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Axial-Flow Compressor Model Based on a Cascade Stacking Technique and Neural Networks

[+] Author Affiliations
Ernesto Benini, Andrea Toffolo

Università di Padova, Padova, Italy

Paper No. GT2002-30443, pp. 793-801; 9 pages
  • ASME Turbo Expo 2002: Power for Land, Sea, and Air
  • Volume 5: Turbo Expo 2002, Parts A and B
  • Amsterdam, The Netherlands, June 3–6, 2002
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3610-X | eISBN: 0-7918-3601-0
  • Copyright © 2002 by ASME


This paper introduces a cascade-stacking technique for the development of a gas turbine multi-stage axial-flow compressor model. A large database of stationary and rotating cascade performance is first obtained by quasi three-dimensional CFD simulations and used to train neural networks for the prediction of cascade performance under generalized conditions. Then the model directly calculates the operating point of a compressor having known geometry characteristics, including variable inlet guide/stator vane effects, as a function of mass flow rate and rotational speed. The model can also be used as a valuable preliminary design tool, obtaining geometry characteristics by imposing flow patterns.

Copyright © 2002 by ASME



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