This paper extends the methodology to use physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for LES subfilter modeling in turbulent flows with finite-rate chemistry and shows a successful application to a non-premixed temporal jet case. This is an important topic considering the need for more efficient and carbon-neutral energy devices to fight the climate change. Multiple a priori and a posteriori results are presented and discussed. As part of this, the impact of the underlying mesh on the prediction quality is emphasized, and a multi-mesh approach is developed. It is demonstrated how LES based on PIESRGAN can be employed to predict cases at Reynolds numbers which were not used for training. Finally, the amount of data needed for a successful prediction is elaborated.

翻译：本文扩展了使用物理学知情增强超分辨率超分辨率对抗性网络(PIESRGANs)的方法,用于对有限率化学的动荡流动进行LES子过滤模型,并展示了对非预设时间喷射机案的成功应用,这是一个重要的专题,因为考虑到需要有更高效和碳中性能源装置来应对气候变化,提出并讨论了多种先验和后验结果,作为这一方法的一部分,强调了基础网目对预测质量的影响,并开发了多种网目方法,说明了如何利用基于PIESRGAN的LES来预测没有用于培训的Reynolds数量的案例。最后,阐述了成功预测所需的数据数量。