A Priori Analysis on Deep Learning of Filtered Reaction Rate

Abstract

A filtered reaction rate model driven by deep learning is proposed and analyzed a priori in the context of large eddy simulation (LES). A deep artificial neural network (ANN) is trained on the explicitly filtered reaction rate source term extracted from a database comprised of turbulent premixed planar flame direct numerical simulations (DNSes) employing single-step chemistry. The filtered DNS database to be used for the training of the ANN covers a wide range of turbulence intensities and LES filter widths. An interpretation technique of deep learning is employed to search the principal input parameters in the high dimensional database to alleviate the model complexity. The deep learning filtered reaction rate model is then tested on the unseen filtered planar flames featuring untrained turbulence intensities and LES filter widths, in conjunction with another canonical type of flame configuration that it has not been trained on. The deep learning filtered reaction rate model achieves good agreement with the filtered DNS results and also provides a quantitatively accurate surrogate model when compared to existing algebraic models and other combustion models from the literature.