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Rapidly Varying Flow01:24

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Stochastic cycle selection in active flow networks.

Francis G Woodhouse1, Aden Forrow2, Joanna B Fawcett3

  • 1Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge CB3 0WA, United Kingdom; F.G.Woodhouse@damtp.cam.ac.uk.

Proceedings of the National Academy of Sciences of the United States of America
|July 7, 2016
PubMed
Summary
This summary is machine-generated.

Scientists uncovered rules governing active flow networks, from blood vessels to slime molds. Network topology dictates flow patterns, enabling prediction of complex cycles in biological and nonbiological systems.

Keywords:
active transportnetworksstochastic dynamicstopology

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Area of Science:

  • Physics, Biology, Network Science

Background:

  • Active biological flow networks are ubiquitous across scales.
  • Principles of self-organization in these nonequilibrium networks remain poorly understood.

Purpose of the Study:

  • To elucidate how network topology controls dynamics in actively driven flow networks.
  • To identify generalizable rules for flow statistics in nonequilibrium systems.

Main Methods:

  • Integrated concepts from lattice field theory, graph theory, and transition rate theory.
  • Developed a generic model for actively driven flow on a network.
  • Performed combined theoretical and numerical analysis.

Main Results:

  • Identified symmetry-based rules governing flow cycle selection.
  • Demonstrated that network topology predicts flow statistics.
  • Established a correspondence between active flow networks and generalized ice-type models.

Conclusions:

  • Network topology is a key determinant of dynamics in active flow systems.
  • The developed framework applies to diverse biological and nonbiological far-from-equilibrium networks.
  • Symmetry rules enable classification and prediction of flow patterns.