Las Vegas, Nevada October 26, 2007 Kearn Patrick Lee TSPA Analyst Yucca Mountain Project

1. Streamlining the Yucca Mountain Project Total System Performance Assessment Model with Looping Containers and Submodels Las Vegas, Nevada October 26, 2007 Kearn Patrick Lee TSPA Analyst Yucca Mountain Project Not LSN Relevant Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company,for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

2. Overview Purpose: To describe how new features in GoldSim are used to streamline a large model • Background • Model Description • Previous Model Architecture • Use of Copied & Cloned Containers • Current Model Architecture • Use of Looping Containers and Submodels • Treatment of Uncertainty • Separation of Aleatory and Epistemic Uncertainties • Use of Submodels • Summary Streamlining the YMP TSPA Model with Looping Containers and Submodels

3. Background • The Total System Performance Assessment (TSPA) Model has been developed to support the evaluation of a geologic repository for the safe disposal of spent nuclear fuel (SNF) and high‑level radioactive waste (HLW) at Yucca Mountain, Nevada • The TSPA is one of a series of iterative performance assessments (PAs) conducted over the life of the Yucca Mountain Project (YMP) • The TSPA Model was developed to analyze the ability of the natural and engineered systems of the Yucca Mountain repository to isolate nuclear waste following repository closure Streamlining the YMP TSPA Model with Looping Containers and Submodels

4. Background-Natural System Streamlining the YMP TSPA Model with Looping Containers and Submodels

5. Background-Engineered Barrier System Streamlining the YMP TSPA Model with Looping Containers and Submodels

6. Background-Biosphere Streamlining the YMP TSPA Model with Looping Containers and Submodels

7. Background • The TSPA Model integrates conceptual, mathematical, and computational models of the relevant processes that may affect repository performance as informed by site‑specific information, relevant laboratory data, and natural analogues • The TSPA Model incorporates uncertainty in parameter values and event occurrence • Probabilistic simulations are carried out using GoldSim v9.60 Service Pack 1 coupled with the Radionuclide Transport Module Streamlining the YMP TSPA Model with Looping Containers and Submodels

8. Previous Model Architecture • Previous iterations of the TSPA Model included 20 or more sets of similar calculations performed in parallel • Two waste package types were modeled separately: • Commercial Spent Nuclear Fuel (CSNF) • High Level Waste & DOE Spent Nuclear Fuel (co-disposed) • To account for spatial variability, waste packages were placed into 1 of 20 groups • Two waste package types (CSNF and Co-Disposed) • Five thermal hydrologic profiles (temperature, relative humidity, saturation, etc.) • Two water flux conditions (dripping and non-dripping) Streamlining the YMP TSPA Model with Looping Containers and Submodels

9. Previous Model Architecture Streamlining the YMP TSPA Model with Looping Containers and Submodels

10. Previous Model Architecture • Each waste package group had spatially variable inputs to similar calculations • Each of the calculation containers were comprised of 800 to 1100 model elements • 11,250 model elements to evaluate the different CSNF waste package groups • 13,218 model elements to evaluate the different Co-Disposed waste package groups • The TSPA Model was comprised of 32,341 model elements • The TSPA Model file size was 412 Mb Streamlining the YMP TSPA Model with Looping Containers and Submodels

11. Previous Model Architecture -CSNF Waste Package Group Streamlining the YMP TSPA Model with Looping Containers and Submodels

12. Current Model Architecture • Utilizing new features in GoldSim v9.60, implementation redundancy has been eliminated • The TSPA Model uses DO-UNTIL looping to evaluate the Engineered Barrier System calculations • Engineered Barrier System calculations are performed in a dynamic submodel embedded within nested loops • Looping containers are embedded in a conditional container that is evaluated when Time=0 yr • The outer loop changes the thermal hydrologic properties of the waste package group • The inner loop changes the dripping conditions of the waste package group Streamlining the YMP TSPA Model with Looping Containers and Submodels

13. Current Model Architecture Streamlining the YMP TSPA Model with Looping Containers and Submodels

14. Current Model Architecture DO UNTIL ~LoopCount >= 5 DO UNTIL ~LoopCount >= 2 i=1 to 5 j = 1 to 2 Streamlining the YMP TSPA Model with Looping Containers and Submodels

