Bayesian analysis method on processing reliability data of high flux engineering test reactor

Hui Bao, Yun Guo, Hang Zhang, Changhong Peng and Jianchao Lu

Reliability Engineering and System Safety, 2020, vol. 199, issue C

Abstract: Probabilistic safety analysis (PSA) is widely used for safety evaluation of research reactors at present. Reliability data analysis is a major foundation for conducting research reactor quantitative safety analysis. The components of research reactors usually have high reliability and reliability data recording of Chinese research reactors lacks standardization, which lead to the insufficiency of reliability data and the inapplicability of the classical method to reliability data evaluation. Bayesian analysis is a useful approach to compensate the shortage through comprehensive utilization of all kinds of information. In this paper, the Bayesian Analysis Method on Processing Reliability Data of High Flux Engineering Test Reactor (HFETR) is introduced. The existing reliability data of the similar research reactor are elected as the prior distribution, which are combined with the reliability data of HFETR to calculate the posterior distribution applying Bayesian Analysis Method. The analysis results show that Bayesian analysis is efficient for expanding reliability data sample space of HFETR. According to the results, it can be found that: (1) the mean (ME) of the posterior distribution is between the mean of prior data and the mean of sample data. (2) the error factor (EF) of posterior data is smaller than either the prior data or the sample data, which indicate a lower uncertainty of posterior data. (3) the information with smaller factor (EF) has more influence on the characteristics of posterior distribution

Keywords: Research reactor; Reliability data; Bayesian analysis (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:199:y:2020:i:c:s0951832019301589

DOI: 10.1016/j.ress.2020.106912

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