWASTElC,SMB39SlC,SMBABSlC , SMB38NlC,

50

& SMB38S1C,SMB39NlC,SMBABNlC,SCULBRlC , SHUP1C,SURFSHlC,SSHMlC,

& BHUPlC,SURFBHlC,SBHMlC,SSALADlC

END IF

SUMMB = SMB38NlC+SMB38SlC+SMB39NlC+SMB39SlC+SMBABN1C+SMBABSlC

IF (SUMMB .GT. 0.0000001) THEN ~

ICOUNT = !COUNT + 1

WRITE (20,50) IVECTORl,CHAR2

FORMA'!' (2X, IS, 2X, AlO)

END IF

IF (SHUP1C+BHUP1C .GT. 0.0000001) THEN

ICOUNT = ICOUNT + 1

WRITE (20,50) IVECTORl ,CHARl

END IF

100 CONTINUE

Informaf on Only

With ActiveChart.Axes(xiVaJue)

.HasMajorGridlines =False

.HasMinorGridlines =False

End With

'Change size of chart and center chart on page

ActiveChart.PiotArea.Select

Selection. Width= 487

ActiveChart.PlotArea.Select

Selection.Left = 1 I 0

'Move title and left justify

ActiveChart.chartTitle.Select

Selection.Left = 161

Selection .Top= 34

With Selection

.HorizontalAlignment = xiLeft

. VerticalAiignment =xi Center

.Orientation= xlHorizonlal

End With

ActiveChart.ChartArea.Select

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ERMS 11530164

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'Move chart to permanent workbook

Sheets(Array(dSheet, cSheet)).Select

Sheets( Array( dSheet, cSheet)).Move Before:=W orkbooks(pBook).Sheets( oldSheet)

o ldSheet = o ldSheet + 2

Next inukes

Next scenario

End Sub

..

Information Only '


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WRITE (20,120) ICOUNT

120 FORMAT (/2X, 'TOTAL = ',!3, / /)

200 CONTINUE

END

Information Only


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Appendix C: Sample NUTS screening input file for replicate 1, scenario 1.

•• NUTS TITLE • •

'NUTS 2.05A TRACER SCREENING TEST FOR CRAl Rl S1 (UNDISTURBED SCENARIO)'

• • 1.11 OF SITES.~ OF MATERIAL. (2.SJTE NAME,H COMP. TO BE MODELED)l , . . ,NSI'l'ES 1,30 'WIPP_SITE' 1

** (1. SITE, 2.COMP., DAUGHTER, PARENT, GROOP NAMES]l , . . ,NSITES • •

'WI PP _Sl'rE' 'TWASTE' 'NONE' 'NONE' 'WASTE' • • 1.~ OF ELEMENT, (2.ELEM. NAME, TEMP. DEPEND., TABLE LOOK-UP)l, .. ,NELEMENT ••

1

'WASTE ' .FALSE. .FALSE. • • COLLOIDAL TRANSPORT FLAG (T/Fl ••

.F1\LSE . • • PH DEPENDENT SOLUBILITY(IS PH REQUIRED (Y/Nl) • •

'N ' ** ORDER OF THE METHOD ·~

1

• • DEGREE OF IMPLICITNESS ••

1. DO

PRECIPITATE IMPLICITNESS; 1.T/F,IF IMPLICIT 2.1 OF ITERATION,TOL. • • .FALSE.

•• IS MATRIX ADSORPTION REQUIRED (Y/NI • • 'N'

•• DO YOU HAVE DISPERSION IN THE MATRIX (Y/ N)

' N'

•• DOES MATRIX HAVE SYMMETRIC DISPERSION (T/F) : ANS'tlER IF DISPERSION IS Y • • H DO YOU HAVE INJECTION/PRODUCTION IN THE MATRIX (Y/ N)

'N'

** DO YOU HAVE DIRICHLET B. CS . IN TRE MATRIX (F/T) • • .TROE.

•• IS CONCENTRATION INITIALIZED MANUALLY IN THE MATRIX (F/T) • • . FALSE.

• • OPEN NUTS UNDISTURBED COB FOR INTRUSION TIME OTHER THAN 350 , 1000 YRS • • . FALSE. • • PRINT FLAGS OF MATRIX VARIABLE,S IN A BINARY FILE • • 0,0,0,0,0,0,1,0 . 0,0,0,0,0,0

• • TEMP. DEPEND. OF Kd (ENTER DATA I F ADSORP. IS (Y ) AND TEMP. DEPEND.) *• .- PRINTING FREQUENCY IN A BINARY FILE H

1,1. 014

u DO YOU HAVE EXTERNAL NUCLIDE SOURCE? (T/F) .FALSE.

* • MINIMUM LIMITS OF TIME TO BE SET IF ZERO ENCOUNTERED • • l . D-18

•• INTRUSION TIME, ITERPOLATED INTRUSION TIME, TOLERANCE • •• END MATERIAL MAP AND START NUCLIDES PROPERTIES • • •

•• IF NOT TEMP . DEPEND. (ELEMENT N~. SOLUBILITY LIMIT) 1, . . ,NELEMENT •• 'WASTE' -2.DO ** (CQ~iP. NAME, MOL. (ATOMIC) WI' . , INITIAL INVENTS. , HALF LIFE)l, .. ,NUCLIDE 'TWASTE' .100 0.00 0.00 O.DO

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• • GROUND WATER PH INPUT r•

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STANDARD BR. DENS. I F NOT BRAGFLO RON (READ ASCII FILE FOR FLUX FIELD) ••

MOLECULAR DIFFUSION OF EACH COMPONENT ' '

ROCK GRAIN DENSITY INPUT (REQUIRED ONLY IF SORPTION OR SOIL BASE CONC.) •• • • WASTE MATRIX INPUT (1.i OF IS0, 2.NAME, LOC . IN THE INPUT, WASTE SITE #) •~

1

' TWASTE' 1 1 ·~~ (1 . SITE NAME, NUMBER OF GRIDS IN THE SITE 2.INDECES)1 ... NSITES • ••

'WIPP_SITE' 33

23.10,1 24,10.1 25.10,1 32,10,1 33,10,1 35,10,1

23,11,1 24 ,11,1 25,11,1

32,11,1 33,11,1 35,11.1

23.12.1 24.12,1 25.12 , 1 32,12,1 33,12,1 35,12,1

MATRIX ADSORPTION INPUT • •

• • MATRIX DISPERSION INPUT ••

26,10,1 27,10,1 37,10, 1

26, 11,1 27,11,1

3?.11.1 26,12,1 27,12.1

37 , 12, 1

MATRIX SOURCE INPUT (INJECTED NUCLIDES IF ANY) ••

28.10. 1 29,10,1

28,11,1 29, 11,1

28.12 ,1 29,12,1

MATRIX OIR. B.CS. I NPUT (REP.='GENERAL',ANYWHERE= 'NOT_GENERAL') ••

1 'NOT_GENERAL' ' TWASTE ' 1 33

23,10,1 24,10 , 1 25,10,1 26,10,1 27,10,1 28,10,1 29,10.1

32 , 10,1 33.10.1 35,10.1 37,10,1

23,11,1 24 ,11,1 25,11,1 26,11,1 27,11 , 1 28,11, 1 29,11,1

32,11 . 1 33,11 , 1 35,11,1 37,11,1

23,12,1 24.12.1 25,12,1 26, 12 ,1 27,12,1 28,12,1 29,12,1

32,12 . 1 33.12.1 35,12,1 37,12,1

' TWASTE '

l.DO 1.00 1.00 1. DO l.DO 1.00 1.00 1. DO l.DO 1.00 1. DO

1.00 1.00 1. DO 1. DO l.DO 1.00 l . DO l.DO 1. DO 1.00 1.DO

1.00 1.00 1.00 1. DO l.DO 1 . DO l.DO l.DO l. DO 1.00 1 . 00 .. TIME DEPENDENT SOURCE IN TH.E MATRIX • • MATRIX CONCENTRATION INITIALIZATION .. .. COLLOID TRANSPORT VELOCITY SCALING FACTORS IN THE MATRIX • .

