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<p>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.</p>

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Criteria for dynamical clustering in permanently excited granular gases: comparison and estimation with machine learning approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月21日

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  • Sai Preetham Sata,
  • Ralf Stannarius,
  • Dmitry Puzyrev

摘要

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.