| Issue |
EPJ Web Conf.
Volume 340, 2025
Powders & Grains 2025 – 10th International Conference on Micromechanics on Granular Media
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|
|---|---|---|
| Article Number | 12013 | |
| Number of page(s) | 4 | |
| Section | Emerging Topics: Additive Manufacturing & Meta Materials, Microgravity, Tribo-Charging, Active Particles, and Artificial Intelligence & Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202534012013 | |
| Published online | 01 December 2025 | |
https://doi.org/10.1051/epjconf/202534012013
AI-aided visual data analysis for granular gases: Complex particles, high density, and other challenges
1 MTRM, Medical Faculty, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany
2 Dept. of Engineering, Brandenburg University of Appl. Sciences, Magdeburger Str. 50, 14770 Brandenburg an der Havel, Germany
3 IKS, Faculty of Informatics, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany
4 MARS, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany
5 Department of Physics and Applied Mathematics, University of Navarra, 31080 Pamplona, Spain
* e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 1 December 2025
Abstract
Microgravity experiments with three-dimensional (3D) granular gases, i.e., ensembles of freemoving macroscopic particles which collide inelastically, produce large amounts of stereo video footage which require processing and analysis. The main steps of data treatment are particle detection, 3D matching and tracking in stereoscopic views, and quantification of ensemble statistical properties such as, e.g. mean kinetic energy or collision processes. Frequent overlapping or clustering of particles and their complicated movement patterns require smart software solutions. In recent years, Artificial Intelligence/Machine Learning (AI/ML) methods were successfully used for analysis of granular systems. We have applied such techniques to the granular gases of rod-like particles and developed a software tool which enables a full cycle of semi-automatic experimental data analysis. The approach is now tested on more complex, non-convex particles, shaped as 3D crosses (hexapods). Another challenge is optical analysis of dense granular gases, where individual particles cannot be tracked. We present a preliminary result of application of an ML method for number density profiles extraction in VIP-Gran experiment with dense ensemble of rod-like particles.
© The Authors, published by EDP Sciences, 2025
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