Complex Network from Nonlinear Time Series with Application to Inclined Oil‐Water Flow Pattern Identification

We study the inclined oil‐water two‐phase flow using complex networks and construct flow pattern complex network with the conductance fluctuating signals measured from oil‐water two‐phase flow experiments. A new method, based on Time‐Delay Embedding and modularity, is proposed to construct network from nonlinear time series. Through detecting the community structure of the resulting network using the community‐detection algorithm based on K‐means clustering, useful and interesting results are found which can be used to identify three inclined oil‐water flow patterns. In this paper, from a new perspective, we not only introduce complex network theory to the study of oil‐water two‐phase flow, but also demonstrate that complex network may be a powerful tool for exploring nonlinear time series in practice.