Cheng Ke, Hou Meiying, Sun Wei, Qiao Zhihong, Li Xiang, Li Tuo, Yang Mingcheng
Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics, Institute of Physics, Beijing, 100190, China.
College of Physics and Electronic Engineering, Hainan Normal University, 571158, Haikou, China.
Sci Data. 2025 Feb 5;12(1):219. doi: 10.1038/s41597-025-04517-8.
This present investigation employs an advanced magnetic particle tracking method to trace the trajectories of an intruder within a vibration-driven granular medium under artificial low-gravity conditions. The experiments are carried out within the centrifuge of the Chinese Space Station, encompassing six distinct low-gravity environments. Trajectories under various vibration modes are captured and analysed for each gravity level. This paper offers an exhaustive account of data collection and algorithms used for data processing, ensuring the dependability and precision of the datasets obtained. Additionally, we make the raw magnetic field data, processing scripts, and visualization tools accessible to the public. This research contributes a comprehensive dataset that is instrumental in exploring the mechanisms of granular segregation under low gravity and aids in the verification of novel physical models for understanding intruder dynamics in granular systems under such conditions.
本研究采用先进的磁粒子跟踪方法,在人工低重力条件下追踪振动驱动颗粒介质中入侵者的轨迹。实验在中国空间站的离心机内进行,涵盖六种不同的低重力环境。针对每个重力水平,捕捉并分析各种振动模式下的轨迹。本文详细介绍了用于数据收集和数据处理的算法,确保所获得数据集的可靠性和精确性。此外,我们还向公众提供原始磁场数据、处理脚本和可视化工具。这项研究贡献了一个全面的数据集,有助于探索低重力下颗粒分离的机制,并有助于验证用于理解此类条件下颗粒系统中入侵者动力学的新物理模型。