Abstract
A new evaluation scheme for double exposure three-dimensional particle-tracking velocimetry is proposed. Its main feature, a robust multi-pass matching algorithm, is presented and validated by investigating its performance when applied to a synthetic data set. To evaluate real measurement data, the approach is supplemented by an iterative triangulation scheme, in which the resulting particle positions are validated through the matching algorithm. The comparison with tomographic particle-image velocimetry data shows good agreement. The proposed algorithm allows this approach to be applied to volumetric measurements with seeding densities exceeding standard particle-tracking applications. Therefore, it can serve as a drop-in replacement for tomographic particle-image velocimetry at significantly reduced computational cost.Graphic abstract
Highlights
In recent years, three-dimensional flow measurement techniques have rapidly been developed, in the form of tomographic particle-image velocimetry (PIV) (Elsinga et al 2006)
Following the initial triangulation and matching pass, such image peaks which have already been used in a detected displacement, i.e., at least one resulting particle has successfully been matched, are not considered in the subsequent triangulation pass. The particles of such matches are retained from the previous triangulation pass. In this one match per peak (OMPP) approach, a match between two particles serves as some kind of validation since the fraction of coherent ghost particles fulfilling the rigidity condition is much lower than the total number of coherent ghost particles
The result quality in real measurements is assessed by comparing the particle tracking and the tomographic PIV results in two planes within the measurement volume using 1050 snapshots
Summary
Three-dimensional flow measurement techniques have rapidly been developed, in the form of tomographic particle-image velocimetry (PIV) (Elsinga et al 2006). The most widespread approach is a probability relaxation algorithm proposed by Baek and Lee (1996), which is based on the assumption that neighboring particles exhibit similar displacements. Using this rigidity assumption, it iteratively adapts the probability of all possible displacements. Mikheev and Zubtsov (2008) proposed to use the particle intensity to identify similar particles across exposures to have more information for the correct identification of particle correspondences While this method can be applied in twodimensional measurements, its suitability for volumetric flows has yet to be shown. Since the final intensity of reconstructed particles in a measurement volume is subject to multiple influences beyond the particles’ properties, e.g., overlapping particle images or intensity being assigned to ghost particles, a robust use of this technique might prove difficult
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