Integrating cross-correlation and relaxation algorithms for particle tracking velocimetry

Abstract

An integrated cross-correlation/relaxation algorithm for particle tracking velocimetry is presented. The aim of this integration is to provide a flexible methodology able to analyze images with different seeding and flow conditions. The method is based on the improvement of the individual performance of both matching methods by combining their characteristics in a two-stage process. Analogous to the hybrid particle image velocimetry method, the combined algorithm starts with a solution obtained by the cross-correlation algorithm, which is further refined by the application of the relaxation algorithm in the zones where the cross-correlation method shows low reliability. The performance of the three algorithms, crosscorrelation, relaxation method and the integrated crosscorrelation/ relaxation algorithm, is compared and analyzed using synthetic and large-scale experimental images. The results show that in case of high velocity gradients and heterogeneous seeding, the integrated algorithm improves the overall performance of the individual algorithms on which it is based, in terms of number of valid recovered vectors, with a lower sensitivity to the individual control parameters.