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Numerical models to predict the performance of tidal stream turbines working under off-design conditions

Ordone-Sanchez, S., Ellis, R., Porter, K. E., Allmark, M. ORCID:, O'Doherty, T. ORCID:, Mason-Jones, A. ORCID: and Johnstone, C. 2019. Numerical models to predict the performance of tidal stream turbines working under off-design conditions. Ocean Engineering 181 , pp. 198-211. 10.1016/j.oceaneng.2019.04.027

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As previously experienced by the wind industry, it is envisaged that tidal stream turbine blades will present misalignments or blade deformations over time as they are constantly working under harsh and highly unsteady environments. Blade misalignment will affect the power capture of a tidal stream turbine and if not detected in time could affect other components of the drive train. Therefore, the aim of this paper is to compare the use of two numerical modelling techniques to predict the performance of a tidal stream turbine working under off-design conditions, in this case, the misalignment of one or more blades. The techniques used in this study are Blade Element Momentum Theory and Computational Fluid Dynamics. The numerical models simulate the performance of a three-bladed horizontal axis tidal stream turbine with one or two blades offset from the optimum pitch setting. The simulations were undertaken at 1.0 m/s flow speeds. The results demonstrated that both unsteady BEMT and steady or transient CFD are able to predict power coefficients when there is a certain level of misalignment in one or even two blades. However, both techniques failed to accurately predict a loss of power performance at high rotational speeds

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Advanced Research Computing @ Cardiff (ARCCA)
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
Publisher: Elsevier
ISSN: 0029-8018
Funders: Engineering and Physical Sciences Research Council
Date of First Compliant Deposit: 16 April 2019
Date of Acceptance: 7 April 2019
Last Modified: 23 Nov 2022 18:44

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