增强的预测建模框架用于乘用车后座的多目标全球优化,使用混合近似模型
Xuan Zhou1, Renjie Zou1, Xigui Xie2
1College of Mechanical and Electrical Engineering, Wenzhou University, Chashan Street, Ouhai District, Wenzhou, 325035, China.
Scientific reports
|February 12, 2025
概括
基于多种类近似模型 (HAM-MSAM) 的新混合近似模型改善了汽车座椅设计. 这种方法增强了全球多目标优化,使汽车座椅更轻,更经济,同时满足安全标准.
科学领域:
- 汽车工程 汽车工程
- 计算力学 计算力学 计算力学
- 优化理论 优化理论
背景情况:
- 单个近似模型在汽车座椅设计中与高度非线性数据作斗争.
- 精确的复杂特征数据的匹配对于有效的多目标优化至关重要.
- 现有的方法可能无法完全捕捉在座椅碰撞等动态条件下的非线性反应.
研究的目的:
- 提出基于多种类近似模型 (HAM-MSAM) 的混合近似模型,以获得高匹配精度.
- 引入基于HAM-MSAM的基于近似的全球多目标优化设计 (ABGMOOD) 战略.
- 为了优化乘用车后座的多目的设计.
主要方法:
- 使用实验验证的有限元模型和实验设计生成的训练集构建了HAM-MSAM.
- 采用基于HAM-MSAM的ABGMOOD战略来优化后座椅.
- 与现有的混合模型相比,验证了HAM-MSAM在碰撞条件下捕获非线性反应的能力.
- 将ABGMOOD的结果与经典的本地多目标优化策略进行比较.
主要成果:
- 与现有的方法相比,HAM-MSAM在非线性座椅碰撞数据上显示出更高的适配精度.
- 通过ABGMOOD战略,在经济性和后座减重方面取得了显著的改进.
- 优化后座满足了监管安全要求,尽管略低于本地优化方案.
- 优化的设计在安全性,经济性和减重方面取得了显著的改进.
结论:
- 基于HAM-MSAM的ABGMOOD策略对于汽车座椅多目标优化是可行的和有效的.
- 这种方法在经济和减重方面提供了实质性的优势.
- 优化的后座设计平衡了性能,安全和效率,为未来的工程挑战提供了宝贵的见解.
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