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A MATLAB Script for vortices in experimental velocity fields measured using particle image velocimetry (PIV) with a novel combinatorial vortex detection (CVD) algorithm.

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Combinatorial-Vortex-Detection-Algorithm

A MATLAB Script for vortices in experimental velocity fields measured using particle image velocimetry (PIV) with a novel combinatorial vortex detection (CVD) algorithm.

The CVD Algorithm implements an automated workflow for vortex identification and characterization in fluid flow data. It combines three techniques:

1. Maximum Vorticity (MV) - Detects vortex candidates based on local vorticity maxima
2. Cross-Sectional Lines (CSL) - Refines vortex core locations
3. Winding Angle (WA) - Confirms and characterizes vortices using winding angle thresholding

This combinatorial approach enables robust vortex detection across varied flow conditions.

Usage: The main CVD algorithm script sequentially calls the MV, CSL, and WA functions:

1. Load input vector field data
2. Set vortex detection parameters
3. Initialize data storage
4. Loop through vector field frames
	- Apply MV method to identify vortex candidates
	- Use CSL to refine vortex core locations
	- Perform WA analysis to confirm and characterize vortices
	- Store vortex data for each frame
5. Generate visualizations and analyze results

Code Structure: - CombinatorialVortexDetection.m: Main CVD algorithm script - MaximumVorticityMethod.m: MV vortex candidate detection - CrossSectionalLinesMethod.m: CSL core localization - RegionsOfInterest.m: This function defines regions of interest (ROIs) around potential vortex cores - WindingAngleMethod.m: WA vortex confirmation/characterization - Circulation.m: Vortex circulation computation - Visualization functions: plotProfiles.m, plotStreamlines.m, plotStreamlinesWA.m, and plotVorticesMap.m

Requirements: - Image Processing Toolbox - Signal Processing Toolbox - Wavelet Toolbox - PIVMat Toolbox

References:

- R.C. Strawn, D.N. Kenwright, J. Ahmad, Computer Visualization of Vortex 
  Wake Systems, AIAA Journal. 37 (1999) 511–512. https://doi.org/10.2514/2.744.

- H. Vollmers, Detection of vortices and quantitative evaluation of their 
  main parameters from experimental velocity data, Meas. Sci. Technol. 12 (2001) 1199. 
  https://doi.org/10.1088/0957-0233/12/8/329.

- L.M. Portela, Identification and characterization of vortices in the 
  turbulent boundary layer, Ph.D., Stanford Uni-versity, 1998.

Authors: Mathew Bussière, Guilherme Bessa, Bob Koch, and David Nobes. Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta

Contact: dnobes@ualberta.ca

Version: 1.0

Date: 10/6/2023

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A MATLAB Script for vortices in experimental velocity fields measured using particle image velocimetry (PIV) with a novel combinatorial vortex detection (CVD) algorithm.

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