Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence
Chenyue Xie, Jianchun Wang, Hui Li, Minping Wan, Shiyi Chen
Abstract
The subgrid-scale (SGS) stress and SGS heat flux are modeled by using an artificial neural network (ANN) for large eddy simulation (LES) of compressible turbulence. The input features of ANN model are based on the first-order and second-order derivatives of filtered velocity and temperature at different spatial locations. The proposed spatial artificial neural network (SANN) model gives much larger correlation coefficients and much smaller relative errors than the gradient model in an a priori analysis. In an a posteriori analysis, the SANN model performs better than the dynamic mixed model (DMM) in the prediction of spectra and statistical properties of velocity and temperature, and the instantaneous flow structures.