Analysis of Gas-water Flow Transition Characteristics Based on Multiscale Limited Penetrable Visibility Graph

Jun Han

doi:10.1038/s41598-020-64021-4

Abstract

It’s a significant challenge for gas-water flow transition characteristics from experimental measurements in the study of multiphase flow systems. The limited penetrable visibility graph has been proved to be an efficient methodology for revealing nonlinear dynamical behaviors of time series. In order to uncovering gas-water flow transitions, gas-water flow experiment was carried out to obtain time series signals related to the transitions of three flow patterns. Then a novel multiscale limited penetrable visibility graph (MLPVG) is used to construct complex networks from many different experimental flow conditions. The multiscale network measures associated with node degree are employed to describe the topological features of the constructed MLPVG. The results show that the multiscale limited penetrable visibility graph can not only effectively characterize flow transition but also yields novel insights into the identification of gas-water flow patterns.

Highlights

Multiphase flow is a complex fluid phenomenon, widely exists in many fields of industry[1,2]
The relationship between flow parameters is different under different flow patterns, which results in the influence of flow patterns on the accuracy of measurement methods
The multiphase flow in the pipeline presents different flow patterns with different geometric and dynamic characteristics, which can be described by component or phase morphology, but it is difficult to achieve quantitative description, because the flow parameters change with the flow pattern, and the relationship between the forces acting on the fluid and parameters is very complex

Summary

Introduction

Multiphase flow is a complex fluid phenomenon, widely exists in many fields of industry[1,2]. We have carried out experiments of gas-water two-phase flow and obtained the differential pressure time series signals related to the three flow pattern transitions. We use multiscale limited penetrable visibility graph (MLPHVG)[22] to analyze the signals and infer complex networks from many different flow conditions.

Results

Conclusion