Convolutional Neural Networks for Image Analysis of High-Speed Videos from Two Slab Burners

Abstract

High-speed video recordings of slab burner experiments were analyzed using a machine learning approach with convolutional neural networks in order to com- pute the regression rate of hybrid rocket fuels over time. Combustion tests of paraffin-based fuel grains performed in two different hybrid rocket slab burners were recorded with high-speed video cameras and the resulting image data are analyzed in order to determine the height of the fuel in each frame. To this end, a deep neural network with U-net architecture is trained in a supervised fashion to segment the shape of the fuel slab. It is demonstrated that this approach is more capable to segment combustion images in unsteady flow conditions than classical computer vision methods based on thresholding or edge detection. Furthermore, methods in the area of uncertainty quantification of neural networks are applied to estimate the errors in the neural network prediction to new previously unseen data. Finally, the regression rate of the fuel is computed as the rate of change of this height. The method allows to automatically analyze a large amount of video data, and takes full advantage of the optical access capabilities of slab burners. More precisely, the method delivers not only the time and space average values of the fuel regression rate, but also quantifies its variation over time and over the length of the slab, providing deeper insights into the combustion mechanics of hybrid rockets.