Artificial neural network model for large-eddy simulation of compressible turbulence

The Spatial Artificial Neural Network (SANN) model is applied to perform Large Eddy Simulations (LES) of highly compressible turbulence at high turbulent Mach numbers of 0.6, 0.8 and 1.0 under the National Numerical Windtunnel (NNW) Project. In our previous studies, we developed the SANN model for incompressible and weakly compressible turbulence based on multi-scale spatial structures of turbulence. However, generations of shock waves in highly compressible turbulence pose great challenges to LES. This paper discusses the applicability of the SANN models for LES of highly compressible turbulence. It has been demonstrated that the correlation coefficients of the SANN model can be larger than 0.995. The relative errors of the SANN model can be smaller than 11%, which are much smaller than those of the traditional gradient model and the approximate deconvolution model in an a priori analysis for highly compressible turbulence. In an a posteriori analysis, we make a comparison of the results of the SANN model, the Implicit Large Eddy Simulation (ILES), the Dynamic Smagorinsky Model (DSM) and the Dynamic Mixed Model (DMM). It is shown that the SANN model performs better in the prediction of spectra and statistical properties of velocity and temperature, and instantaneous flow structures for highly compressible turbulence. The artificial neural network model with consideration of spatial features can deepen our understanding of subgrid-scale modeling for LES of highly compressible turbulence. At the same time, the model can contribute to the construction of the turbulence models of the NNW Project. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.