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An efficient reduced-order framework for active/passive hybrid flutter suppression

An efficient reduced-order framework for active/passive hybrid flutter suppression
An efficient reduced-order framework for active/passive hybrid flutter suppression
Flutter suppression is an important measure to improve fatigue life and enhance the performance of aircraft in modern aircraft design. In order to design more effective controllers for flutter suppression with high efficiency, an
efficient reduced-order framework for active/passive hybrid flutter suppression is proposed. The traditional CFD-based ROMs have been successfully applied to active flutter suppression with high accuracy and efficiency. But, when a structure modification is made such as in aeroelastic tailoring and aeroelastic structural optimisation, the structural model should be updated, and the expensive, time-consuming CFD-based ROMs have to be reconstructed; such a process is impractical for passive flutter suppression. To overcome the realistic challenge, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed by extending an efficient aeroelastic CFD-based POD/ROM which we have developed. The proposed framework is demonstrated and evaluated using an improved AGARD 445.6 wing model. The results show that the proposed framework can accurately predict the aeroelastic response for active/passive hybrid flutter suppression with high efficiency. It provides a powerful tool for active/passive hybrid flutter suppression, and therefore, is ideally suited to design more effective controllers, and may have the potential to reduce the overall cost of aircraft design.
0001-9240
24-40
Li, D.F.
0337c123-d313-4fb1-8bba-9981887c5cf2
Wang, Z.Z.
33fe34f7-0b01-46c4-a81c-d9efd4f2d5fd
Da Ronch, A.
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Chen, G.
fd387732-a735-4dc4-b935-2e8347456267
Li, D.F.
0337c123-d313-4fb1-8bba-9981887c5cf2
Wang, Z.Z.
33fe34f7-0b01-46c4-a81c-d9efd4f2d5fd
Da Ronch, A.
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Chen, G.
fd387732-a735-4dc4-b935-2e8347456267

Li, D.F., Wang, Z.Z., Da Ronch, A. and Chen, G. (2022) An efficient reduced-order framework for active/passive hybrid flutter suppression. The Aeronautical Journal, 127 (1307), 24-40. (doi:10.1017/aer.2022.42).

Record type: Article

Abstract

Flutter suppression is an important measure to improve fatigue life and enhance the performance of aircraft in modern aircraft design. In order to design more effective controllers for flutter suppression with high efficiency, an
efficient reduced-order framework for active/passive hybrid flutter suppression is proposed. The traditional CFD-based ROMs have been successfully applied to active flutter suppression with high accuracy and efficiency. But, when a structure modification is made such as in aeroelastic tailoring and aeroelastic structural optimisation, the structural model should be updated, and the expensive, time-consuming CFD-based ROMs have to be reconstructed; such a process is impractical for passive flutter suppression. To overcome the realistic challenge, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed by extending an efficient aeroelastic CFD-based POD/ROM which we have developed. The proposed framework is demonstrated and evaluated using an improved AGARD 445.6 wing model. The results show that the proposed framework can accurately predict the aeroelastic response for active/passive hybrid flutter suppression with high efficiency. It provides a powerful tool for active/passive hybrid flutter suppression, and therefore, is ideally suited to design more effective controllers, and may have the potential to reduce the overall cost of aircraft design.

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More information

Accepted/In Press date: 7 April 2022
e-pub ahead of print date: 2 May 2022

Identifiers

Local EPrints ID: 483657
URI: http://eprints.soton.ac.uk/id/eprint/483657
ISSN: 0001-9240
PURE UUID: 036138ac-06e7-4112-bc6c-bdc15d9a323c
ORCID for A. Da Ronch: ORCID iD orcid.org/0000-0001-7428-6935

Catalogue record

Date deposited: 02 Nov 2023 18:27
Last modified: 18 Mar 2024 03:25

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Contributors

Author: D.F. Li
Author: Z.Z. Wang
Author: A. Da Ronch ORCID iD
Author: G. Chen

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