Optimizing a Straight‐Bladed Vertical Axis Wind Turbine With Computational Fluid Dynamics (CFD), Artificial Neural Network (ANN), and Genetic Algorithm (GA)
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Key Points
The optimization process improved the power coefficient of the vertical axis wind turbine to 0.319, reflecting a 15.16% increase from the original value.
By adjusting design variables like turbine diameter and airfoil chord length, the study conducted 180 CFD simulations to estimate air forces.
This approach combined computational fluid dynamics with artificial neural networks and genetic algorithms to enhance efficiency while reducing costs.
The optimal turbine diameter and blade tip speed ratio were increased, supporting better performance in urban settings.
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Implication
This research demonstrates a method to optimize vertical axis wind turbines for power efficiency using CFD and ANN.