Issue |
Mechanics & Industry
Volume 22, 2021
|
|
---|---|---|
Article Number | 42 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.1051/meca/2021041 | |
Published online | 21 September 2021 |
Regular Article
Aeroelastic scaling of flying demonstrator: flutter matching
1
Institut Clément Ader, Université de Toulouse, ISAE SUPAERO-CNRS-INSA-Mines Albi-UPS, Toulouse, France
2
ONERA/DTIS, Université de Toulouse, Toulouse, France
3
University of Michigan,
Ann Arbor,
MI
48109, USA
* e-mail: joseph.morlier@isae-supaero.fr
Received:
15
March
2021
Accepted:
22
August
2021
The traditional approach for the design of aeroelastically scaled models assumes that either there exists flow similarity between the full-size aircraft and the model, or that flow non-similarities have a negligible effect. However, when trying to reproduce the behavior of an airliner that flies at transonic conditions using a scaled model that flies at nearly-incompressible flow conditions, this assumption is no longer valid and both flutter speed and static aerodynamic loading are subject to large discrepancies. To address this issue, we present an optimization-based approach for wing planform design that matches the scaled flutter speeds and modes of the reference aircraft when the Mach number cannot be matched. This is achieved by minimizing the squared error between the full-size and scaled aerodynamic models. This method is validated using the Common Research Model wing at the reference aircraft Mach number. The error in flutter speed is computed using the same wing at model conditions, which are in the nearly-incompressible regime. Starting from the baseline wing, its planform is optimized to match the reference response despite different conditions, achieving a reduction of the error in the predicted flutter speed from 7.79% to 2.13%.
Key words: Aeroelastic Scaling / Flutter Matching / Modal Basis / Optimization
© J.M. Colomer et al., Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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