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While direct numerical simulations (DNS) are the most accurate method for studying turbulence, their large computational cost restricts their use to idealized configurations and to Reynolds numbers well below those found in practical…

流体动力学 · 物理学 2025-12-09 Chang Hsin Chen , Arnab Moitro , Alexei Y. Poludnenko

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alternative that is computationally less demanding, but is…

流体动力学 · 物理学 2021-09-09 Shengyu Chen , Shervin Sammak , Peyman Givi , Joseph P. Yurko1 , Xiaowei Jia

The simulation of high Reynolds number (Re) separated turbulent flows faces significant problems for decades: large eddy simulation (LES) is computationally too expensive, and Reynolds-averaged Navier-Stokes (RANS) methods and hybrid…

流体动力学 · 物理学 2024-12-02 Stefan Heinz , Adeyemi Fagbade

A new approach to turbulence simulation, based on a combination of large-eddy simulation (LES) for the whole flow and an array of non-space-filling quasi-direct numerical simulations (QDNS), which sample the response of near-wall turbulence…

流体动力学 · 物理学 2017-10-20 Neil D. Sandham , Roderick Johnstone , Christian T. Jacobs

The precise simulation of turbulent flows holds immense significance across various scientific and engineering domains, including climate science, freshwater science, and energy-efficient manufacturing. Within the realm of simulating…

流体动力学 · 物理学 2024-12-31 Shengyu Chen , Peyman Givi , Can Zheng , Xiaowei Jia

Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simulation (LES) has been widely used as an alternative to direct…

流体动力学 · 物理学 2023-12-13 Shengyu Chen , Tianshu Bao , Peyman Givi , Can Zheng , Xiaowei Jia

Turbulent flow has been extensively studied using computational fluid dynamics (CFD) simulations since turbulent flow regime is so frequently encountered in both academic and engineering applications. The high-fidelity simulation of the…

流体动力学 · 物理学 2024-05-21 Minghan Chu

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

Within the domain of Computational Fluid Dynamics, Direct Numerical Simulation (DNS) is used to obtain highly accurate numerical solutions for fluid flows. However, this approach for numerically solving the Navier-Stokes equations is…

流体动力学 · 物理学 2021-03-16 Pranshu Pant , Amir Barati Farimani

Direct numerical simulation (DNS), mostly used in fundamental turbulence research, is limited to low turbulent intensities due the current and future computer resources. Standard turbulence models, like RaNS (Reynolds averaged…

流体动力学 · 物理学 2015-06-17 Christoph Glawe , Heiko Schmidt , Alan R. Kerstein , Rupert Klein

We study the numerical errors of large-eddy simulation (LES) in isotropic and wall-bounded turbulence. A direct-numerical-simulation (DNS)-aided LES formulation, where the subgrid-scale (SGS) term of the LES is computed by using filtered…

流体动力学 · 物理学 2022-08-05 H. Jane Bae , Adrian Lozano-Duran

High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among many others. The rising popularity of high-fidelity…

流体动力学 · 物理学 2019-03-06 Arvind Mohan , Don Daniel , Michael Chertkov , Daniel Livescu

Wall-bounded turbulence is relevant for many engineering and natural science applications, yet there are still aspects of its underlying physics that are not fully understood, particularly at high Reynolds numbers. In this study, we…

流体动力学 · 物理学 2024-04-04 Himani Garg , Lei Wang , Martin Andersson , Christer Fureby

The Large Eddy Simulation (LES) approach - solving numerically the large scales of a turbulent system and accounting for the small-scale influence through a model - is applied to nonlinear gyrokinetic systems that are driven by a number of…

等离子体物理 · 物理学 2015-06-18 A. Bañón Navarro , B. Teaca , F. Jenko , G. W. Hammett , T. Happel , the ASDEX Upgrade Team

The problem of an accurate Eulerian-Lagrangian modeling of inertial particle dispersion in Large Eddy Simulation (LES) of turbulent wall-bounded flows is addressed. We run Direct Numerical Simulation (DNS) for turbulent channel flow at…

流体动力学 · 物理学 2009-11-13 C. Marchioli , M. V. Salvetti , A. Soldati

Deep learning (DL) has recently emerged as a candidate for closure modeling of large-eddy simulation (LES) of turbulent flows. High-fidelity training data is typically limited: it is computationally costly (or even impossible) to…

流体动力学 · 物理学 2023-03-07 Justin Sirignano , Jonathan F. MacArt

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…

The rational large eddy simulation (RLES) model is applied to turbulent channel flows. This approximate deconvolution model is based on a rational (subdiagonal Pade') approximation of the Fourier transform of the Gaussian filter and is…

数值分析 · 数学 2025-10-20 T. Iliescu , P. Fischer

The shock induced mixing of two gases separated by a perturbed interface is investigated through Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS). In a simulation, physical dissipation of the velocity field and species mass…

流体动力学 · 物理学 2015-06-18 Britton J. Olson , Jeffrey A. Greenough

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
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