Instability of Low-Velocity and High Water-Cut Oil–Water Two-Phase Flow Based on Microwave Signals

Abstract

Accurately understanding the nonlinear and complex dynamics underlying low-velocity and high water-cut oil–water two-phase flow is a significant but challenging problem to the study of flow pattern boundary transformation and flow heat transfer. In this article, we propose a nonlinear analysis method to characterize and distinguish patterns from low-velocity and high water-cut oil–water two-phase flow based on the fractional entropy algorithm that have found great usage in complex science. First, we obtain the variable measurement and images of the oil–water two-phase flow by a microwave sensor and a high-speed camera, respectively. Second, we analyze the complexity of flow structure using recurrence algorithms and adaptive optimal kernel time–frequency representation. Finally, we propose a dynamic nonlinear analysis framework to characterize flow instability based on multiscale fractional weighted permutation entropy. The results show that the entropy rate and its mean value are sensitive to the flow structure. These interesting and significant findings suggest that the multiscale fractional weighted permutation entropy can potentially be a powerful tool for uncovering the underlying dynamics leading to the formation of two-phase flow.