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

Abstract

The volumetric Lagrangian measurements of droplet or turbulent flow using particle tracking methods have attracted intensive attentions recently. The performance of three-dimensional particle tracking velocimetry (3D PTV) is highly relying on the algorithms. Most of the existing 3D PTV algorithms are developed for multi-view systems, which cannot be applied directly to two-view systems due to the lack of enough geometry constraints. In the current study, three different 3D PTV algorithms applicable for two-view systems have been investigated parametrically using synthetic data. The imaging model is established on a two-view collimated shadowgraph imaging setup, which features with high framing rate, large test volume and long depth focus. The performance of three algorithms has been 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 has been achieved through dedicated designed algorithms. The comparative study has revealed 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.

Neural-network-enhanced line-of-sight method for 3D particle cloud reconstruction in particle tracking velocimetry

“…This issue leads to the problem of selecting a proper threshold ε T d for the minimum distance ε d of the distance between two light rays. The smaller ε T d , the higher probability that one valid matching might be rejected; meanwhile, the larger ε T d , the higher probability that ghost particles might be reconstructed, the latter of which becomes a critical constraint in the case of high seeding density 6,18,19 . According to Jahn et al 11 , for a typical particle seeding density of n ppp =0.12, when ε T d =0.6 pixel, the ratio of potential ghost particles to true particles is 2.5, while the ghost-particle ratio increases to 5.0 when ε T d =0.8 pixel 11 .…”