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View article: Deep-reinforcement-learning-based separation control in a two-dimensional airfoil
Deep-reinforcement-learning-based separation control in a two-dimensional airfoil Open
The aim of this study is to discover new active-flow-control (AFC) techniques for separation mitigation in a two-dimensional NACA 0012 airfoil at a Reynolds number of 3000. To find these AFC strategies, a framework consisting of a deep-rei…
View article: Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning
Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning Open
Active flow control strategies for three-dimensional bluff bodies are challenging to design, yet critical for industrial applications. Here we explore the potential of discovering novel drag-reduction strategies using deep reinforcement le…
View article: Active Flow Control for Drag Reduction Through Multi-agent Reinforcement Learning on a Turbulent Cylinder at $$Re_D=3900$$
Active Flow Control for Drag Reduction Through Multi-agent Reinforcement Learning on a Turbulent Cylinder at $$Re_D=3900$$ Open
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 cylind…
View article: Deep-reinforcement-learning-based separation control in a two-dimensional airfoil
Deep-reinforcement-learning-based separation control in a two-dimensional airfoil Open
The aim of this study is to discover new active-flow-control (AFC) techniques for separation mitigation in a two-dimensional NACA 0012 airfoil at a Reynolds number of 3000. To find these AFC strategies, a framework consisting of a deep-rei…
View article: Towards Active Flow Control Strategies Through Deep Reinforcement Learning
Towards Active Flow Control Strategies Through Deep Reinforcement Learning Open
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% decreas…
View article: Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning
Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning Open
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 red…
View article: Active flow control for three-dimensional cylinders through deep reinforcement learning
Active flow control for three-dimensional cylinders through deep reinforcement learning Open
This paper presents for the first time successful results of active flow control with multiple independently controlled zero-net-mass-flux synthetic jets. The jets are placed on a three-dimensional cylinder along its span with the aim of r…
View article: Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes
Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes Open
The increase in emissions associated with aviation requires deeper research into novel sensing and flow-control strategies to obtain improved aerodynamic performances. In this context, data-driven methods are suitable for exploring new app…
View article: Deep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes
Deep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes Open
Deep artificial neural networks (ANNs) used together with deep reinforcement learning (DRL) are receiving growing attention due to their capabilities to control complex problems. This technique has been recently used to solve problems rela…