Comparing Raindrop Size and Velocity Measurement Accuracy Using Shadowgraphy, Disdrometery, and Pie Pan Measurement Techniques

2015 ◽  
Vol 44 (6) ◽  
pp. 20150048
Author(s):  
Blake P. Tullis
Keyword(s):  
Velocity Measurement ◽  
Measurement Accuracy ◽  
Measurement Techniques ◽  
Raindrop Size

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

Research on Velocity Measurement of PD Radar Based on Non-Coherent Accumulation and Waveform Analysis

2014 ◽  
Vol 513-517 ◽  
pp. 2999-3002
Author(s):  
Xu Dong Zhang ◽  
Yun Neng Yuan ◽  
Jun Wang
Keyword(s):  
Radial Velocity ◽  
Target Tracking ◽  
Velocity Measurement ◽  
Measurement Accuracy ◽  
Waveform Analysis ◽  
Radar Target ◽  
Running Time ◽  
Coherent Accumulation ◽  
High Measurement ◽  
High Measurement Accuracy

According to the process of PD radar target tracking, the radial motion of target has a significant impact on velocity measurement. Simulation shows that there is an apparent relation between radial velocity and non-coherent accumulated waveform. Based on the calculation of the entropy of non-coherent accumulated waveform, a method based on waveform entropy in velocity measurement is proposed. Experiments show that the new method can achieve high measurement accuracy, and is insensitive to velocity. At the same time, a rough estimation of velocity is used to shorten the running time.

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

Development of accurate linewidth measurement techniques for in-process wafers

1988 ◽  
Vol 46 ◽  
pp. 54-55
Author(s):  
Robert. L. Van Asselt ◽  
Heinrich Becker
Keyword(s):  
Integrated Circuits ◽  
Integrated Circuit ◽  
Measurement Accuracy ◽  
Measurement Techniques ◽  
Three Dimensional ◽  
Optical Measurements ◽  
Surface Charging ◽  
Standard Tool ◽  
Linewidth Measurement ◽  
Diffraction Effects

Measurements of the device geometries on “in-process” wafers present some interesting challenges. Sample coating is not possible and the measurement probe must not damage the devices. The Scanning Electron Microscope (SEM) is the standard tool for submicrometer measurements. However, operating at the low electron beam accelerating voltage required to avoid damage to integrated circuits introduces problems in resolution. Also, the measurement accuracy may be limited by the effects of surface charging and topography. Further, SEM linewidth standards do not exist at the present time. Optical measurements are attractive because, in general, they display greater precision, are typically less expensive to implement and have a higher throughput. However, diffraction effects associated with the complex three-dimensional geometries of integrated circuit structures make accurate measurements very difficult. The "blur" regions that occur in the optical image at each edge must be interpreted to predict the actual location of the structure edges.

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