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Automated Operational Modal Identification of a Rotor Blade

Daiju Uehara, Marc Eitner, Jayant Sirohi, The University of Texas at Austin

May 14, 2018

https://doi.org/10.4050/F-0074-2018-12757

Abstract:
This study describes the deformation measurement and automated operational modal analysis (OMA) of a rotor blade in hover. Blade deformation of a 0.4 m-diameter two-bladed rotor was measured at two different root pitch angles and four rotational speeds up to 1500 RPM by a time-resolved digital image correlation technique (DIC). The DIC technique successfully measured the time history of 3D displacements over the entire rotor blade at approximately 900 measurement locations for the flap, lead-lag, and torsional degrees of freedom. The measured blade deformation data were then processed with the Complexity Pursuit (CP) algorithm, which is one of several Blind Source Separation (BSS) techniques, to determine the dynamic characteristics of the rotor blade without input excitation information. The modal identification process was able to reduce the number of manual steps that would have to be performed by an analyst in a conventional operational modal analysis. The identified modal parameters were compared with that obtained by another OMA technique called NExT/ERA as well as a numerical model. These results agreed quite well for modal frequency identifications within the range of 5% difference. The mode shape estimated by the CP algorithm were almost similar to the numerical results for identified modes, however, there was non-negligible discrepancy between the results of the CP and NExT/ERA approach. Modal damping was also extracted by the CP algorithm although the resultant values were less than 3% for all test cases, which is quite low under the influence of aerodynamic damping. Overall, the automated OMA with the CP algorithm was favorable to the conventional OMA approach in terms of accuracy of modal parameter identification as well as process simplification.


Automated Operational Modal Identification of a Rotor Blade

  • Presented at Forum 74 - Best Paper for this session
  • 14 pages
  • SKU # : F-0074-2018-12757
  • Dynamics

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Automated Operational Modal Identification of a Rotor Blade

Authors / Details:
Daiju Uehara, Marc Eitner, Jayant Sirohi, The University of Texas at Austin