4.7 Article

Design, analysis and test of a model turbine blade for a wave basin test of floating wind turbines.

Journal

RENEWABLE ENERGY
Volume 97, Issue -, Pages 414-421

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2016.06.008

Keywords

Floating wind turbine; Wave basin test; Reynolds number scaling effect; Rotor thrust; Blade design

Funding

  1. National Basic Research Program of China (973 Program) [2014CB046205]

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Froude scaling is a generally reliable way to design models of floating wind turbines for wave basin testing. However, the resulting rotor thrust of the model is far lower than the Froude-scaled value of a full-size turbine, because the reduction in Reynolds number decreases the lift coefficients and increases the drag coefficients (the Reynolds number scaling effect). A 1/50th scale model wind turbine based on a NREL-5MW reference turbine is examined here. To mitigate the Reynolds number scaling effect in the model, the original aerofoils of the reference turbine (DU series and NACA 64-618) were replaced by an aerofoil at a low Reynolds number (NACA 4412). Such a model with aerofoil-adjusted blades was used in the mathematical optimization of rotor thrust. The design objective was to guarantee that while the rotor thrust of the model equalled the Froude-scaled rotor thrust of the reference, the smallest chord lengths were achieved, considering the weight control in building the model blade. The distribution of chord lengths fitted a fourth-order polynomial curve, and the distribution of twist angles along the blade fitted a second-order polynomial curve. The eight coefficients of the two curves were chosen as optimization variables, and pattern search method was used to solve the optimization model. The model blade was designed at zero pitch angle and further tested in FAST, a fully coupled simulation tool. A model test was conducted using the optimized blade geometry in the State Key Laboratory of Ocean Engineering in Shanghai, China, and the thrusts were compared with the predicted values. (C) 2016 Elsevier Ltd. All rights reserved.

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