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In this paper, we demonstrate that void fraction could be predicted independent of type of flow regime in two-phase flows using 60Co source and one scintillator NaI detector. For this purpose, firstly three features (Feature No. 1: counts under Compton continuum; Feature No. 2: counts under full energy peak of 1173 keV; Feature No. 3: counts under full energy peak of 1333 keV) were extracted from registered gamma-ray spectrum in detector. Secondly, these three features were utilized as the inputs of artificial neural network model of multilayer perceptron (MLP) in order to achieve the best structure for predicting the void fraction. In each structure, void fraction was considered constantly as the output of MLP network. Using the optimum MLP network structure, void fraction was predicted independent of type of flow regime in gas–liquid two-phase flow with MRE of less than 2.5%. Although obtained error using one detector for predicting the void fraction is more than when two or more detectors are utilized, using fewer detectors has advantages such as making the detection system simpler and reducing economical expenses. [ABSTRACT FROM AUTHOR]

In this paper, based on dual-energy broad beam gamma ray attenuation technique (using two transmission 1-inch NaI detectors and a dual-energy gamma ray source), an artificial neural network (ANN) model was used in order to predict the volume fraction of gas, oil and water in three-phase flows independent of the flow regime. A multilayer perceptron (MLP) neural network was used for developing the ANN model in MATLAB 8.1.0.604 software. The input parameters of the MLP model were registered counts under first and second full energy peaks of the both transmission NaI detectors, and the outputs were gas and oil percentage. The volume fractions were obtained precisely independent of flow regime using the presented model. Mean absolute error of the presented model was less than 2.24%. [ABSTRACT FROM AUTHOR]

In this study, a simple detection system comprised of one 60 Co source and just one NaI detector was investigated in order to identify flow regime and measure void fraction in gas–liquid two phase flows. For this purpose, 3 main flow regimes of two-phase flows including stratified, homogenous and annular with void fractions in the range of 5–95% were simulated by Monte-Carlo N Particle (MCNP) code. At first step, 3 features (count under full energy peaks of 1.173 and 1.333 MeV, and count under Compton continuum) were extracted from registered gamma spectrum. These 3 extracted features were used as inputs of artificial neural network (ANNs). A primary network was trained for identifying the flow regimes, but after testing many different structures, it was found that just two regimes of stratified and annular could be completely identified from each other. After identifying the mentioned two flow regimes by the first ANN, two specific ANNs were also implemented for predicting the void fraction. Using the proposed method in this work, void fraction percentages were predicted with a mean relative error (MRE) of less than only 0.42%. Using fewer detectors is of advantage in industrial nuclear gauges, because of reducing economical expenses and also simplicity of working with these systems. [ABSTRACT FROM AUTHOR]