Development of the Unified Bayesian Inference method for improving the pressurized water reactor simulation. (15th June 2022)

• Record Type: Journal Article
• Authors: Wu, Qu; Peng, Xingjie; Rao, Junjie; Yu, Yingrui; Li, Qing
• Is Part Of: Annals of nuclear energy. Volume 171(2022)
• Publication Date: 2022-06-15
• Subjects: The pressurized water reactor simulation -- Bayesian inference -- Sensitivity and uncertainty analysis -- Nuclear data -- Monte Carlo; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2022.109019
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: The Unified Bayesian Inference method is used to improve the PWR simulation. Sensitivity analysis for the Hainan nuclear power plant is performed by using RMC. The accuracy and precision of the PWR simulation are greatly improved. Abstract: Nuclear data is an important input in nuclear power plant core simulation. With the development of advanced simulation methods and the gradual improvement of computing power, the measurement uncertainty of nuclear data becomes the main source of the uncertainty of core physical response. In this paper, firstly, the sensitivity and uncertainty analysis of the nuclear data for the first cycle core loading of Unit 1 of the Hainan nuclear power plant are performed by using the Reactor Monte Carlo (RMC) code. The results show that the uncertainty of the eigenvalue k eff introduced by the nuclear data is 519 pcm, and the uncertainty of critical boron concentration is 57 ppm. Secondly, the UBI method based on Bayesian inference is proposed in the actual simulation of nuclear power plants. By combining the measurement results of core critical boron concentration with the calculation results of the NESTOR software package, the multi-group nuclear data library used by the NESTOR software package is adjusted, and the posterior estimation of the nuclear data and calculated values is obtained. The results show that the accuracy and precision of the first cycle simulation of Unit 1&2 are greatly improved.