15. Current Model Architecture • Time series recorders are used to record calculated results as a function of time • TS_Proc.DLL aggregates the recorded histories • Histories from each j loop are added together • Summed histories from each i loop are kept separate • As the model clock advances, time series elements playback the recorded histories for each i loop • Downstream elements perform calculations with the histories as they are played back Streamlining the YMP TSPA Model with Looping Containers and Submodels

16. Current Model Architecture Streamlining the YMP TSPA Model with Looping Containers and Submodels

17. Current Model Architecture • The software modifications by GoldSim Technology Group took several months • After testing, the architecture change was fully implemented in one week • The model element count was reduced from 32,341 to 8,456 • The model file size was reduced from 412 Mb to 88Mb • Run time was reduced by as much as 12% • Calculated results were typically within 10% of previous results, but time step oscillations did produce larger differences Streamlining the YMP TSPA Model with Looping Containers and Submodels

18. Treatment of Uncertainty • The TSPA Model incorporates uncertainty in parameter values and event occurrence • Uncertainty in the TSPA Model is characterized as either epistemic or aleatory uncertainty • Epistemic UncertaintyEpistemic uncertainty pertains to the state of uncertainty in the state of knowledge concerning parameter values because there are limited data or there are alternative interpretations of the available data. The state of knowledge about the exact value of the parameter can increase through testing and data collection. Therefore, epistemic uncertainty can also be referred to as ‘reducible uncertainty.’ • Aleatory UncertaintyAleatory uncertainty concerns whether or not there is a chance of occurrence of a feature, event, or process. Aleatory uncertainty may also be referred to as ‘irreducible uncertainty’ because no amount of knowledge will determine whether or not a chance event will or will not occur. Streamlining the YMP TSPA Model with Looping Containers and Submodels

19. Treatment of Uncertainty • Uncertain parameters were identified as either epistemic uncertainty or aleatory uncertainty • All epistemic parameters were placed into a submodel within the TSPA Model • All aleatory parameters were placed into a separate submodel within the TSPA Model • Separate sampling of epistemic parameters and aleatory parameters can be accomplished • A single model file can have a separate number of epistemic realizations and aleatory realizations Streamlining the YMP TSPA Model with Looping Containers and Submodels

20. Treatment of Uncertainty • For both submodels, Monte Carlo Sampling using Latin Hypercube Sampling is applied • For both submodels, repeat sampling sequences are enabled • The sample size for each submodel is set independently • Epistemic Sample Size = Ne • Aleatory Sample Size = Na • The total number of realizations in a single model file is the product of the two sample sizes • NumofReal = Ne*Na Streamlining the YMP TSPA Model with Looping Containers and Submodels

21. Setting the Submodel Simulation Settings Streamlining the YMP TSPA Model with Looping Containers and Submodels

22. Algorithm for Separate Sampling • When applying separate sampling, each epistemic realization is applied to each aleatory realization Na = 10, Ne = 100 Real_To_Run_Aleatory= mod(Realization-1, Na)+1 Real_To_Run_Epistemic= trunc((Realization+Na-1)/Na) Realization #1: a(1) e(1) Realization #2: a(2) e(1) …. Realization #10: a(10) e(1) Realization #11: a(1) e(2) .… Realization #999: a(9) e(100) Realization #1000: a(10) e(100) Streamlining the YMP TSPA Model with Looping Containers and Submodels

23. Treatment of Uncertainty • Benefits: • When performing 10 aleatory realizations for each epistemic realization, one model file is required, not 10 • Run time efficiency • Less analyst time required to produce results • One model file to run • One model file to open to extract the desired results • No data assembly required to combine results • Drawbacks: • More realizations are performed in one model file, therefore less information can be saved • Model files with a large number of saved results are time consuming to load Streamlining the YMP TSPA Model with Looping Containers and Submodels

24. Summary • GoldSim v9.60 provided the tools required to streamline the TSPA Model • Streamlining the TSPA Model leads to many efficiencies: • Smaller model opens in less time • Model with fewer elements parses in less time • Model with fewer implementation redundancies gets built in less time • Model with fewer redundancies can be checked and reviewed in less time • Changing the treatment of uncertainty also increases the efficiency of running the TSPA Model • Fewer models to run • Less work to extract and analyze the desired results Streamlining the YMP TSPA Model with Looping Containers and Submodels