Achyut Sapkota

Particle Tracking Velocimetry Using Cellular Neural Network

Recent advances in digital image processing techniques, electronic and optical hardware have facilitated the investigation of fluid mechanics among the others. Flow visualization is the indispensable tool in the investigation of complex flow structures. The present study is focused in the development of an algorithm for particle tracking velocimetry (PTV), a powerful flow visualization tool, using cellular neural network. Significant improvement in computation time has been achieved with the proposed algorithm citing comfortable way to the use of neural network in flow visualization which was previously seemed tedious due to long computation time.

Detection of PIV outliers using rule-based fuzzy logic

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.

Fuzzy logic algorithm for PTV data validation

The fuzzy logic algorithm has been used for the validation strategy of PTV results. The authors already applied the fuzzy logic model as a particle tracking algorithm with successful results. In this study the basic concept of the fuzzy PTV model seemed no less suitable for the validation strategy of other types of PTV results. The noteworthy point of the fuzzy particle tracking algorithm is the confidence of two adjacent velocity vectors. In the fuzzy PTV this confidence value is updated at each stage of the confidence level estimation between any two adjacent candidate velocity vectors. Similarly the confidence level estimation could be done between any two adjacent velocity vectors resulting from other PTV algorithms. This is the base line of the present new validation strategy.