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A systematic decision-making methodology to formalize the selection of degree of realism in screening analysis of probabilistic risk assessment

Author

Listed:
  • Sari Alkhatib
  • Tatsuya Sakurahara
  • Seyed Reihani
  • Zahra Mohaghegh

Abstract

In the nuclear power domain, Probabilistic Risk Assessment (PRA) is used to inform decision-making for Nuclear Power Plants (NPPs). Recently, there has been an increase in the utilization of modeling and simulation (M&S) to support the estimation of PRA inputs. Risk analysts should carefully select the PRA items that require M&S and their degree of realism (DoR) with consideration of the required resources. To support this selection, this article formulates a systematic decision-making approach for the DoR selection. The DoR selection is made based on two predictive decision-making attributes: the predicted differences in safety risk estimate (ΔSaRi) and the cost of analysis (ΔCAN). This research also develops and quantifies causal models to estimate ΔSaRi and ΔCAN. The causal model-based prediction of ΔSaRi and ΔCAN helps reduce the trial-and-error nature of the DoR selection in the PRA screening analysis and provides insights for DoR selection and the gradual refinements of PRA realism. This approach is demonstrated for a case study on fire PRA of NPPs, where an adequate DoR is selected from two fire models: an engineering correlation and a zone model.

Suggested Citation

  • Sari Alkhatib & Tatsuya Sakurahara & Seyed Reihani & Zahra Mohaghegh, 2025. "A systematic decision-making methodology to formalize the selection of degree of realism in screening analysis of probabilistic risk assessment," Journal of Risk and Reliability, , vol. 239(6), pages 1309-1331, December.
    • Handle: RePEc:sae:risrel:v:239:y:2025:i:6:p:1309-1331
    DOI: 10.1177/1748006X251334481
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    References listed on IDEAS

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