activity
20172026
most citedImproving aircraft performance using machine learning: a review

194 citations · 450 across the 19 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.LG2022★ 194 cited

Improving aircraft performance using machine learning: a review

Soledad Le Clainche, Esteban Ferrer, Sam Gibson +3

This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (expe…

math.NA2022★ 3 cited

Truncation Error-Based Anisotropic -Adaptation for Unsteady Flows for High-Order Discontinuous Galerkin Methods

Andrés M. Rueda-Ramírez, Gerasimos Ntoukas, Gonzalo Rubio +2

In this work, we extend the -estimation method to unsteady problems and use it to adapt the polynomial degree for high-order discontinuous Galerkin simulations of unsteady flows…

physics.flu-dyn2022★ 3 cited

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…

physics.flu-dyn2022★ 6 cited

Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers

Kenza Tlales, Kheir-Eddine Otmani, Gerasimos Ntoukas +2

We present a machine learning-based mesh refinement technique for steady and unsteady flows. The clustering technique proposed by Otmani et al. arXiv:2207.02929 [physics.flu-dyn] i…

physics.flu-dyn2022★ 22 cited

Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations

Fernando Manrique de Lara, Esteban Ferrer

We propose to accelerate a high order discontinuous Galerkin solver using neural networks. We include a corrective forcing to a low polynomial order simulation to enhance its accur…

physics.flu-dyn2022★ 14 cited

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…