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A framework for verifying Dynamic Probabilistic Risk Assessment models

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  • Picoco, Claudia
  • Rychkov, Valentin
  • Aldemir, Tunc

Abstract

Recent development of more powerful computational and technological resources has led to significant improvements in the utilization of dynamic methodologies for the Probabilistic Risk Assessment (PRA) of nuclear power plants. These methodologies integrate deterministic and probabilistic analyses and are generally referred to as Dynamic PRA (DPRA) methods. DPRA is performed through the generation and simulation of possibly thousands of different accident scenarios. To ensure the quality and the correctness of the results, DPRA models should be verified. Since DPRA generates large amount of data, a visual inspection of results to verify the correctness of the model used is neither practical nor reliable. As one of the steps for DPRA analysis, a framework is proposed to systematically explore the DPRA model prior to its simulation using statecharts which provide a graphical notation for describing dynamic aspects of system behavior. The application of the framework is illustrated using two case studies: (i) performance assessment of a heated room using the PyCATSHOO DPRA tool, and, (ii) DPRA performed with RAVEN-MAAP5-EDF codes for loss of off-site power as the initiating event in a pressurized water reactor.

Suggested Citation

  • Picoco, Claudia & Rychkov, Valentin & Aldemir, Tunc, 2020. "A framework for verifying Dynamic Probabilistic Risk Assessment models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020306001
    DOI: 10.1016/j.ress.2020.107099
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    References listed on IDEAS

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    1. Catalyurek, Umit & Rutt, Benjamin & Metzroth, Kyle & Hakobyan, Aram & Aldemir, Tunc & Denning, Richard & Dunagan, Sean & Kunsman, David, 2010. "Development of a code-agnostic computational infrastructure for the dynamic generation of accident progression event trees," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 278-294.
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    Cited by:

    1. Feng, Jian Rui & Yu, Guanghui & Zhao, Mengke & Zhang, Jiaqing & Lu, Shouxiang, 2022. "Dynamic risk assessment framework for industrial systems based on accidents chain theory: The case study of fire and explosion risk of UHV converter transformer," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    2. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Kim, Man Cheol, 2022. "Systematic approach and mathematical development for conditional core damage probabilities under station blackout of a nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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