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A novel variant of Improved Delayed Detached-Eddy Simulation based on a differential Reynolds-stress background model is presented. The approach aims to combine the advantages of anisotropy-resolving Reynolds-stress closures in the modelled…

流体动力学 · 物理学 2023-08-25 Marius Herr , Rolf Radespiel , Axel Probst

In wildfires, atmospheric turbulence plays a major role in the transfer of turbulent kinetic energy. Understanding how turbulence feeds back into a dynamical system is important, down to the varying small scales of fuel structures (i.e.…

流体动力学 · 物理学 2024-09-11 Dorianis M. Perez , Jesse M. Canfield , Rodman R. Linn , Kevin Speer

A unified subgrid-scale (SGS) and wall model for large-eddy simulation (LES) is proposed by devising the flow as a collection of building blocks that enables the prediction of the eddy viscosity. The core assumption of the model is that…

流体动力学 · 物理学 2022-12-20 Yuenong Ling , Gonzalo Arranz , Emily Williams , Konrad Goc , Kevin Griffin , Adrián Lozano-Durán

An adjoint-based variational optimal mixed model (VOMM) is proposed for subgrid-scale (SGS) closure in large-eddy simulation (LES) of turbulence. The stabilized adjoint LES equations are formulated by introducing a minimal regularization to…

流体动力学 · 物理学 2023-07-19 Zelong Yuan , Yunpeng Wang , Xiaoning Wang , Jianchun Wang

We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for stochastic modeling. The ideal LES…

An innovative \textit{deep learning} approach has been adopted to formulate the eddy-viscosity for large eddy simulation (LES) of wall-bounded turbulent flows. A deep neural network (DNN) is developed which learns to evaluate the…

流体动力学 · 物理学 2019-05-31 Anikesh Pal

We propose a new model of turbulence for use in large-eddy simulations (LES). The turbulent force, represented here by the turbulent Lamb vector, is divided in two contributions. The contribution including only subfilter fields is…

流体动力学 · 物理学 2009-11-11 Jean-Philippe Laval , Berengere Dubrulle

Turbulent flow across an in-line array of tube-banks with transverse and longitudinal pitch PT /D = 2.67, and PL /D = 2.31, has been simulated successfully by Large Eddy Simulation (LES) based on the dynamic Smagorinsky subgrid scale model…

流体动力学 · 物理学 2016-05-30 C. Jin , I. Potts , D. C. Swailes , M. W. Reeks

A deep learning (DL) closure model for large-eddy simulation (LES) is developed and evaluated for incompressible flows around a rectangular cylinder at moderate Reynolds numbers. Near-wall flow simulation remains a central challenge in…

流体动力学 · 物理学 2023-07-19 Justin Sirignano , Jonathan F. MacArt

With the continuing progress in large eddy simulations (LES), and ever increasing computational resources, it is currently possible to numerically solve the time-dependent and anisotropic large scales of turbulence in a large variety of…

流体动力学 · 物理学 2021-12-14 Ryan D. Hass , Aditya S. Ghate , Sanjiva K. Lele

The effect of grid resolution on large eddy simulation (LES) of wall-bounded turbulent flow is investigated. A channel flow simulation campaign involving systematic variation of the streamwise ($\Delta x$) and spanwise ($\Delta z$) grid…

流体动力学 · 物理学 2018-05-23 Saleh Rezaeiravesh , Mattias Liefvendahl

We conduct wall-modeled LES (WMLES) of a pressure-driven three-dimensional turbulent boundary layer (3DTBL) developing on the floor of a bent square duct to investigate the predictive capability of three widely used wall models, namely, a…

流体动力学 · 物理学 2023-04-26 Xiaohan Hu , Imran Hayat , George Ilhwan Park

A new method for the localization of the regions where small scale turbulent fluctuations are present in hypersonic flows is applied to the large-eddy simulation (LES) of a compressible turbulent jet with an initial Mach number equal to 5.…

流体动力学 · 物理学 2015-06-12 D. Tordella , M. Iovieno , S. Massaglia , A. Mignone

Wall-modeled large-eddy simulation (WMLES) is widely recognized as a useful method for simulation of turbulent flows at high Reynolds numbers. Nevertheless, a continual issue in different wall models is the shift of the mean velocity…

流体动力学 · 物理学 2024-11-20 Huan-Cong Liu , Chun-Xiao Xu , Wei-Xi Huang

Turbulence governs the transport of momentum, energy, and scalars in many geophysical and engineering flows. In large-eddy simulations (LES), parameterizing subgrid-scale (SGS) stresses remains a central challenge, as unresolved physical…

流体动力学 · 物理学 2025-10-02 Yu Cheng , Tianle Liu

The work of Ansumali \textit{et al.}\cite{Ansumali} is extended to Two Dimensional Magnetohydrodynamic (MHD) turbulence in which energy is cascaded to small spatial scales and thus requires subgrid modeling. Applying large eddy simulation…

等离子体物理 · 物理学 2017-12-21 Christopher Flint , George Vahala

We present large-eddy-simulation (LES) modeling approaches for the simulation of atmospheric boundary layer turbulence that are of direct relevance to wind energy production. In this paper, we study a GABLS benchmark problem using…

计算工程、金融与科学 · 计算机科学 2024-10-02 Ananias Tomboulides Matthew Churchfield , Paul Fischer , Michael Sprague , Misun Min

This study develops invariance-embedded machine learning sub-grid-scale (SGS) stress models admitting turbulence kinetic energy (TKE) backscatter towards more accurate large eddy simulation (LES) of meso-scale turbulent hurricane boundary…

流体动力学 · 物理学 2025-04-22 Md Badrul Hasan , Meilin Yu , Tim Oates

Large Eddy Simulations of turbulent flows are powerful tools used in many engineering and geophysical settings. Choosing the right value of the free parameters for their subgrid scale models is a crucial task for which the current methods…

流体动力学 · 物理学 2021-02-03 M. Buzzicotti , P. Clark Di Leoni

High-fidelity wall-resolved large-eddy simulations (LES) are utilized to investigate the flow-physics of small-amplitude pitch oscillations of an airfoil at Re = 100,000. The investigation of the unsteady phenomenon is done in the context…

流体动力学 · 物理学 2019-04-18 Prabal S. Negi , Ricardo Vinuesa , Ardeshir Hanifi , Philipp Schlatter , Dan S. Henningson