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The accuracy of tomographic particle image velocimetry for measurements of a turbulent boundary layer

type de publication      article dans une revue internationale avec comité de lecture
date de publication 2011
auteur(s) Atkinson Callum; Coudert Sébastien; Foucaut Jean-Marc; Stanislas Michel; Soria Julio
journal (abréviation) Experiments in Fluids (Exp Fluid)
volume (numéro) 50 (4)
  
pages 1031 – 1056
résumé To investigate the accuracy of tomographic particle image velocimetry (Tomo-PIV) for turbulent boundary layer measurements, a series of synthetic image-based simulations and practical experiments are performed on a high Reynolds number turbulent boundary layer at Re-theta = 7,800. Two different approaches to Tomo-PIV are examined using a full-volume slab measurement and a thin-volume fat light sheet approach. Tomographic reconstruction is performed using both the standard MART technique and the more efficient MLOS-SMART approach, showing a 10-time increase in processing speed. Random and bias errors are quantified under the influence of the near-wall velocity gradient, reconstruction method, ghost particles, seeding density and volume thickness, using synthetic images. Experimental Tomo-PIV results are compared with hot-wire measurements and errors are examined in terms of the measured mean and fluctuating profiles, probability density functions of the fluctuations, distributions of fluctuating divergence through the volume and velocity power spectra. Velocity gradients have a large effect on errors near the wall and also increase the errors associated with ghost particles, which convect at mean velocities through the volume thickness. Tomo-PIV provides accurate experimental measurements at low wave numbers; however, reconstruction introduces high noise levels that reduces the effective spatial resolution. A thinner volume is shown to provide a higher measurement accuracy at the expense of the measurement domain, albeit still at a lower effective spatial resolution than planar and Stereo-PIV.
mots clés PIV; FLOW; OPTIMIZATION; DEFORMATION; FIELDS
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