Non-invasive on-line two-phase flow regime identification employing artificial neural networks
Journal article, 2009

A novel non-invasive approach to the on-line identification of BWR two-phase flow regimes is investigated. The proposed approach receives neutron radiography images of coolant flow recordings as its input and performs feature extraction on each image via simple and directly computable statistical operators. The extracted features are subsequently used as inputs to an ensemble of self-organizing maps whose outputs demonstrate swift and accurate classification of each image into its corresponding flow regime. The novelty of the approach lies in the use of the self-organizing map which generates the different classes by itself, according to feature similarity of the corresponding images; this contrasts traditional artificial neural networks where the user has to define both the number of distinct classes as well as to supply separate training vectors for each class.

Two-phase flow identification

artificial neural networks


Tatiani Tampouratzi

Chalmers, Applied Physics, Nuclear Engineering

Imre Pazsit

Chalmers, Applied Physics, Nuclear Engineering

Annals of Nuclear Energy

0306-4549 (ISSN)

Vol. 36 4 464 - 469

Subject Categories

Other Engineering and Technologies not elsewhere specified



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