Particle Tracking Velocimetry (PTV) can visualize flow fields by reconstructing particle trajectories, but its performance degrades in environments with high particle concentration. In contrast, Particle Image Velocimetry (PIV) is effective in analyzing flows with dense particle population using continuous grayscale image data, yet it has difficulty in capturing the fine-scale local motions of individual particles.
To tackle the technical challenge of accurate Lagrangian reconstruction in flow fields with high particle concentration, this study presents a new hybrid algorithm, which integrates the PIV algorithm with a cluster-based PTV algorithm enhanced by Voronoi Diagram (VD) technology. Different from traditional hybrid methods, it innovatively realizes a Dual Fusion (DF) of PIV and PTV principles and a Dual Correction (DC) mechanism for optimizing particle matching results, thus named DF-DC. Through comparative analysis with existing algorithms using synthetic grayscale images, DF-DC exhibits stronger robustness: When the particle number reaches 30,000, DF-DC maintains an accuracy of over 80 %, exceeding SRPIV and MQ-PTV by 17 % and 47 %, respectively. Furthermore, under realistic measurements with a ghost particle ratio of 5 %, DF-DC sustains an accuracy above 90 %. Then the practical value of DF-DC is verified by successful applications in reconstructing particle motion in a fluidized bed and in the flow downstream of a heart valve, which affirms its real-world applicability.
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