The influence of the heights of low-level jets (LLJs) on the rotor power and aerodynamic loads of a horizontal axis wind turbine were investigated using the fatigue, aerodynamics, structures, and turbulence code. The LLJ and shear inflow wind fields were generated using an existing wind speed spectral model. We found that the rotor power predicted by the average wind speed of the hub height is higher than the actual power in relatively weak and shallow LLJ inflow conditions, especially when the LLJ height is located inside the rotor-swept area. In terms of aerodynamic loads, when the LLJ height is located inside the rotor-swept area, the root mean square (RMS) rotor thrust coefficient and torque coefficient increase, while the RMS rotor unbalanced aerodynamic load coefficients, including lateral force, longitudinal force, tilt moment, and yaw moment, decreased. This means that the presence of both positive and negative wind shear in the rotor-swept area not only increases the rotor power but also reduces the unbalanced aerodynamic loads, which is beneficial to the operation of wind turbine. Power spectrum analysis shows no obvious difference in the power spectrum characteristics of the rotor torque and thrust in LLJ inflow conditions with different heights.

The airfoil plays an important role in improving the performance of wind turbines. However, there is less research dedicated to the airfoils for Vertical Axis Wind Turbines (VAWTs) compared to the research on Horizontal Axis Wind Turbines (HAWTs). With the objective of maximizing the aerodynamic performance of the airfoil by optimizing its geometrical parameters and by considering the law of motion of VAWTs, a new airfoil, designated the LUT airfoil (Lanzhou University of Technology), was designed for lift-driven VAWTs by employing the sequential quadratic programming optimization method. Afterwards, the pressure on the surface of the airfoil and the flow velocity were measured in steady conditions by employing wind tunnel experiments and particle image velocimetry technology. Then, the distribution of the pressure coefficient and aerodynamic loads were analyzed for the LUT airfoil under free transition. The results show that the LUT airfoil has a moderate thickness (20.77%) and moderate camber (1.11%). Moreover, compared to the airfoils commonly used for VAWTs, the LUT airfoil, with a wide drag bucket and gentle stall performance, achieves a higher maximum lift coefficient and lift–drag ratios at the Reynolds numbers 3 × 10⁵ and 5 × 10⁵.

In order to quantitatively analyze the data measurement accuracy of ocean buoys under normal and extreme sea conditions, in this study, we simulated the six-degree-of-freedom motion response of self-designed ocean buoys under different sea conditions based on a separated vortex simulation and the fluid volume method and analyzed the impact of the unsteady motion of buoys on data measurement. The results indicate that under normal sea conditions, the deviation between the numerical method used in this paper and the experimental results is less than 10%. The heaving motion of a buoy is most sensitive to changes in wave conditions. The fluctuation intensity of buoy motion is modulated by the height and wavelength of waves. When the wave height and wavelength are similar to the overall geometric size of a buoy, the wave characteristics of the buoy’s heave, yaw, and pitch motion are significant. In addition, under extreme sea conditions, the movement of the buoy can also cause a deviation in the measured velocity in the transverse flow direction, but the overall deviation is less than 10%. In extreme sea conditions, the wind speed measurement results should be corrected to improve the measurement accuracy of a buoy.

Studies demonstrate that the reduced frequency k is influenced by the incoming wind speed U₀ and the rotor speed n. As a dimensionless parameter, k characterizes the stability of the flow field, which is a critical factor affecting the performance of vertical-axis wind turbines (VAWTs). This paper investigates the impact of k on the performance of straight-blade vertical-axis wind turbines (H-VAWT). The findings indicate that 0.05 is the critical value of k. The same k results in a similar flow field structure, yet the performance changes vary with different U₀. A decrease in n or an increase in U₀ leads to an increase in the average value and fluctuation of k, which subsequently reduces the rotor rotation torque C_m and decreases the maximum wind energy utilization rate Cp_max. This reduction in Cp_max weakens the stability of the flow field. Additionally, the high-speed area of the blade’s trailing edge velocity trajectory at $\theta =0°$, $\theta =120°$, and $\theta =240°$ expands with increasing range. Velocity dissipation in the high-speed area of the trailing edge affects the stability of the flow field within the rotor.

Wind turbine blade surface defect detection is of great significance in ensuring the safety and operational efficiency of wind power systems. However, accurately detecting subtle and small‐scale defects remains challenging under complex imaging conditions. In particular, progressive downsampling in deep layers tends to cause severe feature loss for subtle and small‐scale defects, whereas insufficient cross‐layer feature compensation further weakens spatial detail preservation and multiscale discrimination. To tackle these challenges, this paper introduces an enhanced defect detection algorithm based on You Only Look Once version 8 nano (YOLOv8n). The main improvements include (1) introducing the C2f_EMBC module in the backbone network to replace the original C2f module to enhance the feature extraction capability; (2) adopting the Gold‐YOLO architecture in the neck network and utilizing the innovative gather‐and‐distribute (GD) mechanism to optimize the multiscale feature fusion through low‐GD and high‐GD branches. Compared with the original YOLOv8n algorithm, the proposed method improves the recall rate by 6.7% on the wind turbine defect dataset and increases the mean Average Precision at Intersection over Union (IoU) threshold 0.5 (mAP@0.5) by 2.2%. The accompanying efficiency metrics, including inference speed in frames per second (FPS), the number of parameters (Params), and computational complexity in giga floating‐point operations (GFLOPs), further suggest that these gains are achieved with acceptable computational overhead, indicating suitability for real‐time UAV‐based inspection and potential deployment on edge platforms.