多胞薄壁铝合金方管载荷特性与多目标优化研究

Mechanical Performance and Multi-objective Optimization of Multi-cell Thin-walled Aluminum Alloy Square Tubes

中文关键词: 多胞薄壁方管  边长系数  响应面模型  非支配排序遗传算法(NSGA-Ⅱ)  多目标优化

英文关键词: multi-cell thin-walled square tube  coefficient of side  RSM  NSGA-Ⅱ  multi-objective optimization

中文摘要:

      目的 通过引入边长系数γ，得到构型不同的W型和C型多胞薄壁方管结构，改善多胞薄壁方管的吸能效率。方法 本文利用实验和数值方法对2种类型方管进行研究，材料选用6060T4铝合金，深入分析边长系数γ和壁厚t对结构的载荷特性、能量吸收及变形模式的影响。然后采用非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm, NSGA-Ⅱ)求解，针对结构的峰值载荷(Pc)和比吸能(Sa)进行多目标优化。结果 数值仿真所得载荷曲线与实验结果一致，折叠波数均为7个，边长系数γ对2类结构的载荷和变形有显著影响。结论 本文所得结果为改善多胞薄壁方管结构的缓冲吸能及冲击防护提供了数据支持。

英文摘要:

      The work aims to obtain W-type and C-type multi-cell thin-walled square tubes with different configurations through the introduction of side coefficient γ to improve the energy absorption efficiency of multi-cell thin-walled square tubes. Experimental and numerical methods were adopted to study two types of square tubes made of 6060T4 aluminum alloy. The effects of side coefficient γ and wall thickness t on the mechanical performance, energy absorption and deformation mode of the structure were analyzed in depth. Then, the Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) was used to analyze the thin-walled square tubes. Multi-objective optimization was carried out for the peak load (Pc) and specific energy absorption (Sa). The load curves obtained by numerical simulation were consistent with the experimental results. There were 7 folded waves. The side coefficient γ had a significant influence on the load and deformation of class 2 structures. The results obtained in this work provide data support for improving the buffering energy absorption and impact protection of thin-walled square tube structures.