A Methodology to Enable Concurrent Trade Space Exploration of Space Campaigns and Transportation Systems
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Space exploration campaigns detail the ways and means to achieve goals for our human spaceflight programs. Significant strategic, financial, and programmatic investments over long timescales are required to execute them, and therefore must be justified to decision makers. To make an informed down-selection, many alternative campaign designs are presented at the conceptual-level, as a set and sequence of individual missions to perform that meets the goals and constraints of the campaign, either technical or programmatic. Each mission is executed by in-space transportation systems, which deliver either crew or cargo payloads to various destinations. Design of each of these transportation systems is highly dependent on campaign goals and even small changes in subsystem design parameters can prompt significant changes in the overall campaign strategy. However, the current state of the art describes campaign and vehicle design processes that are generally performed independently, which limits the ability to assess these sensitive impacts. The objective of this research is to establish a methodology for space exploration campaign design that represents transportation systems as a collection of subsystems and integrates its design process to enable concurrent trade space exploration. More specifically, the goal is to identify existing campaign and vehicle design processes to use as a foundation for improvement and eventual integration. In the past two decades, researchers have adopted terrestrial logistics and supply chain optimization processes to the space campaign design problem by accounting for the challenges that accompany space travel. Fundamentally, a space campaign is formulated as a network design problem where destinations, such as orbits or surfaces of planetary bodies, are represented as nodes with the routes between them as arcs. The objective of this design problem is to optimize the flow of commodities within network using available transport systems. Given the dynamic nature and the number of commodities involved, each campaign can be modeled as a time-expanded, generalized multi-commodity network flow and solved using a mixed integer programming algorithm. To address the challenge of modeling complex concept of operations (ConOps), this formulation was extended to include paths as a set of arcs, further enabling the inclusion of vehicle stacks and payload transfers in the campaign optimization process. Further, with the focus of transportation system within this research, the typical fixed orbital nodes in the logistics network are modified to represent ranges of orbits, categorized by their characteristic energy. This enables the vehicle design process to vary each orbit in the mission as it desires to find the best one per vehicle. By extension, once integrated, arc costs of dV and dT are updated each iteration. Once campaign goals and external constraints are included, the formulated campaign design process generates alternatives at the conceptual level, where each one identifies the optimal set and sequence of missions to perform. Representing transportation systems as a collection of subsystems introduces challenges in the design of each vehicle, with a high degree of coupling between each subsystem as well as the driving mission. Additionally, sizing of each subsystem can have many inputs and outputs linked across the system, resulting in a complex, multi-disciplinary analysis, and optimization problem. By leveraging the ontology within the Dynamic Rocket Equation Tool, DYREQT, this problem can be solved rapidly by defining each system as a hierarchy of elements and subelements, the latter corresponding to external subsystem-level sizing models. DYREQT also enables the construction of individual missions as a series of events, which can be directly driven and generated by the mission set found by the campaign optimization process. This process produces sized vehicles iteratively by using the mission input, subsystem level sizing models, and the ideal rocket equation. By conducting a literature review of campaign and vehicle design processes, the different pieces of the overall methodology are identified, but not the structure. The specific iterative solver, the corresponding convergence criteria, and initialization scheme are the primary areas for experimentation of this thesis. Using NASA’s reference 3-element Human Landing System campaign, the results of these experiments show that the methodology performs best with the vehicle sizing and synthesis process initializing and a path guess that minimizes dV. Further, a converged solution is found faster using non-linear Gauss Seidel fixed point iteration over Jacobi and set of convergence criteria that covers vehicle masses and mission data. To show improvement over the state of the art, and how it enables concurrent trade studies, this methodology is used at scale in a demonstration using NASA’s Design Reference Architecture 5.0. The LH2 Nuclear Thermal Propulsion (NTP) option is traded with NH3and H2O at the vehicle-level as a way to show the impacts of alternative propellants on the vehicle sizing and campaign strategy. Martian surface stay duration is traded at the campaign-level through two options: long-stay and short-stay. The methodology was able to produce four alternative campaigns over the course of two weeks, which provided data about the launch and aggregation strategy, mission profiles, high-level figures of merit, and subsystem-level vehicle sizes for each alternative. Expectedly, with their lower specific impulses, alternative NTP propellants showed significant growth in the overall mass required to execute each campaign, subsequently represented the number of drop tanks and launches. Further, the short-stay campaign option showed a similar overall mass required compared to its long-stay counterpart, but higher overall costs even given the fewer elements required. Both trade studies supported the overall hypothesis and that integrating the campaign and vehicle design processes addresses the coupling between then and directly shows the impacts of their sensitivities on each other. As a result, the research objective was fulfilled by producing a methodology that was able to address the key gaps identified in the current state of the art.
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