High-resolution reconstruction and a-priori modeling of turbulent flames in the context of large eddy simulation using the convolutional neural network

Abstract

In large eddy simulation (LES) of turbulent combustion, accurate modeling of the unresolved scalar flux and filtered reaction source terms is challenging. In the present work, a convolutional neutral network (CNN) was developed for the high-resolution reconstruction of the unfiltered progress variable, velocity and reaction rate based on the filtered quantities that are available from LES or by filtering the direct numerical simulation (DNS) data. The unclosed terms in the filtered progress variable transport equation were then modeled using the reconstructed quantities from the proposed CNN model and the approximate deconvolution method (ADM). Two DNS cases, i.e., case A and case B, with different Karlovitz numbers (Ka) were performed to assess the performance of the models a−priori. The unfiltered and filtered DNS results were first presented. It was found that the small-scale wrinkling structures of the flames and turbulence are largely filtered out, and the reaction zone is broadened by filtering. Then, the progress variable, velocity and reaction rate were reconstructed from the filtered DNS data. The results of reconstruction by ADM and the CNN model were compared with those from the DNS. It was found that the distributions of various quantities predicted by the CNN model agree well with those of the DNS. Finally, the unresolved scalar flux and reaction source terms in the filtered progress variable transport equation were modeled. The statistics of the modeled results were analyzed and it was shown that the CNN model performs better than ADM. Overall, the CNN model is promising for the data reconstruction and model development of turbulent combustion.