Metaheuristic Optimization of Dual-Element Vertical Axis Wind Turbine Using Genetic Algorithm
Volume 2: Combustion, Fuels, and Emissions; Renewable Energy: Solar and Wind; Inlets and Exhausts; Emerging Technologies: Hybrid Electric Propulsion and Alternate Power Generation; GT Operation and Maintenance; Materials and Manufacturing (Including Coatings, Composites, CMCs, Additive Manufacturing); Analytics and Digital Solutions for Gas Turbines/Rotating Machinery(2019)
Abstract
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.
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Key words
Wind Energy, Genetic Algorithm, Invasive Weed Optimization, Vertical Axis Wind Turbine, Computational Fluid Dynamics, OpenFOAM
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