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. Firstly, separate particle reconstructions with a sparsity-based algorithm are performed on a fine grid. Secondly, 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). 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), while its root-mean-square error of velocity estimation is lower to that of state-of-the-art similar methods. Results from an experimental campaign on a transitional round air jet at Reynolds number 4600 are also presented. During the tests, seeding density varies from 0.06 to 0.03 ppp on average. Associated to an outlier rejection scheme based on temporal statistics, DF-TPTV vector fields truthfully correspond to the instantaneous jet dynamics. Quantitative performance assessment is 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, or even higher.

Main results of the first Lagrangian Particle Tracking Challenge

“…August 1-4, 2021 3.1.5 ONERA: Iterative double-frame tomographic PTV The algorithm used by ONERA for processing the TP data is an improved version of the Double Frame Tomographic PTV (DF-TPTV) algorithm from Cornic et al (2020). The DF-TPTV approach involves three stages.…”

“…The sparsity of the particles distribution in three-dimensional space has been exploited to enhance the reconstruction accuracy (Champagnat et al, 2014) as well as to increase the computational efficiency (Cornic et al, 2015). For double-frame recordings, Cornic et al (2020) recently introduced the double-frame tomographic PTV (DF-TPTV) approach that first uses voxel grids to find the possible position of particle candidates and obtain a coarse predictor of their displacements via a correlation analysis, and finally determines the individual particles' intensities and exact positions via a global optimisation procedure. Wieneke (2013) proposed an iterative particle reconstruction (IPR) algorithm where the distribution of the tracer particles in the volumetric measurement domain is represented from the beginning by three-dimensional positions and intensity values rather than by a voxel-based intensity distribution.…”

Advanced iterative particle reconstruction for Lagrangian particle tracking

“…The approach to tomographically reconstruct the volume in a socalled voxel-space (Atkinson and Soria 2009), followed by a 3D cross-correlation, was later extended by methods using the temporal information, like MTE (Novara et al 2010) or SMTE (Lynch and Scarano 2015). Hybrid approaches, combining aspects of tomographic and particle-based reconstruction, emerged (Schröder et al 2011;Novara and Scarano 2013;Cornic et al 2020).…”

Lagrangian Particle Tracking: a link between localization error and fraction of missed particles

“…Schanz et al (2016)) and double-frame data (then also referred to as 3D PTV, see e.g. Fuchs et al (2016), Yang et al (2019), Lasinger et al (2019), Cornic et al (2020)). Whereas 3D PIV, relying on cross-correlation, induces important spatial averaging, and thus smoothing of corresponding turbulent scales, residual error on LPT vectors is much smaller, of the order of a fraction of the particle image size.…”