Articolo in rivista, 2022, ENG, 10.2478/caim-2022-0006

Machine learning assisted droplet trajectories extraction in dense emulsions

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

Fdn Ist Italiano Tecnol IIT; CNR; Univ Roma Tre; Harvard Univ

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.

Communications in Applied and Industrial Mathematics 13 (1), pp. 70–77

Keywords

Lattice Boltzmann methods, YOLO, DeepSORT

CNR authors

Succi Sauro, Lauricella Marco, Tiribocchi Adriano

CNR institutes

IAC – Istituto per le applicazioni del calcolo "Mauro Picone"

ID: 476792

Year: 2022

Type: Articolo in rivista

Creation: 2023-01-20 14:23:19.000

Last update: 2023-02-09 12:51:40.000

External links

OAI-PMH: Dublin Core

OAI-PMH: Mods

OAI-PMH: RDF

DOI: 10.2478/caim-2022-0006

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:476792

DOI: 10.2478/caim-2022-0006

ISI Web of Science (WOS): 000878266600001