一个基于机器学习的防撞性优化,用于在曲下使用一种新松式的多细胞管
Rui Liang1, Xuebang Tang2, Jie Huang3,4
1School of Automobile Engineering, Guilin University of Aerospace Technology, Guilin, 541004, China.
Heliyon
|September 26, 2024
概括
松灵感的多细胞管 (PCMTs) 显示了增强的防撞性. 机器学习优化显著提高了车辆安全应用的能量吸收和结构性能.
科学领域:
- 汽车工程 汽车工程
- 材料科学是一种材料科学.
- 计算力学是计算力学.
背景情况:
- 生物管在汽车工程中提供了卓越的防撞潜力.
- 以松为灵感的多细胞管 (PCMTs) 正在研究它们的结构性能.
研究的目的:
- 为了调查PCMT的防撞反应.
- 使用机器学习进行PCMT的多目标优化.
主要方法:
- 基础PCMT模型与现有实验的相关性.
- 对各种PCMT配置的动态响应评估.
- 使用机器学习算法构建代理模型.
- 使用非主导排序遗传算法II (NSGA-II) 的多目标优化.
主要成果:
- 厚度变化显著影响初始峰值力 (IPF) 和平均压碎力 (MCF).
- 最佳PCMT设计显示IPF (36.82%),MCF (61.66%) 和特定能量吸收 (SEA) (72.95%) 与总和情况相比大幅增加.
- 嵌入内管可以增强能量吸收,使IPF增加最小.
- 与原始管相比,最佳设计在MCF中实现了18.01%的差异,在SEA中达到5.91%.
结论:
- 作为车辆车身中有效的能量吸收结构,PCMT显示出显著的潜力.
- 机器学习驱动的优化对于最大限度地提高生物结构的防撞性至关重要.
- 该研究强调了生物灵感设计对先进汽车安全系统的好处.
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