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Double-frame 3D-PTV using a tomographic predictor

Abstract

Thanks to the technological progress, 3D velocimetry techniques are becoming more popular. In particular, the time-resolved flow analysis by means of particle tracking is very attractive. Compared to double-frame recordings, higher seeding concentrations are feasible, yielding high spatial resolution results without bias errors due to strong velocity gradients. However, hardware restrictions still limit time-resolved measurements to rather small flow velocities and low magnifications. In aerodynamics, especially, this is a drawback, since often higher flow velocities are of interest. To conduct reliable 3D-PTV measurements from double-frame recordings, the well-established techniques tomographic particle imaging and 3D-PTV are employed for a novel processing approach. In this combined approach, the tomographic reconstruction is used as a predictor for the sensor locations of the corresponding particle images of the reconstructed particles. Furthermore, the reconstruction helps to identify and reject non-corresponding sets of particle images, reducing the amount of ghost particles to a minimum. A probabilistic tracking algorithm is then applied to estimate the flow field.

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Acknowledgments

The investigations were conducted as part of the joint research programme AG Turbo 2020 in the frame of AG Turbo. The work was supported by the Bundesministerium für Wirtschaft und Technologie (BMWi) as per resolution of the German Federal Parliament under Grant No. 03ET2013M. The authors gratefully acknowledge AG Turbo and MTU Aero Engines AG for their support and permission to publish this paper. The responsibility for the content lies solely with its authors.

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Correspondence to Thomas Fuchs.

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Fuchs, T., Hain, R. & Kähler, C.J. Double-frame 3D-PTV using a tomographic predictor. Exp Fluids 57, 174 (2016). https://doi.org/10.1007/s00348-016-2247-0

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Keywords

  • Particle Image Velocimetry
  • Particle Image
  • Tomographic Reconstruction
  • Particle Tracking Velocimetry
  • Ghost Particle