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Machine learning assisted droplet trajectories extraction in dense emulsions

Academic Article
Publication Date:
2022
abstract:
This work analyzes trajectories obtained by YOLO and DeepSORT algorithms of dense emulsion systems simulated via lattice Boltzmann methods. The results indicate that the individual droplet's moving direction is influenced more by the droplets immediately behind it than the droplets in front of it. The analysis also provide hints on constraints of a dynamical model of droplets for the dense emulsion in narrow channels.
Iris type:
01.01 Articolo in rivista
Keywords:
Lattice Boltzmann methods; YOLO; DeepSORT
List of contributors:
Succi, Sauro; Lauricella, Marco; Tiribocchi, Adriano
Authors of the University:
LAURICELLA MARCO
TIRIBOCCHI ADRIANO
Handle:
https://iris.cnr.it/handle/20.500.14243/447344
Published in:
COMMUNICATIONS IN APPLIED AND INDUSTRIAL MATHEMATICS
Journal
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