14 citations · 18 across the 7 of their papers we have counts for
7 papers
Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
Martín de Frutos, Oscar A. Marino, David Huergo +1
We develop a torque-pitch control framework using deep reinforcement learning for wind turbines to optimize the generation of wind turbine energy while minimizing operational noise…
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers
David Huergo, Martín de Frutos, Eduardo Jané +3
We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p solvers using Reinforcement Learning (RL). The dynamic RL adaptation uses the evolvi…
Modelling Wind Turbines via Actuator Lines in High-Order h/p Solvers
Oscar A. Marino, Raúl Sanz, Stefano Colombo +2
This paper compares two actuator line methodologies for modelling wind turbines employing high-order h/p solvers and large-eddy simulations. The methods combine the accuracy of hig…
Low-cost wind turbine aeroacoustic predictions using actuator lines
Laura Botero-Bolivar, Oscar A Marino, Cornelis H. Venner +2
Aerodynamic noise is a limitation for further exploitation of wind energy resources. As this type of noise is caused by the interaction of turbulent flow with the airframe, a detai…
Reinforcement learning to maximise wind turbine energy generation
Daniel Soler, Oscar Mariño, David Huergo +2
We propose a reinforcement learning strategy to control wind turbine energy generation by actively changing the rotor speed, the rotor yaw angle and the blade pitch angle. A double…
A comparative study of explicit and implicit Large Eddy Simulations using a high-order discontinuous Galerkin solver: application to a Formula 1 front wing
Gerasimos Ntoukas, Gonzalo Rubio, Oscar Marino +4
This paper explores two Large Eddy Simulation (LES) approaches within the framework of the high-order discontinuous Galerkin solver, Horses3D. The investigation focuses on an Inver…