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Motivated by the need to characterize the spatio-temporal structure of turbulence in wall-bounded flows, we study wavenumber-frequency spectra of the streamwise velocity component based on large-eddy simulation (LES) data. The LES data are…

流体动力学 · 物理学 2015-03-17 Michael Wilczek , Richard J. A. M. Stevens , Charles Meneveau

Neural networks of simple structures are used to construct a turbulence model for large-eddy simulation (LES). Data obtained by direct numerical simulation (DNS) of homogeneous isotropic turbulence are used to train neural networks. It is…

流体动力学 · 物理学 2020-12-04 Satoshi Miyazaki , Yuji Hattori

Large eddy simulation (LES) of forced, homogeneous, isotropic, two-dimensional (2D) turbulence in the energy transfer subrange is the subject of this paper. A difficulty specific to this LES and its subgrid scale (SGS) representation is in…

chao-dyn · 物理学 2008-02-03 Semion Sukoriansky , Alexei Chekhlov , Boris Galperin , Steven A. Orszag

Large-eddy simulations (LES) are widely-used for computing high Reynolds number turbulent flows. Spatial filtering theory for LES is not without its shortcomings, including how to define filtering for wall-bounded flows, commutation errors…

流体动力学 · 物理学 2022-02-02 Perry L. Johnson

In many engineering and industrial applications, the investigation of rotating turbulent flow is of great interest. In rotor-stator cavities, the centrifugal and Coriolis forces have a strong influence on the turbulence by producing a…

A wall-modeled large eddy simulation approach is proposed in a Discontinuous Galerkin (DG) setting, building on the slip-wall concept of Bae et al. (JFM'19) and the universal scaling relationship by Pradhan and Duraisamy (JFM'23). The…

流体动力学 · 物理学 2025-04-25 Pratikkumar Raje , Karthik Duraisamy

Wall-modeled large-eddy simulation (WMLES) is performed for flow over a wing with a focus on documenting grid resolution requirements to predict both the laminar and turbulent regions accurately. Flow over a spanwise extruded NACA0012…

流体动力学 · 物理学 2026-02-13 P. Balakumar , Prahladh S. Iyer

FEARLESS (Fluid mEchanics with Adaptively Refined Large Eddy SimulationS) is a new numerical scheme arising from the combined use of subgrid scale (SGS) model for turbulence at the unresolved length scales and adaptive mesh refinement (AMR)…

宇宙学与河外天体物理 · 物理学 2014-11-20 L. Iapichino , A. Maier , W. Schmidt , J. C. Niemeyer

Large-eddy simulation developments and validations are presented for an improved simulation of turbulent internal flows. Numerical methods are proposed according to two competing criteria: numerical qualities (precision and spectral…

流体动力学 · 物理学 2008-01-15 Jérôme Boudet , Joëlle Caro , L. Shao , Emmanuel Lévêque

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different spatial points are used as input features of the DANN…

流体动力学 · 物理学 2020-12-02 Zelong Yuan , Chenyue Xie , Jianchun Wang

The present work is concerned with a study of large eddy simulations (LES) of unsteady turbulent jet flows. In particular, the present analysis is focused on the effects of the subgrid-scale modeling used when a second-order spatial…

流体动力学 · 物理学 2022-12-26 Carlos Junqueira-Junior , Sami Yamouni , João Luiz F. Azevedo , William R. Wolf

A new modeling approach for large-eddy simulation (LES) is obtained by combining a `regularization principle' with an explicit filter and its inversion. This regularization approach allows a systematic derivation of the implied…

混沌动力学 · 物理学 2009-11-07 Bernard J. Geurts , Darryl D. Holm

One promising decomposition of turbulent dynamics is that into building blocks such as equilibrium and periodic solutions and orbits connecting these. While the numerical approximation of such building blocks is feasible for flows in small…

流体动力学 · 物理学 2018-11-14 Lennaert van Veen , Genta Kawahara , Tatsuya Yasuda

A nonlocal subgrid-scale stress (SGS) model is developed based on the convolution neural network (CNN), a powerful supervised data-driven approach. The CNN is an ideal approach to naturally consider nonlocal spatial information in…

流体动力学 · 物理学 2023-01-27 Bo Liu , Huiyang Yu , Haibo Huang , Xi-Yun Lu

The logarithmic law for the mean velocity in turbulent boundary layers has long provided a valuable and robust reference for comparison with theories, models, and large-eddy simulations (LES) of wall-bounded turbulence. More recently,…

流体动力学 · 物理学 2014-12-23 Richard J. A. M. Stevens , Michael Wilczek , Charles Meneveau

Large eddy simulation has been widely used to simulate turbulence at balanced computational cost and accuracy. Many Subgrid-Scale (SGS) models have been proposed over the years, where data-driven and machine learning-aided approaches set…

流体动力学 · 物理学 2026-05-13 Takeru Hashimoto , Takahiro Tsukahara , Ryo Araki

Restrictive phenomenological assumptions represent a major roadblock for the development of accurate subgrid-scale models of fluid turbulence. Specifically, these assumptions limit a model's ability to describe key quantities of interest,…

流体动力学 · 物理学 2026-02-18 Matteo Ugliotti , Brandon Choi , Mateo Reynoso , Daniel R. Gurevich , Roman O. Grigoriev

Floods, tides and tsunamis are turbulent, yet conventional models are based upon depth averaging inviscid irrotational flow equations. We propose to change the base of such modelling to the Smagorinksi large eddy closure for turbulence in…

混沌动力学 · 物理学 2008-05-22 A. J. Roberts , D. J. Georgiev , D. V. Strunin

In this paper we employ renormalized viscosity and thermal diffusivity to construct a subgrid-scale model for large eddy simulation (LES) of turbulent thermal convection. For LES, we add $\nu_\mathrm{ren} \propto \Pi_u^{1/3}…

流体动力学 · 物理学 2018-10-31 Sumit Vashishtha , Mahendra K. Verma

Data-driven subgrid-scale (SGS) modeling in the large-eddy simulations (LES) suffers from the inconsistency between the \textit{a priori} tests and the a posteriori tests, which make training accurate SGS models a difficult task. We study…

流体动力学 · 物理学 2025-11-21 Xinyi Huang , Sze Chai Leung , H. Jane Bae