Geometric deep learning for loads prediction of manoeuvring aircraft
Geometric deep learning for loads prediction of manoeuvring aircraft
A framework based on deep learning is developed for the rapid prediction of unsteady aerodynamic loads and flowfield distributions around a full-scale, maneuvering vehicle. The framework is demonstrated for the Future Fighter Demonstrator configuration, studied within the NATO Science & Technology Organisation Research Task Group 351, performing static and forced-motion maneuvers in pitch up to high angles of attack. The complexity of the problem is characterized by the dynamics of a set of vortices that form and grow in size and intensity until they break down, strongly dependent on the three-dimensional planform shape. For applicability to both structured and unstructured grids, we leverage geometric deep learning. For deployment on a large number of surface grid points, in the order of millions, we developed a multimesh scheme to operate at coarser levels of refinement. For robustness when predicting unsteady flow features, we proposed two schemes to limit the well-known pitfall of long-term error accumulation, namely a quasi-steady and a recurrent scheme. For efficiency in generating the model, training is limited to one single signal that provides sufficient coverage of the three-parameter space, namely static angle of attack, pitch motion amplitude, and frequency. Accuracy of the unsteady models is assessed on four forced-motion pitch signals and on predicting steady-state aerodynamic characteristics. We found the recurrent scheme to outperform the quasi-steady scheme across multiple error metrics. This is attributed to better capturing the flow hysteresis via inclusion of past flow information at each time step iteration.
145-162
Massegur, David
d5bc71e8-f1b8-4c9f-9537-7ff63ad19426
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Massegur, David
d5bc71e8-f1b8-4c9f-9537-7ff63ad19426
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Massegur, David and Da Ronch, Andrea
(2025)
Geometric deep learning for loads prediction of manoeuvring aircraft.
Journal of Aircraft, 63 (1), .
(doi:10.2514/1.C038235).
Abstract
A framework based on deep learning is developed for the rapid prediction of unsteady aerodynamic loads and flowfield distributions around a full-scale, maneuvering vehicle. The framework is demonstrated for the Future Fighter Demonstrator configuration, studied within the NATO Science & Technology Organisation Research Task Group 351, performing static and forced-motion maneuvers in pitch up to high angles of attack. The complexity of the problem is characterized by the dynamics of a set of vortices that form and grow in size and intensity until they break down, strongly dependent on the three-dimensional planform shape. For applicability to both structured and unstructured grids, we leverage geometric deep learning. For deployment on a large number of surface grid points, in the order of millions, we developed a multimesh scheme to operate at coarser levels of refinement. For robustness when predicting unsteady flow features, we proposed two schemes to limit the well-known pitfall of long-term error accumulation, namely a quasi-steady and a recurrent scheme. For efficiency in generating the model, training is limited to one single signal that provides sufficient coverage of the three-parameter space, namely static angle of attack, pitch motion amplitude, and frequency. Accuracy of the unsteady models is assessed on four forced-motion pitch signals and on predicting steady-state aerodynamic characteristics. We found the recurrent scheme to outperform the quasi-steady scheme across multiple error metrics. This is attributed to better capturing the flow hysteresis via inclusion of past flow information at each time step iteration.
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Accepted/In Press date: 17 April 2025
e-pub ahead of print date: 17 July 2025
Identifiers
Local EPrints ID: 511841
URI: http://eprints.soton.ac.uk/id/eprint/511841
ISSN: 0021-8669
PURE UUID: 1f16ab8c-8d5c-4aae-9771-726787e6163c
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Date deposited: 08 Jun 2026 16:32
Last modified: 09 Jun 2026 01:45
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Author:
David Massegur
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