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Models for finite-rate-chemistry in underresolved flows still pose one of themain challenges for predictive simulations of complex configurations. Theproblem gets even more challenging if turbulence is involved. This workadvances the recently developed PIESRGAN modeling approach to turbulentpremixed combustion. For that, the physical information processed by thenetwork and considered in the loss function are adjusted, the training processis smoothed, and especially effects from density changes are considered. Theresulting model provides good results for a priori and a posteriori tests ondirect numerical simulation data of a fully turbulent premixed flame kernel.The limits of the modeling approach are discussed. Finally, the model isemployed to compute further realizations of the premixed flame kernel, whichare analyzed with a scale-sensitive framework regarding their cycle-to-cyclevariations. The work shows that the data-driven PIESRGAN subfilter model canvery accurately reproduce direct numerical simulation data on much coarsermeshes, which is hardly possible with classical subfilter models, and enablesstudying statistical processes more efficiently due to the smaller computingcost.

Applying Physics-Informed Enhanced Super-Resolution Generative  Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame  Kernel Direct Numerical Simulation Data