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

140
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...
140

You might also read

Related Articles

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

Sort by
Same author

Fracture-matrix fluid exchange in oil-bearing unconventional mudstones.

Scientific reports·2023
Same author

Origins of pressure dependent permeability in unconventional hydrocarbon reservoirs.

Scientific reports·2023
See all related articles

Related Experiment Video

Updated: Oct 5, 2025

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers
10:21

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Published on: May 5, 2016

10.8K

Multiphase flow detection with photonic crystals and deep learning.

Lang Feng1, Stefan Natu2,3, Victoria Som de Cerff Edmonds4

  • 1Corporate Strategic Research, ExxonMobil Research and Engineering, 1545 Route 22 East, Annandale, NJ, 08801, USA. lang.feng@exxonmobil.com.

Nature Communications
|January 29, 2022
PubMed
Summary

This study introduces a novel photonic crystal sensor for real-time multiphase flow characterization. The technology offers accurate phase fraction, flow morphology, and flow rate measurements, enabling industrial process optimization.

More Related Videos

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

263
Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.5K

Related Experiment Videos

Last Updated: Oct 5, 2025

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers
10:21

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Published on: May 5, 2016

10.8K
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

263
Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.5K

Area of Science:

  • Physics
  • Engineering
  • Material Science

Background:

  • Multiphase flows are crucial in industrial processes.
  • Current characterization methods lack frequency, accuracy, and cost-efficiency for process optimization.

Purpose of the Study:

  • To present a new physics-based concept for real-time multiphase flow characterization.
  • To demonstrate the efficacy of photonic crystals for this application.

Main Methods:

  • Utilizing low-power microwave transmission through photonic crystals filled with fluid mixtures.
  • Applying deep learning analysis to interrogate microwave transmission data.
  • Inferring flow rate from differential pressure measurements.

Main Results:

  • Achieved fast and accurate characterization of phase fraction and flow morphology.
  • Successfully inferred flow rate based on known flow characteristics.
  • Validated the concept with lab and field prototypes.

Conclusions:

  • Photonic crystals offer a novel, inexpensive, and accurate method for multiphase flow characterization.
  • This technology can significantly enhance industrial process optimization.
  • The developed technique is convenient and suitable for real-world applications.