Vortex-induced resonance is a complex coupling phenomenon. Vortex-induced vertical force (VIVF) model describes the characteristics of the vortex-induced force in resonance. Improper parameters can distort the model, so accurately identifying the parameters of the model is a current research hotspot. In this paper, a parameter identification method based on model degradation is proposed. A constant term is used to replace the harmonic force term in the model. The degenerated model is a linear combination of the nonlinear basis functions. Therefore, the linear fitting method is used to identify the model parameters. Based on the fit of VIVF, the performance of identification algorithm is evaluated. The results of Wilcoxon’s and Friedman’s nonparametric tests show that the identification algorithm is the best.
Published in | Science Discovery (Volume 6, Issue 6) |
DOI | 10.11648/j.sd.20180606.28 |
Page(s) | 489-494 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2018. Published by Science Publishing Group |
Long-Span Bridge, Vortex Induced Resonance, Parameter Identification, Nonparametric Test
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APA Style
Xiaoxia Tian. (2018). Parameter Identification Method Based on Model Degradation. Science Discovery, 6(6), 489-494. https://doi.org/10.11648/j.sd.20180606.28
ACS Style
Xiaoxia Tian. Parameter Identification Method Based on Model Degradation. Sci. Discov. 2018, 6(6), 489-494. doi: 10.11648/j.sd.20180606.28
@article{10.11648/j.sd.20180606.28, author = {Xiaoxia Tian}, title = {Parameter Identification Method Based on Model Degradation}, journal = {Science Discovery}, volume = {6}, number = {6}, pages = {489-494}, doi = {10.11648/j.sd.20180606.28}, url = {https://doi.org/10.11648/j.sd.20180606.28}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20180606.28}, abstract = {Vortex-induced resonance is a complex coupling phenomenon. Vortex-induced vertical force (VIVF) model describes the characteristics of the vortex-induced force in resonance. Improper parameters can distort the model, so accurately identifying the parameters of the model is a current research hotspot. In this paper, a parameter identification method based on model degradation is proposed. A constant term is used to replace the harmonic force term in the model. The degenerated model is a linear combination of the nonlinear basis functions. Therefore, the linear fitting method is used to identify the model parameters. Based on the fit of VIVF, the performance of identification algorithm is evaluated. The results of Wilcoxon’s and Friedman’s nonparametric tests show that the identification algorithm is the best.}, year = {2018} }
TY - JOUR T1 - Parameter Identification Method Based on Model Degradation AU - Xiaoxia Tian Y1 - 2018/12/12 PY - 2018 N1 - https://doi.org/10.11648/j.sd.20180606.28 DO - 10.11648/j.sd.20180606.28 T2 - Science Discovery JF - Science Discovery JO - Science Discovery SP - 489 EP - 494 PB - Science Publishing Group SN - 2331-0650 UR - https://doi.org/10.11648/j.sd.20180606.28 AB - Vortex-induced resonance is a complex coupling phenomenon. Vortex-induced vertical force (VIVF) model describes the characteristics of the vortex-induced force in resonance. Improper parameters can distort the model, so accurately identifying the parameters of the model is a current research hotspot. In this paper, a parameter identification method based on model degradation is proposed. A constant term is used to replace the harmonic force term in the model. The degenerated model is a linear combination of the nonlinear basis functions. Therefore, the linear fitting method is used to identify the model parameters. Based on the fit of VIVF, the performance of identification algorithm is evaluated. The results of Wilcoxon’s and Friedman’s nonparametric tests show that the identification algorithm is the best. VL - 6 IS - 6 ER -