Simulating the Potential of an In-Space Fragmentation of the Lunar Reconnaissance Orbiter

Nicolò Trabacchin, Cinzia Giacomuzzo, Lorenzo Olivieri
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Abstract

The rapid expansion of activities in the cislunar region introduces new challenges for ensuring the long-term safety and sustainability of lunar operations. With hundreds of missions planned in the coming decades, the risk of orbital congestion in Low Lunar Orbit (LLO) is becoming increasingly relevant. Recent conjunctions involving active spacecraft, such as NASA’s Lunar Reconnaissance Orbiter (LRO) and KARI’s Korea Pathfinder Lunar Orbiter (KPLO), underline the need for effective Space Situational Awareness (SSA) and Space Traffic Management (STM) strategies in this domain. This work presents a preliminary effort to characterize the potential debris environment in LLO through high-fidelity fragmentation simulations using the Collision Simulation Tool Solver (CSTS), a semi-empirical code developed at the University of Padova. Specifically, this study investigates two critical breakup scenarios: an explosion of the LRO and a collision between the LRO and the KPLO. The tool enables the modeling of complex breakup scenarios and the derivation of statistically representative debris populations for subsequent dynamical analyses. The resulting synthetic datasets provide an initial foundation for understanding the spatial distribution and evolution of debris clouds in the lunar environment. Such analyses are critical for supporting future mission design, risk assessment, and operational decision-making, ultimately contributing to the establishment of a sustainable framework for cislunar space operations.

模拟月球侦察轨道器在空间破碎的可能性
地月区域活动的迅速扩大为确保月球行动的长期安全和可持续性带来了新的挑战。随着未来几十年计划进行的数百次任务,低月球轨道(LLO)轨道拥堵的风险正变得越来越重要。最近涉及主动航天器的联合,如NASA的月球侦察轨道器(LRO)和KARI的韩国探路者月球轨道器(KPLO),强调了在该领域有效的空间态势感知(SSA)和空间交通管理(STM)战略的必要性。这项工作通过使用碰撞模拟工具求解器(CSTS)(由帕多瓦大学开发的半经验代码)进行高保真碎片模拟,初步描绘了LLO中潜在碎片环境的特征。具体来说,本研究研究了两种关键的解体情景:LRO爆炸和LRO与KPLO之间的碰撞。该工具能够对复杂的破碎场景进行建模,并为随后的动力学分析推导具有统计代表性的碎片种群。由此产生的合成数据集为了解月球环境中碎片云的空间分布和演化提供了初步的基础。这种分析对于支持未来的任务设计、风险评估和业务决策至关重要,最终有助于建立可持续的地月空间业务框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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