Stochastic Fourier spectrum model and probabilistic information analysis for wind speed process

From the view point of physical mechanism, stochastic dynamic excitations, such as earthquake and strong wind, subjecting on structures can be represented by a physical model with elemental random variables. In this article, a physical model for wind speed process is investigated to obtain the probability density function of wind speed process. Firstly, the stochastic Fourier spectrum, consisting of wave-number spectrum and phase spectrum, is introduced. The wave-number spectrum displays the energy distribution over frequency domain. The phase spectrum is viewed as the evolutionary result driven by characteristic velocity of air vortex. After that, the elemental variables are carefully selected in term of physical relation and the measurement data collected at a wind observation station is analyzed to gain the statistics of the elemental random variables. With the help of probability density evolution method (PDEM), the probability density information of wind speed process can be obtained. The comparison with the measurement data validates the effectiveness of the stochastic Fourier spectrum to simulate the wind speed process.