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

Applying Physics-Informed Enhanced Super-Resolution Generative  Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform  Meshes and Demonstration of an Accelerated Simulation Workflow