SOM based Particle Matching for Volumetric Particle Tracking Velocimetry

Basanta Joshi, Kazuo Ohmi, and Kazuo Nose


Particle tracking velocimetry, Volumetric PTV, Tomographic reconstruction, Self organising map


Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two time differential samples are matched by using Self Organising Map (SOM) neural network scheme. SOM neural network tracking algorithm is highly adaptive to time differential tracking even with loss-of-pair particles. The particle location and velocity results of the present new approach turned out accurate, reliable and robust in comparison to the conventional 3D PTV approaches.

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