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...

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Main Authors: Sun, Bihai, Shehzad, Muhammad, Jovic, Daniel, Cuvier, Christophe, Willert, Christian, Ostovan, Yasar, Foucaut, Jean-Marc, Atkinson, Callum, Soria, Julio
Format: Journal Article
Language:English
Published: 26-02-2021
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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