收稿日期: 2016-09-30
网络出版日期: 2017-08-30
基金资助
国家自然科学基金资助项目(51378304)
Simulation of non-Gaussian fluctuating wind pressure based on LPZ spectral analysis
Received date: 2016-09-30
Online published: 2017-08-30
提出一种基于线性预测和Z转换(linear prediction and Z-transform, LPZ)谱分析法的非高斯脉动风压模拟算法. 运用Johnson变换系统实现高斯随机过程到非高斯白噪声的转换; 再使用LPZ对其进行数字滤波, 进而得到所需的非高斯脉动风压. 采用基于LPZ谱分析法对单变量非高斯随机信号和非高斯脉动风压进行了数值模拟. 通过对模拟的非高斯信号和脉动风压的统计参数(峰度和偏度)与目标统计参数, 以及模拟的功率谱与目标功率谱进行比较,验证了基于LPZ谱分析法的非高斯脉动风压模拟算法的有效性.
关键词： Johnson变换系统; 数字滤波; 线性预测和Z转换谱分析法; 非高斯脉动风压模拟
蒋磊, 李春祥, 邓莹 . 基于LPZ谱分析法的非高斯脉动风压模拟[J]. 上海大学学报(自然科学版), 2017 , 23(4) : 600 -608 . DOI: 10.12066/j.issn.1007-2861.1852
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.
[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.
/
| 〈 |
|
〉 |
