CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

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.


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


Läs direkt!

Länk till annan sajt (kan kräva inloggning)

Institutioner (Chalmers)

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



Chalmers infrastruktur