Optical measurement of ultrasonic Poynting and velocity vector fields

2002 ◽  
Vol 49 (2) ◽  
pp. 193-203 ◽  
Author(s):  
T.A. Pitts ◽  
J.F. Greenleaf
Keyword(s):  
Velocity Vector ◽  
Vector Fields ◽  
Optical Measurement

Reconstructing Three-Dimensional Fluid Velocity Vector Fields from Acoustic Transmission Measurements

1977 ◽  
pp. 307-326 ◽  
Author(s):  
S. A. Johnson ◽  
J. F. Greenleaf ◽  
C. R. Hansen ◽  
W. F. Samayoa ◽  
M. Tanaka ◽  
...  
Keyword(s):  
Velocity Vector ◽  
Vector Fields ◽  
Fluid Velocity ◽  
Three Dimensional ◽  
Acoustic Transmission ◽  
Transmission Measurements

scholarly journals On the Application of Proper Orthogonal Decomposition (POD) for In-Cylinder Flow Analysis

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2261 ◽  
Author(s):  
Mohammed El-Adawy ◽  
Morgan Heikal ◽  
A. A. Aziz ◽  
Ibrahim Adam ◽  
Mhadi Ismael ◽  
...  
Keyword(s):  
Physical Properties ◽  
Proper Orthogonal Decomposition ◽  
Pressure Difference ◽  
Velocity Vector ◽  
Vector Fields ◽  
Orthogonal Decomposition ◽  
Stereoscopic Particle Image Velocimetry ◽  
Pod Analysis ◽  
Cylinder Flow ◽  
Proper Orthogonal

Proper orthogonal decomposition (POD) is a coherent structure identification technique based on either measured or computed data sets. Recently, POD has been adopted for the analysis of the in-cylinder flows inside internal combustion engines. In this study, stereoscopic particle image velocimetry (Stereo-PIV) measurements were carried out at the central vertical tumble plane inside an engine cylinder to acquire the velocity vector fields for the in-cylinder flow under different experimental conditions. Afterwards, the POD analysis were performed firstly on synthetic velocity vector fields with known characteristics in order to extract some fundamental properties of the POD technique. These data were used to reveal how the physical properties of coherent structures were captured and distributed among the POD modes, in addition to illustrate the difference between subtracting and non-subtracting the ensemble average prior to conducting POD on datasets. Moreover, two case studies for the in-cylinder flow at different valve lifts and different pressure differences across the air intake valves were presented and discussed as the effect of both valve lifts and pressure difference have not been investigated before using phase-invariant POD analysis. The results demonstrated that for repeatable flow pattern, only the first mode was sufficient to reconstruct the physical properties of the flow. Furthermore, POD analysis confirmed the negligible effect of pressure difference and subsequently the effect of engine speed on flow structures.

scholarly journals Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

2016 ◽  
Author(s):  
Navvab Afrashteh ◽  
Samsoon Inayat ◽  
Mostafa Mohsenvand ◽  
Majid H. Mohajerani
Keyword(s):  
Optical Flow ◽  
Optical Imaging ◽  
Velocity Vector ◽  
Vector Fields ◽  
Brain Activity ◽  
Simulated Data ◽  
Spatiotemporal Dynamics ◽  
Sources And Sinks ◽  
Quantitative Analyses ◽  
Sensory Evoked

AbstractWide-field optical imaging techniques constitute powerful tools to sample and study mesoscale neuronal activity. The sampled data constitutes a sequence of image frames in which one can perceive the flow of brain activity starting and terminating at source and sink locations respectively. The most common data analyses include qualitative assessment to identify sources and sinks of activity as well as their trajectories. The quantitative analyses is mostly based on computing the temporal variation of the intensity of pixels while a few studies have also reported estimates of wave motion using optical-flow techniques from computer vision. A comprehensive toolbox for the quantitative analyses of mesoscale brain activity data however is still missing. We present a graphical-user-interface based Matlab® toolbox for investigating the spatiotemporal dynamics of mesoscale brain activity using optical-flow analyses. The toolbox includes the implementation of three optical-flow methods namely Horn-Schunck, Combined Local-Global, and Temporospatial algorithms for estimating velocity vector fields of perceived flow in mesoscale brain activity. From the velocity vector fields we determine the locations of sources and sinks as well as the trajectories and temporal velocities of activity flow. Using our toolbox, we compare the efficacy of the three optical-flow methods for determining spatiotemporal dynamics by using simulated data. We also demonstrate the application of optical-flow methods onto sensory-evoked calcium and voltage imaging data. Our results indicate that the combined local-global method we employ, yields results that correlate with the manual assessment. The automated approach permits rapid and effective quantification of mesoscale brain dynamics and may facilitate the study of brain function in response to new experiences or pathology.Conflicts of InterestnoneAuthor contribution statementMHM, MM, NV, and SI designed the study. NA and SI wrote Matlab® code for the toolbox and designed the simulated data. MHM, and NA performed the experiments. NA and SI analyzed the data. SI, NA, and MHM wrote the manuscript.

