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Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework

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  • Noman, Abdullah Al
  • Tasneem, Zinat
  • Sahed, Md. Fahad
  • Muyeen, S.M.
  • Das, Sajal K.
  • Alam, Firoz

Abstract

Currently, the Savonius wind turbine (SWT) has established itself as a reliable wind turbine solution, particularly for small-scale wind farms. It is a reliable form of power generation owing to its self-starting capability, lack of reliance on wind direction, and low vibration and noise. As a result, it has been gaining popularity worldwide. The main technological challenge in this field, however, is the inferior efficiency of Savonius turbines compared to their other counterparts. A large number of studies have been conducted to enhance the power coefficient of SWT. These studies primarily focused on blade profile and augmentation strategies using simulation-based optimization approaches. Some recent studies, in contrast, have attempted to integrate Artificial Intelligence (AI) into the SWT optimization method. However, to ensure the maximum efficiency and commercial feasibility of SWTs, additional strategy is required. Based on previous and current research trends, this review presents a next-generation SWT blade design framework. The importance of AI in output optimization at low computing costs has been emphasized. Finally, future design concerns have raised the possibility of using a smart blade and digital twin model to enhance the efficiency of SWT blades. Moreover, the existing roadblocks and their possible solutions are also highlighted.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:168:y:2022:i:c:s1364032122004324
    DOI: 10.1016/j.rser.2022.112531
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