Application of Global Importance Measures in RISMC for Dynamic Risk Assessment

Abstract Risk-Informed Safety Margin Characterization (RISMC) integrates traditional deterministic safety analysis methods with probabilistic safety analysis approaches by employing stochastic sampling of uncertain parameters and dynamic branching simulations of nuclear power plants. It enables time-dependent, uncertainty-aware, and multi-level risk assessments. In the process of RISMC analysis, quantifying the impacts of uncertainty parameters, response events, and system components on nuclear power plant risk can support the implementation of risk analysis results in nuclear power plants. However, traditional importance measurement methods cannot directly apply to RISMC. This study adopts the global importance measurement method to measure the time-dependent importance. The selected global importance measurement method considers input parameter uncertainties and uncertainties from interactions between input parameters. It is demonstrated on Station Blackout (SBO) accidents modeled with dynamic event trees and compared with traditional importance metrics used in probabilistic risk assessment. The risk importance of systems and physical design parameters in SBO accidents is ranked. Results indicate timely AC power recovery significantly reduces risk, while the importance of TAFW failure diminishes over time. Future research is needed to investigate the discrepancies between the global importance measurement method and traditional methods.