Shake The Box: A highly efficient and accurate Tomographic Particle Tracking Velocimetry (TOMO-PTV) method using prediction of particle positions

Abstract

A novel approach to the evaluation of time resolved particle-based tomographic data is introduced. By seizing the time
\ninformation contained in such datasets, a very fast and accurate tracking of nearly all particles within the measurement domain is achieved at seeding densities comparable to (and probably above) the thresholds for tomographic PIV. The method relies on predicting the position of already tracked particles and refining the found position by an image matching scheme (‘shaking’ all particles within the measurement ‘box’ until they fit the images: ‘Shake The Box’ -
\nSTB). New particles entering the measurement domain are identified using triangulation on the residual images.
\nApplication of the method on a high-resolution time-resolved experimental dataset showed a reliable tracking of the
\nvast majority of available particles for long time-series with many particles being tracked for their whole length of stay
\nwithin the measurement domain. The image matching process ensures highly accurate particle positioning. Comparing
\nthe results to tomographic PIV evaluations by interpolating vector volumes from the discrete particles shows a high
\nconformity of the results. The availability of discrete track information additionally allows for Lagrangian evaluations
\nnot possible with PIV data, as well as easy temporal smoothing and a reliable determination of derivations.
\nThe processing time of a not fully optimized version of STB proved to be a factor of 3 to 4 faster compared to the
\nfastest methods available for TOMO-PIV.