We report a computational method based on deep learning (DL) to generate planar distributions of soot particles in turbulent flames from line-of-sight luminosity images. A conditional generative adversarial network (C-GAN) was trained using flame luminosity and planar laser-induced incandescence (LII) images simultaneously recorded in a turbulent sooting flame with an exit Reynolds number of 15,000. Such a training built up the underlying relationship between the two types of images i.e., a predictive model which was then used to predict LII images from luminosity images and the accuracy was assessed using four different methods. Results show that the model is effective and capable of generating LII images with acceptable prediction accuracies of around 0.75. The model was also found to be applicable over a range of heights in the flames, as well as for the flames with a range of exit Reynolds numbers spanning from 8000 to 20,000. Besides, the probability density function (PDF) of LII signals in different flames can also be predicated using the model. This work, for the first time, demonstrates the feasibility of predicting planar signals from corresponding line-of-sight signals from turbulent flames, which potentially offers a much simpler optical arrangement for a modest trade-off in accuracy.

中文翻译：

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使用深度学习从湍流火焰中的光度图像生成烟尘粒子的平面分布

我们报告了一种基于深度学习 (DL) 的计算方法，可从视线光度图像生成湍流火焰中烟尘粒子的平面分布。条件生成对抗网络 (C-GAN) 使用火焰光度和平面激光诱导白炽度 (LII) 图像进行训练，这些图像在出口雷诺数为 15,000 的湍流烟尘火焰中同时记录。这种训练建立了两种类型图像之间的潜在关系，即一个预测模型，然后使用该模型从光度图像预测 LII 图像，并使用四种不同的方法评估准确性。结果表明，该模型是有效的，能够生成具有可接受的预测精度约为 0.75 的 LII 图像。还发现该模型适用于火焰中的一系列高度，以及具有范围从 8000 到 20,000 的退出雷诺数的火焰。此外，还可以使用该模型来预测不同火焰中 LII 信号的概率密度函数（PDF）。这项工作首次证明了根据来自湍流火焰的相应视线信号预测平面信号的可行性，这可能提供更简单的光学布置，以实现适度的精度权衡。