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一个混合模拟-物理数据驱动的框架,用于预测车辆底部结构中乘客受伤的情况
Xinge Si1, Changan Di1, Peng Peng1
1Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
优化车辆底部结构的爆炸保护是通过一个新的框架得到了改进. 这种方法使用有限的物理测试和增强模拟来准确预测乘客受伤,降低成本.
科学领域:
- 工程 工程师 工程师 工程师
- 计算力学 计算力学 计算力学
- 车辆安全 车辆安全
背景情况:
- 优化车辆底部结构的爆炸保护面临的挑战是平衡物理测试的成本与模拟准确性的挑战.
- 由于爆炸冲击信号的复杂性和可用于增强的有限数据,模拟通常缺乏准确性.
研究的目的:
- 引入一个预测框架,将有限的物理测量与系统增强的模拟数据集成在一起.
- 为解决极小样本集的有限元模拟增强负载信号的困难.
主要方法:
- 在波形域中使用条件生成对抗网络 (CGAN) 的小规模数据增强模型.
- 引入由累积分布函数控制的实时扰动,用于数据多样化.
- 一个高斯过程回归 (GPR) 模型,集成物理数据和增强波段特征 (PCA减小波段尺度能量) 来估计损伤指数.
主要成果:
- 混合模型结合了物理数据和增强模拟,与单独的模拟相比,其准确性更高.
- 交叉验证证实了拟议框架的增强预测能力.
- 一个案例研究表明,增加船体的角度和深度可以有效地减少乘客的伤害.
结论:
- 开发的预测框架为优化车辆底部结构以应对爆炸威胁提供了具有成本效益和准确的解决方案.
- 该方法成功克服了复杂的爆炸冲击信号数据增强方面的局限性.
- 增加船体角度和深度等设计修改被验证为提高乘客安全的有效策略.
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