35 citations · 77 across the 14 of their papers we have counts for
5 papers · 1 filter
Towards Active Flow Control Strategies Through Deep Reinforcement Learning
Ricard Montalà, 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…
WaterLily.jl: A differentiable and backend-agnostic Julia solver for incompressible viscous flow around dynamic bodies
Gabriel D. Weymouth, Bernat Font
Integrating computational fluid dynamics (CFD) solvers into optimization and machine-learning frameworks is hampered by the rigidity of classic computational languages and the slow…
Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at
P. Suárez, F. Álcantara-Ávila, A. Miró +4
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…
Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning
P. Suárez, F. Alcántara-Ávila, J. Rabault +4
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 pot…
Active flow control of a turbulent separation bubble through deep reinforcement learning
Bernat Font, Francisco Alcántara-Ávila, Jean Rabault +2
The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at on the upstream region be…