Nonlinear Aerodynamic Force Identification and Nonlinear Flutter Analysis Based on Autoencoder

MEI Hanyu; LIAO Haili; WANG Changjiang

doi:10.3969/j.issn.0258-2724.20230261

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Journal of Southwest Jiaotong University > 2024 > Accepted Manuscript

MEI Hanyu, LIAO Haili, WANG Changjiang. Nonlinear Aerodynamic Force Identification and Nonlinear Flutter Analysis Based on Autoencoder[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230261

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MEI Hanyu, LIAO Haili, WANG Changjiang. Nonlinear Aerodynamic Force Identification and Nonlinear Flutter Analysis Based on Autoencoder[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230261

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Nonlinear Aerodynamic Force Identification and Nonlinear Flutter Analysis Based on Autoencoder

doi: 10.3969/j.issn.0258-2724.20230261

1.
Zhejiang Institute of Communications Co., Ltd., Hangzhou 310006, China
2.
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
3.
Zhejiang Scientific Research Institute of Transport, Hangzhou 310012, China

Received Date: 30 May 2023
Accepted Date: 17 Dec 2024
Rev Recd Date: 16 Oct 2023

Available Online: 30 Dec 2024

Abstract

Abstract

In order to identify the nonlinear aerodynamic forces and calculate nonlinear flutters of a nonlinear dynamic system, an autoencoder model based on the neural network method and numerical solution of motion equation was proposed. The 5∶1 rectangular cross-section was taken as the research object. Through free vibration wind tunnel tests of the sectional model, the amplitude dependence of the nonlinear damping and the steady-state amplitude responses of the nonlinear flutter of the system were tested, and it was clarified that the tested cross-section had the only steady-state flutter response at different reduced wind speeds. Based on the experimental data, the proposed autoencoder model was trained. The nonlinear aerodynamic force encoder model that accurately described displacement and speed was obtained to realize motion time-history analysis of the nonlinear flutter of the 5∶1 rectangular cross-section under different dynamic parameters. Research results show that the proposed autoencoder model can accurately identify the nonlinear aerodynamic force time-history containing high-order harmonic components only by relying on a free vibration wind tunnel test without the need to carry out force or pressure tests; the proposed model can accurately reproduce the motion time-history of nonlinear flutter under different initial conditions and the steady-state amplitude responses at different reduced wind speeds. The maximum error of torsional steady-state amplitude is less than 5%, and the average error is 1.15%. It has high extensibility and can provide a reference for subsequent related research.
- nonlinear flutter,
- wind tunnel test,
- neural network,
- encoder,
- decoder

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References

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