Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy
Yunpeng Wang, Zhijie Li, Zelong Yuan, Wenhui Peng, Tianyuan Liu, Jianchun Wang
Abstract
The implicit U-Net enhanced Fourier neural operator (IUFNO) combines the loop structure of implicit FNO (IFNO) with U-Net, leading to enhanced long-term predictive ability in the large-eddy simulations (LES) of turbulent channel flow. It is found that the IUFNO outperforms the traditional dynamic Smagorinsky model (DSM) and the wall-adapted local eddy-viscosity (WALE) model at coarse LES grids. The predictions of both the mean and fluctuating quantities by IUFNO are closer to the filtered direct numerical simulation (fDNS) benchmark compared to the traditional LES models, while the computational cost of IUFNO is much lower.
Topics & Concepts
TurbulenceBenchmark (surveying)Artificial neural networkFourier transformLarge eddy simulationTurbulence modelingOperator (biology)Computer scienceFlow (mathematics)Fast Fourier transformAlgorithmArtificial intelligenceFourier seriesMechanicsApplied mathematicsMathematicsPhysicsMathematical analysisGeologyChemistryGeneBiochemistryGeodesyTranscription factorRepressorFluid Dynamics and Turbulent FlowsHeat Transfer MechanismsModel Reduction and Neural Networks