Double-frame tomographic PTV at high seeding densities

Abstract

A novel method performing 3D PTV from double-frame multi-camera images is introduced. Particle velocities are estimated by following three steps: First, separate particle reconstructions with a sparsity based algorithm are performed on a fine grid. Second, they are expanded on a coarser grid on which 3D correlation is performed, yielding a predictor displacement field that allows to efficiently match particles at the two time instants. As these particles are still located on a voxel grid, the third, final step achieves particle position refinement to their actual subvoxel position by a global optimization process, also accounting for their intensities. As it strongly leverages on principles from tomographic reconstruction, the technique is termed Double-Frame Tomo-PTV (DF-TPTV). Standard synthetic tests on a complex turbulent flow show that the method achieves high particle and vector detection efficiency, up to seeding densities of around 0.08 particles per pixel (ppp). On these tests, it also shows a higher robustness to noise and lower root-mean-square errors on velocity estimation than similar state-of-the-art methods. Results from an experimental campaign on a transitional round air jet at Reynolds number 4600 are also presented. Average seeding density varies in time from 0.06 to 0.03 ppp during the considered run, with different densities and signal-to-noise ratios being observed with time in the jet and ambient air regions, supplied by two different seeding systems. The strong polydisperse nature of the seeding, as well as the coexistence of two spatial zones of significantly different particle densities and signal-to-noise ratios, are observed to be the most influential sources of limitation for DF-TPTV performance. However, the method still successfully reconstructs a large amount of particles, and, associated with an outlier rejection scheme based on temporal statistics, truthfully reconstructs the instantaneous jet dynamics. Further quantitative performance assessment is then provided by introducing statistics performed by bin averaging, upon assuming statistical axisymmetry of the jet. Mean and fluctuating axial velocity components in the jet near-field are compared with reference results obtained from planar PIV at higher seeding density, with an interrogation window of size comparable to that of the bins. Results are found to be in excellent agreement with one another, confirming the high performance of DF-TPTV to yield reliable volumetric vector fields at seeding densities usually considered for tomographic PIV processing.