预测子布的频率响应和研究高阶微分衍生粘弹性模型
Bao Chen1,2, Lunyang Chen3, Feng Zhou2
1Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing, 400054, China.
Scientific reports
|July 9, 2024
概括
这项研究优化了使用先进的人工智能和微积分计算的布模型,以获得更好的车辆动态. 新的分数导数模型显著减少了拟合误差,提高了模拟准确性.
科学领域:
- 机械工程 机械工程
- 汽车工程 汽车工程
- 计算力学 计算力学 计算力学
背景情况:
- 罩是车辆悬架系统的关键组件,影响行驶舒适度和操控.
- 精确的罩动态建模对于有效的车辆模拟和设计至关重要.
研究的目的:
- 开发和验证后部子框架套的先进动态模型.
- 为了提高预测精确度的布施行为跨越广泛的频率范围.
- 为了增强车辆动态建模和模拟能力.
主要方法:
- 利用粒子集群优化 (PSO) 在动态刚性测试数据上训练反向传播 (BP) 神经网络.
- 开发了一种新的动态模模型,其中包含了对粘弹性元素的高阶微分衍生物.
- 采用了增强的PSO算法,使用预测数据识别模型参数.
主要成果:
- 在1-50 Hz的频率范围内,BP神经网络实现了较低的测试误差 (1.03%-3.05%).
- 与传统模型相比,新型分数衍生模型显示了较少的拟合误差 (3.61%-5.49%).
- 能够准确预测高达70 Hz的形行为.
结论:
- 拟议的分数导数动态模型为罩提供了卓越的准确性.
- 这项研究为先进的车辆动态建模和模拟提供了坚实的基础.
- 优化的人工智能和分数微积分的集成增强了机械元件行为的预测.
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