Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2017, Vol. 28 ›› Issue (7): 91 doi: 10.1007/s41365-017-0255-2

• NUCLEAR ENERGY SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Biasing transition rate method based on direct MC simulation for probabilistic safety assessment

Xiao-Lei Pan1, Jia-Qun Wang2, Run Yuan2, Fang Wang2, Han-Qing Lin2, Li-Qin Hu2, Jin Wang2   

  1. 1 University of Science and Technology of China, Hefei 230027, China
    2 Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academic of Sciences, Hefei 230031, China
  • Supported by:

    Supported by the Special Projects of International Thermonuclear Experimental Reactor (2015GB116000), the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA03040000), the Informatizational Special Projects of Chinese Academy of Sciences (No. XXH12504-1-09), the Major/Innovative
    Program of Development Foundation of Hefei Center for Physical Science and Technology (No. 2014FXCX004).

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Xiao-Lei Pan, Jia-Qun Wang, Run Yuan, Fang Wang, Han-Qing Lin, Li-Qin Hu, Jin Wang. Biasing transition rate method based on direct MC simulation for probabilistic safety assessment.Nuclear Science and Techniques, 2017, 28(7): 91     doi: 10.1007/s41365-017-0255-2

Abstract:

Direct Monte Carlo (MC) simulation is a powerful probabilistic safety assessment method for accounting dynamics of the system. But it is not efficient at simulating rare events. A biasing transition rate method based on direct MC simulation is proposed to solve the problem in this paper. This method biases transition rates of the components by adding virtual components to them in series to increase the occurrence probability of the rare event, hence the decrease in the variance of MC estimator. Several cases are used to benchmark this method. The results show that the method is effective at modeling system failure and is more efficient at collecting evidence of rare events than the direct MC simulation. The performance is greatly improved by the biasing transition rate method.

Key words: Direct Monte Carlo simulation, Probabilistic safety assessment, Biasing transition rate method