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An integrated methodology for spatio-temporal incorporation of underlying failure mechanisms into fire probabilistic risk assessment of nuclear power plants

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  • Sakurahara, Tatsuya
  • Mohaghegh, Zahra
  • Reihani, Seyed
  • Kee, Ernie
  • Brandyberry, Mark
  • Rodgers, Shawn

Abstract

In this research, an Integrated probabilistic risk assessment (I-PRA) methodological framework for Fire PRA is developed to provide a unified multi-level probabilistic integration, beginning with spatio-temporal simulation-based models of underlying failure mechanisms (i.e., physical phenomena and human actions), connecting to component-level failures, and then linking to system-level risk scenarios in classical PRA. The simulation-based module, called the fire simulation module (FSM), includes state-of-the-art models of fire initiation, fire progression, post-fire failure damage propagation, fire brigade response, and scenario-based damage. Fire progression is simulated using a CFD code, fire dynamics simulator (FDS), which solves Navier–Stokes equations governing the turbulent flow field. Uncertainty quantification is conducted to address parameter uncertainties. The I-PRA paves the way for reducing excessive conservatisms derived from the modeling of (i) fire progression and damage and (ii) the interactions between fire progression and manual suppression. Global importance measure analysis is used to rank the risk-contributing factors. A case study demonstrates the implementation of I-PRA for a regulatory-documented fire scenario.

Suggested Citation

  • Sakurahara, Tatsuya & Mohaghegh, Zahra & Reihani, Seyed & Kee, Ernie & Brandyberry, Mark & Rodgers, Shawn, 2018. "An integrated methodology for spatio-temporal incorporation of underlying failure mechanisms into fire probabilistic risk assessment of nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 242-257.
  • Handle: RePEc:eee:reensy:v:169:y:2018:i:c:p:242-257
    DOI: 10.1016/j.ress.2017.09.001
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    References listed on IDEAS

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    1. M Kloos & J Peschke, 2008. "Consideration of human actions in combination with the probabilistic dynamics method Monte Carlo dynamic event tree," Journal of Risk and Reliability, , vol. 222(3), pages 303-313, September.
    2. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    3. Hansen, Clifford W. & Helton, Jon C. & Sallaberry, Cédric J., 2012. "Use of replicated Latin hypercube sampling to estimate sampling variance in uncertainty and sensitivity analysis results for the geologic disposal of radioactive waste," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 139-148.
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    Cited by:

    1. Sakurahara, Tatsuya & Schumock, Grant & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 84-99.
    2. 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.
    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. Tan, Samson & Moinuddin, Khalid, 2019. "Systematic review of human and organizational risks for probabilistic risk analysis in high-rise buildings," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 233-250.
    5. Takeda, Satoshi & Kitada, Takanori, 2021. "Simple method based on sensitivity coefficient for stochastic uncertainty analysis in probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. 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.
    7. Lyu, Dong & Si, Shubin, 2020. "Dynamic importance measure for the K-out-of-n: G system under repeated random load," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    8. Pence, Justin & Sakurahara, Tatsuya & Zhu, Xuefeng & Mohaghegh, Zahra & Ertem, Mehmet & Ostroff, Cheri & Kee, Ernie, 2019. "Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 240-260.
    9. Lyu, Dong & Si, Shubin, 2021. "Importance measure for K-out-of-n: G systems under dynamic random load considering strength degradation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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