Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence

In this work, artificial neural network-based nonlinear algebraic models (ANN-NAMs) are developed for the subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence at the Taylor Reynolds number Re-lambda ranging from 180 to 250. An ANN architecture is applied to construct the coefficients of the general NAM for the SGS anisotropy stress. It is shown that the ANN-NAMs can reconstruct the SGS stress accurately in the a priori test. Furthermore, the ANN-NAMs are analyzed by calculating the average, root mean square values, and probability density functions of dimensionless model coefficients. In an a posteriori analysis, we compared the performance of the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and ANN-NAM. The ANN-NAM yields good agreement with a filtered direct numerical simulation dataset for the spectrum, structure functions, and other statistics of velocity. Besides, the ANN-NAM predicts the instantaneous spatial structures of SGS anisotropy stress much better than the DSM and DMM. The NAM based on the ANN is a promising approach to deepen our understanding of SGS modeling in LES of turbulence.