主动学习基自适应 PC-Kriging 模型的复合材料结构可靠度算法

To address the complex，high dimensional，highly nonlinear，and long computing time-consuming problems of random natural frequency reliability analysis of composite wings，a reliability algorithm based on active learning basis-adaptive PC-Kriging model is proposed in this paper. A basis-adaptive strategy is used in this model to determine the orthogonal polynomial basis of the polynomial chaos expansion to approximate the global response of the numerical model，and Kriging is used for higher-order nonlinear interpolation to approximate the local response of the numerical model. In the framework of active learning reliability calculation，weighted K mean clustering is introduced，which means that K candidate sample points with greater contribution to failure probability are added in one iteration to reduce the number of iterations and accelerate the convergence rate. The effectiveness and accuracy of the proposed method are proved by a highly nonlinear numerical example. The proposed method is applied to the random natural frequency reliability analysis of composite plate and composite wing，and the accurate and efficient reliability calculation results are obtained.