Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence

TL;DR

This paper introduces LESnets, a physics-informed neural operator framework that accurately and efficiently predicts high Reynolds number wall-bounded turbulence without relying on labeled data.

Contribution

LESnets integrates LES equations into a neural operator, enabling stable, long-term turbulence predictions at high Reynolds numbers with coarse grids and physics-informed loss.

Findings

LESnets achieves accuracy comparable to traditional LES methods.

LESnets offers higher computational efficiency than data-driven models.

Performance is validated on turbulent channel flows at Re_τ=180, 590, 1000.

Abstract

Accurate and efficient prediction of three-dimensional (3D) wall-bounded turbulent flows poses a significant challenge for machine learning methods, particularly in scenarios where flow field data are limited. Physics-informed neural operator (PINO) combines neural operator and physics constraint methods, and shows great potential for solving a wide range of partial differential equations. Nevertheless, the multi-scale vortex structures in wall-bounded turbulence make it difficult for most existing PINO methods to make stable and accurate long-term predictions at high Reynolds numbers. To address this challenge, we develop the large-eddy simulation nets (LESnets) that integrates large-eddy simulation (LES) equations into the factorized Fourier neural operator (F-FNO) for wall-bounded turbulence. The LESnets framework does not rely on labeled data for training, which enables it to generate temporal solutions over flexible time horizons during the training process. Moreover, the law of the wall is integrated into the LESnets framework through a wall model for the physics-informed loss, thus enabling reliable simulations of wall-bounded turbulence at high Reynolds number using coarse grids. The proposed LESnets methods are demonstrated in turbulent channel flows at three friction Reynolds numbers: 180, 590, and 1000. Numerical experiments show that the performance of the LESnets in terms of prediction accuracy and efficiency is comparable to that of two data-driven models, namely the implicit U-Net enhanced Fourier neural operator (IUFNO) and F-FNO. Meanwhile, the LESnets model achieves prediction accuracy comparable to traditional LES methods while offering a higher computational efficiency. Thus, the LESnets model demonstrates strong potential for efficient and long-term prediction of wall-bounded turbulent flows.