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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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Determining 3D Flow Fields via Multi-camera Light Field Imaging
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Observing flow of He II with unsupervised machine learning.

X Wen1,2,3, L McDonald2,4, J Pierce2

  • 1Department of Physics and Astronomy, University of Tennessee, Knoxville, TN, 37996, USA.

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|November 27, 2022
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Summary
This summary is machine-generated.

Researchers developed a novel machine learning method to analyze fluid flow using excimer fluorescence. This technique overcomes limitations of traditional particle tracking velocimetry for complex flow dynamics.

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Area of Science:

  • Fluid dynamics
  • Neutron capture diagnostics
  • Machine learning applications

Background:

  • Understanding fluid flow around complex objects requires time-dependent velocity field analysis.
  • Traditional particle tracking velocimetry (PTV) and particle imaging velocimetry (PIV) are limited in certain experimental setups.

Purpose of the Study:

  • To develop a method for observing and analyzing fluid flow using excimer fluorescence.
  • To overcome the limitations of PTV and PIV in capturing fluorescence data from neutron capture-induced excimers.

Main Methods:

  • Utilized thermal gradients to induce fluid flow within a ~1 cm³ volume.
  • Observed fluid flow by recording fluorescence of neutron capture-produced excimers.
  • Applied an unsupervised machine learning algorithm to identify clusters of excimers.
  • Tracked cluster centroids using a particle displacement determination algorithm adapted from PTV.

Main Results:

  • Successfully identified and tracked ensembles of excimers (clusters) from their fluorescence.
  • Enabled the analysis of fluid flow dynamics where individual excimer photon detection is improbable.
  • Provided a new approach for structure function analysis in fluid dynamics.

Conclusions:

  • The developed unsupervised machine learning approach is effective for analyzing fluid flow from excimer fluorescence.
  • This method offers a viable alternative to PTV and PIV for specific fluid dynamics studies.
  • The technique advances the capability to model and understand fluid flow around complex objects.