Numerical and experimental comparison of complete three-dimensional particle tracking velocimetry algorithms for indoor airflow study. Issue 4 (April 2018)

• Record Type: Journal Article
• Authors: Fu, Sijie; Biwole, Pascal Henry; Mathis, Christian; Maïssa, Philippe
• Is Part Of: Indoor + built environment. Volume 27:Issue 4(2018)
• Page Start: 528
• Page End: 543
• Publication Date: 2018-04
• Subjects: 3D Particle tracking velocimetry -- Comparison -- Algorithm -- Tracking density; Indoor air pollution -- Periodicals; Sick building syndrome -- Periodicals; Buildings -- Environmental engineering -- Periodicals; Environmental health -- Periodicals; 613.5
• DOI: 10.1177/1420326X16682294
• Languages: English
• ISSNs: 1420-326X

Abstract

The experimental data retrieved from three-dimensional particle tracking velocimetry (3D PTV) are crucial for indoor environment engineering when designing ventilation strategies or monitoring airborne pollutants dispersion in inhabited spaces as well as in livestock compounds. The performance of the measurement algorithm, as the core part of the 3D PTV technology, has a strong influence on the final results. This study presents a method to select the better 3D PTV algorithm depending on the application targeted. This study first compared the performance of seven 3D PTV algorithms on two types of 3D laminar macro scale flows of known Navier–Stokes solutions, namely the Kovasznay and Beltrami flows. The comparison was based on the accuracy, the measuring volume coverage and the achievable trajectory length of each algorithm with respect to the particle tracking density and the total number of frames. Then, the measurement algorithms with better performances were further compared with the experimental measurement of real 3D airflows in a cubic cavity. The results suggest that different measurement algorithms have different advantages and drawbacks depending on the application targeted, and that the algorithms using the epipolar constraint as the method of spatial matching generally perform better.