基于深度学习的平板燃烧器固液混合火箭固体燃料退燃率测量
Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning
摘要：
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2D平板燃烧器实验中的燃料回归率。设计了平板燃烧室实验，通过测量燃料退移速率来验证固液火箭反应边界层燃烧机理模型。使用具有高强度闪光灯的DSLR相机在整个燃烧过程中捕获图像，然后使用图像找到燃料边界以计算回归率。探索了一种U-net卷积神经网络架构，以从实验图像中分割燃料。Monte-Carlo Dropout过程用于量化网络产生的回归率不确定性。U-网计算的回归率与其他技术从文献中的值进行比较，显示误差小于10%。的氧化剂通量依赖性的研究进行，并显示回归率的U-网络预测是准确的，独立的氧化剂通量，当在训练集中的图像不过饱和。探索了使用单色图像的训练，并且在从具有高噪声的图像预测燃料回归率方面并不成功。与传统的图像处理技术（如阈值二进制转换和空间滤波）相比，该网络在滤除由室玻璃上的烟灰、点蚀和蜡沉积以及火焰引入的噪声方面更优越。U-net始终如一地提供低误差图像分割，以准确计算燃料的回归率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
