Open AccessPageRank versatility analysis of multilayer modality-based network for exploring the evolution of oil-water slug flow
Author(s) -
Zhongke Gao,
Weidong Dang,
Shan Li,
Yuxuan Yang,
Hong-Tao Wang,
Jing-Ran Sheng,
Xiaofan Wang
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/s41598-017-05890-0
Subject(s) - pagerank , slug flow , flow (mathematics) , coalescence (physics) , computer science , clustering coefficient , salient , cluster analysis , data mining , biological system , theoretical computer science , mechanics , artificial intelligence , physics , two phase flow , biology , astrobiology
Numerous irregular flow structures exist in the complicated multiphase flow and result in lots of disparate spatial dynamical flow behaviors. The vertical oil-water slug flow continually attracts plenty of research interests on account of its significant importance. Based on the spatial transient flow information acquired through our designed double-layer distributed-sector conductance sensor, we construct multilayer modality-based network to encode the intricate spatial flow behavior. Particularly, we calculate the PageRank versatility and multilayer weighted clustering coefficient to quantitatively explore the inferred multilayer modality-based networks. Our analysis allows characterizing the complicated evolution of oil-water slug flow, from the opening formation of oil slugs, to the succedent inter-collision and coalescence among oil slugs, and then to the dispersed oil bubbles. These properties render our developed method particularly powerful for mining the essential flow features from the multilayer sensor measurements.