Void fraction is an important parameter in the oil industry. This quantity is necessary for
volume rate measurement in multiphase flows. In this study, the void fraction percentage
was estimated precisely, independent of the flow regime in gaseliquid two-phase flows by
using g-ray attenuation and a multilayer perceptron neural network. In all previous studies
that implemented a multibeam g-ray attenuation technique to determine void fraction
independent of the flow regime in two-phase flows, three or more detectors were used
while in this study just two NaI detectors were used. Using fewer detectors is of advantage
in industrial nuclear gauges because of reduced expense and improved simplicity. In this
work, an artificial neural network is also implemented to predict the void fraction percentage
independent of the flow regime. To do this, a multilayer perceptron neural
network is used for developing the artificial neural network model in MATLAB. The
required data for training and testing the network in three different regimes (annular,
stratified, and bubbly) were obtained using an experimental setup. Using the technique
developed in this work, void fraction percentages were predicted with mean relative error
of <1.4%.
