6 papers
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
Ricard MontalÃ, Bernat Font, Oriol Lehmkuhl +2
This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Re = 450,000 and angle of attack = 23 using wallresolved large-ed…
Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
R. MontalÃ, B. Font, P. Suárez +4
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…
Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
R. MontalÃ, B. Font, P. Suárez +4
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…
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
Maochao Xiao, Yuning Wang, Felix Rodach +15
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…
Turbulent Boundary Layer in a 3-Element High-LiftWing: Coherent Structures Identification
Ricard MontalÃ, Benet Eiximeno, Arnau Miró +2
A wall-resolved large-eddy simulation (LES) of the fluid flow around a 30P30N airfoil is conducted at a Reynolds number of Rec=750,000 and an angle of attack (AoA) of 9 degrees. Th…
Towards Active Flow Control Strategies Through Deep Reinforcement Learning
Ricard MontalÃ, Bernat Font, Pol Suárez +3
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 a…