Abstract
A novel approach is devised for the quantification of systematic uncertainty due to peak locking in particle image velocimetry (PIV), which also leads to correction of the peak-locking errors. The approach, applicable to statistical flow properties such as time-averaged velocity and Reynolds stresses, relies on image recordings with multiple time separations Δt and a least-squares regression of the measured quantities. In presence of peak locking, the measured particle image displacement is a non-linear function of Δt due to the presence of measurement errors which vary non-linearly with the sub-pixel particle image displacement. Additionally, the measured displacement fluctuations are a combination of the actual flow fluctuations and the measurement error. When the image recordings are acquired with multiple Δt's, a least-squares regression among the statistical results yields a correction where systematic errors due to peak locking are significantly diminished. The methodology is assessed for planar PIV measurements of the flow over a NACA0012 airfoil at 10 degrees angle of attack. Reference measurements with much larger Δt than the Δt's of the actual measurements, such that relative peak-locking errors are negligible for the former, are used to assess the validity of the proposed approach.
Original language | English |
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Article number | 054003 |
Number of pages | 19 |
Journal | Measurement Science and Technology |
Volume | 32 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- least-squares regression
- multi-Δt
- particle image velocimetry
- peak-locking errors
- uncertainty quantification
- t
- multi-Δ
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Figures created during the work on "Multi-Δt approach for peak-locking error correction and uncertainty quantification in PIV"
Adatrao, S. (Creator), Sciacchitano, A. (Creator) & Bertone, M. (Creator), TU Delft - 4TU.ResearchData, 22 Mar 2021
DOI: 10.4121/13379174
Dataset/Software: Dataset
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MATLAB scripts created during the work on "Multi-Δt approach for peak-locking error correction and uncertainty quantification in PIV"
Adatrao, S. (Creator), Sciacchitano, A. (Creator) & Bertone, M. (Creator), TU Delft - 4TU.ResearchData, 22 Mar 2021
DOI: 10.4121/13379120
Dataset/Software: Software