Mea Sci Technol 20(4):045401Ĭasa LDC, Krueger PS (2013) Radial basis function interpolation of unstructured, three-dimensional, volumetric particle tracking velocimetry data. Karri S, Charonko J, Vlachos PP (2009) Robust wall gradient estimation using radial basis functions and proper orthogonal decomposition (pod) for particle image velocimetry (piv) measured fields. Tecplot (2012) Tecplot 360 User’s Manual. Spence C, Buchmann N, Jermy M (2012) Unsteady flow in the nasal cavity with high flow therapy measured by stereoscopic PIV. Stamatopoulos C, Mathioulakis D, Papaharilaou Y, Katsamouris A (2011) Experimental unsteady flow study in a patient-specific abdominal aortic aneurysm model. Report IMM-REP-2002-12, informatics and mathematical modelling, Technical University of Denmark, p 34, tech. Lophaven S, Nielsen H, Søndergaard J (2002) DACE - A Matlab Kriging Toolbox. Raben SG, Charonko JJ, Vlachos PP (2012) Adaptive gappy proper orthogonal decomposition for particle image velocimetry data reconstruction. Gunes H, Rist U (2008) On the use of kriging for enhanced data reconstruction in a separated transitional flat-plate boundary layer. Gunes H, Rist U (2007) Spatial resolution enhancement/smoothing of stereoparticle-imagevelocimetry data using proper-orthogonal decomposition based and kriging interpolation methods. Gunes H, Sirisup S, Karniadakis GE (2006) Gappy data: to krig or not to krig?. Venturi D, Karniadakis GE (2004) Gappy data and reconstruction procedures for flow past a cylinder. Math Geol 22(3):239–252Ĭressie N (1993) Statistics for spatial data. Translated by Israel Program for Scientific Translations, JerusalemĬressie N (1990) The origins of Kriging. Gandin L (1965) Objective analysis of meteorological fields: Gidrometeorologicheskoe Izdatel’stvo (GIMIZ), Leningrad. Matheron G (1963) Principles of Geostatistics. Cambridge University Press, Cambridge Aerospace Series, Cambridge Springer, BerlinĪdrian R, Westerweel J (2010) Particle image velocimetry. Raffel M, Willert CE, Wereley ST, Kompenhans J (2007) Particle Image Velocimetry: a practical guide (2nd ed). Percin M, Eisma H, van Oudheusden B, Remes B, Rujsink R, de Wagter C (2012) Flow visualization in the wake of flapping-wing MAV ‘DelFly II’ in forward flight, In AIAA fluid dynamics and co-located conferences and exhibit New Orleans Scarano F (2013) Tomographic PIV: principles and practice. Meas Sci Technol 18(1):275Įlsinga G, Scarano F, Wieneke B, Oudheusden B (2006) Tomographic particle image velocimetry. Theunissen R, Scarano F, Riethmuller ML (2007) An adaptive sampling and windowing interrogation method in PIV. Westerweel J, Scarano F (2005) Universal outlier detection for PIV data. Scarano F, Riethmuller ML (2000) Advances in iterative multigrid PIV image processing. 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 interpolation.Īdrian R (2005) Twenty years of particle image velocimetry. By qualitatively comparing the large-scale vortical structures, we show that Kriging LE performs better than cubic spline interpolation. We subsequently apply Kriging LE for spatial regression of stereo-PIV data to reconstruct the three-dimensional wake of a flapping-wing micro air vehicle. Kriging LE is found to increase the accuracy of interpolation to a finer grid dramatically at severe reflection and low seeding conditions. The performance of Kriging LE is first tested on synthetically generated PIV images of a two-dimensional flow of four counter-rotating vortices with various seeding and illumination conditions. In Kriging LE, each velocity vector must be accompanied by an estimated measurement uncertainty. The postprocessing method we propose is Kriging regression using a local error estimate (Kriging LE). 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.
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