In this paper, the aerodynamic performance of the Basalt Fiber Reinforced Polymer (BFRP) bionic plate wind turbine blade with different pitch angles, incoming wind speeds and rotational speeds was investigated. The influence of the tower shadow effect on the wake velocity, aerodynamic load, displacement and stress of BFRP bionic plate wind turbine blade under the rated condition was obtained by establishing the whole machine model including tower tube, and the error analysis of the simplified calculation formula of aerodynamic load was carried out. Results show that the incoming wind speed has a great influence on the stress and wind speed backflow and the tower shadow effect has a great influence on the horizontal thrust and torque of BFRP bionic plate wind turbine blade. The simplified calculation formula of aerodynamic load can accurately simulate the displacement and stress trend of BFRP bionic plate wind turbine blade and the recommended values of the pitch angle, incoming wind speed and the rotational speed of BFRP bionic plate wind turbine blade were given. The research results can provide the dynamic parameter reference for the engineering design of BFRP bionic plate wind turbine blade.

The current research on the wind resistance of standing seam metal roofs primarily focuses on the failure modes of the entire roof panel and the contact areas between the seams and supports, with little consideration given to the synergy between the roof seam reinforcements, the web, and the supports. As a result, the failure mechanisms of roof systems cannot be accurately represented. This paper, based on wind uplift tests and ABAQUS simulation modeling, provides a detailed analysis of the wind resistance and failure mechanisms of reinforced standing seam metal roof systems. The study reveals that the deformation and failure of the roof system under wind load can be divided into three stages: elastic deformation, plastic deformation, and failure. In the elastic deformation stage, the areas with higher stress are mainly distributed in the mid-span of the roof panels and along the ribs, where the roof stress remains below the material's yield strength, and the displacement at the roof panel seams is minimal. During the plastic deformation stage, as the load increases, significant vertical deformations appear in the roof panels, the lateral displacement at the seams gradually increases, and the stress growth is pronounced. Without reinforcement, the roof panel withstands a maximum wind pressure of 3.2 kPa, with a central vertical displacement of 109 mm, while the ultimate lateral displacement at the seams reaches 2.3 mm, resulting in unseating failure, marking the structural failure. With reinforcement, the roof panel can withstand a maximum wind pressure of 4.3 kPa, corresponding to a central vertical displacement of 122 mm. The growth of lateral displacement at the seams slows down, and the reinforcement significantly suppresses seam displacement. As the load continues to increase, the reinforcements and the web work synergistically, exhibiting reciprocating counterclockwise and clockwise rotations, with the maximum lateral displacement at the seams increasing to 3.05 mm. Ultimately, unseating occurs at the roof panel seams or tearing at the web. Therefore, the reinforcement system significantly enhances the wind resistance of the roof system, providing theoretical guidance for wind-resistant design in roofing engineering.

The multiple-fan wind tunnel is an important facility for reproducing target wind field. Existing control methods for the multiple-fan wind tunnel can generate wind speeds that satisfy the target statistical characteristics of a wind field (e.g., power spectrum). However, the frequency-domain features cannot well represent the nonstationary winds of extreme storms (e.g., downburst). Therefore, this study proposes a multiple-fan wind tunnel control scheme based on Deep Reinforcement Learning (DRL), which will completely transform into a data-driven closed-loop control problem, to reproduce the target wind field in the time domain. Specifically, the control scheme adopts the Deep Deterministic Policy Gradient (DDPG) paradigm in which the strong fitting ability of Deep Neural Networks (DNN) is utilized. It can establish the complex relationship between the target wind speed time series and the current control strategy in the DRL-agent. To address the fluid memory effect of the wind field, this study innovatively designs the system state and control reward to improve the reproduction performance based on historical data. To validate the performance of the model, we established a simplified flow field based on Navier Stokes equations to simulate a two-dimensional numerical multiple-fan wind tunnel environment. Using the strategy of DRL decision maker, we generated a wind speed time series with minor error from the target under low Reynolds number conditions. This is the first time that the AI methods have been used to generate target wind speed time series in a multiple-fan wind tunnel environment. The hyperparameters in the DDPG paradigm are analyzed to identify a set of optimal parameters. With these efforts, the trained DRL-agent can simultaneously reproduce the wind speed time series in multiple positions.

An accurate assessment of aerodynamic effects on structures is essential for a reliable wind design for high-rise buildings. Turbulence model is a key ingredient of computational fluid dynamics (CFD) in calculating the wind flow fields. This paper aims to identify the properties of representative RANS and LES models particularly for wind load determination. The models investigated are the realizable k-ε model for RANS and the dynamic Smagorinsky model for LES. In this study, their application aspects are discussed to provide enhanced reproducibility and reliability. The airflow around a building at Reynolds number 76,000 is simulated and the numerical results are also compared with wind tunnel experiment data. The wind design loads, such as story shear forces and overturning or torsional moments, are calculated based on the numerical results. Both RANS and LES models accurately capture surface pressure profiles, while LES results demonstrate proper energy decay in the power spectra. The numerical results highlight the effects of aspect ratio of building and the attack angle on the wind loads. This information would be of great help in designing tall buildings resilient to wind environments using CFD models.

This paper reviews measurements of wind profiles in the atmospheric boundary layer in strong wind (thermally neutral) conditions over open water and the ocean, and the variation of the roughness parameters with mean wind speed. Based on the wind data recorded on the coast of the island of Frøya (Norway) in the 1980s, and dropwindsonde profiles in hurricanes, the paper shows that 'capping', or saturation, of the surface drag coefficient becomes apparent at a mean wind speed at 10m height of about 25 m/s. Wind speed models used in the offshore industries were investigated, (the ISO model, the API 'tropical cyclone' model and the IEC model). The ISO model, although based on good quality data from Frøya, does not allow for the saturation of the roughness above about 25-30 m/s, even though that was apparent in the Frøya data. 'Capping' of the aerodynamic roughness length for wind speeds greater than 28 m/s is represented appropriately in the API 'tropical cyclone'model, and hence the model represents the mean wind properties reasonably well in severe tropical cyclone conditions. However, the turbulence intensities in the API 'tropical cyclone' model, based on over-land measurements (ESDU), are overpredicted for winds over the ocean, at heights above 20m. The IEC models are entirely based on over-land measurements, and hence are not representative of over-water conditions such as those required for offshore wind farms. New model profiles for over-ocean strong winds are proposed for wind speeds up to hurricane strength, based on the ISO profiles, but with capping of the surface drag coefficient at a value of 0.0025, at a mean wind speed at 10m height of 25 m/s. The proposed turbulence intensity model is also a revision of the ISO profile, also with capping above 25 m/s. The proposed model profiles are in better general agreement with recorded data in strong winds than those currently specified in international standards, and are applicable to all wind speeds in synoptic-scale events, including those in tropical cyclones, typhoons and hurricanes. As well as the Frøya data, the revised strong-wind models are supported by measurements from Atlantic hurricanes, gales in the North Sea, landfalling typhoons in Japan and Cyclone 'Yasi' in Queensland, Australia.