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Related Experiment Video

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Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
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Quantitative classification of vortical flows based on topological features using graph matching.

Paul S Krueger1, Michael Hahsler2, Eli V Olinick2

  • 1Department of Mechanical Engineering, Information, and Systems, Southern Methodist University, Dallas, TX 75275, USA.

Proceedings. Mathematical, Physical, and Engineering Sciences
|September 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel graph-theoretic method to compare and classify fluid flow patterns. The technique accurately categorizes complex vortical flows, offering insights into fluid dynamics and animal propulsion.

Keywords:
Gabriel graphgraph matchingtopologyvorticeswakes

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Area of Science:

  • Fluid dynamics
  • Graph theory
  • Computational science

Background:

  • Vortical flow patterns are crucial for understanding fluid dynamic drag and propulsive performance in systems like swimming animals.
  • Current methods for flow field comparison lack quantitative precision, especially for complex or variable patterns.

Purpose of the Study:

  • To develop a novel, quantitative method for comparing and classifying fluid flow fields.
  • To introduce a graph-theoretic approach for analyzing vortical flow structures.

Main Methods:

  • Utilized critical points of the velocity vector field as vertices in a weighted Gabriel graph.
  • Encoded local geometric structure and flow features using graph edges and weights.
  • Quantified flow dissimilarity via graph matching and employed hierarchical clustering for classification.

Main Results:

  • The weighted Gabriel graph method demonstrated robustness to minor flow field perturbations.
  • Achieved high classification accuracy for artificially generated periodic vortical flows, even with significant variations.
  • Showed insensitivity to scale and the number of periods in flow patterns.

Conclusions:

  • The proposed graph-theoretic approach provides a robust and accurate method for quantitative flow field comparison and classification.
  • This technique offers a generalizable framework applicable to diverse flow-generating mechanisms, including biological propulsion.