Simplified Particle Models and Properties Analysis Designed for DEM Lunar Soil Simulants

The discrete element method (DEM) is one of the most popular methods for simulating lunar soil simulants due to the lack of real lunar soil. To reduce the computational consumption and difficulty because of complex particle models, simplified particle models, in which a single particle consists of two, four, or six elements, are discussed in this paper. Three steps, including random generation, particle replacement, and sedimentation, can generate the proposed simulant. The relationship between the mechanical properties of the simulant and microscopic parameters defined in DEM was analyzed by the orthogonal array testing (OATS) technique. Then, the prediction functions, which can calculate mechanical properties from inputting the microscopic parameters without carrying out the DEM, are also established by a back-propagation artificial neural network (BP-ANN). The widely used physical simulants JSC-1 from the USA and FJS-1 from Japan are simulated in DEM from the prediction function with high accuracy.

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Publication Details

Journal
Aerospace
Published
2025-04-11
DOI
https://doi.org/10.3390/aerospace12040330
Primary Topic
Planetary Science and Exploration
Type
article
Field-Weighted Citation Impact
0.00

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article

Simplified Particle Models and Properties Analysis Designed for DEM Lunar Soil Simulants

Qian Li, Junhao Liu, Xiong Xiong, Lanlan Xie
Aerospace
Planetary Science and Exploration
article

Simplified Particle Models and Properties Analysis Designed for DEM Lunar Soil Simulants

Qian Li, Junhao Liu, Xiong Xiong, Lanlan Xie
article en

Abstract

The discrete element method (DEM) is one of the most popular methods for simulating lunar soil simulants due to the lack of real lunar soil. To reduce the computational consumption and difficulty because of complex particle models, simplified particle models, in which a single particle consists of two, four, or six elements, are discussed in this paper. Three steps, including random generation, particle replacement, and sedimentation, can generate the proposed simulant. The relationship between the mechanical properties of the simulant and microscopic parameters defined in DEM was analyzed by the orthogonal array testing (OATS) technique. Then, the prediction functions, which can calculate mechanical properties from inputting the microscopic parameters without carrying out the DEM, are also established by a back-propagation artificial neural network (BP-ANN). The widely used physical simulants JSC-1 from the USA and FJS-1 from Japan are simulated in DEM from the prediction function with high accuracy.

AerospaceVol. 12(4)
Chengdu University of Technology (CN)
National Natural Science Foundation of China, Chengdu University of Technology, University of Tsukuba, Chengdu University, Natural Science Foundation of Sichuan Province
Openalex Percentile: Top 95%
Planetary Science and Exploration
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