摘要
Fourier neural operator(FNO)model is developed for large eddy simulation(LES)of three-dimensional(3D)turbulence.Velocity fields of isotropic turbulence generated by direct numerical simulation(DNS)are used for training the FNO model to predict the filtered velocity field at a given time.The input of the FNO model is the filtered velocity fields at the previous several time-nodes with large time lag.In the a posteriori study of LES,the FNO model performs better than the dynamic Smagorinsky model(DSM)and the dynamic mixed model(DMM)in the prediction of the velocity spectrum,probability density functions(PDFs)of vorticity and velocity increments,and the instantaneous flow structures.Moreover,the proposed model can significantly reduce the computational cost,and can be well generalized to LES of turbulence at higher Taylor-Reynolds numbers.
基金
supported by the National Natural Science Foundation of China(Nos.91952104,92052301,12172161,and 12161141017)
National Numerical Windtunnel Project(No.NNW2019ZT1-A04)
Shenzhen Science and Technology Program(No.KQTD20180411143441009)
Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(No.GML2019ZD0103)
CAAI-Huawei Mind Spore open Fund
and by Department of Science and Technology of Guangdong Province(No.2019B21203001)
supported by Center for Computational Science and Engineering of Southern University of Science and Technology。
