2014
DOI: 10.5334/jors.bl
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PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB

Abstract: Digital particle image velocimetry (DPIV) is a non-intrusive analysis technique that is very popular for mapping flows quantitatively. To get accurate results, in particular in complex flow fields, a number of challenges have to be faced and solved: The quality of the flow measurements is affected by computational details such as image pre-conditioning, sub-pixel peak estimators, data validation procedures, interpolation algorithms and smoothing methods. The accuracy of several algorithms was determined and th… Show more

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Cited by 1,779 publications
(1,299 citation statements)
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References 46 publications
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“…Using particle image velocimetry (PIV) [36], we found that the flow fields and relative flow rates into the growth chamber and bypass channel match the design criteria (figure 1d). Furthermore, the ratio of flow rates Q bypass / Q inlet , 1 remain constant even after 24 h of bacterial flow, ensuring that delivery of nutrients is consistent throughout the experiment (see the electronic supplementary material, methods for details).…”
Section: Results (A) Development Of a Custom Worm Growthchipmentioning
confidence: 99%
See 1 more Smart Citation
“…Using particle image velocimetry (PIV) [36], we found that the flow fields and relative flow rates into the growth chamber and bypass channel match the design criteria (figure 1d). Furthermore, the ratio of flow rates Q bypass / Q inlet , 1 remain constant even after 24 h of bacterial flow, ensuring that delivery of nutrients is consistent throughout the experiment (see the electronic supplementary material, methods for details).…”
Section: Results (A) Development Of a Custom Worm Growthchipmentioning
confidence: 99%
“…One micrometre green fluorescent polystyrene beads suspended in the S-Medium supplemented with OP50 E. coli were used to visualize and quantify flow fields using PIVLab [36].…”
Section: (E) Particle Image Velocimetrymentioning
confidence: 99%
“…Cell motility was also studied by particle image velocimetry [39] using PIVlab 1.35 software [40] of MATLAB (The MathWorks, Natick, MA). Image sequences recorded for 255 min with Δt´= 15 min were filtered and analyzed with an interrogation window of 82 × 82 pixels with 50% overlap.…”
Section: Evaluation Of Cell Displacementsmentioning
confidence: 99%
“…This velocity is found to remain uniform over a substantial region of the device until the time that the cloud runs out of dust particles. We have measured this velocity for different lowering of hill height by open PIV tool in MATLAB [5]. For subsonic flow of the dust fluid, wake fields are excited in the left side of the wire traveling at the speed of ≈ 2.30 cm/s.…”
Section: Resultsmentioning
confidence: 99%