Congratulations to SoTeRiA graduate student Dr. Sari Alkhatib on successfully completing his Ph.D. requirements in the Department of Nuclear Plasma and Radiological Engineering (NPRE) at the Grainger College of Engineering, University of Illinois at Urbana-Champaign (UIUC)!
Sari’s research has focused on advancing methodologies for screening analysis to enhance the strategic use of modeling and simulation in Probabilistic Risk Assessment (PRA) of Nuclear Power Plants (NPPs). His unique research area leverages methodologies that support efficient operations of NPPs and the licensing of advanced reactors. The executive summary can be found below.
Sari was a Graduate Research Assistant in the SoTeRiA Research Laboratory, supervised by Prof. Zahra Mohaghegh, and a Mavis Future Faculty Fellow of the Grainger College of Engineering. He is joining Argonne National Lab (ANL) as a postdoctoral researcher in the Licensing & Risk Assessments group, to work under the supervision of Dr. Dave Grabaskas.
The SoTeRiA team is very excited about Sari’s achievements and looking forward to continued collaboration and wishing him good luck in his career!!
Special thanks to Sari’s Ph.D. advisor, Prof. Zahra Mohaghegh, and other Ph.D. committee members, Prof. Terje Aven, Prof. Christopher P.L. Barkan, Dr. Seyed Reihani, Prof. Tatsuya Sakurahara, Prof. Rizwan Uddin, and Prof. Jianqi Xi, for their support, feedback, and guidance on Sari’s research.
Executive Summary:
Methodologies to Strategize the Utilization of Modeling and Simulation for Probabilistic Risk Assessment (PRA) of Nuclear Power Plants: Applications in Fire PRA
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.
