فهرست نشریات علمی دسترسی آزاد ایران

This study focuses on optimizing wind turbine blade design and hub height for enhanced energy performance in the Ain al-Tamr area of Karbala, Iraq. Site-specific wind speed data from NASA Giovanni (2019) revealed a vertical wind speed gradient, increasing from 5.66 m/s at 10 m to 14.22 m/s at 100 m (P = 0.21). Statistical analysis indicated that the lognormal distribution provided a better fit (R² = 0.94–0.96) for modeling local wind speeds compared to the Weibull distribution. To optimize the blade geometry and hub height, Blade Element Momentum (BEM) theory was combined with a genetic algorithm (GA) optimization framework. The objective function minimized negative power coefficient (Cp) values under aerodynamic and structural constraints. The applied GA parameters included a population size of 100, a crossover rate of 0.8, and a mutation rate of 0.05. The optimized turbine design achieved significant improvements over the baseline GE 2.5-120 model. The chord length was reduced from 0.303 m at the root to 0.015 m at the tip, while the twist angle decreased from 1.433° to 0.150° along the span. These changes resulted in an increase in the power coefficient (Cp) from 0.26 to 0.51, reaching approximately 86% of the theoretical Betz limit. Annual energy production improved by 72.4%, rising from 21,834.50 MWh to 37,665.83 MWh. Validation against factory data for the GE 2.5-120 turbine showed a deviation of only 3%, confirming the accuracy of the proposed model. This work demonstrates that integrating advanced statistical modeling with BEM-GA optimization techniques can significantly enhance wind turbine performance in arid and desert environments such as Iraq. The results highlight the importance of tailoring wind turbine designs to local climatic conditions to maximize energy output and support sustainable development goals.A hybrid Blade Element Momentum (BEM) theory and genetic algorithm (GA) approach optimized blade geometry and hub height, minimizing negative power coefficient (Cp) values under aerodynamic and structural constraints. GA parameters included a population size of 100, crossover rate of 0.8, and mutation rate of 0.05.The optimized design significantly outperformed the baseline GE 2.5-120 model. Chord length decreased from 0.303 m (root) to 0.015 m (tip), while twist angle reduced from 1.433° to 0.150° along the span. These modifications increased Cp from 0.26 to 0.51, achieving 86% of the Betz limit. Annual energy production rose 72.4% from 21,834.50 MWh to 37,665.83 MWh. Model validation showed only 3% deviation from factory data.The results demonstrate that combining statistical modeling with BEM-GA optimization substantially improves turbine performance in arid regions. Site-specific design adaptation proves crucial for maximizing energy output and supporting sustainable development in comparable environments. The methodology provides a replicable framework for wind energy optimization in desert climates.