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The development of a wall model using machine learning methods for the large-eddy simulation (LES) of separated flows is still an unsolved problem. Our approach is to leverage the significance of separated flow data, for which existing…

流体动力学 · 物理学 2024-12-25 Zhideng Zhou , Xin-lei Zhang , Guo-wei He , Xiaolei Yang

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…

流体动力学 · 物理学 2026-04-22 Michael Heisel , Rahul Deshpande , Gabriel G. Katul

The evolution with Reynolds number of the dissipation function, normalized by wall variables, is investigated using direct numerical simulation (DNS) databases for incompressible turbulent Poiseuille flow in a plane channel, at friction…

流体动力学 · 物理学 2012-11-01 Faouzi Laadhari

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…

流体动力学 · 物理学 2016-04-18 Benjamin Krank , Wolfgang A. Wall

It is well known that in a neutrally-stratified turbulent flow in a deep constant-stress layer above a flat surface, the variation of the mean velocity with respect to the distance from the surface obeys the logarithmic law (the so-called…

流体动力学 · 物理学 2009-11-07 S. Besio , A. Mazzino , C. F. Ratto

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…

流体动力学 · 物理学 2024-04-04 Himani Garg , Lei Wang , Martin Andersson , Christer Fureby

In the present paper, we apply the framework of volume-filtering for particle-laden flows, to large eddy simulations (LES) of wall-bounded flows leading to a new perspective on wall-modeled LES (WMLES) that we refer to as volume-filtered…

流体动力学 · 物理学 2025-04-29 Max Hausmann , Berend van Wachem

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

The hybrid wall-modeled large-eddy simulation (WMLES) and immersed boundary (IB) method offers significant flexibility for simulating high Reynolds number flows involving complex boundaries. However, the approximate boundary conditions…

流体动力学 · 物理学 2025-06-30 Zhideng Zhou , Fengshun Zhang , Xiaolei Yang

Accurate and efficient prediction of three-dimensional (3D) wall-bounded turbulent flows poses a significant challenge for machine learning methods, particularly in scenarios where flow field data are limited. Physics-informed neural…

流体动力学 · 物理学 2026-04-30 Sunan Zhao , Yunpeng Wang , Huiyu Yang , Zhihong Guo , Jianchun Wang

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 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…

流体动力学 · 物理学 2023-01-25 Aniruddhe Pradhan , Karthik Duraisamy

This survey investigates wall modeling in large eddy simulations (LES) using data-driven machine learning (ML) techniques. To this end, we implement three ML wall models in an open-source code and compare their performances with the…

流体动力学 · 物理学 2023-05-23 Aurélien Vadrot , Xiang I. A. Yang , Mahdi Abkar

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…

流体动力学 · 物理学 2022-12-20 Yuenong Ling , Gonzalo Arranz , Emily Williams , Konrad Goc , Kevin Griffin , Adrián Lozano-Durán

We propose a model for the streamwise velocity variance in wall-bounded turbulent flows. It hypothesizes that the wall-parallel motions of the attached eddies induce internal turbulent boundary layers. A logarithmic variance profile is…

流体动力学 · 物理学 2024-05-07 Chenning Tong

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

Townsend (1976) proposed a structural model for the logarithmic layer (log-layer) of wall turbulence at high Reynolds numbers, where the dominant momentum-carrying motions are organised into a multi-scale population of eddies attached to…

流体动力学 · 物理学 2021-10-26 Adrián Lozano-Durán , Hyunji Jane Bae

In this Letter we suggest a simple and physically transparent analytical model of the pressure driven turbulent wall-bounded flows at high but finite Reynolds numbers Re. The model gives accurate qualitative description of the profiles of…

混沌动力学 · 物理学 2009-02-18 Victor S. L'vov , Itamar Procaccia , Oleksii Rudenko

The prediction of aircraft aerodynamic quantities of interest remains among the most pressing challenges for computational fluid dynamics. The aircraft aerodynamics are inherently turbulent with mean-flow three-dimensionality, often…

流体动力学 · 物理学 2021-05-25 Adrián Lozano-Durán , Hyunji Jane Bae

We investigate eddy-viscosity distributions in pressure-driven wall turbulence for three canonical configurations: plane closed-channel flow, open-channel flow with a free-slip surface, and pipe flow. Using direct numerical simulation (DNS)…

流体动力学 · 物理学 2026-04-13 Ben-Rui Xu , Ao Xu