低重力下振动三维颗粒床中磁球的运动轨迹。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Ke Cheng, Meiying Hou, Wei Sun, Zhihong Qiao, Xiang Li, Tuo Li, Mingcheng Yang
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引用次数: 0

摘要

本研究采用一种先进的磁粒子跟踪方法,在人工低重力条件下,在振动驱动的颗粒介质中追踪入侵者的轨迹。这些实验是在中国空间站的离心机内进行的,包括六个不同的低重力环境。捕获并分析了每个重力水平下各种振动模式下的轨迹。本文提供了一个详尽的数据收集和算法用于数据处理,确保获得的数据集的可靠性和精度。此外,我们还向公众提供了原始磁场数据、处理脚本和可视化工具。本研究提供了一个全面的数据集,有助于探索低重力下颗粒偏析的机制,并有助于验证新的物理模型,以理解在这种条件下颗粒系统中的入侵者动力学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Trajectories of a magnetic sphere in a shaken three-dimensional granular bed under low gravity.

Trajectories of a magnetic sphere in a shaken three-dimensional granular bed under low gravity.

Trajectories of a magnetic sphere in a shaken three-dimensional granular bed under low gravity.

Trajectories of a magnetic sphere in a shaken three-dimensional granular bed under low gravity.

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.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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