In light of extreme value distribution probability, an improved prediction method of the Recurrence Period Wind Speed (RPWS) is constructed considering wind direction, with the Equivalent Independent Wind Direction Number (EIWDN) introduced as a parameter variable. Firstly, taking the RPWS prediction of Beijing city as an example, the traditional Cook method is used to predict the RPWS of each wind direction based on the measured wind speed data in Beijing area. On basis of the results, the empirical formulae to determine the parameter variables are fitted to construct an improved expression of the non-exceedance probability of the RPWS. In this process, the statistical model of the optimal threshold is established, and thus the independent wind speed samples exceeding the threshold are extracted and fitted to follow the Generalized Pareto Distribution (GPD) model for analysis. In addition, the Extreme Value Type I (EVT I) distribution model is used to predict and analyze the RPWS. To verify its wide applicability, the improved method is further used in cities like Jinan, Nanjing, Wuxi, Shanghai and Shenzhen to predict and analyze the RPWS of each wind direction, and the prediction results are compared against those gained via the traditional Cook method and the whole direction. Results show that the 50-year RPWS results predicted by the improved method are basically consistent with those predicted by the traditional method, and the RPWS prediction values of most wind directions are within the envelope range of the whole wind direction prediction value. Compared with the traditional method, the improved method can readily predict the RPWS under different return periods through empirical formulae, and avoid the repeated operation process and some assumptions in the traditional Cook method, and then improve the efficiency of prediction. In addition, the improved RPWS prediction results corresponding to the GPD model are slightly larger than those of the EVT I distribution model.

Tall buildings are distinguished by their slenderness, making them sensitive to wind loads. A huge amount of resources is typically dedicated to controlling loads and vibrations caused by wind. Enhancing tall buildings' aerodynamic performance can save a large portion of these expenses. This enhancement can be achieved through aerodynamic optimization that can be tackled either by altering the outer shape of the building locally through modifying the corners (e.g., corner chamfering) or globally through changing the whole form of the building (e.g., twisting). In this paper, a newly developed aerodynamic optimization procedure (AOP) is adopted to enhance tall buildings' aerodynamic performance. This procedure is a combination of computational fluid dynamics (CFD), Artificial Neural Networks (ANN) and Genetic algorithm (GA). An ANN-based surrogate model is used to evaluate the aerodynamic parameters through the optimization procedure to reach a reliable aerodynamic shape. Helical twisting and corner modifications of the buildings are used to reduce the along-wind base moment.

The atmospheric turbulence characteristics measured at a meteorological station in northwest part of the Czech Republic are presented for selected time periods in the year 2017. The terrain of this region is influenced by surface coal mining and the related industry. The datasets used in this study were measured using four ultrasonic anemometers installed on an 80 m high meteorological mast at heights of 20, 40, 60 and 80 m, respective. From the primary high-frequency datasets, time intervals in order of hours were selected and integral turbulence characteristics (ITCs), turbulence intensities and turbulence spectra were analyzed. The time intervals were selected with respect to atmospheric stability parameter, known as Obukhov number. We concentrated on the days with higher wind velocity and neutral atmospheric stratification. The wind characteristics investigated in this study include the wind speed, wind direction and its histograms, turbulence intensity, friction velocity and wind power spectra. The ITCs and spectral characteristics were compared with the theoretical models and values from the literature. The resulting ITCs showed the values for urban locations similar to those found in other studies and can be used in practical design. The computed turbulence spectra followed the shape of theoretical spectra of turbulence for both horizontal and vertical velocity components. The computed integral length scales have shown to be unsuitable for further use due to their highly scattered values.

The simulation of non-stationary wind velocity is particularly crucial for the wind resistant design of slender structures. Recently, some data-driven simulation methods have received much attention due to their straightforwardness. However, as the number of simulation points increases, it will face efficiency issues. Under such a background, in this paper, a time-varying coherence matrix decomposition method based on Diagonal Proper Orthogonal Decomposition (DPOD) interpolation is proposed for the data-driven simulation of non-stationary wind velocity based on S-transform (ST). Its core idea is to use coherence matrix decomposition instead of the decomposition of the measured time-frequency power spectrum matrix based on ST. The decomposition result of the time-varying coherence matrix is relatively smooth, so DPOD interpolation can be introduced to accelerate its decomposition, and the DPOD interpolation technology is extended to the simulation based on measured wind velocity. The numerical experiment has shown that the reconstruction results of coherence matrix interpolation are consistent with the target values, and the interpolation calculation efficiency is higher than that of the coherence matrix time-frequency interpolation method and the coherence matrix POD interpolation method. Compared to existing data-driven simulation methods, it addresses the efficiency issue in simulations where the number of Cholesky decompositions increases with the increase of simulation points, significantly enhancing the efficiency of simulating multivariate non-stationary wind velocities. Meanwhile, the simulation data preserved the time-frequency characteristics of the measured wind velocity well.

Engineering structures often suffer significant damage in the horizontal outflow region of downburst. The wall jet model, which simplifies the simulation device by only modeling the horizontal outflow region of downburst, has been widely employed to study downburst flow characteristics. However, research on downburst wind fields over hilly terrain using the wall jet model is limited, and the relationship between the downburst wind fields generated by wall jet and impinging jet remains unclear. This study investigates the flow characteristics of downburst-like wind over a 3D ideal hill model using wind tunnel tests with the wall jet and impinging jet models. The effects of hill height, slope, shape, and radial position on the speed-up ratio are examined using the wall jet flow. The results indicate that slope and radial position significantly affect the speed-up ratio, while hill height have a slight impact and shape have a minimal impact. Additionally, this study investigates the wind field characteristics over flat terrain using the impinging jet, and investigated the connection between the impinging jet model and the wall jet. Based on this connection, a comparison of the downburst-like flow characteristics over the same 3D ideal hill using the wall jet and impinging jet models is conducted, which further validates the reliability of the wall jet model for studying downburst flow characteristics over hilly terrain.