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A general regressio...
A general regression artificial neural network for two-phase flow regime identification
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- Tampouratzi, Tatiani, 1965 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Pazsit, Imre, 1948 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- Elsevier BV, 2010
- 2010
- English.
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In: Annals of Nuclear Energy. - : Elsevier BV. - 0306-4549 .- 1873-2100. ; 37:5, s. 672-680
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Fysik -- Subatomär fysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Subatomic Physics (hsv//eng)
Keyword
- CLASSIFICATION
- FLUCTUATIONS
- TRANSITIONS
- RECTANGULAR CHANNEL
- PATTERNS
Publication and Content Type
- art (subject category)
- ref (subject category)
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