Analysis, comparison and modification of various Particle Image Velocimetry (PIV) algorithms

A program based on particle tracking velocimetry (PTV) was developed in this work. The program was successfully validated by means of artificial images where parameters such as radius, concentration, and noise were varied in order to test their influence on the results. This program uses the mask cross correlation technique for particle centroid location. The sub-pixel accuracy is achieved using two different methods, the three point Gaussian interpolation method and the center of gravity method. The second method is only used if the first method fails. The object matching algorithm between frames uses cross correlation with a non binarized image. A performance comparison between different particle image velocimetry (PIV) and PTV algorithms was done using the international standard PIV challenge artificial images. The best performance was obtained by the program developed in this work. It showed the best accuracy, and the best spatial resolution by finding the larger number of correct vectors of all algorithm tested. A procedure is proposed to obtain error estimates for real images based on errors calculated with experimental ones. Using this procedure a real PIV image with 20% noise has an estimated average error of 0.1 pixel. Results of the analysis of 200 experimental images are shown for the two best PTV algorithms.