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Classification of Two-Phase Flow Regimes via Image Analysis by a Neuro-Wavelet Approach

Carl Sunde (Institutionen för reaktorfysik) ; Senada Avdic ; Imre Pázsit (Institutionen för reaktorfysik)
Applied Computational Intelligence, Proceedings of the 6th International FLINS Conference p. 236-239. (2004)
[Konferensbidrag, refereegranskat]

A non-intrusive method of two-phase flow identification is investigated in this paper. It is based on image processing of data obtained from dynamic neutron radiography recordings. Classification of the flow regime types is performed by an artificial neural network (ANN) algorithm. The input data to the ANN are some statistical functions (mean and variance) of the wavelet transform coefficients of the pixel intensity data. The investigations show that bubbly and annular flows can be identified with a high confidence, but slug and churn-turbulent flows are more often mixed up in between themselves.

Denna post skapades 2006-08-29. Senast ändrad 2014-09-02.
CPL Pubid: 9165


Institutioner (Chalmers)

Institutionen för reaktorfysik (1960-2005)



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