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The accurate prediction of small scales in underresolved flows is still oneof the main challenges in predictive simulations of complex configurations.Over the last few years, data-driven modeling has become popular in many fieldsas large, often extensively labeled datasets are now available and training oflarge neural networks has become possible on graphics processing units (GPUs)that speed up the learning process tremendously. In fact, the successfulapplication of deep neural networks in fluid dynamics, such as forunderresolved reactive flows, is still challenging. This work advances therecently introduced PIESRGAN to reactive finite-rate-chemistry flows. However,since combustion chemistry typically acts on the smallest scales, the originalapproach needs to be extended. Therefore, the modeling approach of PIESRGAN ismodified to accurately account for the challenges in the context of laminarfinite-rate-chemistry flows. The modified PIESRGAN-based model gives goodagreement in a priori and a posteriori tests in a laminar lean premixedcombustion setup. Furthermore, a reduced PIESRGAN-based model is presented thatsolves only the major species on a reconstructed field and employs PIERSGANlookup for the remaining species, utilizing staggering in time. The advantagesof the discriminator-supported training are shown, and the usability of the newmodel demonstrated in the context of a model gas turbine combustor.

Applying Physics-Informed Enhanced Super-Resolution Generative  Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean  Premixed Gas Turbine Combustors