摘要
The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.
基金
supported by the National Natural Science Foundation of China(Grant No.52271277)
the Natural Science Foundation of Jiangsu Province(Grant.No.BK20211343)
the State Key Laboratory of Ocean Engineering(Shanghai Jiao Tong University)(Grant.No.GKZD010081)
Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant.No.SJCX22_1906).