Turbulent jet flows require significant computational resources at high Reynolds numbers when simulated using large-eddy simulation (LES). To address this challenge, a convolutional neural network (CNN) was used to reproduce small-scale fluctuations from large-scale velocity fields in turbulent jet flows. High-resolution LES data were used as ground truth, and the fast Fourier transform was applied to separate large-scale and subgrid-scale (SGS) components. The CNN was trained to predict SGS velocity fields using the large-scale components extracted from coarse-grid LES data. The performance of the model was evaluated under a 20% and 40% decrease in data resolution, and with a 50% increase in Reynolds number. The CNN-generated velocity fields reproduced energy spectra and turbulent kinetic energy, which closely agreed with those of the high-resolution LES. Although turbulence production and dissipation were less accurately predicted, due to complex energy transfer mechanisms, the CNN outputs demonstrated physical consistency in terms of mass and momentum conservation.
Choi et al. (Thu,) studied this question.