2 citations · 2 across the 2 of their papers we have counts for
3 papers · 1 filter
Deep-reinforcement-learning-based separation control in a two-dimensional airfoil
Xavier Garcia, Arnau Miró, Pol Suárez +5
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…
Deep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes
Pau Varela, Pol Suárez, Francisco Alcántara-Ávila +6
Deep artificial neural networks (ANNs) used together with deep reinforcement learning (DRL) are receiving growing attention due to their capabilities to control complex problems. T…
Deep learning of the spanwise-averaged Navier-Stokes equations
Bernat Font, Gabriel D. Weymouth, Vinh-Tan Nguyen +1
Simulations of turbulent fluid flow around long cylindrical structures are computationally expensive because of the vast range of length scales, requiring simplifications such as d…