Particle Pairing Using Genetic Algorithms for PIV

Authors:
I. Kimura;A. Hattori;M. Ueda
Affiliations:
Osaka Electro-Communication University, 18-8, Hatsu-cho, Neyagawa, Osaka 572-8530, Japan. e-mail: ichiro@kimlab.osakac.ac.jp;Osaka Electro-Communication University, 18-8, Hatsu-cho, Neyagawa, Osaka 572-8530, Japan. e-mail: ichiro@kimlab.osakac.ac.jp;Osaka Electro-Communication University, 18-8, Hatsu-cho, Neyagawa, Osaka 572-8530, Japan. e-mail: ichiro@kimlab.osakac.ac.jp
Venue:
Journal of Visualization - International Conference on Optical Technology and Image Processing in Fluid, Thermal, and Combustion Flow, Yokohama, Japan, December 1998
Year:
1999

Citing 1
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

An improved ant colony optimization based particle matching algorithm for time-differential pairing in particle tracking velocimetry

ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing

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Abstract

This paper presents a new particle pairing algorithm using "Genetic Algorithms" for DPIV (Digital Particle Image Velocimetry), which are searching algorithms for obtaining an optimal solution based on the mechanism of evolution. The particle pairing between two tracer images with a constant time interval is needed to obtain a velocity vector field. Since the algorithm adopts a fitness function which totally evaluates the similarity between respective small particle patterns in the two tracer images over the field, it promises to give a more correct velocity vector distribution than the conventional PTV (Particle Tracking Velocimetry) which identifies each particle based on its local information. In addition, a particle pattern matching for the similarity is performed after correcting fluid rotation. It therefore is robust against a high particle density and an increase in the time interval. The algorithm is applied to the PIV standard images distributed through the Internet (http://www.vsj.or.jp/piv). It gives a correct velocity vector distribution as a result even if a pair of the successive images has a large time interval.