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Prediction of the fouling penalty on the tidal turbine performance and development of its mitigation measures

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  • Song, Soonseok
  • Demirel, Yigit Kemal
  • Atlar, Mehmet
  • Shi, Weichao

Abstract

The power coefficient for a horizontal axis tidal turbine is the determinant factor for the efficiency of a tidal energy system. To guarantee a highly efficient tidal turbine operating in the real sea environment for an enduring long period is of critical importance to the power production and hence the cost of energy. However, this performance is under the threat of marine biofouling and the biofouling effect on tidal turbine systems are barely known neither quantified. This paper focuses on the study of the roughness effect due to biofouling on the performance of a tidal turbine. A Reynolds Averaged Navier-Stokes model based Computational Fluid Dynamics (CFD) was developed to predict the effect of biofouling on a full-scale turbine. A roughness modelling that involves modified wall-functions in the CFD model was used representing the surface roughness caused by barnacle fouling. The simulations were conducted under different fouling scenarios for a range of tip speed ratios (TSR). The surface fouling resulted in up to 13% decrease in the power coefficient at the designed operating condition. The effect proved to be even more severe at higher TSRs, bringing narrower operating range of TSRs. The results also suggest that by lowering the operating TSRs for fouled turbines the fouling effect on efficiency losses can be minimised to ensure efficient operation between maintenances.

Suggested Citation

  • Song, Soonseok & Demirel, Yigit Kemal & Atlar, Mehmet & Shi, Weichao, 2020. "Prediction of the fouling penalty on the tidal turbine performance and development of its mitigation measures," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920310102
    DOI: 10.1016/j.apenergy.2020.115498
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    References listed on IDEAS

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    Cited by:

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    4. Wang, Longyan & Xu, Jian & Luo, Wei & Luo, Zhaohui & Xie, Junhang & Yuan, Jianping & Tan, Andy C.C., 2022. "A deep learning-based optimization framework of two-dimensional hydrofoils for tidal turbine rotor design," Energy, Elsevier, vol. 253(C).

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