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The dynamical flow behaviors underlying the transitions of oil\u2013water bubbly flows are still elusive. We carry out oil\u2013water 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. <\/jats:p>","DOI":"10.1142\/s0218127417500596","type":"journal-article","created":{"date-parts":[[2017,5,8]],"date-time":"2017-05-08T05:30:57Z","timestamp":1494221457000},"page":"1750059","source":"Crossref","is-referenced-by-count":40,"title":["Multilayer Network from Multivariate Time Series for Characterizing Nonlinear Flow Behavior"],"prefix":"10.1142","volume":"27","author":[{"given":"Zhong-Ke","family":"Gao","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. 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