4.7 Article

Influence of blade deformation and yawed inflow on performance of a horizontal axis tidal stream turbine

Journal

RENEWABLE ENERGY
Volume 92, Issue -, Pages 321-332

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2016.02.025

Keywords

Horizontal axis tidal stream turbine (HATST); Computational fluid dynamics (CFD); Fluid-structure interaction (FSI) analysis; Blade deformation; Yawed inflow

Funding

  1. National Research Foundation of Korea - Korea government [2009-0083510, 2011-0020563, 2013R1A1A2012597, 2015037577]
  2. IT R&D Program of MOTIE/KEIT - Korea government [10060329]
  3. Korea Evaluation Institute of Industrial Technology (KEIT) [10060329] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  4. National Research Foundation of Korea [2011-0020563] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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For a better design of tidal stream turbines operated in off-design conditions, analyses considering the effects of blade deformation and yawed inflow conditions are necessary. The flow load causes deformation of the blade, and the deformation affects the turbine performance in return. Also, a yawed inflow influences the performance of the turbine. As a validation study, a computational fluid dynamics (CFD) simulation was carried out to predict the performance of a horizontal axis tidal stream turbine (HATST) with rigid blades. The numerical uncertainty for the turbine performance with blade deformation and a yawed inflow was evaluated using the concept of the grid convergence index (GCI). A fluid-structure interaction (FSI) analysis was carried out to estimate the performance of a turbine with flexible composite blades, with the results then compared to those of an analysis with rigid blades. The influence of yawed inflow conditions on the turbine performance was investigated and found to be important in relation to power predictions in the design stages. (C) 2016 Elsevier Ltd. All rights reserved.

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