Study On The Evoiution Characteristics Of Gas-Liquid Two-Phase Flow Patterns

Gas-liquid two-phase flow is a complex nonlinear system, it widely exists in the field of industrial processes. The flow pattern, the distribution of the flowing media between phases in two-phase flow, plays an important role in the study of two-phase flow parameters. It can largely affect the two-phase flow pressure gradient and heat and mass transfer rate, but also affect the measurement accuracy of flow process parameters and other relevant characteristics. So, identify the different gas-liquid two-phase flow patterns and uncover the complexity of systems are very significance to the study of gas-liquid two-phase flows.In this paper, the base-scale entropy (BE) method is employed to extract the features of gas-liquid two-phase flows. The root mean square (RMS) is another way of extracting feature and is used to compare the base-scale entropy. And we can identify the different gas-liquid two-phase flow pattern according to extracted features. In order to test the effectiveness of the extracted features, we calculate their separability measure values. Finally, flow pattern identification of different gas-liquid two-phase flows was achieved.In order to characterize the dynamic and evolution of gas-liquid two-phase flow patterns, the multi-scale order recurrence analysis method was involved in the study. In view of the non-linearity and non-stability of two-phase flow pattern signals, we propose the adaptive analysis method ensemble empirical mode decomposition (EEMD) and use this EEMD method to process the flow pattern signals. Thus, feature vectors made from intrinsic mode function components on different scales could be obtained, and then draw the order recurrence plot (ORP) of each scale signal. The results show that the multi-scale order recurrence analysis method can identify the different gas-liquid two-phase flow patterns at the macro perspective, and it also can characterize the evolution characteristics of gas-liquid two-phase flow patterns at the micro perspective.