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

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...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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...
Rapidly Varying Flow01:24

Rapidly Varying Flow

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...
Gradually Varying Flow01:29

Gradually Varying Flow

Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
Velocity and Acceleration in Steady and Unsteady Flow01:11

Velocity and Acceleration in Steady and Unsteady Flow

In fluid mechanics, velocity and acceleration are key concepts for analyzing particle motion in both steady and unsteady flow. Consider a fluid particle moving along a pathline, where its velocity depends on its position and time. The particle's acceleration is obtained by differentiating the velocity with respect to time.
The acceleration can be generalized to any point in the flow, and expressed as components along three perpendicular directions, representing changes in velocity over time.
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...

You might also read

Related Articles

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

Sort by
Same author

Metal cation effects on the structural, optical and thermal properties of double tungstates AM(WO<sub>4</sub>)<sub>2</sub> (A = Li, Na, K; M = Y, La, Ce, Pr, Nd, Sm, Bi).

Dalton transactions (Cambridge, England : 2003)·2026
Same author

A ULK1-MTFR1L feedback loop links mitochondrial fission, mitophagy and apoptosis.

Journal of cell science·2026
Same author

Is Self-Report of Attachment Patterns in Young People Stable From Three to Nine Months After a Concussion?

Scandinavian journal of psychology·2025
Same author

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Sensors (Basel, Switzerland)·2024
Same author

Efficacy of patient education and duloxetine, alone and in combination, for patients with multisystem functional somatic disorder: Study protocol for the EDULOX trial.

Contemporary clinical trials·2024
Same author

Aetiological Understanding of Fibromyalgia, Irritable Bowel Syndrome, Chronic Fatigue Syndrome and Classificatory Analogues: A Systematic Umbrella Review.

Clinical psychology in Europe·2024

Related Experiment Video

Updated: May 26, 2026

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

Variational adaptive correlation method for flow estimation.

Florian Becker1, Bernhard Wieneke, Stefania Petra

  • 1Heidelberg Collaboratory for Image Processing and the Image and Pattern Analysis Group, University of Heidelberg, Heidelberg, Germany. becker@math.uni-heidelberg.de

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 29, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new variational method for estimating turbulent fluid flow from particle images. The robust approach accurately measures flow dynamics across various conditions without needing parameter tuning.

More Related Videos

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
09:17

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Related Experiment Videos

Last Updated: May 26, 2026

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
09:17

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Area of Science:

  • Experimental fluid mechanics
  • Turbulence research
  • Image velocimetry

Background:

  • Accurate estimation of turbulent fluid flow is crucial for understanding complex fluid dynamics.
  • Particle Image Velocimetry (PIV) is a key technique, but its accuracy can be limited by factors like particle density and image noise.
  • Existing methods often require data-specific parameter tuning, limiting their general applicability.

Purpose of the Study:

  • To present a novel variational approach for estimating turbulent fluid flow from particle image sequences.
  • To develop a method that is robust to varying particle densities and image noise levels.
  • To achieve high estimation accuracy without data-specific parameter tuning.

Main Methods:

  • A variational approach involving two coupled optimizations.
  • Adaptation of Gaussian correlation window size and shape at each location.
  • Estimation of fluid flow using a multiscale nonlinear optimization technique.

Main Results:

  • The method demonstrates robustness across typical experimental scenarios.
  • Highest estimation accuracy achieved on an international benchmark dataset (PIV Challenge).
  • Effective handling of a wide range of particle densities and image noise levels.

Conclusions:

  • The proposed variational approach offers a robust and accurate solution for turbulent flow estimation from particle images.
  • The method eliminates the need for data-specific parameter tuning, enhancing its practical utility.
  • This technique advances the field of experimental fluid mechanics and PIV analysis.