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This is the second part to our companion paper. The novel method to quantify artificial dissipation proposed in Part 1 is further applied in turbulent channel flow at $\mathrm{Re_\tau}=180$ using various subgrid-scale models, with an…

流体动力学 · 物理学 2025-03-03 Jing Sun , Roel Verstappen

We propose a novel method to quantify artificial dissipation in large eddy simulation. Here, artificial dissipation is defined as the residual of the discrete turbulent kinetic energy (TKE) equation. This method is applied to turbulent…

流体动力学 · 物理学 2025-03-03 Jing Sun , Roel Verstappen

A previously developed modeling procedure for large eddy simulations (LESs) is extended to allow physical space implementations for inhomogeneous flows. The method is inspired by the well-established theoretical analyses and numerical…

流体动力学 · 物理学 2022-10-28 Guangrui Sun , J. Andrzej Domaradzki

Most sub-grid scale (SGS) models employed in LES (large eddy simulation) formulations were originally developed for incompressible, single phase, inert flows and assume transfer of energy based on the classical energy cascade mechanism.…

流体动力学 · 物理学 2023-09-13 Jhon Cordova , Cesar Celis , Andres Mendiburu , Luis Bravo , Prashant Khare

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

In this review, the methodology of large eddy simulations (LES) is introduced and applications in astrophysics are discussed. As theoretical framework, the scale decomposition of the dynamical equations for compressible neutral fluids by…

星系天体物理 · 物理学 2025-09-09 Wolfram Schmidt-Brückner

The performance of the nonlinearly stable flux reconstruction (NSFR) schemes for resolving subsonic viscous turbulent free-shear flows is investigated. The schemes are extensively verified for the direct numerical simulation (DNS) of the…

流体动力学 · 物理学 2024-11-20 Julien Brillon , Siva Nadarajah

In this article, we utilize machine learning to dynamically determine if a point on the computational grid requires implicit numerical dissipation for large eddy simulation (LES). The decision making process is learnt through \emph{a…

流体动力学 · 物理学 2019-02-07 Romit Maulik , Omer San , Jamey D Jacob

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

Implicitly filtered large-eddy simulation (LES) is by nature numerically under-resolved. With the sole exception of Fourier-spectral methods, discrete numerical derivative operators cannot accurately represent the dynamics of all of the…

流体动力学 · 物理学 2021-10-04 Gopal R. Yalla , Todd A. Oliver , Robert D. Moser

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

In this review, the methodology of large eddy simulations (LES) is introduced and applications in astrophysics are discussed. As theoretical framework, the scale decomposition of the dynamical equations for neutral fluids by means of…

宇宙学与河外天体物理 · 物理学 2014-04-10 Wolfram Schmidt

We present a one-equation subgrid scale model that evolves the turbulence energy corresponding to unresolved velocity fluctuations in large eddy simulations. The model is derived in the context of the Germano consistent decomposition of the…

天体物理学 · 物理学 2009-11-11 W. Schmidt , J. C. Niemeyer , W. Hillebrandt

(Abridged) In the implicit large eddy simulation (ILES) paradigm, the dissipative nature of high-resolution shock-capturing schemes is exploited to provide an implicit model of turbulence. Recent 3D simulations suggest that turbulence might…

高能天体物理现象 · 物理学 2015-08-21 David Radice , Sean M. Couch , Christian D. Ott

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

In high Reynolds number turbulent flows, energy dissipation refers to the process of energy transfer from kinetic energy to internal energy due to molecular viscosity. In large eddy simulation (LES) with one-equation turbulence models, the…

流体动力学 · 物理学 2020-01-22 Hongjiang Li , Christopher J. Rutland , Francisco E. Hernandez Perez , Hong G. Im

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

Large-eddy simulations (LES) and implicit LES (ILES) are wise and affordable alternatives to the unfeasible direct numerical simulations (DNS) of turbulent flows at high Reynolds numbers (Re). However, for systems with few observational…

太阳与恒星天体物理 · 物理学 2022-04-13 H. D. Nogueira , G. Guerrero , P. K. Smolarkiewicz , A. G. Kosovichev

In this paper, we have used renormalized viscosity for performing large eddy simulations (LES) of decaying homogeneous and isotropic turbulence inside a periodic cube on coarse grids ($32^3$, $64^3$ and $128^3$) at initial Taylor Reynolds…

流体动力学 · 物理学 2018-04-25 Sumit Vashishtha , Anando Chatterjee , Abhishek Kumar , Mahendra K Verma

Due to the prohibitive cost of resolving all relevant scales, direct numerical simulations of turbulence remain unfeasible for most real-world applications. Consequently, dynamically simplified formulations are needed for coarse-grained…

流体动力学 · 物理学 2025-12-30 F. Xavier Trias , Jesús Ruano , Alexey Duben , Andrey Gorobets
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