Modelling and simulating thunderstorm/downburst winds using S-transform and discrete orthonormal S-transform. Issue 212 (May 2021)

• Record Type: Journal Article
• Authors: Hong, H.P.; Cui, X.Z.; Xiao, M.Y.
• Is Part Of: Journal of wind engineering and industrial aerodynamics. Issue 212(2021)
• Publication Date: 2021-05
• Subjects: Nonstationary wind -- Simulation -- S-transform -- Discrete orthonormal S-transform -- Downburst -- Peak responses; Wind-pressure -- Periodicals; Buildings -- Aerodynamics -- Periodicals; Pression du vent -- Périodiques; Constructions -- Aérodynamique -- Périodiques; Buildings -- Aerodynamics; Wind-pressure; Periodicals
• DOI: 10.1016/j.jweia.2021.104598
• Languages: English
• ISSNs: 0167-6105

Abstract

Abstract: Fluctuating winds from intense thunderstorm/downburst are often modelled as the amplitude modulated evolutionary stochastic processes and simulated based on the spectral representation method. Aspects of the modelling and simulation include assessing time and frequency dependent power spectrum, and the identification of the amplitude and frequency modulations are not straightforward. The present study uses the S-transform and discrete orthonormal S-transform to evaluate the time-frequency power spectral density (TFPSD) functions and coherence functions of nonstationary winds. The results for a thunderstorm/downburst event indicate that the coherence is time-dependent. The use of the TFPSD functions and a newly developed simulation method to generate winds acted on single and multiple points with a time-dependent coherence is shown. A comparison of the mean of the TFPSD of the simulated winds to its corresponding target is used to validate the adequacy of the simulation models. The use of the simulated records to assess the record-to-record variability on the maximum responses of a nonlinear inelastic single-degree-of-freedom system is given. Highlights: Applied S-transform (ST) and discrete orthonormal ST to nonstationary winds to provide time-frequency representation. Provided a time-frequency characterization of the high-intensity wind event. Characterize the time-dependent coherence characteristics using ST. Simulated and validated vector of nonstationary wind with time-dependent coherence based on ST.