Sensitivity analysis of generic deep geologic repository with focus on spatial heterogeneity induced by stochastic fracture network generation. (November 2022)

• Record Type: Journal Article
• Authors: Brooks, D.M.; Swiler, L.P.; Stein, E.; Mariner, P.E.; Basurto, E.; Portone, T.; Eckert, A.; Leone, R.
• Is Part Of: Advances in water resources. Volume 169(2022)
• Publication Date: 2022-11
• Subjects: Nuclear waste -- Spent nuclear fuel -- Performance assessment -- Uncertainty; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2022.104310
• Languages: English
• ISSNs: 0309-1708

Abstract

Abstract: 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. Highlights: Spatial heterogeneity is a significant uncertainty in geologic repository models. Summary measures of spatial heterogeneity enable global sensitivity analysis. Including spatial heterogeneity improves understanding of system performance. Flux-based quantities of interest aid interpretation of system behavior over time.