With the increasingly serious shortage of resources and environmental pollution caused by fossil fuels, green renewable energy has become a key focus of global development. As a crucial field, wind power is developing towards large‐scale and high efficiency. The dynamic response of wind turbine blade, as a core load‐bearing component, directly affects the safety and stability of the whole machine. Aiming at the trade‐off between the efficiency and accuracy of traditional multibody dynamics (MBD) and finite element analysis (FEA), this study proposes two blade response prediction models based on the long‐short‐term memory network (LSTM) and Bayesian optimization (BO), which can efficiently predict the response of MBD and FEA simulation data, respectively. The results show that the optimized LSTM model achieves mean square error (MSE) 0.0011, mean absolute error (MAE) 0.0196, and coefficient of determination (R 2 ) 0.9964 in MBD prediction, and MSE 0.0025, MAE0.0322, and R 2 0.9922 in FEA prediction, which demonstrate high fitting accuracy and generalization ability. For the first time, based on the high‐precision simulation data of WeMoLab platform, the modeling and prediction of two types of dynamic responses are realized with high efficiency, and the computational cost is greatly reduced. The proposed model provides reliable data‐driven support for wind turbine blade load prediction, structural optimization, and operational state assessment and has wide engineering application value.
Jia et al. (Wed,) studied this question.