Abstract
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.
Highlights
Due to some of the pressing issues of modern society, that include climate change, global warming, pollution, etc, the extraction of wind energy is one of the fastest developing engineering fields today
The values of minimum, average and maximum velocity along the characteristic line were registered. They were recorded as two distinct goal functions: average speed Vmean and min-to-max speed ratio Vmin/Vmax that serves to quantify the uniformity of the velocity field in the downstream part of the rotor that is generally more critical for vertical-axis wind turbines (VAWTs) performance
Some typical applications in aerospace include: flight simulations, control systems, autopilots, aircraft component behavior simulations, aircraft component fault detection, maintenance analysis, image/signal identification, processing, and compression. They were employed in this research because a fast and accurate prediction of output velocity profile based on four input geometric parameters was necessary for concentrator optimization, contrary to the solution of the nonlinear Navier-Stokes equations that takes up too much time
Summary
Due to some of the pressing issues of modern society, that include climate change, global warming, pollution, etc, the extraction of wind energy is one of the fastest developing engineering fields today. The effects of rotor rotation are simulated by sliding-mesh approach, where in every time-step Dt, the inner zone is rotated for a small angular increment Dc. Unsteady Reynolds-averaged Navier-Stokes (URANS) equations were closed by a two-equation kv SST turbulence model,[20] based on Boussinesq hypothesis, that provides good results and is often employed in the engineering problems from the field of computational aerodynamics. Computations were performed for 1000 iterations that is, until reaching the converged values of minimum, average and maximum speeds along the characteristic line located 0.75 m downstream from the rotational axis
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