Main results of the first Lagrangian Particle Tracking Challenge

Abstract

This work presents the main results of the first Lagrangian Particle Tracking challenge, conducted within the framework of the European Union’s Horizon 2020 project HOMER (Holistic Optical Metrology for Aero-Elastic Research), grant agreement number 769237. The challenge, jointly organised by the research groups of DLR, ONERA and TU Delft, considered a synthetic experiment reproducing the wall-bounded flow in the wake of a cylinder which was simulated by LES. The participants received the calibration images and sets of particle images acquired by four virtual cameras, and were asked to produce as output the particles positions, velocities and accelerations (when possible) at a specific time instant. Four different image acquisition strategies were addressed, namely two-pulse (TP), four-pulse (FP) and time-resolved (TR) acquisitions, each with varying tracer particle concentrations (or number of particles per pixel, ppp). The participants’ outputs were analysed in terms of percentages of correctly reconstructed particles, missed particles, ghost particles, correct tracks and wrong tracks, as well as in terms of position, velocity and acceleration errors, along with their distributions. The analysis of the results showed that the best-performing algorithms allow for a correct reconstruction of more than 99% of the tracer particles with positional errors below 0.1 pixels even at ppp values exceeding 0.15, whereas other algorithms are more prone to the presence of ghost particles already for ppp < 0.1. While the velocity errors remained contained within a small percentage of the bulk velocity, acceleration errors as large as 50% of the actual acceleration magnitude were retrieved.

A robust pairing method for two-pulse particle tracking velocimetry based on coherent point drift

“…The triangulation errors can be modelled by altering the ground truth particles positions with a Gaussian noise. The standard deviation of this Gaussian noise is set here to 2% of the average displacement of the particles, which would correspond to an error of 0.1 pixels for an average displacement of 5 pixels, consistently with observed errors in former benchmarks [35].…”

Thermographic 3D particle tracking velocimetry for turbulent gas flows

“…Increasing the particle seeding density beyond this level leads to lower numbers of usable particles due to increasing overlap of individual particle images. Implementing volume selfcalibration [38,39] and advanced iterative volume reconstruction algorithms [40][41][42][43] will allow us to process images at this seeding density, offering an approximated improvement in spatial measurement density by a factor of almost 4.5 compared to the current results. The use of a high-speed PTV sub-system can allow for time-resolved 3D-PTV [9,44], leading to more accurate particle position reconstructions, velocity measurements, and with the possibility of supplementary acceleration measurements.…”

3D Lagrangian Particle Tracking in Fluid Mechanics

“…Increased particle track densities in the measurement volume can be achieved with scanning 3D PTV techniques (Hoyer et al 2005, Kozul et al 2019, reaching higher ppv values for relatively low flow velocities. Nowadays, state-of-the-art 3D LPT techniques can reach high particle image densities of ∼0.05-0.2 ppp using the Shake-The-Box (STB) method (Schanz et al 2016, Jahn et al 2021, Leclaire et al 2021, Sciacchitano et al 2021b.…”

“…Introducing moderate image noise and particle intensity variations only slightly worsens these results, apart from the positional error that Helium-filled soap bubbles (HFSB): tracers, reflecting light, used for large-scale measurements in air; often used with LED illumination rises to ∼0.1 px. Heavy image noise and strong intensity variations inhibit the reconstruction of all true particles; however, ghost levels still remain low at moderate N I ( Jahn et al 2021, Sciacchitano et al 2021b).…”