A Multi-Parametric Particle Pairing Algorithm for Particle Tracking Velocimetry in Single and Multiphase Flows

The measurement of turbulent flows becomes problematic when considering a dispersed multiphase flow, which typically requires special techniques focusing on the simultaneous resolution of both the carrier and discrete phases present in the flowfield. The method presented in this paper, a multi-parametric particle pairing algorithm for particle tracking velocimetry (MP3-PTV), provides a powerful and flexible technique for the measurement of multiphase flows. Combined with a traditional Particle Image Velocimetry (PIV) system, the MP3-PTV employs a variable pair-matching algorithm which utilizes displacement preconditioning in combination with estimated particle size and intensity to match particle pairs between successive images. To improve the method’s efficiency, a new particle identification and segmentation routine was also developed. Validation of the new method was performed on two artificial data sets: a traditional single-phase flow published by the Visualization Society of Japan (VSJ) and an in-house generated multiphase flow having a bi-modal distribution of particles diameters. On the VSJ data set, the newly presented segmentation routine delivered a two-fold increase in identifying particles compared to other published methods. For the simulated multiphase flow data set, measurement efficiency of the dispersed phase improved from 9% to 41% for MP3-PTV as compared to traditional hybrid PTV.