Particle Tracking Velocimetry using the genetic algorithm

Abstract

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.

Relaxation algorithm-based PTV with dual calculation method and its application in addressing particle saltation

“…The test is conducted using synthetic particle images of PIV Standard Image project (Okamoto et al 2000a, b), which has been extensively used to verify PTV/PIV algorithms (Brevis et al 2011;Cardwell et al 2011;Mikheev and Zubtsov 2008;Ohmi and Li 2000;Ohmi and Panday 2009;Ohmi et al 2010). Three sets of 2D image series, numbered as 01, 04 and 23, are used here.…”

Stereoscopic Correspondence of Particles for 3-Dimensional Particle Tracking Velocimetry by using Genetic Algorithm

“…Of these two processes of particle pairing, for the temporal particle pairing almost the entire 2-D particle tracking algorithms are applicable without any additional complexity [7,9,15,24]. Two of the present authors have even successfully applied the GA to the temporal particle pairing in their previous article [16]. However, the spatial process of particle pairing always possesses some challenges when 3-D particle coordinates must be calculated with accuracy and with high recovery ratio.…”

A Neural Network Based Algorithm for Particle Pairing Problem of PIV Measurements

“…g (i, j) stands for the Euclid distance between the two particles i and j in the first frame and similarly g (k, l) stands for the Euclid distance between the particles k and l in the second frame, where particle k and particle l are the matched partners of particle i and particle j respectively. This condition addresses the assumptions made in binaryimage cross-correlation methods [4] and in relaxation based method [14]- [19] with enough flexibility, and in a global manner. The second and the third conditions are, in some way, involved in the idea of fuzzy logic based tracking [5]- [11].…”

“…The relaxation algorithms [14]- [19] based on the probability of particle matching [20] seem promising in comparison to the above methods. In the relaxation algorithm, every first-frame particle selects its candidate partners from the second frame using a certain distance threshold.…”