Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Hammond, Glenn E.; Hu, Bill; Zachara, John M.
Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance-based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real-world biogeochemical model at the groundwater-surface water interface within the Hanford Site's 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon-consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multiphysics models for complex systems.
R&D addressing the disposal of commercial spent nuclear fuel in the U.S. is currently generic (i.e., “non-site-specific”) in scope. However, to prepare for the eventuality of a repository siting process, the former Used Fuel Disposition (UFD) Campaign of the Nuclear Energy (NE) Office of the U.S. DOE formulated an R&D Roadmap in 2012 outlining generic R&D activities and their priorities appropriate for developing safety cases and associated performance assessment (PA) models for deep geologic repositories in several potential host-rock environments in the contiguous United States. This 2012 UFD Roadmap identified the importance of re-evaluating priorities in future years as knowledge is gained from the DOE's ongoing R&D activities. Since 2012, significant knowledge has been gained from these activities through R&D in the U.S. and via international collaborations, especially with countries that operate underground research laboratories (URLs). The 2019 R&D Roadmap Update, introduced here, summarizes the progress of ongoing R&D activities, re-assesses R&D priorities, and identifies new activities of high priority, such as R&D on disposal of DPCs (dual purpose canisters), which now contain a significant fraction of the Nation's spent fuel activity.
PFLOTRAN is well-established in single-phase reactive transport problems, and current research is expanding its visibility and capability in two-phase subsurface problems. A critical part of the development of simulation software is quality assurance (QA). The purpose of the present work is QA testing to verify the correct implementation and accuracy of two-phase flow models in PFLOTRAN. An important early step in QA is to verify the code against exact solutions from the literature. In this work a series of QA tests on models that have known analytical solutions are conducted using PFLOTRAN. In each case the simulated saturation profile is rigorously shown to converge to the exact analytical solution. These results verify the accuracy of PFLOTRAN for use in a wide variety of two-phase modelling problems with a high degree of nonlinearity in the interaction between phase behavior and fluid flow.