Development of a novel finite element submodeling technique for fatigue reliability modeling and assessment of wind turbine blades

Wind turbine blades are critical to the overall performance of wind turbine systems , and their fatigue resistance is paramount to ensuring long-term operational safety and reliability. Current investigations into blade fatigue behavior heavily rely on full-scale structural testing, a process that is both resource-intensive and time-consuming. As a result, there has been increasing interest in simulation-based methods. Existing simulation approaches face challenges including prolonged computation times, decreased accuracy, and limitations in analyzing critical blade components. In response to these challenges, this study presents a novel localized fatigue simulation analysis methodology based on the finite element submodeling technique specifically tailored for wind turbine blades, subsequently employed for fatigue reliability modeling and assessment. The manuscript begins by elucidating the fundamental principles and workflow of the submodeling technique-based finite element simulation method, demonstrating its effectiveness through rigorous experimental validation. Subsequently, the method is implemented to analyze fatigue and forecast the fatigue life of wind turbine blades. Moreover, a fatigue reliability model is constructed, taking into account uncertainties in blade fatigue performance. The results emphasize the improved simulation accuracy of the proposed method and its substantial implications for the advancement of blade safety analysis. • A submodeling technique-based fatigue simulation methodology is developed with integration of Solidworks, Abaqus, and Fesafe. • Submodels, including those incorporating the adhesive layer, are constructed for wind turbine blade, followed by fatigue analysis. • Fatigue reliability modeling and assessment are conducted for wind turbine blade, considering multiple uncertainties. • Sensitivity and uncertainty analyses are carried out to identify critical parameters influencing fatigue reliability.