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Free keywords:
particle tracking, fluid dynamics experiment, Lagrangian statistics, data processing
Abstract:
Lagrangian particle-tracking algorithms are susceptible to intermittent loss of particle images on the sensors. The measured trajectories are often interrupted into short segments and the long-time Lagrangian statistics are difficult to obtain. We present an algorithm to connect the segments of Lagrangian trajectories from common particle-tracking algorithms. Our algorithm tracks trajectory segments in the six-dimensional position and velocity space. We describe the approach to determine parameters in the algorithm and demonstrate the validity of the algorithm with data from numerical simulations and the improvement of long-time Lagrangian statistics on experimental data. The algorithm has important applications in measurements with high particle seeding density and in obtaining multi-particle Lagrangian statistics.
