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Neural network based objective flow regime identification in air‐water two phase flow
Author(s) -
Cai Shiqian,
Toral Haluk,
Qiu Jianhung,
Archer John S.
Publication year - 1994
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450720308
Subject(s) - flow (mathematics) , turbulence , artificial neural network , range (aeronautics) , two phase flow , self organizing map , feature (linguistics) , identification (biology) , computer science , mechanics , meteorology , artificial intelligence , geography , physics , materials science , linguistics , philosophy , botany , composite material , biology
The Kohonen self‐organising neural network was applied to identify flow regimes in horizontal air‐water flow. The neural network was trained with stochastic features derived from turbulent absolute pressure signals obtained across a range of flow regimes. The feature map succeeded in classifying samples into distinctive flow regime classes consistent with the visual flow regime observation.