Wind and Structures

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Enhanced data-driven simulation of non-stationary winds using DPOD based coherence matrix decomposition

  • Liyuan Cao (Department of Civil Engineering, School of Mechanics and Engineering Science, Shanghai University) ;
  • Jiahao Lu (Department of Civil Engineering, School of Mechanics and Engineering Science, Shanghai University) ;
  • Chunxiang Li (Department of Civil Engineering, School of Mechanics and Engineering Science, Shanghai University)
  • Received : 2024.01.23
  • Accepted : 2024.07.14
  • Published : 2024.08.25
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Abstract

The simulation of non-stationary wind velocity is particularly crucial for the wind resistant design of slender structures. Recently, some data-driven simulation methods have received much attention due to their straightforwardness. However, as the number of simulation points increases, it will face efficiency issues. Under such a background, in this paper, a time-varying coherence matrix decomposition method based on Diagonal Proper Orthogonal Decomposition (DPOD) interpolation is proposed for the data-driven simulation of non-stationary wind velocity based on S-transform (ST). Its core idea is to use coherence matrix decomposition instead of the decomposition of the measured time-frequency power spectrum matrix based on ST. The decomposition result of the time-varying coherence matrix is relatively smooth, so DPOD interpolation can be introduced to accelerate its decomposition, and the DPOD interpolation technology is extended to the simulation based on measured wind velocity. The numerical experiment has shown that the reconstruction results of coherence matrix interpolation are consistent with the target values, and the interpolation calculation efficiency is higher than that of the coherence matrix time-frequency interpolation method and the coherence matrix POD interpolation method. Compared to existing data-driven simulation methods, it addresses the efficiency issue in simulations where the number of Cholesky decompositions increases with the increase of simulation points, significantly enhancing the efficiency of simulating multivariate non-stationary wind velocities. Meanwhile, the simulation data preserved the time-frequency characteristics of the measured wind velocity well.

Keywords

Acknowledgement

This research described in this paper is supported by the National Natural Science Foundation of China (Grant No. 52108460).

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