Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy

TL;DR

This paper introduces an enhanced Fourier neural operator model that accurately predicts long-term turbulent channel flow dynamics on coarse grids, outperforming traditional models in accuracy and computational efficiency.

Contribution

The paper presents an implicit U-Net enhanced Fourier neural operator (IUFNO) that improves long-term prediction accuracy of turbulent flows on coarse meshes, surpassing existing models.

Findings

IUFNO outperforms traditional LES models in flow statistics.

IUFNO achieves faster predictions than conventional LES methods.

The model maintains accuracy over long simulation times.

Abstract

Fast and accurate predictions of turbulent flows are of great importance in the science and engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural operator (IUFNO) in the stable prediction of long-time dynamics of three-dimensional (3D) turbulent channel flows. The trained IUFNO models are tested in the large-eddy simulations (LES) at coarse grids for three friction Reynolds numbers: $Re_{\tau}\approx180$, $395$ and $590$. The adopted near-wall mesh grids are tangibly coarser than the general requirements for wall-resolved LES. Compared to the original Fourier neural operator (FNO), the implicit FNO (IFNO) and U-Net enhanced FNO (UFNO), the IUFNO model has a much better long-term predictive ability. The numerical experiments show that the IUFNO framework outperforms the traditional dynamic Smagorinsky model (DSM) and the wall-adapted local eddy-viscosity (WALE) model in the predictions of a variety of flow statistics and structures, including the mean and fluctuating velocities, the probability density functions (PDFs) and joint PDF of velocity fluctuations, the Reynolds stress profile, the kinetic energy spectrum, and the Q-criterion (vortex structures). Meanwhile, the trained IUFNO models are computationally much faster than the traditional LES models. Thus, the IUFNO model is a promising approach for the fast prediction of wall-bounded turbulent flow.