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A dynamic event tree informed approach to probabilistic accident sequence modeling: Dynamics and variabilities in medium LOCA

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  • Karanki, Durga Rao
  • Kim, Tae-Wan
  • Dang, Vinh N.

Abstract

In Probability Safety Assessments, accident scenario dynamics are addressed in the accident sequence analysis task. In an analyst-driven, iterative process, assumptions are made about equipment responses and operator actions and simulations of the scenario evolution are performed. To calculate how scenario dynamics and stochastic variabilities may affect the results of this process in terms of estimated risk, this work applies Dynamic Event Trees (DETs) to more comprehensively examine the accident scenario space. Alternative event tree models are developed and the core damage frequency is quantified to reveal the effects of different delineations of the sequences and of the bounding assumptions underlying success criteria. The results from a case study on Medium-break Loss of Coolant Accident scenarios in a Pressurized Water Reactor are presented, considering the break size, available injection trains, and the timing of rapid cooldown and the switchover to recirculation. The results show not only that estimated risk can be very sensitive to the numerous assumptions made in current accident sequence analysis but also that bounding assumptions do not always result in conservative risk estimates, thereby confirming the benefits that DETs provide in terms of characterizing scenario dynamics.

Suggested Citation

  • Karanki, Durga Rao & Kim, Tae-Wan & Dang, Vinh N., 2015. "A dynamic event tree informed approach to probabilistic accident sequence modeling: Dynamics and variabilities in medium LOCA," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 78-91.
    • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:78-91
    DOI: 10.1016/j.ress.2015.04.011
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    Cited by:

    1. Taleb-Berrouane, Mohammed & Khan, Faisal & Amyotte, Paul, 2020. "Bayesian Stochastic Petri Nets (BSPN) - A new modelling tool for dynamic safety and reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 193(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. Santosh B. Rane & Prathamesh R. Potdar & Suraj Rane, 2019. "Accelerated life testing for reliability improvement: a case study on Moulded Case Circuit Breaker (MCCB) mechanism," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(6), pages 1668-1690, December.
    4. Di Maio, Francesco & Picoco, Claudia & Zio, Enrico & Rychkov, Valentin, 2017. "Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 122-138.
    5. Karanki, Durga Rao & Dang, Vinh N., 2016. "Quantification of Dynamic Event Trees – A comparison with event trees for MLOCA scenario," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 19-31.
    6. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
    7. Karanki, D.R. & Rahman, S. & Dang, V.N. & Zerkak, O., 2017. "Epistemic and aleatory uncertainties in integrated deterministic and probabilistic safety assessment: Tradeoff between accuracy and accident simulations," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 91-102.

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