A New Monte-Carlo Model for the Space Environment
Daniel Jang, Davide Gusmini, Peng Mun Siew, Andrea D'Ambrosio, Simone, Servadio, Pablo Machuca, Richard Linares

TL;DR
This paper presents a novel Monte Carlo simulation method for modeling the space environment, significantly improving computational efficiency to analyze long-term debris evolution and scenarios involving large satellite constellations.
Contribution
The paper introduces a new Monte Carlo approach that enhances orbit propagation efficiency, enabling large-scale, long-term space debris environment simulations with over 80,000 payloads.
Findings
Validated against IADC study results
Enabled simulation of over 80,000 payloads over 200 years
Achieved a fivefold increase in object count compared to previous models
Abstract
This paper introduces a novel Monte Carlo (MC) method to simulate the evolution of the low-earth orbit environment, enhancing the MIT Orbital Capacity Analysis Tool (MOCAT). In recent decades, numerous space environment models have been developed by government agencies and research groups to understand and predict the dynamics of space debris. Our MC approach advances this by simulating the trajectories of space objects and modeling their interactions, such as collisions and explosions. This aids in analyzing the trends of space-object and debris populations. A key innovation of our method is the computational efficiency in orbit propagation, which is crucial for handling potentially large numbers of objects over centuries. We present validation results against the IADC (Inter-Agency Space Debris Coordination Committee) study and explore various scenarios, including ones without future…
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Taxonomy
TopicsSpacecraft Design and Technology
