Empirical Flutter Prediction Method.

Abstract

Design of advanced technology engines is often limited by compressor blade instability or flutter. Test points from the annular cascade data base were analyzed, to predict from aeromechanical data which of 14 types of stability or instability would result. The basic approach was to identify for each pair of stability regions, linear combinations (hyperplanes) of the aeromechanical variables, whose numerical value would be above a critical level for all test points in one stability region and would be below the critical level for test points in the other stability region. It was found that 76% of the pairs of stability regions allowed a hyperplane to discriminate between the two regions, but for 24% a curved surface or nonlinear combination variables would be needed. Review of 85% of the 891 test points used to construct the hyperplanes revealed that the hyperplanes correctly identify the stability condition of 59% of the points in a literal sense, but are correct in a broader practical sense for 79% of the points. When the hyperplanes were applied to 51 validation test points taken from several actual engine/rig test data, they gave virtually no correct results. This result is not immediately explainable. Keywords: Stall; Choke; Discriminant; Cluster. (edc)

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Document Details

Document Type
Technical Report
Publication Date
Mar 05, 1988
Accession Number
ADA195699

Entities

People

  • J. K. Casey

Organizations

  • GE Aerospace

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Center Of Gravity
  • Classification
  • Computer Programming
  • Computer Programs
  • Computers
  • Databases
  • Discriminant Analysis
  • Equations
  • Linear Programming
  • Plastic Explosives
  • Security
  • Simplex Method
  • Stability Conditions
  • Tank Guns
  • Three Dimensional
  • Turbofan Engines
  • Two Dimensional

Readers

  • Aerospace Engineering
  • Fluid Mechanics and Fluid Dynamics.
  • Graph Algorithms and Convex Optimization.