Improvement of the Delaunay-Tessellation Particle Tracking Algorithm in the Flow Visualization Research

2013 ◽  
Vol 411-414 ◽  
pp. 2134-2137
Author(s):  
Yang Zhang ◽  
Yuan Wang ◽  
Bin Yang
Keyword(s):  
Flow Visualization ◽  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
Simple Structure ◽  
High Accuracy ◽  
Tracking Algorithm ◽  
Delaunay Tessellation ◽  
Complex Flows ◽  
Particle Tracking Algorithm ◽  
Novel Concept

The particle tracking velocimetry (PTV) algorithm is one of the most important branches in the flow visualization research. An efficient two-frame PTV based on Delaunay tessellation was updated by a novel concept called Dual Computation. The updated algorithm was tested using CFD flows with changeable parameters and random erasing of particles as perturbation. In addition to the simple structure and the minimal dependence on algorithmic assumptions, the advantages of this updated algorithm also include the high accuracy in addressing complex flows with noticeable ratio of particles having no match.

scholarly journals Improvement of Algorithm in the Particle Tracking Velocimetry Using Self-Organizing Maps

1970 ◽  
Vol 7 (1) ◽  
pp. 6-23 ◽  
Author(s):  
Shashidhar Ram Joshi
Keyword(s):  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
A Priori ◽  
Maximum Flow ◽  
Tracking Algorithm ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Particle Tracking Algorithm ◽  
Modified Algorithm ◽  
Self Organizing

The neural network techniques are becoming a useful tool for the particle tracking algorithm of the PIV system software and among others, the self-organizing maps (SOM) model seems to have turned out particularly effective for this purpose. This is mainly because of the performance of the particle tracking itself, capacity of dealing with unpaired particles between two frames and no necessity for a priori knowledge on the flow field (e.g. maximum flow rate) to be measured. Initially, concept of SOM was applied to PIV by Labonte. It was modified by Ohmi and further modified algorithm is developed using the concept of Delta-Bar-Delta rule. It is a heuristic algorithm for modifying the learning rate as training progresses. Earlier, the treatment of unpaired particles, a specific problem to any type of PIV, is not fully considered and thereby, the tracking goes unsuccessfully for some particles. The present research is to bring about further improvement and practicability in this promising particle tracking algorithm. The computational complexity can be reduced employing modified algorithm compared to other algorithms. The modified algorithm is tested in the light of the synthetic PIV standard image as well as in particle images obtained from visualization experiments.Key words: Delta-Bar Delta, Dynamic Threshold Binarization, HVD Algorithm, Labonte's SOM, Modified Algorithm, Ohmi's SOM, Particle Image Velocimetry(PIV), Particle Tracking Velocimetry(PTV), Self-Organizing Map(SOM), Single Threshold Binarization.Journal of the Institute of Engineering, Vol. 7, No. 1,  2009, July, pp. 6-23doi: 10.3126/jie.v7i1.2057

scholarly journals Visualization of submerged turbulent jets using particle tracking velocimetry

2021 ◽  
Author(s):  
Franklin Shaffer ◽  
Eric Ibarra ◽  
Ömer Savaş
Keyword(s):  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
Turbulent Jets ◽  
Reynolds Numbers ◽  
Tracking Algorithm ◽  
Small Scale ◽  
High Particle ◽  
Tracer Particles ◽  
Abrupt Changes ◽  
Particle Tracking Algorithm

Abstract Over the past few decades, advances have been made in using particle image velocimetry (PIV) and particle tracking velocimetry (PTV) for mapping of Lagrangian velocity and acceleration flow fields. With PIV, Lagrangian trajectories are not measured directly; rather, hypothetical trajectories must be constructed from sequences of Eulerian velocity snapshots. Because PTV directly measures actual trajectories, it provides distinct advantages over PIV, especially for trajectories with abrupt changes in direction. In this work, a novel particle tracking algorithm is described, then applied to track trajectories of tracer particles in submerged turbulent jets. The Reynolds numbers ranged from 1000 to 25,000, thereby covering laminar, transitioning-to-turbulence, and fully turbulent flow regimes. The novel particle tracking algorithm is designed to handle flows with very high particle concentrations, thereby resolving small-scale flow structures. Trajectories are tracked with high velocity gradients, sharp curvatures, cycloids, abrupt changes in direction, and strong recirculation—all of which are inaccessible via construction from PIV sequences. Most trajectories measured in this work are at least 500 camera frames (time steps) long, with many being more than 3000 frames long. Graphic abstract

scholarly journals Adaptive settings for the nearest-neighbor particle tracking algorithm

2014 ◽  
Vol 31 (8) ◽  
pp. 1279-1285 ◽  
Author(s):  
Javier Mazzaferri ◽  
Joannie Roy ◽  
Stephane Lefrancois ◽  
Santiago Costantino
Keyword(s):  
Particle Tracking ◽  
Nearest Neighbor ◽  
Tracking Algorithm ◽  
Neighbor Particle ◽  
Particle Tracking Algorithm
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