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Appendix 0: Sample ALGEBRA screening input file for replicate 1, scenario 1 . .

ALGEBRA INPUT FILE FOR NVTS SCREENING

Modified for CRA grid by Thomas Lowry, 11-April-2003

ALLTIMES !FIRST ISOTOP !MASS FLUXES LEAVING THE WASTE REGION

! WASTE TOP LAYER

SWASTEl =IFGT0(FLUXJMl[E:ll65] ,FLUXJMl[E : l165],0. ) SWASTE1 SWAST£1

SWASTEl SWASTEl SWAST£1 SI~ASTEl

SWASTEl SWAST£1

SWASTE1 SWASTEl

SWASTEl + 'IFGTO t FLUXJMl 1 E: 11661 , FLUXJMl 1 E: 11661 • o. l . SWASTEl + IFGTO(FLUXJMl[E:l l67],FLUXJMl [E: ll67].0.1

= SWAST£1 + IFGT0(FLUXJMl[E:ll68],FLUXJMl[E:ll68],0.)

SWASTEl + IFGT0tFLUXJM1[E:~l69],FLUXJMl(E:ll69],0. )

SWASTEl + IFGT0 (FLUXJMl(E:ll70 ),FLOXJMl (E:1170) ,0.) SWASTEl + IFGTO(FLUXJMl[E:ll7l),FLUXJMl[E:ll71J,O.I

SWASTEl + IFGTO(FLUXJMl[E:ll72),FLUXJMl(E:l172] ,0.) SWASTEl + IFGT0tFLUXJMl(E:l469],FLUXJMl(E:l469],0.)

SWASTEl ~ IFGTO(FLUXJMl(E:ll97],FLUXJMl(E:l29?j,O.I SWASTEl

SWASTEl -= SWASTEl + IFGTO(FLUXJMl[E:1198],FLUXJMl(E:l298],0. ) + IFGTO(FLUXJM1(E:ll99],FLUXJMl(E:1299],0.1

+ IFGTO(FLUXJMl(E:l475],FLUXJMl[E:l475),0 .) + IFGTO(FLUXJM1[E:1209], FLUXJMl[E:l209],0.) + IFGTO(FLUXJMl[E:l210],FLUXJMl[E:l210],0.)

+ IFGTO(FLUXJMl(E:12lll,FLUXJMl[E:l211),0.)

+ IFGTO(FLUXJMl (E:l48l ] ,FLUXJMl[E: l 481],0 .)

SWASTEl SWASTEl SWASTEl SV1ASTE1 SWASTEl = SWASTEl S\'/AST£1 SWASTEl SWASTEl SWASTEl

!WASTE LOWER LAYER

SWASTEl

SWASTEl SWASTEl SWASTE1 SWASTE1

SWASTE1 SWASTE1 SWASTE1

SWASTE1 SWASTE1 SWASTEl SWASTEl

SWASTEl SWASTE1 SWASTE1 SWASTE1 SWASTEl

SWASTEl + IFGT0(-FLUXJMl [E:l407],-FLUXJMl[E:l407],0.)

: SWAS'l'J::l + lFGTO (-FLUXJMl IE: 1 408]. -FLUXJMll E: 1408 J I 0 . I SWASTEl ~ IFGTO(-FLUXJMl [E: l 409j,-FLOXJMl[E:1409) ,0 .) SWASTEl + IFGT0(-FLUXJMl [E:l410], -FLUXJMl(E:l410),0 .) SWASTEl + lFGT0(-FLUXJMl [E:14llj,-FLUXJMl(E: l 411 ],0. ) SWASTEl + IFGTO(-FLUXJM1 [E:l412 ] . -FLUXJMl[E:l412},0.)

: SWASTEl + IFGTO(-FLUXJMl[E:l413],-FLUXJMl[E:l413),0.1 SWASTEl + !FGT0(-FLUXJMrr [E:l455],-FLUXJMl[E:l455 ],0 .1

SWASTEl + IFGT0(-FLUXJMl [E:l466), -FLUXJMl (E: l466 ] ,0 .) : SWAST£1 + IFGT0(-FLUXJMl[E:l428 ] ,-FLUXJMl[E:l428] ,0.1

SWASTEl + IFGT0 (-FLUXJMl [E:1429],-FLUXJMl (E :1429 ) .0 .) SWASTEl + IFGT0(-FLUXJMl[E:l458),-FLUXJMl [E :l458] ,0 . ) SWAST£1 + IFGT0(-FLUXJMl[E:l472],-FLUXJMl[E:l472].0.) SWASTEl + lFGTO(-FLUXJMl(E:1434 ) 1 -PLUXJMl [E :l434 ],0 .1

SWASTEl + IFGTO(-FLUXJMl[E:l435],-FLUXJMl[E:l435] ,0. ) SWASTEl + IFGTO(-FLUXJMl[E:1461],-FLUXJMl[E:1461] ,0.)

SWASTEl + IFGT0(-FLUXJMl[E:l4781,-FLUXJMl[E:l478], 0.1

WASTE LEFT LAYER

Information Only

SWAS'l'El

SWASTEl SWAS'l'El


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of76 SWASTEl + IFGT0(-FLUXIMl(E:1421),-FLUXIMl [E: l421] , 0.)

SWASTEl + IFGT0(-FLUXIM1(E:l414], -FLUXIMl [E:1414J ,0.) SWASTEl + IFGT0(-FLUXIMl(E:1407 ) ,-FLUXIMl[E:l407J ,0. )

WASTE RIGHT LA~ER

SWASTEl SWASTEl

SWASTEl

I

= SWASTEl + IFGT0(FLUXIM1 [E:l446 ),FLUXIM1 [E: l446 ) ,Q , ) SWASTEl .~ IFGT0(FLUXIMl[E:l443),FLUX~Ml[E: l443),0.)

SWASTEl + TFGTO (FLUXIMl [E:l440), FLUX:IM1 [E:l440 ) , O.)

! MASS FLUXES REACHI NG CULEBRA LOWER BOUNDARY

SCULBRl = lFGTO(FLUXJMl[E:l82S],FLUXJMl[E :lB25),0 .)

SCULBRl SCULBRl

SCULBRl SClJLBRl SClJLBRl

SCULBRl

SCULBRl SCULBRl

SCULBRl SCULBRl SCULB.Rl

SCULBRl

SCULBRl

SCULBRl

SCULBRl SCULBRl

SCULBRl SCULBRl

SCULBRl SCULBRl SCULBRl SCULBRl

SCULBRl SCULBRl

SCULBRl SCULBRl

SCULBRl SCULBRl SCULBRl SCULBRl SCULBRl

SCULBRl SCULBRl

SCULBR1 SCULBRl SCULBRl

SCULBR1 SCULBRl SCULBRl

SCULBRl + IFGTO (FLUXJM1 [ E:l826 ] ,FLUXJM1 [E:l826),0 ,)

SCULBRl + IFGT0(FLUXJMl [E:l827 ],FLUXJMl[E:1827],0.) SCUI.BRl + IFGT0(FLUXJMl[E:l828),FLUXJMl(E:1828),0.)

