A novel methodology to scientifically justify the formulation of modeling assumptions in screening analysis of Probabilistic Risk Assessment (PRA)

Abstract

In Probabilistic Risk Assessment (PRA) of nuclear power plants (NPPs), there is a growing reliance on modeling and simulation ( M&S). Due to time and resource constraints, PRA analysts do not conduct M&S for every PRA event or its underlying factors; instead, they selectively, through screening analysis, determine the required level of realism for M&S. Since plant data for estimating the values of input parameters may not yet be fully collected, modeling assumptions would be required. This paper proposes a new approach for systematically selecting a set of proper modeling assumptions that minimize a false negative result in the screening analysis. The proposed methodology conducts uncertainty quantification and sensitivity analysis and generates a sensitivity dataset, which can then be utilized to guide and justify modeling assumptions to generate surrogate values of M&S input parameters. The proposed methodology is applied to the screening analysis of a multi-compartment fire scenario at an NPP.