Particle Cluster Tracking Algorithm in Particle Image Velocimetry

Abstract

The cross-correlation tracking technique is widely used to analyze image data, in particle image velocimetry(PIV). The technique assumes that the fluid motion, within small regions of the flow field, is parallel over short time intervals. However, actual flow fields may have some distorted motion, such as rotation, shear and expansion. Therefore, if the distortion of the flow field is not negligible, the fluid motion cannot be tracked well using the cross-correlation technique. The author proposed a new particle tracking technique, based on the particle cluster matching using linear affine transformation. The algorithm can be applied to flow fields which exhibit characteristics such as rotation, shear and expansion. The algorithm is based on pattern matching of particle clusters between the first and second image. The deformation of the cluster pattern is expressed by the linear affine transformation. The parameter of the transformation can be determined using the least squares technique from the particle positions. The residue of the particle deformation is taken as the pattern matching parameter. The smallest residue pattern in the second image is the most probable pattern match to the correspondent original pattern in the first image. Therefore by finding the best matches, particle movements can be tracked between the two images. The effectiveness of the proposed technique was verified with synthetic data of three-dimensional flow. It demonstrated a high degree of accuracy for the three-dimensional calculation.