相关论文: Duty-cycle actuation for drag reduction of deep dy…

High-fidelity simulations are performed to study active flow control techniques for alleviating deep dynamic stall of a SD7003 airfoil in plunging motion. The flow Reynolds number is $Re=60{,}000$ and the freestream Mach number is $M=0.1$.…

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease…

Active flow control is applied to improve the aerodynamic performance of a NACA0018 airfoil operating as a single-bladed vertical axis wind turbine (VAWT). Results computed by wall-resolved large-eddy simulations (LES) highlight the…

This study investigates the influence of aerofoil shape optimisation on blade aerodynamic performance under curvilinear and unsteady kinematics characteristic of vertical-axis turbines and cycloidal propellers. Using a cyclorotor in hover…

Flutter in lightweight airfoils under unsteady flows presents a critical challenge in aeroelastic stability and control. This study uncovers phase-localized mechanisms that drive the onset and suppression of flutter in a freely pitching…

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…

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…

This research computationally investigates the complex dynamic stall phenomena of a cross-flow turbine blade utilizing modal analysis to identify pertinent events within the cycle. The blade rotation perpendicular to the freestream…

We integrate a discrete vortex method with complex network analysis to strategize dynamic stall mitigation over a pitching airfoil with active flow control. The objective is to inform actuator placement and timing to introduce control…

Dynamic stall at low Reynolds numbers, $\mathit{Re} \sim \mathcal{O}(10^4)$, exhibits complex flow physics with co-existing laminar, transitional, and turbulent flow regions. Current state-of-the-art stall onset criteria use parameters that…

Motivated by the need to better understand flow unsteadiness in hydraulic turbines, we perform a local linear stability and adjoint-based sensitivity analysis of the turbulent swirling jet at the outlet of a Francis turbine. We use measured…

Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control strategies for drag…

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement learning as the…

Rotor blades of wind turbines in the atmospheric boundary layer regularly experience the aerodynamic phenomenon of dynamic stall consisting of a temporary overshoot of lift and detrimental fatigue loads. Particularly the formation of…

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…

The effect of duty cycle (DC) and blowing ratio on synthetic jet flow control over a stalled NACA 0025 airfoil at Re_c=10^5 was investigated experimentally. A finite-span microblower array operating with burst modulation was tested across a…

This study employed smoothed particle hydrodynamics (SPH) as the numerical environment, integrated with deep reinforcement learning (DRL) real-time control algorithms to optimize the sloshing suppression in a tank with a centrally…

Active flow control of compressible transonic shock-boundary layer interactions over a two-dimensional RAE2822 airfoil at Re = 50,000 is investigated using deep reinforcement learning (DRL). The flow field exhibits highly unsteady dynamics,…

This study showcases an experimental deployment of deep reinforcement learning (DRL) for active flow control (AFC) of vortex-induced vibrations (VIV) in a circular cylinder at a high Reynolds number (Re = 3000) using rotary actuation.…

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,…