Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Direct fault-tree modeling of human failure event dependency in probabilistic safety assessment

  • Ji Suk Kim (Department of Energy Systems Engineering, Chung-Ang University) ;
  • Sang Hoon Han (Risk and Reliability Research Team, Korea Atomic Energy Research Institute) ;
  • Man Cheol Kim (Department of Energy Systems Engineering, Chung-Ang University)
  • Received : 2022.04.13
  • Accepted : 2022.08.27
  • Published : 2023.01.25
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Abstract

Among the various elements of probabilistic safety assessment (PSA), human failure events (HFEs) and their dependencies are major contributors to the quantification of risk of a nuclear power plant. Currently, the dependency among HFEs is reflected using a post-processing method in PSA, wherein several drawbacks, such as limited propagation of minimal cutsets through the fault tree and improper truncation of minimal cutsets exist. In this paper, we propose a method to model the HFE dependency directly in a fault tree using the if-then-else logic. The proposed method proved to be equivalent to the conventional post-processing method while addressing the drawbacks of the latter. We also developed a software tool to facilitate the implementation of the proposed method considering the need for modeling the dependency between multiple HFEs. We applied the proposed method to a specific case to demonstrate the drawbacks of the conventional post-processing method and the advantages of the proposed method. When applied appropriately under specific conditions, the direct fault-tree modeling of HFE dependency enhances the accuracy of the risk quantification and facilitates the analysis of minimal cutsets.

Keywords

Acknowledgement

This work was supported by the Nuclear Safety Research Program of the Korea Foundation of Nuclear Safety funded by the Korean government's Nuclear Safety and Security Commission [grant number 2203027-0122-CG100]; and the Nuclear Research & Development Program of the National Research Foundation of Korea funded by the Korean government's Ministry of Science and ICT [grant number NRF-2017M2B2B1071973].

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