2 citations · 2 across the 1 of their papers we have counts for
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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…
Accelerating high order discontinuous Galerkin solvers through a clustering-based viscous/turbulent-inviscid domain decomposition
Kheir-Eddine Otmani, Andrés Mateo-Gabín, Gonzalo Rubio +1
We explore the unsupervised clustering technique introduced in [25] to identify viscous/turbulent from inviscid regions in incompressible flows. The separation of regions allows so…
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…