Machine learning assisted droplet trajectories extraction in dense emulsions
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31 ott 2022
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Pubblicato online: 31 ott 2022
Pagine: 70 - 77
Ricevuto: 24 mag 2022
Accettato: 03 ott 2022
DOI: https://doi.org/10.2478/caim-2022-0006
Parole chiave
© 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.