Radiation heat transfer in ablating boundary layer combustion theory used for hybrid rocket motor analysis

Budzinski, Kenneth; Aphale, Siddhant S.; Ismael, Elektra Katz; Surina, Gabriel; DesJardin, Paul E.

doi:10.1016/j.combustflame.2020.04.011

Cited by 23 publications

(11 citation statements)

References 12 publications

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“…Figure 1a shows the 2D slab burner experiment for paraffin wax-gaseous oxygen hybrid combustion. The experiments are conducted in the setup that is based on [4] and [15]. The experiment uses solid paraffin wax (or candle wax) as the fuel because of its low cost and high regression rate characteristics.…”

Section: Methodsmentioning

confidence: 99%

“…Hybrid rockets are recently gaining attention and becoming a competitive propulsion system because they have the benefits of solid and liquid rockets in one platform [17,24]. The fuel and oxidizer are in two different states: typically the fuel is a solid and the oxidizer is a gas/liquid, providing a highly dense fuel source similar to a bi-propellant solid motor but with the throttle control of a liquid motor [4,10,28]. The fuel regression rate ( ṙ) corresponds to the rate that the fuel recedes over the course of a burn and is an important measurement since it directly defines the motor thrust and geometry (e.g., a higher regression rate results in smaller combustion chambers) [54].…”

Section: Introductionmentioning

confidence: 99%

“…Other methods of regression rate measurement include using combustion chamber pressure [33], x-ray radiography [10], initial and final port diameter [26,46,54], and ultrasonic transducers [7,9,32]. More recently, [4] developed an imaging-based regression rate measurement for a 2D slab burner experiment. They collected images of the fuel profiles captured continuously throughout the burn and manually traced the fuel boundary to segment the fuel from the background (binary masks where a value of "0" corresponds to background and a value of "1" corresponds to fuel).…”

Section: Introductionmentioning

confidence: 99%

“…The overall research focus of this study is on automating the manual regression rate measurement technique in [4] using U-net to produce the fuel segmentation masks instead of manually tracing them. The manual approach is used as the ground truth and serves as a baseline to validate the U-net results.…”

Section: Introductionmentioning

confidence: 99%

“…To accomplish this goal, a series of U-net models of similar architecture are built. Each model is trained on all or a subset of the data collected in [4], a 2D slab burner experiment with four different oxidizer fluxes (5.91, 9.58, 18.59, 22.19 [kg/m 2 − s]). The data included, in total, 150 RGB images and their corresponding manually segmented fuel masks.…”

Section: Introductionmentioning

confidence: 99%

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

Surina,

Georgalis,

Aphale

et al. 2021

Preprint

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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.

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Section: Methodsmentioning

confidence: 99%