Distinctive impact responses of extraterrestrial regolith simulants: Insights from crater morphology classification and granular dynamics through machine learning and x-ray computed tomography
Journal
Physics of Fluids
Journal Volume
37
Journal Issue
2
ISSN
1070-6631
1089-7666
Date Issued
2025-02-01
Author(s)
Abstract
Impact responses of granular materials remain poorly understood, posing significant challenges to extraterrestrial exploration activities such as landing, sampling, drilling, and construction. We studied the crater morphology formed in the granular media and their granular behavior on low-velocity impact cratering by integrating three-dimensional surface scanning, machine learning-based classification, and x-ray computed tomography (X-CT) imaging. Our laboratory experiments focused on lunar (LHS-1 and LMS-1) and Martian regolith simulants (MGS-1) and terrestrial fine silica sand (T-8) as studied granular materials. The profiles of formed craters were analyzed under various experimental conditions, defined by different spherical projectile diameters and fall heights, accounting for the low kinetic energy at the granular surface contact of 0.1-0.5 mJ. In machine learning, we applied logistic regression models to classify target granular materials based on crater profile features, revealing primary morphological differences influenced by the given granular properties. The particle distribution from X-CT imaging revealed notable differences in the granular behavior of regolith simulants due to impact compared with T-8. These results underscored significant differences between the granular properties of terrestrial fine sand and regolith simulants. Additionally, our X-CT data emphasized that the high cohesion of LMS-1 significantly enhanced its resistance to impact, resulting in the porosity decline beneath the crater bottom of 0.20 and 0.03 on average in the LHS-1 and LMS-1 media, respectively, when the kinetic energy was 0.3 mJ. Our findings highlight the distinctive granular properties of regolith particles, advancing our understanding of their granular responses to impact.
Subjects
Computerized tomography
Jurassic
Logistic regression
Martian surface analysis
Crater morphology
Energy
Extraterrestrial exploration
Granular dynamics
Impact response
Machine-learning
Morphology classification
Property
Tomography imaging
X-ray computed tomography
Silica sand
Publisher
AIP Publishing
Type
journal article