Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator

Li，Zhijie1,2; Peng，Wenhui1,2; Yuan，Zelong1,2; Wang，Jianchun1,2

2023-07-01

10.1063/5.0158830

Physics of Fluids   影响因子和分区

1070-6631

1089-7666

Long-term predictions of nonlinear dynamics of three-dimensional (3D) turbulence are very challenging for machine learning approaches. In this paper, we propose an implicit U-Net enhanced Fourier neural operator (IU-FNO) for stable and efficient predictions on the long-term large-scale dynamics of turbulence. The IU-FNO model employs implicit recurrent Fourier layers for deeper network extension and incorporates the U-net network for the accurate prediction on small-scale flow structures. The model is systematically tested in large-eddy simulations of three types of 3D turbulence, including forced homogeneous isotropic turbulence, temporally evolving turbulent mixing layer, and decaying homogeneous isotropic turbulence. The numerical simulations demonstrate that the IU-FNO model is more accurate than other FNO-based models, including vanilla FNO, implicit FNO (IFNO), and U-Net enhanced FNO (U-FNO), and dynamic Smagorinsky model (DSM) in predicting a variety of statistics, including the velocity spectrum, probability density functions of vorticity and velocity increments, and instantaneous spatial structures of flow field. Moreover, IU-FNO improves long-term stable predictions, which has not been achieved by the previous versions of FNO. Moreover, the proposed model is much faster than traditional large-eddy simulation with the DSM model and can be well generalized to the situations of higher Taylor-Reynolds numbers and unseen flow regime of decaying turbulence.

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China["91952104","92052301","12172161","91752201"] ; NSFC Basic Science Center Program[11988102] ; Shenzhen Science and Technology Program[KQTD20180411143441009] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0103] ; Department of Science and Technology of Guangdong Province[2020B1212030001]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:001034275700005

AIP Publishing

20233114468048

Forecasting ; Fourier transforms ; Network layers ; Probability density function ; Reynolds number ; Turbulence

Fluid Flow:631 ; Fluid Flow, General:631.1 ; Computer Software, Data Handling and Applications:723 ; Mathematics:921 ; Mathematical Transformations:921.3 ; Probability Theory:922.1

PHYSICS

2-s2.0-85166167464

Scopus

1.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,Southern University of Science and Technology,Shenzhen,518055,China

Li，Zhijie,Peng，Wenhui,Yuan，Zelong,et al. Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator[J]. Physics of Fluids,2023,35(7).

Li，Zhijie,Peng，Wenhui,Yuan，Zelong,&Wang，Jianchun.(2023).Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator.Physics of Fluids,35(7).

Li，Zhijie,et al."Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator".Physics of Fluids 35.7(2023).