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We study the motion of a rigid sphere falling in a two-layer stratified fluid under the action of gravity in the potential flow regime. Experiments at a moderate Reynolds number of approximately 20 to 450 indicate that a sphere with the…

We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5\times10^4$ and Mach number 0.4. Direct numerical simulation and…

Fluid Dynamics · Physics 2025-10-09 Xuemin Liu , Tom Hickling , Jonathan F. MacArt

Direct numerical simulation of open-channel flow over a bed of spheres arranged in a regular pattern has been carried out at bulk Reynolds number and roughness Reynolds number (based on sphere diameter) of approximately 6900 and 120,…

Fluid Dynamics · Physics 2017-07-20 Marco Mazzuoli , Markus Uhlmann

In this study, a simple model based closed-loop algorithm is used to control the separated flow downstream a backward-facing step. It has been shown in previous studies that the recirculation bubble can be minimized when exciting the shear…

Fluid Dynamics · Physics 2013-11-05 N. Gautier , J. -L. Aider

A fundamental problem in the field of turbulent skin-friction drag reduction is to determine the performance of the available control techniques at high values of the Reynolds number $Re$. We consider active, predetermined strategies based…

Fluid Dynamics · Physics 2015-06-12 Davide Gatti , Maurizio Quadrio

The present study proposes an active flow control (AFC) approach based on deep reinforcement learning (DRL) to optimize the performance of multiple plasma actuators on a square cylinder. The investigation aims to modify the control inputs…

We study the adaptability of deep reinforcement learning (DRL)-based active flow control (AFC) technology for bluff body flows with complex geometries. It is extended from a cylinder with an aspect ratio $Ar = 1$ to a flat elliptical…

Fluid Dynamics · Physics 2024-09-27 Wang Jia , Hang Xu

The present study investigates the passive flow control phenomena over a two-dimensional circular cylinder using numerical simulations in the laminar regime. The aim is to explore one of the passive control techniques, which involves the…

Fluid Dynamics · Physics 2021-02-03 Alok Mishra , Mohd. Hanzla , Ashoke De

This study presents novel drag reduction active-flow-control (AFC) strategies} for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity and cylinder diameter of $Re_D=3900$. The cylinder in this…

Fluid Dynamics · Physics 2025-02-20 P. Suárez , F. Álcantara-Ávila , A. Miró , J. Rabault , B. Font , O. Lehmkuhl , R. Vinuesa

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

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…

Fluid Dynamics · Physics 2015-06-19 Daniel J. Wise , Pierre Ricco

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,…

Fluid Dynamics · Physics 2021-12-02 Pierre Ricco , Martin Skote , Michael A. Leschziner

We study the snapping instability of a spherical elastic shell induced by a viscous flow, the umbrella flipping problem when life is at low Reynolds numbers. We combine precision desktop-scale experiments, fluid-structure simulations, shell…

Machine learning has recently become a promising technique in fluid mechanics, especially for active flow control (AFC) applications. A recent work [J. Fluid Mech. (2019), vol. 865, pp. 281-302] has demonstrated the feasibility and…

Fluid Dynamics · Physics 2021-03-22 Feng Ren , Jean Rabault , Hui Tang

We perform fully coupled numerical simulations using immersed boundary methods of finite-size spheres and fibres suspended in a turbulent flow for a range of Taylor Reynolds numbers $12.8<Re_\lambda<442$ and solid mass fractions $0\leq…

Fluid Dynamics · Physics 2024-05-16 Ianto Cannon , Stefano Olivieri , Marco E. Rosti

Super hydrophobic surfaces have been the focus of research in the recent years.One of the reasons for this is the self cleaning property of these surfaces which emerges from the ability of the droplets to roll freely over them.However…

Fluid Dynamics · Physics 2015-11-30 Indrajit P. Wadgaonkar , T. Sundararajan , Sarit K. Das

Deep reinforcement learning (DRL) algorithms are rapidly making inroads into fluid mechanics, following the remarkable achievements of these techniques in a wide range of science and engineering applications. In this paper, a deep…

Fluid Dynamics · Physics 2020-12-21 M. A. Elhawary

Patterned surfaces with large effective slip lengths, such as super-hydrophobic surfaces containing trapped gas bubbles, have the potential to reduce hydrodynamic drag. Based on lubrication theory, we analyze an approach of a hydrophilic…

Fluid Dynamics · Physics 2015-03-14 Aleksey V. Belyaev , Olga I. Vinogradova

Biological locomotion, observed in the flexible wings of birds and insects, bodies and fins of aquatic mammals and fishes, consists of their ability to morph the wings/fins. The morphing capability holds significance in the abilities of…

Fluid Dynamics · Physics 2022-10-19 Pragalbh Dev Singh , Ishan Neogi , Vardhan Niral Shah , Vaibhav Joshi

Fluid-structure interactions are ubiquitous in nature and technology. However, the systems are often so complex that numerical simulations or ad hoc assumptions must be used to gain insight into the details of the complex interactions…