Data-driven modeling of transonic nonlinear flutter via convolutional neural network autoencoder

This paper proposes a novel data-driven aeroelastic modeling method based on the autoencoder (AE) and the nonlinear state-space identification. This method allows high-dimensional flow field nonlinear features to be characterized with high accuracy by lower-dimensional latent vectors, which facilita...

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Bibliographic Details
Published in:	Nonlinear dynamics Vol. 113; no. 13; pp. 15741 - 15760
Main Authors:	Peng, Zhijie, Yao, Xiangjie, Liu, Haojie, Huang, Rui
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.07.2025 Springer Nature B.V
Subjects:	Aerodynamic forces Aerodynamics Aeroelasticity Applications of Nonlinear Dynamics and Chaos Theory Artificial neural networks Classical Mechanics Control Coupling Dynamical Systems Flow mapping Limit cycle oscillations Neural networks Physics Physics and Astronomy Statistical Physics and Dynamical Systems Structural models Transonic flow Transonic flutter Unsteady flow Vibration Data-driven model Unsteady flow Autoencoder Transonic aeroelastic Nonlinear state-space
ISSN:	0924-090X, 1573-269X
Online Access:	Get full text
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