A hybrid particle tracking algorithm for characterizing particle motions in pneumatic dispersion

Accurate measurement of particle motion in pneumatic dispersion is essential, yet challenging, because current imaging techniques cannot clearly resolve individual particles within dense agglomerations. Anticipating future high-resolution imaging, this study proposes a hybrid particle tracking velocimetry (PTV) algorithm. The initial version, Hybrid1, integrates Voronoi diagrams, minimum enclosing ellipses, particle image velocimetry, and the new relaxation method. To better handle source-like flow fields, Hybrid2 was developed by introducing Hu moment shape descriptors, which significantly enhances particle matching accuracy. Because conventional experiments only yield macroscopic metrics such as dispersion radius, we utilized high-fidelity numerical simulations—validated against experimental data—to generate complete Lagrangian particle trajectories for rigorous algorithm testing. The results show that Hybrid2 achieves a matching rate exceeding 91% at a challenge index of CPTV ≈ 1.2, demonstrating excellent robustness and applicability. This research delivers a highly accurate, validated approach for tracking particles in heavily dispersed, high-concentration fields, effectively bridging the gap between current experimental limitations and future imaging capabilities.