Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows

Nista, L., Schumann, C. D.K., Grenga, T., Attili, A. and Pitsch, H. (2023) Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows. Proceedings of the Combustion Institute, 39 (4), 5279-5288. (doi:10.1016/j.proci.2022.07.244).

Abstract

In the past decades, Deep Learning (DL) frameworks have demonstrated excellent performance in modeling nonlinear interactions and are a promising technique to move beyond physics-based models. In this context, super-resolution techniques may present an accurate approach as subfilter-scale (SFS) closure model for Large Eddy Simulations (LES) in premixed combustion. However, DL models need to perform accurately in a variety of physical regimes and generalize well beyond their training conditions. In this work, a super-resolution Generative Adversarial Network (GAN) is proposed as closure model for the unresolved subfilter-stress and scalar-flux tensors of the filtered reactive Navier-Stokes equations solved in LES. The model trained on a premixed methane/air jet flame is evaluated a-priori on similar configurations at different Reynolds and Karlovitz numbers. The GAN generalizes well at both lower and higher Reynolds numbers and outperforms existing algebraic models when the ratio between the filter size and the Kolmogorov scale is preserved. Moreover, extrapolation at a higher Karlovitz number is investigated indicating that the ratio between the filter size and the thermal flame thickness may not need to be conserved in order to achieve high correlation in terms of SFS field. Generalization studies obtained on substantially different flame conditions indicate that successful predictive abilities are demonstrated if the generalization criterion is matched. Finally, the reconstruction of a scalar quantity, different from that used during the training, is evaluated, revealing that the model is able to reconstruct scalar fields with large gradients that have not been explicitly used in the training. The a-priori investigations carried out assess whether out-of-sample predictions are even feasible in the first place, providing insights into the quantities that need to be conserved for the model to perform well between different regimes, and represent a crucial step toward future embedding into LES numerical solvers.

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Contributors

Author: T. Grenga

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