Two-phase flow is of great importance in various industrial processes. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems and it holds great significance in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for

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... ification of flow regime using dynamic pressure signals and deep learning techniques. Stratified, slug and annular flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure signals were collected at a strategic location. These signals were converted to scalograms and used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures were effective in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This study can be extended by collecting data over broader range of flow parameters and different geometries.