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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

131
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
131
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

174
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 cross-section...
174
Streamlines, Streaklines, and Pathlines01:18

Streamlines, Streaklines, and Pathlines

1.5K
A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over...
1.5K
Introduction to Types of Flows01:23

Introduction to Types of Flows

1.4K
Fluid flows are categorized by dimensionality and behavior, with one-dimensional flow being the simplest form, where properties like velocity and pressure change only along a single axis. Water moving through straight pipes exemplifies this flow type, as variations in other directions are minimal. One-dimensional analysis helps simplify understanding such flows, focusing solely on changes along the pipe's length.
Two-dimensional flow involves changes in both length and height, as seen in...
1.4K
Plane Potential Flows01:23

Plane Potential Flows

457
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
457
Rapidly Varying Flow01:24

Rapidly Varying Flow

146
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...
146

You might also read

Related Articles

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

Sort by
Same author

Enhanced Molecular Packing of a Conjugated Polymer with High Organic Thermoelectric Power Factor.

ACS applied materials & interfaces·2016
Same author

Effects of Three Types of Inactivation Agents on the Antibody Response and Immune Protection of Inactivated IHNV Vaccine in Rainbow Trout.

Viral immunology·2016
Same author

An Integrated Model of RAF Inhibitor Action Predicts Inhibitor Activity against Oncogenic BRAF Signaling.

Cancer cell·2016
Same author

Ischemic post-conditioning attenuates acute lung injury induced by intestinal ischemia-reperfusion in mice: role of Nrf2.

Laboratory investigation; a journal of technical methods and pathology·2016
Same author

Liquid-solid joining of bulk metallic glasses.

Scientific reports·2016
Same author

An Autoinhibited Dimeric Form of BAX Regulates the BAX Activation Pathway.

Molecular cell·2016

Related Experiment Video

Updated: Sep 18, 2025

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

225

A flow pattern recognition method for gas-liquid two-phase flow based on dilated convolutional channel attention

Jie Liu1, Yang Wu1

  • 1School of Intelligent Equipment Engineering, Wuxi Taihu University, Wuxi, China.

Plos One
|June 24, 2025
PubMed
Summary

This study introduces Enhanced DenseNet with transfer learning (ED-DenseNet) for improved flow pattern identification. The novel model achieves high accuracy in recognizing gas-liquid two-phase flow patterns, outperforming existing methods.

More Related Videos

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

5.7K
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

10.6K

Related Experiment Videos

Last Updated: Sep 18, 2025

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

225
Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

5.7K
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

10.6K

Area of Science:

  • Fluid dynamics
  • Machine learning
  • Image recognition

Background:

  • Existing deep learning methods struggle with feature extraction for flow pattern identification, leading to low recognition rates.
  • Accurate identification of multiphase flow patterns is crucial for process control and safety.

Purpose of the Study:

  • To propose a novel flow pattern image recognition model, Enhanced DenseNet with transfer learning (ED-DenseNet), to address limitations in feature extraction.
  • To enhance deep feature extraction capabilities for more accurate flow pattern identification.

Main Methods:

  • Developed ED-DenseNet incorporating a multi-branch structure, ECA attention mechanism, and dilated convolutions for multi-scale feature extraction.
  • Utilized transfer learning by applying pretrained DenseNet121 weights (ImageNet) to the ED-DenseNet model.
  • Evaluated the model on gas-liquid two-phase flow and nitrogen condensation two-phase flow datasets.

Main Results:

  • ED-DenseNet achieved an overall recognition accuracy of 97.82% on a gas-liquid two-phase flow dataset.
  • The model outperformed state-of-the-art methods like Flow-Hilbert-CNN, particularly in complex flow scenarios.
  • Demonstrated superior generalization and robustness on a nitrogen condensation two-phase flow dataset.

Conclusions:

  • The proposed ED-DenseNet model significantly improves deep feature extraction for flow pattern identification.
  • ED-DenseNet offers superior accuracy, generalization, and robustness compared to existing methods.
  • This advancement has implications for enhanced monitoring and control in multiphase flow systems.