摘要
为了更深入地了解设计参数对混合薄壁管性能的影响,从混合结构中组合件的吸能分担角度出发,采用ABAQUS有限元软件建立有限元模型,通过验证的有限元模型分析各材料成分在变形模式和能量吸收上的差异,研究混合管的耦合增能效应。考虑轻量化与材料成本,在保证吸能量的同时,选取缠绕层数为6层的混合管作为优化对象,选取比吸能和压溃力峰值作为优化目标函数,以铝管厚度和碳纤维增强复合材料(CFRP)缠绕角度作为优化变量,采用改进的非支配排序遗传算法(NSGA-Ⅱ)在约束范围内确定最优铝管厚度和铺层角度,所得到的混合管优化结果在满足压溃力峰值限制的情况下具有最佳的吸能特性。
To further understand the influence of design parameters on the performance of composite thin-walled tubes,from the perspective of the energy absorption sharing of the components in composite structure,finite element model was established using ABAQUS finite element software,and the difference of deformation mode and energy absorption of each material composition was analyzed by the validated finite element model,and the coupling energizing effect of composite tube was studied.Considering lightweight and material cost,while ensuring energy absorption amount,a composite tube with six winding layers was selected as the optimization object,and the specific energy absorption(SEA)and peak crushing force(PCF)were selected as the optimization objective function,and the almuminum tube thickness and winding angle of carbon fibre-reinforced polymer(CFRP)were selected as the optimization variables.The optimal aluminum tube thickness and ply angle were determined within the constraints range usign the improved non-dominated sorting genetic algorithm(NSGA-Ⅱ),and the obtained optimization results of composite tube have the best energy absorption characteristic under the condition that the peak crushing force limit is satisfied.
作者
马其华
董帆
甘学辉
MA Qi-hua;DONG Fan;GAN Xue-hui(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;State Key Laboratory for Modification of Chemical Fibers and Polymer,Donghua University,Shanghai 201620,China)
出处
《塑性工程学报》
CAS
CSCD
北大核心
2021年第9期117-128,共12页
Journal of Plasticity Engineering
基金
中央高校基本科研业务费专项资金资助项目(2232018A3-02)
纤维材料改性国家重点实验室开放课题(KF1826)。
关键词
Al-CFRP混合管
耦合增能效应
吸能特性
多目标优化
Al-CFRP composite tubes
coupling energizing effect
energy absorption characteristics
multi-objective optimization