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We propose a framework for developing wall models for large-eddy simulation that is able to capture pressure-gradient effects using multi-agent reinforcement learning. Within this framework, the distributed reinforcement learning agents…

Fluid Dynamics · Physics 2024-07-29 Di Zhou , H. Jane Bae

Filtered budgets for anelastic turbulence and a general expression of the turbulent sensible heat flux are derived for a multicomponent fluid with an arbitrary equation of state. A family of subgrid-scale closures is then found under the…

Fluid Dynamics · Physics 2026-01-26 Thomas Dubos

The effects of turbulent dynamic range on scalar mixing in stably stratified turbulence are investigated by an adaptation of the theoretical passive scalar modelling arguments of Beguier et al. (1978) and demonstrated statistically using…

Fluid Dynamics · Physics 2022-04-05 G. D. Portwood , S. M. de Bruyn Kops , C. P. Caulfield

Turbulence models, such as the Smagorinsky model herein, are used to represent the energy lost from resolved to under-resolved scales due to the energy cascade (i.e. non-linearity). Analytic estimates of the energy dissipation rates of a…

Numerical Analysis · Mathematics 2019-04-24 Ali Pakzad

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

In this paper a procedure for large-eddy simulation (LES) has been devised for fluid and magnetohydrodynamic turbulence in Fourier space using the renormalized parameters. The parameters calculated using field theory have been taken from…

Chaotic Dynamics · Physics 2009-11-10 Mahendra K. Verma , Shishir Kumar

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…

Fluid Dynamics · Physics 2019-05-31 Anikesh Pal

We present two families of sub-grid scale (SGS) turbulence models developed for large-eddy simulation (LES) purposes. Their development required the formulation of physics-informed robust and efficient Deep Learning (DL) algorithms which,…

Fluid Dynamics · Physics 2023-07-20 Rikhi Bose , Arunabha M. Roy

We study the spatial statistics of velocity gradient volatility (i,e., the energy dissipation rate) in turbulent flow. We extend the Kolmogorov-Obukhov theory but also narrow its scope. The models are log normal, with verification from…

Fluid Dynamics · Physics 2017-03-01 James Glimm , Vinay Mahadeo

The discrete direct deconvolution model (D3M) is developed for the large-eddy simulation (LES) of turbulence. The D3M is a discrete approximation of previous direct deconvolution model studied by Chang et al. ["The effect of sub-filter…

Fluid Dynamics · Physics 2024-02-16 Ning Chang , Zelong Yuan , Yunpeng Wang , Jianchun Wang

A stochastic approach based on generalized Polynomial Chaos (gPC) is used to quantify the error in Large-Eddy Simulation (LES) of a spatially-evolving mixing layer flow and its sensitivity to different simulation parameters, viz. the grid…

Fluid Dynamics · Physics 2013-09-27 Marcello Meldi , Maria Vittoria Salvetti , Pierre Sagaut

We establish and discuss {\em a priori} estimates on subgrid stress and subgrid flux for filtering schemes used in the turbulence modelling method of Large-Eddy Simulation (LES). Our estimates are derived as rigorous consequences of the…

chao-dyn · Physics 2008-02-03 Gregory L. Eyink

We construct a new subgrid scale (SGS) stress model for representing the small scale effects in large eddy simulation (LES) of incompressible flows. We use the covariance tensor for representing the Reynolds stress and include Clark's model…

Fluid Dynamics · Physics 2016-01-14 Rukiye Kara , Mine Caglar

We use the recently developed Macroscopic Forcing Method [Mani and Park, Physical Review Fluids, 6:054607, 2021] to compute the scale-dependent eddy diffusivity characterizing ensemble-averaged scalar and momentum transport in…

Fluid Dynamics · Physics 2022-01-19 Yasaman Shirian , Ali Mani

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…

Fluid Dynamics · Physics 2020-01-22 Hongjiang Li , Christopher J. Rutland , Francisco E. Hernandez Perez , Hong G. Im

In practically all turbulent flows, turbulent energy decay is present and competes with numerous other phenomena. In Kolmogorov's theory, decay proceeds by transfer from large energy-containing scales towards small viscous scales through…

Fluid Dynamics · Physics 2007-05-23 Antoine Llor

This study aims to identify the properties, advantages, and drawbacks of some common (and some less common) sub-grid scale (SGS) models for large eddy simulation of low Mach compressible flows using high order spectral elements. The models…

We perform direct numerical simulation (DNS) and large eddy simulation (LES) of an initially spherical region of turbulence evolving in free space. The computations are performed with a lattice Green's function method, which allows the…

Fluid Dynamics · Physics 2021-04-22 Ke Yu , Tim Colonius , D. I. Pullin , Gregoire Winckelmans

The explicit filtering method for large eddy simulation (LES,) which comprises integration of the governing equations without any added terms for sub-grid-scale modeling, and the application of a low-pass filter to transported fields, is…

Fluid Dynamics · Physics 2017-10-06 Joseph Mathew

Direct numerical simulations (DNS) are one of the main ab initio tools to study turbulent flows. However, due to their considerable computational cost, DNS are primarily restricted to canonical flows at moderate Reynolds numbers, in which…

Fluid Dynamics · Physics 2024-09-17 Arnab Moitro , Sai Sandeep Dammati , Alexei Y. Poludnenko
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