Structural Engineering and Mechanics

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ML-based prediction method for estimating vortex-induced vibration amplitude of steel tubes in tubular transmission towers

  • Jiahong Li (School of Civil Engineering, Chongqing University) ;
  • Tao Wang (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Zhengliang Li (School of Civil Engineering, Chongqing University)
  • Received : 2022.07.17
  • Accepted : 2024.04.01
  • Published : 2024.04.10
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Abstract

The prediction of VIV amplitude is essential for the design and fatigue life estimation of steel tubes in tubular transmission towers. Limited to costly and time-consuming traditional experimental and computational fluid dynamics (CFD) methods, a machine learning (ML)-based method is proposed to efficiently predict the VIV amplitude of steel tubes in transmission towers. Firstly, by introducing the first-order mode shape to the two-dimensional CFD method, a simplified response analysis method (SRAM) is presented to calculate the VIV amplitude of steel tubes in transmission towers, which enables to build a dataset for training ML models. Then, by taking mass ratio M*, damping ratio ξ, and reduced velocity U* as the input variables, a Kriging-based prediction method (KPM) is further proposed to estimate the VIV amplitude of steel tubes in transmission towers by combining the SRAM with the Kriging-based ML model. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by using three full-scale steel tubes with C-shaped, Cross-shaped, and Flange-plate joints, respectively. The results show that the SRAM can reasonably calculate the VIV amplitude, in which the relative errors of VIV maximum amplitude in three examples are less than 6%. Meanwhile, the KPM can well predict the VIV amplitude of steel tubes in transmission towers within the studied range of M*, ξ and U*. Particularly, the KPM presents an excellent capability in estimating the VIV maximum amplitude by using the reduced damping parameter SG.

Keywords

Acknowledgement

This work presented in this paper was fully supported by the Special Support of Chongqing Postdoctoral Research Project (Grant No.2022CQBSHBT3009), Chongqing Natural Science Foundation-Innovation Development Joint Foundation Project (Grant No. CSTB2023NSCQLZX0080) and the NSFC-JSPS China-Japan Scientific Cooperation Project (NSFC Grant No. 51611140123). The authors would like to express their gratitude for all supports.

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