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

Journal Article · Tue Jul 14 00:00:00 EDT 2015 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2015.07.001· OSTI ID:1249548

Maljovec, D. ^[1]; Liu, S. ^[1]; Wang, B. ^[1]; Mandelli, D. ^[2]; Bremer, P. -T. ^[3]; Pascucci, V. ^[1]; Smith, C.  ^[2]

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1. Univ. of Utah, Salt Lake City, UT (United States)
2. Idaho National Lab. (INL), Idaho Falls, ID (United States)
3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Here, 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.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC07-05ID14517; AC52-07NA27344; LLNL-CONF-658933
• OSTI ID: 1249548
• Alternate ID(s): OSTI ID: 1396747
• Report Number(s): INL/JOU-15-34662; PII: S095183201500191X
• Journal Information: Reliability Engineering and System Safety, Vol. 145, Issue C; ISSN 0951-8320
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
probabilistic risk assessment
computational topology
clustering
high-dimensional data analysis