基于FOSS的方法用于薄壁结构的变形感知和形状重建
Huifeng Wu1,2, Rui Dong3, Qiwei Xu1
1School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China.
Micromachines
|July 8, 2023
概括
这项研究引入了一种结合光纤传感器系统 (FOSS) 和机器学习的新方法,以准确地重建柔性薄壁结构的变形. 该方法增强了飞机机翼和太阳能电池板等应用程序的实时监控能力.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 灵活的薄壁结构在航空航天和可再生能源方面至关重要.
- 准确的变形感知和形状重建对于它们的性能和安全至关重要.
- 现有的方法在精度和实时监控方面经常面临挑战.
研究的目的:
- 开发一种准确的方法来对柔性薄壁结构的变形感知和形状重建.
- 将光纤传感器系统 (FOSS) 与机器学习技术相结合.
- 为实时监控和分析提供一种新方法.
主要方法:
- 使用ANSYS有限元分析进行应变测量和变形数据收集.
- 采用一类支向量机 (OCSVM) 模型来识别和删除异常值.
- 开发了一个神经网络模型,以建立应变值和变形变量 (x,y,z轴) 之间的映射关系.
主要成果:
- 提出的方法实现了2.01% (x轴),29.49% (y轴) 和15.52% (z轴) 的最大误差.
- 尽管在小变形时,y和z方向的错误较大,但重建的形状与实际变形状态保持良好的一致性.
- 在变形分析中,FOSS和机器学习的整合被证明是有效的.
结论:
- 结合FOSS和机器学习方法,为灵活的薄壁结构的实时监控和形状重建提供了高度准确的解决方案.
- 这种方法为分析诸如翅膀,直升机叶片和太阳能电池板等结构提供了宝贵的工具.
- 这项研究表明了先进的计算和传感技术在结构健康监测中的潜力.
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