Coupling Coherent Point Drift And Affine Least-Squares Transformation To Build Trajectories In TR-PTV

Abstract

The robustness and accuracy of state-of-the-art time-resolved Particle Tracking Velocimetry algorithms is strongly supported by the use of the temporal coherence of particle trajectories. However, to exploit this coherence, the particle needs to be tracked for a sufficiently long period. This condition cannot be achieved if the time of flight of the particles across the measurement volume is not long enough, for example because the limited illumination energy requires the thickness of the volume to be thin. The authors propose to address this difficulty by providing an algorithm based on the spatial coherence of the trajectories rather than the temporal one. This algorithm results from the coupling of two methods; the Coherent Point Drift (CPD), which has proven to be accurate in pairing particles from two instants, but with an insufficient recall for time-resolved application, and the Affine Least Square Transformation (ALST), which allows transporting particles that were missed by the CPD. This algorithm is integrated into a global process that includes an Iterative Particle Reconstruction (IPR) algorithm to extract a list of particles from the images. First tests performed on a synthetic test case consisting of images of particles advected by a turbulent flow highlighted the potential of the method for tracking short tracks. With six frames, the recall is found to be 99.6% with the present method against 98.3% with the DaVis 10.2 implementation of ShakeThe-Box (STB). However, the error is much larger with a false positive rate of 5.1% with our method but only 0.2% with (STB). The cause of this high error rate is analysed and it is speculated that it is partly explained by the tendency of the IPR to detect the same real particle multiple times, which is a concern that has to be addressed in future work.