Abstract
The self-organizing maps (SOM) neural network is applied to the particle matching algorithm of the 3D particle tracking velocimetry (PTV). In the particle tracking velocimetry, the matching result of particles between two time-differential image frames is directly related to the velocity of particles, i.e., the velocity of the fluid flow in which the particles are suspended. The new particle matching method is basically based on the SOM model by Labonté [G. Labonté, A new neural network for particle tracking velocimetry, Experiments in Fluids 26-4 (1999) 340–346] but has been improved in many aspects for more reliable matching at larger numbers of distributed particles, larger dynamic range of velocity and more robustness against loss-of-pair particles between two image frames. In addition the new method is now applied to 3D particle flows for the use in 3D particle tracking velocimetry. In the present study, the new method is tested with 2D and 3D synthetic particle images as well as with 2D experimental images with a large number of particles.
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