Nonlinear Analysis for Identification of Vertical Upward Oil–Water Two-Phase Flow Patterns

Oil–water two-phase flow exists widely in the industrial process. At present, it is very difficult to describe the oil–water two-phase flow system theoretically. The mechanism of droplet fragmentation and coalescence, which restricts droplet size and flow stability, is still unclear. Accurate identification of oil–water two-phase flow patterns is of great significance to flow control and measurement. In this study, a refined composite multiscale rescaled range fractional permutation entropy (MRSFPE) is proposed for identifying the flow pattern of oil–water two-phase flow in vertical pipes. First, the proposed algorithm is simulated numerically to demonstrate its ability to uncover the complexity of nonlinear time series. Then, the conductance sensor signals are collected in the flow loop test facility of vertical upward oil–water two-phase flow. In the experiment, the observed flow patterns, captured by a high-speed camera, include very fine dispersed oil-in-water flow (VFD O/W), dispersed oil-in-water flow (D O/W), dispersed oil-in-water slug flow (D OS/W), and transition flow (TF). Finally, the proposed algorithm is applied to the experimental signal analysis, extracting two scale indices of the entropy rate at high scale (MRSFPE_rate) and the mean value entropy at low scale (MRSFPE_ave). The results show that the four flow patterns can be identified in the joint distribution of the two scale indices. Furthermore, the MRSFPE can be a useful approach to characterize the nonlinear dynamics of multiphase flow based on sensor measurement signals.