SCULBRl + IFGTO(FLUXJMl (E:l829],FLUXJMl(E:1829), 0.) SCULBRl + IFGTO(FLUXJMl[E: 1830 ),FLUXJMl (E: l830),0.)

= SCULBRl + IFGT0(FLUXJMl [ E: l83l].FLUXJMl(E: l831 ) ,0 ,)

SCULBRl + IFGTO(FLUXJMl (E:l832 ],FLUXJMl [E:1832 ) ,0 .)

SCULBRl + rFGT0(FLOXJMl(E:l833),FLUXJMl(E: l833 ) .0 ,)

SCULBRl + IFGTO(FLUXJMl(E: lB34 ),FLUXJMl(E:lB34),0.)

SCULBRl + IFGTO(FLUXJMl (E: l835],FLUXJMl [E:l835),0.l

SCULBRl + IFGTO(FLUXJMl (E:1836),FLUXJMl(E: l B36],0 .)

SCULBRl + IFGT0(FLUXJMl(E:1837),FLUXJMl[E: l837),0.)

SCULBRl + TFGT0(FLOXJM1 (E: 1838),FLUXJM1(E:l838), 0.)

SCULBRl + IFGTO(FLUXJMl(E:1839],FLUXJMl(E:l839),0 , )

= SCULBRl + IFGT0(FLUXJMl[E:l840],FLUXJMl [E:l840),0.)

SCULBRl + IFGTO(FLUXJMl(E:l84l] ,FLUXJMl[E:l841), 0 .)

SCULBRl + IFGTO(FLUXJMl[E:l842] ,FLUXJMl(E: l84 2),0.)

SCULBRl + rFGT0(FLUXJMl [E:l843],FLUXJMl(E:1843),0 .)

SCULBRl + IFGTO(FLUXJMl[E:l844),FLUXJMl(E:l844 ] ,0.)

SCULBRl + IFGT0 (PLUXJMl(E:l845 ],FLUXJMl(E:1845).0.)

SClJLBRl + lFGT0(FLUXJM1[E:l846],FLUXJM1 (E:l8461,0.)

SCULBRl + IFGT0(FLUXJMl[E:1847],FLUXJMl(E: l 847],0 . ) SCULBRl + IFGTO(FLUXJMJ[E:l848),FLUXJMl[E:l848],0.) SCULBRl + IFGTO(FLUXJM1[E:l849), FLUXJMl [E:l849],0.)

SCULBRl + IFGTO (FLUXJMl[E:l850],FLUXJMl [ E:l850),~.)

SCULBRl + IFGT0(FLUXJMl[E: 1851] , FLUXJMl [E:l851],0.)

SCULBRl + IFGT0(FLUXJMl[E :l852] ,FLUXJMl(E :1852),0 . )

SCULBRl + IFGTO(FLUXJMl[E:l853],FLUXJMl[E:1853 ). 0.)

= SCULBRl + IFGT0(FLUXJMl(E:1854] ,FLUXJMl[E: l854),0.)

SCULBRl • IFGTO(FLUXJMl (E:1855],FLUXJMl[E:1855], 0 . l SCULBRl + IFGTO(FLUXJMl [E:l856), FLUXJMl(E:1856),0 . )

SCULBRl + I FGTO(FLUXJMl (E:l857],FLUXJMl [E: l857],0.) SCULBRl + IFGTO(PLUXJMl [ E:1858J.FLUXJMl (E:1858 ), 0.)

SCULBRl + IFGT0(FLUXJMl(E:l859],FLUXJMl(E:l859),0.)

SCULBRl + IFG'rO (FLUXJMl ( E: 1860 I , FLUXJMl{ E: 1860] , 0 . )

"' SCULBRl + IFGTO(FLUXJMl [E:186l],FLUXJMl[E:l861),0.)

SCULBRl .. IFGT0(FLUXJMl(E:l489] ,FLUXJMl[E:1489),0.)

SCULBRl + IFGTO (FLUXJMl [E: 1862), FLUXJMl[ E: 1862 I , 0. )'

SCULBRl + IFGT0(FLUXJMl (E:l8631.FLUXJMl[E:l863],0.)

Information Only

SCULBRl

SCULBRl SCULBRl

SCULBRl

SCUL.BRl SCULBRl SCULBRl

SCULBRl SCOLBRl

SCULBRl SCOLBRl SCULBRl

SCULBRl

SCULBRl SCULBRl SCULBRl

SCUL.BRl

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SCULBRl + IFGTO( FLUXJMl[E:l864],FLUXJMl[E:1864),0. )

SCULBRl + IFGTO(FLUXJMl[E:l865),FLUXJMl(E:l865),0 . 1 SCULBRl + IFGT0(FLUXJMliE:l866l,FLUXJMl[E:l866],0.)

SCULBRl + !FGT0(FLUXJMl(E:l867),FLUXJMl(E:18671,0.)

SCULBRl + IFGTO(FLUXJMl{E:l868],FLUXJJill(E:1868],0 . 1 SCULBRl + IFGTO(FLUXJMl[E:1869],FLUXJMl[E:l869] ,0 . ) SCULBRl + IFGT0(FLOXJM1 [ E:l870),FLOXJM1(E;l870],0 . )

: SCULBRl + IFGTO(FLUXJMl[E:l87l] , FLUXJMl[E:187l) ,0.) SCULBRl + IFGT0(FLUXJM1[E:l872],FLOXJMl(E:l872],0 . ) SCULBRl + IFGTO(FLUXJMl[E:l873),FLUXJMl(E:1873),0.)

SCULBRl + IFGTO(FLUXJMl[E:l874),FLUXJMl(E:l874].0. ) ScULBRl + LFGTO(FLUXJMlfE:l875],FLUXJMl[E:l875],0. )

SCULBRl + IFGTO(FLUXJM1(E:l876) ,FLOXJMl(E:1876],0 .) = SCULBRl + IFGTO(FLUXJMl(E:l877),FLUXJMl[E:l877].0.)

SCULBRl + IFGT0(FLUXJM1(E:l878),FLUXJMl[E:1878),0. ) SCULBRl + IFGTO(FLOXJMl[ E:l879),FLOXJMl(E:l879),0 , )

SCULBRl + IFGTO(FLUXJMl[E:l880),FLUXJl'1l(E:1880],0 . )

!MASS FLUXES TNTO MB139 SOUTH MARKER BED

!

SMB139Sl = IPGTO(-FLUXJMliE:278),-FLUXJMl[E:278),0.)

SMB139Sl SMB139Sl + IFGTO(FLUXJMl[E:l246), FLUXJMl(E:l246),0.)

SMB139Sl SMB139Sl + IFGTO (-FLUX!Ml[E: 1247]. -FLUXIHll E: 1247]. 0.) SMB139Sl SMB139Sl + IFGTO(FLUXIMl[E:l246],FLUXIMllE:l246),0.)

