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Intelligent parameter optimization of Savonius rotor using Artificial Neural Network and Genetic Algorithm

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  • Mohammadi, M.
  • Lakestani, M.
  • Mohamed, M.H.

Abstract

Power coefficient, the most significant criterion for evaluating the performance of Savonius rotor is a multi-dimensional function of numerous parameters like overlap ratio, number of stages, blade rotation, etc. All these parameters have been examined separately and an approximate span in which optimum performance can be attained is proposed for each one. Furthermore, neither any attempt on scrutinizing this range accurately nor any investigations on probing the probability of existence of any interacting relation among these parameters have been reported so far. Using computational intelligence, an accurate study toward this span and a probable relation among these parameters has been conducted. Power coefficient is considered as a function of six independent input parameters, according to experimental data extracted from a related paper. An Artificial Neural Network has been assigned to investigate a logical interaction among dependent and independent variables and define a cost function based on same empirical data. This function is then optimized by Genetic Algorithm and best amount for each parameter has been determined. Suggested geometry and flow field conditions have then been simulated by Computational Fluid Dynamics and acceptable agreement is detected.

Suggested Citation

  • Mohammadi, M. & Lakestani, M. & Mohamed, M.H., 2018. "Intelligent parameter optimization of Savonius rotor using Artificial Neural Network and Genetic Algorithm," Energy, Elsevier, vol. 143(C), pages 56-68.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:56-68
    DOI: 10.1016/j.energy.2017.10.121
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    Cited by:

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    5. 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).
    6. Kumail Abdulkareem Hadi Al-Gburi & Balasem Abdulameer Jabbar Al-quraishi & Firas Basim Ismail Alnaimi & Ee Sann Tan & Ali Hussein Shamman Al-Safi, 2022. "Experimental and Simulation Investigation of Performance of Scaled Model for a Rotor of a Savonius Wind Turbine," Energies, MDPI, vol. 15(23), pages 1-23, November.
    7. Mohammadi, M. & Mohammadi, R. & Ramadan, A. & Mohamed, M.H., 2018. "Numerical investigation of performance refinement of a drag wind rotor using flow augmentation and momentum exchange optimization," Energy, Elsevier, vol. 158(C), pages 592-606.
    8. Zhang, Yongchao & Kang, Can & Ji, Yanguang & Li, Qing, 2019. "Experimental and numerical investigation of flow patterns and performance of a modified Savonius hydrokinetic rotor," Renewable Energy, Elsevier, vol. 141(C), pages 1067-1079.
    9. Li, Yaopeng & Jia, Ming & Han, Xu & Bai, Xue-Song, 2021. "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)," Energy, Elsevier, vol. 225(C).
    10. 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).
    11. Mohamed, M.H. & Dessoky, A. & Alqurashi, Faris, 2019. "Blade shape effect on the behavior of the H-rotor Darrieus wind turbine: Performance investigation and force analysis," Energy, Elsevier, vol. 179(C), pages 1217-1234.

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