Assessing Programmatic Variables and Uncertainties in Space Exploration Campaign Planning
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Gollins, Nicholas J.
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Abstract
All space exploration programs begin life as a set of requirements, driven by one or more scientific or technological objectives. Historically, the majority of those objectives were achieved using tailor-made spacecraft and systems to deliver their payloads, be they science experiments or astronaut crews, to their intended location. For example, the Apollo spacecraft were designed to deliver and return a specific amount of mass to and from a specific set of locations on the Moon; and most ISS-servicing vehicles were designed specifically to do just that. More recently, however, the number of vehicles that could service space exploration logistics needs has greatly increased. This is particularly true in the case of lunar exploration, largely due to the Artemis and CLPS programs, supported by government space agencies and private companies in the U.S. and across the world.
In the earliest stages of space program planning, the program or mission architect must find the most effective method by which to achieve the program objectives. ``Effective'', in this case, covers a wide range of needs, such as affordability, reliability, and robustness to uncertainties in the development cycle of the mission(s). It is the program planner's role to uncover the architecture that best satisfies these needs from among the mire of available options and associated uncertainties. The increasingly-large number of options of novel technologies and logistics vehicles, some that already exist but many that are still under development, coupled with the ambitious exploration objectives of programs like Artemis creates a large decision space for the program planner.
Space logistics, as a field, is concerned with the efficient planning of space missions. A wide number of classical logistics problems, when applied to space, offer methods for assessing the effectiveness of a range of mission types. Exploration missions can be modeled using network flow formulations, in which mixed-integer linear programming is used to find the most efficient flow of commodities through a network given a set of supplies, demands, and vehicles. However, in the early stages of program planning, many of the parameters of the network flow model are undefined, representing, for example, uncertain vehicle performances, payload masses, or launch schedules. Some of these are uncertainties inherent to the fact that some payloads and vehicles are still in their development cycles. Others represent decisions to be made by the program architect. Therefore, this thesis will explore methods by campaign model parameters can be analyzed, and their effect on the results of the model optimization can be assessed.
With a focus on launch schedule as an example of a programmatic variable, the methods developed here are used to find optimal launch schedules for a cislunar exploration campaign. In the deterministic case, the method is also used to study the impact of varying logistic provider availability on the optimal mission plan, and therefore make recommendations about logistics provider redundancies. In the stochastic case, the analyses are extended to identify the most-likely-optimal launch time for each payload in the campaign and most-likely-optimal overall schedule. The probability of a specific logistics vehicle be used in the campaign, and the number of times that the specific vehicle design is likely used, is also able to be calculated using these methods. This provides the campaign planner with some insights into the value of each available logistics vehicle in improving the robustness of the campaign under launch uncertainty.
Finally, a parametric programming method is employed to study how the optimal cislunar logistics plan varies as vehicle, payload, or commodity dynamics parameters change. The end result of this is the identification of regions within the parameter space for which the optimal plan remains feasible. These regions in parameter space can then be used to define requirements for the systems to which those parameters pertain. In the cislunar logistics case study, for example, the method is used to identify the bounds on in-situ-produced rocket propellant production rates and infrastructure maintenance requirements such that ISRU capability should, or should not, be incorporated into a logistically-optimal mission plan.
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2025-12
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Dissertation (PhD)