基于三维热传导模型的风力机叶片缺陷深度检测

基于等效源法建立了含缺陷叶片三维热传导模型,解决了一维导热模型不能有效预测各向异性材料缺陷周围三维热流的问题,实现了用脉冲红外热像技术对大型风力机叶片缺陷深度进行准确评估。通过线性坐标变换将含缺陷叶片三维导热方程简化为各向同性问题,再用分离变量法求得第三类边界条件下的缺陷区表面过余温度解析解,建立了缺陷几何尺寸、过余温度峰值时刻与缺陷深度之间的定量关系。对1.5 MW风力机玻璃纤维增强复合材料(GFRP)复合材料叶片进行了现场检测,证明了方法的可行性。测试结果表明,量程可达7.8 mm,检测误差低于10%,相较于一维模型方法可使精度提高10%~31.4%。此外,当缺陷深度超过3 mm,边界换热不可被忽略,否则将导致10.0%以上的检测误差。文中方法可为其他各向异性材料的缺陷检测提供参考。

Wind turbine blade defect depth detection based on three-dimensional heat conduction model

A three-dimensional heat conduction model of wind turbine blade with defect is established based on equivalent source theory, which solves the problem that one-dimensional heat conduction model cannot effectively predict the three-dimensional heat flow around the anisotropic material defect, and the accurate evaluation of the defect depth of the large-scale wind turbine blade is realized with pulsed infrared thermal imaging technology. The three-dimensional heat conduction equation of the blade with defect is simplified to isotropic problem with linear coordinate transformation, and then the analytical solution of the surface excess temperature in the defect area under the third boundary condition is obtained using separating variable method. Finally, the quantitative relationship among geometry dimensions of the defect, the peak time of excess temperature and the depth of defect is established. The on-site detection of 1.5 MW wind turbine blade made of GFRP composite material was carried out, which proves the feasibility of the proposed method, and the detection results show that the detection range reaches 7.8 mm, the detection error is less than 10%, and the detection accuracy is improved by 10%~31.4% compared with the one-dimensional model method. In addition, when the defect depth exceeds 3 mm, the boundary heat transfer cannot be ignored, otherwise more than 10.0% detection error will be caused. The method proposed in this paper can provide a reference for the defect detection of other anisotropic materials.