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.
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Reference
Liqing Liu and Chunzheng Chen, Predicting performance of helium expansion turbines using similarity principles, ICEC16 Proceedings, Elsevier Science (1996), Part 1, 225–228
Whitfield, A. and Baines, N. C. In: Design of Radial Turbomachines, John Wiley Sons Inc., New York, USA(1990): 26–41
Misao, H., A one-dimensional analysis and performance prediction of subsonic radial turbines, Bulletin of the JSME (1980) 23 2064–2070
Ji, G. H., In: Expansion turbine, Jixie Gongye Press, Beijing, China (1989) 161–163
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© 1998 Springer Science+Business Media New York
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Liu, L., Li, Y., Chen, C. (1998). Predicting Performance of Expansion Turbines Using Different Working Fluids Based on the Artificial Neural Network. In: Kittel, P. (eds) Advances in Cryogenic Engineering. Advances in Cryogenic Engineering, vol 43. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9047-4_80
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DOI: https://doi.org/10.1007/978-1-4757-9047-4_80
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-9049-8
Online ISBN: 978-1-4757-9047-4
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