Predicting Performance of Expansion Turbines Using Different Working Fluids Based on the Artificial Neural Network

  • Liqiang Liu
  • Yanzhong Li
  • Chunzheng Chen
Part of the Advances in Cryogenic Engineering book series (ACRE, volume 43)

Abstract

In order to obtain the efficiency curve of a helium Expansion Turbine (ET) in the factory, a method which can be used to transform the performance of an ET with one kind of Working Fluid (WF) to another must be investigated because of the lack of the helium in the factory. A performance prediction program based on an one-dimensional analysis of ETs has been developed and has proved valid. On the basis of the program, the Artificial Neural Network(ANN) (Back-Propagation Algorithm) has been used to deal with the transformation problem. The method has proved effective by the efficiency experiments of an ET using the CO2 and the air as the WF respectively.

Keywords

Helium Expansion Turbine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. 1.
    Liqing Liu and Chunzheng Chen, Predicting performance of helium expansion turbines using similarity principles, ICEC16 Proceedings, Elsevier Science (1996), Part 1, 225–228Google Scholar
  2. 2.
    Whitfield, A. and Baines, N. C. In: Design of Radial Turbomachines, John Wiley Sons Inc., New York, USA(1990): 26–41Google Scholar
  3. 3.
    Misao, H., A one-dimensional analysis and performance prediction of subsonic radial turbines, Bulletin of the JSME (1980) 23 2064–2070CrossRefGoogle Scholar
  4. 4.
    Ji, G. H., In: Expansion turbine, Jixie Gongye Press, Beijing, China (1989) 161–163Google Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Liqiang Liu
    • 1
  • Yanzhong Li
    • 1
  • Chunzheng Chen
    • 1
  1. 1.Institute of Cryogenic EngineeringXi’an Jiaotong UniversityXi’anChina

Personalised recommendations