Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study

摘要: Abstract Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller RAVEN ADAPT). Whereas model dynamics deterministically, introduce both deterministic control logic operating procedures) stochastic component failures parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of set parameters simulating behavior for that specific values. For complex systems, major challenge in using to analyze large number scenarios generated, where clustering techniques are typically employed better organize interpret data. In this paper, we focus on analysis two nuclear datasets part risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide domain experts software tool encodes traditional topological within an interactive visualization environment, understanding structures such high-dimensional datasets. demonstrate through our study types complement each other enhanced structural