1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CE2025★ 1 cited
Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
R. Montalà, 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…
cs.CE2025★ 1 cited
Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
R. Montalà, 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…
physics.flu-dyn2024
Turbulent Boundary Layer in a 3-Element High-LiftWing: Coherent Structures Identification
Ricard Montalà, 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…