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

Paper in proceeding, 2004

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.