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 - 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 - 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 - Stannarius, Ralf A1 - Wang, Jing A1 - Pongó, Tivadar A1 - Fan, Bo A1 - Börzsönyi, Tamás A1 - Hidalgo, Raúl Cruz ED - Alam, M. ED - Das, S.L. ED - Bose, M. ED - Murthy, T.G. ED - Chikkadi, V. ED - Singh, A. ED - Luding, S. T1 - Forces on a sphere suspended in flowing granulate JF - EPJ Web of Conferences N2 - We investigate the forces of flowing granular material on an obstacle. A sphere suspended in a discharging silo experiences both weight of the overlaying layers and drag of the surrounding moving grains. In experiments with frictional hard glass beads, the force on the obstacle was found to be practically flow-rate independent. In contrast, flow of nearly frictionless soft hydrogel spheres added drag forces which increased with the flow rate until reaching saturation at high flow speeds. The total force grew quadratically with the obstacle diameter in the soft, low friction material, while it grew much weaker, nearly linearly with the obstacle diameter, in the bed of hard, frictional glass spheres. In addition to the drag, obstacles embedded in the flowing hydrogel spheres experience a weight force from the top as if immersed in a hydrostatic pressure profile, but negligible counter-forces from below. In contrast, the frictional hard particles create a strong pressure gradient near the upper surface of the obstacle. Numerical simulations provide additional information that is difficult to access experimentally. They reproduce the experimental results and give hints for the origin of the different force contributions. The results have considerable practical importance for the discharge of storage containers with large objects suspended in flowing granular material. Y1 - 2025 U6 - https://doi.org/10.1051/epjconf/202534002019 VL - 340 PB - EDP Sciences ER - TY - JOUR A1 - Sata, Sai Preetham A1 - Stannarius, Ralf A1 - Puzyrev, Dmitry T1 - Criteria for dynamical clustering in permanently excited granular gases: comparison and estimation with machine learning approaches JF - Granular Matter N2 - When granular gases in microgravity are continuously excited mechanically, spatial inhomogeneities of the particle distribution can emerge. At a sufficiently large overall packing fraction, a significant share of particles tend to concentrate in strongly overpopulated regions, so-called clusters, far from the excitation sources. This dynamical clustering is caused by a complex balance between energy influx and dissipation. The mean number density of particles, the geometry of the container, and the excitation strength influence cluster formation. A quantification of clustering thresholds is not trivial. We generate ‘synthetic’ data sets by Discrete Element Method simulations of frictional spheres in a cuboid container and apply established criteria to classify the local packing fraction profiles. Machine learning approaches that predict dynamic clustering from known system parameters on the basis of classical test criteria areoposed and tested. It avoids the necessity of complex numerical simulations. Y1 - 2025 U6 - https://doi.org/10.1007/s10035-025-01560-5 VL - 27 IS - 4 PB - Springer 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 - TY - JOUR A1 - Preetham Sata, Sai A1 - Stannarius, Ralf A1 - Puzyrev, Dmitry ED - Alam, M. ED - Das, S.L. ED - Bose, M. ED - Murthy, T.G. ED - Chikkadi, V. ED - Singh, A. ED - Luding, S. T1 - Machine learning for prediction of dynamical clustering in granular gases JF - EPJ Web of Conferences N2 - Continuously excited dense granular gases in microgravity can develop spatial inhomogeneities of the particle distribution. Dynamical clustering is a phenomenon where a significant share of particles concentrate in strongly overpopulated regions. It is caused by a complex interplay between the energy influx and dissipation in collisions. The overall packing fraction, container geometry, and excitation parameters influence the gas-cluster transition. We perform Discrete Element Method (DEM) simulations for frictional spheres in a cuboid container and apply statistical criteria to the packing fraction profiles. Machine learning (ML) methods are used to study the dependence of the gas-cluster transition on system parameters. It is a promising alternative to predict the state of the system without the need for the time-consuming DEM simulations. We identify the best models for predicting the dynamical clustering of frictional spheres in a specific experimental geometry. Y1 - 2025 U6 - https://doi.org/10.1051/epjconf/202534012012 VL - 340 PB - EDP Sciences ER - TY - JOUR A1 - Klopp, Christoph A1 - Trittel, Torsten A1 - Stannarius, Ralf T1 - Coarsening of quasi two-dimensional emulsions formed by islands in free-standing smectic films JF - ChemPhysChem N2 - We study the coarsening behavior of assemblies of islands on smectic A freely suspended films in ISS microgravity experiments. The islands can be regarded as liquid inclusions in a two-dimensional fluid in analogy to liquid droplets of the discontinuous phase of an emulsion. The coarsening is effectuated by two processes, predominantly by island coalescence, but to some extend also by Ostwald ripening, whereby large islands grow at the expense of surrounding smaller ones. A peculiarity of this system is that the continuous and the discontinuous phases consist of the same material. We determine the dynamics, analyze the self-similar aging of the island size distribution and discuss characteristic exponents of the mean island growth. Y1 - 2024 U6 - https://doi.org/10.1002/cphc.202400166 VL - 25 IS - 12 SP - 1 EP - 12 PB - Wiley 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 -