摘要
针对非能动系统功能故障概率评估,提出一种新的自适应重要抽样方法。这种方法先对失效域进行预抽样,然后拟合出失效域中样本分布的密度函数,以之作为重要抽样密度函数。以1000 MW非能动先进压水堆(AP1000)非能动余热排出系统为研究对象,考虑模型和输入参数的不确定性,将响应面法和自适应重要抽样法相结合,对其进行功能故障概率评估。结果表明:与传统的概率评估方法相比,自适应重要抽样法具有较高的计算效率,同时又能保证很高的计算精度。
In order to estimate the functional failure probability of passive systems,an innovative adap-tive importance sampling methodology is presented.In the proposed methodology,information of variables is extracted with some pre-sampling of points in the failure region.An important sampling density is then con-structed from the sample distribution in the failure region.Taking the AP1000 passive residual heat removal system as an example,the uncertainties related to the model of a passive system and the numerical values of its input parameters are considered in this paper.And then the probability of functional failure is estimated with the combination of the response surface method and adaptive importance sampling method.The numeri-cal results demonstrate the high computed efficiency and excellent computed accuracy of the methodology compared with traditional probability analysis methods.
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2012年第2期30-36,共7页
Nuclear Power Engineering
基金
国家自然科学基金资助项目(61174110)
关键词
功能故障
概率安全评估
非能动系统
自适应重要抽样法
Functional failure
Probabilistic safety assessment
Passive system
Adaptive importance sampling method
