On the uncertainty of digital PIV and PTV near walls

By Christian J. Kähler, Sven Scharnowski, Christian Cierpka. 2012

327 citations recorded by the source corpus.

The reliable measurement of mean flow properties near walls and interfaces between different fluids or fluid and gas phases is a very important task, as well as a challenging problem, in many fields of science and technology. Due to the decreasing concentration of tracer particles and the strong flow gradients, these velocity measurements are usually biased. To investigate the reason and the effect of the bias errors systematically, a detailed theoretical analysis was performed using window-correlation, singe-pixel ensemble-correlation and particle tracking evaluation methods. The different findings were validated experimentally for microscopic, long-range microscopic and large field imaging conditions. It is shown that for constant flow gradients and homogeneous particle image density, the bias errors are usually averaged out. This legitimates the use of these techniques far away from walls or interfaces. However, for inhomogeneous seeding and/or nonconstant flow gradients, only PTV image analysis techniques give reliable results. This implies that for wall distances below half an interrogation window dimension, the singe-pixel ensemble-correlation or PTV evaluation should always be applied. For distances smaller than the particle image diameter, only PTV yields reliable results.

Subfields: Aerospace Engineering, Computational Mechanics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/4c40b7752a0e182fe3f9d6c556ce0305f3477c01

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Citations in the corpus

Shake-The-Box: Lagrangian particle tracking at high particle image densities (2016, methodology): “The superiority of this approach to correlation-based techniques has been shown in detail for the 2D case (Kähler et al. 2012a, b).”
Estimation of uncertainty bounds for individual particle image velocimetry measurements from cross-correlation peak ratio (2013, background): “Close examination did reveal the presence of peak-locking bias error in the alternating overunder pattern of deviations from the best-fit line, and a nearwall bias of the type described in [21], but neither appreciably affected the overall fit.”
Higher order multi-frame particle tracking velocimetry (2013, background): “These error levels can easily be reached for elaborate particle detection algorithms and high-quality imaging (Kähler et al. 2012b).”
Uncertainty on PIV mean and fluctuating velocity due to bias and random errors (2013, methodology): “The accuracy for PIV, single-pixel PIV and particle tracking velocimetry (PTV) near a wall was recently investigated by Kaehler et al [13].”