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基于分离变量的地震PSA相关性分析方法研究
引用本文:冯丙辰,杨建峰,张晓明,王照.基于分离变量的地震PSA相关性分析方法研究[J].原子能科学技术,2021,55(3):488-493.
作者姓名:冯丙辰  杨建峰  张晓明  王照
作者单位:苏州热工研究院有限公司,广东 深圳518000
摘    要:为解决现有地震概率安全评价(PSA)相关性分析简化假设存在的问题,建立更准确反映核电厂构筑物、系统和部件(SSC)地震相关性的分析方法,对基于分离变量的易损度相关性分析开展了研究。结合易损度模型对分析方法进行了理论推导,并对方法的实施过程进行了介绍。利用该方法对不同条件下SSC的联合失效开展案例分析,得到了联合失效的易损度曲线和失效频率分析结果,并与现有相关性简化假设得到的结果进行了对比。研究结果表明,基于分离变量的地震PSA相关性分析方法能弥补现有方法的不足,支持核电厂地震PSA开发和应用。

关 键 词:概率安全评价    相关性    易损度    分离变量

Research on Correlation Analysis Method for Seismic PSA Based on Separation of Variables
FENG Bingchen,YANG Jianfeng,ZHANG Xiaoming,WANG Zhao.Research on Correlation Analysis Method for Seismic PSA Based on Separation of Variables[J].Atomic Energy Science and Technology,2021,55(3):488-493.
Authors:FENG Bingchen  YANG Jianfeng  ZHANG Xiaoming  WANG Zhao
Affiliation:Suzhou Nuclear Power Research Institute, Shenzhen 518000, China
Abstract:In order to solve the problems of existing simplified correlation analysis assumptions of seismic probabilistic safety assessment (PSA), and establish methods that more accurately reflect the seismic correlation of structures, systems, and components (SSC) of nuclear power plant, correlation analysis of fragility based on separation of variables was studied. Theoretical derivations of the method were carried out with the fragility model, and the implementation process of the method was introduced. Case studies on joint failure of SSCs under different conditions using proposed method were carried out, and the fragility curves and frequencies of joint failure were obtained, which were compared with the results obtained by the existing simplified methods. The results show that the seismic PSA correlation analysis method based on separation of variables can make up for the deficiencies of existing methods and support the development and application of seismic PSA for nuclear power plants.
Keywords:probabilistic safety assessment                                                                                                                        correlation                                                                                                                        fragility                                                                                                                        separation of variables
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