Particle Tracking Method of Fluid Flow Fields by Genetic Algorithms

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A combination of flow visualization and digital image processing is one of great advantageous method for understanding phenomena of fluid flow. Many methods of particle image velocimetry have proposed and developed in accordance with the advancement of computer science and technologies. An important factor for measuring velocity distribution of flow is not only the fluid flow visualization but also an analysis of flow images created by illuminated particles. It is also a subject of study attracting the interest of researchers. From such a viewpoint, the authors have investigated the particle tracking method that the one-to-one correspondence of illuminated particles is found by processing two sequential flow images. This paper proposes the new particle tracking method by taking advantages of combinatorial optimization process using the genetic algorithm which is a method of sequential improvement for comginatorial optimization derived from a theory of biological evolution. Since the concept of particle tracking becomes to a combinatorics for identifying particles between two sequential flow images, the authors make use of the genetic algorithm for the combinatorics. The method has been investigated to search out the identical particles between two sequential flow images. The particle identification is realized by optimizing the evolutive gene strings which are encoding the particle combinations. The method is particularly effective in the tracking for a large number of particles by one image processing at one image channel.
東海大学の論文

Particle Tracking Method of Fluid Flow Fields by Genetic Algorithms

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