基于GPC的环肋耐压圆柱壳结构失稳概率分析

为评估具有小失效概率特性的深潜环肋耐压圆柱壳结构的失稳概率，提出了一种新的基于高斯过程分类和重要抽样的自适应分析方法.该方法通过引入马尔科夫链蒙特卡洛法和欧式距离，开发了一种新的考虑预测不确定性和取样均匀性的自适应试验设计策略，以便更高效地构造高斯过程分类器；采用核密度估计构造准最优重要抽样密度函数；基于失效概率估计的稳定性，提出了一种更精确的迭代停止准则.通过某一分段函数验证了所提分析方法的准确性及高效性.应用所提方法得到某深潜环肋耐压圆柱壳结构的失稳概率约为8.242×10^-5.

Instability Probability Analysis of Ring Stiffened Pressure Cylindrical Shell Structures Based on GPC

To evaluate the instability probability of deep submergence ring stiffened pressure cylindrical shell structures with small failure probability， an innovative adaptive analysis method based on Gaussian process classification (GPC) and importance sampling (IS) was proposed. By introducing the Markov Chain Monte Carlo (MCMC) and the Euclidean distance， a new adaptive strategy for design of experiments (DoE)， considering the prediction uncertainty and the sampling uniformity， was developed to establish the Gaussian process classifier more efficiently. Furthermore， the quasi-optimal importance sampling density function was constructed by adopting the kernel density estimation (KDE). Based on the stability of failure probability estimation， a more accurate stopping criterion was also proposed. A piecewise function was utilized to verify the accuracy and efficiency of the proposed analysis method. The instability probability of a deep submergence ring stiffened pressure cylindrical shell structure obtained by the proposed method is about 8.242×10^-5.