Comparison of Numerical Results With Experiment Using Digital Image Correlation System of Multiobjective Structural Optimised Wind Turbine Blade

ABSTRACT This paper presents a novel approach to the structural design of small‐scale turbine blades using the Artificial Bee Colony (ABC) Algorithm to optimise both mass and cost (objective functions), with a comparison to experimental results obtained using a Digital Image Correlation (DIC) system. The optimisation algorithm defined several variables, including structural constraints, the type of composite material and the number of composite layers to form a mathematical model. Also, prototypes were designed to test and refine the proposed concept. After constructing the prototypes, structural tests were performed to evaluate their performance and durability under various conditions. These structural tests involved subjecting the prototypes to different loads and stresses to assess their response and ability to withstand expected usage conditions. In the experimental study, the structural response of the optimised composite turbine blades was evaluated under an extreme wind load of 42 m/s in Kayseri using a DIC system. The findings demonstrated that the structurally optimised small‐scale turbine blades provided a sustainable solution with improved efficiency compared to traditional designs. Furthermore, material characterisation techniques, considered for the first time in this study, highlight the importance of accounting for structural behaviour in optimising turbine blade designs. This innovative approach offers significant insights for designing more efficient and sustainable turbine blades.