Two-frame 3D particle tracking

Abstract

A whole-field three-dimensional (3D) particle tracking velocimetry (PTV) tool for diagnostics in fluid mechanics is presented. Specifically, it is demonstrated why and when PTV is the natural choice in 3D applications compared to particle image velocimetry (PIV). Three different tracking methods are investigated, namely the nearest neighbour, the neural network and the relaxation method. In order to demonstrate the use of PTV for 3D applications, the selected tracking schemes are implemented for use with the defocusing digital particle image velocimetry (DDPIV) technique. The performance of the tracking algorithms is evaluated based on synthetic 3D information. Furthermore, the potential benefit of a merging between the PIV and PTV approaches is explored within the DDPIV framework. The results show that the relaxation tracking method is the most robust and efficient, while the combined PIV/PTV analysis brings significant improvements solely with the neural network scheme. In terms of errors, PTV is found to be more sensitive to particle reconstruction errors than the DDPIV cross-correlation analysis.