Christopher J. D. Pickering

Laser speckle photography and particle image velocimetry: photographic film noise

Laser speckle photography (LSP) applied in fluid mechanics is more appropriately named particle image velocimetry (PIV). Traditional use of holographic films requires the use of high powered pulsed lasers to arrest the flow and record the seeding particle images. If resolution limits are satisfied, the use of more sensitive films can remove this restriction. This paper examines the noise contributed by films used in PIV and LSP and shows that, although sensitive films exhibit high noise levels due to phase distortions, signal-to-noise ratios can be preserved with the use of an index matching liquid gate during analysis of the negative.

Particle image velocimetry: Estimation of measurement confidence at low seeding densities

Abstract In order to optimise the spatial resolution of particle image velocimetry, it is often necessary to take measurements from regions of the photographic record which contain few particle images. If Youngs fringe analysis is used in these cases, the observed fringe pattern is confused by extraneous spatial frequency components or speckle noise. If spatial correlation analysis is employed, spurious correlation peaks occur. In this paper we introduce a definition of signal to noise ratio appropriate to particle image velocimetry and investigate the statistical variation of this ratio as a function of the seeding particle size and concentration.

Particle Image Velocimetry: A new approach to fringe analysis

Abstract In recent years there has been a great deal of interest in automatic fringe analysis for Particle Image Velocimetry (PIV). To achieve this it is necessary to measure the wavelength and orientation of Youngs-type fringes which have inherently poor signal-to-noise ratio. Current methods can be classified into two groups. In the first the fringe orientation is found so that the wavelength can be determined using 1 -D algorithms, and in the second 2 -D algorithms are used to measure wavelength and orientation directly. Since the signal-to-noise ratio of PIV fringes is generally poor, full automation of either technique is fraught with difficulties and some interactive control is usually required. In this paper, a new technique is reported which uses the cross-correlation function calculated from two separated lines of data in the fringe pattern. It is shown that an efficient automatic data reduction system can be realised using this approach.

Automatic analysis of Young's fringes in speckle photography and particle-image velocimetry

Abstract Laser-speckle photography (LSP) and particle-image velocimetry (PIV) are two closely related optical techniques for the measurement of two-dimensional in-place displacement. LSP is primarily used for the measurement of solid-surface motion, whereas PIV is used in fluid-flow applications. In both cases, data are obtained from the optical-power spectrum of a double-exposure recording of the moving object by measuring the wavelength and orientation of Youngs-type fringes corresponding to the displacement magnitude and direction, respectively. Typically, in any single experiment, of the order of 10 4 fringe patterns must be analysed, and this would be extremely tedious to perform manually. For this reason, a number of schemes have been reported in the technical literature for automated analysis of Youngs fringes. In this paper, we examine two techniques, 1 -D integration and 2 -D autocorrelation, and discuss their application to LSP and PIV. A unified theoretical model is used to show important differences between the two techniques, and resulting implications with respect to analysis procedures are discussed.

Particle Image Velocimetry: The Ambiguity Problem

The accurate determination of the velocity vector from a particle image velocimetry (PIV) transparency depends on the number of image pairs that have been recorded in the region under examination. If one particle pair is present, there is an ambiguity of rad in direction. Two particle pairs give four ambiguous velocity vectors. This paper describes a new approach in PIV that resolves the ambiguity problem and potentially increases the dynamic range of the technique.

Particle Image Velocimetry: Automatic Fringe Analysis By Cross-Correlation

A new method of automatic fringe analysis for particle image velocimetry (PIV) data reduction is presented. The technique is based on calculating the positions of coincident peaks in the cross-correlation of pairs of separated parallel lines of data in the fringe pattern. Preliminary results show that an efficient automatic data reduction system can be realized using this approach.