Please note that frequently used variables are listed in Table 1. At high seeding concentrations, two major random errors need to be taken into account.Įrrors related to the determination of the particle position.Įrrors due to wrong particle image pairingĪ crit is the critical area in which a particle image starts to overlap with the boundaries of another particle image. 2012b), the technique is well suited for accurate flow field measurements at any magnification provided the seeding concentration is sufficiently low for a reliable particle image pairing. The particle image size increases beyond the optimal range for spatial cross-correlation analysis.Ĭorrelation-based methods may show bias errors due to a spatial variation in the particle image density.Īs PTV does not show the bias error at all (Kähler et al. The particle image density becomes too sparse for spatial cross-correlation methods. To better resolve strong flow gradients, the optical magnification can be increased, but this leads to three major problems (Kähler et al. 2012a Westerweel 2008 Keane and Adrian 1992). However, when flows with strong gradients are investigated, the measurements are biased (Kähler et al. For the investigation of flows with relatively weak spatial gradients, the technique is very reliable and the measurement accuracy is usually sufficient to estimate spatial derivatives. Therefore, the size of the interrogation windows, and thus, the spatial resolution depend on the seeding concentration and the particle image diameter. For a robust and precise cross-correlation analysis, interrogation windows covering 6–10 particle images are usually required (Raffel et al. The velocity is estimated within interrogation windows by cross-correlating the images of small tracer particles recorded at time t and \(t+\Updelta t\). Particle image velocimetry (PIV) is a well-established technique for non-intrusive flow field investigations in transparent fluids. The improvements increase the PTV working range as reliable and accurate measurements become possible at seeding concentrations typically used for PIV measurements. Furthermore, it is shown that the accuracy and precision can be increased by using vector reallocation and displacement estimation using a fit of the trajectory in the case of curved particle paths. In this paper, it is shown that the particle image information acquired at four or more time steps greatly enhances a reliable particle pairing even at high seeding concentrations. However, at high seeding concentrations, the reliable particle pairing is challenging, and the measurement precision decreases rapidly due to overlapping particle images and wrong particle image pairing. The possibility to simultaneously measure the velocity with the temperature, ph-value, or pressure of the flow at the particle location by means of fluorescent particles is another advantage of PTV. In addition, they are not biased due to inhomogeneous seeding concentration or in-plane and out-of-plane gradients so that the measurement precision can be increased as well. Particle tracking velocimetry methods (PTV) have a great potential to enhance the spatial resolution compared to spatial correlation-based methods (PIV).
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