Prediction of cavitation vortex dynamics in the draft tube of a francis turbine using radial basis neural networks
Hočevar, Marko (Author), Širok, Brane (Author), Blagojević, Bogdan (Author)

URLURL - Presentation file, Visit http://dx.doi.org/10.1007/s00521-004-0458-4 This link opens in a new window

Application of radial basis neural networks (RBNN) for prediction of cavitation vortex dynamics in a Francis turbine draft tube is presented. The dynamics of the cavitation vortex was established by fluctuations of a void fraction in a selected region of the draft tube. The void fraction was determined by image acquisition and analysis. Pressure in the draft tube and images of the cavitation vortex were acquired simultaneously for the experiment. RBNN were used for prediction. The void fraction in the selected region of the cavitation vortex was predicted on the basis of experimentally provided pressure data. The learning set consisted of pressure - void fractionpairs. The prediction consisted in providing only the pressure. Regression coefficients r between the predicted and measured void fractions were in an interval of 0.82-0.98. A good agreement between power spectra and correlation functions of measured and predicted void fractions was shown.

Keywords:izbor cevi, numerični modeli, analitični modeli, predvidevanja, cavitation vortex, draft tube, Francis turbine, prediction, radial basis neural networks, void fraction
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Number of pages:str. 229-234
Numbering:Letn. 14, št. 3
ISSN on article:0941-0643
COBISS.SI-ID:8455963 Link is opened in a new window
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Record is a part of a journal

Title:Neural computing & applications
Shortened title:Neural comput. appl.
COBISS.SI-ID:1607958 This link opens in a new window

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