Phase correlation processing for DPIV measurements

Abstract

A novel digital particle image velocimetry (DPIV) correlation method is presented, the Gaussian transformed phase correlation (GTPC) estimator, using nonlinear filtering techniques coupled with the phase-transform (PHAT) generalized cross-correlation filter. The use of spatial windowing is shown to be ideally suited for the use of phase correlation estimators, due to their invariance to the loss of correlation effects. Error analysis demonstrates the increased valid vector detection and measurement accuracy of the windowed GTPC over the traditional Fourier based estimator in a series of uniform displacement Monte Carlo simulations. Analysis of the GTPC performance in the PIV standard image sets shows error reductions on the order of 15–45% over the range of simulations. Experimental DPIV images from a turbulent rib roughened channel flow are used to validate the use of the GTPC, which shows a strong reduction in peak locking effects, background noise errors, and erroneous vectors. Together, these results demonstrate the coupled benefits provided by the use of advanced filtering techniques applied to the phase correlation estimator. With the correct implementation of these filters, the GTPC is able to provide substantial improvements to the robustness of DPIV estimation.