5 citations · 5 across the 4 of their papers we have counts for
4 papers
Physics-guided surrogate learning enables zero-shot control of turbulent wings
Yuning Wang, Pol Suarez, Mathis Bode +1
Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spatial variability, p…
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially…
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…