Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning

TL;DR

This paper develops a deep learning imaging tool using U-net CNN to accurately measure fuel regression rates in hybrid rocket experiments, outperforming traditional methods and quantifying uncertainty.

Contribution

It introduces a novel U-net based segmentation approach for fuel regression rate measurement in hybrid rockets, with uncertainty quantification and robustness against noise.

Findings

U-net achieves less than 10% error in regression rate estimation.

The method is robust against soot, pitting, and wax deposition noise.

Regression rate predictions are accurate and independent of oxidizer flux when images are not over-saturated.

Abstract

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.