A digital image processing algorithm was developed to identify flow patterns in high speed imaging. This numerical tool allows to quantify the fluid dynamic features in compressible flows of relevance in aerospace and space related applications. This technique was demonstrated in a harsh environment with poor image quality and illumination fluctuations. This original pattern recognition tool is based on image binarization and object identification. The geometrical properties of the detected elements are obtained by measuring the characteristics of each object in the binary image. In case of multiple shock waves or shock bifurcations, a “decision-making” algorithm chooses the best shock-wave path, based on the original image intensity and local pattern orientation. The algorithm was successfully used for validation on numerical Schlieren images, where the shock-wave fluctuation was triggered by vortex shedding. The applicability of the algorithm was finally evaluated in two Schlieren imaging studies: at the trailing edge of supersonic airfoils and for hypersonic research. The program correctly identified the fuzzy flow features present in all applications. 
A decision-making algorithm for automatic flow pattern identification in high-speed imaging