1 February 2003 Robust genetic algorithm for high-accuracy particle position estimation in three-dimensional particle image velocimetry applications
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Optical Engineering, 42(2), (2003). doi:10.1117/1.1533038
Abstract
A method for three-dimensional particle position estimation employed in particle image velocimetry applications is presented. The method includes the application of a robust optimization process, which involves the use of a genetic algorithm. The algorithm derives particle position by pattern matching theoretical to experimental images, using the concept of image peak signal-to-noise ratio as the objective error measure for this comparison. To produce sufficiently accurate theoretical images comparable to experimental images for positioning purposes, it is found that a Lorenz-Mie treatment of the seeding scattering field was required, which also took into consideration the incident wavefront. The use of a genetic algorithm for positioning proved to be more accurate and faster than a Nelder-Mead algorithm combined with neural nets used previously. This method has also been shown to be an effective means of isolating contaminant particles in velocimetry images, which can substantially increase the overall error. We discuss some aspects of the theory regarding this method, illustrate the ideas with a simple experimental image, as well as detail our implementation of a pattern-matching approach combined with a genetic algorithm for positioning purposes.
P. Padilla Sosa, J. E. Valdez, L. R. Berriel, L. R. Sahagun Ortiz, Marcelo Funes-Gallanzi, "Robust genetic algorithm for high-accuracy particle position estimation in three-dimensional particle image velocimetry applications," Optical Engineering 42(2), (1 February 2003). https://doi.org/10.1117/1.1533038
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