Realistic estimation of human error probability through Monte Carlo thermal-hydraulic simulation

Jaehyun Cho, Yochan Kim, Jaewhan Kim, Jinkyun Park and Dong-San Kim

Reliability Engineering and System Safety, 2020, vol. 193, issue C

Abstract: Human reliability analysis (HRA) plays an important role in probabilistic safety assessment because human error probability (HEP) strongly influences not only core damage frequency in the nuclear field, for example, but also risk insight across many industries. Conventional HRA methods though use thermal-hydraulic analysis with best-estimate or conservative conditions to estimate the available time for a given operator action, thus significantly overestimating HEP. In response, this paper suggests a new method to more precisely quantify HEP using realistic thermal-hydraulic analyses with exhaustive conditions. The developed method was applied to the rapid cooldown operation in a small loss-of-coolant accident in the APR1400 nuclear power plant. As a result, it was found that conventional HRA methods currently overestimate the risk-significance of this rapid cooldown operation by a factor of six. It is expected that the new HRA method can be applied to not only the nuclear field but also other complex systems to accurately estimate system failure probabilities for specific scenarios by means of more realistic HEP.

Keywords: Probabilistic safety assessment; Monte Carlo simulation; Thermal-hydraulics; Human reliability analysis; Human error probability (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:193:y:2020:i:c:s095183201930359x

DOI: 10.1016/j.ress.2019.106673

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