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Understanding the dynamics of multi-phase flows has been a
challenge in the fields of nonlinear dynamics and fluid mechanics.
This chapter reviews our work on two-phase flow dynamics in
combination with complex network theory. We systematically carried
out gas-water/oil-water two-phase flow experiments for measuring
the time series of flow signals which is studied in terms of the
mapping from time series to complex networks. Three network mapping
methods were proposed for the analysis and identification of flow
patterns, i.e. Flow Pattern Complex Network (FPCN), Fluid Dynamic
Complex Network (FDCN) and Fluid Structure Complex Network (FSCN).
Through detecting the community structure of FPCN based on K-means
clustering, distinct flow patterns can be successfully
distinguished and identified. A number of FDCN's under different
flow conditions were constructed in order to reveal the dynamical
characteristics of two-phase flows. The FDCNs exhibit universal
power-law degree distributions. The power-law exponent and the
network information entropy are sensitive to the transition among
different flow patterns, which can be used to characterize
nonlinear dynamics of the two-phase flow. FSCNs were constructed in
the phase space through a general approach that we introduced. The
statistical properties of FSCN can provide quantitative insight
into the fluid structure of two-phase flow. These interesting and
significant findings suggest that complex networks can be a
potentially powerful tool for uncovering the nonlinear dynamics of
two-phase flows.
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