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Novel method of dynamic event tree keeping the number of simulations in risk analysis small

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  • Kaneko, Fujio
  • Yuzui, Tomohiro

Abstract

Conducting a risk analysis on nuclear plants using the dynamic event tree (DET) to improve the accuracy of consequence analysis of the system state significantly increases the number of event tree (ET) branches. Several methods have been developed to reduce the number of branches and event sequences of DET. In this study, we developed a new risk analysis method using a relatively small ET to estimate risk considering time changes in the system state of a target system, in a drastic short time. The main features of the proposed method are: to set some headings which cause the same branches on every event sequence outside of the ET for keeping it small; to set the probability distribution function or cumulative distribution function of the time to activating each of safety measures which have the monotonous relation to the risk; and to estimate the maximum and minimum risk values of every event sequence using these probabilistic distributions.

Suggested Citation

  • Kaneko, Fujio & Yuzui, Tomohiro, 2023. "Novel method of dynamic event tree keeping the number of simulations in risk analysis small," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s095183202200624x
    DOI: 10.1016/j.ress.2022.109009
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    References listed on IDEAS

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    1. Hu, Yunwei & Parhizkar, Tarannom & Mosleh, Ali, 2022. "Guided simulation for dynamic probabilistic risk assessment of complex systems: Concept, method, and application," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Katrina M Groth & Matthew R Denman & Michael C Darling & Thomas B Jones & George F Luger, 2020. "Building and using dynamic risk-informed diagnosis procedures for complex system accidents," Journal of Risk and Reliability, , vol. 234(1), pages 193-207, February.
    3. Maljovec, D. & Liu, S. & Wang, B. & Mandelli, D. & Bremer, P.-T. & Pascucci, V. & Smith, C., 2016. "Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 262-276.
    4. Mandelli, Diego & Yilmaz, Alper & Aldemir, Tunc & Metzroth, Kyle & Denning, Richard, 2013. "Scenario clustering and dynamic probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 146-160.
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    Cited by:

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    2. Wang, Qianlin & Han, Jiaqi & Chen, Feng & Hu, Su & Yun, Cheng & Dou, Zhan & Yan, Tingjun & Yang, Guoan, 2024. "Modeling risk characterization networks for chemical processes based on multi-variate data," Energy, Elsevier, vol. 293(C).
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    4. Wang, Chenyushu & Cai, Baoping & Shao, Xiaoyan & Zhao, Liqian & Sui, Zhongfei & Liu, Keyang & Khan, Javed Akbar & Gao, Lei, 2023. "Dynamic risk assessment methodology of operation process for deepwater oil and gas equipment," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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