scholarly journals Local strategy of particle tracking with TrackNETv2 on the BES-III CGEM inner detector

2021 ◽  
Author(s):  
Anastasiya Nikolskaia ◽  
Egor Schavelev ◽  
Pavel Goncharov ◽  
Gennadiy Ososkov ◽  
Yury Nefedov ◽  
...  
Keyword(s):  
Particle Tracking ◽  
Local Strategy

VELOCITY MEASUREMENTS IN BUBBLY FLOWS WITH PARTICLE TRACKING VELOCIMETRY

2001 ◽  
Vol 8 (2-3) ◽  
pp. 10
Author(s):  
Javier Ortiz-Villafuerte ◽  
D. R. Todd ◽  
Yassin A. Hassan
Keyword(s):  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
Bubbly Flows ◽  
Velocity Measurements

3D Particle Tracking Velocimetry on Transverse Injection in Supersonic Flow

2003 ◽  
Author(s):  
H. G. Hornung ◽  
N. Ponchaut ◽  
C. Mouton
Keyword(s):  
Supersonic Flow ◽  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
Transverse Injection

Efficiency prediction for storage chambers using computational fluid dynamics

1996 ◽  
Vol 33 (9) ◽  
pp. 163-170 ◽  
Author(s):  
Virginia R. Stovin ◽  
Adrian J. Saul
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Particle Tracking ◽  
Critical Value ◽  
Complex Geometries ◽  
Bed Shear Stress ◽  
Breadth Ratio ◽  
Computational Fluid Dynamics Cfd ◽  
Simulated Flow

Research was undertaken in order to identify possible methodologies for the prediction of sedimentation in storage chambers based on computational fluid dynamics (CFD). The Fluent CFD software was used to establish a numerical model of the flow field, on which further analysis was undertaken. Sedimentation was estimated from the simulated flow fields by two different methods. The first approach used the simulation to predict the bed shear stress distribution, with deposition being assumed for areas where the bed shear stress fell below a critical value (τcd). The value of τcd had previously been determined in the laboratory. Efficiency was then calculated as a function of the proportion of the chamber bed for which deposition had been predicted. The second method used the particle tracking facility in Fluent and efficiency was calculated from the proportion of particles that remained within the chamber. The results from the two techniques for efficiency are compared to data collected in a laboratory chamber. Three further simulations were then undertaken in order to investigate the influence of length to breadth ratio on chamber performance. The methodology presented here could be applied to complex geometries and full scale installations.

DEVELOPMENT OF PARTICLE TRACKING SIMULATION AND ITS APPLICATION TO COILIA NASUS IN CHIKUGO RIVER ESTUARY

2019 ◽  
Vol 75 (2) ◽  
pp. I_475-I_480
Author(s):  
Masashi FUJIMOTO ◽  
Naoya IWAMOTO ◽  
Tetsuya SHINTANI ◽  
Katsuhide YOKOYAMA
Keyword(s):  
Particle Tracking ◽  
River Estuary ◽  
Coilia Nasus ◽  
Chikugo River

Multiple particle tracking

2018 ◽  
Author(s):  
Eric M. Furst ◽  
Todd M. Squires
Keyword(s):  
Brownian Motion ◽  
Best Practices ◽  
Particle Tracking ◽  
Heterogeneous Material ◽  
Quantitative Measurements ◽  
Particle Tracking Microrheology ◽  
Shell Models ◽  
Multiple Particle Tracking ◽  
Microscopy Data ◽  
Multiple Particle

The fundamentals and best practices of multiple particle tracking microrheology are discussed, including methods for producing video microscopy data, analyzing data to obtain mean-squared displacements and displacement correlations, and, critically, the accuracy and errors (static and dynamic) associated with particle tracking. Applications presented include two-point microrheology, methods for characterizing heterogeneous material rheology, and shell models of local (non-continuum) heterogeneity. Particle tracking has a long history. The earliest descriptions of Brownian motion relied on precise observations, and later quantitative measurements, using light microscopy.

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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