AI Super-Resolution: Application to Turbulence and Combustion

Lecture notes in energy, pp. 279–305

Abstract

Abstract This article summarizes and discusses recent developments with respect to artificial intelligence (AI) super-resolution as a subfilter model for large-eddy simulations. The focus is on the application of physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for subfilter closure in turbulence and combustion applications. A priori and a posteriori results are presented for various applications, ranging from decaying turbulence to finite-rate chemistry flows. The high accuracy of AI super-resolution-based subfilter models is emphasized, and advantages and shortcoming are described.

Authors 1

1. Mathis Bode corresponding Aachen Fakultät für Machinenwesen

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Fakultät für Machinenwesen, RWTH Aachen University, Templergraben 64, 52056, Aachen, NRW, Germany

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