摘要
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.
基金
This work was supported by the National Natural Science Foundation of China(Grants 91952104,11702127,and 91752201)
the Technology and Innovation Commission of Shenzhen Municipality(Grants KQTD20180411143441009,JCYJ20170412151759222,and ZDSYS201802081843517).This work was also supported by Center for Computational Science and Engineering of Southern University of Science and Technology.J.Wang acknowledges the support from Young Elite Scientist Sponsorship Program by CAST(Grant 2016QNRC001).
