Detecting gas-liquid two-phase flow pattern determinism from experimental signals with missing ordinal patterns

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas-liquid two-phase flow

The complex phase interactions of the two-phase flow are a key factor in understanding the flow pattern evolutional mechanisms, yet these complex flow behaviors have not been well understood. In this paper, we employ a series of gas-liquid two-phase flow multivariate fluctuation signals as observations and propose a novel interconnected ordinal pattern network to investigate the spatial coupling behaviors of the gas-liquid two-phase flow patterns. In addition, we use two network indices, which are the global subnetwork mutual information (I) and the global subnetwork clustering coefficient (C), to quantitatively measure the spatial coupling strength of different gas-liquid flow patterns. The gas-liquid two-phase flow pattern evolutionary behaviors are further characterized by calculating the two proposed coupling indices under different flow conditions. The proposed interconnected ordinal pattern network provides a novel tool for a deeper understanding of the evolutional mechanisms of the multi-phase flow system, and it can also be used to investigate the coupling behaviors of other complex systems with multiple observations.

Hard c-mean transition network method for analysis of time series

Transition network is a powerful tool to analyze nonlinear dynamic characteristics of complex systems, which characterizes the temporal transition property. Few, if any, existing approaches map different time series into transition networks with the same size so that temporal information of time series can be captured more effectively by network measures including typical average node degree, average path length, and so on. To construct a fixed size transition network, the proposed approach uses the embedding dimension method to reconstruct phase space from time series and divides state vectors into different nodes based on the hard c-mean clustering algorithm. The links are determined by the temporal succession of nodes. Our novel method is illustrated by three case studies: distinction of different dynamic behaviors, detection of parameter perturbation of dynamical system, and identification of seismic airgun based on sound data recorded in central Atlantic Ocean. The results show that our proposed method shows good performance in capturing the underlying nonlinear and nonstationary dynamics from short and noisy time series.