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Sensitivity analysis of generic deep geologic repository with focus on spatial heterogeneity induced by stochastic fracture network generation

Advances in Water Resources

Brooks, Dusty M.; Swiler, Laura P.; Stein, Emily S.; Mariner, Paul M.; Basurto, Eduardo B.; Portone, Teresa P.; Eckert, Aubrey C.; Leone, Rosemary C.

Geologic Disposal Safety Assessment Framework is a state-of-the-art simulation software toolkit for probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste developed by the United States Department of Energy. This paper presents a generic reference case and shows how it is being used to develop and demonstrate performance assessment methods within the Geologic Disposal Safety Assessment Framework that mitigate some of the challenges posed by high uncertainty and limited computational resources. Variance-based global sensitivity analysis is applied to assess the effects of spatial heterogeneity using graph-based summary measures for scalar and time-varying quantities of interest. Behavior of the system with respect to spatial heterogeneity is further investigated using ratios of water fluxes. This analysis shows that spatial heterogeneity is a dominant uncertainty in predictions of repository performance which can be identified in global sensitivity analysis using proxy variables derived from graph descriptions of discrete fracture networks. New quantities of interest defined using water fluxes proved useful for better understanding overall system behavior.

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Sensitivity Analysis Comparisons on Geologic Case Studies: An International Collaboration

Swiler, Laura P.; Becker, Dirk-Alexander B.; Brooks, Dusty M.; Govaerts, Joan G.; Koskinen, Lasse K.; Plischke, Elmar P.; Röhlig, Klaus-Jürgen R.; Saveleva, Elena S.; Spiessl, Sabine M.; Stein, Emily S.; Svitelman, Valentina S.

Over the past four years, an informal working group has developed to investigate existing sensitivity analysis methods, examine new methods, and identify best practices. The focus is on the use of sensitivity analysis in case studies involving geologic disposal of spent nuclear fuel or nuclear waste. To examine ideas and have applicable test cases for comparison purposes, we have developed multiple case studies. Four of these case studies are presented in this report: the GRS clay case, the SNL shale case, the Dessel case, and the IBRAE groundwater case. We present the different sensitivity analysis methods investigated by various groups, the results obtained by different groups and different implementations, and summarize our findings.

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Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2021)

Swiler, Laura P.; Basurto, Eduardo B.; Brooks, Dusty M.; Eckert, Aubrey C.; Leone, Rosemary C.; Mariner, Paul M.; Portone, Teresa P.; Smith, Mariah L.; Stein, Emily S.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-21SN01030404) level 3 milestone, Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework (FY2021) (M3SF-21SN010304042). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY21, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of GDSA Framework. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF-21SN01030405), the GDSA Repository Systems Analysis work package (SF-21SN01030406), and the GDSA PFLOTRAN Development work package (SF-21SN01030407). This report builds on developments reported in previous GDSA Framework milestones, particularly M3SF 20SN010304032.

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Progress in Deep Geologic Disposal Safety Assessment in the U.S. since 2010

Mariner, Paul M.; Connolly, Laura A.; Cunningham, Leigh C.; Debusschere, Bert D.; Dobson, David C.; Frederick, Jennifer M.; Hammond, Glenn E.; Jordan, Spencer H.; LaForce, Tara; Nole, Michael A.; Park, Heeho D.; Perry, Frank V.; Rogers, Ralph D.; Seidl, Daniel T.; Sevougian, Stephen D.; Stein, Emily S.; Swift, Peter N.; Swiler, Laura P.; Vo, Jonathan V.; Wallace, Michael G.

Abstract not provided.

Methods of sensitivity analysis in geologic disposal safety assessment (GDSA) framework

International High-Level Radioactive Waste Management 2019, IHLRWM 2019

Stein, Emily S.; Swiler, Laura P.; Sevougian, Stephen D.

Probabilistic simulations of the post-closure performance of a generic deep geologic repository for commercial spent nuclear fuel in shale host rock provide a test case for comparing sensitivity analysis methods available in Geologic Disposal Safety Assessment (GDSA) Framework, the U.S. Department of Energy's state-of-the-art toolkit for repository performance assessment. Simulations assume a thick low-permeability shale with aquifers (potential paths to the biosphere) above and below the host rock. Multi-physics simulations on the 7-million-cell grid are run in a high-performance computing environment with PFLOTRAN. Epistemic uncertain inputs include properties of the engineered and natural systems. The output variables of interest, maximum I-129 concentrations (independent of time) at observation points in the aquifers, vary over several orders of magnitude. Variance-based global sensitivity analyses (i.e., calculations of sensitivity indices) conducted with Dakota use polynomial chaos expansion (PCE) and Gaussian process (GP) surrogate models. Results of analyses conducted with raw output concentrations and with log-transformed output concentrations are compared. Using log-transformed concentrations results in larger sensitivity indices for more influential input variables, smaller sensitivity indices for less influential input variables, and more consistent values for sensitivity indices between methods (PCE and GP) and between analyses repeated with samples of different sizes.

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18 Results
18 Results