A general regression artificial neural network for two-phase flow regime identification
Artikel i vetenskaplig tidskrift, 2010

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.

CLASSIFICATION

FLUCTUATIONS

TRANSITIONS

RECTANGULAR CHANNEL

PATTERNS

Författare

Tatiani Tampouratzi

Chalmers, Teknisk fysik, Nukleär teknik

Imre Pazsit

Chalmers, Teknisk fysik, Nukleär teknik

Annals of Nuclear Energy

0306-4549 (ISSN)

Vol. 37 5 672-680

Ämneskategorier

Subatomär fysik

DOI

10.1016/j.anucene.2010.02.004