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.
This paper presents a novel capacitance probe,i.e.,parallel-wire capacitance probe(PWCP),for two-phase flow measurement.Using finite element method(FEM),the sensitivity field of the PWCP is investigated and the optimum sensor geometry is determiend in term of the characterisitc parameters.Then,the response of PWCP for the oil-water stratified flow is calculated,and it is found the PWCP has better linearity and sensitivity to the variation of water-layer thickness,and is almost independant of the angle between the oil-water interface and the sensor electrode.Finally,the static experiment for oil-water stratified flow is carried out and the calibration method of liquid holdup is presented.
We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.
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.
Based on the conductance fluctuation signals measured from vertical upward oil-gas-water three-phase flow experiment, time frequency representation and surrogate data method were used to investigate dynamical characteristics of oil-in-water type bubble and slug flows. The results indicate that oil-in-water type bubble flow will turn to deterministic motion with the increase of oil phase fraction f o and superficial gas velocity U sg under fixed flowrate of oil-water mixture Q mix . The dynamics of oil-in-water type slug flow becomes more complex with the increase of U sg under fixed flowrate of oil-water mixture. The change of f o leads to irregular influence on the dynamics of slug flow. These interesting findings suggest that the surrogate data method can be a faithful tool for characterizing dynamic characteristics of oil-in-water type bubble and slug flows.
This paper presents the characteristics of a double helix capacitance sensor for measurement of the liquid holdup in horizontal oil–water two-phase flow. The finite element method is used to calculate the sensitivity field of the sensor in a pipe with 20 mm inner diameter and the effect of sensor geometry on the distribution of sensitivity field is presented. Then, a horizontal oil–water two-phase flow experiment is carried out to measure the response of the double helix capacitance sensor, in which a novel method is proposed to calibrate the liquid holdup based on three pairs of parallel-wire capacitance probes. The performance of the sensor is analyzed in terms of the flow structures detected by mini-conductance array probes.
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.
