A wall model learned from the periodic hill data and the law of the wall

Zhideng Zhou; Xiang I.A. Yang; Fengshun Zhang; Xiaolei Yang

doi:10.1063/5.0143650

A wall model learned from the periodic hill data and the law of the wall

Zhideng Zhou, Xiang I.A. Yang, Fengshun Zhang, Xiaolei Yang

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Toward data-driven wall-modeled large-eddy simulations of different wall-bounded turbulent flows, a wall model is learned in this work using the wall-resolved large-eddy simulation (WRLES) data of the flow over periodic hills (PH) and the law of the wall (LoW). The feedforward neural network (FNN) is employed to construct the model. The obtained FNN_PH-LoW model is successfully tested using the direct numerical simulation data of turbulent channel flows and the WRLES data of PH cases, and applied to turbulent channel flows for a wide range of Reynolds numbers.

Original language	English (US)
Article number	055108
Journal	Physics of Fluids
Volume	35
Issue number	5
DOIs	https://doi.org/10.1063/5.0143650
State	Published - May 1 2023

All Science Journal Classification (ASJC) codes

Computational Mechanics
Condensed Matter Physics
Mechanics of Materials
Mechanical Engineering
Fluid Flow and Transfer Processes

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10.1063/5.0143650

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title = "A wall model learned from the periodic hill data and the law of the wall",

abstract = "Toward data-driven wall-modeled large-eddy simulations of different wall-bounded turbulent flows, a wall model is learned in this work using the wall-resolved large-eddy simulation (WRLES) data of the flow over periodic hills (PH) and the law of the wall (LoW). The feedforward neural network (FNN) is employed to construct the model. The obtained FNN_PH-LoW model is successfully tested using the direct numerical simulation data of turbulent channel flows and the WRLES data of PH cases, and applied to turbulent channel flows for a wide range of Reynolds numbers.",

author = "Zhideng Zhou and Yang, {Xiang I.A.} and Fengshun Zhang and Xiaolei Yang",

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AU - Zhou, Zhideng

AU - Yang, Xiang I.A.

AU - Zhang, Fengshun

AU - Yang, Xiaolei

PY - 2023/5/1

Y1 - 2023/5/1

N2 - Toward data-driven wall-modeled large-eddy simulations of different wall-bounded turbulent flows, a wall model is learned in this work using the wall-resolved large-eddy simulation (WRLES) data of the flow over periodic hills (PH) and the law of the wall (LoW). The feedforward neural network (FNN) is employed to construct the model. The obtained FNN_PH-LoW model is successfully tested using the direct numerical simulation data of turbulent channel flows and the WRLES data of PH cases, and applied to turbulent channel flows for a wide range of Reynolds numbers.

AB - Toward data-driven wall-modeled large-eddy simulations of different wall-bounded turbulent flows, a wall model is learned in this work using the wall-resolved large-eddy simulation (WRLES) data of the flow over periodic hills (PH) and the law of the wall (LoW). The feedforward neural network (FNN) is employed to construct the model. The obtained FNN_PH-LoW model is successfully tested using the direct numerical simulation data of turbulent channel flows and the WRLES data of PH cases, and applied to turbulent channel flows for a wide range of Reynolds numbers.

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ER -

A wall model learned from the periodic hill data and the law of the wall

Abstract

All Science Journal Classification (ASJC) codes

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