With the trend towards larger and lighter designs of wind turbines, blades are progressively being developed to have longer and more flexible configurations. Under standstill conditions, the separated flow induced by a wide range of incident flow angles can cause complex aerodynamic elastic phenomena on blades. However, classical momentum blade element theory methods show limited applicability at high angles of attack, leading to significant inaccuracies in wind turbine performance prediction. In this paper, the geometrically accurate beam theory and high-fidelity CFD method are combined to establish a bidirectional fluid–structure coupling model, which can be used for the prediction of the aeroelastic response of wind turbine blades and the analysis of fluid–structure coupling. Aeroelastic calculations are carried out for a single blade under different working conditions to analyze the influence of turbulence, gravity and other parameters on the aeroelastic response of the blade. The results show that the dominant frequency of the vibration deformation response in the edgewise direction is always the same as the first-order edgewise frequency of the blade when the incoming flow condition is changed. The loading of gravity will make the aeroelastic destabilization of the blade more significant, which indicates that the influence of gravity should be taken into account in the design of the aeroelasticity of the wind turbine. Increasing the turbulence intensity will change the dominant frequency of the vibration response in the edgewise direction, and at the same time, it will be beneficial to the stabilization of the aeroelasticity response.

With the gradual increase in the maturity of wind energy technology, floating offshore wind turbines have progressively moved from small-capacity demonstrations to large-capacity commercial applications. As a direct component of wind turbines used to capture wind energy, an increase in the blade length directly leads to an increase in blade flexibility and a decrease in aerodynamic performance. Furthermore, if the floater has an additional six degrees of freedom, the movement and load of the blade under the combined action of wind and waves are more complicated. In this work, two types of semi-submersible wind turbines with different capacities are used as the research objects, and the load and motion characteristics of the blades of these floating offshore wind turbines are studied. Through the analysis of the simulation data, the following conclusions are drawn: with the increase in the capacity of the wind turbine, the flexible deformation of the blade increases, the movement range of the blade tip becomes larger, the blade root load increases, and the power fluctuation is more obvious. Compared with the bottom-fixed wind turbine, the flexible blade deformation of the floating offshore wind turbine is smaller; however, the blade root load is more dispersed, and the power output is more unstable and lower.

Real-time monitoring of wind turbine aeroelastic damping is crucial for dynamically adjusting operational strategies and enhancing turbine stability and economic efficiency. However, since aeroelastic damping cannot be directly measured and effective industrial methodologies remain limited, this study proposes an innovative hybrid prediction framework for aeroelastic damping of wind turbine blades based on field-measured turbine data. First, a general model for calculating blade root reaction forces was developed using blade element momentum theory, considering multiple influencing factors such as motor torque, gravitational force, and centrifugal force. Linear regression and decision tree algorithms were employed to identify key coefficients in the theoretical model, thereby providing accurate hub load inputs for finite element (FE) calculations of tower aeroelastic damping through blade physical modeling. Second, a full-scale FE model of the wind turbine was constructed in Abaqus, where dynamic responses were computed using hub axial forces as inputs and compared with field data to obtain aeroelastic damping values, yielding high-quality labeled data for training machine learning models. Finally, an aeroelastic damping dataset was generated through data analysis and downsampling, and a long short-term memory recurrent neural network was trained as the prediction model. Simulation results demonstrated high accuracy, with mean prediction errors below 2.2% and maximum errors below 2.5% on real turbine datasets. In addition, experimental validation further confirmed the effectiveness of the proposed method. The model features a computationally efficient architecture, strong real-time applicability for high-dimensional inputs, and considerable potential for practical implementation.