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A general regression artificial neural network for two-phase flow regime identification

Tatiana Tambouratzis (Institutionen för teknisk fysik, Nukleär teknik) ; Imre Pázsit (Institutionen för teknisk fysik, Nukleär teknik)
Annals of Nuclear Energy (0306-4549). Vol. 37 (2010), 5, p. 672-680.
[Artikel, refereegranskad vetenskaplig]

Supplementing the collection of artificial neural network methodologies devised for monitoring energy producing installations, a general regression artificial neural network is proposed for the identification of the two-phase flow that occurs in the coolant channels of boiling water reactors. The utilization of a limited number of image features derived from radiography images affords the proposed approach with efficiency and non-invasiveness. Additionally, the application of counter-clustering to the input patterns prior to training accomplishes an 80% reduction in network size as well as in training and test time. Cross-validation tests confirm accurate on-line flow regime identification. (C) 2010 Elsevier Ltd. All rights reserved.

Nyckelord: RECTANGULAR CHANNEL, CLASSIFICATION, FLUCTUATIONS, TRANSITIONS, PATTERNS



Denna post skapades 2010-06-10. Senast ändrad 2014-09-02.
CPL Pubid: 122558

 

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Institutioner (Chalmers)

Institutionen för teknisk fysik, Nukleär teknik (2006-2015)

Ämnesområden

Kärnfysik

Chalmers infrastruktur