!MASS FLUXES INTO MB139 NORTH MARKER BED

SMB139Nl IFGT0(-FLUXJMl[E:421),-FLUXJMl[E:421] ,0.)

SMB139Nl SMB139Nl + IFGTO(FLUXJMl[E:l283),PLUXJMl(E:l283),0.) SMB139Nl SMB139Nl + IFGTO(FLUXIMl (E:1283],FLUX!Ml[E:1283],0.) SMB139Nl SMB139Nl + IFGT0(-FLUXIMl[E:1284) , -FLUXIMl (E:1284 ) ,0.)

! MASS 'FLUXES INTO SOUTH MBAAB MJ\RXER BED

SMBABSl

SMBABSl SMBABSl SMBABSl

IFGTO(-FLUXJM1[E:S48],-FLUXJM1(E:548],0.)

SMBABSl + IFGTO(FLUXJM1[E:1295] ,FLUXJMl[£:1295),0. ) SMBABSl + IFGT0(FLUXIMl[E:1295],FL.UXLMl(E:l295),0.) SMBABSl + IFGTO(-FLUXIMl(E:1296],-FLUXLMl[E:1296].0.)

!MASS FLUXES INTO NORTH MBAAB MARKER BED

SMBABNl IFGT0(-FLUXJMl[E:603),-FLUXJMl[E:603],0.)

SMBABNl SMB~BNl + IFGTO(FLUXJMl(E:l332],FLUXJMl(E : l332),0 .) SMBABNl SMBABNl + IFGTO(FLUXLMl[E:l332],FLUXLMl[E:l332],0.) SMBABNl SMBABNl ~ IPGTO ( -FLUXIMl {E: 1333]. -FLUX!Ml[E: 1333].-0. I I

!MASS FLUXES LNTO SOUTH MB138 MARKER BED

SMB138Sl = IFGT0(-FLUXJMl[E:638}, - FLUXJMl[E:638] ,0.) SMB138Sl SMB138Sl + IFGT0(FLUXJMl (E:l344],FLUXJMl(E:1344},0.) SMB138Sl SMB138Sl + IFGTO(FLUXIMl {E:lJ44],FLUXIMl[E:l344},0 , )

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SMB138Sl ~ SMB138Sl + IFGTOI-FLUXIMl(E:l345),-FLUX1Ml(E:l345),0.1

l~SS FLUXES rNTO NORTH MB139 MARKER BED

l

SMB138Nl c IFGT0(-FLUXJMl(E:945},-FLUXJMl[E:945},0.)

SMB138Nl = SMB138Nl + IFGTO(FLUXJM1(£:1400},FLUXJM1(£:1400},0.)

SMB138Nl: SMB138Nl + IFGT0(FLUX!Ml[E:l400),FLUXJM1(£:1400},0.)

SMB138Nl SMB138Nl + IFGT0(-FLUX!Ml(E:1401},-FLUXIMl(E:l401],0.)

!POINTS OF INTEREST

SHUPl a IFGTO(FLUXJMl!E:l489),FLUXJMl!E:l489),0.1

BHUPl ~ IFGT0(FLUXJMl[E:l845),FLOXJMl[E:l845}.0. )

SURFBHl = lFGT0(FLUXJMl[E:2155), FLUXJMl[E:2155),0.)

SURFSHl: IFGTOIFLUXJMl[E:1496).FLUXJMl[E:1496),0.)

!MASS FLUXES REACHING BOREHOLE !N CULEBRA

I BOREHOLE COMMENTED OUT FOR SCENARIO 1 (UNDISTURBED SCENARIO)

!T.Lowry 4-18-03 !SBHMl BHUPl • IFGTO(-FLUXJMl(£:2155), - FLUXJMl(£:2155),0.)

ISBHMl !SBHMl

ISBHMl !SBHMl

!SBHMl

!SBHMl !SBHMl !SBHMl

ISBHMl ISBHMl

!SBHMl !SBHMl ISBHMl

!SBHMl I

s SBHMl ~ 1FGT0(FLUXIMl(E:1845),FLUXIMl[E:1845},0.)

SBHMl + IFGTO (-FLUXIMl(£ :1846],-FLUXIMl(£:1846} ,0.)

= SBHMl + IFGTO(FLUXIM11E:17ll},FLUXIMl(E:l711},0.)

SBHMl + IFGT0(-FLUXIMl[E:l712).-FLUXIMl(E:l712),0.1

= SBHMl + IFGTO(FLUXIMli £: 1912),FLUXIMl(E:l912),0.)

: SBHM1 + IFGTOI-FLUX!Ml(£:1913],-FLUXIM1(E:l913),0.)

= SBHMl + IFGT0(FLUXIMllE:l778],FLUXIMl [£:1778},0 . )

SBHM1 + IFGTO(-FLUXIMl[£:1779},-FLUXIMl(£:1779),0.)

= SBHMl ~ IFGT0(FLUXIM11E:l979),FLUXIM1[£:1979],0. I

SBHMl + IFGT01-FLUXIMl[E:l980),-FLUXIM1(£ : 1980} ,0.)

SBHMl + IFGTO(FLUXIM1 JE:202l],FLUXIM1(E:2021),0 . 1

SBHM1 + IFGT0(-FLUX1Ml[E:2022),-FLUXIM1(E:2022},0.1 SBHM1 + IFGTO(FLUXIM1[£:2113),FLUXIM1(£:2113},0 . )

SBHMl ~ IFGTO(-FLUXIMl(£:2114),-FLUXrMl(£:2114 ),0.)

!MASS FLUXES REACHING SHAFT IN COLEBRA

SSHMl SSHM1 SSHMl SSHMl

SSHMl SSHMl SSHMl

SSHMl SSHMl SSHM1 SSHMl

SSHMl SSHM1 SSHMl SSHMl

SHUPl + IFGTOI-FLUXJM1~E:1496),-FLUXJMl (E:1496),0.)

: SSHMl • IFGTO(FLUXl~u (E:1489),FLUXIMl[E:l489),0.)

SSHMl- IFGT0(-FLUXIMl:E:1862),-FLUXIM1(£:1862),0.)

SSHM1 ~ IFGTOIFLUXIMl! E:l490),FLUXIMl(E:l490),0.)

= SSHMl • IFGT0(-FLUXIM1~E~l728},-FLUXIM1(E:l728),0.)

SSHMl • IFGT0(FLUXrMl(E:l491],FLUXIMl(E:l491),0. 1

SSHMl + IFGTO (-FLUX!Ml[E: 1929), -FLUX!Ml (E: 1929), 0.)

• SSHMl + IFGTOIFLUXIMl[£:1492},FLUXIMl(E:l492} ,0. 1

: SSHM1 + IFCT0(-FLUXIM1[E:l795).-FLUXIM1(£:1795),0.)

: SS~11 + IFGTO(PLUXIM1{E:l493),FLUX1Ml(E:1493},0.1

• SSHMl + IFGT0(-FLUXIMl[E:2038],-FLUXIMl(£:2038),0.l

SSHM1 + IFCT0(FLUXIMl[E:l494),FLUXIMl(E:1494],0.)

SSHMl + IFGTO(-FLUXIMli£:2063],-FLUXIMl(£:2063},0.)

• SSHMl + IFGT0(FLUX1Ml(E:l495),FLUXIMl(E:l495),0.)

SSHMl • IFGT0( - FLUXIMl(E:2172},-FLUXIMl(E:2172).0.)

