A parametric study of 3D PTV algorithms based on a two-view collimated imaging model

Abstract

Volumetric Lagrangian measurements of droplet or turbulent flow using particle tracking methods have attracted intensive attention recently. The performance of three-dimensional particle tracking velocimetry (3D PTV) is highly reliant on the algorithms. Most existing 3D PTV algorithms are developed for multi-view systems, which cannot be applied directly to two-view systems due to the insufficient geometry constraints. In the current study, three different 3D PTV algorithms applicable for two-view systems are investigated parametrically using synthetic data. The imaging model is established on a two-view collimated shadowgraph imaging setup, which features a high framing rate, large test volume and long depth focus. The performances of the three algorithms are tested under different image particle densities and displacement–spacing ratios. The correctness of 3D reconstruction and tracking, as well as the number of ghost particles, are obtained and compared comprehensively. The results indicate that significant improvement is achieved through the dedicated designed algorithms. The comparative study reveals the potential of each algorithm with extremely limited geometry constraints in two-view systems, which may serve as guidance for choosing appropriate algorithms under different test conditions.