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A filtered density function (FDF) model based on deep neural network (DNN),termed DNN-FDF, is introduced for large eddy simulation (LES) of turbulentflows involving conserved scalar transport. The primary objectives of thisstudy are to develop the DNN-FDF models and evaluate their predictivecapability in accounting for various filtered moments, including that ofnon-linear source terms. A systematic approach is proposed to select DNNtraining sample size and architecture via learning curves to minimize bias andvariance. Two DNN-FDF models are developed, one utilizing FDF data from DirectNumerical Simulations (DNS) of constant-density temporal mixing layer, and theother from zero-dimensional pairwise mixing stirred reactor simulations. Thelatter is particularly intended for cases where generating DNS data iscomputationally infeasible. DNN-FDF models are applied for LES of avariable-density temporal mixing layer. The accuracy and consistency of bothDNN-FDF models are established by comparing their predicted filtered scalarmoments with those of conventional LES, where moment transport equations aredirectly solved. The DNN-FDF models are shown to outperform a widely usedpresumed-FDF model, especially for multi-modal FDFs and higher variance values.Results are further assessed against DNS and the transported FDF method. Thelatter couples LES with Monte Carlo for mixture fraction FDF computation. Mostimportantly, the study shows that DNN-FDF models can accurately filter highlynon-linear functions within variable-density flows, highlighting theirpotential for turbulent reacting flow simulations. Overall, the DNN-FDFapproach is shown to offer an accurate yet computationally economical approachfor describing turbulent scalar transport.

Investigation of Deep Learning-Based Filtered Density Function for Large Eddy Simulation of Turbulent Scalar Mixing