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