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!MASS FLUXES REACHING SALADO UPPER BOUNDARY

SSALAOl

SSALAOl SSALADl SSALAOl

SSALAOl SSALAOl SSALAOl

SSALAOl SSALAOl SSALADl

SSALAOl SSALAOl SSALADl

SSALAOl SSALAOl

SSALADl

SSALADl SSALAOl SSALADl

SSALAOl SSALADl SSALADl

SSALAOl SSALADl

SSALADl

SSALAOl SSALAOl

IFGTO(FLUXJMl[E:885),FLUXJMl[t:885],0.)

SSALADl + IFGT0(FLUXJMl[E:886},FLUXJMl[E:886 ] 1 0.) SSALADl SSALADl

SSALADl SSALADl SSALADl

:: SSALAOl SSALAOl SSALAOl


SSALADl SSALAOl

= SSALADl

+ lFGTO(FLUXJMl [E:887],FLUXJMl[E:887],0.)

~ IFGTOIFLUXJMl [E:888] ,FLUX~~l[E:BBB],O.) + IFGTO(FLUXJMl [E:889],FLUXJMl[E:889],0.) ~ IFGT0(FLUXJMl[E:890],FLUXJMl[E:890],0.)

+ IFGTO (FLUXJMJ. [E: 891], FLOXJMJ [E: 891 I, 0.) ~ IFGTO(FLUXJMl [E:892] ,FLUXJMl[£:892],0.) .,. IFGTO (FLUXJMl [ E: 893] , FLUXJMl [E: 893], 0 . )

+ IFGTO(FLUXJMl[E:894),FLOXJMl[E:894),0.) + IFGTO(FLUXJMl[E:B95],FLOXJMl[E:895],0.) + IFGTO(FLUXJMU[E:896],FLUXJMl[E:896),0.) + IFGTO(FLUXJMl[E:897],FLUXJMl(E:897),0.)

+ IFGTO(PLUXJMl(E:898],FLUXJMl[E:898],0.) + IFGTO(FLUXJMl[E:899],FLUXJMl(E:899],0.)

+ IFGTO(FLUXJMl[E:900),FLOXJMl[E:900] , 0.) + IFGT0(FLUXJMl[E:90l),FLUXJMl[E:901],0.) + IFGTO(FLUXJMl[E:902],FLUXJMl[E:902),0.)

SSALADl SSALADl

SSALADl + IFGTO ( FLUXJMl [ E: 903] , PLUXJMl [E: 903) , 0. ) SSALAOl + IFGTO(FLOXJMl[E:904],FLUXJMl[E:904),0.)

SSALAOl + IFGTO(FLUXJMl[E:905J.~LUXJMl[E:905) 1 0.) SSALADl + IFGTO(FLUXJMl[E:906],FLUXJMl[E:906],0.)

SSALADl + IFGTO(FLUXJMl[E:907),FLUXJMl[E:907},0.) SSALADl + IFGTO(FLUXJM1[E:908],FLUXJMl[E:908),0.)

SSALADl + lYGTO(FLUXJMl [£:909) ,FLOXJMl [E:909}, 0.) SSALADl + lFGTO(FLUXJMl[E:910),FLUXJMl[E:910),0.) SSALAOl + IFGTO(FLOXJMl[E:9ll ),FLUXJMl(E:911),0.)

SSALAOl = SSALADl * IFGTO(FLUXJMl[E:912],FLUXJMl(E:912],0.) SSALAOl SSALAOl + IFGTO(FLUXJMl[E:913],FLUXJMl[E:913],0.) SSALADl

SSALAOl SSALAOl SSALAOl SSALADl


SSALAOl SSALADl

SSALADl SSALADl SSALAOl SSALADl

SSALAOl SSALADl SSALAOl

SSALADl SSALADl

SSALADl

SSALADl + IFGTO (FLUXJMl ( E: 914], FLUXJMl[ E: 914 J , 0.)

SSALADl + !FGT0(FLUXJMl[E:915),FLOXJMl[E:915],0.) :: SSALADl + lFGTO(FLUXJMl(E:916].FLUXJMl[E:916].0.)

SSALADl + IFGT0(FLOXJMl[E:9l7),FLUXJMl[E:917),0.) SSALADl + IFGT0(FLUXJMl[E:918},FLUXJMl[E:918),0.) SSALADl + IPGTO(FLUXJMl[E:919),FLUXJMl(E:919),0.) SSALADl ~ IFGT0(FLOXJMl[E:920],FLUXJMl(E:920],0.) SSALADl + IFGTO(FLUXJMl[E:92l],FLUXJMl[ E:92l],O.) SSALADl + IPGT0(FLUXJMl[E:922],FLUXJMl(E:922),0.) SSALADl + IFGT0(FLUXJMl[E:923),FLUXJMl[E:923),0.) SSALADl T IFGTO(FLUXJMl[E:924],FLUXJMl(E:924),0.)

SSALADl + IFGTO(FLUXJMl[E:925],FLUXJMl(E:925],0.) SSALADl ~ IfGT0(FLUXJMl(E:926],FLUXJM1[E:926],0.) SSALADl + IFGTO(FLUXJM1(E:l503],FLUXJMl(E:l503],0 .) SSALADl + IFGT0(FLUXJMl[E:l077},FLUXJMllE:l077),0 .) SSALADl + IFGTO(FLUXJMl(E:l078),FLUXJMl(E:l078} ,0.)

SSALAOl + IFGT0(FLUXJMl(E:1079),FLUXJMl(E:l079],0.) SSALADl + IFGTO(FLUXJMl[E:l080},FLUXJMl[E:l080),0 . ) SSALADl + IFGTO(FLUXJMl[E:l08l),FLUXJMl[E:1081),0. ) SSALADl + IFGTO(FLUXJMl[E:l082],FLUXJMl[E:l082],0. )

I · formation Only

SSA.LADl

SSALAOl SSALJ\01

SSALADl


SSALADl

SSALADl SSALAOl

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SSALADl + IFGTO(FLUXJMl[E:1083],FLUXJMl[E:l083],0.)

SSALAOl + IFGTO(FLUXJMl[E:1084),FLUXJMl[E:l084),0.) SSALADl + 1FGT0(FLUXJMl[E: l085),FLUXJMl[E: l085] ,0.) SSALADl ~ IFGTO(FLUXJMl[E:1086),FLUXJMl[E:l086),0.)

SSALAOl + IFGT0(FLUXJMl[E: 1087),FLUXJMl[E:l087) 1 0 .) SSALADl + IFGT0(FLUXJMl[E:l088],FLUXJMl[E:1088),0.)

SSALADl + IFGTO(FLUXJM1[E:1089],FLUXJMl[E:l089),0. ) : SSALADl + IFGTO(FLUXJMl[E:l090),FLUXJMl[E:1090),0.)

SSALADl + IFGTO(FLUXJMl[E:1091],FLUXJMl[E:l091),0 .) ~ SSALADl + IFGT0(FLUX~l[E:l092],FLUXJMl[E:l092),0.)

SSALJ\01 SSALADl + ITGTO ( FLUXJMl [ E: 1093]. FLUXJMl [ E: 1093]. 0 . ) SSALAOl : SSALADl + IFGT0(FLUXJMl[E:l094],FLUXJMl[E:l094],0 .)

SSALADl


SSALADl SSALADl

SSALADl

"'

SSALAOl + IFGTO(FLUXJM1[E:1095],FLUXJMl[E:l095),0 .)


