  This study presents an imaging-based deep learning tool to measure the fuel
regression rate in a 2D slab burner experiment for hybrid rocket fuels. The
slab burner experiment is designed to verify mechanistic models of reacting
boundary layer combustion in hybrid rockets by the measurement of fuel
regression rates. A DSLR camera with a high intensity flash is used to capture
images throughout the burn and the images are then used to find the fuel
boundary to calculate the regression rate. A U-net convolutional neural network
architecture is explored to segment the fuel from the experimental images. A
Monte-Carlo Dropout process is used to quantify the regression rate uncertainty
produced from the network. The U-net computed regression rates are compared
with values from other techniques from literature and show error less than 10%.
An oxidizer flux dependency study is performed and shows the U-net predictions
of regression rates are accurate and independent of the oxidizer flux, when the
images in the training set are not over-saturated. Training with monochrome
images is explored and is not successful at predicting the fuel regression rate
from images with high noise. The network is superior at filtering out noise
introduced by soot, pitting, and wax deposition on the chamber glass as well as
the flame when compared to traditional image processing techniques, such as
threshold binary conversion and spatial filtering. U-net consistently provides
low error image segmentations to allow accurate computation of the regression
rate of the fuel.
