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Optimization of Savonius wind turbine with additional blades by surrogate model using artificial neural networks

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  • Haddad, Hassan Z.
  • Mohamed, Mohamed H.
  • Shabana, Yasser M.
  • Elsayed, Khairy

Abstract

The aim of the current investigation is to obtain the optimum configuration of Savonius wind turbine which results in maximum power coefficient (Cp). In order to achieve that Surrogate-based optimization (SBO) was used for obtaining the optimum values of investigated parameters. Design of experiment (DoE) was applied on four variables which are: the arc angle of original blade (ψ), the shape factor of original blade (p/q), the arc angle of additional blades (β), and the additional blade radii ratio (Rr). Computational fluid dynamics (CFD) simulations using ANSYS FLUENT were conducted to feed the artificial neural networks (ANN) with the sufficient data for training for the traditional and optimized rotors. The considered original blade arc angle (ψ) and the additional blades angle (β) varied from 70° to 180°, the original blade shape factor (p/q) ranges from 0.00 to 0.70, and the additional blade radii ratio (Rr) changes from 0.20 to 1.25 of that of the original blade. The optimized rotor showed a maximum Cp of 0.2836 results in 44.5% increase in Cp over 0.1962 of conventional one for the tip speed ratio (TSR) of 0.75. The maximum increase is 66.12% at TSR = 1.3.

Suggested Citation

  • Haddad, Hassan Z. & Mohamed, Mohamed H. & Shabana, Yasser M. & Elsayed, Khairy, 2023. "Optimization of Savonius wind turbine with additional blades by surrogate model using artificial neural networks," Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223003468
    DOI: 10.1016/j.energy.2023.126952
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    References listed on IDEAS

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