Abstract
Cross-section shape and thickness distribution are essential to the dynamic crashing behaviors of thin-walled tubes. Therefore, this paper designs a novel functional gradient tube with three variable wall thicknesses along the axial direction. The best regular hexagon (RH) cross-section shape is predetermined by comparing with different cross-section tubes. On this basis, six circular fillets are added to each corner of the RH cross-section tube to reduce its stress concentration. Furthermore, to obtain the surrogate models more accurately and effectively, a hybrid TSSA–GRNN method is proposed by combing the adaptive t-distribution sparrow search algorithm (TSSA) and the generalized regression neural network (GRNN). Multi-objective optimization of the variable gradient tube is conducted by integrating the hybrid TSSA–GRNN method and the non-dominated sorting genetic algorithm (NSGA-II). The results show that the energy absorption, crashworthiness, and lightweight of the optimal variable gradient regular hexagon (VG-RH) tube are better than those obtained by the initial counterpart. The VG-RH tube can be recommended as a good absorber in engineering applications.
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Acknowledgements
This work was supported by the Natural Science Foundation of Jiangsu Province (Grant BK20180482) and the Fundamental Research Funds for Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX19_0158).
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The results are obtained by integrating CATIA, LS-DYNA, MATLAB, and ISIGHT. And the corresponding data are presented in the text. Moreover, the other non-confidential data relevant to the present study are available upon request.
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Wang, W., Dai, S., Zhao, W. et al. Optimal design of variable gradient tube under axial dynamic crushing based on hybrid TSSA–GRNN method. Struct Multidisc Optim 65, 11 (2022). https://doi.org/10.1007/s00158-021-03105-9
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DOI: https://doi.org/10.1007/s00158-021-03105-9