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Anti-cavitation optimal design and experimental research on tidal turbines based on improved inverse BEM

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  • Sun, ZhaoCheng
  • Li, Dong
  • Mao, YuFeng
  • Feng, Long
  • Zhang, Yue
  • Liu, Chao

Abstract

To capture tidal current energy to the greatest extent possible, the turbines must to be large in-scale. When the turbine is close to the free surface, with high energy-flow density, unsteady cavitation on the blade surface has a large negative impact on the efficiency and life of the turbine. This paper presents a revised theoretical analysis of hydrodynamics optimization of horizontal-axis tidal turbines, including cavitation effects, based on improved inverse blade element momentum (BEM) theory. The cavitation performance is reflected by the minimum pressure coefficient peak value on the blade surface, based on which the cavitation prediction model is established. The cavitation prediction model and improved inverse BEM theory are combined; additionally, the mathematical model of multi-objective optimization is established. To verify the effectiveness of the proposed methodology, a 10-kW current turbine was designed, and computational fluid dynamics (CFD) was used to validate the hydrodynamics characteristics of the resulting turbine blades. The experimental model was designed according to the similarity theory, and a cavitation mechanism visualization experiment and a performance parameter test experiment were carried out in a cavitation water tunnel. The simulation and experimental results revealed that the proposed design method achieves the optimization goal.

Suggested Citation

  • Sun, ZhaoCheng & Li, Dong & Mao, YuFeng & Feng, Long & Zhang, Yue & Liu, Chao, 2022. "Anti-cavitation optimal design and experimental research on tidal turbines based on improved inverse BEM," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221025111
    DOI: 10.1016/j.energy.2021.122263
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    1. Xu, Jian & Wang, Longyan & Yuan, Jianping & Luo, Zhaohui & Wang, Zilu & Zhang, Bowen & Tan, Andy C.C., 2024. "DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade," Renewable Energy, Elsevier, vol. 224(C).

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