PIV uncertainty quantification from correlation statistics

By Bernhard Wieneke. 2015

727 citations recorded by the source corpus.

The uncertainty of a PIV displacement field is estimated using a generic post-processing method based on statistical analysis of the correlation process using differences in the intensity pattern in the two images. First the second image is dewarped back onto the first one using the computed displacement field which provides two almost perfectly matching images. Differences are analyzed regarding the effect of shifting the peak of the correlation function. A relationship is derived between the standard deviation of intensity differences in each interrogation window and the expected asymmetry of the correlation peak, which is then converted to the uncertainty of a displacement vector. This procedure is tested with synthetic data for various types of noise and experimental conditions (pixel noise, out-of-plane motion, seeding density, particle image size, etc) and is shown to provide an accurate estimate of the true error.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/83ec3a5de95eb3253ad9e02f28d55a96292e9e6d

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Adaptive separation control of a laminar boundary layer using online dynamic mode decomposition (2020, methodology): “The uncertainty in each vector field measurement is calculated using the correlation-statistics method for estimating PIV uncertainty (Wieneke 2015).”
Shake-The-Box: Lagrangian particle tracking at high particle image densities (2016, background): “…to planar PIV, it can be stated that STB delivers average velocity accuracies that are at least on par with the ones gained by planar PIV (a typical velocity error of 0.1 px was repeatedly found for planar PIV, see, for example, Nobach and Bodenschatz 2009; Sciacchitano et al. 2013; Wieneke 2015).”
Development and evaluation of gappy-POD as a data reconstruction technique for noisy PIV measurements in gas turbine combustors (2016, methodology): “0 software package; details of this technique can be found in Wieneke (2015). Presented in last column of Table 1 is the average relative uncertainty in the vector magnitude, averaged over space and time for each case.”
Experimental characterization of the turbulent boundary layer over a porous trailing edge for noise abatement (2019, methodology): “These values are verified using the error quantification method introduced by Wieneke [72].”
PIV uncertainty propagation (2016, methodology): “The uncertainty at P is evaluated with the correlation statistics method (Wieneke 2015).”
De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets (2015, methodology): “14 The error field associated with the TDMD pre-processing step indicates that most of the “data corrections” are made in the same spatial regions of high data uncertainty, as estimated from the correlation statistics method of the TR-PIV data [54].”