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The quest for reductions in fuel consumption and CO2 emissions in transport has been a powerful driving force for scientific research into methods that might underpin drag-reducing technologies for a variety of vehicular transport on roads,…

流体动力学 · 物理学 2021-12-02 Pierre Ricco , Martin Skote , Michael A. Leschziner

Waves of spanwise velocity imposed at the walls of a plane turbulent channel flow are studied by Direct Numerical Simulations. We consider sinusoidal waves of spanwise velocity which vary in time and are modulated in space along the…

流体动力学 · 物理学 2015-05-13 M. Quadrio , P. Ricco , C. Viotti

The changes of a turbulent channel flow subjected to oscillations of wall flush-mounted rigid discs are studied by means of direct numerical simulations. The Reynolds number is $R_\tau$=$180$, based on the friction velocity of the…

流体动力学 · 物理学 2015-06-19 Daniel J. Wise , Pierre Ricco

We experimentally assess the capabilities of an active, open-loop technique for drag reduction in turbulent wall flows recently introduced by Quadrio et al. [J. Fluid Mech., v.627, 161, (2009)]. The technique consists in generating…

流体动力学 · 物理学 2015-05-19 Franco Auteri , Arturo Baron , Marco Belan , Gabriele Campanardi , Maurizio Quadrio

We show that the energy required by a turbulent flow to displace a given amount of fluid through a straight duct in a given time interval can be reduced by modulating in time the pumping power. The control strategy is hybrid: it is passive,…

流体动力学 · 物理学 2024-05-15 Giulio Foggi Rota , Alessandro Monti , Marco E. Rosti , Maurizio Quadrio

The underlying mechanisms of three different flow-control strategies on drag reduction in a channel flow are investigated by direct numerical simulations at friction Reynolds numbers ranging from 65 to 85. These strategies include the…

流体动力学 · 物理学 2021-10-27 Alex Rogge , Jae Sung Park

The drag reduction properties of a turbulent channel flow modified by spanwise sinusoidal oscillations of the walls are investigated by direct numerical simulations. The work is based on the linear relation between the drag reduction and…

流体动力学 · 物理学 2008-02-04 Pierre Ricco , Maurizio Quadrio

A new idea for turbulent skin-friction reduction is proposed, wherein the shape of the solid wall is designed to create the spanwise pressure gradient acting similarly to the well-known method of drag reduction by in-plane spanwise wall…

流体动力学 · 物理学 2013-04-18 Sergei Chernyshenko

We study suspensions of oblate rigid particles in a viscous fluid for different values of the particle volume fractions. Direct numerical simulations have been performed using a direct-forcing immersed boundary method to account for the…

流体动力学 · 物理学 2021-02-23 M. Niazi Ardekani , P. Costa , W. -P. Breugem , F. Picano , L. Brandt

We introduce a reinforcement learning (RL) environment to design and benchmark control strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The environment provides a framework for computationally-efficient,…

流体动力学 · 物理学 2023-02-09 L. Guastoni , J. Rabault , P. Schlatter , H. Azizpour , R. Vinuesa

Opposition flow control is a robust strategy that has been proved effective in turbulent wall-bounded flows. Its conventional setup consists of measuring wall-normal velocity in the buffer layer and opposing it at the wall. This work…

流体动力学 · 物理学 2022-02-16 Anna Guseva , Javier Jimenez

Wall-based spanwise forcing has been experimentally used with success by Auteri et al. (Phys. Fluids vol. 22, 2010, 115103) to obtain large reductions of turbulent skin-friction drag and considerable energy savings in a pipe flow. The…

流体动力学 · 物理学 2024-01-23 Emanuele Gallorini , Maurizio Quadrio

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…

流体动力学 · 物理学 2025-03-19 Zisong Zhou , Mengqi Zhang , Xiaojue Zhu

Solid-fuel ramjets offer a compact, energy-dense propulsion option for long-range, high-speed flight but pose significant challenges for thrust regulation due to strong nonlinearities, limited actuation authority, and complex multi-physics…

最优化与控制 · 数学 2025-11-07 Gohar T. Khokhar , Kyle Hanquist , Parham Oveissi , Alex Dorsey , Ankit Goel

In this work we compare different drag-reduction strategies that compute their actuation based on the fluctuations at a given wall-normal location in turbulent open channel flow. In order to perform this study, we implement and describe in…

流体动力学 · 物理学 2023-09-07 L. Guastoni , J. Rabault , H. Azizpour , R. Vinuesa

This study investigates a mechanism of controlling separated flows around an airfoil using a synthetic jet (SJ). A large-eddy simulation (LES) was performed for a leading-edge separation flow around a NACA0015 airfoil at the chord Reynolds…

流体动力学 · 物理学 2023-06-21 Yoshiaki Abe , Taku Nonomura , Kozo Fujii

The present study applies a Deep Reinforcement Learning (DRL) algorithm to Active Flow Control (AFC) of a two-dimensional flow around a confined square cylinder. Specifically, the Soft Actor-Critic (SAC) algorithm is employed to modulate…

流体动力学 · 物理学 2024-09-27 Wang Jia , Hang Xu

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

The paper presents a high Reynolds number experimental study of turbulent boundary layer separation control on a convex plate using the wavy-wall method, which was initially proposed for a flat plate by Dr\'o\.zd\.z et al. 2021 (Exp Therm…

流体动力学 · 物理学 2026-05-22 Artur Dróżdż , Mathias Romańczyk , Witold Elsner

The ability of streamwise-travelling waves of spanwise velocity to reduce the turbulent skin friction drag is assessed in the compressible regime. Direct numerical simulations are carried out to compare drag reduction in subsonic, transonic…

流体动力学 · 物理学 2023-12-18 Federica Gattere , Massimo Zanolini , Davide Gatti , Matteo Bernardini , Maurizio Quadrio