Abstract
Although it is common for automated image processing techniques to claim subpixel accuracy in the identification of particles, or centroids of displacements of groups of particles, additional errors are inevitably introduced when and if these data are reinterpolated back onto a grid mesh whose nodes lie at different locations from the original data. Moreover, these errors can be large compared to the errors introduced in the original image processing step.
Two different techniques, convolution with an adaptive Gaussian window (AGW), and a two-dimensional thin-shell spline (STS), have been compared and contrasted for interpolating irregularly spaced data onto a regular grid. Both techniques are global interpolators; the Gaussian kernel applies an ad hoc choice of smooth function, while the thin-shell spline minimises a global functional proportional to the Laplacian of the velocity field. In this way, the smoothness constraint on the spline coefficients may be thought of as akin to a viscous smoothing of the fluid flow.
Performance curves are given, enabling the investigator to make an informed choice of interpolating routine and grid interpolation parameters to minimise the interpolation errors, given various external constraints. Some illustrative example applications on real experimental data are described. In general, the importance of matching the interpolation technique to the characteristics of the original data is stressed. It is also pointed out that a correct interpretation of grid interpolated data must be based on a basic knowledge of the performance characteristics of that interpolator. Finally, recommendations are made concerning the development of surface spline techniques for problems involving large numbers of data points.
Similar content being viewed by others
References
Adrian, R. J. 1988: Statistical properties of particle image velocimetry measurements in turbulent flow. In: Laser anemometry in fluid mechanics (eds. Adrian, R. J.; Asanuma, T. S.; Durao, D. F. G.; Durst, F.; Whitelaw, J. H.), 115–129
Adrian, R. J. 1991: Particle imaging techniques for experimental fluid mechanics. Ann. Rev. Fluid Mech. 23, 261–304
Agüí, J. C.; Jimenez, J. 1987: On the performance of particle tracking, J. Fluid Mech. 185, 447–468
Duchon, J. 1977: Splines minimizing rotation invariant norms in Sobolev spaces. In: Constructive theory of functions of several variables, (eds. Schempp, W.; Zeller, K.) Lecture Notes in Math. 571, Springer NY, 85–100
Efron, B. 1982: The jackknife, the bootstrap and other resampling plans, SIAM, Philadelphia, PA.
Franke, R. 1979: A critical comparison of some methods for interpolation of scattered data. Naval Postgraduate school, TR #NPS-53-79-003, Monterey, California
Franke, R. 1982: Scattered data interpolation: tests of some methods. Math. Computation 38, 181–200.
Gharib, M.; Hernan, M. A.; Yavrouian, A. H.; Sarohia, V. 1985: Flow velocity measurement by image processing of optically activated tracers. AIAA Paper 85-0172
Gharib, M.; Dyne, B.; Thomas, O.; Yap, C. 1986: Flow velocity measurements by image processing of optically modulated tracers. AGARD-CPP-413, No. 22
Hardy, R. L. 1971: Multiquadric equations of topography and other irregular surfaces. J. Geophys. Res. 76, 1905–1915
Hardy, R. L.; Gopfert, W. M. 1975: Least squares predictions of gravity anomalies, geoidal undulations, and deflections of the vertical with multiquadric harmonic functions. Geophys. Res. Lett. 10, 423–426
Hesselink, L. 1988: Digital image processing in flow visualisation. Ann. Rev. Fluid. Mech. 20, 421–485
Imaichi, K.; Ohmi, K. 1983: Numerical processing of flow-visualization pictures — measurement of two-dimensional vortex flow. J. Fluid. Mech. 129, 283–311
Jimenez, J.; Agüi, L. C. 1986: Approximate reconstruction of randomly sampled signals. Signal Proc 12, 153–168.
Lancaster, P.; Šalkauskas, K. 1986: Curve and surface fitting: an introduction. Academic Press, London
Malik, N. A. 1993: Particle tracking velocimetry in three dimensional flows. Part II.: Particle tracking, Exp. Fluids 4/5, 279–294
Meinguet, J. 1979a: An intrinsic approach to multivariate spline interpolation at arbitrary points. In Polynomial and Spline Approximation. (ed. Sahney, B. N.), Reidel, Dordrecht, 163–190
Meinguet, J. 1979b: Multivariate interpolation at arbitrary points made simple. ZAMP 30, 292–304
Nguyen Duc, J. M.; Sommeria, J. 1988: Experimental characterization of steady two-dimensional vortex couples. J. Fluid. Mech. 192, 175–192
Paihua Montes, L. 1978: Quelques méthodes numériques pour le calcul de fonctions splines à une et plusieurs variables. Thèse de 3e Cycle, Université de Grenoble, France
Perkins, R. I.; Hunt, J. C. R. 1989: Particle tracking in turbulent flows. Department of Trade and Industry, Warren Sprig Technical Report, LR-706
Rignot E. J. M.; Spedding, G. R. 1988: Performance analysis of automated image processing and grid interpolation techniques for fluid flows. University of Southern California, Department of Aerospace Engineering Report, USCAE 143
Shepard, D. 1968: A two-dimensional interpolation function for irregularly spaced data. Proc. 23rd Nat. Conf. ACM, 517-523
Spedding, G. R.; Maxworthy, T. 1986: The generation of circulation and lift in a rigid two-dimensional fling. J. Fluid Mech. 165, 247–272
Spedding, G. R.; Maxworthy, T.; Rignot, E. J. M.; 1987: Unsteady vortex flows over delta wings. Proc. 2nd AFOSR Workshop on Unsteady and Separated Flows. Colorado Springs, 283–287
Utami, T.; Ueno, T. 1984: Visualization and picture processing of turbulent flow. Exp. in Fluids 2, 25–32
Utami, T.; Ueno, T. 1987: Experimental study on the coherent structure of turbulent open-channel flow using visualization and picture processing. J. Fluid Mech. 174, 399–440
Utami, T.; Blackwelder, R. F.; Ueno, T. 1990: Flow visualisation and image processing of three-dimensional features of coherent structures in an open-channel flow. In Near-Wall Turbulence, (eds. Kline, S. J.), Hemisphere, NY, 289–305
Utami, T.; Blackwelder, R. F.; Ueno, T. 1991: A cross-correlation technique for velocity field extraction from particulate visualisation. Exp. in Fluids 10, 213–223
Wahba, G. 1979: Convergence rates of thin plate smoothing splines when the data are noisy, University of Wisconsin, Department of Statistics Technical Report UWIS-DS-TR-557
Wahba, G. 1982: Vector splines on the sphere, with application to the estimation of vorticity and divergence from discrete, noisy data. University of Wisconsin, Department of Statistics Technical Report UWIS-DS-TR-674
Wahba, G. 1990: Spline models for observation data. SIAM, Philadelphia, PA
Willert, C. E.; Gharib, M. 1991: Digital particle image velocimetry. Exp. Fluids 10, 181–193
Yeung, P. K.; Pope, S. B. 1988: An algorithm for tracking fluid particles in numerical simulations of homogeneous turbulence. J. Comp. Phys. 79, 373–416
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Spedding, G.R., Rignot, E.J.M. Performance analysis and application of grid interpolation techniques for fluid flows. Experiments in Fluids 15, 417–430 (1993). https://doi.org/10.1007/BF00191784
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF00191784