Abstract
Abstract Particle Image Velocimetry (PIV) is a non-intrusive measurement technique, which can be used to study the structure of various fluid flows. PIV is a very efficient measurement technique since it can obtain both qualitative and quantitative spatial information about the flow field being studied. This information can be further processed into information such as vorticity and pathlines. Other flow measurement techniques (Laser Doppler Velocimetry, Hot Wire Anemometry, etc..) only provide quantitative information at a single point. A study on the performance of the Sub-Grid Genetic Tracking Algorithm for use in Particle Image Velocimetry was performed. A comparison with other tracking routines as the Cross Correlation, Spring Model and Neural Network tracking techniques was conducted. All four algorithms were used to track the synthetic data, and the results are compared with those obtained from a Large Eddy Simulation computational fluid dynamics program. The simulated vectors were compared with the results from the four tracking techniques, to determine the yield and reliability of each tracking algorithm.
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