A GMDH-type neural network model for predicting the effect of sister hole parameters on the film cooling effectiveness over a turbine blade-Reference-Cited by-同舟云学术

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A GMDH-type neural network model for predicting the effect of sister hole parameters on the film cooling effectiveness over a turbine blade

Published:2020-07 Issue:7 Volume:135 Page:
ISSN:2190-5444
Container-title:The European Physical Journal Plus
language:en
Short-container-title:Eur. Phys. J. Plus

Author:

Kazemi M. J.,Amanifard N.,Deylami H. M.,Dolati F.

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy

Link

https://link.springer.com/content/pdf/10.1140/epjp/s13360-020-00597-0.pdf

Reference28 articles.

1. V.K. Garg, Int. J. Heat Fluid Flow 21, 134 (2000)

2. A. Sinha, D. Bogard, M. Crawford, J. Turbomach. 113, 442 (1991)

3. B.A. Haven, M. Kurosaka, J. Fluid Mech. 352, 27 (1997)

4. A. Javadi, K. Javadi, M. Taeibi-Rahni, M. Darbandi, Momentum 2, 1–9 (2003)

5. M.J. Ely, B. Jubran, in ASME Turbo Expo 2012: Turbine Technical Conference and Exposition (American Society of Mechanical Engineers, 2012), p. 1185

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1. General correlations for hydrothermal and hydrodynamic features of a nanofluid affected by FHD: a GMDH-type neural network;The European Physical Journal Plus;2022-12-06

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