Abstract
Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.
Highlights
Nazemi et al carried out research into two-phase flow regimes for volume fraction calculations autonomously of the various types of regimes in the pipe, using the gamma-ray attenuation technique [10]
Several statistical features were extracted from the approximation signals of both detectors
All these features were analyzed in order to find the best features with the highest ability to classify the relevant flow regimes, for use as the inputs to the networks
Summary
Nazemi and co-authors performed research into void fraction measurement that was independent of density changes in the liquid phase, by employing a dual modality densitometry technique [3]. Nazemi et al carried out research into two-phase flow regimes for volume fraction calculations autonomously of the various types of regimes in the pipe, using the gamma-ray attenuation technique [10]. Roshani et al studied void fraction measurements independently of the type of flow regimes using a dual-energy broad beam technique. In this context, several features were extracted from the signals from the detector to use in network inputs [12]. This method reduced the rate of errors in terms of the void fraction
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