Measuring dynamic deformation of a rotating blade by point cloud matching algorithm

In this paper, a point cloud matching algorithm is proposed for the scenario of measuring the dynamic deformation field of a rotating blade. Unlike conventional scheme based on Digital Image Correlation (DIC), the essence of this scheme is to reconstruct individual 3D feature points, instead of a group of feature points in a finite-size subset, on the measurement surface and then track their relative offset from reference baseline. The principles of 3D feature-point reconstruction and straddle-frame tracking are similar to those being used in the algorithm of Particle Tracking Velocimetry (PTV); therefore, no additional tags on feature points to facilitate the perspective matching and tracking is needed. Experiment test shows that this scheme works well in the scenario of high feature-point density (0.026 points per pixel), which cannot be achieved by existing feature-point-tagging methods. Both the rigid motion of the rotating blade and its dynamic deformation field can be reliably obtained. Comparing to DIC-based scheme, the benefit of the present proposed scheme includes the improvement of spatial resolution, the insensitivity to feature-point density, as well as the avoidance of outliers due to insufficient texture information.