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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…

流体动力学 · 物理学 2013-04-23 Rashad Moarref , Mihailo R. Jovanović

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

This study aims to quantify how turbulence in a channel flow mixes momentum in the mean sense. We applied the macroscopic forcing method (Mani and Park, Physical Review Fluids, 2021, p.054607) to direct numerical simulation (DNS) of a…

流体动力学 · 物理学 2024-11-05 Danah Park , Ali Mani

We investigate the role of the bulk eddy-viscosity variation on the well-known log-layer mismatch problem. An analysis of the mean momentum-balance shows that the modeled stress term close to the wall can dominate because of the bulk…

流体动力学 · 物理学 2017-12-22 Rey DeLeon , Inanc Senocak

To date no satisfying model exists to explain the mean velocity profile within the whole turbulent layer of canonical wall bounded flows. We propose a modification of the velocity profile expression that ensues from a recently proposed…

流体动力学 · 物理学 2018-12-10 Benoit Pinier , Etienne Mémin , Sylvain Laizet , Roger Lewandowski

This is the second part to our companion paper. The novel method to quantify artificial dissipation proposed in Part 1 is further applied in turbulent channel flow at $\mathrm{Re_\tau}=180$ using various subgrid-scale models, with an…

流体动力学 · 物理学 2025-03-03 Jing Sun , Roel Verstappen

The aim of this study is to improve the prediction of near-wall mean streamwise velocity profile $U^+$ by using a simple method. The $U^+$ profile is obtained by solving the momentum equation which is written as an ordinary differential…

流体动力学 · 物理学 2011-06-07 Rafik Absi

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 predictions of resolvent analysis for turbulent channel flow are evaluated for a friction Reynolds number of Retau = 550. In addition to the standard resolvent operator with kinematic viscosity, a resolvent operator augmented with the…

流体动力学 · 物理学 2022-05-24 Sean Symon , Anagha Madhusudanan , Simon J. Illingworth , Ivan Marusic

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

Fluid turbulence is an important problem for physics and engineering. Turbulence modeling deals with the development of simplified models that can act as surrogates for representing the effects of turbulence on flow evolution. Such models…

流体动力学 · 物理学 2021-11-16 J P Panda

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…

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

We use the recently developed Macroscopic Forcing Method [Mani and Park, Physical Review Fluids, 6:054607, 2021] to compute the scale-dependent eddy diffusivity characterizing ensemble-averaged scalar and momentum transport in…

流体动力学 · 物理学 2022-01-19 Yasaman Shirian , Ali Mani

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

Non-equilibrium wall turbulence with mean-flow three-dimensionality is ubiquitous in geophysical and engineering flows. Under these conditions, turbulence may experience a counter-intuitive depletion of the turbulent stresses, which has…

流体动力学 · 物理学 2020-01-08 Adrián Lozano-Durán , Marco Giometto , George I. Park , Parviz Moin

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

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

We employ a resolvent-based methodology to estimate velocity and pressure fluctuations within turbulent channel flows at friction Reynolds numbers of approximately 180, 550 and 1000 using measurements of shear stress and pressure at the…

流体动力学 · 物理学 2021-10-04 Filipe R. Amaral , André V. G. Cavalieri , Eduardo Martini , Peter Jordan , Aaron Towne

This paper presents an in-depth analysis of a novel subgrid-scale stress model proposed in 2022, which utilizes the rotational part of the velocity gradient as the velocity scale for computing eddy viscosity. This study investigates the…

流体动力学 · 物理学 2025-07-29 Jiawei Chen , Yifei Yu , Emran Hossen , Chaoqun Liu

A promising and cost-effective method for numerical simulation of high Re wall-bounded flows is wall-modeled large-eddy simulation. Most wall models are formulated from the Reynolds-averaged Navier-Stokes equations (RANS). These RANS-based…

流体动力学 · 物理学 2021-01-08 Ahmed Elnahhas , Adrián Lozano-Durán , Parviz Moin
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