Metaheuristic Optimization of Dual-Element Vertical Axis Wind Turbine Using Genetic Algorithm

Abstract This paper presents a framework for the optimization of Dual-Element Vertical Axis Wind Turbine (VAWT) Blade configurations for improvement in power generation. Multi-element nature of the turbine was specifically chosen as this configuration offers better-attached flow over a conventional single element H-type turbine. The framework was based on a genetic evolutionary algorithm which is a metaheuristic optimization technique based on the principle of survival of the fittest. The class of genetic algorithm used was Invasive Weed Optimization. The geometry of the turbine consists of a rotor with three sets of dual-element airfoil oriented symmetrically. Effective chord length and relative chord angle were taken as modifying parameters for generating new configurations. The fitness of each individual was evaluated by performing two-dimensional Computational Fluid Dynamics Simulations. OpenFOAM was used for performing numerical simulations. Qualitative data of torque, pressure, velocity, and turbulence kinetic energy of best configuration is shown. A considerable increase in torque in the final geometry. The model was found ideal for optimizing multi-element VAWT configuration.