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Non-invasive on-line two-phase flow regime identification employing artificial neural networks

Tatiana Tambouratzis (Institutionen för teknisk fysik, Nukleär teknik) ; Imre Pázsit (Institutionen för teknisk fysik, Nukleär teknik)
Annals of Nuclear Energy (0306-4549). Vol. 36 (2009), 4, p. 464 - 469 .
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Two-phase flow identification, artificial neural networks



Denna post skapades 2009-12-20. Senast ändrad 2014-09-02.
CPL Pubid: 104453

 

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Institutioner (Chalmers)

Institutionen för teknisk fysik, Nukleär teknik (2006-2015)

Ämnesområden

Övrig teknisk fysik

Chalmers infrastruktur