SSALADl SSALAOl

SSALAOl

+ IFGTO(FLUXJMl[E:l096],FLUXJMl[E:l096],0.)

+ IFGT0(FLUXJMl[E:l097),FLUXJMl[E:1097],0.) + IFGTO(FLUXJMl[E:1098],FLUXJMl(E:l098],0.) + IFGTO ( FLUXJMl [E: 1099), FLUXJMl [E: 1099), 0 .)

+ IFGTO(FLUXJMl(E:llOOl,FLUXJMl(E:llOO),O.) + lFGT0(FLUXJMl(E:110l),FLUXJMl[E:ll0l],O.)

INTEGRATION OF MASSES

SWASTElC

SMB39SlC SMBABSlC SMB38NlC

SMB38SlC

SMB39NlC SMBABNlC SCULBRlC

SHUPlC

SURFSHlC SSHMlC

BRUPlC

SURFBHlC !SBHMlC

SSALAOlC

intright(SWASTEl) iflt0(intright(SMB139Sll - l .e-7,0.,intright(SMB139Slll

= ifltO(intright(SMBABSl)-1.e-7,0.,intright(SMBABSlll iflt0(intright(SMB13BN1)-l.e-7,0.,intright(SMB138Nl)l

= iflt0(intright(SMB138Sl)-l . e - 7,0 .. intrighc(SMB138Slll

iflt0(intright(SMB139Nl)-l.e-7,0 . ,intright(SMB139Nl))

iflt0(intright(SMBABNl) - l.e- 7,0.,tntright(SMBABNlll iflt0(intright(SCULBR1)-l . e - 7,0., intright(SCULBR1))

: ifltO(intright(SHUPl)-l.e-7,0.,intright(SHUPlll

ifltO(intright(SURFSHl)-l.e-7,0 ., intrigbt(SURFSHl)l ifltO(intright(SSHMl)-l.e-7,0.,intright(SSHMl))

= ifltO(intright(BHUPl)-l.e-7,0.,1ntright(BHUPll) iflt0(intright(SURFBH1)-l.e-7,0.,intrigbt(SURFBH1))

= ifltO(intright{SBHMl)-l.e-7,0.,incright(SBHMl)) = intright(SSALAOl)

DELETE ATTRIBUTE, PROPERTY, HISTORY, ELEMENT, NODAL

END

Information Only


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Appendix E: Sample SUMMARIZE screening input file for replicate 1, scenario 1.

•INPUT FILES

DIR = PAWORK: [ANALYSIS .CRAl.mJT.SCN.OATA.RlSll TEMPLATE = ALG_NUT_CRA1_SCN_Rl_S1_ V%%%

TYPE = COB

*VECTORS 10 = %

VECTOR = 1 TO 100 *TIMES

*ITEM

READ "' SECONDS INPUT = YEARS OUTPUT = YEARS

TIMES = 10000

TYPE .: GLOBAL

NAME = SWASTE1C, SMB39S1C, SMBABSlC, SMB38NlC, SMB38SlC, SMB39N1C, SMBABN1C, SCULBRlC, SHUP1C, SURFSHlC, SSHM1C, BHUPlC, SURFBHlC, SSALAOlC

•OUTPUT DRIVER = EXCEL

WRITE = TIME VS ITEM

NAME = SUM_NUT_CRAl_SCN_Rl_Sl.OAT

Information Only

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Appendix F: ALGEBRA input file for analysis of R1S1v082.

!====================================================================== ALGEBRA file for post-processing NUTS (non-screening runs) output

Reduced file for calculating concentration of isotopes at several locations as time histories in units of Curies/liter

8 July 1996

Author: Joel D. Miller, SNL Org. 9363

!======================================================================

Eliminate excess output

DELETE ALL

!**************************************************************** ****** !** ********************************************** ***•********** ********

Al

A2

A3

A4

AS

Conversion factors (Curies per kilogram) , where radionuclides are numbered

1 = Am- 241

2 = Pu-239

3 Pu-238

4 U-234

5 = Th-230

= 3431.154

= 62.14574

17115.25

= 6. 247269

= 20.18264

To convert from kg/mA3 to Ci/1, multiply by 0.001 mA3/l

!* * ******** ****** **************** •***** ****** **************** ********** !*** ** **************•**************• * ************* * *****W*********** ***

Isotope concentration (Ci /1 ) in shaft at top of Salado (El 1488)

Param 1: Am- 241 concen. in shaft at top of Salado (el.1488) -->

C1SH_661

Param 2: Pu-239 concen. in shaft at top of Salado (el.1488) -->

C2SH_661

Information Only

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Par am 3: Pu-238 concen. in shaft at top of Salado (el.1488) --> C3SH_661

Par am 4: u-- 234 concen. in shaft at top of Salado (el.1488) -->

C4SH - 661

Par am 5: Th-230 concen. in shaft at top of Salado (el.1488) --)o

C5SH_661

Isotope concentration (Ci/1) in shaft at Culebra Dolomite (El 1489)

Par am 6: Am-241 cone. in shaft at Culebra Dolomite (el.l489) --> ClSH_662

Par am 7: Pu-239 cone. in shaft at Culebra Dolomite ( el.1489) -->

C2SH_662

Par am 8: Pu-238 cone. in shaft at Culebra Dolomite (el.1489) --> C3SH_662

Par am 9: u--234 cone. in shaft at Culebra Dolomi te (el.l489) --> C4SH_662

Par am 10: Th-230 cone. in shaft at Culebra Dolomite (el . 1489) --> CSSH_662

!*********** ****************************

Isot ope concentration (Ci/1) in shaft at top of Salado (El 1488)

C1SH_661 = CM1[E:l488]*A1*0.001

C2SH_661 CM2[E:1488]*A2*0.001

C3SH_661 CM3 [E:l488]*A3*0.001

C4SH_661 = CM4[E:l488]*A4*0.001

CSSH_66l = CM5[E:1488]*A5*0.001

Isotope concentration (Ci/1) in shaft at Culebra Dolomite (El 1489)

C1SH_662 CM1[E:1489]*Al*0.001

C2SH_662 = CM2[E:1489]*A2*0.001

C3SH_662 CM3[E:1489]*A3*0.001

C4SH_662 =·CM4 [E:1489]*A4*0.001

C5SH_ 662 = CM5(E:l489]~A5*0.001

!********************************************* ** *********** **** ****•••· !******************************************************************~***

! Isotope concentration (Ci/1) at South L-W boundary in MB 138 (El 1344)

Param 11: Am-241 concen. in MB 138 inside L-W bndry (el.l344) -->

C1LWM38S

.. Information Only

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Par am 12: Pu-239 concen. in MB 138 inside L- W bndry (el.1344) -->

C2 LWM38S

Par am 13: Pu-238 concen. in MB 138 inside L-W bndry (el.1344) -->

C3LWM38S

Par am 14: u--234 concen. in MB 138 inside L-W bndry (el.1344) -->

C4LWM38S

Par am 15: Th-230 concen. in MB 138 inside L-W bndry (el.1344) -->

CSLWM38S

! Isotope concentration (Ci/1) at South L-W bndry in Anhydrite A/B (El 1295)

