Fuzzy inference systems for efficient non-invasive on-line two-phase flow regime identification
Paper i proceeding, 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

Chalmers University of Technology

Imre Pazsit

Chalmers, Teknisk fysik, Nukleär teknik

Lecture Notes in Computer Science

0302-9743 (ISSN)

Vol. 5495 LNCS 423-429


Annan teknik