Machine Learning for 3D Particle Tracking in Granular Gases

Puzyrev, Dmitry; Harth, Kirsten; Trittel, Torsten; Stannarius, Ralf

doi:10.1007/s12217-020-09800-4

Cited by 19 publications

(12 citation statements)

References 46 publications

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“…Here we can expect more influence of particle-particle collisions relative to collisions with the walls, highly pronounced orientational inhomogeneity as well as clustering effects. Taking into account the recent advances in the analysis of experimental data in similar systems [25], one expects more possibilities to fine-tune the simulations and get more insight into the dynamics of granular gases. Further investigations can be envisioned in the direction of ensembles of particles of more elaborate shapes, as well as mixtures of different particles.…”

Section: Resultsmentioning

confidence: 99%

Continuously heated granular gas of elongated particles

et al. 2021

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Some years ago, Harth et al. experimentally explored the steady state dynamics of a heated granular gas of rod-like particles in microgravity [K. Harth et al. Phys. Rev. Lett. 110, 144102 (2013)]. Here, we report numerical results that quantitatively reproduce their experimental findings and provide additional insight into the process. A system of sphero-cylinders is heated by the vibration of three flat side walls, resulting in one symmetrically heated direction, one non-symmetrically heated direction, and one non-heated direction. In the non-heated direction, the speed distribution follows a stretched exponential distribution $$p(\upsilon )\, \propto \,{\rm{exp}}\left( { - {{\left( {{{\left| \upsilon \right|} \mathord{\left/ {\vphantom {{\left| \upsilon \right|} C}} \right. \kern-\nulldelimiterspace} C}} \right)}^{1.5}}} \right)$$. In the symmetrically heated direction, the velocity statistics at low speeds is similar but it develops pronounced exponential tails at high speeds. In the non-symmetrically heated direction (not accessed experimentally), the distribution also follows $$p(\upsilon )\, \propto \,{\rm{exp}}\left( { - {{\left( {{{\left| \upsilon \right|} \mathord{\left/ {\vphantom {{\left| \upsilon \right|} C}} \right. \kern-\nulldelimiterspace} C}} \right)}^{1.5}}} \right)$$ , but the velocity statistics of rods moving toward the vibrating wall resembles the indirectly excited direction, whereas the velocity statistics of those moving away from the wall resembles the direct excited direction.

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Section: Resultsmentioning

confidence: 99%

Continuously heated granular gas of elongated particles

et al. 2021

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“…So far, there is no reliable way to reconstruct the 3D local density data from the experimental videos. The possibility of such a reconstruction, e.g., by means of machine learning techniques [23] could be envisioned as an aim of future research.…”

Section: Observation In the Vip-gran Experimentsmentioning

confidence: 99%

Cluster dynamics in dense granular gases of rod-like particles

et al. 2021

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Granular gases are interesting multiparticle systems which, irrespective of the apparent simplicity of particle interactions, exhibit a rich scenario of so far only little understood features. We have numerically investigated a dense granular gas composed of frictional spherocylinders which are excited mechanically by lateral vibrating container walls. This study was stimulated by experiments in microgravity on parabolic flights. The formation of spatial inhomogeneities (clusters) was observed in a region near the corners of the container, about halfway from the excitation plates. The particles in the clusters show a tendency to align parallel to the container walls, seemingly increasing the stabilizing effect of friction. The simulation results provide hints that the phase difference of the vibrations of the two excitation walls might affect the cluster dynamics.

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“… 5 , 25 , 52 . This demanding task can be tackled manually 25 or using Machine Learning algorithms 53 , but even 2D tracking becomes complicated when the filling fraction of the observed volume is more than a few percent, because particles in front screen background particles. Particle assignment in subsequent videos is not always trivial.…”

Section: Introductionmentioning

confidence: 99%

Visual analysis of density and velocity profiles in dense 3D granular gases

Puzyrev

Fischer

Harth

et al. 2021

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Granular multiparticle ensembles are of interest from fundamental statistical viewpoints as well as for the understanding of collective processes in industry and in nature. Extraction of physical data from optical observations of three-dimensional (3D) granular ensembles poses considerable problems. Particle-based tracking is possible only at low volume fractions, not in clusters. We apply shadow-based and feature-tracking methods to analyze the dynamics of granular gases in a container with vibrating side walls under microgravity. In order to validate the reliability of these optical analysis methods, we perform numerical simulations of ensembles similar to the experiment. The simulation output is graphically rendered to mimic the experimentally obtained images. We validate the output of the optical analysis methods on the basis of this ground truth information. This approach provides insight in two interconnected problems: the confirmation of the accuracy of the simulations and the test of the applicability of the visual analysis. The proposed approach can be used for further investigations of dynamical properties of such media, including the granular Leidenfrost effect, granular cooling, and gas-clustering transitions.

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Machine Learning for 3D Particle Tracking in Granular Gases

Cited by 19 publications

References 46 publications

Continuously heated granular gas of elongated particles

Continuously heated granular gas of elongated particles

Cluster dynamics in dense granular gases of rod-like particles

Visual analysis of density and velocity profiles in dense 3D granular gases

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