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

arXiv (Cornell University)

Abstract

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.

Authors 1

1. Mathis Bode corresponding Aachen Fakultät für Maschinenwesen

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Fakultät für Maschinenwesen , RWTH Aachen University , Templergraben 64 , 52056 Aachen , Germany

   Jülich Supercomputing Centre , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany