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- W2410592732 abstract "The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the overall objective to help sustain the existing commercial nuclear power plants (NPPs). To accomplish this program objective, there are multiple LWRS “pathways,” or research and development (R&D) focus areas. One LWRS focus area is called the Risk-Informed Safety Margin and Characterization (RISMC) pathway. Initial efforts under this pathway to combine probabilistic and plant multi-physics models to quantify safety margins and support business decisions also included HRA, but in a somewhat simplified manner. HRA experts at Idaho National Laboratory (INL) have been collaborating with other experts to develop a computational HRA approach, called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), for inclusion into the RISMC framework. The basic premise of this research is to leverage applicable computational techniques, namely simulation and modeling, to develop and then, using RAVEN as a controller, seamlessly integrate virtual operator models (HUNTER) with 1) the dynamic computational MOOSE runtime environment that includes a full-scope plant model, and 2) the RISMC framework PRA models already in use. The HUNTER computational HRA approach is a hybrid approach that leverages past work from cognitive psychology, human performance modeling, and HRA, but it is also a significant departure from existing static and even dynamic HRA methods. This report is divided into five chapters that cover the development of an external flooding event test case and associated statistical modeling considerations." @default.
- W2410592732 created "2016-06-24" @default.
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- W2410592732 date "2015-09-01" @default.
- W2410592732 modified "2023-09-27" @default.
- W2410592732 title "Proof-of-Concept Demonstrations for Computation-Based Human Reliability Analysis. Modeling Operator Performance During Flooding Scenarios" @default.
- W2410592732 doi "https://doi.org/10.2172/1244632" @default.
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