4.7 Article

Framework to model severe accident management guidelines into Level 2 probabilistic safety assessment of a nuclear power plant

期刊

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2021.108076

关键词

Level 2 PSA; SAMG; NPP risk; Cs-137; Severe accident; Event tree; Fault tree

资金

  1. Ministry of Science, ICT, and Future Planning of the Republic of Korea
  2. National Research Foundation of Korea [2017M2A8A4015287, NRF-2020M2C9A1061638]
  3. National Research Foundation of Korea [2020M2C9A1061638, 2017M2A8A4015287] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Modeling severe accident management guidelines (SAMG) into Level 2 probabilistic safety assessment (PSA) is crucial for realistically quantifying the safety of nuclear power plants (NPPs) and the effectiveness of SAMG. The inclusion of SAMG in the Level 2 PSA model led to a significant increase in the conditional containment intact probability and a considerable decrease in NPP risk, as demonstrated in the application to the OPR-1000 plant.
Severe accident management guidelines (SAMG) play a significant role in mitigating severe accidents in nuclear power plants (NPPs), and thus the modeling of SAMG into Level 2 probabilistic safety assessment (PSA) has become essential to more realistically quantify the safety of NPPs and the effectiveness of SAMG. This study developed a systematic framework to model SAMG into Level 2 PSA, in which SAMG event trees and fault trees are modeled to formulate SAMG in a probabilistic manner. Application results to the internal events of a real plant, the OPR-1000, showed that the inclusion of SAMG in the Level 2 PSA model led to not only a significant increase in the conditional containment intact probability, from 37 to 70%, but also a considerable decrease in NPP risk, namely by 44% compared to the case without SAMG. The method can be used for the estimation of the effectiveness of each mitigation strategy, as well as for the identification of the risk-significant factors regarding SAMG.

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