Detection of PIV outliers using rule-based fuzzy logic

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Particle 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. However, such velocity information is always prone to outliers. The outliers degrade the quantitative information of the velocity field and gives misleading information of velocity based quantities like vorticity, streamlines, divergence etc. In this paper, a novel technique based on rule-based fuzzy logic has been proposed for the detection of such outliers. This technique overcomes the limitation of most of the detection techniques which are based on simple type of nearest neighborhood similarity constraint. The methodology is demonstrated to different PTV results.

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