High-speed video recordings of slab burner experiments were analyzed using a machine learning approach with convolutional neural networks in order to compute the regression rate of hybrid rocket fuels over time. Combustion tests of paraffin-based fuel grains performed in two different hybrid rocket slab burners were recorded with high-speed video cameras and the resulting image data are analyzed in order to determine the height of the fuel in each frame. To this end, a deep neural network with U-net architecture is trained in a supervised fashion to segment the shape of the fuel slab. It is demonstrated that this approach is more capable to segment combustion images in unsteady flow conditions than classical computer vision methods based on thresholding or edge detection. Furthermore, methods in the area of uncertainty quantification of neural networks are applied to estimate the errors in the neural network prediction to new previously unseen data. Finally, the regression rate of the fuel is computed as the rate of change of this height. This method enables automatic analysis of a large amount of video data, taking full advantage of the optical access capabilities of slab burners. Additionally, the method delivers not only the time and space average values of the fuel regression rate, but also quantifies its variation over time and over the length of the slab, providing deeper insights into the combustion mechanics of hybrid rockets.

中文翻译：

---

用于对两个板坯燃烧器的高速视频进行图像分析的卷积神经网络


使用带有卷积神经网络的机器学习方法分析板坯燃烧器实验的高速视频记录，以计算混合火箭燃料随时间的回归率。使用高速摄像机记录在两种不同的混合火箭板燃烧器中进行的石蜡基燃料颗粒的燃烧测试，并对所得图像数据进行分析，以确定每帧中燃料的高度。为此，采用监督方式训练具有 U-net 架构的深度神经网络来分割燃料板的形状。事实证明，与基于阈值或边缘检测的经典计算机视觉方法相比，该方法更能够在不稳定流动条件下分割燃烧图像。此外，应用神经网络不确定性量化领域的方法来估计神经网络对新的先前未见过的数据的预测中的误差。最后，燃料的回归率被计算为该高度的变化率。该方法能够自动分析大量视频数据，充分利用板坯燃烧器的光学访问能力。此外，该方法不仅提供燃料回归率的时间和空间平均值，还量化其随时间和板片长度的变化，从而为混合火箭的燃烧力学提供更深入的了解。