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PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB

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TLDR
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.
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.

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Journal ArticleDOI

MacCormack’s technique-based pressure reconstruction approach for PIV data in compressible flows with shocks

TL;DR: In this article, the authors proposed an improved approach for extraction of pressure fields from velocity data, such as obtained by particle image velocimetry (PIV), especially for steady compressible flows with strong shocks.
Journal ArticleDOI

Generation and characterization of fully developed state in open channel flow

TL;DR: In this article , a planar particle image velocimetry is used to study the characteristics of fully developed smooth open channel flow at a constant Reynolds number of 3.9 × 104 based on the maximum velocity and flow depth.
Journal ArticleDOI

Experimental comparison of PIV-based pressure measurements in supersonic flows with shock waves

TL;DR: In this article, the performance of the novel MacCormack method for particle image velocimetry (PIV)-based pressure reconstruction in real supersonic PIV experiments that include oblique shock waves generated by three different wedges of 10°, 15°, and 20° with a free stream Mach number of 2.91 was evaluated.
Posted ContentDOI

Spatial distribution of tracers for optical sensing of stream surface flow

TL;DR: In this article, numerical simulations were performed to consider different levels of particle aggregation, particle color (in terms of greyscale intensity), seeding density, and background noise, and an error reduction of about 15.9 and 16.1% was calculated by employing the optimal frame window.
Journal ArticleDOI

Laboratory study of suspended sediment dynamics over a mildly sloping sandy seabed

TL;DR: In this paper, the results of laboratory measurements of suspended sediment movement induced by regular non-linear water waves propagating over a mildly sloping sandy seabed covered with ripples are presented.
References
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Journal ArticleDOI

The Design and Implementation of FFTW3

TL;DR: It is shown that such an approach can yield an implementation of the discrete Fourier transform that is competitive with hand-optimized libraries, and the software structure that makes the current FFTW3 version flexible and adaptive is described.
Book

Particle Image Velocimetry: A Practical Guide

TL;DR: In this paper, the authors present a practical guide for the planning, performance and understanding of experiments employing the PIV technique, which is primarily intended for engineers, scientists and students, who already have some basic knowledge of fluid mechanics and nonintrusive optical measurement techniques.
Journal ArticleDOI

Particle-Imaging Techniques for Experimental Fluid Mechanics

TL;DR: A review of these methods can be found in articles by Lauterborn & Vogel (1984), Adrian (1986a), Hesselink (1988), and Dudderar et al..
Journal ArticleDOI

Adaptive histogram equalization and its variations

TL;DR: It is concluded that clipped ahe should become a method of choice in medical imaging and probably also in other areas of digital imaging, and that clip ahe can be made adequately fast to be routinely applied in the normal display sequence.
Journal ArticleDOI

Digital Particle Image Velocimetry

TL;DR: In this article, the directional ambiguity associated with PIV and LSV is resolved by implementing local spatial cross-correlations between two sequential single-exposed particle images, and the recovered velocity data are used to compute the spatial and temporal vorticity distribution and the circulation of the vortex ring.
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