Measuring risk-importance in a Dynamic PRA framework

Risk Importance Measures (RIMs) are indexes that are used to rank Structures, Systems, and Components (SSCs). The most used measures are: Risk Reduction Worth, Risk Achievement Worth, Birnbaum and Fussell-Vesely. Once obtained from Classical Probabilistic Risk Assessment (PRA), these risk measures can be effectively employed to identify the most risk-important SSCs. The objective of this paper is to present a series of methods that can be employed to measure risk importance of SSCs from Dynamic PRA. In contrast to Classical PRA methods, Dynamic PRA methods couple stochastic models with system simulators to determine risk associated to complex systems such as nuclear plants. Compared to Classical PRA methods, Dynamic PRA approaches can evaluate with higher resolution the safety impact of timing and sequencing of events on the accident progression. The developed set of RIMs are directly derived from Classical RIMs and adapted to deal with simulation-based data. We present a series of analytical tests to show how RIMs can be obtained from a Dynamic PRA data and a comparison of the RIMs obtained from Classical and Dynamic PRA for a Large Break Loss Of Coolant Accident (LB-LOCA) initiating event of a Pressurized Water Reactor (PWR). The obtained results have highlighted differences among the two PRA approaches in the predicted final outcome of few accident sequences. This have consequently affected the risk importance of a subset of basic events.