Optimization Method for the Hydrodynamic Performance of Tidal Turbines Considering Blockage Effect

Abstract

As crucial components in the advancement of tidal energy, tidal turbines are progressively transitioning from large standalone units to multi-unit arrays. In the context of tidal arrays, the blockage effect has become a key factor that necessitates careful consideration due to its substantial impact on turbine performance. This paper introduces an innovative optimization methodology aimed at improving the hydrodynamic performance of tidal turbines while specifically addressing the environmental blockage effect. The proposed approach integrates the blade element momentum—computational fluid dynamics hydrodynamic model, radial basis function neural networks, and the nondominated sorting genetic algorithm III optimization algorithm. A case study is used to demonstrate this methodology, focusing on the optimization of a tidal turbine under varying blockage intensity levels. Comparative analyses of optimal turbine configurations indicate that strategic modifications in design parameters can substantially improve hydrodynamic performance in response to varying blockage intensity. Specifically, increasing the root chord length and decreasing the tip chord length effectively reduce the turbine’s load while maintaining operational efficiency as blockage intensity increases. Conversely, the blade pitch angle exhibits minimal sensitivity to the blockage effect. Moreover, the optimized turbines demonstrate lower velocity deficits in their near-field wake compared to the prototype. The case study not only validates the effectiveness of the proposed optimization method but also highlights the substantial impact of blockage effects on optimal turbine configurations. This research provides valuable theoretical insights for designing turbines intended for deployment in tidal arrays.

Keywords

Tidal current turbine / Blade optimization / Blockage effect / BEM—CFD / RBF neural networks / NSGA-III

RIGHTS & PERMISSIONS

Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature