Multilayer Network From Multivariate Time Series For Characterizing Nonlinear Flow Behavior

The exploration of two-phase flows, as a multidisciplinary subject, has drawn a great deal of attention on account of its significance. The dynamical flow behaviors underlying the transitions of oil-water bubbly flows are still elusive. We carry out oil-water two-phase flow experiments and capture multichannel flow information. Then we propose a novel methodology for inferring multilayer network from multivariate time series, which enables to fuse multichannel flow information at different frequency bands. We employ macro-scale, meso-scale and micro-scale network measures to characterize the generated multilayer networks, and the results suggest that our analysis allows uncovering the nonlinear flow behaviors underlying the transitions of oil-in-water bubbly flows.