Priestley’s oscillatory process theory is widely employed to model nonstationary excitation. However, the underlying coherence function of this theory can only be time invariant. To take into account the time-varying coherence function, the Sigma-oscillatory process theory and Wold-Cramer decomposition model have been introduced in literature. Due to its straightforwardness, the Wold-Cramer decomposition model is used in this study. Based on the Wold-Cramer decomposition model of nonstationary wind excitation, the alongwind response analysis framework of tall buildings to nonstationary winds is presented. The time-varying coherence function models of two downburst events are developed using the measured downburst data. The influence of time-varying coherence of nonstationary winds on the alongwind tall building response is investigated. Numerical examples show that the time-varying coherence function may lead to larger structural response compared with the corresponding time-invariant coherence function model. Considering the large variation of nonstationary downburst winds, the observation drawn from this study should be further evaluated using more measurement wind data. It can be expected that the time-varying coherence function may play an important role in better modeling the nonstationary winds and their load effects on structures.

The classical spectral representation method for simulation of multivariate nonstationary Gaussian random processes may be less efficient due to difficulty in a straightforward application of the fast Fourier transform (FFT). Although several attempts have been made to invoke the FFT, there remains the need to further improve the efficiency for cases where a large number of points need to be simulated. In this paper, a stochastic wave-based simulation scheme for the multivariate nonstationary random process along a straight line is introduced in conjunction with either a direct summation of cosine functions or the application of a two-dimensional (2D) FFT. Central to the proposed schemes is the transformation of the simulation of a multivariate nonstationary random process to that of a nonstationary one-dimensional stochastic wave. The stochastic wave can be simulated based on a direct summation of cosine functions or by invocation of a 2D FFT. The latter requires that the points to be simulated are evenly distributed. To implement FFT, proper orthogonal decomposition (POD) is employed to factorize the time-dependent and space-dependent 2D decomposed evolutionary power spectral density of the converted stochastic wave. The proposed hybrid approach of a stochastic wave and POD is quite general, and can be used to simulate nonstationary multivariate random processes with complex coherence functions. Numerical examples show that the proposed hybrid approach is very efficient in comparison with existing approaches when the number of simulation locations is large, and it offers a desired level of simulation accuracy when appropriate discretization parameters are selected.

Estimating the peak value of wind load effects is an important task for engineers of structural and cladding design. Because of the various sources of uncertainties, such as the aerodynamic effect, model selection, and calibration errors, the peak wind load effects may have large variations. This study evaluates the uncertainty of the peak value of wind load effects across multiple samples from a wind tunnel. The Hermite polynomial model (HPM)-based translation process method is adopted to estimate the peak wind load effect. An empirical formula is proposed to estimate the parameters of peak value distribution. Two analytical approaches to model the uncertainty of the peak value at any fractile are proposed based on the probability function and the Gaussianity inference of the peak value. Numerical examples including the wind pressure coefficient and wind-induced response illustrate the performance of the proposed approaches.

In order to extract more wind energy, the wind turbine rotor becomes larger and the tower becomes taller. With more flexibility and smaller damping, wind turbine tower is prone to vibrate in winds. Meanwhile, the tower suffers the periodic loadings caused by the rotor rotation in the operational condition. The excessive vibrations could not only significantly affect the power generation but shorten the structural life due to the fatigue as well. It is challenging to reduce the vibration caused by the rotor rotation using the passive tuned mass damper (TMD) and traditional LQR controller due to the limited effective bandwidth. Therefore, an active tuned mass damper (ATMD) using a virtual TMD algorithm is proposed to mitigate the along-wind vibration of the tower under parked and operational conditions. The virtual TMD algorithm exhibits wide effective bandwidth and only requires the acceleration information on the top of the tower or the relative displacement of the active TMD. Firstly, the aerodynamic-structure-servo coupling (ASSC) model of the wind turbine is established which considers the interaction among the aerodynamic load, structure, and servo system. Secondly, the accuracy of the ASSC model is then verified using the onshore 5 MW wind turbine by the National Renewable Energy Laboratory (NREL). Thirdly, the ATMD feedback control force is designed by the virtual TMD algorithm. Finally, the reduction effect on the along-wind vibration by the proposed controller is evaluated at both of operational and parked conditions using the ASSC model. The TMD and LQR controller are utilized for comparison. The numerical results demonstrate that tuned mass damper (TMD) system with fixed parameters becomes detuned and may loses its effectiveness at different wind speeds. In contrast, active control can suppress the vibration of wind turbines at different wind speeds. Compared to the LQR controller, the proposed controller can enhance the reduction effect of wind turbine response with smaller stroke and control force at operational conditions.

A downburst is a typical local highly intensive wind all over the world, which is attributed to be the main cause of wind damage to transmission lines in inland areas worldwide. The collapse accidents of transmission towers under the downburst still occur every year. Therefore, it is of great significance to assess the safety of the transmission towers under downbursts. The motivation of the present study is to propose a fragility assessment method for transmission towers under the action of a downburst considering the uncertainty of wind-resistance capacity and the stochastic wind load effect. First, the downburst wind field of the transmission tower with different wind attack angles and different radial distances is simulated according to the mixed stochastic model. Then, random material characteristic samples are generated by the Latin hypercube sampling technique and applied to establish uncertain finite element models for transmission towers. Next, the static nonlinear buckling analysis is carried out by numerical methods to determine the ultimate capacity under the downburst wind load. The parameter analysis of different wind attack angles and radial distances between the downburst and the tower is conducted to determine the most unfavorable location of the maximum response. The failure mode of the transmission tower and the probabilities of the initial failure main members are summarized. Finally, the fragility curves of the transmission tower under the downburst and the atmospheric boundary layer (ABL) wind are compared. The results show that the maximum response is located at R = 1.6D. Most of the initial buckling members are located close to the first section of the tower. The fragility curves of the tower under the downburst are more dangerous than the ABL wind with the attack angle increasing from 0° to 90°. Furthermore, considering the probability model of intensity and direction of the downburst and based on the previous fragility analysis, the collapse probability of the transmission tower caused by the downburst is obtained. By probability analysis of the parameters, including layout conditions, different directions, and different wind speeds, it is found that the most favorable arrangement is 157.5°, and the most unfavorable arrangement is 112.5°.

The prediction of the extreme value of the wind pressure receives significant attention from the wind engineering community. However, the correlation among extreme wind pressures associated with different locations on the building surface is less addressed. In this study, the characterization of the dependence structure of extreme wind pressures and its application to roofing damage estimation are presented. First, three methods to model the dependence structure of extreme wind pressures, including multivariate extreme value theory, copula function, and Nataf transformation, are reviewed. Then, the asymptotic property of the bivariate non-Gaussian random vector will be established and the partial correlation for the data within finite duration is discussed. Furthermore, the dependence structure and asymptotical properties of extreme wind pressure coefficients are investigated based on the very long wind tunnel data. The correlation of extreme pressures considering the randomness of the annual maxi...