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

Wang, Yunpeng; Li, Zhijie; Yuan, Zelong; Peng, Wenhui; Liu, Tianyuan; Wang, Jianchun

doi:10.1103/physrevfluids.9.084604

Phys. Rev. Fluids

2024

DOI: 10.1103/physrevfluids.9.084604

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

Yunpeng Wang,

Zhijie Li,

Zelong Yuan

et al.

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Cited by 4 publications

References 89 publications

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An implicit factorized transformer with applications to fast prediction of three-dimensional turbulence

Yang,

Li,

Wang

et al. 2024

Theoretical and Applied Mechanics Letters

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An implicit factorized transformer with applications to fast prediction of three-dimensional turbulence

Yang,

Li,

Wang

et al. 2024

Theoretical and Applied Mechanics Letters

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Fourier neural operator for large eddy simulation of compressible Rayleigh–Taylor turbulence

Luo,

Li,

Yuan

et al. 2024

Physics of Fluids

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The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh–Taylor turbulence with miscible fluids at Atwood number At=0.5, stratification parameter Sr = 1.0, and Reynolds numbers Re = 10 000 and 30 000. The FNO model is first used for predicting three-dimensional compressible turbulence. The different magnitudes of physical fields are normalized using root mean square values for an easier training of FNO models. In the a posteriori tests, the FNO model outperforms the velocity gradient model, the dynamic Smagorinsky model, and implicit large eddy simulation in predicting various statistical quantities and instantaneous structures, and is particularly superior to traditional LES methods in predicting temperature fields and velocity divergence. Moreover, the computational efficiency of the FNO model is much higher than that of traditional LES methods. FNO models trained with short-time, low Reynolds number data exhibit a good generalization performance on longer-time predictions and higher Reynolds numbers in the a posteriori tests.

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Koopman neural operator approach to fast flow prediction of airfoil transonic buffet

Meng,

Zhu,

Wang

et al. 2024

Physics of Fluids

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Transonic buffet on airfoil is of great importance in the aerodynamic characteristics of aircraft. In the present work, a modified Koopman neural operator (KNO) is applied to predict flow fields during the transonic buffet process of the OAT15A [ONERA (National Office for Aerospace Studies and Research) Aerospatiale Transport aircraft 15 Airfoil] airfoil. Transonic buffet flow with different angles of attack is simulated by Reynolds averaged numerical simulation with the Menter's k−ω shear stress transport (SST) model at Reynolds number Re=3×106. A prediction model is directly constructed between the flow fields at several previous time nodes and that at the future time node by KNO. The predictions of flow fields with single sample and multi samples are performed to demonstrate the prediction accuracy and efficiency of KNO. The prediction of sequence flow fields based on the iterative prediction strategy is achieved for the transonic buffet process. The results indicate that KNO can achieve a fast and accurate prediction of flow physical quantities for the transonic buffet. Compared with other deep learning models including Unet and Fourier neural operator, KNO has a more advanced capability of predicting airfoil transonic buffet flow fields with higher accuracy and efficiency and less hardware requirements.

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

Cited by 4 publications

References 89 publications

An implicit factorized transformer with applications to fast prediction of three-dimensional turbulence

An implicit factorized transformer with applications to fast prediction of three-dimensional turbulence

Fourier neural operator for large eddy simulation of compressible Rayleigh–Taylor turbulence

Koopman neural operator approach to fast flow prediction of airfoil transonic buffet

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