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

Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters, and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated, where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station bl...

• Type: Journal
• Year: 2016
• Where: RESS
• Authors: Dan Maljovec, Shusen Liu, Bei Wang, Diego Mandelli, Peer-Timo Bremer, Valerio Pascucci, C. Smith
• Keywords: RESS 2016