Par am 16: Arn-241 cone. in Anhyd.A/B inside L-W bndry (el.1295) -->

C1LWAABS

Par am 17: Pu-239 cone. in Anhyd.A/B inside L-W bndry (el.1295) -->

C2LWAABS

Par am 18: Pu-238 cone. in Anhyd.A/B inside L-W bndry (el.1295) -->

C3LWAABS

Par am 19: u--234 cone. in Anhyd.A/B inside L-W bndry (el.l295) -->

C4LWAABS

Par am 20: Th-230 cone. in Anhyd.A/B inside L-W bndry (el.1295) -->

CSLWAABS

! Isotope concentration (Ci/1) at South L-W boundary in MB 139 (El 1246)

Par am 21: Arn-241 concen. in MB 139 inside L-W bndry (el.1246) C1LWM39S

Par am 22: Pu-239 concen. in MB 139 inside L-W bndry (el.1246-) C2LWM39S

Par am 23: Pu-238 concen. in MB 139 inside L-W bndry (el.1246) C3LWM39S

Par am 24: u--234 concen. in MB 139 inside L-W bndry (el.1246) C4LWM39S

Par am 25: Th- 230 concen. in MB 139 inside L-W bndry (el.1246) CSLWM39S

!***************************************

Isotope concentration (Ci/1) at South L-W boundary in MB 138 (El 1344)

C1LWM38S

C2LWM38S

C3LWM38S

C4LWM38S

C5LWM38S

CM1(E:1344]*A1*0.001

CM2[E:1344)*A2*0.001

= CM3(E:1344 )*A3*0.001

CM4[E:1344) *A4*0.001

CMS( E:1344 )*A5*0.001

Information Only

-->

-->

-->

-->

-->

Salado Transport Calculalions: Compliance Recertification Applicallon

ERMS 11530164

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Isotope concentration (Ci/1) at South L-W bndry in Anhydrite A/B (El 1295)

ClLWMAAB CMl(E:1295)*A1*0.001

C2LWMAAB = CM2(E:l295)*A2*0.001

C3LWMAAB = CM3(E:1295]*A3*0.001

C4LWMAAB = CM4[E:l295)*A4*0.001

CSLWMAAB CM5(E:1295)*A5*0.001

Isotope concentration (Ci/1) at South L-W boundary in MB 139 (El 1246).

C1LWM39S = CMl[E:l246l*Al*0.001

C2LWM39S CM2[E:l246)*A2*0.001

C3LWM39S = CM3[E:l246 J *A3*o:ool

C4LWM39S = CM4[E:l246)*A4*0.001

C5LWM39S ::. CMS(E:1246)*A5*0.001

!********************************************************************** !****************************************************************** ****

DELETE Al, A2, A3, A4, AS

!********************************************************************** !********************************************************************** END

Information Only


ERMS #530164

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Appendix G: SUMMARrZE input file for analysis of R1S1v082. *input files

name"' disk=

PA_NUTS_ISO_R1Sl_CONC_V082 Ul

directory=[JWGARNE.cra2.NUTS.MB)

*times

*items

type= CDB

seconds

input; years

output= years

times~ o to 1~00 by 50, 1400 to 10000 by 200

type= GLOBAL

name= ClSH_661, C2SH_ 661, C3SH_661, C4SH_661, C5SH_661, &

C1SH_662 , C2SH_662, C3SH_662 , C4SH_662, C5SH_662, & C1LWM38S, C2LWM38S, C3LWM38S, C4LWM38S, C5LWM38S, & ClLWMAAB, C2LWMAAB, C3LWMAAB , C4LWMAAB, CSLWMAAB , & C1LWM39S, C2LWM39S, C3LWM39S , C4LWM39S, C5LWM39S

name= ClLWM38S, C2LWM38S , C3LWM3HS, C4LWM38S, C5LWM38S, &

ClLWMAAB, C2LWMAAB, C3LWMAAB, C4LWMAAB, CSLWMAAB, & ClLWM39S, C2LWM39S, C3LWM39S, C4LWM39S , C5LWM39S .

*output

driver= EXCEL write= time vs item

name; PA_NUTS_ISO_ Sl_ CONC.TBL

*end

Information Only

Snlado T111nsport Cnlculations: Compliance Recertification Apphcallon

ERMS 11530164

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Appendix H: Tl Time Series Plots Am-2"1 Total Activity Up Borehole Repllcate 1, Scenario 2: lnt1@ 100 vrs

1[~2~--~----~-----------=~~~--------~

l'E~I

! t.E+OO

~ I E.OI i 1 1!.0?

.. ::t

1.1!.03

'1 11!-04

0: I.E-05

IE~?

i I Eo01

~ 1 E+OO

~ I E-01 Jl I.

I E-02 J .. 1 E-03 :>

I I E-04 f .. 1E.OS

1 c.oe 0

-----------------------------------------------------------------

8000 10000

limo (\Ira~

U·23-C Total Activity Up Borehole Repllcete 1, Scenario 2: lnt1 @ 100 yna

~ ::::::::--

~ ~ 2000 4000 6000 8000 10000

limo (yro)

Am-241 Total Activity Up Borehole Repllcete 2, Scenario 2: lnt1 @ 100 !fnl

1E~2r---~-----------------=~~~--------~

I I .E•01

c 1 E+OO

~ t.E.Ol

1 I E.02

" ::t 1 E.Cl

• . lE-04 . ! IE .OS

1E-Oe

0 2000 4000 6000

U.23-C Total Activity Up Borehole Replic:at.e 2. Scenario 2: lnt1 @ 100 yrs

8000 10000

1Eo02r-------------------------------------,

i 1[~1

! 1 E+OO c .. !!. 1 E.OI

I I E.02

.. I E-03

~ ::t

j 1 E-04

~ ~ ~ 1 E.OS

IE.of 0 2000 l9o P"'" 'OMii

Tl o ral

Pu-239 Total Activity Up Borehole Repllcete 1, Scenario 2: lnt1 @ 100 yrs

1E•02r-----------------------------------~

i IE•Ot c: ::t I E•OO

i - 1 E-01 i t .E-02

" 1.E.03 ::t

j 1(.()C~~51~ I.E-05

'e.oe 0 2000

Th·230 Total Activity Up Borehole Replicate 1, Scenario 2: lnt1 @ 100 yn

8000 10000

1Eo02r-------------------------------------,

i I EoOI

! t E•OO

;

I.Eo02

I t EoOt

c 11!>00

~ 1,E.CI

I 1 E.C2 J ! 11!.03

: , E-04 i 0: 1[.(1$

1E-Oe

0

2000 6000 8000

limo (\Ira)

Pu-239 Total Activity Up Borehole Replicate 2, Scenario 2: lnt1 @ 100 yrs

2000 4000 6000

Tlmo , , .. ,

8000

10000

10000

Th·230 Tout Activity Up Borehole Replicate 2, Scenario 2: lnt1 @ 100 yr.;

1E~2r-------------------------~----------,

i I E•OI < ::t I E•OO

~ 1 !.(II

i I E.O?

