A Neural Network Based Algorithm for Particle Pairing Problem of PIV Measurements

Abstract

SUMMARYParticle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time. key words: flow measurement, particle image velocimetry, particle tracking velocimery, neural network, optimization

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“…The particle velocities u p were measured by cross correlation algorithm of PIV technique. [29][30][31] The particle images in the flow field are taken sequentially at the rate of 60 frames per second by the CCD camera, the shutter speed is 1/1000 s, the time-mean velocity vectors u p were obtained by processing the 20 successive particle images. 30 Resolution of each image was 1024 Â 1024 pixels.…”