Characterizing countercurrent flow structures in an inclined oil-water two-phase flow from one-dimensional measurement is of great importance for model building and sensor design.Firstly,we conducted oil-water two-phase flow experiments in an inclined pipe to measure the conductance signals of three typical water-dominated oil-water flow patterns in inclined flow,i.e.,dispersion oil-in-water pseudo-slug flow （PS）,dispersion oil-in-water countercurrent flow （CT）,and transitional flow （TF）.In pseudo-slug flow,countercurrent flow and transitional flow,oil is completely dispersed in water.Then we used magnitude and sign decomposition analysis and multifractal analysis to reveal levels of complexity in different flow patterns.We found that the PS and CT flow patterns both exhibited high complexity and obvious multifractal dynamic behavior,but the magnitude scaling exponent and singularity of the CT flow pattern were less than those of the PS flow pattern; and the TF flow pattern exhibited low complexity and almost monofractal behavior,and its magnitude scaling was close to random behavior.Meanwhile,at short time scales,all sign series of two-phase flow patterns exhibited very similar strong positive correlation; at high time scales,the scaling analysis of sign series showed different anti-correlated behavior.Furthermore,with an increase in oil flow rate,the flow structure became regular,which could be reflected by the decrease in the width of spectrum and the difference in dimensions.The results suggested that different oil-water flow patterns exhibited different nonlinear features,and the varying levels of complexity could well characterize the fluid dynamics underlying different oil-water flow patterns.
We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of different oil-in-water flows. We first take several typical time series for example to investigate the characteristic of the MS-CECP and find that the MS-CECP not only describes the continuous loss of dynamical structure with the increase of scale, but also reflects the determinacy of the system. Then we calculate the MS-CECP for the conductance fluctuating signals measured from oil–water two-phase flow loop test facility. The results indicate that the MS-CECP could be an intrinsic measure for indicating oil-in-water two-phase flow structures.
We investigate the dynamic characteristics of oil-gas-water three-phase flow in terms of chaotic attractor comparison. In particular, we extract a statistic to characterize the dynamical difference in attractor probability distribution. We first take time series from Logistic chaotic system with different parameters as examples to demonstrate the effectiveness of the method. Then we use this method to investigate the experimental signals from oil-gas-water three-phase flow. The results indicate that the extracted statistic is very sensitive to the change of flow parameters and can gain a quantitatively insight into the dynamic characteristics of different flow patterns.
