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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

Wall-bounded turbulence is relevant for many engineering and natural science applications, yet there are still aspects of its underlying physics that are not fully understood, particularly at high Reynolds numbers. In this study, we…

Fluid Dynamics · Physics 2024-04-04 Himani Garg , Lei Wang , Martin Andersson , Christer Fureby

A novel approach to wall modeling for the incompressible Navier-Stokes equations including flows of moderate and large Reynolds numbers is presented. The basic idea is that a problem-tailored function space allows prediction of turbulent…

Fluid Dynamics · Physics 2016-04-18 Benjamin Krank , Wolfgang A. Wall

A wall model for large-eddy simulation (LES) is proposed by devising the flow as a combination of building blocks. The core assumption of the model is that a finite set of simple canonical flows contains the essential physics to predict the…

Fluid Dynamics · Physics 2023-06-07 Adrián Lozano-Durán , H. Jane Bae

In this paper, we propose an approach for simulating wall-bounded incompressible turbulent flows by integrating the technology of random vortex method with the core principles of large-eddy simulations (LES). In particular, we employ the…

Fluid Dynamics · Physics 2025-11-11 Zihao Guo , Zhongmin Qian

A new approach to turbulence simulation, based on a combination of large-eddy simulation (LES) for the whole flow and an array of non-space-filling quasi-direct numerical simulations (QDNS), which sample the response of near-wall turbulence…

Fluid Dynamics · Physics 2017-10-20 Neil D. Sandham , Roderick Johnstone , Christian T. Jacobs

We propose a supervised-machine-learning-based wall model for coarse-grid wall-resolved large-eddy simulation (LES). Our consideration is made on LES of turbulent channel flows with a first grid point set relatively far from the wall…

Fluid Dynamics · Physics 2021-06-18 Naoki Moriya , Kai Fukami , Yusuke Nabae , Masaki Morimoto , Taichi Nakamura , Koji Fukagata

We study the error scaling properties of large-eddy simulation (LES) in the outer region of wall-bounded turbulence at moderately high Reynolds numbers. In order to avoid the additional complexity of wall-modeling, we perform LES of…

Fluid Dynamics · Physics 2021-10-26 Adrián Lozano-Durán , Hyunji Jane Bae

Wall modelling in large-eddy simulation (LES) is necessary to overcome the prohibitive near-wall resolution requirements in high-Reynolds-number turbulent flows. Most existing wall models rely on assumptions about the state of the boundary…

Fluid Dynamics · Physics 2021-10-26 H. Jane Bae , Adrián Lozano-Durán , Sanjeeb T. Bose , Parviz Moin

The study presents wall-modeled large-eddy simulations (LES) characterizing the flow features of a neutral atmospheric boundary layer over two urban-like roughness geometries: an array of three-dimensional square prisms and the…

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…

A unified subgrid-scale (SGS) and wall model for large-eddy simulation (LES) is proposed by devising the flow as a collection of building blocks that enables the prediction of the eddy viscosity. The core assumption of the model is that…

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…

Fluid Dynamics · Physics 2026-01-29 Rong Ma , Adrian Lozano-Duran

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…

Fluid Dynamics · Physics 2022-03-14 Ryley McConkey , Eugene Yee , Fue-Sang Lien

In this work, we propose a single formula for the law of the wall, which is dubbed as the logarithmic-exponential (LOG-EXP) formula, for predicting the mean velocity profile in different regions near the wall. And then a feedforward neural…

Fluid Dynamics · Physics 2022-04-05 Fengshun Zhang , Zhideng Zhou , Xiaolei Yang , Huan Zhang

Turbulent flows have high requirements for very fine meshes near the boundary to ensure accuracy. In the context of topology optimization (TO), such fine meshes become unrealistic and common approaches are hampered by low accuracy and…

Fluid Dynamics · Physics 2026-01-06 Amirhossein Bayat , Hao Li , Joe Alexandersen

The variance and spectra of wall-normal velocities are investigated for direct numerical simulations of turbulent flow in a channel, pipe, and zero-pressure-gradient boundary layer across a decade of friction Reynolds numbers. Spectra along…

Fluid Dynamics · Physics 2026-04-22 Michael Heisel , Rahul Deshpande , Gabriel G. Katul

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…

Fluid Dynamics · Physics 2016-11-15 Alexandre Marques , Qiqi Wang , Johan Larsson , Gregory Laskowski , Sanjeeb Bose

The main objective of this work is to develop a unified framework that can be used as a lens to quantitatively assess and augment a wide range of coarse-grained models of turbulence, viz. large eddy simulations (LES), hybrid…

Fluid Dynamics · Physics 2023-01-25 Aniruddhe Pradhan , Karthik Duraisamy

Motivated by the needs of wall modeled Large Eddy Simulation (LES), we introduce fits to numerical solutions of the Reynolds Averaged Navier-Stokes equations in their simplest near-wall, boundary layer approximation including a…

Fluid Dynamics · Physics 2020-12-02 Charles Meneveau
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