Measurement of two overlapped velocity vector fields in microfluidic devices using time-resolved PIV

2008 ◽  
Vol 11 (1) ◽  
pp. 33-34 ◽  
Author(s):  
N. Erkan ◽  
K. Shinohara ◽  
K. Okamoto ◽  
T. Okamoto ◽  
T. Fujii
Keyword(s):  
Velocity Vector ◽  
Vector Fields ◽  
Microfluidic Devices ◽  
Time Resolved

scholarly journals Evolution of vortical structures behind an inclined flat plate

2018 ◽  
Vol 168 ◽  
pp. 05003 ◽  
Author(s):  
Pavel Procházka ◽  
Václav Uruba ◽  
Vladislav Skála
Keyword(s):  
Flat Plate ◽  
High Velocity ◽  
Velocity Vector ◽  
Vector Fields ◽  
Vortical Structures ◽  
Piv Technique ◽  
Streamwise Structures ◽  
New Hypothesis ◽  
Instantaneous Velocity Vector ◽  
Good Agreement

2D3C TR-PIV technique was utilized to investigate streamwise-oriented vortical structures behind an inclined flat plate. The angle of attack was set to 7 deg, several fields of view in the wake were investigated. The instantaneous velocity vector fields were captured, dynamics of the flow was studied using POD method. The streamwise structures are determined by vorticity and low- and high-velocity streaks are defined. The acquired results are in a good agreement with the new hypothesis of a principle of flight.

scholarly journals Estimation of Flow Velocity Vector Fields Using Neural Networks

1998 ◽  
Vol 34 (12) ◽  
pp. 1800-1805 ◽  
Author(s):  
Ichiro KIMURA ◽  
Atsuhiko HATTORI ◽  
Yasuaki KUROE ◽  
Akikazu KAGA
Keyword(s):  
Neural Networks ◽  
Flow Velocity ◽  
Velocity Vector ◽  
Vector Fields

Flow of a Viscoelastic Fluid Around a Falling Sphere

2011 ◽  
Author(s):  
Takahisa Shiratori ◽  
Yuji Tasaka ◽  
Yuichi Murai ◽  
Kazuya Oyama ◽  
Ichiro Kumagai ◽  
...  
Keyword(s):  
Strain Rate ◽  
Velocity Vector ◽  
Vector Fields ◽  
Flow Region ◽  
Moving Frame ◽  
Strain Rate Tensor ◽  
Tensor Fields ◽  
Falling Sphere ◽  
History Of ◽  
Negative Wake

Velocity vector fields around a falling sphere in a 1.0 wt % polyacrylamide (PAA) solution are obtained on a vertical cross section by particle image velocimetry (PIV). PAA solution is known as non-Newtonian fluid, which has shear thinning and viscoelastic property. Strain rate tensor fields and deformation fields are calculated from the velocity vector fields in order to visualize the dynamic behavior of the fluid quantitatively. In velocity vector field, two typical flow regions are observed in the wake of the sphere: approaching flow to the sphere, rising flow called “negative wake” [1]. Results show that the strain rate tensor field gives fluid strain at the approaching flow region and the edge of the negative wake. Furthermore deformation history of one portion of the fluid shows that fluid is strained in the approaching flow region, and the strain rate at the edge of the negative wake represents their recovery to the original status of the fluid in the moving frame.

Sign in / Sign up

Export Citation Format

Share Document