62 citations · 67 across the 10 of their papers we have counts for
10 papers
HORSES3D-GPU: A high-order discontinuous Galerkin solver for multi-GPU systems
Gerasimos Ntoukas, Gonzalo Rubio, Abbas Ballout +13
We present the GPU acceleration and large-scale performance assessment of HORSES3D, an open-source high-order discontinuous Galerkin solver for computational fluid dynamics. The so…
A High-Order Immersed Boundary Method for Fluid-Structure Interaction Problems
Yingjie Xia, Stefano Colombo, David Huergo +3
Accurate and efficient simulation of fluid-structure interaction (FSI) problems remains a central challenge in computational physics. High-order discontinuous Galerkin (DG) methods…
An Empirical Wall-Pressure Spectrum Model for Aeroacoustic Predictions Based on Symbolic Regression
Laura Botero Bolívar, David Huergo, Fernanda L. dos Santos +3
Fast-turn around methods to predict airfoil trailing-edge noise are crucial for incorporating noise limitations into design optimization loops of several applications. Among these…
A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers
David Huergo, Laura Alonso, Saumitra Joshi +3
We explore a reinforcement learning strategy to automate and accelerate h/p-multigrid methods in high-order solvers. Multigrid methods are very efficient but require fine-tuning of…
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…
Implementation of Immersed Boundaries via Volume Penalization in the Industrial Aeronautical Computational Fluid Dynamics Solver CODA
Jonatan Nunez, David Huergo, Diego Lodares +5
We present the implementation and validation of an immersed boundary volume penalization method in the computational fluid dynamics solver CODA (from ONERA, DLR, and Airbus). Our g…
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 reinforcement learning strategy for p-adaptation in high order solvers
David Huergo, Gonzalo Rubio, Esteban Ferrer
Reinforcement learning (RL) has emerged as a promising approach to automating decision processes. This paper explores the application of RL techniques to optimise the polynomial or…
HORSES3D: a high-order discontinuous Galerkin solver for flow simulations and multi-physics applications
E. Ferrer, G. Rubio, G. Ntoukas +10
We present the latest developments of our High-Order Spectral Element Solver (HORSES3D), an open source high-order discontinuous Galerkin framework, capable of solving a variety of…