Application of a quantitative human performance model to the operational procedure design of a fuel storage pool cooling system

Marcos Coelho Maturana, Marcelo Ramos Martins and Paulo Fernando Ferreira Frutuoso e Melo

Reliability Engineering and System Safety, 2021, vol. 216, issue C

Abstract: Probabilistic safety analyses and Human Reliability Analysis have a potential contribution for designing new systems. For this, it is essential to develop models that are able to feed analyses at the design stage and to bring results that can be used in decision-making. This reinforces the need to develop quantitative techniques for performing human reliability still in the design phase of complex systems – a stage with little information available for Human Reliability Analysis. Currently, one can find publications of this kind for equipment reliability but not for human reliability. This paper presents a technique for human performance analysis that can be used in system design phase. This technique is based on the use of different information sources to obtain probability estimates of the various human error types that may occur during a specific action, and to estimate human error probabilities of generic actions. Thus, considering this model and a methodology for the early quantitative consideration of reliability in the design of complex systems, this paper presents the conception of an operational procedure for a Fuel Storage Pool Cooling System for cooling the spent fuel of a pressurized nuclear reactor, by configuring the human factors without changing the equipment preset for the system.

Keywords: Systems design; Human Reliability Analysis; Bayesian Networks; Technique for the Early Consideration of Human Reliability – TECHR; Probabilistic Safety Assessment; Human Performance Analysis (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004993

DOI: 10.1016/j.ress.2021.107989

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