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

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

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.

Suggested Citation

  • Storti, Bruno A. & Dorella, Jonathan J. & Roman, Nadia D. & Peralta, Ignacio & Albanesi, Alejandro E., 2019. "Improving the efficiency of a Savonius wind turbine by designing a set of deflector plates with a metamodel-based optimization approach," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219314860
    DOI: 10.1016/j.energy.2019.07.144
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    Citations

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    Cited by:

    1. Hu, Wenyu & E, Jiaqiang & Leng, Erwei & Zhang, Feng & Chen, Jingwei & Ma, Yinjie, 2023. "Investigation on harvesting characteristics of convective wind energy from vehicle driving on multi-lane highway," Energy, Elsevier, vol. 263(PE).
    2. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    3. Noman, Abdullah Al & Tasneem, Zinat & Sahed, Md. Fahad & Muyeen, S.M. & Das, Sajal K. & Alam, Firoz, 2022. "Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    4. Marinić-Kragić, Ivo & Vučina, Damir & Milas, Zoran, 2022. "Robust optimization of Savonius-type wind turbine deflector blades considering wind direction sensitivity and production material decrease," Renewable Energy, Elsevier, vol. 192(C), pages 150-163.
    5. Hu, Wenyu & E, Jiaqiang & Tan, Yan & Zhang, Feng & Liao, Gaoliang, 2022. "Modified wind energy collection devices for harvesting convective wind energy from cars and trucks moving in the highway," Energy, Elsevier, vol. 247(C).
    6. Murshed, Muntasir, 2023. "Efficacies of technological progress and renewable energy transition in amplifying national electrification rates: contextual evidence from developing countries," Utilities Policy, Elsevier, vol. 81(C).
    7. Zahra Sefidgar & Amir Ahmadi Joneidi & Ahmad Arabkoohsar, 2023. "A Comprehensive Review on Development and Applications of Cross-Flow Wind Turbines," Sustainability, MDPI, vol. 15(5), pages 1-39, March.
    8. Altaf Hussain Rajpar & Imran Ali & Ahmad E. Eladwi & Mohamed Bashir Ali Bashir, 2021. "Recent Development in the Design of Wind Deflectors for Vertical Axis Wind Turbine: A Review," Energies, MDPI, vol. 14(16), pages 1-23, August.
    9. Reza Norouztabar & Seyed Soheil Mousavi Ajarostaghi & Seyed Sina Mousavi & Payam Nejat & Seyed Saeid Rahimian Koloor & Mohamed Eldessouki, 2022. "On the Performance of a Modified Triple Stack Blade Savonius Wind Turbine as a Function of Geometrical Parameters," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    10. Abed, Bouabdellah & Benzerdjeb, Abdelouahab & Benmansour, Abdeljellil & Achache, Habib & Ferhat, Rabia & Debz, Abderrahmene & Gorlov, Alaxender M., 2021. "An efficient hydrodynamic method for cross-flow turbines performance evaluation and comparison with the experiment," Renewable Energy, Elsevier, vol. 180(C), pages 993-1003.
    11. Bizhanpour, Ali & Hasanzadeh, Nima & Najafi, Amir F. & Magagnato, Franco, 2023. "Investigation of different deflector geometry and mechanism effect on the performance of an in-pipe hydro Savonius turbine," Applied Energy, Elsevier, vol. 350(C).
    12. Zhang, Yelin & Zhang, Xingxing & Huang, Pei & Sun, Yongjun, 2020. "Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization," Applied Energy, Elsevier, vol. 279(C).
    13. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Power prediction formula for blade design and optimization of Dual Darrieus Wind Turbines based on Taguchi Method and Genetic Expression Programming model," Renewable Energy, Elsevier, vol. 192(C), pages 583-605.

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