Edgewise Structural Damping of a 2.8-MW Land-Based Wind Turbine Rotor Blade: Article No. 022069

Research output: Contribution to journalArticlepeer-review

Abstract

Modern wind turbines push the predictive capabilities of state-of-the-art aero-servo-elastic tools. The existing limitations hide across the numerical tool chain and can result in serious issues, such as missing aeroelastic instabilities during the design phase. Structural damping is an input that is usually hard to estimate, but also has a major impact on the turbine behavior. In this paper, we discuss an experiment that aims to accurately quantify the structural damping characterizing the edgewise modes of modern wind turbine blades. The experiment is carried out on a 2.8-MW land-based wind turbine and features a fast yaw actuation that induces an edgewise motion on one of the three blades. The Covariant-subspace system identification (Cov-SSI) method is then used to post-process the blade root moment to estimate the short-term edgewise structural damping. Despite limitations of the Cov-SSI method, which consistently under-predicts the absolute values of damping, we observe that structural damping decreases across the first three blade edgewise modes, which is different from the stiffness-proportional damping model that assumes that structural damping increases with the modes. This paper argues that a stiffness-proportional damping model, which is implemented in most aeroelastic tools, is therefore not conservative and might hide aeroelastic instabilities that can instead appear in the field.
Original languageAmerican English
Number of pages8
JournalJournal of Physics: Conference Series
Volume2767
Issue number2
DOIs
StatePublished - 2024

NREL Publication Number

  • NREL/JA-5000-88643

Keywords

  • aeroelastic stability
  • big adaptive rotor
  • structural damping
  • wind turbine

Fingerprint

Dive into the research topics of 'Edgewise Structural Damping of a 2.8-MW Land-Based Wind Turbine Rotor Blade: Article No. 022069'. Together they form a unique fingerprint.

Cite this