Particle Tracking Velocimetry in Noisy Environment

Abstract

The encoded Particle Tracking Velocimetry (ePTV) is introduce in this paper as a specific approach of Particle Tracking Velocimetry (PTV). This method is applied to track particles obtained from flow images that contain significant background noise and relatively low particle density. Encoding is achieved by illuminating the flow with a series of light pulses within individual image exposures. Dependent upon the velocity, each particle will be illuminated multiple times in each image frame with spacing determined by both the pulse train timing and the particle velocity. A search algorithm is used that identifies each particle and seeks the encoded pattern with other particles in the image, repeating this until all encoded particles are found. Based on probability analysis and finite image size an analytic model is developed to determine the ratio of true particles, false particles and those that are ‘lost’ by exiting the image frame. This ePTV technique has been experimentally implemented to track spherical particles suspended in stationary vortices. By using a suspension of micro-particles, subsequent imaging with encoded pulse trains provided snap-shots of the complex flow patterns. Typically, even after filtering, the images show around 100 to 200 particles from which encoded trajectories have been extracted and typically account for about 30% of the objects identified in the image.