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 the best performing methods were implemented in a user-friendly open-source tool for performing DPIV flow analysis in Matlab.
Highlights
Digital particle image velocimetry (DPIV) is a non-intrusive analysis technique that is very popular for mapping flows quantitatively
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
In most DPIV analyses, two images (A and B) of the illuminated plane are captured at t0 and t0+Δt
Summary
PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB. A DPIV analysis typically consists of three main steps Thielicke and Stamhuis: PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB. The workflow is menu-based, starting at the left with image input and pre-processing options, and continuing to the right of the menu (image evaluation / PIV analysis, post-processing, data exploration). This workflow is demonstrated in tutorials and screen capture videos that can be found on the project website. We present a selected number of pre-processing techniques that are implemented in PIVlab (see Figure 3 for examples)
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