Image Processing and Edge Detection Techniques to Quantify Shock Wave Dynamics Experiments

Abstract

Experimental studies of multiple shock wave interaction to study transition from regular to irregular reflection rely on the processing of a large amount of schlieren photographs. Here we present an automated algorithm to track individual shock fronts and triple points. First, correction to any optical distortions is applied to the photographs. Next, noise removal and edge detection algorithms are implemented to extract the pixel locations of the shocks. The edge detection algorithm takes advantage of the light intensity feature of the shock waves to distinguish shock fronts from background noise. This algorithm is also capable of separating entangled shock fronts through pattern recognition, which utilizes a discretization method to reduce complex shock geometries to localized linear patterns. Collectively, the algorithms can track shock wave characteristics to sub-pixel precision. This algorithm has been deployed for post processing of shock wave experiments to extract shock wave characteristics including positions and propagation velocities of shock fronts, vertical and horizontal velocities of Mach stems, and triple point trajectories during shock-shock interactions. Results show that the algorithm can process large volumes of data with minimal manual operations, making image processing more precise, efficient and productive while allowing for tracking of Mach stems and triple points.