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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…

Fluid Dynamics · Physics 2024-11-05 Danah Park , Ali Mani

Well-resolved direct numerical simulations (DNSs) have been performed of the flow in a smooth circular pipe of radius $R$ and axial length $10\pi R$ at friction Reynolds numbers up to $Re_\tau=5200$. Various turbulence statistics are…

Fluid Dynamics · Physics 2023-02-15 Jie Yao , Saleh Rezaeiravesh , Philipp Schlatter , Fazle Hussain

We propose a Navier-Stokes-driven analysis of the mean and fluctuating wall shear stress (WSS) applied to turbulent channel flow data from direct numerical simulations at friction Reynolds numbers up to $Re_\tau\approx 2000$. Starting from…

Fluid Dynamics · Physics 2026-02-05 Le Yin , Yongyun Hwang , John Christos Vassilicos

The predictive accuracy of wall-modeled large eddy simulation is studied by systematic simulation campaigns of turbulent channel flow. The effect of wall model, grid resolution and anisotropy, numerical convective scheme and subgrid-scale…

Fluid Dynamics · Physics 2019-04-01 Saleh Rezaeiravesh , Timofey Mukha , Mattias Liefvendahl

The pressure strain correlation plays a critical role in the Reynolds stress transport modelling. Accurate modelling of the pressure strain correlation leads to proper prediction of turbulence stresses and subsequently the other terms of…

Fluid Dynamics · Physics 2021-03-02 J P Panda , H V Warrior

We investigate statistics of large-scale structures from large-eddy simulation (LES) of turbulent channel flow at friction Reynolds numbers $Re_\tau = 2 {\rm k}$ and $200 {\rm k}$. To properly capture the behaviour of large-scale…

Fluid Dynamics · Physics 2015-05-18 D. Chung , B. J. McKeon

The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are performed by training a fully-convolutional network (FCN) to…

Turbulent wall flows offer the most direct means for understanding the effects of boundaries and viscosity on turbulent fluctuations. Available data on mean-square fluctuations in these flows show apparent contradiction with classical…

Fluid Dynamics · Physics 2025-08-05 Xi Chen , Katepalli R. Sreenivasan

Resolvent analysis is a powerful tool that can reveal the linear amplification mechanisms between the forcing inputs and the response outputs about a base flow. These mechanisms can be revealed in terms of a pair of forcing and response…

We perform numerical simulations of a turbulent channel flow over an hyper-elastic wall. In the fluid region the flow is governed by the incompressible Navier-Stokes (NS) equations, while the solid is a neo-Hookean material satisfying the…

Fluid Dynamics · Physics 2017-11-22 Marco E. Rosti , Luca Brandt

Recent simulations indicate that streamwise-preferential porous materials have the potential to reduce drag in wall-bounded turbulent flows(Gomez-de-Segura & Garcia-Mayoral 2019). This paper extends the resolvent formulation to study the…

Fluid Dynamics · Physics 2021-03-26 Andrew Chavarin , Garazi Gomez-de-Segura , Ricardo Garcia-Mayoral , Mitul Luhar

Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and aerodynamic designs to weather and climate forecasting. While…

Fluid Dynamics · Physics 2024-02-14 Xiao Xue , Shuo Wang , Hua-Dong Yao , Lars Davidson , Peter V. Coveney

The use of extended surfaces find wide range of applications in heat transfer devices for achieving heat transfer augmentation like gas turbine blade cooling and nuclear reactor core since the last few decades. So, understanding the…

Fluid Dynamics · Physics 2022-09-01 Auronil Mukherjee , Arnab Chakraborty

Direct Numerical Simulations (DNSs) are one of the most powerful tools for studying turbulent flows. Even if achievable Reynolds numbers are lower than those obtained with experimental means, there is a clear advantage since the entire…

Fluid Dynamics · Physics 2024-06-03 Sergio Hoyas , Ricardo Vinuesa , Peter Schmid , Hassan Nagib

Deep reinforcement learning (DRL) is employed to develop control strategies for drag reduction in direct numerical simulations (DNS) of turbulent channel flows at high Reynolds numbers. The DRL agent uses near-wall streamwise velocity…

Fluid Dynamics · Physics 2025-03-19 Zisong Zhou , Mengqi Zhang , Xiaojue Zhu

Coherent turbulent wave-packet structures in a jet at Reynolds number 460000 and Mach number 0.4 are extracted from experimental measurements and are modeled as linear fluctuations around the mean flow. The linear model is based on harmonic…

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

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…

Fluid Dynamics · Physics 2020-01-08 Adrián Lozano-Durán , Marco Giometto , George I. Park , Parviz Moin

Resolvent analysis identifies the most responsive forcings and most receptive states of a dynamical system, in an input--output sense, based on its governing equations. Interest in the method has continued to grow during the past decade due…

This study is concerned with the simulation of a complex fluid flow problem involving flow past a wedge mounted on a wall for channel Reynolds numbers $Re_c=1560$, $6621$ and $6873$ in uniform and accelerated flow medium. The transient…

Fluid Dynamics · Physics 2022-11-29 Jiten C Kalita , Pankaj Kumar