Sari Alkhatib, Ph.D. Candidate
Dr. Zahra Mohaghegh, Director of Research
August 5, 2024 | 10:45am - 12:45pm CST
This final examination will be held in 101A Talbot Laboratory.
Methodologies to Strategize the Utilization of Modeling and Simulation for Probabilistic Risk Assessment (PRA) of Nuclear Power Plants: Applications in Fire PRA
ABSTRACT: Probabilistic Risk Assessment (PRA) is essential for ensuring the safety and efficiency of nuclear power plants (NPPs). As the role of modeling and simulation (M&S) in PRA grows, it is increasingly important to carefully select PRA events and determine the appropriate degree of realism for M&S applications. These selections, made during the “screening analysis” phase of PRA, significantly influence the background knowledge and introduce uncertainties. This research advances methodologies for screening analysis to enhance the strategic use of M&S in PRA for NPPs, contributing in the following ways:
- Developed a multi-criteria decision-making methodology to determine the appropriate degree of realism for M&S in PRA, taking into account predicted safety risks and anticipated resource requirements prior to conducting M&S analysis. This methodology is illustrated through a fire PRA case study, which employs two models with varying degrees of realism: an engineering correlation model and a two-zone model.
- Introduced a methodology based on phenomenological nondimensional parameter (PNP) decomposition to generate surrogate values for M&S input parameters. This approach reduces the need for precise input data, thereby minimizing extensive data collection efforts during screening analysis. Its feasibility is demonstrated with a multi-compartment fire analysis case study for fire PRA of NPPs.
- Developed a novel approach that integrates uncertainty quantification and sensitivity analysis to guide and scientifically justify the formulation of modeling assumptions during screening analysis. This approach contrasts with current PRA practices, which typically conduct sensitivity analysis as a post-processing step on PRA outputs, after modeling assumptions have been established and screening decisions made based on them. The new method aims to minimize false negatives in screening by addressing constraints related to background knowledge and unquantified uncertainties. It is integrated into the PNP decomposition methodology to enhance the justification for deriving surrogate values for physical input parameters in M&S. Its effectiveness is demonstrated through a multi-compartment fire analysis case study.