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

Author

Tatiani Tampouratzi

Chalmers, Applied Physics, Nuclear Engineering

Imre Pazsit

Chalmers, Applied Physics, Nuclear Engineering

Annals of Nuclear Energy

0306-4549 (ISSN)

Vol. 37 5 672-680

Subject Categories

Subatomic Physics

DOI

10.1016/j.anucene.2010.02.004

More information

Created

10/6/2017