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

• Published in: Reliability engineering & system safety Vol. 145; no. C; pp. 262 - 276
• Main Authors: Maljovec, D., Liu, S., Wang, B., Mandelli, D., Bremer, P.-T., Pascucci, V., Smith, C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2016; Elsevier
• Subjects: Boiling water reactors; Clustering; Computational topology; Computer programs; Computer simulation; Controllers; Dynamical systems; Dynamics; High-dimensional data analysis; Mathematical models; MATHEMATICS AND COMPUTING; Probabilistic risk assessment; Probabilistic risk assessment; High-dimensional data analysis; Clustering; Computational topology
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2015.07.001

Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and 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 blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data. •We analyze the safety margins associated to simulations of BWR station blackout.•We present an integrated visualization framework for viewing analysis results.•Employing the use of two complementary clusterings reveals complex structure.•Hierarchical clustering groups points with similar parameter settings.•Topological clustering finds areas in the input domain with similar gradient flow.