Framework to model severe accident management guidelines into Level 2 probabilistic safety assessment of a nuclear power plant. (January 2022)

• Record Type: Journal Article
• Authors: Cho, Jaehyun; Lee, Sang Hun; Kim, Jaewhan; Park, Seong Kyu
• Is Part Of: Reliability engineering & system safety. Volume 217(2022)
• Publication Date: 2022-01
• Subjects: Level 2 PSA -- SAMG -- NPP risk -- Cs-137 -- Severe accident -- Event tree -- Fault tree; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.108076
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Systematic framework to model severe accident management guidelines into Level 2 PSA. Approach covers SAMG event tree/fault tree modeling. Application was conducted with OPR-1000 internal event Level 1 and 2 PSA models. A significant increase was found in containment intact probability, from 37 to 70%. Significant decrease in NPP risk found, by 44% compared to the case without SAMG. Abstract: 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.