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机器学习加速模拟用于设计被静止障碍物限制的安全气囊
José E Valenzuela Del Río1, Richard Lancashire2, Karan Chatrath2
1Siemens Technology.
Stapp car crash journal
|March 21, 2024
概括
新的机器学习 (ML) 加速模型大大加快了安全气囊部署几何预测. 这些模型将物理与数据驱动技术结合在一起,实现了显著的加快速度,同时保持了车辆安全设计的准确性.
科学领域:
- 计算力学是计算力学.
- 汽车安全工程 汽车安全工程
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确的安全气囊部署几何预测对于满足汽车安全标准至关重要.
- 目前的模拟方法,如有限元分析 (FEA) 和计算流体动力学 (CFD),在计算上昂贵,限制了设计空间探索.
- 复杂性源于安全气囊部署模拟中固有的多学科相互作用.
研究的目的:
- 开发参数模拟模型,加速安全气囊部署几何预测.
- 为了保持预测准确性,同时显著减少模拟时间.
- 为了能够更快,更彻底地探索安全气囊设计空间.
主要方法:
- 提出了新的机器学习 (ML) 加速模型,将物理系统模式与数据驱动技术集成在一起.
- 开发了安全气囊和冲击器参数的参数模型.
- 使用越来越复杂的虚拟测试案例评估模型,包括不同的障碍类型和多达六个设计变量.
- 记录了ML培训时间,以进行完整的预算评估.
主要成果:
- 用ML加速的模型实现了比高保真多物理方法快三倍的预测速度.
- 在经过测试的设计空间内保持对安全气囊部署几何的合理准确性.
- 在一系列虚拟测试案例中证明了有效性,从简单到复杂的场景.
结论:
- 机器学习加速模型为快速准确的安全气囊部署几何预测提供了可行的解决方案.
- 这些模型显著降低了计算成本,使工程师能够更有效地设计太空探索.
- 该方法成功地平衡了计算速度与汽车安全应用的可接受准确性.
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