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

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), slid...

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Main Authors: Yeong-Bae Kim, Hyun-Jae Jeong, Shin-Mu Park, Jae Hyuk Lim, Hoon-Hee Lee
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Aerospace
Subjects:
Online Access:https://www.mdpi.com/2226-4310/8/12/380
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author Yeong-Bae Kim
Hyun-Jae Jeong
Shin-Mu Park
Jae Hyuk Lim
Hoon-Hee Lee
author_facet Yeong-Bae Kim
Hyun-Jae Jeong
Shin-Mu Park
Jae Hyuk Lim
Hoon-Hee Lee
author_sort Yeong-Bae Kim
collection DOAJ
description 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.
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spelling doaj.art-64d428d36b494576835ead57735f0c8f2023-11-23T03:18:09ZengMDPI AGAerospace2226-43102021-12-0181238010.3390/aerospace8120380Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft GroundYeong-Bae Kim0Hyun-Jae Jeong1Shin-Mu Park2Jae Hyuk Lim3Hoon-Hee Lee4Division of Mechanical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si 54896, KoreaAgency for Defense Development, 160, Bugyuseong-daero 488beon-gil, Yuseong-gu, Daejeon 34060, KoreaKorea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 34133, KoreaDivision of Mechanical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si 54896, KoreaKorea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 34133, KoreaIn 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.https://www.mdpi.com/2226-4310/8/12/380lunar landerlanding stabilityclassification mapMonte Carlo simulationlanding success rate
spellingShingle Yeong-Bae Kim
Hyun-Jae Jeong
Shin-Mu Park
Jae Hyuk Lim
Hoon-Hee Lee
Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground
Aerospace
lunar lander
landing stability
classification map
Monte Carlo simulation
landing success rate
title Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground
title_full Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground
title_fullStr Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground
title_full_unstemmed Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground
title_short Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground
title_sort prediction and validation of landing stability of a lunar lander by a classification map based on touchdown landing dynamics simulation considering soft ground
topic lunar lander
landing stability
classification map
Monte Carlo simulation
landing success rate
url https://www.mdpi.com/2226-4310/8/12/380
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