Application of feature matching trajectory detection algorithm for particle streak velocimetry

Abstract

We detect the trajectory of particles using the feature matching method to improve the resolution of particle streak velocimetry (PSV), which is used to measure the velocity of particles from a visualized path line. PSV has a more reliable performance in particle matching as compared to particle tracking velocimetry and is therefore less likely to cause erroneous matching even in high-density images. The center of gravity of the first and last trajectories is obtained to calculate the displacement. The trajectory of the particle is illuminated using a diode laser and imaged using a digital single-lens reflex camera; the trajectory is then divided into three parts and recorded in a single frame using coded illumination. The first and second trajectories are short, and the third trajectory is long. The asymmetry of the trajectories is then used to determine the flow direction. We first evaluate the detection rate by increasing the trajectory density of synthetic images. The image size was fixed at 500 × 500 pixels, and the number of trajectories was increased from 28 to 280, and the detection rate was examined. Then, we evaluated the accuracy of detection of the center of gravity of the first and last trajectories using the root mean square error. Finally, we used the coded illumination method to visualize the swirling flow inside a device to examine its applicability to real flows.