SUBGRID-SCALE STRESS MODELING BASED ON ARTIFICIAL NEURAL NETWORK

Subgrid-scale (SGS) stress modelling can be of particular importance in large-eddy simulation (LES) of turbulent flows. Traditional SGS stress models usually suffer from the drawbacks of large relative errors, excessive dissipations, etc. With the rapid progress in computer technology, machine learning methods such as artificial neural network (ANN) have gradually become a new research paradigm for SGS stress modeling. In the present paper, an ANN is employed to establish the SGS stress model for incompressible turbulent channel flow with particular attention devoted to the effect of filter width and Reynolds number. To this end, the filtered direct numerical simulation (fDNS) flow field and filter width are used as the inputs and the SGS stress at the corresponding filter width as the outputs. After training based on the data at different filter widths and different Reynolds numbers, the SGS stress predicted by ANN model is in acceptable agreement with the direct numerical simulation (DNS) data. Furthermore, excellent performance can also be found in non-modeling quantities of ANN such as SGS dissipation. The correlation coefficients between the ANN-based quantities and those calculated using DNS data are all above 0.9, indicating obvious improvements of the present ANN model over the gradient model and Smagorinsky model. In the a posteriori test, the ANN model can give better predictions on the streamwise mean velocity as compared with a variety of traditional LES models including the gradient model, Smagorinsky model and implicit LES. For the prediction of root-mean-square (RMS) fluctuating velocity, the ANN-based model is generally superior to the other three models except for some specific wall-normal locations. However, the RMS fluctuating velocities predicted by ANN-based model deviate from the fDNS results with the increase of grid size. It is suggested that ANN should have great potential for development of SGS stress models with high accuracy.