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Reduced Order Modeling and Analysis of Airfoil Flutter Using Dynamics-Based Autoencoders.

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Title: Reduced Order Modeling and Analysis of Airfoil Flutter Using Dynamics-Based Autoencoders.
Authors: Mogharabin, Nami1, Epureanu, Bogdan I.2, Ghadami, Amin3
Source: AIAA Journal. Oct2025, Vol. 63 Issue 10, p4427-4435. 9p.
Abstract: Nonlinear flutter analysis is essential for ensuring the safety and performance of modern aeroelastic systems. Performing nonlinear stability analysis, however, is a challenging task for aeroelastic systems when relying on traditional approaches. This paper introduces a data-driven approach for nonintrusive nonlinear reduced order modeling and flutter analysis in aeroelastic systems. The proposed approach integrates nonlinear stability analysis for dynamical systems theory with machine learning techniques, enabling nonlinear flutter analysis with a limited number of simulated time-domain trajectories. This data-driven method determines reduced order models of systems exhibiting flutter instabilities and the transformation to and from the state space and the reduced order coordinates. Numerical results are provided to demonstrate the performance of the proposed method for a typical nonlinear airfoil section exhibiting supercritical and subcritical flutter. [ABSTRACT FROM AUTHOR]
Copyright of AIAA Journal is the property of American Institute of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: Reduced Order Modeling and Analysis of Airfoil Flutter Using Dynamics-Based Autoencoders.
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  Data: <searchLink fieldCode="AR" term="%22Mogharabin%2C+Nami%22">Mogharabin, Nami</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Epureanu%2C+Bogdan+I%2E%22">Epureanu, Bogdan I.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ghadami%2C+Amin%22">Ghadami, Amin</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22AIAA+Journal%22">AIAA Journal</searchLink>. Oct2025, Vol. 63 Issue 10, p4427-4435. 9p.
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Nonlinear flutter analysis is essential for ensuring the safety and performance of modern aeroelastic systems. Performing nonlinear stability analysis, however, is a challenging task for aeroelastic systems when relying on traditional approaches. This paper introduces a data-driven approach for nonintrusive nonlinear reduced order modeling and flutter analysis in aeroelastic systems. The proposed approach integrates nonlinear stability analysis for dynamical systems theory with machine learning techniques, enabling nonlinear flutter analysis with a limited number of simulated time-domain trajectories. This data-driven method determines reduced order models of systems exhibiting flutter instabilities and the transformation to and from the state space and the reduced order coordinates. Numerical results are provided to demonstrate the performance of the proposed method for a typical nonlinear airfoil section exhibiting supercritical and subcritical flutter. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of AIAA Journal is the property of American Institute of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.2514/1.J064881
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        Text: English
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              Text: Oct2025
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