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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.
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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Uniform Depth Channel Flow: Problem Solving01:18

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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...
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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...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

Updated: Sep 19, 2025

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
12:34

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

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Multi-camera orientation tracking method for anisotropic particles in particle-laden flows.

Mees M Flapper1, Elian Bernard2, Sander G Huisman1

  • 1Physics of Fluids Department and Max Planck Center for Complex Fluid Dynamics, J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7522NB Enschede, The Netherlands.

The Review of Scientific Instruments
|June 18, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new method for tracking the 3D location and orientation of anisotropic particles using multi-camera systems. This robust algorithm accurately tracks multiple particles in various conditions, aiding fluid dynamics research.

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Area of Science:

  • Physics
  • Fluid Dynamics
  • Particle Science

Background:

  • Accurate tracking of particle dynamics is crucial for understanding fluid flow.
  • Existing methods often struggle with anisotropic particles or simultaneous multi-particle tracking.

Purpose of the Study:

  • To develop and validate a novel method for 3D particle orientation tracking.
  • To enable detailed analysis of particle location, orientation, and rotation statistics over time.

Main Methods:

  • Utilized high-speed multi-camera recordings from multiple viewpoints.
  • Reconstructed 3D particle location and orientation based on known particle shapes.
  • Developed an algorithm for simultaneous tracking of multiple anisotropic particles.

Main Results:

  • Quantified the robustness and error of the tracking method.
  • Investigated the impact of noise, image size, camera count, and arrangement using synthetic data.
  • Demonstrated successful application in experiments with quiescent and turbulent fluids.

Conclusions:

  • The developed method is effective for tracking diverse anisotropic particle shapes.
  • The algorithm successfully tracks multiple particles simultaneously and distinguishes between particle types.
  • Broad applicability is shown across various experimental conditions and fluid behaviors.