Toward the Global Description of Supercritical Flutter Dynamics via Koopman Theory

This paper presents a novel parameterization approach for aeroelastic systems utilizing Koopman theory, specifically leveraging the Koopman bilinear form (KBF) model. To address the limitations of linear parametric dependence in the KBF model, we introduce the extended KBF (EKBF) model, which enables a global linear representation of aeroelastic dynamics while capturing stronger nonlinear dependence on, e.g., the flutter parameter. Currently, this study considers the supercritical case, where the system does not involve unstable limit cycles. The effectiveness of the proposed methodology is demonstrated through two case studies: a 2D academic example and a panel flutter problem. Results show that EKBF effectively interpolates and extrapolates principal eigenvalues, capturing flutter mechanisms and accurately predicting the flutter boundary even when the data is corrupted by noise. Furthermore, parameterized isostable and isochron identified by EKBF provide valuable insights into the nonlinear flutter system.

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Publication Details

Journal
AIAA Journal
Published
2026-02-01
DOI
https://doi.org/10.2514/1.j065836
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Toward the Global Description of Supercritical Flutter Dynamics via Koopman Theory

Jiwoo Song, Daning Huang
AIAA Journal
Model Reduction and Neural Networks
article

Toward the Global Description of Supercritical Flutter Dynamics via Koopman Theory

Jiwoo Song, Daning Huang
article en

Abstract

This paper presents a novel parameterization approach for aeroelastic systems utilizing Koopman theory, specifically leveraging the Koopman bilinear form (KBF) model. To address the limitations of linear parametric dependence in the KBF model, we introduce the extended KBF (EKBF) model, which enables a global linear representation of aeroelastic dynamics while capturing stronger nonlinear dependence on, e.g., the flutter parameter. Currently, this study considers the supercritical case, where the system does not involve unstable limit cycles. The effectiveness of the proposed methodology is demonstrated through two case studies: a 2D academic example and a panel flutter problem. Results show that EKBF effectively interpolates and extrapolates principal eigenvalues, capturing flutter mechanisms and accurately predicting the flutter boundary even when the data is corrupted by noise. Furthermore, parameterized isostable and isochron identified by EKBF provide valuable insights into the nonlinear flutter system.

AIAA Journal
Pennsylvania State University (US)
Openalex Percentile: Top 68%
Model Reduction and Neural Networks
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Toward the Global Description of Supercritical Flutter Dynamics via Koopman Theory — Jiwoo Song, Daning Huang · AIAA Journal (2026) | TGRS Research Map | TGRS