Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows

This work presents the full reconstruction of coarse-grained turbulence fields in a planar turbulent jet flow by a deep learning framework for large-eddy simulations (LES). Turbulent jet flows are characterized by complex phenomena such as intermittency and external interfaces. These phenomena are strictly non-universal and conventional LES models have shown only limited success in modeling turbulent mixing in such configurations. Therefore, a deep learning approach based on physics-informed enhanced super-resolution generative adversarial networks (Bode et al., Proceedings of the Combustion Institute, 2021) is utilized to reconstruct turbulence and mixture fraction fields from coarse-grained data. The usability of the deep learning model is validated by applying it to data obtained from direct numerical simulations (DNS) with more than 78 Billion degrees of freedom. It is shown that statistics of the mixture fraction field can be recovered from coarse-grained data with good accuracy.