摘要
提出一种新颖的圆形多胞复合填充结构,该结构采用蜂窝和泡沫两类材料的交错复合填充。采用实验验证与数值研究相结合的方法,系统地研究了蜂窝和泡沫材料在全填充、部分填充及交互填充结构中的耐撞性。研究结果表明,针对单一材料填充的多胞圆管,部分填充结构比全填充结构具有更好的耐撞性能,其中,环形蜂窝填充结构(H40)和中心泡沫填充结构(F01)具有更优异的能量吸收特性。针对双材料复合填充的多胞圆管,则是中心泡沫填充与环形蜂窝填充的复合结构(F01H40)具有最佳的耐撞吸能性。最后,进一步结合Kriging近似技术与粒子群数值优化方法,对复合填充结构进行多目标优化设计,探索其最优耐撞性与最优参数匹配。结果表明,环形蜂窝部分填充结构(H40)、中心泡沫填充与环形蜂窝填充的复合全填充结构(F01H40)具有最优的耐撞性能。
A novel circular multi-cell composite filling structure with honeycomb and foam materials was proposed.Based on the methods of experiment validation and numerical simulation,the crashworthiness of honeycomb and foam materials in full filling,partly filling and interaction filling structures were investigated.The results show that the partial filling structures have better crashworthiness performance than the full filling structures for multi-cell circular tubes with single filling material,in which the annular honeycomb filling structure（H40）and the center foam filling structure（F01）have more excellent crashworthiness performance.The center foam filling and annular honeycomb filling compound structure（F01 H40）has the best crashworthiness performance for the composite filling tube with double filling materials.Lastly,the Kriging approximation technique and the Particle Swarm Optimization（PSO）algorithm were employed to explore the optimal crashworthiness performance and match the optimal parameters.The results show that partial filling the annular honeycomb partially filling structure（H40）and the center foam filling and annular honeycomb compound full filling structure（F01 H40）yield the optimal crashworthiness performance.
作者
闫晓刚
张勇
林继铭
徐翔
赖雄鸣
YAN Xiaogang;ZHANG Yong;LIN Jiming;XU Xiang;LAI Xiongming(College of Mechanical Engineering and Automation,Huaqiao University,Xiamen 361021,China)
出处
《复合材料学报》
EI
CAS
CSCD
北大核心
2018年第8期2166-2176,共11页
Acta Materiae Compositae Sinica
基金
国家自然科学基金(51675190
51305143)
福建省自然科学基金(2015J01204)
福建省高等学校新世纪优秀人才计划
华侨大学研究生科研创新能力培育计划资助项目
关键词
薄壁结构
复合填充结构
耐撞性
多孔材料
多目标优化
thin-walled structures
composite filling structures
crashworthiness
porous material
multi-objectiveoptimization
