Heterogeneous Satellite Constellation Design for Cislunar Space Situational Awareness Using Real Options Analysis
Author(s)
Wachs, Jordan
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Advisor
Cahoy, Kerri
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The number of commercial satellites in Low Earth Orbit (LEO) increased more than four-fold in the decade between 2011 and 2021, a trend that is likely to continue due to the combination of increasingly capable technologies and business models [23] [24]. Building on the success of the LEO economy, there is potential for the establishment of a cislunar economy, supporting NASA and providing commercial goods and services well beyond LEO [20].
Successful creation of a cislunar economy will require commercial and government cooperation at an altogether new scale. The vastness of this new domain, however, makes the implementation of an adequate Space Situational Awareness (SSA) capability both necessary and extraordinarily difficult. Future aerospace leaders may find them selves breaking from traditional paradigms for architecture definition, systems engineering, and program costing [43]. Rather than wholesale abandonment of the processes that resulted in decades of successful innovation, however, future architects will benefit from the adoption of a new framework of systems thinking that combines the hard-won knowledge from the Aerospace domain with similarly deep lessons learned from other domains such as Finance and Real Options.
This thesis explores a new framework for the analysis of large, dynamic, heterogeneous satellite constellation architectures by focusing on a multi-mode cislunar (SSA) capability. The combination of technical performance models, financial models, and real options analyses into a single tool allows system architects to identify and maintain multiple paths to program success by preserving flexibility in design and implementation throughout the program life cycle. The analyses presented in this thesis explore how Real Options can be used to increase the probability of mission success from as low as 2% to as high as 72% despite wide ranging uncertainty in system cost and performance.
Date issued
2023-02Department
System Design and Management Program.Publisher
Massachusetts Institute of Technology