Importance Measure for Dynamic Probabilistic Risk Assessment and Cost Savings for Safety Systems and Components

Importance measures (IMs) in probabilistic risk assessment are used to evaluate the significance of the basic event in terms of its contribution to the risk. In traditional PRA, the IMs are calculated based on an event's occurrence and non-occurrence, which is invalid in dynamic probabilistic risk assessment (DPRA). The uncertainties associated with the random variables (RVs) usually have their distributions based on their likelihood of occurrences. The DPRA framework suggested by the author in the previous research provides the cumulative distribution-based conditional core damage probability profile for the accident scenarios. Conventional measures such as Fussell-Vessely (FV) and Risk Achievement Worth (RAW) are designed for traditional PRA to identify the safety significance of the basic events and therefore difficult to apply to DPRA. In this research, the cumulative distribution function (CDF) based IM was leveraged to identify the importance of basic events in an SBO accident. FV and RAW, were calculated and compared with CDF based IM. Since the CDF-based IM provides a single value for the effect of uncertainties in RV over a complete risk profile, this measure was leveraged to re-evaluate the safety significance of the basic events after implementing the new system, the US diverse and flexible coping strategies. The criteria for the CDF-based IM were established to re-categorize the components from risk-informed safety classification (RISC)-1 and RISC-3 categories to RISC-2 and RISC-4 categories. However, since the initial selection of the RVs was based on safety significance, almost all the components were identified as safety significant. However, if the domain of RVs is expanded, this re-categorization may lead to substantial cost savings.