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With the continuing progress in large eddy simulations (LES), and ever increasing computational resources, it is currently possible to numerically solve the time-dependent and anisotropic large scales of turbulence in a large variety of…

流体动力学 · 物理学 2021-12-14 Ryan D. Hass , Aditya S. Ghate , Sanjiva K. Lele

A new tuning-free subgrid-scale model, termed `locally-averaged scale-dependent dynamic' (LASDD) model, is developed and implemented in large-eddy simulations (LESs) of stable boundary layers. The new model dynamically computes the…

大气与海洋物理 · 物理学 2016-08-16 Sukanta Basu , Fernando Porté-Agel

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 study of turbulent flows calls for measurements with high resolution both in space and in time. We propose a new approach to reconstruct High-Temporal-High-Spatial resolution velocity fields by combining two sources of information that…

流体动力学 · 物理学 2017-07-10 Linh Van Nguyen , Jean-Philippe Laval , Pierre Chainais

One of the greatest challenges to using large-eddy simulations (LES) in engineering applications is the large number of grid points required near walls. To mitigate this issue, researchers often couple LES with a simplified model of the…

流体动力学 · 物理学 2016-11-15 Alexandre Marques , Qiqi Wang , Johan Larsson , Gregory Laskowski , Sanjeeb Bose

The observability of the flow field away from the wall in turbulent channel flow is studied using only wall observations. Reconstructions are generated from noiseless but limited wall data using linear stochastic estimation, with emphasis…

流体动力学 · 物理学 2019-11-13 Miguel P. Encinar , Javier Jiménez

The present research proposes a new memory-efficient method using diffusion models to inject turbulent inflow conditions into Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) for various flow problems. A guided diffusion…

An online Data Assimilation strategy based on the Ensemble Kalman Filter (EnKF) is used to improve the predictive capabilities of Large Eddy Simulation (LES) for the analysis of the turbulent flow in a plane channel, $Re_\tau \approx 550$.…

流体动力学 · 物理学 2023-10-30 Lucas Villanueva , Karine Truffin , Marcello Meldi

Current design constraints have encouraged the studies of aeroacoustics fields around compressible jet flows. The present work addresses the numerical study of unsteady turbulent jet flows for aeroacoustic analyses of main engine rocket…

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

Explicit filtering in large eddy simulation (LES) of a turbulent barotropic flow on the sphere in spectral space is studied and compared to implicit filtering. Here, a smooth filter is applied to the nondivergent barotropic vorticity…

流体动力学 · 物理学 2014-04-08 Leila N. Azadani , Anne E. Staples

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

The effect of grid resolution on large eddy simulation (LES) of wall-bounded turbulent flow is investigated. A channel flow simulation campaign involving systematic variation of the streamwise ($\Delta x$) and spanwise ($\Delta z$) grid…

流体动力学 · 物理学 2018-05-23 Saleh Rezaeiravesh , Mattias Liefvendahl

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

In the Large Eddy Simulation (LES) framework for modeling a turbulent flow, when the large scale velocity field is defined by low-pass filtering the full velocity field, a Taylor series expansion of the full velocity field in terms of the…

流体动力学 · 物理学 2012-10-09 Balasubramanya T. Nadiga , Freddy Bouchet

Serrations are commonly employed to mitigate the turbulent boundary layer trailing-edge noise. However, significant discrepancies persist between model predictions and experimental observations. In this paper, we show that this results from…

流体动力学 · 物理学 2023-12-27 Haopeng Tian , Benshuai Lyu

In many practical fluid dynamics experiments, measuring variables such as velocity and pressure is possible only at a limited number of sensor locations, \textcolor{black}{for a few two-dimensional planes, or for a small 3D domain in the…

流体动力学 · 物理学 2023-07-14 Ali Girayhan Özbay , Sylvain Laizet

We propose a new model of turbulence for use in large-eddy simulations (LES). The turbulent force, represented here by the turbulent Lamb vector, is divided in two contributions. The contribution including only subfilter fields is…

流体动力学 · 物理学 2009-11-11 Jean-Philippe Laval , Berengere Dubrulle

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

The Eulerian-Lagrangian approach based on Large-Eddy Simulation (LES) is one of the most promising and viable numerical tools to study turbulent dispersed flows when the computational cost of Direct Numerical Simulation (DNS) becomes too…

流体动力学 · 物理学 2017-06-02 Alessio Innocenti , Cristian Marchioli , Sergio Chibbaro

Recent developments in vortex particle methods for simulating three-dimensional incompressible flows are presented. A lightweight, dynamic Large-Eddy Simulation model is tested, featuring a dynamic procedure that relies solely on Lagrangian…

流体动力学 · 物理学 2026-01-13 Flavio A. C. Martins , Alexander van Zuijlen , Carlos J. Simao Ferreira