Impact of rubber membrane on lunar regolith mechanical properties under low effective confining pressure

被引:0
|
作者
Wang, Siyuan [1 ]
Jiang, Mingjing [2 ,3 ]
Lin, Jiayu [1 ]
机构
[1] Tianjin Univ, Sch Civil Engn, Tianjin 300350, Peoples R China
[2] Suzhou Univ Sci & Technol, Sch Civil Engn, Suzhou 215009, Jiangsu, Peoples R China
[3] Tongji Univ, State Key Lab Disaster Reduct Civil Engn, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
Lunar soil; discrete-element method (DEM); The flexible membrane condition; Triaxial compression; Persistent homology; STRAIN LOCALIZATION; GRANULAR-MATERIALS; FLEXIBLE MEMBRANE; CEMENTED SANDS; DEM SIMULATION; BEHAVIOR;
D O I
10.1016/j.asr.2024.10.048
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The existing lunar exploration activities and associated equipment interactions are limited to the surface environment, where the stress state of the lunar regolith is significantly lower than that in laboratory tests conducted on Earth. To address this, this paper proposes a new framework for discrete modeling of large-scale triaxial tests on lunar regolith under low confining pressure. The framework incorporates particle shapes from the Chang'E-5 mission (CE-5) and flexible boundary conditions. Firstly, the shape characteristics of the lunar regolith particles were adopted in the Discrete Element Method (DEM) model to reproduce the mechanical properties of the lunar regolith as accurately as possible. Then, experiments with varying membrane particle stiffness ratios were conducted to explore the effect of the rubber membrane's properties on the mechanical characteristics of lunar regolith under low effective confining pressure. Topological Data Analysis (TDA) tools from persistent homology were utilized to quantify the dynamic response of particles during the onset and development of strain localization. The results indicate that under low effective confining pressure, selecting appropriate rubber membrane types is crucial for accurately determining the mechanical properties of lunar regolith. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:2340 / 2360
页数:21
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