Integration of Machine Learning (ML) and Finite Element Analysis (FEA) for Predicting the Failure Modes of a Small Horizontal Composite Blade

article aims to integrate machine learning (ML) methodologies and Finite Element Analysis (FEA) to analyze wind turbine blades made of composite material. The methods for wind speed forecasting were examined in this article. A suitable technique was employed for creating synthetic wind speed over four years in Baghdad, Iraq, and applied to structural analysis. Composite materials were considered to simulate a small horizontal-axis wind turbine blade. Baghdad's long-term wind speed pattern was established after the machine learning forecasting models based on autoregressive integrated moving averages (ARIMA). This wind forecast prediction was then used to mimic the dynamic loads acting on the blade. The structural behavior of a wind turbine under various loads was modeled using ABAQUS software employing three composite wind blades with varied stacking sequences. Hashin's criterion determined the structure's failure modes and most vulnerable areas. The main objectives are identifying an integrated methodology requiring high accuracy in blade modeling and wind forecasting. Damage analysis has been developed for small horizontal-axis wind turbine blades to evaluate the optimum stacking sequences of composite materials.