Particle image velocimetry (PIV): use of photo-thermoplastic film for photographic recording of particle images

1995 ◽  
Vol 20 (2) ◽  
pp. 136-139
Author(s):  
K. Banke ◽  
K. Wutzke ◽  
H. Helmers
Keyword(s):  
Particle Image Velocimetry ◽  
Particle Image ◽  
Photographic Recording ◽  
Image Velocimetry ◽  
Particle Images

Multi-Cross-Correlation Method in Particle Image Velocimetry

2011 ◽  
Vol 27 (3) ◽  
pp. 365-377
Author(s):  
T.-W. Hsu ◽  
C.-Y. Shin ◽  
S.-H. Ou ◽  
Y.-T. Li
Keyword(s):  
Particle Image Velocimetry ◽  
Cross Correlation ◽  
Particle Image ◽  
Processing System ◽  
Correlation Method ◽  
Seeding Density ◽  
Image Velocimetry ◽  
Accuracy Of Measurements ◽  
Out Of Plane ◽  
Particle Images

ABSTRACTA multi-cross-correlation method (MCCM) was developed in a particle image velocimetry (PIV) auto-processing system to reduce spurious vectors and improve accuracy of measurements. This technique is an improvement based on conventional cross-correlation method (CCM). Four typical neighboring interrogation windows were specified to be overlapped and calculated by MCCM. A high cross-correlation value is obtained in which many particle images match up with their corresponding spatially shifted partners, and small cross-correlation peaks due to interference of noises during experiments are reduced. Several parameters such as out-of-plane motions, particle size, and seeding density are considered for checking both MCCM and conventional PIV algorithms. The examination gives authenticity to the merits of MCCM for avoiding particles loss or mistaken velocity vectors.

Particle Image Velocimetry on the Flow Analysis of Stenosed Cardiovascular Structures

2019 ◽  
Vol 9 (2) ◽  
pp. 314-318
Author(s):  
Mingliang Zhang ◽  
Tao Yin ◽  
Xiaoqing Zhuo ◽  
Yongtao Zhang
Keyword(s):  
Particle Image Velocimetry ◽  
Particle Image ◽  
Flow Analysis ◽  
Computational Modelling ◽  
Estimation Algorithm ◽  
Flow Fields ◽  
Correlation Technique ◽  
Motion Field ◽  
Image Velocimetry ◽  
Particle Images

In this paper, we present Particle images velocimetry (PIV), based on digital scanning of illuminated fluid suspended microscopic glass seeds, together with flow fields generated by a cross-correlation technique and a standard motion estimation algorithm. Flow information calculated by both methods are presented. The experiments carried out here demonstrate the capability of flow in estimating parametric motion field using particle images. However, there is a need to have a comparison technique that detects the deviation of the flow fields based on the different computational modelling schemes to determine its accuracy. The study carried out by us adequately covers the different computational aspects of particle image velocimetry and presents the fundamental tools for post-processing and analysing cardiovascular structures effectively and efficiently.

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