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Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground

Authors :
Yeong-Bae Kim
Hyun-Jae Jeong
Shin-Mu Park
Jae Hyuk Lim
Hoon-Hee Lee
Source :
Aerospace, Vol 8, Iss 12, p 380 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

In this paper, a method for predicting the landing stability of a lunar lander by a classification map of the landing stability is proposed, considering the soft soil characteristics and the slope angle of the lunar surface. First, the landing stability condition in terms of the safe (=stable), sliding (=unstable), and tip-over (=statically unstable) possibilities was checked by dropping a lunar lander onto flat lunar surfaces through finite-element (FE) simulation according to the slope angle, friction coefficient, and soft/rigid ground, while the vertical touchdown velocity was maintained at 3 m/s. All of the simulation results were classified by a classification map with the aid of logistic regression, a machine-learning classification algorithm. Finally, the landing stability status was efficiently predicted by Monte Carlo (MC) simulation by just referring to the classification map for 10,000 input datasets, consisting of the friction coefficient, slope angles, and rigid/soft ground. To demonstrate the performance, two virtual lunar surfaces were employed based on a 3D terrain map of the LRO mission. Then, the landing stability was validated through landing simulation of an FE model of a lunar lander requiring high computation cost. The prediction results showed excellent agreement with those of landing simulations with a negligible computational cost of around a few seconds.

Details

Language :
English
ISSN :
22264310 and 49457683
Volume :
8
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Aerospace
Publication Type :
Academic Journal
Accession number :
edsdoj.64d428d36b494576835ead57735f0c8f
Document Type :
article
Full Text :
https://doi.org/10.3390/aerospace8120380