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Darrieus type vertical axis wind turbine (VAWT) is optimized using the genetic algorithm (GA). The airfoil shape is parameterized using the Class-Shape Transformation (CST) method. The double multiple stream tube (DMST) method with the Gormont dynamic stall modification is used for the calculation of the VAWT performance parameters. Once the numerical codes are validated using availible experimental results, the airfoil parameters are varied as to achieve the optimum value of the genetic algorithm fitness function.

Vertical-axis wind turbines (VAWTs) are attractive tools for wind energy extraction particularly suitable for small consumers or off-grid areas. Although their geometry is simple (here, rectangular blade of constant airfoil is assumed), aerodynamic analysis may be quite complex. Computational fluid dynamics (CFD) approach is employed for the estimation of rotor aerodynamic performances. This paper provides a review of possible multiobjective optimization strategies for the design of small-scale VAWT laminate blades in terms of its main structural parameters: ply-order and ply-number. Numerous structural analyses of the composite turbine blades were performed by finite element method (FEM). Multi-criteria constrained optimizations, by an evolutionary method − particle swarm optimization (PSO), were performed with respect to blade total mass, maximum blade tip deflection under static loading, computed natural frequencies and failure index along the blade. By combining different input and output parameters (cost functions and constraints) a large variety of feasible solutions can be achieved.

Wind energy extraction is one of the fastest developing engineering branches today. Number of installed wind turbines is constantly increasing. Appropriate solutions for urban environments are quiet, structurally simple and affordable small-scale vertical-axis wind turbines (VAWTs). Due to small efficiency, particularly in low and variable winds, main topic here is development of optimal flow concentrator that locally augments wind velocity, facilitates turbine start and increases generated power. Conceptual design was performed by combining finite volume method and artificial intelligence (AI). Smaller set of computational results (velocity profiles induced by existence of different concentrators in flow field) was used for creation, training and validation of several artificial neural networks. Multi-objective optimization of concentrator geometric parameters was realized through coupling of generated neural networks with genetic algorithm. Final solution from the acquired Pareto set is studied in more detail. Resulting computed velocity field is illustrated. Aerodynamic performances of small-scale VAWT with and without optimal flow concentrator are estimated and compared. The performed research demonstrates that, with use of flow concentrator, average increase in wind speed of 20%–25% can be expected. It also proves that contemporary AI techniques can significantly facilitate and accelerate design processes in the field of wind engineering.

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