Bernat Font
YOU?
Author Swipe
View article: Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations
Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations Open
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. Depa…
View article: Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning Open
In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a threedimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack of AoA = 14 degrees. In the uncontrolled baseline case, the…
View article: Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000 Open
This study explores the use of deep reinforcement learning (DRL) for active flow control (AFC) to reduce flow separation on wings at high angles of attack. Concretely, here the DRL agent controls the flow over the three-dimensional NACA001…
View article: SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms Open
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and discovery of novel numerical schemes. We introduce SmartFlow, a …
View article: WaterLily.jl: A differentiable and backend-agnostic Julia solver for incompressible viscous flow around dynamic bodies
WaterLily.jl: A differentiable and backend-agnostic Julia solver for incompressible viscous flow around dynamic bodies Open
Integrating computational fluid dynamics (CFD) solvers into optimization and machine-learning frameworks is hampered by the rigidity of classic computational languages and the slow performance of more flexible high-level languages. In this…
View article: Barriers to Immunosuppressant Medication Adherence in Thoracic Transplant Recipients: Initial Findings
Barriers to Immunosuppressant Medication Adherence in Thoracic Transplant Recipients: Initial Findings Open
Although transplantation remains the gold-standard treatment for patients with end-organ failure, lifelong adherence to immunosuppressant medication is required to prevent rejection, graft failure, and mortality. Given the increase in thor…
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: Author Correction: Deep reinforcement learning for active flow control in a turbulent separation bubble
Author Correction: Deep reinforcement learning for active flow control in a turbulent separation bubble Open
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: Deep reinforcement learning for active flow control in a turbulent separation bubble
Deep reinforcement learning for active flow control in a turbulent separation bubble Open
The control efficacy of deep reinforcement learning (DRL) compared with classical periodic forcing is numerically assessed for a turbulent separation bubble (TSB). We show that a control strategy learned on a coarse grid works on a fine gr…
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: Data-driven wall modeling for LES involving non-equilibrium boundary layer effects
Data-driven wall modeling for LES involving non-equilibrium boundary layer effects Open
Purpose Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall region. However, its use is limited in flows that have hi…
View article: Deep reinforcement learning for active flow control in a turbulent separation bubble
Deep reinforcement learning for active flow control in a turbulent separation bubble Open
The control efficacy of deep reinforcement learning (DRL) compared with classical periodic forcing is assessed for a turbulent separation bubble (TSB) reaching a friction Reynolds number of Re𝝉=750. The TSB is a simplified repre…
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 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 cylinder in this…
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 of a turbulent separation bubble through deep reinforcement learning
Active flow control of a turbulent separation bubble through deep reinforcement learning Open
The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at Re τ = 180 on the upstream region before separation occurs. The TSB can resemble a separation p…
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: 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: WaterLily.jl: A differentiable fluid simulator in Julia with fast heterogeneous execution
WaterLily.jl: A differentiable fluid simulator in Julia with fast heterogeneous execution Open
Integrating computational fluid dynamics (CFD) software into optimization and machine-learning frameworks is hampered by the rigidity of classic computational languages and the slow performance of more flexible high-level languages. WaterL…
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…
View article: Deep learning of the spanwise-averaged Navier–Stokes equations
Deep learning of the spanwise-averaged Navier–Stokes equations Open
View article: A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees
A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees Open
View article: Brote de endocarditis por M. chimaera en Barcelona
Brote de endocarditis por M. chimaera en Barcelona Open