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We extend the approach of wall modeling via function enrichment to detached-eddy simulation. The wall model aims at using coarse cells in the near-wall region by modeling the velocity profile in the viscous sublayer and log-layer. However,…

Fluid Dynamics · Physics 2018-08-28 Benjamin Krank , Martin Kronbichler , Wolfgang A. Wall

This article analyses the simulation methodology for wall-modeled large-eddy simulations using solvers based on the spectral-element method (SEM). To that end, algebraic wall modeling is implemented in the popular SEM solver Nek5000. It is…

Fluid Dynamics · Physics 2024-04-09 Timofey Mukha , Philipp Schlatter

FEARLESS (Fluid mEchanics with Adaptively Refined Large Eddy SimulationS) is a new numerical scheme arising from the combined use of subgrid scale (SGS) model for turbulence at the unresolved length scales and adaptive mesh refinement (AMR)…

Cosmology and Nongalactic Astrophysics · Physics 2014-11-20 L. Iapichino , A. Maier , W. Schmidt , J. C. Niemeyer

Measurement techniques such as Magnetic Resonance Velocimety (MRV) and Magnetic Resonance Concentration (MRC) are useful for obtaining 3D time-averaged flow quantities in complex turbulent flows, but cannot measure turbulent correlations or…

Systems comprising a turbulent channel flow overlaying a permeable bed can be found in a variety of industrial and natural applications (e.g. urban planning, fracking, submerged vegetation). One important realization of this system is at…

Fluid Dynamics · Physics 2017-08-01 Benjamin H. Sonin

The weights of a deep neural network model are optimized in conjunction with the governing flow equations to provide a model for sub-grid-scale stresses in a temporally developing plane turbulent jet at Reynolds number $Re_0=6\,000$. The…

Fluid Dynamics · Physics 2023-03-23 Jonathan F. MacArt , Justin Sirignano , Jonathan B. Freund

We examine and benchmark the emerging idea of applying the large-eddy simulation (LES) formalism to unconventionally coarse grids where RANS would be considered more appropriate at first glance. We distinguish this idea from…

Fluid Dynamics · Physics 2023-10-17 Yuanwei Bin , George I. Park , Yu Lv , Xiang I. A. Yang

A shear-improved Smagorinsky model is introduced based on recent results concerning shear effects in wall-bounded turbulence by Toschi et al. (2000). The Smagorinsky eddy-viscosity is modified subtracting the magnitude of the mean shear…

Chaotic Dynamics · Physics 2015-06-26 E. Leveque , F. Toschi , L. Shao , J. -P. Bertoglio

Over the last two decades, both experiments and simulations have demonstrated that transverse wall oscillations with properly selected amplitude and frequency can reduce turbulent drag by as much as 40%. In this paper, we develop a…

Fluid Dynamics · Physics 2013-04-23 Rashad Moarref , Mihailo R. Jovanović

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

One promising decomposition of turbulent dynamics is that into building blocks such as equilibrium and periodic solutions and orbits connecting these. While the numerical approximation of such building blocks is feasible for flows in small…

Fluid Dynamics · Physics 2018-11-14 Lennaert van Veen , Genta Kawahara , Tatsuya Yasuda

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…

Plasma Physics · Physics 2015-06-18 A. Bañón Navarro , B. Teaca , F. Jenko , G. W. Hammett , T. Happel , the ASDEX Upgrade Team

Turbulent flows are fundamental in engineering and the environment, but their chaotic and three-dimensional (3-D) nature makes them computationally expensive to simulate. In this work, a dimensionality reduction technique is investigated to…

Fluid Dynamics · Physics 2020-12-16 Bernat Font

This study proposes a rotationally invariant data-driven subgrid-scale (SGS) model for large-eddy simulation (LES) of wall-bounded turbulent flows. Building upon the multiscale convolutional neural network subgrid-scale model, which outputs…

Fluid Dynamics · Physics 2026-02-03 Bahrul Jalaali , Kie Okabayashi

This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high-dimensional problems, especially in domains such as games.…

Fluid Dynamics · Physics 2023-05-24 Aurélien Vadrot , Xiang I. A. Yang , H. Jane Bae , Mahdi Abkar

A turbulent side-wall quenching (SWQ) flame in a fully developed channel flow is studied using Large-Eddy Simulation (LES) with a tabulated chemistry approach. Three different flamelet manifolds with increasing levels of complexity are…

Fluid Dynamics · Physics 2023-08-16 Yujuan Luo , Matthias Steinhausen , Driss Kaddar , Christian Hasse , Federica Ferraro

We present results of implicit large eddy simulation (LES) and different Reynolds-averaged Navier-Stokes (RANS) models of the MTU 161 low pressure turbine at an exit Reynolds number of 90,000 and exit Mach number of 0.6. The LES results are…

Recently, Kami\'nski et al. [1] demonstrated that a two-dimensional streamwise waviness with carefully selected amplitude and period can be effectively used in postponement of a flow separation at high Reynolds number which is out of reach…

Fluid Dynamics · Physics 2025-05-29 Piotr Kamiński , Artur Tyliszczak , Witold Elsner , Paweł Niegodajew

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…

Fluid Dynamics · Physics 2026-01-13 Flavio A. C. Martins , Alexander van Zuijlen , Carlos J. Simao Ferreira

Predicting the large-scale dynamics of three-dimensional (3D) turbulence is challenging for machine learning approaches. This paper introduces a transformer-based neural operator (TNO) to achieve precise and efficient predictions in the…

Fluid Dynamics · Physics 2024-06-07 Zhijie Li , Tianyuan Liu , Wenhui Peng , Zelong Yuan , Jianchun Wang
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