Distortion correction of two-component - two-dimensional PIV using a large imaging sensor with application to measurements of a turbulent boundary layer flow at $Re_{\tau} = 2386
Exp Fluids 62, 183 (2021) In the past decade, advances in electronics technology have made larger imaging sensors available to the experimental fluid mechanics community. These advancements have enabled the measurement of 2-component 2-dimensional (2C-2D) velocity fields using particle image velocim...
Saved in:
Main Authors: | , , , , , , , , |
---|---|
Format: | Journal Article |
Language: | English |
Published: |
26-02-2021
|
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Exp Fluids 62, 183 (2021) In the past decade, advances in electronics technology have made larger
imaging sensors available to the experimental fluid mechanics community. These
advancements have enabled the measurement of 2-component 2-dimensional (2C-2D)
velocity fields using particle image velocimetry (PIV) with much higher spatial
resolution than previously possible. However, due to the large size of the
sensor, the lens distortion needs to be taken into account as it will now have
a more significant effect on the measurement quality that must be corrected to
ensure accurate high-fidelity 2C-2D velocity field measurements. In this paper,
two dewarping models, a second-order rational function (R2) and a bicubic
polynomial (P3) are investigated with regards to 2C-2D PIV measurements of a
turbulent boundary layer (TBL) using a large imaging sensor. Two approaches are
considered and compared: (i) dewarping the images prior to the PIV
cross-correlation analysis and (ii) undertaking the PIV cross-correlation
analysis using the original recorded distorted images then followed by using
the mapping functions derived for image dewarping to provide the correct
spatial location of the velocity measurement point. The results demonstrate
that the use of P3 dewarping model to correct lens distortion yields better
results than the R2 dewarping model. Furthermore, both approaches for the P3
dewarping model yield results which are statistically indistinguishable. |
---|---|
DOI: | 10.48550/arxiv.2103.00115 |