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Validation of Linearized Navier-Stokes Based Flutter Prediction Tool: Part 2—Quantification of the Prediction Accuracy on a Turbine Test Case

[+] Author Affiliations
Antonio Sanz Luengo, Damian M. Vogt, Torsten H. Fransson

Royal Institute of Technology, Stockholm, Sweden

Stefan Schmitt

Siemens AG, Mülheim an der Ruhr, Germany

Paper No. GT2012-69682, pp. 1581-1592; 12 pages
  • ASME Turbo Expo 2012: Turbine Technical Conference and Exposition
  • Volume 7: Structures and Dynamics, Parts A and B
  • Copenhagen, Denmark, June 11–15, 2012
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4473-1
  • Copyright © 2012 by Siemens Energy Inc.


This is the second part of two papers describing the validation of a tool chain for flutter prediction. The first paper provides an overview of the numerical methods and their verification. The second paper presents the detailed validation of the tool chain on the basis of experimental data obtained from measurements of an annular cascade sector comprising 3D twisted turbine blades.

Aeroelastic test data has been acquired in an isolated blade row consisting of seven free-standing low-pressure (LP) turbine blades. The middle blade has been oscillated in controlled manner in three orthogonal modes and at various frequencies while measuring the unsteady blade surface pressure on several blades. The data has been reduced to aerodynamic influence coefficients and finally recombined to travelling wave mode stability curves. By acquiring data at various spanwise positions, a basis for validating three-dimensional effects has been provided.

The validation of the investigated flutter prediction tool has been performed in a detailed manner and on various levels, started from a critical reduced frequency over stability curves to local work coefficients. At the lowest level of condensation, the unsteady blade surface pressures have been compared. Correlation to test data is shown and discussed rigorously at these various levels giving a detailed assessment of the prediction accuracy of the investigated tool.

Copyright © 2012 by Siemens Energy Inc.



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