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An algorithm for enhancing spatiotemporal resolution of probabilistic risk assessment to address emergent safety concerns in nuclear power plants

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  • Bui, Ha
  • Sakurahara, Tatsuya
  • Pence, Justin
  • Reihani, Seyed
  • Kee, Ernie
  • Mohaghegh, Zahra

Abstract

Emergent safety concerns often involve complex spatiotemporal phenomena. In addressing these concerns, the classical Probabilistic Risk Assessment (PRA) of Nuclear Power Plants (NPPs) has limitations in generating the required resolution for risk estimations. The existing dynamic PRAs have yet to demonstrate their feasibility for implementation in a plant. In addition, due to the widespread use of classical PRA in the nuclear industry and by the regulatory agency, a transition to a fully dynamic PRA would require a significant investment of resources. As a more feasible alternative, the authors have developed the Integrated PRA (I-PRA) methodology to add realism to risk estimations by explicitly incorporating time and space into underlying models of the events in the plant PRA while avoiding significant changes to its structure. In I-PRA, the failure mechanisms associated with the areas of concern (e.g., fire, Generic Safety Issue 191) were modeled in separate simulation modules, which were then integrated with the plant PRA through a probabilistic interface. This paper (i) provides theoretical foundations for the incorporation of time and space into PRA and (ii) introduces an algorithm that helps execute I-PRA in a way to gradually enhance spatiotemporal resolution of plant PRAs to efficiently address emergent safety concerns.

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  • Bui, Ha & Sakurahara, Tatsuya & Pence, Justin & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "An algorithm for enhancing spatiotemporal resolution of probabilistic risk assessment to address emergent safety concerns in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 405-428.
    • Handle: RePEc:eee:reensy:v:185:y:2019:i:c:p:405-428
    DOI: 10.1016/j.ress.2019.01.004
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    Cited by:

    1. Tatsuya Sakurahara & Seyed Reihani & Ernie Kee & Zahra Mohaghegh, 2020. "Global importance measure methodology for integrated probabilistic risk assessment," Journal of Risk and Reliability, , vol. 234(2), pages 377-396, April.
    2. Hong Xu & Baorui Zhang, 2022. "Diverse and Flexible Coping Strategy for Nuclear Safety: Opportunities and Challenges," Energies, MDPI, vol. 15(17), pages 1-21, August.
    3. Justin Pence & Zahra Mohaghegh, 2020. "A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1183-1211, June.
    4. Sakurahara, Tatsuya & O'Shea, Nicholas & Cheng, Wen-Chi & Zhang, Sai & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.

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