  In this paper a data analytical approach featuring support vector machines
(SVM) is employed to train a predictive model over an experimentaldataset,
which consists of the most relevant studies for two-phase flow pattern
prediction. The database for this study consists of flow patterns or flow
regimes in gas-liquid two-phase flow. The term flow pattern refers to the
geometrical configuration of the gas and liquid phases in the pipe. When gas
and liquid flow simultaneously in a pipe, the two phases can distribute
themselves in a variety of flow configurations. Gas-liquid two-phase flow
occurs ubiquitously in various major industrial fields: petroleum, chemical,
nuclear, and geothermal industries. The flow configurations differ from each
other in the spatial distribution of the interface, resulting in different flow
characteristics. Experimental results obtained by applying the presented
methodology to different combinations of flow patterns demonstrate that the
proposed approach is state-of-the-art alternatives by achieving 97% correct
classification. The results suggest machine learning could be used as an
effective tool for automatic detection and classification of gas-liquid flow
patterns.
