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Simulation of non-Gaussian fluctuating wind pressure based on LPZ spectral analysis
Received date: 2016-09-30
Online published: 2017-08-30
This paper presents an algorithm based on linear prediction and Z-transform(LPZ) spectral analysis to simulate non-Gaussian fluctuating wind pressure. The Gaussian process is converted into a non-Gaussian white noise process by Johnson translator system. The non-Gaussian white noise process is then filtered with LPZ spectral analysis to obtain fluctuating wind pressure. The univariate non-Gaussian random signals and non-Gaussian fluctuating wind pressure are simulated using the algorithm. By comparing the statistical parameters including skewness, kurtosis and power spectral density of the non-Gaussian white noise process and fluctuating wind pressure with their target values, it is confirmed that the algorithm based on LZP spectral analysis can effectively simulate non-Gaussian fluctuating wind pressure.
JIANG Lei, LI Chunxiang, DENG Ying . Simulation of non-Gaussian fluctuating wind pressure based on LPZ spectral analysis[J]. Journal of Shanghai University, 2017 , 23(4) : 600 -608 . DOI: 10.12066/j.issn.1007-2861.1852
[1] Gurley K, Kareem A. Analysis interpretation modeling and simulation of unsteady wind and pressure data [J]. Journal of Wind Engineering and Industrial Aerodynamics, 1997, 69: 657-669.
[2] Seong S H, Peterka J A. Computer simulation of non-Gaussian multiple wind press time series [J]. Journal of Wind Engineering and Industrial Aerodynamics, 1997, 72(1): 95-105.
[3] Suresh K K, Stathopoulos T. Synthesis of non-Gaussian wind pressure time series on low building roofs [J]. Engineering Structures, 1999, 21(12): 1086-1100.
[4] Seong S H, Peterka J A. Digital generation of surface-pressure fluctuations with spiky features [J]. Journal of Wind Engineering and Industrial Aerodynamics, 1998, 73(2): 181-192.
[5] Suresh K K, Stathopoulos T. Computer simulation of fluctuating wind pressures on low building roofs [J]. Journal of Wind Engineering and Industrial Aerodynamics, 1997, 69(1): 485-495.
[6] Vorchovský M. Simulation of simply cross correlated random fields by series expansion methods[J]. Structural Safety, 2008, 30(4): 337-373.
[7] Yang Q, Tian Y. A model of probability density function of non-Gaussian wind pressure with multiple samples [J]. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 140: 67-78.
[8] Huang G, Luo Y, Gurley K R, et al. Revisiting moment-based characterization for wind pressures [J]. Journal of Wind Engineering and Industrial Aerodynamics, 2016, 151: 158-168.
[9] Yang L, Gurley K R. Efficient stationary multivariate non-Gaussian simulation based on a Hermite PDF model [J]. Probabilistic Engineering Mechanics, 2015, 42(4): 31-41.
[10] Ma X L, Xu F Y, Kareem A, et al. Estimation of surface pressure extremes: hybrid data and simulation-based approach [J]. Journal of Engineering Mechanics, 2016, DOI: 10.1061/(ASCE)EM.1943-7889.0001127.
[11] Tang J, Norris J R. LPZ spectral analysis using linear prediction and the Z transform [J]. Journal of Chemical Physics, 1986, 84(9): 5210-5211.
[12] Hill I D, Hill R, Holder R L. Fitting Johnson curves by moments [J]. Applied Statistics, 1976, 25: 149-176.
[13] Hu Y Z, Tonder K. Simulation of 3-D random rough surface by 2-D digital filter and Fourier analysis [J]. International Journal of Machine Tools Manufacturing, 1992, 32(1/2): 83-90.
[14] 李锦华, 李春祥. 土木工程随机风场数值模拟研究的进展[J]. 振动与冲击, 2008, 19(9): 116-125, 187.
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