玄武岩纤维增强复合材料(basalt fiber-reinforced polymer,BFRP)具有优异的力学性能和可熔融再生特性,在汽车轻量化领域的应用前景广阔。针对某车型的铝制防撞装置,开展BFRP/铝混合防撞装置的多目标优化设计。首先,对BFRP层合板开展力...玄武岩纤维增强复合材料(basalt fiber-reinforced polymer,BFRP)具有优异的力学性能和可熔融再生特性,在汽车轻量化领域的应用前景广阔。针对某车型的铝制防撞装置,开展BFRP/铝混合防撞装置的多目标优化设计。首先,对BFRP层合板开展力学性能测试,并利用HyperMesh软件建立防撞装置有限元模型;其次,采用拉丁超立方抽样生成代理模型的训练样本,结合敏感度分析识别关键设计参数,并通过基于加权欧式距离的空间填充采样法来提升代理模型对响应指标的预测精度;最后,以防撞装置峰值载荷、总质量及横梁最大位移最小为优化目标,运用MOPSO(multi-objective particle swarm optimization,多目标粒子群优化)算法求解Pareto前沿,并基于熵权-TOPSIS(technique for order preference by similarity to an ideal solution,逼近理想解排序法)确定最优设计参数组合。结果显示:优化后防撞装置的峰值载荷降低了36.15%,总质量减小了12.23%,显著提升了耐撞性能并实现了轻量化目标。所提出的方法可为BFRP/铝混合防撞装置的轻量化设计提供一套系统性的解决方案。展开更多
The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composit...The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composite designs for EVs.A comprehensive materials genome database was developed,encompassing composites based on carbon,glass,and natural fibers.This database systematically records critical parameters such as mechanical properties,density,cost,and environmental impact.Machine learning models,including Random Forest,Support Vector Machines,and Artificial Neural Networks,were employed to construct a predictive system for material performance.Subsequent material composition optimization was performed using amulti-objective genetic algorithm.Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45%weight reduction compared to conventional steel,while maintaining equivalent structural strength.The predictive accuracy of the models reached 94.2%.A cost-benefit analysis indicated that despite a 15%increase in material cost,the overall vehicle energy consumption decreased by 12%,leading to an 18%total cost saving over a five-year operational lifecycle,under a representative mid-size battery electric vehicle(BEV)operational scenario.展开更多
文摘玄武岩纤维增强复合材料(basalt fiber-reinforced polymer,BFRP)具有优异的力学性能和可熔融再生特性,在汽车轻量化领域的应用前景广阔。针对某车型的铝制防撞装置,开展BFRP/铝混合防撞装置的多目标优化设计。首先,对BFRP层合板开展力学性能测试,并利用HyperMesh软件建立防撞装置有限元模型;其次,采用拉丁超立方抽样生成代理模型的训练样本,结合敏感度分析识别关键设计参数,并通过基于加权欧式距离的空间填充采样法来提升代理模型对响应指标的预测精度;最后,以防撞装置峰值载荷、总质量及横梁最大位移最小为优化目标,运用MOPSO(multi-objective particle swarm optimization,多目标粒子群优化)算法求解Pareto前沿,并基于熵权-TOPSIS(technique for order preference by similarity to an ideal solution,逼近理想解排序法)确定最优设计参数组合。结果显示:优化后防撞装置的峰值载荷降低了36.15%,总质量减小了12.23%,显著提升了耐撞性能并实现了轻量化目标。所提出的方法可为BFRP/铝混合防撞装置的轻量化设计提供一套系统性的解决方案。
文摘The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composite designs for EVs.A comprehensive materials genome database was developed,encompassing composites based on carbon,glass,and natural fibers.This database systematically records critical parameters such as mechanical properties,density,cost,and environmental impact.Machine learning models,including Random Forest,Support Vector Machines,and Artificial Neural Networks,were employed to construct a predictive system for material performance.Subsequent material composition optimization was performed using amulti-objective genetic algorithm.Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45%weight reduction compared to conventional steel,while maintaining equivalent structural strength.The predictive accuracy of the models reached 94.2%.A cost-benefit analysis indicated that despite a 15%increase in material cost,the overall vehicle energy consumption decreased by 12%,leading to an 18%total cost saving over a five-year operational lifecycle,under a representative mid-size battery electric vehicle(BEV)operational scenario.
