Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Experiments in Fluids
Erschienen in: Experiments in Fluids 1/2014

01.01.2014 | Research Article

Kriging regression of PIV data using a local error estimate

verfasst von: Jouke H. S. de Baar, Mustafa Percin, Richard P. Dwight, Bas W. van Oudheusden, Hester Bijl

Erschienen in: Experiments in Fluids | Ausgabe 1/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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 two-dimensional flow of four counter-rotating 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 stereo-PIV data to reconstruct the three-dimensional wake of a flapping-wing micro air vehicle. By qualitatively comparing the large-scale 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 interpolation.
Vorheriger Artikel Instabilities and elastic recoil of the two-fluid circular hydraulic jump
Nächster Artikel Mesler entrainment in alcohols

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
The error matrix is not a ‘nugget effect’, as it does not directly change the correlation matrix and does not represent a cascade of unresolved smaller flowscales [36].
 
2
To avoid negative or infinitely large \(\epsilon\), we implement: \(\epsilon_{{\textsc{snr}},i}=\frac{c}{\max(\rm{SNR}_i-SNR_{\rm min},0.01)}\).
 
3
A MLE optimization of the Kriging correlation ranges typically results in θ x  = θ y  = 0.26, and in the following, we use a fixed range of θ x  = θ y  = 0.25.
 
4
One might conclude that the Low-Fi UM slightly underestimates the measurement uncertainty for high PMR. This conclusion could be attributed to the extremely simple Low-Fi UM; a slightly more sophisticated Low-Fi UM might incorporate the uncertainty associated with the subpixel fit as a lower uncertainty bound–which would roughly amount to \(\epsilon_{\rm subpix} \approx 0.1 {\hbox{m/s}}\) in the present case.
 
