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

• 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.

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NATURVETENSKAP  -- Fysik -- Subatomär fysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Subatomic Physics (hsv//eng)

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