Machine learning assisted droplet trajectories extraction in dense emulsions

Mihir Durve; Andriano Tiribocchi; Andrea Montessori; Marco Lauricella; Sauro Succi

doi:10.2478/caim-2022-0006

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.

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Machine learning assisted droplet trajectories extraction in dense emulsions

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