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Academic Blade Geometries for Baseline Comparisons of Industry-Specific Forced Response Predictions

[+] Author Affiliations
James H. Little, II, Jeffrey L. Kauffman

University of Central Florida, Orlando, FL

Matthias Huels

Siemens AG, Mülheim an der Ruhr, Germany

Paper No. GT2017-64877, pp. V07BT35A028; 12 pages
doi:10.1115/GT2017-64877
From:
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 7B: Structures and Dynamics
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5093-0
  • Copyright © 2017 by Siemens Energy, Inc.

abstract

Predicting the energy dissipation associated with contact of underplatform dampers remains a critical challenge in turbomachinery blade and friction damper design. Typical turbomachinery blade forced vibration response analyses rely on reduced order models and simplified nonlinear codes to predict blade vibration characteristics in a computationally tractable manner. Recent research has focused on both the model reduction process and simulation of the contact dynamics. This paper proposes two academic turbine blade geometries with coupled underplatform dampers as vehicles by which these model reduction and forced response simulation techniques may be compared. The blades correspond to two types of freestanding turbine blades and demonstrate the same qualitative behavior as more complex industry geometries. The blade geometries are fully described here and analyzed using the same procedure as used for an industry-specific blade. Standard results are presented in terms of resonance frequency, amplitude, and damping across a range of aerodynamic excitation. In addition, the predicted blade vibration characteristics are examined under variations in the contact interface: friction coefficient, damper / platform surface roughness, and damper mass, with relative sensitivities to each term generated. Finally, the effect of the number of modes retained in the reduced order model is studied to uncover patterns of convergence as well as to provide additional sets of standard data for comparison with other model reduction and forced response simulation methods.

Copyright © 2017 by Siemens Energy, Inc.
Topics: Blades

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