JOURNAL ARTICLE
Nonlinear Effects of Mechanical and Aerodynamic Damping on a Motion-Amplitude-Dependent VIV Model.
Published In: International Journal of Structural Stability & Dynamics, 2025, v. 25, n. 11. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Qie, Kai; Zhang, Zhitian 3 of 3
Abstract
Based on VIV time histories growing from still to limit cycle oscillations, the motion-amplitude dependence of the VIV loading model is determined. Energy-trapping properties corresponding to the entire process of motion evolution are described by the model parameters. The influence of nonlinear mechanical damping on the VIV loading model parameter is analyzed. Prediction of VIVs under higher mechanical damping ratios is performed by the identified VIV model. The results show that, while the model parameter varies drastically with the motion amplitude, the aerodynamic damping involves gently and monotonically towards a final value corresponding to an LCO state. Neglecting the nonlinearities in mechanical damping would result in significant deviations in model parameters and predicted VIV responses. By comparing the traditional fixed- model with the proposed nonlinear- model, it is found that the VIVs predicted by the former are about 1.5 ∼ 3.0 times those predicted by the latter or tested, which underlies the necessity of consideration of motion-amplitude-dependence of an aerodynamic model. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Structural Stability & Dynamics. 2025/06, Vol. 25, Issue 11, p1
- Document Type:Article
- Subject Area:Physics
- Publication Date:2025
- ISSN:0219-4554
- DOI:10.1142/S0219455425501202
- Accession Number:186630058
- Copyright Statement:Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company 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.)
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