ABSTRACT
Human reliability analysis (HRA) for severe accidents is an important component of a Level 2 probabilistic risk assessment (PRA) for nuclear power plants. In this study, a Bayesian network (BN) is used to construct a qualitative causal model of human error during severe accidents by identifying the main cognitive functions (MCFs), crew failure modes (CFMs), and performance influencing factors (PIFs) for emergency personnel implementing severe accident management guidelines (SAMGs) after core damage. This qualitative model is used to develop a quantitative HRA method for severe accidents in nuclear power plants by improving the definition of performance-shaping factors (PSFs), levels, and multiplier criteria of the standardized plant analysis risk human reliability analysis (SPAR-H) method and applying BNs to quantify the failure probability of human performance. The usability of the proposed method is verified using a case study of emergency personnel following SAMGs to depressurize a reactor cooling system.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Grant Nos.71371070,71771084), Hunan Provincial Innovation Foundation For Postgraduate (No.CX20200908), The Provincial Characteristic Application Discipline of Safety Science and Engineering of Hunan Institute of Technology (KFB20020).
Disclosure statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.