Digital particle image velocimetry

On errors of digital particle image velocimetry

The goal of the present study is to quantify and reduce, when possible, errors in two-dimensional digital particle image velocimetry (DPIV). Two major errors, namely the mean bias and root-mean-square (RMS) errors, have been studied. One fundamental source of these errors arises from the implementation of cross correlation (CC). Other major sources of these errors arise from the peak-finding sc...

Multiple CCD Array Digital Particle Image Velocimetry

Digital image processing is a technologically limited subject, and has seen tremendous growth over the last ten years with the exponential increase in computing power commonly available. This trend can be seen in Particle Image Velocimetry (PIV) as the migration from traditional wet film cameras to fully digital systems. The use of digital cameras in PJV measurements has been common place for a...

Digital particle image velocimetry studies of nasal airflow.

Understanding the properties of airflow in the nasal cavity is essential to understanding physiologic and pathologic aspects of nasal breathing. Many attempts have been made to evaluate nasal airflow patterns using the best possible analytical methods available at the time. Recently, digital particle image velocimetry (DPIV) and computational fluid dynamic methods have been applied to this area...

Digital Particle Image Velocimetry: a challenge for feature based tracking

Measurement of digital particle image velocimetry precision using electro-optically created particle-image displacements

The displacement (velocity) precision achieved with digital particle image velocimetry (PIV) was measured. The purpose of this work was to determine the precision and sensitivity of digital PIV using real rather than theoretical images at 1 and 2 mm spatial resolution. The displacement measurement precision was determined by measuring the RMS noise from 60 identical displacement distributions. ...