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Multi-unit risk aggregation with consideration of uncertainty and bias in risk metrics

Author

Listed:
  • Zhou, Taotao
  • Modarres, Mohammad
  • Droguett, Enrique López

Abstract

The risk significance of multi-unit events has received much interest, especially since the 2011 Fukushima–Daiichi accident. However, there have been limited experiences in performing a multi-unit probabilistic risk assessment (MUPRA), considering the interactions among multiple reactor units and fuel storage facilities on a site. While considerable research and development efforts have been devoted over the past few years to MUPRAs, there is still no consensus on a unified MUPRA methodology. A site-based risk model is of great importance for comprehensive risk-informed applications and safety goals evaluations. Further, a site-based MUPRA model needs to aggregate risks from various reactor units’ internal and external sequences of events and modes of operation. Risk values obtained from various initiating events and modes of operation, however, are usually biased very differently due to the various factors such as conservatism and modeling assumption used during their developments. As such, to appropriately aggregate various sequences of events, they should be least biased. This paper discusses the framework of a probabilistic aggregation approach for the multi-unit risk metrics to make them least biased using expert elicitation. More importantly, the paper, through a sensitivity analysis, shows that a biased risk metric, even when it is not aggregated, could mask important contributors to risk and thereby yield incorrect risk contributors. This is done by comparing the risk insights from importance measures for various modeling scope and assumptions that make a risk metric biased. Through an example, it is shown the masking of important risk contributors and human error events would be an important impediment in the MUPRAs. The example provides an illustration of the aggregation process of multi-unit risk metrics for both external events and internal events. The paper demonstrates the importance of biased correction before approaching to multi-unit risk aggregation, especially for the external events.

Suggested Citation

  • Zhou, Taotao & Modarres, Mohammad & Droguett, Enrique López, 2019. "Multi-unit risk aggregation with consideration of uncertainty and bias in risk metrics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 473-482.
  • Handle: RePEc:eee:reensy:v:188:y:2019:i:c:p:473-482
    DOI: 10.1016/j.ress.2019.04.001
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    References listed on IDEAS

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    1. Schroer, Suzanne & Modarres, Mohammad, 2013. "An event classification schema for evaluating site risk in a multi-unit nuclear power plant probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 40-51.
    2. Zhou, Taotao & Modarres, Mohammad & Droguett, Enrique López, 2018. "An improved multi-unit nuclear plant seismic probabilistic risk assessment approach," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 34-47.
    3. Stan Kaplan, 2000. "‘Combining Probability Distributions from Experts in Risk Analysis’," Risk Analysis, John Wiley & Sons, vol. 20(2), pages 155-156, April.
    4. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    5. Modarres, Mohammad & Zhou, Taotao & Massoud, Mahmoud, 2017. "Advances in multi-unit nuclear power plant probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 87-100.
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    Citations

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    Cited by:

    1. Kim, Yongjin & Jang, Seunghyun & Jae, Moosung, 2022. "Evaluation of inter-unit dependency effect on site core damage frequency: Internal and seismic event," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Arigi, Awwal Mohammed & Park, Gayoung & Kim, Jonghyun, 2020. "Dependency analysis method for human failure events in multi-unit probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Jin, Kyungho & Hwang, Yujeong & Heo, Gyunyoung, 2021. "Development of Dependence Indexes for Multi-Unit Risk Assessment and its Estimation Using Copula," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Xiaohui Chen & Lin Zhang & Ze Zhang, 2020. "An integrated model for maintenance policies and production scheduling based on immune–culture algorithm," Journal of Risk and Reliability, , vol. 234(5), pages 651-663, October.
    5. Zhou, Taotao & Modarres, Mohammad & Droguett, Enrique López, 2021. "Multi-unit nuclear power plant probabilistic risk assessment: A comprehensive survey," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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