ARTIFICIAL NEURAL NETWORK-BASED SUBGRID-SCALE MODELS FOR LARGE-EDDY SIMULATION OF TURBULENCE

Large eddy simulation (LES) is an important method to investigate different types of complex turbulent flows, which has been widely applied to the turbulent flows in aerospace, combustion, acoustics, atmospheric boundary layer, etc. Large eddy simulation effectively solves the large-scale motions of turbulence and models the effects of small-scale dynamics on the large-scale structures by using subgrid-scale (SGS) models. Traditional SGS models only use the single-point information based on some simple forms of analytical functions to approximate the SGS terms. Thus, traditional models exhibit quite large relative errors in the a priori study, and have excessive dissipations in the a posteriori study. Recently, machine learning approaches have been widely used to develop turbulence models, including the Reynolds-averaged Navier-Stokes (RANS) models and LES models. In this paper, we review the recent developments of artificial neural network (ANN) methods for SGS models in LES of turbulence. We discuss three different ANN-based SGS models, including artificial neural network mixed model (ANNMM), spatial artificial neural network (SANN) model and deconvolutional artificial neural network (DANN) model. Due to the strong data interpolation capability of artificial neural networks, the new SGS models exhibit improved accuracy in both a priori studyand a posteriori study. In the a priori study, the new SGS models can predict the SGS stress much more accurately than the traditional SGS models: the correlation coefficients predicted by new SGS models can be made larger than 99%. In the a posteriori study, the new SGS models can give better predictions on turbulence statistics and instantaneous flow structures, as compared to a variety of traditional SGS models including the implicit LES (ILES), dynamic Smagorinsky model (DSM), and dynamic mixed model (DMM). It is shown that artificial neural network-based methods have strong potentials for the developments of advanced SGS models in the LES of complex turbulence.