Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning
Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning
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DOI：
10.1016/j.actaastro.2021.09.046
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Gabriel Surina;G. Georgalis;Siddhant S. Aphale;A. Patra;P. DesJardin
中科院分区：
文献类型：
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作者：
Gabriel Surina;G. Georgalis;Siddhant S. Aphale;A. Patra;P. DesJardin
这项研究提出了一种基于成像的深度学习工具，用于测量混合火箭燃料的二维平板燃烧器实验中的燃料退化率。设计了平板燃烧器实验，通过测量燃料退化率来验证混合火箭反应边界层燃烧的机理模型。使用高强度闪光灯的单反相机捕捉整个烧伤过程中的图像，然后使用图像找到燃料边界来计算退化率。采用U-Net卷积神经网络结构从实验图像中分割出燃料。采用蒙特卡罗失落法对网络产生的回归速度不确定性进行量化。U-Net计算的回归率与文献中其他技术的值进行了比较，误差小于10%。进行了氧化剂通量依赖关系的研究，结果表明，当训练集中的图像没有过饱和时，回归速度的U网预测是准确的，并且与氧化剂通量无关。对单色图像的训练进行了探索，但不能成功地从高噪声图像中预测燃料退化率。与传统的图像处理技术(如阈值二值转换和空间滤波)相比，该网络在滤除镜面玻璃上的烟尘、点蚀和蜡沉积以及火焰方面具有优势。U-Net始终提供低误差的图像分割，以便准确计算燃料的退化率。
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.