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The present study gives an overview and emphasizes principal moments of the applications of the turbulence-resolving modeling with large-eddy simulation (LES) numerical technique to planetary boundary layer (PBL) research and climate…

流体动力学 · 物理学 2009-07-02 Igor Esau

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

We adopt the stretched spiral vortex sub-grid model for large-eddy simulation (LES) of turbulent convection at extreme Rayleigh numbers. We simulate Rayleigh-B\'enard convection (RBC) for Rayleigh numbers ranging from $10^6$ to $10^15$ and…

流体动力学 · 物理学 2024-06-12 Roshan Samuel , Ravi Samtaney , Mahendra K. Verma

Large eddy simulation (LES) of forced, homogeneous, isotropic, two-dimensional (2D) turbulence in the energy transfer subrange is the subject of this paper. A difficulty specific to this LES and its subgrid scale (SGS) representation is in…

chao-dyn · 物理学 2008-02-03 Semion Sukoriansky , Alexei Chekhlov , Boris Galperin , Steven A. Orszag

Computational fluid dynamics (CFD) is a useful tool for prediction of turbulence in aerodynamic and biomedical applications. The choice of appropriate turbulence models is key to reaching accurate predictions. The present investigation…

流体动力学 · 物理学 2018-03-13 Fardin Khalili , Peshala P. T. Gamage , Hansen A. Mansy

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

In the present work, we explore the capability of artificial neural networks (ANN) to predict the closure terms for large eddy simulations (LES) solely from coarse-scale data. To this end, we derive a consistent framework for LES closure…

计算物理 · 物理学 2022-12-05 Marius Kurz , Andrea Beck

This study proposes a multiscale convolutional neural network subgrid-scale (MSC-SGS) model for large-eddy simulation (LES). This model incorporates multiscale representations obtained via filtering to capture turbulent vortices…

流体动力学 · 物理学 2025-02-18 Bahrul Jalaali , Kie Okabayashi

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…

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

With increasing emphasis on carbon neutrality, accurate and efficient combustion prediction has become essential for the design and optimization of new generation combustion systems. This study established a computational framework by…

The present work is concerned with a study of large eddy simulations (LES) of unsteady turbulent jet flows. In particular, the present analysis is focused on the effects of the subgrid-scale modeling used when a second-order spatial…

流体动力学 · 物理学 2022-12-26 Carlos Junqueira-Junior , Sami Yamouni , João Luiz F. Azevedo , William R. Wolf

In large-eddy simulations (LES) a computational-domain translation velocity can be used to improve performance by allowing longer time-step intervals. The continuous equations are Galilean invariant, however, standard…

大气与海洋物理 · 物理学 2021-02-03 Oumaima Lamaakel , Georgios Matheou

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,…

流体动力学 · 物理学 2023-07-20 Rikhi Bose , Arunabha M. Roy

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

Acquisition of large datasets for three-dimensional (3D) partial differential equations (PDE) is usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and…

流体动力学 · 物理学 2025-04-10 Sunan Zhao , Zhijie Li , Boyu Fan , Yunpeng Wang , Huiyu Yang , Jianchun Wang

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

This study proposes a novel method for developing discretization-consistent closure schemes for implicitly filtered Large Eddy Simulation (LES). Here, the induced filter kernel, and thus the closure terms, are determined by the properties…

流体动力学 · 物理学 2023-12-14 Andrea Beck , Marius Kurz

We present a numerical method for Large Eddy Simulations (LES) of compressible two-phase flows. The method is validated for the flow in a micro channel with a step-like restriction. This setup is representative for typical cavitating…

流体动力学 · 物理学 2014-01-28 S Hickel , M. Mihatsch , S. J. Schmidt

In many engineering and industrial applications, the investigation of rotating turbulent flow is of great interest. In rotor-stator cavities, the centrifugal and Coriolis forces have a strong influence on the turbulence by producing a…