小失效概率情况下的全局可靠性灵敏度分析的高效方法

发布时间：2018-01-03 04:38

本文关键词：小失效概率情况下的全局可靠性灵敏度分析的高效方法　出处：《航空学报》2016年09期 　论文类型：期刊论文

【摘要】：针对目前很多算法都无法准确、高效地计算小失效概率(10-4,甚至更小)情况下的全局可靠性灵敏度问题,本文提出了一种高效求解小失效概率情况下的全局可靠性灵敏度新算法。所提算法通过扩大标准差构造重要抽样密度函数来进行空间分割(SP),再与无迹变换(UT)结合,利用函数在分割后的子空间内非线性程度的降低和无迹变换方法可以高效计算低非线性程度函数的前二阶矩,来高效准确地计算小失效概率情况下的全局可靠性灵敏度。所提算法的优点有:重要抽样密度函数的选择可以使得空间分割时向重要区域偏移,并且在分割区域内功能函数的复杂性被降低,从而可以利用无迹变换方法高效计算失效概率,进而高效求得全局可靠性灵敏度。与已有的算法相比,算例说明了本文所提方法的优势。
[Abstract]:At present, many algorithms are unable to calculate the global reliability sensitivity problem efficiently and efficiently in the case of small failure probability of 10-4 or even less. In this paper, a new global reliability sensitivity algorithm is proposed to solve the problem of global reliability sensitivity in the case of small failure probability. The proposed algorithm constructs an important sampling density function by expanding the standard deviation for spatial segmentation. Combining with the unscented transform (UTT), the first order moment of the low nonlinear degree function can be calculated efficiently by using the method of reducing the degree of nonlinearity in the segmented subspace and using the method of unscented transformation. The advantages of the proposed algorithm are that the selection of the important sampling density function can make the space segmentation shift to the important region. And the complexity of the function function is reduced in the segmentation area, so the failure probability can be calculated efficiently by using the unscented transformation method, and the global reliability sensitivity can be obtained efficiently, compared with the existing algorithms. An example is given to illustrate the advantages of the proposed method.
【作者单位】：西北工业大学航空学院;
【基金】：国家自然科学基金(51475370) 中央高校基本科研业务费专项资金(3102015BJ(II)CG009)~~
【分类号】：TB114.3
【正文快照】： 网络出版地址:www.cnki.net/kcms/detail/11.1929.V.20160309.1447.006.html引用格式:柳诗雨,吕震宙,员婉莹,等.小失效概率情况下的全局可靠性灵敏度分析的高效方法[J].航空学报,2016,37(9):2766-2774.LIU S Y,LYU Z Z,YUN W Y,et al.Efficient method for global reliability

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