Vision-based correspondence using relaxation algorithms for particle tracking velocimetry

Abstract

A new particle tracking algorithm is derived based on consolidated methods, with the aim of overcoming the current limits encountered with high particle density flows. The proposed method consists of an integration of the relaxation algorithm based on matching probabilities into vision-based features association concepts. Hybridization with PIV pre-analysis is suggested to help with the estimation of parameters. A dual calculation strategy is also developed in order to reduce the amount of spurious vectors. Simulation tests using synthetically generated images are carried out to evaluate the sensitivity of the proposed method to the particle image density, the background noise and the nature of the flow. Three flow configurations with a growing degree of complexity are successively considered: a 2D flow over a moving wall, a steady 2D Lamb–Oseen vortex ring, and a 3D unsteady homogeneous isotropic turbulence. The ability of the new tracking algorithm to provide better matching performances with high reliability than conventional techniques, out of a dense particle image field, is demonstrated.