• Energy Research

Wind energy is gaining special interest worldwide due to the necessity of reducing pollutant emissions and employ renewable resources. Traditionally, horizontal axis wind turbines have been employed but certain situations require vertical axis wind turbines. With a view to improve the efficiency of a vertical axis wind turbine Savonius type, the present work proposes a bioinspired design blade profile relying on the Fibonacci spiral. This shape is repeatedly presented in nature and thus it leads to a bio-inspired blade profile. A numerical model was carried out and it was found that the Fibonacci shape improves the performance of the original Savonius shape, based on semicircular blade profiles. Particularly, the Fibonacci blade profile increases around 14% the power in comparison with the Savonius blade profile. Besides this comparison between Savonius and Fibonacci, a research study was carried out to improve the efficiency of the Fibonacci turbine. To this end, the effect of several parameters was analyzed: number of blades, aspect ratio, overlap, separation gap, and twist angle. Improvements on the average power greater than 30% were obtained.

Hydrokinetic energy constitutes a source of renewable energy. However, many regions have flow velocities that are too low for effective energy extraction, and conventional turbines are not suitable for these sites. In order to address this challenge, the present work proposes a novel vertical axis hydrokinetic turbine designed for environments where conventional turbines are not feasible due to a low water velocity. The turbine’s design is inspired by biological principles, enhancing the traditional Savonius turbine by incorporating a Fibonacci spiral-inspired blade configuration. The turbine’s performance was subjected to a rigorous analysis through Computational Fluid Dynamics (CFD). The results demonstrate a notable improvement, with a 15.1% increase in the power coefficient compared to the traditional Savonius turbine. This innovative approach not only extends the applicability of hydrokinetic turbines to low-flow regions but also underscores the potential of biomimicry in optimizing renewable energy technologies. The findings of this study indicate that integrating natural design principles can result in more efficient and sustainable energy solutions, thereby paving the way for the broader adoption of hydrokinetic power in diverse geographical settings.

The present work proposes an artificial neural network (ANN) to analyze vertical axis wind turbines of the Savonius type. These turbines are appropriate for low wind velocities due to their low starting torque. Nevertheless, their efficiency is too low. In order to improve the efficiency, several modifications are analyzed. First of all, an innovative blade profile biologically inspired is proposed. After that, the influence of several parameters such as the aspect ratio, overlap, and twist angle was analyzed through a CFD (computational fluid dynamics) model. In order to characterize the most appropriate combination of aspect ratio, overlap, and twist angle, an artificial neural network is proposed. A data set containing 125 data points was obtained through CFD. This data set was used to develop the artificial neural network. Once established, the artificial neural network was employed to analyze 793,881 combinations of different aspect ratios, overlaps, and twist angles. It was found that the maximum power coefficient, 0.3263, corresponds to aspect ratio 7.5, overlap/chord length ratio 0.1125, and twist angle 112°. This corresponds to a 32.4% increment in comparison to the original case analyzed with aspect ratio 1, overlap 0, and twist angle 0.