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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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13 Results
13 Results