UCLA

Dynamic Probabilistic Risk Assessment (PRA) refers to an emerging class of PRA methods that generate risk scenarios through the model-based simulation of systems such as nuclear power plants (NPPs) and their crew response to accident initiators. The dynamic PRA approach offers several advantages over the conventional approaches currently used by the nuclear industry worldwide. These advantages include: (1) time-dependent prediction of the operator error-forcing contexts, (2) better representation of the thermal-hydraulic success criteria, and (3) considerable reduction in analyst-to-analyst variability of the results. An example of such a simulation platform is the Accident Dynamics Simulator coupled with the Information, Decision and Action in a Crew context cognitive model (ADS-IDAC), and a realistic NPP thermal-hydraulic model. The aim of this research is to integrate qualitative and quantitative hybrid causal logic into the ADS-IDAC dynamic PRA platform. This makes ADS-IDAC a more practical and realistic analysis tool for specific applications. These applications are primarily event assessments, but also include the ability to analyze highly dynamic and complex accident scenarios in support of conventional PRAs. This work offers major modeling enhancements of ADS-IDAC, including dynamically linked fault trees (FTs) for support and frontline systems modeling, more advanced system and operating crew modeling capabilities, comprehensive quantification features modeling human failure evens (HFEs), and uncertainty propagation through the generated discrete dynamic event tree (DDET). The new risk assessment process was streamlined with the help of a newly developed user-friendly graphical interface, which provides efficient and convenient access to all the capabilities of the ADS-IDAC simulation engine.