主动学习基自适应PC⁃Kriging模型的复合材料结构可靠度算法
收稿日期: 2023-05-09
修回日期: 2023-06-12
录用日期: 2023-06-14
网络出版日期: 2023-06-16
基金资助
国家重点研发计划(2021YFB1715000)
Reliability algorithm of composite structure based on active learning basis-adaptive PC-Kriging model
Received date: 2023-05-09
Revised date: 2023-06-12
Accepted date: 2023-06-14
Online published: 2023-06-16
Supported by
National Key R & D Program of China(2021YFB1715000)
针对复合材料机翼随机固有频率可靠性分析复杂、高维、高度非线性和计算时间长的问题，本文提出了一种主动学习基自适应PC-Kriging模型的可靠度算法。在基自适应PC-Kriging模型中，采用一种基自适应策略来确定多项式混沌展开部分的正交多项式基，以近似数值模型的全局响应，Kriging用于高阶非线性插值以近似数值模型的局部响应。在主动学习可靠度计算框架中，引入加权
龚煜廉 , 张建国 , 吴志刚 , 褚光远 , 范晓铎 , 黄赢 . 主动学习基自适应PC⁃Kriging模型的复合材料结构可靠度算法[J]. 航空学报, 2024 , 45(8) : 228982 -228982 . DOI: 10.7527/S1000-6893.2023.28982
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.
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