Di Wu, Massachusetts Institute of Technology; Daniel Jang, Massachusetts Institute of Technology; Guillermo D Mendoza Contreras, Massachusetts Institute of Technology; Richard Linares, Massachusetts Institute of Technology
Keywords: SSA/SDA, Space Population, Temporal Model, Policy
Abstract:
The ability to analyze and assess the changing scenes of space population and orbit capacity is foundational for impactful space situational and domain awareness (SSA/SDA). Trustworthy analysis and characterization of the evolution of space population, in particular, are essential in ensuring effective space traffic management (STM) and sustainable space development. Existing analysis has shown the unabated expansion of space population, leading to the infeasible orbit environment in the future. This ominous future leads to the development of debris mitigation techniques and discussion on policy changes. Analysis on these various techniques and policies, ranging from collision avoidance maneuvers, responsible satellite disposal practices, and other proactive measures, despite showing the grand benefits they could bring to the long-term space development, is plagued by the assumption of a prompt implementation of the changes. The hidden question on the timeline of consecutive changes and their effects is largely unexplored (i.e., due to adapting to policy and gradual improvement in technology), along with the analysis on temporal difference for when to initiate such changes. We have adapted and conducted extensive development on MOCAT-MC (MIT Orbital Capacity Assessment Tool – Monte Carlo), enabling robust, trackable, and explainable space population behaviors. The quantification of the temporal analysis, using MOCAT-MC, on possible changes for space sustainability offers a groundbreaking investigation for answering the implementation problems in sustainable space development.
Monte-Carlo-based framework has been widely employed in predicting the future of space population due to its ability to capture the complex space population evolution characterized by the chaotic interaction between space objects, the randomness in debris generation events, and the existence of nonlinear dynamics in circumterrestrial space. The explicit dynamics and trajectory history for a single object in the evolution of the whole space environment, effectively a digital twin of the circumterrestrial space environment, are vital in providing self-evident reasoning behind any Monte Carlo running. Simplification in the dynamics ensures that the propagation of a single space object, the basic component of the system, is efficient while maintaining space population’s statistical stability — close-encounters and natural decays are statistically reliable. The temporal evolution of the space population is determined by the sequential interaction between propagation, decaying, new launching, control, debris generation from explosion and collision. Environment parameters that dictate this evolution are typically predetermined throughout the Monte Carlo simulation, representing certain fundamental assumptions.These assumptions include, but are not limited to, simplifications in the dynamics model (such as the oblateness coefficient and atmosphere density) and operational parameters (such as the post-mission disposal rate). MOCAT is developed in such a way that even a single laptop could efficiently run and generate analyses of space population evolution.
The temporal modeling in the framework of MOCAT for various collision mitigation strategies and regulations has not been adequately explored, partly due to the traditional emphasis on qualitative performance. In addition, the question of which policy or combination of multiple policies and in what sequence could offer a paradigm-shifting framework for solving the cardinal problem in SSA/SDA and ensuring sustainable use of the congested orbital environment remains unexplored. The quantification of “sooner or later”, i.e., how late is too late for a sequence of policies change in space sustainability, is also a rarely asked question. We have developed an extensive set of advanced computational models for simulating the long-term space population around Earth, balancing both stability and computational efficiency. We will show herein how to convert policies and techniques for collision mitigation into temporal model to feed into the MOCAT framework. Subsequently, we will quantitatively evaluate their effectiveness in curbing the growth of space population and mitigating the risk of space debris proliferation. For example, the analysis of an early implementation of active debris removal could likely induce a much lower space population in the long run; but quantifying the extent to which it would be lower compared to implementing it later is crucial for assessing its effectiveness. We will also explore existing economical model as a way to connect the physical implication from space population with intuitive economic metrics. This work will not only contribute to our understanding of the evolving space environment but also informs policymakers and stakeholders on the necessary steps to ensure a sustainable and secure orbital environment for future generations.
Date of Conference: September 17-20, 2024
Track: Space Domain Awareness