62 citations · 98 across the 15 of their papers we have counts for
15 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…
Projection-Based Reconstruction for Achieving High-Order Accuracy from Low-Order DGSEM Simulations
Xukun Wang, Suyash Shrestha, Oscar A. Marino +1
High-order discontinuous Galerkin spectral element methods (DGSEM) based on Legendre-Gauss-Lobatto (LGL) nodes provide accurate and efficient discretizations for conservation laws.…
Accelerating high-order energy-stable discontinous Galerkin solver using auto-differentiation and neural networks
Xukun Wang, Oscar A. Marino, Esteban Ferrer
High-order Discontinuous Galerkin Spectral Element Methods (DGSEM) provide excellent accuracy for complex flow simulations, but their computational cost increases sharply with high…
Can Explicit Subgrid Models Enhance Implicit LES Simulations? A Very High-Order Solver Perspective
Gonzalo Rubio, Gerasimos Ntoukas, Miguel Chávez-Módena +5
High-order discontinuous Galerkin (DG) methods offer excellent accuracy for turbulent-flow simulations and are increasingly attractive on GPU-oriented architectures, where high pol…
Acoustic Propagation/Refraction Through Diffuse Interface Models
Abbas Ballout, Oscar A. Marino, Gerasimos Ntoukas +2
We present a novel approach for simulating acoustic (pressure) wave propagation across different media separated by a diffuse interface through the use of a weak compressibility fo…
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…
Aeroacoustic airfoil shape optimization enhanced by autoencoders
Jiaqing Kou, Laura Botero-Bolívar, Román Ballano +4
We present a framework for airfoil shape optimization to reduce the trailing edge noise for the design of wind turbine blades. Far-field noise is evaluated using Amiet's theory cou…
Jump penalty stabilisation techniques for under-resolved turbulence in discontinuous Galerkin schemes
Jiaqing Kou, Oscar A. Marino, Esteban Ferrer
Jump penalty stabilisation techniques have been recently proposed for continuous and discontinuous high order Galerkin schemes [1,2,3]. The stabilisation relies on the gradient or…
towards a robust detection of viscous and turbulent flow regions using unsupervised machine learning
Kheir-Eddine Otmani, Gerasimos Ntoukas, Esteban Ferrer
We propose an invariant feature space for the detection of viscous dominated and turbulent regions (i.e., boundary layers and wakes). The developed methodology uses the principal i…
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…