Fuzzy inference systems for efficient non-invasive on-line two-phase flow regime identification
Paper in proceedings, 2009

The identification of two-phase flow regimes that occur in heated pipes is of paramount importance for monitoring nuclear installations such as boiling water reactors. A Sugeno-type fuzzy inference system is put forward for non-invasive, on-line flow regime identification. The proposed system is particularly efficient in that it employs a single directly computable input, four outputs calculated via subtractive clustering - each corresponding to one flow regime -, and four fuzzy inference rules. Despite its simplicity, the system accomplishes accurate identification of the flow regime of sequences of images from neutron radiography videos.


Tatiani Tampouratzi


Panepistimion Pireos

Imre Pazsit

Chalmers, Applied Physics, Nuclear Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 5495 LNCS 423-429

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Other Engineering and Technologies





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