Nano-Particle Image Velocimetry (NPIV): A New Technique for Measuring Near-Wall Velocity Fields with Submicron Spatial Resolution

2004 ◽  
Author(s):  
Minami Yoda ◽  
Reza Sadr
Keyword(s):  
Particle Image Velocimetry ◽  
Spatial Resolution ◽  
Particle Image ◽  
Velocity Fields ◽  
New Technique ◽  
Nano Particle ◽  
Wall Velocity ◽  
Image Velocimetry ◽  
A New Technique ◽  
Near Wall

scholarly journals Experimental Validation of Falling Liquid Film Models: Velocity Assumption and Velocity Field Comparison

Polymers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1205
Author(s):  
Ruiqi Wang ◽  
Riqiang Duan ◽  
Haijun Jia
Keyword(s):  
Experimental Data ◽  
Particle Image Velocimetry ◽  
Velocity Field ◽  
Experimental Validation ◽  
Particle Image ◽  
Velocity Fields ◽  
Number Range ◽  
Comparison Results ◽  
Image Velocimetry ◽  
Experimental Particle

This publication focuses on the experimental validation of film models by comparing constructed and experimental velocity fields based on model and elementary experimental data. The film experiment covers Kapitza numbers Ka = 278.8 and Ka = 4538.6, a Reynolds number range of 1.6–52, and disturbance frequencies of 0, 2, 5, and 7 Hz. Compared to previous publications, the applied methodology has boundary identification procedures that are more refined and provide additional adaptive particle image velocimetry (PIV) method access to synthetic particle images. The experimental method was validated with a comparison with experimental particle image velocimetry and planar laser induced fluorescence (PIV/PLIF) results, Nusselt’s theoretical prediction, and experimental particle tracking velocimetry (PTV) results of flat steady cases, and a good continuity equation reproduction of transient cases proves the method’s fidelity. The velocity fields are reconstructed based on different film flow model velocity profile assumptions such as experimental film thickness, flow rates, and their derivatives, providing a validation method of film model by comparison between reconstructed velocity experimental data and experimental velocity data. The comparison results show that the first-order weighted residual model (WRM) and regularized model (RM) are very similar, although they may fail to predict the velocity field in rapidly changing zones such as the front of the main hump and the first capillary wave troughs.

Analysis of local velocity gradients in rapid mixer using particle image velocimetry technique

2002 ◽  
Vol 2 (5-6) ◽  
pp. 47-55
Author(s):  
N.-S. Park ◽  
H. Park
Keyword(s):  
Particle Image Velocimetry ◽  
Particle Image ◽  
Velocity Fields ◽  
Local Velocity ◽  
Mixing Condition ◽  
Particle Image Velocimetry Technique ◽  
Velocity Gradients ◽  
Image Velocimetry ◽  
Current Design ◽  
G Value

Recognizing the significance of factual velocity fields in a rapid mixer, this study focuses on analyzing local velocity gradients in various mixer geometries with particle image velocimetry (PIV) and comparing the results of the analysis with the conventional G-value, for reviewing the roles of G-value in the current design and operation practices. The results of this study clearly show that many arguments and doubts are possible about the scientific correctness of G-value, and its current use. This is because the G-value attempts to represent the turbulent and complicated factual velocity field in a jar. Also, the results suggest that it is still a good index for representing some aspects of mixing condition, at least, mixing intensity. However, it cannot represent the distribution of velocity gradients in a jar, which is an important factor for mixing. This study as a result suggests developing another index for representing the distribution to be used with the G-value.

Investigation of Field Amplified Sample Stacking With Particle Image Velocimetry

2002 ◽  
Author(s):  
Shankar Devasenathipathy ◽  
Rajiv Bharadwaj ◽  
Juan G. Santiago
Keyword(s):  
Particle Image Velocimetry ◽  
Electroosmotic Flow ◽  
Particle Image ◽  
Velocity Fields ◽  
Concentration Profiles ◽  
Conductivity Ratio ◽  
Number Density ◽  
Electrokinetic Flow ◽  
Sample Stacking ◽  
Image Velocimetry

This paper presents an experimental investigation of field amplified sample stacking (FASS) with micron resolution particle image velocimetry (μPIV). The preliminary experiments reported in this work show particle velocity fields in electrokinetic flow in a glass microchannel with a single buffer-buffer interface. The buffer-to-buffer conductivity ratio is 10. Stacking of latex microspheres (i.e., increases in their number density) in the presence of a background electroosmotic flow is demonstrated. The generation of an internal pressure gradient is quantified using μPIV. This work is part of an ongoing study of the spatial and temporal development of the velocity and concentration profiles of FASS systems.

Particle image velocimetry investigation of a turbulent boundary layer manipulated by spanwise wall oscillations

2002 ◽  
Vol 467 ◽  
pp. 41-56 ◽  
Author(s):  
GAETANO MARIA DI CICCA ◽  
GAETANO IUSO ◽  
PIER GIORGIO SPAZZINI ◽  
MICHELE ONORATO
Keyword(s):  
Boundary Layer ◽  
Particle Image Velocimetry ◽  
Turbulent Boundary Layer ◽  
Particle Image ◽  
Oscillation Amplitude ◽  
Research Programme ◽  
Oscillation Period ◽  
Wall Turbulence ◽  
Image Velocimetry ◽  
Near Wall

Particle image velocimetry has been applied to the study of a canonical turbulent boundary layer and to a turbulent boundary layer forced by transversal wall oscillations. This work is part of the research programme at the Politecnico di Torino aerodynamic laboratory with the objective of investigating the response of near-wall turbulence to external perturbations. Results are presented for the optimum oscillation period of 100 viscous time units and for an oscillation amplitude of 320 viscous units. As expected, turbulent velocity fluctuations are considerably reduced by the wall oscillations. Particle image velocimetry has allowed comparisons between the canonical and forced flows in an attempt to find the physical mechanisms by which the wall oscillation influences the near-wall organized motions.

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