文献类型：期刊文献

英文题名：Application of physics-informed machine learning methods in buckling design of axially compressed cylindrical shells

作者：Liu, Fang[1];Chen, Haofeng[2];Yang, Jie[3];Wang, Xiaoxiao[2]

机构：[1]Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai 200093, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety, Shanghai 200237, Peoples R China;[3]Univ Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China

年份：2024

卷号：200

外文期刊名：THIN-WALLED STRUCTURES

收录：;EI(收录号：20242016087592);WOS:【SCI-EXPANDED(收录号:WOS:001241286900001)】；

基金：The authors gratefully acknowledge the supports from the National Natural Science Foundation of China (52305159, 52150710540 and 52375145) and the National Key R & D Program of China (2023YFF0614903) .

语种：英文

外文关键词：Physics-informed model; Artificial neural network; Cylindrical shells; Buckling design; Local reduced stiffness method

摘要：A physics-informed artificial neural network (ANN) is developed for the buckling design of cylindrical shells under axial compression, and two strategies are applied to incorporate physical knowledge into the ANN model. One strategy is to introduce physics-informed features derived from the local reduced stiffness method (LRSM) as additional input features. The other strategy is to incorporate physical constraints based on elastic buckling theory into the loss function of ANN. The accuracy of the proposed model is verified using a buckling dataset of metal and non-metal cylindrical shells. Results demonstrate that the proposed physics-informed ANN model with four hidden layers achieves better predictive performance than pure data-driven random forest (RF), support vector machine (SVM) and ANN. Furthermore, the physics-informed model could predict moderately conservative buckling loads with a design factor of 1.25.