Estimation of the likelihood of severe accident management decision-making using a fuzzy logic model. (1st September 2020)

• Record Type: Journal Article
• Authors: Suh, Young A; Kim, Jaewhan
• Is Part Of: Annals of nuclear energy. Volume 144(2020)
• Publication Date: 2020-09-01
• Subjects: Human reliability analysis (HRA) -- Decision-making -- Severe accident management (SAM) -- Severe accident management guideline (SAMG) -- Fuzzy logic model; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2020.107581
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: We investigate the severe accident management guideline (SAMG) decision-making from an HRA perspective. The fuzzy logic model (FLM) is used for quantifying the likelihood of SAMG decision-making. A range of probabilistic value is suggested for each of Fuzzy linguistic terms. Abstract: This paper investigates a reasonable fuzzy logic model (FLM) for quantifying the likelihood of decision-making actions to be used in the human reliability analysis (HRA) of actions required for Level 2 probabilistic safety assessment (PSA). The reliability or likelihood of a decision while following a severe accident management guideline (SAMG) is a critical part of HRA for Level 2 PSA. As FLMs are resourceful tools for solving HRA problems having knowledge uncertainty, a FLM approach for quantifying the likelihood of SAMG decision-making is proposed. The relationships between input parameters and output are discussed as quantitative findings, which show that the proposed FLM can obtain practical and quantified values of decision actions with consideration of uncertainties. A case study applying the FLM to a total loss of component cooling water accident is compared with the results of expert judgement, which demonstrates that the developed FLM is feasible to quantify SAMG decision actions. Ultimately, this study implies that FLM can be applied into evaluations of human decision likelihood in overall HRAs as well as SAMG HRA.