Technical challenges in modeling human and organizational actions under severe accident conditions for Level 2 PSA

Jaewhan Kim and Jaehyun Cho

Reliability Engineering and System Safety, 2020, vol. 194, issue C

Abstract: After the Fukushima Daiichi accident, new safety measures and equipment, enhanced accident mitigation guidelines or procedures, and reinforced emergency response organizations have been established. The HRA technology for Level 2 PSA, which deals with probabilistic assessment of human and organizational actions under severe accident conditions, becomes more important because newly adopted systems and guidelines are targeted to reduce the likelihood of severe accident phenomena. Under this context, this paper provides a preliminary process as a case study to identify candidate accident management actions (CAMAs) based on SAMG decision-making process under specific plant damage states from Level 2 PSA, and illustrate how those CAMAs could affect the major parameters associated with Level 2 accident progression and phenomena. From the case study, it was identified that the availability and level of composition of emergency staff as well as the availability of means to implement a specific strategy and evaluation of their positive and negative consequences, are considered to be important contributors to the decision-making of the given strategy. Apart from the emphasis on adequate modeling of the decision-making process, this paper presents several important technical elements and challenges that need to be resolved for an improved Level 2 HRA method and application.

Keywords: Level 2 HRA; Accident management; Severe accident management guidelines; Candidate accident management action; Plant damage state; Containment event tree (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:194:y:2020:i:c:s0951832017310839

DOI: 10.1016/j.ress.2018.08.003

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