Improving the efficiency of a Savonius wind turbine by designing a set of deflector plates with a metamodel-based optimization approach

Bruno A. Storti, Jonathan J. Dorella, Nadia D. Roman, Ignacio Peralta and Alejandro E. Albanesi

Energy, 2019, vol. 186, issue C

Abstract: Savonius wind turbines are the most suitable devices used in urban areas to produce electrical power. This is due to their simplicity, ease of maintenance, and acceptable power output with a low speed and highly variable wind profile. However, their efficiency is low, and the development of optimization tools is necessary to increase the total power output. This work presents a metamodel-based method to optimize the size and shape of a set of deflector plates to reduce the reverse moment of the turbine, using a genetic algorithm combined with an artificial neural network, reducing the computational cost. A parametrization of the deflectors geometry is proposed, and a Computational Fluid Dynamics model was implemented to train and validate the artificial neural network. The method was applied to design the deflectors of an actual 8-blade, 1[kW], 2.5[m] height turbine. Results showed an efficiency increment of 30%, from 0.215, to 0.279 in the turbine with the optimized deflectors. Furthermore, it is capable of producing power at 4[m/s], while the reference design had null power at that point. This methodology demanded 159 h, a substantial reduction of the computational cost of up to 97% in contrast to the classical simulation-based optimization approach.

Keywords: Savonius wind turbine; Optimization; Artificial neural networks; Genetic algorithm; Deflectors plates; Computational fluid dynamics (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:186:y:2019:i:c:s0360544219314860

DOI: 10.1016/j.energy.2019.07.144

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