Authors: Jouke H S de Baar Mustafa Percin Richard P Dwight Bas W van Oudheusden Hester Bijl
Publish Date: 2014/01/05
Volume: 55, Issue: 1, Pages: 1650-
Abstract
The objective of the method described in this work is to provide an improved reconstruction of an original flow field from experimental velocity data obtained with particle image velocimetry PIV technique by incorporating the local accuracy of the PIV data The postprocessing method we propose is Kriging regression using a local error estimate Kriging LE In Kriging LE each velocity vector must be accompanied by an estimated measurement uncertainty The performance of Kriging LE is first tested on synthetically generated PIV images of a twodimensional flow of four counterrotating vortices with various seeding and illumination conditions Kriging LE is found to increase the accuracy of interpolation to a finer grid dramatically at severe reflection and low seeding conditions We subsequently apply Kriging LE for spatial regression of stereoPIV data to reconstruct the threedimensional wake of a flappingwing micro air vehicle By qualitatively comparing the largescale vortical structures we show that Kriging LE performs better than cubic spline interpolation By quantitatively comparing the interpolated vorticity to unused measurement data at intermediate planes we show that Kriging LE outperforms conventional Kriging as well as cubic spline interpolationFor the crosscorrelation of the synthetic images in Sect 3 we use the following general settings nx window = 32 ny window = 32 overlap x = 0 overlap y = 0 iu max = 0 iv max = 0 dt = 001 piv type = ’cor’ i recur = 2 i plot = 0 By default mpiv assigns NaN to vectors with a SNR or PPR below 300 however we want to control the SNR min toplevel Therefore in the files piv corm and piv crsm we change these settings r SNR = 000 r PPR = 000
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