Summary of the Potential Impacts of Nuclear Fuel Cycle Options on the Permanent Disposal of Spent Fuel in the U.S
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The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Departmentof Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel and Waste Disposition(SFWD) is conducting research and development (R&D) on deep geologic disposal of spentnuclear fuel (SNF) and high-level nuclear waste (HLW). R&D addressing the disposal ofSNF/HLW in the U.S. is currently generic (i.e., "non-site-specific") in scope, following thesuspension of the Yucca Mountain Repository Project in 2010. However, to prepare for theeventuality of a repository siting process, the former Used Fuel Disposition Campaign (UFDC) ofDOE-NE, which was succeeded by the SFWST Campaign, formulated an R&D Roadmap in 2012outlining generic R&D activities and their priorities appropriate for developing safety cases andassociated performance assessment (PA) models for generic deep geologic repositories in severalpotential host-rock environments in the contiguous United States. This 2012 UFDC Roadmap alsoidentified the importance of re-evaluating priorities in future years as knowledge is gained fromthe DOE's ongoing R&D activities.
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International High-Level Radioactive Waste Management 2019, IHLRWM 2019
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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