14 citations · 20 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…