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Related Concept Videos

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...
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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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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.
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Rapidly Varying Flow01:24

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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...
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Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:

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Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

Optical flow estimation for a periodic image sequence.

Ling Li1, Yongyi Yang

  • 1Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 18, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel temporal modeling method for periodic image motion analysis. The approach enhances motion field estimation robustness, especially in noisy image sequences.

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Signal processing

Background:

  • Accurate motion estimation is crucial for analyzing dynamic image sequences.
  • Traditional frame-by-frame methods struggle with noise and complex motion patterns.

Purpose of the Study:

  • To develop a robust temporal modeling approach for periodic image motion.
  • To improve motion field estimation accuracy in noisy environments.

Main Methods:

  • Utilized a Fourier harmonic representation to model temporal motion evolution.
  • Estimated motion field parameters simultaneously across image frames.
  • Employed model order as a regularization parameter for temporal coherence.

Main Results:

  • Demonstrated robust motion field estimation in the presence of significant imaging noise.
  • Outperformed frame-by-frame estimation approaches in experimental tests.
  • Successfully applied to translational, convergent/divergent, and cardiac motion types.

Conclusions:

  • The proposed temporal modeling approach offers superior robustness for periodic motion analysis.
  • This method effectively leverages the entire image sequence for improved motion estimation.
  • Applicable to various dynamic imaging scenarios requiring motion field determination.