Development of PFLOTRAN Transport Capability for Use in the Waste Isolation Pilot Plant Performance Assessment-20545
Abstract not provided.
Publications
Forecasting Marine Sediment Properties On and Near the Arctic Shelf with Geospatial Machine Learning
Abstract not provided.
An open-source framework for probabilistic gas hydrate systems modeling
Abstract not provided.
Bluff geometry and material variability influence stress states relevant to coastal permafrost bluff failure
Abstract not provided.
Development of a Tightly Coupled Multi-Physics Numerical Model for an Event-Based Understanding of Arctic Coastal Erosion
Abstract not provided.
A PROBABILISTIC MODELING FRAMEWORK UTILIZING OCEAN DRILLING DATA TO FORECAST CURRENT & FUTURE THERMODYNAMIC CONDITIONS ON & BELOW THE SEAFLOOR
Abstract not provided.
Progress in Deep Geologic Disposal Safety Assessment in the U.S. since 2010
Abstract not provided.
Forecasting Shallow Gas Gas Hydrate and Submarine Permafrost Occurrence in Marine Sediments On and Near the Arctic Shelf with Geospatial Machine Learning
Abstract not provided.
A Predictive Model for Arctic Coastal Erosion
Abstract not provided.
Strongly-coupled thermo-mechanical model of permafrost for the simulation of Arctic coastal erosion
Abstract not provided.
Sandia National Laboratories Arctic Science and Security Initiative
Abstract not provided.
Benchmarking and QA Testing PFLOTRAN
Abstract not provided.
Surrogate Modeling of Fuel Dissolution
Abstract not provided.
High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance
Abstract not provided.
High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance
Abstract not provided.
Multiphysics Peridynamic Models for Crack Growth in Brittle Materials
Abstract not provided.
Forecasting Marine Sediment Properties On and Near the Arctic Shelf with Geospatial Machine Learning
Abstract not provided.
High fidelity surrogate modeling of fuel dissolution for probabilistic assessment of repository performance
International High-Level Radioactive Waste Management 2019, IHLRWM 2019
Two surrogate models are under development to rapidly emulate the effects of the Fuel Matrix Degradation (FMD) model in GDSA Framework. One is a polynomial regression surrogate with linear and quadratic fits, and the other is a k-Nearest Neighbors regressor (kNNr) method that operates on a lookup table. Direct coupling of the FMD model to GDSA Framework is too computationally expensive. Preliminary results indicate these surrogate models will enable GDSA Framework to rapidly simulate spent fuel dissolution for each individual breached spent fuel waste package in a probabilistic repository simulation. This capability will allow uncertainties in spent fuel dissolution to be propagated and sensitivities in FMD inputs to be quantified and ranked against other inputs.
Benchmarking and QA testing in PFLOTRAN
International High-Level Radioactive Waste Management 2019, IHLRWM 2019
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.
Development of a Tightly Coupled Numerical Model for Arctic Coastal Erosion Infrastructure Risk and Evaluation of Associated Coastal Hazards
Abstract not provided.
Forecasting Marine Sediment Properties On and Near the Arctic Shelf with Geospatial Machine Learning
Abstract not provided.
High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance
Abstract not provided.
Verification of PFLOTRAN for 3-D WIPP Performance Assessment: PFLOTRAN-BRAGFLO Benchmark
Abstract not provided.
SNL/BER Review 2018
Abstract not provided.
Advances in Geologic Disposal Safety Assessment and an Unsaturated Alluvium Reference Case
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Depat ment of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (OFCT) 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 (DOE 2011, Table 6). These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) work package, which is charged with developing a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes specific GDSA activities in fiscal year 2018 (FY 2018) toward the development of GDSA Framework, an enhanced disposal system modeling and analysis capability for geologic disposal of nuclear waste. GDSA Framework employs the PFLOTRAN thermal-hydrologic-chemical multiphysics code (Hammond et al. 2011a; Lichtner and Hammond 2012) and the Dakota uncertainty sampling and propagation code (Adams et al. 2012; Adams et al. 2013). Each code is designed for massivelyparallel processing in a high-performance computing (HPC) environment. Multi-physics representations in PFLOTRAN are used to simulate various coupled processes including heat flow, fluid flow, waste dissolution, radionuclide release, radionuclide decay and ingrowth, precipitation and dissolution of secondary phases, and radionuclide transport through engineered barriers and natural geologic barriers to the biosphere. Dakota is used to generate sets of representative realizations and to analyze parameter sensitivity.
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