Machine learning assisted droplet trajectories extraction in dense emulsions
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31 oct 2022
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Publicado en línea: 31 oct 2022
Páginas: 70 - 77
Recibido: 24 may 2022
Aceptado: 03 oct 2022
DOI: https://doi.org/10.2478/caim-2022-0006
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© 2022 Mihir Durve et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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.