TY - JOUR A1 - Wang, Jing A1 - Harth, Kirsten A1 - Puzyrev, Dmitry A1 - Stannarius, Ralf T1 - The effect of obstacles near a silo outlet on the discharge of soft spheres JF - New Journal of Physics KW - fluid dynamics Y1 - 2022 U6 - https://doi.org/10.1088/1367-2630/ac8bea VL - 24 PB - IOP ER - TY - JOUR A1 - Trittel, Torsten A1 - Klopp, Christoph A1 - Harth, Kirsten A1 - Stannarius, Ralf T1 - Stability and Rupture of Liquid Crystal Bridges under Microgravity JF - Crystals KW - smectic liquid-crystals KW - Rayleigh-Plateau instability KW - TEXUS suborbital rocket KW - microgravity Y1 - 2022 U6 - https://doi.org/https://doi.org/10.3390/cryst12081092 VL - 12 IS - 8 PB - MDPI ER - TY - JOUR A1 - Puzyrev, Dmitry A1 - Torsten, Torsten A1 - Harth, Kirsten A1 - Stannarius, Ralf T1 - Cooling of a granular gas mixture in microgravity JF - npj microgravity N2 - Granular gases are fascinating non-equilibrium systems with interesting features such as spontaneous clustering and non-Gaussian velocity distributions. Mixtures of different components represent a much more natural composition than monodisperse ensembles but attracted comparably little attention so far. We present the observation and characterization of a mixture of rod-like particles with different sizes and masses in a drop tower experiment. Kinetic energy decay rates during granular cooling and collision rates were determined and Haff’s law for homogeneous granular cooling was confirmed. Thereby, energy equipartition between the mixture components and between individual degrees of freedom is violated. Heavier particles keep a slightly higher average kinetic energy than lighter ones. Experimental results are supported by numerical simulations. Y1 - 2024 U6 - https://doi.org/10.1038/s41526-024-00369-5 VL - 10 IS - 1 SP - 1 EP - 9 PB - Springer CY - Brandenburg an der Havel ER - TY - JOUR A1 - Trittel, Torsten A1 - Puzyrev, Dmitry A1 - Harth, Kirsten A1 - Stannarius, Ralf T1 - Rotational and translational motions in a homogeneously cooling granular gas JF - npj microgravity N2 - A granular gas composed of monodisperse spherical particles was studied in microgravity experiments in a drop tower. Translations and rotations of the particles were extracted from optical video data. Equipartition is violated, the rotational degrees of freedom were excited only to roughly 2/3 of the translational ones. After stopping the mechanical excitation, we observed granular cooling of the ensemble for a period of three times the Haff time, where the kinetic energy dropped to about 5% of its initial value. The cooling rates of all observable degrees of freedom were comparable, and the ratio of rotational and translational kinetic energies fluctuated around a constant value. The distributions of translational and rotational velocity components showed slight but systematic deviations from Gaussians at the start of cooling. Y1 - 2024 U6 - https://doi.org/10.1038/s41526-024-00420-5 VL - 10 IS - 1 SP - 1 EP - 6 PB - Springer ER - TY - JOUR A1 - Harth, Kirsten A1 - Trittel, Torsten A1 - Mohammadi, Mahdieh A1 - Puzyrev, Dmitry A1 - Enezz, Mohammad A1 - Cruz Hidalgo, Raul A1 - Stannarius, Ralf ED - Alam, M. ED - Das, S.L. ED - Bose, M. ED - Murthy, T.G. ED - Chikkadi, V. ED - Singh, A. ED - Luding, S. T1 - Granular gases of rough spheres: Velocity, rotation and collision statistics from in-situ measurements, optical data and simulations JF - EPJ Web of Conferences N2 - Granular gases are not only of interest in fundamental physics, but they can also serve as a test ensembles for the validity of collision models employed in (loose) granular matter. The theoretical literature mainly addresses spheres under ideal conditions and simulations allow full access to all particle parameters, but experiments cannot fulfill these idealizations. We investigate granular gases of soft, rough spheres by combining microgravity experiments and adjusted simulations. We introduce Smart Particles with embedded autarkic micro-sensors for in-situ measurements of rotation rates and accelerations. Additionally, we extract 3D positions, translations and orientations of the particles from stereoscopic video data using Machine-Learning based algorithms. We address the partition of kinetic energy between the degrees of freedom, the angular and translational velocity as well as collision statistics. A simulation is adjusted to experiment parameters, showing good agreement of translational motion, but qualitative differences in the decay of rotational kinetic energy. Y1 - 2025 U6 - https://doi.org/10.1051/epjconf/202534003012 VL - 340 PB - EDP Sciences ER - TY - JOUR A1 - Puzyrev, Dmitry A1 - Trittel, Torsten A1 - Harth, Kirsten A1 - Sata, Sai Preetham A1 - Stannarius, Ralf A1 - Cruz Hidalgo, Raúl ED - Alam, M. ED - Das, S.L. ED - Bose, M. ED - Murthy, T.G. ED - Chikkadi, V. ED - Singh, A. ED - Luding, S. T1 - AI-aided visual data analysis for granular gases: Complex particles, high density, and other challenges JF - EPJ Web of Conferences N2 - 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. Y1 - 2025 U6 - https://doi.org/10.1051/epjconf/202534012013 VL - 340 PB - EDP Sciences ER -