Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Flow of Oil Through Circular Pipes01:25

Design Example: Flow of Oil Through Circular Pipes

593
Understanding fluid flow behavior through pipes is critical in fluid mechanics, especially in applications like oil transportation through pipelines. Hagen-Poiseuille's law provides an exact solution derived from the Navier-Stokes equations for steady, incompressible, and laminar flow within a circular pipe. Hagen-Poiseuille's law helps determine the necessary pressure drop across a pipeline section by determining parameters like pipe length, radius, oil viscosity, and the desired volumetric...
593
Rapidly Varying Flow01:24

Rapidly Varying Flow

750
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...
750
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

890
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant...
890
General Characteristics of Pipe Flow II01:24

General Characteristics of Pipe Flow II

1.2K
When fluid enters a pipe, it first passes through the entrance region, where the velocity profile adjusts due to viscous effects. In this region, a boundary layer forms along the pipe walls and grows until it fully occupies the pipe's cross-section. Once the boundary layer merges, the flow becomes fully developed, with a steady velocity profile that remains consistent along the pipe's length.
The distance to reach a fully developed flow is called the entrance length and depends on the...
1.2K
Typical Model Studies01:30

Typical Model Studies

870
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
870
Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

1.8K
Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
1.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multi-scaling reservoir computing learns noise-induced transitions with Lévy noise.

Chaos (Woodbury, N.Y.)·2025
Same author

Early warnings are too late when parameters change rapidly.

Scientific reports·2025
Same author

Nonmonotonic emergence of order from chaos in turbulent thermoacoustic fluid systems.

Physical review. E·2025
Same author

Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

The circular movement of synchronous extreme precipitation preceding Kerala floods in 2018 and 2019.

Chaos (Woodbury, N.Y.)·2025
Same author

Extreme events in two coupled chaotic oscillators.

Physical review. E·2025

Related Experiment Video

Updated: May 7, 2026

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
08:38

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

10.3K

Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

Zhong-Ke Gao1, Xin-Wang Zhang, Ning-De Jin

  • 1School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China and Department of Physics, Humboldt University, Berlin 12489, Germany and Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 16, 2013
PubMed
Summary
This summary is machine-generated.

A new multisector conductance sensor effectively characterizes complex horizontal oil-water two-phase flows. Multivariate recurrence networks reveal flow pattern transitions, offering insights into flow behavior.

More Related Videos

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp
09:58

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

7.8K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.2K

Related Experiment Videos

Last Updated: May 7, 2026

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
08:38

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

10.3K
Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp
09:58

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

7.8K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.2K

Area of Science:

  • Fluid Dynamics
  • Complex Systems Analysis
  • Sensor Technology

Background:

  • Horizontal oil-water two-phase flows exhibit complex patterns crucial for industrial processes.
  • Accurate characterization of these flow patterns remains a significant challenge.
  • Advanced sensing and analytical techniques are needed for improved understanding.

Purpose of the Study:

  • To develop and validate a novel multisector conductance sensor for measuring multivariate signals in oil-water flows.
  • To apply multivariate recurrence network analysis to experimental flow data.
  • To identify network properties sensitive to flow pattern transitions.

Main Methods:

  • Design and implementation of a multisector conductance sensor.
  • Systematic experimentation with horizontal oil-water two-phase flows.
  • Inference of multivariate recurrence networks from sensor data.
  • Analysis of local cross-network properties, focusing on the cross-clustering coefficient.

Main Results:

  • The multisector conductance sensor successfully captured multivariate signals corresponding to different flow patterns.
  • A cross-clustering coefficient derived from multivariate recurrence networks demonstrated high sensitivity to flow pattern transitions.
  • Quantitative insights into the underlying flow behavior were obtained through network analysis.

Conclusions:

  • Multivariate recurrence networks provide a powerful framework for analyzing complex horizontal oil-water two-phase flows.
  • The cross-clustering coefficient is a key indicator for detecting flow pattern changes.
  • This network-based approach offers a novel perspective for investigating multiphase flow systems.