A new concept genetic algorithm (GA) has been implemented and tested for the use in the 2-D and 3-D Particle Tracking Velocimetry (PTV). The algorithm is applicable to particle images with larger (greater than 2000) number of particles without losing the excellent accuracy in the particle matching results. This is mainly due to a new fitness function as well as unique genetic operations devised especially for the purpose of particle matching problem. The new fitness function is based on the relaxation of movement of a group of particles and is particularly suited for an increased density of particle images. The unique genetic operations give rise to the concentration of more fit genes in the forward part of the gene strings where the crossover and mutation processes are suppressed. The new algorithm also profits from the new genetic encoding scheme which can deal with the loss-of-pair particles (i.e., those particles which exist in one frame but do not have their matching pair in the other frame), a typical problem in the real image particle tracking velocimetry. In the present study, the new method is tested with 2-D and 3-D synthetic as well as real particle images with a large number of particles.

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Abstract:The genetic algorithm (GA) based stereo particle-pairing algorithm has been developed and applied to the spatial particle-pairing problem of the stereoscopic three-dimensional (3-D) PTV system. In this 3-D PTV system, particles viewed by two (or more than two) stereoscopic cameras with a parallax have to be correctly paired at every synchronized time step. This is important because the 3-D coordinates of individual particles cannot be computed without the knowledge of the correct stereo correspondence of the particles. In the present study, the GA algorithm is applied to the epipolar line proximity analysis for establishing correspondence of particles pairs between two co-instantaneous stereoscopic particles images, in order to compute the 3-D coordinates of every individual particle. The results are tested with various standard images and it's found that the new strategy using GA works better than conventional particle pairing methods of 3-D particle tracking velocimetry for steoroscopic PTV.

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