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C. Kähler, T. Astarita, P. Vlachos, J. Sakakibara, R. Hain, S. Discetti, Roderick Foy, C. Cierpka (2016)
Main results of the 4th International PIV ChallengeExperiments in Fluids, 57
C. Cierpka, B. Lütke, C. Kähler (2013)
Higher order multi-frame particle tracking velocimetryExperiments in Fluids, 54
G. Elsinga, J. Westerweel, F. Scarano, M. Novara (2011)
On the velocity of ghost particles and the bias errors in Tomographic-PIVExperiments in Fluids, 50
K. Okamoto, S. Nishio, T. Saga, Toshio Kobayashi (2000)
Standard images for particle-image velocimetryMeasurement Science and Technology, 11
T. Fuchs, R. Hain, C. Kähler (2016)
Double-frame 3D-PTV using a tomographic predictorExperiments in Fluids, 57
C. Kähler, S. Scharnowski, C. Cierpka (2012)
On the uncertainty of digital PIV and PTV near wallsExperiments in Fluids, 52
A Mikheev, V. Zubtsov (2008)
Enhanced particle-tracking velocimetry (EPTV) with a combined two-component pair-matching algorithmMeasurement Science and Technology, 19
C. Kähler, S. Scharnowski, C. Cierpka (2012)
On the resolution limit of digital particle image velocimetryExperiments in Fluids, 52
M. Novara, D. Schanz, N. Reuther, C. Kähler, A. Schröder (2016)
Lagrangian 3D particle tracking in high-speed flows: Shake-The-Box for multi-pulse systemsExperiments in Fluids, 57
K. Ohmi, Han Li (2000)
Particle-tracking velocimetry with new algorithmsMeasurement Science and Technology, 11
C. Kähler, S. Scharnowski, C. Cierpka (2016)
Highly resolved experimental results of the separated flow in a channel with streamwise periodic constrictionsJournal of Fluid Mechanics, 796
D. Schanz, S. Gesemann, A. Schröder (2016)
Shake-The-Box: Lagrangian particle tracking at high particle image densitiesExperiments in Fluids, 57
In recent years, the detection of individual particle images and their tracking over time to determine the local flow velocity has become quite popular for planar and volumetric measurements. Particle tracking velocimetry has strong advantages compared to the statistical analysis of an ensemble of particle images by means of cross-correlation approaches, such as particle image velocimetry. Tracking individual particles does not suffer from spatial averaging and therefore bias errors can be avoided. Furthermore, the spatial resolution can be increased up to the sub-pixel level for mean fields. A maximization of the spatial resolution for instantaneous measurements requires high seeding concentrations. However, it is still challenging to track particles at high seeding concentrations, if no time series is available. Tracking methods used under these conditions are typically very complex iterative algorithms, which require expert knowledge due to the large number of adjustable parameters. To overcome these drawbacks, a new non-iterative tracking approach is introduced in this letter, which automatically analyzes the motion of the neighboring particles without requiring to specify any parameters, except for the displacement limits. This makes the algorithm very user friendly and also offers unexperienced users to use and implement particle tracking. In addition, the algorithm enables measurements of high speed flows using standard double-pulse equipment and estimates the flow velocity reliably even at large particle image densities.
Experiments in Fluids – Springer Journals
Published: Aug 18, 2017
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