Volumetric particle tracking velocimetry with improved algorithms using a two-view shadowgraph system

Determining the time-resolved three-dimensional (3D) three-component (3C) velocity is essential for complex turbulent flow measurements. The current study is an extension of a recently developed temporal-spatial three-dimensional particle tracking velocimetry (TS 3D-PTV) technique established for two-view imaging systems. Two improvements have been embedded in TS 3D-PTV algorithm to improve the accuracy at high particle image densities (up to 0.03 ppp). One is using the neighboring particle information to correct the predicted positions and select the temporal particles with higher probability; the other is to iteratively optimize the 2D particle positions during the tracking process using the temporal and image information. The synthetic particle tests indicate that the correctness can be increased by 4.7%-5.8%, to reach a value about 92% with the improved algorithm around 0.03 ppp. The comparative results also indicate that using an advanced particle identification algorithm can improve the correctness over 20%. Two experiments, including a buoyancy jet in water and a transient droplet splashing process, have been conducted with a two-view shadowgraph imaging system. Different tracking algorithms have been conducted to determine the 3D trajectories of seeding particles or secondary droplets comparatively. The new algorithm has shown the best performance with much longer and more reliable trajectories, which indicates the tracking interruption caused by particle overlapping is reduced. The newly developed algorithms have further improved the performance under high seeding density conditions, which makes the two-view shadowgraph 3D PTV system adaptable to more experimental conditions.

Automated summary

Determining the time-resolved 3D-3C velocity is crucial for studying complex turbulent flows. This study extends a recently developed TS 3D-PTV technique for two-view imaging systems, and introduces two improvements to enhance accuracy at high particle image densities. Synthetic particle tests demonstrate the improved algorithm achieves a correctness increase of 4.7%-5.8% at 0.03 ppp. Comparative experiments confirm that the new algorithm outperforms others, providing longer and more reliable trajectories with reduced tracking interruptions caused by particle overlapping. These newly developed algorithms enhance the performance of the TS 3D-PTV system under high seeding density conditions, making it adaptable to various experimental scenarios.