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We present a framework for Large Eddy Simulations (LES) with Immersed Boundaries (IBs) to simulate high Reynolds number flows over complex walls. In this approach, which we call Immersed Boundary-Modeled LES (IBMLES), we volume-filter the…

流体动力学 · 物理学 2025-11-03 Morrison Rickard , M. Houssem Kasbaoui

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…

流体动力学 · 物理学 2024-07-29 Di Zhou , H. Jane Bae

Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and out-of-distribution generalizability. Here, we use {online}…

Particle Image Velocimetry (PIV) systems are often limited in their ability to fully resolve the spatiotemporal fluctuations inherent in turbulent flows due to hardware constraints. In this study, we develop models based on Rapid Distortion…

流体动力学 · 物理学 2021-01-15 C. Vamsi Krishna , Mengying Wang , Maziar S. Hemati , Mitul Luhar

We present an approximate deconvolution (AD) large eddy simulation (LES) model for the two-layer quasigeostrophic equations. We applied the AD-LES model to mid-latitude two-layer square oceanic basins, which are standard prototypes of more…

大气与海洋物理 · 物理学 2013-10-08 Omer San , Anne E. Staple , Traian Iliescu

In this work, we present a novel data-based approach to turbulence modelling for Large Eddy Simulation (LES) by artificial neural networks. We define the exact closure terms including the discretization operators and generate training data…

计算工程、金融与科学 · 计算机科学 2019-10-09 Andrea D. Beck , David G. Flad , Claus-Dieter Munz

We present a wall model for large-eddy simulation that incorporates surface-roughness effects and is applicable across low- and high-speed flows, for both transitional and fully rough conditions. The model, implemented using an artificial…

流体动力学 · 物理学 2026-01-29 Rong Ma , Adrian Lozano-Duran

Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to a drastic drop in performance when computer vision…

计算机视觉与模式识别 · 计算机科学 2022-04-20 Nithin Gopalakrishnan Nair , Kangfu Mei , Vishal M. Patel

Despite well-known limitations of Reynolds-averaged Navier-Stokes (RANS) simulations, this methodology remains the most widely used tool for predicting many turbulent flows, due to computational efficiency. Machine learning is a promising…

流体动力学 · 物理学 2022-03-14 Ryley McConkey , Eugene Yee , Fue-Sang Lien

Predictive simulation of many complex flows requires moving beyond Reynolds-averaged Navier-Stokes (RANS) based models to representations resolving at least some scales of turbulence in at least some regions of the flow. To resolve…

流体动力学 · 物理学 2018-12-11 Sigfried Haering , Todd A. Oliver , Robert D. Moser

Response modes computed via linear resolvent analysis of a turbulent mean-flow field have been shown to qualitatively capture characteristics of the observed turbulent coherent structures in both wall-bounded and free shear flows. To make…

流体动力学 · 物理学 2021-05-12 Ethan Pickering , Georgios Rigas , Oliver T. Schmidt , Denis Sipp , Tim Colonius

This work examines the flow separation and the resulting pressure distortions at the exit plane of a serpentine diffuser operating at both subsonic and transonic conditions. Wallmodeled large-eddy simulations (WMLES) using the charLES flow…

流体动力学 · 物理学 2025-06-19 Rahul Agrawal , Chad Winkler , Sanjeeb Bose , Parviz Moin

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

Site-specific flow and turbulence information are needed for various practical applications, ranging from aerodynamic/aeroelastic modeling for wind turbine design to optical diffraction calculations. Even though highly desirable, collecting…

大气与海洋物理 · 物理学 2013-07-10 Yao Wang , Sukanta Basu , Lance Manuel

We present a methodology to determine the best turbulence closure for an eddy-permitting ocean model through measurement of the error-landscape of the closure's subgrid spectral transfers and flux. We apply this method to 6 different…

流体动力学 · 物理学 2015-06-05 Jonathan Pietarila Graham , Todd Ringler

One issue associated with the use of Large-Eddy Simulation (LES) to investigate the dispersion of small inertial particles in turbulent flows is the accuracy with which particle statistics and concentration can be reproduced. The motion of…

Large-Eddy Simulations (LES) of two-phase turbulent flows exhibit quantitative differences in particle statistics if compared to Direct Numerical Simulations (DNS) which, in the context of the present study, is considered the exact…

流体动力学 · 物理学 2012-05-04 F. Bianco , S. Chibbaro , C. Marchioli , M. V. Salvetti , A. Soldati

In Navier-Stokes turbulence, a bottleneck effect in the energy cascade near the viscous cutoff causes an overshoot in the energy spectrum, or spectral bump, relative to Kolmogorov's -5/3 scaling. A similar overshoot occurs in large-eddy…

流体动力学 · 物理学 2025-09-24 Mostafa Kamal , Perry L. Johnson

The transport and deposition of heavy particles over complex surface topography by turbulent fluid flow is an important problem in a number of disciplines, including sediment and snow transport, ecology and plant pathology, aeolian…

大气与海洋物理 · 物理学 2019-03-11 Scott T. Salesky , Marco G. Giometto , Marcelo Chamecki , Michael Lehning , Marc B. Parlange

In the present research work, an attempt is made to develop a coupled non-linear turbulence-structure-damper model in a finite volume-finite difference (FV-FD) framework. Tuned liquid damper (TLD) is used as the additional damping system…

流体动力学 · 物理学 2023-10-20 Ananya Majumdar , Biplab Ranjan Adhikary , Partha Bhattacharya