5
Unlike in mpiv , PPR is defined in Davis.
 
Literatur
1.
Adrian R (2005) Twenty years of particle image velocimetry. Exp Fluids 39:159–169CrossRef
2.
Scarano F, Riethmuller ML (2000) Advances in iterative multigrid PIV image processing. Exp Fluids 29:S051–S060CrossRef
3.
Westerweel J, Scarano F (2005) Universal outlier detection for PIV data. Exp Fluids 39:1096–1100CrossRef
4.
Theunissen R, Scarano F, Riethmuller ML (2007) An adaptive sampling and windowing interrogation method in PIV. Meas Sci Technol 18(1):275CrossRef
5.
Elsinga G, Scarano F, Wieneke B, Oudheusden B (2006) Tomographic particle image velocimetry. Exp Fluids 41:933–947CrossRef
6.
Scarano F (2013) Tomographic PIV: principles and practice. Meas Sci Technol 24(1):012001CrossRef
7.
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
8.
Raffel M, Willert CE, Wereley ST, Kompenhans J (2007) Particle Image Velocimetry: a practical guide (2nd ed). Springer, Berlin
9.
Adrian R, Westerweel J (2010) Particle image velocimetry. Cambridge University Press, Cambridge Aerospace Series, Cambridge
10.
Matheron G (1963) Principles of Geostatistics. Econ Geol 58:1246–1266CrossRef
11.
Gandin L (1965) Objective analysis of meteorological fields: Gidrometeorologicheskoe Izdatel’stvo (GIMIZ), Leningrad. Translated by Israel Program for Scientific Translations, Jerusalem
12.
13.
Cressie N (1993) Statistics for spatial data. Wiley: New York
14.
Venturi D, Karniadakis GE (2004) Gappy data and reconstruction procedures for flow past a cylinder. J Fluid Mech 519:315–336CrossRefMATHMathSciNet
15.
Gunes H, Sirisup S, Karniadakis GE (2006) Gappy data: to krig or not to krig?. J Comput Phys 212:358–382CrossRefMATH
16.
Gunes H, Rist U (2007) Spatial resolution enhancement/smoothing of stereoparticle-imagevelocimetry data using proper-orthogonal decomposition based and kriging interpolation methods. Phys Fluids 19(6):064101-1–064101-19
17.
Gunes H, Rist U (2008) On the use of kriging for enhanced data reconstruction in a separated transitional flat-plate boundary layer. Phys Fluids 20(10):104109-1–104109-19
18.
Raben SG, Charonko JJ, Vlachos PP (2012) Adaptive gappy proper orthogonal decomposition for particle image velocimetry data reconstruction. Meas Sci Technol 23(2):025303
19.
Lophaven S, Nielsen H, Søndergaard J (2002) DACE - A Matlab Kriging Toolbox. Report IMM-REP-2002-12, informatics and mathematical modelling, Technical University of Denmark, p 34, tech. rep
20.
Stamatopoulos C, Mathioulakis D, Papaharilaou Y, Katsamouris A (2011) Experimental unsteady flow study in a patient-specific abdominal aortic aneurysm model. Exp Fluids 50:1695–1709CrossRef
21.
Spence C, Buchmann N, Jermy M (2012) Unsteady flow in the nasal cavity with high flow therapy measured by stereoscopic PIV. Exp Fluids 52:569–579CrossRef
22.
23.
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. Mea Sci Technol 20(4):045401
24.
Casa LDC, Krueger PS (2013) Radial basis function interpolation of unstructured, three-dimensional, volumetric particle tracking velocimetry data. Meas Sci Technol 24(6):065304
25.
JC for Guides in Metrology (JCGM) (1995) Guide to the expression of uncertainty in measurement (GUM)
26.
Timmins B, Wilson B, Smith B, Vlachos P (2012) A method for automatic estimation of instantaneous local uncertainty in particle image velocimetry measurements. Exp Fluids 53:1133–1147CrossRef
27.
Charonko JJ, Vlachos PP (2013) Estimation of uncertainty bounds for individual particle image velocimetry measurements from cross-correlation peak ratio. Meas Sci Technol 24(6):065301
28.
29.
Mardia KV, Marshall RJ (1984) Maximum Likelihood Estimation of Models for Residual Covariance in Spatial Regression. Biometrika 71(1):135–146CrossRefMATHMathSciNet
30.
Kitanidis PK, Lane RW (1985) Maximum likelihood parameter estimation of hydrologic spatial processes by the Gauss-Newton method. J Hydrol 79(1–2):53–71CrossRef
31.
Kitanidis PK (1986) Parameter uncertainty in estimation of spatial functions: Bayesian analysis. Water Resour Res 22(4):499–507CrossRef
23.
Mardia KV (1989) Maximum likelihood estimation for spatial models. Proceedings from the symposium on spatial statistics: past, present, and future, pp 203–253
33.
de Baar J, Dwight R, Bijl H (2013) Speeding up kriging through fast estimation of the hyperparameters in the frequency-domain. Comput Geosci 54:99–106CrossRef
34.
Chiles J, Delfiner P (2012) Geostatistics, Modeling Spatial Uncertainty, 2nd Edn. Wiley, New YorkCrossRef
35.
de Baar J, Dwight R, Bijl H Improvements to Gradient-Enhanced Kriging using a Bayesian perspective, Int J Uncertain Quantif, In production
36.
Matheron G (1971) Les Cahiers du Centre de Morphologie Mathematique: The theory of regionalized variables and its application. Ecole Nationale Superieure des Mines de Paris
37.
Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat Assoc, pp 335–341
38.
39.
Keane RD, Adrian RJ (1992) Theory of cross-correlation analysis of piv images. Appl Sci Res 49(3):191–215CrossRef
40.
Witten I, Frank E, Hall M (2011) Data mining, practical machine learning tools and techniques, 3rd Edn. Morgan Kaufmann, Burlington
41.
Percin M, Eisma HE, de Baar JHS, van Oudheusden BW, Remes B, Ruijsink R, de Wagter C (2012) Wake reconstruction of flapping-wing MAV DelFly II in forward flight. Int Micro Air Vehicle Conf Flight Compet, July 2012, Braunschweig Germany
Metadaten
Titel
Kriging regression of PIV data using a local error estimate
verfasst von
Jouke H. S. de Baar
Mustafa Percin
Richard P. Dwight
Bas W. van Oudheusden
Hester Bijl
Publikationsdatum
01.01.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Experiments in Fluids / Ausgabe 1/2014
Print ISSN: 0723-4864
Elektronische ISSN: 1432-1114
DOI
https://doi.org/10.1007/s00348-013-1650-z

Weitere Artikel der Ausgabe 1/2014

Experiments in Fluids 1/2014 Zur Ausgabe

Research Article

Influence of turbulence on the drag of solid discs and turbine simulators in a water current

Research Article

A study of gas bubbles in liquid mercury in a vertical Hele-Shaw cell

Research Article

Experimental investigation of the inclined interface Richtmyer–Meshkov instability before and after reshock

Research Article

PIV experiments in rough-wall, laminar-to-turbulent, oscillatory boundary-layer flows

Research Article

Extensive study of temperature dissipation measurements on the centerline of a turbulent round jet based on the $$\overline{\theta^{2}}/2$$ θ 2 ¯ / 2 budget

Research Article

On the laminar-turbulent transition in injection-driven porous chambers

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise