A GENETIC ALGORITHM PARTICLE PAIRING TECHNIQUE FOR 3D VELOCITY-FIELD EXTRACTION IN HOLOGRAPHIC PARTICLE IMAGE VELOCIMETRY

This research explores a novel technique, using Genetic Algorithm Part icle Pairing (GAPP) to extract three-dimensional (3D) velocity fields of complex flows. It is motivated by Holographic Particle Image Veloci metry (HPIV), in which intrinsic speckle noise hinders the achievement of high particle density required for conventional correlation method s in extracting 3D velocity fields, especially in regions with large v elocity gradients. The GA particle pairing method maps particles recor ded at the first exposure to those at the second exposure in a 3D spac e, providing one velocity vector for each particle pair instead of see king statistical averaging. Hence, particle pairing can work with spar se seeding and complex 3D velocity fields. When dealing with a large n umber of particles from two instants, however, the accuracy of pairing results and processing speed become major concerns. Using GA's capabi lity to search a large solution space parallelly, our algorithm can ef ficiently find the best mapping scenarios among a large number of poss ible particle pairing schemes. During GA iterations, different pairing schemes or solutions are evaluated based on fluid dynamics. Two types of evaluation functions are proposed, tested, and embedded into the G A procedures. Hence, our Genetic Algorithm Particle Pairing (GAPP) tec hnique is characterized by robustness in velocity calculation, high sp atial resolution, good parallelism in handling large data sets, and hi gh processing speed on parallel architectures. It has been successfull y tested on a simple HPIV measurement of a real trapped vortex flow as well as a series of numerical experiments. In this paper, we introduc e the principle of GAFF, analyze its performance under different param eters, and evaluate its processing speed on different computer archite ctures.