On the Performance of Velocity Measurement Techniques in Air-Water Flows

2002 ◽  
Author(s):  
Jorge Matos ◽  
Kathleen H. Frizell ◽  
Stéphanie André ◽  
K. Warren Frizell
Keyword(s):  
Velocity Measurement ◽  
Measurement Techniques ◽  
Water Flows

scholarly journals In vivo whole-field blood velocity measurement techniques

2007 ◽  
Vol 42 (4) ◽  
pp. 495-511 ◽  
Author(s):  
Peter Vennemann ◽  
Ralph Lindken ◽  
Jerry Westerweel
Keyword(s):  
Velocity Measurement ◽  
Measurement Techniques ◽  
Blood Velocity

scholarly journals Approximate Bayesian framework for 3D reconstruction in a volumetric PIV/PTV measurement

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Sayantan Bhattacharya ◽  
Ilias Bilionis ◽  
Pavlos Vlachos
Keyword(s):  
Inverse Problem ◽  
3D Reconstruction ◽  
Velocity Measurement ◽  
Tracer Particle ◽  
Measurement Techniques ◽  
Local Maximum ◽  
Position Estimation ◽  
Map Estimation ◽  
Reconstruction Methods ◽  
Particle Images

Non-invasive flow velocity measurement techniques like volumetric Particle Image Velocimetry (PIV) (Elsinga et al., 2006; Adrian and Westerweel, 2011) and Particle Tracking Velocimetry (PTV) (Maas, Gruen and Papantoniou, 1993) use multi-camera projections of tracer particle motion to resolve three-dimensional flow structures. A key step in the measurement chain involves reconstructing the 3D intensity field (PIV) or particle positions (PTV) given the projected images and known camera correspondence. Due to limited number of camera-views the projected particle images are non-unique making the inverse problem of volumetric reconstruction underdetermined. Moreover, higher particle concentration (>0.05 ppp) increases erroneous reconstructions or “ghost” particles and decreases reconstruction accuracy. Current reconstruction methods either use voxel-based representation for intensity reconstruction (e.g. MART (Elsinga et al., 2006)) or a particle-based approach (e.g. IPR (Wieneke, 2013)) for 3D position estimation. The former method is computationally intensive and has a lesser positional accuracy due to stretched shape of the reconstructed particle along the line of sight. The latter compromises triangulation accuracy (Maas, Gruen and Papantoniou, 1993) due to overlapping particle images for higher particle concentrations. Thus, each method has its own challenges and the error in 3D reconstruction significantly affects the accuracy of the velocity measurement. Though, other methods like maximum-a-posteriori (MAP) estimation have been previously developed (Levitan and Herman, 1987; Bouman and Sauer, 1996) for computed Tomography data, it has not been explored for PIV/ PTV 3D reconstruction. Here, we use a MAP estimation framework to model and solve the inverse problem. The cost function is optimized using a stochastic gradient ascent (SGA) algorithm. Such an optimization can converge to a better local maximum and also use smaller image patches for efficient iterations.

scholarly journals Comparison of particle velocity measurement techniques in a fluidized bed operating in the square-nosed slugging flow regime

2016 ◽  
Vol 296 ◽  
pp. 45-52 ◽  
Author(s):  
Sina Tebianian ◽  
Kristian Dubrawski ◽  
Naoko Ellis ◽  
Ray A. Cocco ◽  
Roy Hays ◽  
...  
Keyword(s):  
Flow Regime ◽  
Fluidized Bed ◽  
Particle Velocity ◽  
Velocity Measurement ◽  
Measurement Techniques ◽  
Particle Velocity Measurement

Comparison of PIV and PDA droplet velocity measurement techniques on two high-pressure water mist nozzles

2009 ◽  
Vol 44 (8) ◽  
pp. 1030-1045 ◽  
Author(s):  
Bjarne Paulsen Husted ◽  
Per Petersson ◽  
Ivar Lund ◽  
Göran Holmstedt
Keyword(s):  
High Pressure ◽  
Velocity Measurement ◽  
Measurement Techniques ◽  
Water Mist ◽  
Droplet Velocity ◽  
Pressure Water

Evaluating velocity measurement techniques in shallow streams

2007 ◽  
Vol 45 (6) ◽  
pp. 752-762 ◽  
Author(s):  
Mark C. Stone ◽  
Rollin H. Hotchkiss
Keyword(s):  
Velocity Measurement ◽  
Measurement Techniques
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