摘要:
针对航空航天、交通运输、土木建筑等领域的碰撞防护需求，提出一种新型多胞梯度结构管（CMGHT）设计方法：在普通六边形管内引入正弦波纹肋板，并融合功能梯度设计理念，以实现结构耐撞性能的提升。首先，构建该结构的有限元（FE）模型并开展数值模拟分析。结果显示，在相同壁厚条件下，CMGHT的关键吸能指标表现显著优于现有结构：相较于普通六边形管（HT），其吸能量（EA）、比吸能（SEA）、平均压缩力（MCF）及压缩效率（CFE）分别提升395%、76%、45%和395%；相较于多胞六边形管（MHT），上述指标分别提升102%、57%、120%和48%；相较于波纹多胞六边形管（CMHT），EA、SEA、MCF、CFE分别提升8%、7%、8%、32%，且初始峰值压缩力（IPCF）降低18%，充分证明其吸能性能更优。随后，以肋板与外管的几何参数为设计变量，通过全因子实验设计生成540组样本，构建支持向量机（SVM）代理模型，并结合冠豪猪优化（CPO）算法完成模型优选，实现对CMGHT耐撞性能的精准预测。最后，采用多目标浣熊优化算法（MOCOA）进行多目标优化，获取最优特征参数组合。优化结果显示，相较于初始结构，优化后结构的SEA提高22%，CFE提升53%，MCF增强270%，进一步验证了设计方法的有效性。
Abstract:
To address the collision protection requirements in fields such as aerospace, transportation, and civil engineering, a novel design method for the corrugated multi-cell gradient hexagonal tube (CMGHT) is proposed: sinusoidal corrugated ribs are introduced into a conventional hexagonal tube, integrated with the functional gradient design concept to enhance the structural crashworthiness. First, the FE model of the structure was established and numerical simulation analysis was conducted. Results indicate that under the same wall thickness condition, the key energy absorption indicators of CMGHT outperform existing structures significantly. Compared with the hexagonal tube (HT), the energy absorption (EA), specific energy absorption (SEA), mean crushing force (MCF), and crushing force efficiency (CFE) are improved by 395%, 76%, 45%, and 395%, respectively; Compared with the multi-cell hexagonal tube (MHT), the aforementioned indicators are increased by 102%, 57%, 120%, and 48%, respectively; Relative to a corrugated multi-cell hexagonal tube (CMHT), the enhancements are 8%, 7%, 8%, and 32% respectively, while the initial peak crushing force (IPCF) is decreased by 18%. These results fully demonstrate its superior energy absorption performance. Subsequently, the geometric parameters of the ribs and outer tube were selected as design variables. A total of 540 sample sets were generated via full factorial experimental design, and a support vector machine (SVM) surrogate model was constructed. Combined with the crested porcupine optimization (CPO) algorithm, model optimization was completed to achieve accurate prediction of CMGHT’s crashworthiness. Finally, the multi-objective coati optimization algorithm (MOCOA) was adopted for multi-objective optimization to obtain the optimal combination of characteristic parameters. Optimization results show that compared with the initial structure, the SEA of the optimized structure is increased by 22%, the CFE by 53%, and the MCF by 270%, further verifying the effectiveness of the proposed design method.
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