Main results of the first Lagrangian Particle Tracking Challenge


  • Andrea Sciacchitano Delft University of Technology, Netherlands
  • Benjamin Leclaire ONERA, France
  • Andreas Schroeder DLR, Germany



Lagrangian particle tracking, LPT challenge, methods assessment


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.

Author Biography

Andrea Sciacchitano, Delft University of Technology, Netherlands

Dr. Sciacchitano is Assistant Professor at Delft University of Technology, the Netherlands. His areas of expertise include the development and application of optical techniques for large-scale flow diagnostics.






3D Methods and Applications