.. I E-03 ::t

j I 1!-04

0: tE-05

1 1;.00

l: 200' (~- " !lC( 8000 10000 ... ,,..,

IIE'o07

i IHI

! I EoOO • & '.:! I E.01 . f I E.02 f

' ! 1E.03 . ! 1£44

0: I.E.05

I E-Oe

0

Am-241 Total Activity Up Borehole Replicate 3, Scenario 2: lnt1 @ 100 yrs

1000 4000 6000

U·234 Total Activity Up Borehole

11000

Salado Transpon Calcula&ions: Compliance Rec:enificanon Application

ERMS #530164

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Poge63 of16

I foO~

i 1 Eo01 'E ::> I EoOO • ~ IE.OI -1 " I E.02

! ! I E.Ol

I 1E44

0: I E.OS

1 e.oe 10000 0

Pu·239 Total Activity Up Borehole Replicate 3, Scenario 2: lnt1 @ 100 yrs

2000 4000 0000

Tlme(JrsJ

Th·230 Total Activity Up Borehole

8000 10000

Replicate 3, Scenario 2: lnt1 @ 100 yrs 1£.02~--~----------------~~~----------~ I Eo02

Replicate 3, Se11nario 2: lnt1 @ 100 yrs

i I EoOI

~ I E>OO

~ \E.01

i ! i

IE.07

IE~J

1£44

•e~s

1 e.oe

I Eo02

i I E•OI

~ I EoOO • & !!! I £.01

i 1.E.02

" IE.OJ ::>

I IE44

0: IE.05

1E-4fl

I £.02

i I E.OI

! IE•OO • & !! 1£.01 j

I E.02 f 0 • & I E.03 ::>

I IE-04 . 0: I E.05

I E.OG

0

0

~

fJt"_

2000 4000 6000 11000 10000

Am-241 Total Activity Up Borehole Realization 1, Scenario 3: lntl@ 3000yrs

{_ lr'

~~""; "k-

~rr

U.234 Total Act ivity Up Borehole Realization 1, Scenario 3: lnt3@ 3000)'ra

tP:.

{ ~

::...--:::

2000 4000 6000 8000

Tlme(Ynl

~

10000

-

10000

I 1 E.OI

lfoOO : !! I.E.OI ~

i 1E~2 ! IE~l

: 1£44

j l f.OS

1 Eo02

i I E.OI

~ 1,£+00 c ~ I £.01

I IE.02 £ • ! IE.OJ

I I E.O•

• IE.OS

'e.oe

I Eo02

! IHI

5 1£.00 : !! 1 E-41

i ! 1 E.02

0

TltM(Yn)

Pu·239 Total Activity Up Borehole Realization 1, Scenario 3: lnt3@ 3000yrs

2000 <1000 11000 11000 10000

n.Cyrs)

Th·230 Total Activity Up Borehole Reallutlon t , Scenario 3: lnt3@ 3000yrs

! I E.OJ

f 1

e.o• ~~::i;;~;;~~~~ o: IE.05

1E.06~ 0 2000 8000 10000

Tlme(Yrs)

rmat1on 0

-· --

1Eo02

i 1 Eo01 c ::> 1 E-oo :. .. i 1£~1

I I E~2 0. ::>

I ,E-00 . : I E-04

! .. 1.E.OS

1.E-06

1 Eo02

! I E•OI 0 ::> I ,EoOO < .. ~ 1.~1

i ! 1.~2 0 • 0. ::>

I.E-03

I I.E.OO

~ 1.E.05

I.E-06

1 E•02

I E-06

I,Eo02

~ I.E+Ot i! ::> I E+OO <I .. !!!. IE.OI ! -e I .E-02

.! 0. I E.OJ ::> . : tE.OO

i I,E.OS

1.E-06

0

0

0

Am-241 Total Activity Up Borehole RP.alinlion 2, Scenario 3: lnt3@ 3000yM

. .

...-

~ ...---,.. -

8000

U·234 Total ActlvHy Up Borehole Realization 2, Scenario 3: lnt3 @ 3000yrs

I~

ri 2000 4000 6000 8000

Am-241 Total Activity Up Borehole Realization 3, Scenario 3: lnt3 @ 3000yrs

~ ~

,J-:;,<"'

/ d1:.

8000

U-234 Total Activity Up Borehole Realization 3, Scenario 3: lntl @ 3000yrs

2000 8000

Salado Trnnspon Calculations: Compliance Recertification Application

ERMS #530164

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10000

10000

10000

i I .E•OI

~ l.E•OO :. !;! I.E-01 ~

~ I E-02

" 1 E-03 ::> : : :

1 E-04

1E.05

Pu·239 Total Activity Up Borehole • Raall:r.allon 2, Scenario 3: lnt3 @ 3000yrs

1.E.08 L _____ _jrt:;J ...... ::::::=:::;::::;;;;::::::;:::;:::::==j 0 2000 4000 6000 8000 10000

Th·230 Total Activity Up Borehole Realization 2, Scenario 3: lnt3 @ 3000yrs

1Eo02r-------------~~~----,

i I EoOI c ;: I.E+OO .. !!!. I.E.OI i ! I E-02 0

CD 0. I.E-03 ::I

: J

I,E.().I

~ I E.05

I .E-06 0 2000 4000 6000 8000 10000

Pu-239 Tota l Activity Up Borehole Realization 3, Scenario 3: lnt3 @ 3000yrs

1,!+02 r-------------~-~----,

i 1,E+01 c ::> I.E•OO

~ ~ I,E.OI . 0 l I.E-02

o. 1 E-03 ::> • ! I E-04 .. 0: 1.E.05

I.E.OG ~-----_.""'---L-----'--..,.....---l--l

0 2000 6000 8000

Tlme(yra)

Th-230 Total Activlly Up Borehole Realization 3, Scenario 3: lnt3 @ 3000yrs

10000

I ,E-o2 ,---------------------,

i I.E'111 c ; 1.£•00

~ 1.E.OI . 0 ~1.E.02

! I E.OJ

~ 1 E-04

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ERMS #530164

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Salado Transpon Calculations: Compliance Recenilication Application

ERMS #530164

Vers•on 00 of76

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Sa13do Transpon Calculations: Compliance: Rcceni fication Applicution

ERMS #530164

Vmion 00

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Salado Transpoft Calculations: Compliance Recertification Application

ERMS #530164 Version 00

of76

Pu-239 Total Activity Up Borehole Realization 1, Scenario 3: ln19 @ 9()00yrs

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U-234 Total Activity Up Borehole Realization 3, Scenario 3; lnt9@ 9000yrs

Salado Transpon Cnlculations: Compliance Recenitication Application

ERMS #530 164

Version 00

Page69of76

I Eo02

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Salado Transport Calculations: Compliance Recertification Applica1ion

ERMS #530164

Version 00

of76

10000

Pu-239 Total Activity Up Borehole Repllute 2, Scenario 4: lnt1 @ 100 yrs

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Salado T~nsport Calculations: Compliance RC(:ertlfication Applica11on

ERMS #530164

Version 00

of76

Am-241 Total Ac:tlvlty Up Borehole Reallutlon 1, Scenario 5: lnt3@ 300()yfs

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Am-241 Total Activity Up Borehole Reallutlon 2, Scenario 5: lnt3 @ 3000yrs

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Salado Transport Calculations: Compliance Recertification 1\pplicntion

ERMS 11530164 Version 00

of76

10000

Pu-239 Total Ac:tlvlty Up Borehole Realization 3, Scenario 5: lnt3 @ 3000yrs

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Salado Transport Calculations; Compliance Recenilication Application

ERMS #530164

Version 00

of 76

Pu·239 Total Activity Up BorehOle Realization 2, Sc:enarlo 5: lnt5 @ SOOOyrs

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ERMS 11530164

· Version 00

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Salado Transpon Calculations: Compliance Recertification Application

ERMS #530164

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