Dynamic PRA: An overview of methods and applications using RAVEN

Dynamic PRA methods couple stochastic tools (i.e., sampling methods) with system simulators (e.g., RELAP5-3D) to determine the risk associated to complex systems such as nuclear power plants. Compared to classical PRA methods they can evaluate with higher resolution the safety impacts of timing and sequencing of events on the accident progression without the need to introduce conservative modeling assumptions and success criteria. This paper provides an overview on how the INL developed code RAVEN can be used to perform DPRA. In addition, it is shown how machine-learning and data mining methods can be successfully employed to reduce the required computational resources and create knowledge out of gigabytes of generated data. Some applications of dynamic PRA methods are also presented.