Multi-Objective Structural Optimization of Composite Wind Turbine Blade Using a Novel Hybrid Approach of Artificial Bee Colony Algorithm Based on the Stochastic Method

The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models. This paper presented a novel approach to the structural design of small-scale turbine blades using the Artificial Bee Colony (ABC) Algorithm based on the stochastic method to optimize both mass and cost (objective functions). The study used computational fluid dynamics (CFD) and structural analysis to consider the fluid-structure interaction. The optimization algorithm defined several variables: structural constraints, the type of composite material, and the number of composite layers to form a mathematical model. The numerical modeling was performed using the Ansys Fluent software and its Fluid-Structure Interaction (FSI) module. The ANSYS Composite PrePost (ACP) advanced composite modeling method was utilized in the structural design of composite materials. This study showed that the structurally optimized small-scale turbine blades provided a sustainable solution with improved efficiency compared to traditional designs. Furthermore, using CFD, structural analysis, and material characterization techniques first considered in this study highlights the importance of considering structural behavior when optimizing turbine blade designs.