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Related papers: Optimal Eddy Viscosity in Closure Models for 2D Tu…

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In this work we study stochastic Oldroyd type models for viscoelastic fluids in $\mathbb{R}^d, d= 2, 3$. We show existence and uniqueness of strong local maximal solutions when the initial data are in $H^s$ for $s>d/2, d= 2, 3$.…

Probability · Mathematics 2017-06-19 Utpal Manna , Debopriya Mukherjee

We investigate statistics of large-scale structures from large-eddy simulation (LES) of turbulent channel flow at friction Reynolds numbers $Re_\tau = 2 {\rm k}$ and $200 {\rm k}$. To properly capture the behaviour of large-scale…

Fluid Dynamics · Physics 2015-05-18 D. Chung , B. J. McKeon

We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm "discretize first, filter and close next". This leads to full model-data consistency and allows for…

Numerical Analysis · Mathematics 2024-11-20 Syver Døving Agdestein , Benjamin Sanderse

By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics…

Atmospheric and Oceanic Physics · Physics 2026-01-21 Karan Jakhar , Yifei Guan , Pedram Hassanzadeh

In the present paper, we address the important point of the proportionality between the longitudinal integral lengthscale ($L$) and the characteristic mean flow width ($\delta$) using experimental data of an axisymmetric wake and a…

Fluid Dynamics · Physics 2018-11-07 Gioacchino Cafiero , Martin Obligado , J. Christos Vassilicos

A model reduction technique based on an optimization principle is employed to coarse-grain inviscid, incompressible fluid dynamics in two dimensions. In this reduction the spectrally-truncated vorticity equation defines the microdynamics,…

Fluid Dynamics · Physics 2016-09-21 Bruce Turkington , Qian-Yong Chen , Simon Thalabard

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…

Fluid Dynamics · Physics 2024-12-02 Stefan Heinz , Adeyemi Fagbade

Sediment transport over wave-induced ripples is a very complex phenomenon where available models fail to provide accurate predictions. For coastal engineering applications, the 1-DV advection-diffusion equation could be used with an…

Geophysics · Physics 2011-06-07 Rafik Absi , Hitoshi Tanaka

Large-eddy simulations of incompressible Newtonian fluid flows with approximate deconvolution models based on the van Cittert method are reported. The Legendre spectral element method is used for the spatial discretization to solve the…

Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES). We leverage the concept of differentiable turbulence, whereby an…

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…

Fluid Dynamics · Physics 2021-09-09 Shengyu Chen , Shervin Sammak , Peyman Givi , Joseph P. Yurko1 , Xiaowei Jia

The vortex particle method (VPM) is a mesh-free approach to computational fluid dynamics (CFD) solving the Navier-Stokes equations in their velocity-vorticity form. The VPM uses a Lagrangian scheme, which not only avoids the hurdles of mesh…

Fluid Dynamics · Physics 2024-01-30 Eduardo J. Alvarez , Andrew Ning

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…

Astrophysics of Galaxies · Physics 2025-09-09 Wolfram Schmidt-Brückner

The results of large eddy simulation (LES) using three sub-grid scale models, namely: constant coefficient Smagorinsky, dynamic Smagorinsky, and a dynamic Clark model, for rotating stratified turbulence in the absence of forcing using…

Fluid Dynamics · Physics 2023-08-17 Kiran Jadhav , Rahul Agrawal , Abhilash J. Chandy

This work investigates the current wall-modeled large-eddy simulation (WMLES) capabilities of the open-source computational fluid dynamics solver OpenFOAM, which is used widely in academia and industry. This is achieved by a simulation…

Fluid Dynamics · Physics 2024-12-24 Christoffer Hansen , Xiang I. A. Yang , Mahdi Abkar

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…

Plasma Physics · Physics 2017-12-21 Christopher Flint , George Vahala

Coarse resolution numerical ocean models must typically include a parameterisation for mesoscale turbulence. A common recipe for such parameterisations is to invoke down-gradient mixing, or diffusion, of some tracer quantity, such as…

Fluid Dynamics · Physics 2016-08-03 Julian Mak , James R. Maddison , David P. Marshall

One of the more promising recent approaches to turbulence modelling is the Variational Multiscale Large Eddy Simulation (VMS LES) method proposed by Hughes et al. [Comp. Visual. Sci., vol. 3, pp. 47-59, 2000]. This method avoids several…

Computational Physics · Physics 2007-05-23 Thor Gjesdal , Carl Erik Wasberg , Bjorn Anders Pettersson Reif , Oyvind Andreassen

We study the Navier-Stokes equations with an extra eddy viscosity term in the whole space in three dimensions. We introduce a suitable regularized system for which we prove the existence of a regular solution defined for all time. We prove…

Analysis of PDEs · Mathematics 2017-06-01 Roger Lewandowski

Accurate and efficient prediction of three-dimensional (3D) wall-bounded turbulent flows poses a significant challenge for machine learning methods, particularly in scenarios where flow field data are limited. Physics-informed neural…

Fluid Dynamics · Physics 2026-04-30 Sunan Zhao , Yunpeng Wang , Huiyu Yang , Zhihong Guo , Jianchun Wang
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