Aerodynamic and structural optimization of wind turbine blade considering natural of frequencies of vibration

In recent years, wind energy has garnered significant global attention due to its immense energy and environmental potential, leading to a substantial increase in both capacity and size of wind turbines, evolving from a nominal power of 75kW to 7.5MW and a rotor diameter of 17m to over 125m. However, this scaling-up has introduced new challenges related to instability caused by the aeroelastic effect, which stems from the interplay between wind loads and the structural deformation of wind turbine blades. Consequently, an effective model necessitates well-defined aerodynamic and structural components. Yet, the complex geometric shapes of turbines demand extensive computational resources for analysis, particularly given their composite materials and intricate configurations.Various research institutions, including NREL, DTU, and ECN, have developed aeroelastic tools for wind turbine studies, integrating structural and aerodynamic modules. The prevalent approach employs the BEMT (Blade Element Moment Theory) model for aerodynamic simulation and FEM models for structural dynamic analysis. The BEMT model partitions the blade into elements to evaluate aerodynamic loads based on local characteristics. This amalgamation offers an efficient solution for predicting element performance parameters. Additionally, the BEM model provides a computationally economical alternative to more intricate models, making it a cost-effective choice for wind turbine analysis and a cheaper tool compared to robust methods, like CFD.This work proposes an aerodynamic optimization method constrained by the structural model, focusing on the natural vibration frequencies of wind turbine blades. The methodology comprises three phases: defining the aerodynamic model using the CCBlade code in Julia Language, implementing the structural analysis method via FEM for rotating Timoshenko beams in MATLAB, and integrating the aerodynamic and structural models on a multi-objective optimization platform. Evolutionary algorithms were employed to enhance wind turbine energetic efficiency and avoid structural instabilities within this study. By leveraging advancements in modeling and optimization techniques, the aim is to contribute to the development of more efficient and reliable wind turbines, further promoting the transition to clean and sustainable energy sources.