This paper explores methods to reduce aircraft design loads through an optimal fuel usage strategy, which maximises the beneficial effects of wing-tank sloshing-induced damping. A reduced-order neural network models the nonlinear sloshing behaviour in various tank fill level scenarios. Integrated into an aeroelastic framework, this model allows for the assessment of incremental sloshing-induced damping in gust response of wings. The optimised fuel usage strategy enables control of fuel consumption from each individual tank to provide maximisation of load alleviation.

Loads-based optimal fuel-usage strategy by using a neural-network-based reduced-order model for vertical sloshing / Nerattini, A.; Pizzoli, M.; Martinez-Carrascal, J.; Saltari, F.; Gonzalez-Gutierrez, L. M.; Mastroddi, F.. - In: AEROSPACE SCIENCE AND TECHNOLOGY. - ISSN 1270-9638. - 152:(2024). [10.1016/j.ast.2024.109408]

Loads-based optimal fuel-usage strategy by using a neural-network-based reduced-order model for vertical sloshing

Nerattini A.;Pizzoli M.;Saltari F.
;
Mastroddi F.
2024

Abstract

This paper explores methods to reduce aircraft design loads through an optimal fuel usage strategy, which maximises the beneficial effects of wing-tank sloshing-induced damping. A reduced-order neural network models the nonlinear sloshing behaviour in various tank fill level scenarios. Integrated into an aeroelastic framework, this model allows for the assessment of incremental sloshing-induced damping in gust response of wings. The optimised fuel usage strategy enables control of fuel consumption from each individual tank to provide maximisation of load alleviation.
2024
Gust load alleviation; Neural networks; Nonlinear vertical sloshing; Optimal fuel usage; Reduced order models
01 Pubblicazione su rivista::01a Articolo in rivista
Loads-based optimal fuel-usage strategy by using a neural-network-based reduced-order model for vertical sloshing / Nerattini, A.; Pizzoli, M.; Martinez-Carrascal, J.; Saltari, F.; Gonzalez-Gutierrez, L. M.; Mastroddi, F.. - In: AEROSPACE SCIENCE AND TECHNOLOGY. - ISSN 1270-9638. - 152:(2024). [10.1016/j.ast.2024.109408]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1730424
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