Wind and Structures

  • 제38권1호
  • /
  • Pages.1-13
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  • 2024
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Separation-hybrid models for simulating nonstationary stochastic turbulent wind fields

  • Long Yan (School of Civil Engineering and Architecture, Wuhan Institute of Technology) ;
  • Zhangjun Liu (School of Civil Engineering and Architecture, Wuhan Institute of Technology) ;
  • Xinxin Ruan (School of Civil Engineering and Architecture, Wuhan Institute of Technology) ;
  • Bohang Xu (School of Civil Engineering and Architecture, Wuhan Institute of Technology)
  • 투고 : 2022.11.14
  • 심사 : 2023.10.15
  • 발행 : 2024.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

In order to effectively simulate nonstationary stochastic turbulent wind fields, four separation hybrid (SEP-H) models are proposed in the present study. Based on the assumption that the lateral turbulence component at one single-point is uncorrelated with the longitudinal and vertical turbulence components, the fluctuating wind is separated into 2nV-1D and nV1D nonstationary stochastic vector processes. The first process can be expressed as double proper orthogonal decomposition (DPOD) or proper orthogonal decomposition and spectral representation method (POD-SRM), and the second process can be expressed as POD or SRM. On this basis, four SEP-H models of nonstationary stochastic turbulent wind fields are developed. In addition, the orthogonal random variables in the SEP-H models are presented as random orthogonal functions of elementary random variables. Meanwhile, the number theoretical method (NTM) is conveniently adopted to select representative points set of the elementary random variables. The POD-FFT (Fast Fourier transform) technique is introduced in frequency to give full play to the computational efficiency of the SEP-H models. Finally, taking a long-span bridge as the engineering background, the SEP-H models are compared with the dimension-reduction DPOD (DR-DPOD) model to verify the effectiveness and superiority of the proposed models.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (Grant No. 51978543), State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering, Jianghan University (No. PBSKL2022C07), and the Plan of Outstanding Young and Middle-aged Scientific and Technological Innovation Team in Universities of Hubei Province [Project No. T2020010]. The above foundations are highly appreciated.

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