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Zili Zhang

Tuned liquid column dampers for mitigation of edgewise vibrations in rotating wind turbine blades

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  • Zili Zhang
  • Biswajit Basu, Ireland
  • Søren R.K. Nielsen, Denmark
Edgewise vibrations in wind turbine blades are lightly damped, and large amplitude vibrations induced by the turbulence may significantly shorten the fatigue life of the blade. This paper investigates the performance of tuned liquid column dampers (TLCDs) for mitigating edgewise vibrations in rotating wind turbine blades. Normally, the centrifugal acceleration at the outboard portion of a rotating blade can reach to a magnitude of 7–8 g, which makes it possible to use a TLCD with a very small mass for suppressing edgewise vibrations effectively. The parameters of the TLCD to be optimized are the mounting position, the mass ratio, the geometries, and the head loss coefficient of the damper. Based on a reduced 2-DOF nonlinear model developed by the authors, the optimization of these parameters are carried out by minimizing the standard deviation of the edgewise tip displacement, with the consideration of both the space limitation inside the blade and the constraint of the liquid motion. The edgewise modal load for the 2-DOF model has been calculated from a more sophisticated 13-DOF aeroelastic wind turbine model, which includes the coupling of the blade-tower-drivetrain vibration and the aerodynamic damping presented in different modes. Various turbulence intensities and rotational speeds of the rotor have been considered to evaluate the performance of the TLCD. Further, the optimized damper is incorporated into the 13-DOF model to verify the application of the decoupled optimization. The investigation shows promising results for the use of the TLCD in mitigating edgewise vibrations in wind turbine blades. Copyright © 2014 John Wiley & Sons, Ltd.
Original languageEnglish
JournalStructural Control and Health Monitoring
Issue3 March
Pages (from-to)500–517
Number of pages8
Publication statusE-pub ahead of print - 2015
Externally publishedYes

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