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Tidal current turbine blade optimisation with improved blade element momentum theory and a non-dominated sorting genetic algorithm

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  • Yeo, Eng Jet
  • Kennedy, David M.
  • O'Rourke, Fergal

Abstract

Tidal current energy has the advantage of predictability over most of the other renewable energy resources. However, due to the harsh operating environment and complicated site conditions, developments in this domain have been gradual. Paramount to these points is device design and optimisation of hydrodynamic performance. Recent developments in the correction models of BEM theory have further improved the accuracy of the prediction model. Using an improved blade element momentum theory model that is capable of accurately capturing the downwash angle and combining it with a well-developed and reliable non-dominated sorting genetic algorithm model, an effective and efficient tidal current turbine blade optimisation tool has been developed and is presented in this paper. This novel work incorporated a NACA generator that is capable of reproducing any NACA profile, such a tool allows the solver to analyse each and every profile used in each spanwise blade element. As a result, the model is very effective at producing tidal current turbine blades that have been optimised not only for local twist angle and chord length, but also for the suitable NACA profiles to be used at a particular spanwise blade element. The use of the non-dominated sorting genetic algorithm in this work allows the model to efficiently explore a wide range of solutions, outputting a number of tidal current turbine blades suitable for a specified operating condition. The accuracy of the performance prediction of the improved BEM model is validated against an experimentally validated tidal current turbine blade. The coefficient of determination (R2) values for power and thrust coefficient are 0.99828 and 0.99488 respectively when comparing this work with experimental measurements found in the literature. Furthermore this proves that the improved BEM model is capable of efficiently predicting hydrodynamic performance of a tidal current turbine blade to a high degree of accuracy. Further work includes implementing computational fluid dynamics for further validation and evaluation.

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  • Yeo, Eng Jet & Kennedy, David M. & O'Rourke, Fergal, 2022. "Tidal current turbine blade optimisation with improved blade element momentum theory and a non-dominated sorting genetic algorithm," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006235
    DOI: 10.1016/j.energy.2022.123720
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    4. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).

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