基于机器学习的新型多胞梯度结构设计与优化OA
Design and optimization of corrugated multi-cell gradient structures based on machine learning
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),其吸收能量(Ea)、比吸能(Esa)、平均压缩力(¯F)及压缩效率(η)分别提升 390%、76%、395%和 46%;相较于多胞六边形管(MHT),上述指标分别提升 121%、58%、121%和 97%;相较于波纹多胞六边形管(CMHT),Ea、Esa、¯F、η分别提升 7%、7%、8%、33%,且初始峰值压缩力(Fmax)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,通过全因子试验设计生成 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.优化结果表明,相较于未经过参数优化的CMGHT基础模型(参数基于工程常用范围初步设定:肋板厚度1 mm、肋板幅值3 mm、外管梯度厚度0.5 mm-1 mm-1.5 mm、外管长度33.3 mm),优化后结构的Esa提高 22%,η提升 53%,-F增强 270%,进一步验证了设计方法的有效性.
To address the collision protection requirements in fields such as aeronautics and space,traffic transportation,and civil construction,a novel design method for the corrugated multi-cell gradient hexagonal tube(CMGHT)was proposed.The sinusoidal corrugated ribs were introduced into a conventional hexagonal tube,integrated with the functional gradient design concept to improve the energy absorption performance of the structure.First,the finite element 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(Esa),mean crushing force(¯F),and crushing force efficiency(η)are improved by 390%,76%,395%,and 46%,respectively;Compared with the multi-cell hexagonal tube(MHT),the aforementioned indicators are increased by 121%,58%,121%,and 97%,respectively;Relative to a corrugated multi-cell hexagonal tube(CMHT),the enhancements are 7%,7%,8%,and 33%respectively,while the initial peak crushing force(Fmax)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 the accurate prediction of the crashworthiness indicators for CMGHT.Finally,the multi-objective coati optimization algorithm(MOCOA)was adopted for multi-objective optimization to obtain the optimal combination of characteristic parameters.The optimization results show that compared with the CMGHT basic model without parameter optimization(the parameters are initially set based on the common range of engineering:rib thickness of 1 mm,rib amplitude of 3 mm,outer tube gradient thickness of 0.5 mm-1 mm-1.5 mm,outer tube length of 33.3 mm),the Esa of the optimized structure is increased by 22%,the η is increased by 53%,and the ¯F is increased by 270%,which further verifies the effectiveness of the design method.
闫凯波;周鹏;陆思思;王俊杰;范志伟
重庆交通大学机电与车辆工程学院,重庆 400074||重庆嘉陵特种装备有限公司,重庆 400032重庆交通大学机电与车辆工程学院,重庆 400074重庆交通大学机电与车辆工程学院,重庆 400074||重庆嘉陵特种装备有限公司,重庆 400032重庆交通大学机电与车辆工程学院,重庆 400074重庆交通大学机电与车辆工程学院,重庆 400074
数理科学
多胞梯度结构有限元分析机器学习多目标优化
multi-cell gradient structuresfinite element simulationmachine learningmulti-objective optimization
《爆炸与冲击》 2026 (6)
197-212,16
国家自然科学基金(52402466)中国博土后科学基金(2022M723001,2022M713014)重庆市自然科学基金(CSTB2025NSCQ-GPX0885)重庆市技术创新与应用发展专项重点项目(CSTB2024TIAD-KPX0081)重庆市博土后研究项目(2022CQBSHTB2020)重庆市教育委员会科学技术研究项目(KJZD-K202400704,KJQN202400718)重庆交通大学研究生科研创新项目(2025S0051)
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