Authors:
Jouni Elfvengren
1
;
Jari Kolehmainen
2
and
Pentti Saarenrinne
2
Affiliations:
1
Mohamed V-Agdal University, Finland
;
2
Tampere University of Technology, Finland
Keyword(s):
Particle Tracking Velocimetry, Fluidized Bed, Particle Sizing, GPU Computing.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Digital Photography
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Segmentation and Grouping
;
Shape Representation and Matching
Abstract:
Fluidized beds are used in wide variety of industrial applications. These applications range from energy production to chemical industry. Particle tracking velocimetry (PTV) is an efficient way to study small scale behavior inside fluidized beds. An accurate PTV algorithm has to be able to perform also in relatively dense suspensions where particles may overlap and form clusters. PTV algorithms typically proceed from locating the particles to tracking their motion. Typically the particle locating has been based on either profile matching or image intensity thresholding. This study proposes a combined method that tries to take advantage of the both methods to overcome difficulties associated with dense suspensions. The method was tested in a synthetic case and in an experimental fluidized bed case. The synthetic tests showed a slight increase in error when the number of particles increased, but the error level remained acceptable. Results obtained from the fluidized bed were visually
inspected. Visual inspection showed that most of the particles were tracked correctly, which suggests that the proposed method performs well also in practice.
(More)