Sensitivity analysis of wall-modeled large-eddy simulation for separated turbulent flow

Zhou, Di; Bae, H. Jane

doi:10.1016/j.jcp.2024.112948

Journal of Computational Physics

2024

DOI: 10.1016/j.jcp.2024.112948

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Sensitivity analysis of wall-modeled large-eddy simulation for separated turbulent flow

Di Zhou,

H. Jane Bae

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2024

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Cited by 4 publications

References 43 publications

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A wall model for separated flows: embedded learning to improve a posteriori performance

Zhou,

Zhang,

et al. 2024

J. Fluid Mech.

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Developing large-eddy simulation (LES) wall models for separated flows is challenging. We propose to leverage the significance of separated flow data, for which existing theories are not applicable, and the existing knowledge of wall-bounded flows (such as the law of the wall) along with embedded learning to address this issue. The proposed so-called features-embedded-learning (FEL) wall model comprises two submodels: one for predicting the wall shear stress and another for calculating the eddy viscosity at the first off-wall grid nodes. We train the former using the wall-resolved LES (WRLES) data of the periodic hill flow and the law of the wall. For the latter, we propose a modified mixing length model, with the model coefficient trained using the ensemble Kalman method. The proposed FEL model is assessed using the separated flows with different flow configurations, grid resolutions and Reynolds numbers. Overall good a posteriori performance is observed for predicting the statistics of the recirculation bubble, wall stresses and turbulence characteristics. The statistics of the modelled subgrid-scale (SGS) stresses at the first off-wall grids are compared with those calculated using the WRLES data. The comparison shows that the amplitude and distribution of the SGS stresses and energy transfer obtained using the proposed model agree better with the reference data when compared with the conventional SGS model.

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A wall model for separated flows: embedded learning to improve a posteriori performance

Zhou,

Zhang,

et al. 2024

J. Fluid Mech.

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Nonequilibrium wall model for large eddy simulations of complex flows exhibiting turbulent smooth body separation

Agrawal,

Bose,

Moin

2024

Phys. Rev. Fluids

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Efficient dynamic mixed subgrid-scale model

Iyer,

Malik

2024

Phys. Rev. Fluids

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Sensitivity analysis of wall-modeled large-eddy simulation for separated turbulent flow

Cited by 4 publications

References 43 publications

A wall model for separated flows: embedded learning to improve a posteriori performance

A wall model for separated flows: embedded learning to improve a posteriori performance

Nonequilibrium wall model for large eddy simulations of complex flows exhibiting turbulent smooth body separation

Efficient dynamic mixed subgrid-scale model

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