Cislunar system of systems architecture evaluation and optimization
| dc.contributor.author | Duffy, Laura, author | |
| dc.contributor.author | Adams, Jim, advisor | |
| dc.contributor.author | Sega, Ronald M., committee member | |
| dc.contributor.author | Herber, Daniel R., committee member | |
| dc.contributor.author | Fankell, Douglas, committee member | |
| dc.date.accessioned | 2023-06-01T23:55:58Z | |
| dc.date.available | 2023-06-01T23:55:58Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Cislunar space is the next frontier of space exploration, but a sustainable architecture is lacking. Cislunar space is considered a complex system of systems because it consists of multiple independent systems that work together to deliver unique capabilities. The independent systems of the cislunar system of systems include the communications, navigation, and domain awareness systems. Additionally, the methodology to design, evaluate and optimize a complex system of systems has not been published. To close the gap, a comprehensive needs analysis is performed for cislunar space. Next, model-based systems engineering is used to design the cislunar system of systems. The cislunar architectures are designed in terms of constellations and payloads. The architectures are each evaluated in terms of cost and performance. An appropriate optimization algorithm is found for the system of systems, and the results of the optimization are evaluated using multiple techniques for comparison. A literature review is included on the topics of cislunar architectures, system of systems, model-based systems engineering, system architecture evaluation, and system architecture optimization. During the research of cislunar architectures, a needs analysis is completed which identifies the three primary missions planned for cislunar space and eight supporting functions to provide the infrastructure for the primary missions. The primary missions identified include science, commerce, and defense. The eight supporting functions identified include transportation, communication, domain awareness, service, energy, shelter, and control. Technologies and programs are identified for each supporting function, included gaps in needed technology or programs. For the evaluation and optimization of the system of systems, the supporting functions are down-selected to include only the three necessary supporting functions for any operations in cislunar space: communications, navigation, and domain awareness. A system architecture is developed using Systems Modeling Language in Cameo Systems ModelerTM. The model is designed using the Model-based Systems Architecture Process which includes the design of the Operational Viewpoint, Logical/Functional Viewpoint, and Physical Viewpoint. The Operational Viewpoint includes structural, behavioral, data, and contextual perspectives. The Logical/Functional Viewpoint includes structural, behavioral, data, and contextual perspectives. The Physical Viewpoint includes design, standards, data, and contextual perspectives. Each of these perspectives are represented in the form of Cameo Systems ModelerTM diagrams or tables. Diagrams include block definition diagrams, internal block diagrams, use case diagrams, activity diagrams, and sequence diagrams. Additional modeling concepts beyond the Model-based Systems Architecture Process are included in the Cameo Systems ModelerTM model and analysis of the model. These topics include allocating requirements, stereotypes, patterns in architecture decisions, architecture optimization, verification, validation, complexity, and open systems architecture. Cislunar constellations and payloads are designed which account for the cislunar physical environment. Six constellations are designed to be included in the optimization algorithm. These constellations include Lagrange light, Lagrange medium, Lagrange heavy, Earth-based, Earth plus Moon, and Earth plus Lagrange. These constellations essentially represent the location of the bus while the payloads provide the functionality of the system. Payloads are designed for the supporting functions deemed essential for a basic cislunar infrastructure, which are communications, navigation, and domain awareness. The optimization algorithm runs through each possible combination of payload and bus, including any opportunities to integrate multiple payloads on a single bus. The total number of possible architecture combinations for the optimization algorithm is 288. The payload sensors are modeled in Systems Tool Kit and evaluated for physical performance. Additionally, each payload and bus possibility are evaluated for cost using the Unmanned Space Vehicle Cost Model and professional estimates. The performance and cost metrics are used in the optimization algorithm. The optimization algorithm uses multi-objective optimization with an integer linear program. The result of the optimization algorithm is a pareto front of the highest-performance, lowest-cost architectures. The architectures along the pareto front are evaluated using multi-criteria decision making with and without evidential reasoning to find the "best" architecture. A Kiviat chart assessment is also performed, though this method is shown to not be practical for the cislunar application. The model and conclusions of the dissertation are validated using a variety of industry-accepted techniques. The cislunar architectures are validated via peer-review. The performance evaluations are validated via a validated physics model. The cost evaluations are validated by a validated cost-model when possible and by peer-review. The optimization algorithm is validated by comparison to a manual optimization method. The Cameo Systems ModelerTM model is validated using validation techniques internal to the tool. Suggestions for future work are presented. Future work could include fully integrating the Cameo Systems ModelerTM model with the Systems Tool Kit model, providing improved cost estimates, using alternative optimization parameters, adding supporting functions as they are identified, evaluating the architectures using additional metrics, evaluating additional constellations, applying integration at the functional level, or assessing non-homogenous requirements. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Duffy_colostate_0053A_17663.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/236673 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | architecture optimization | |
| dc.subject | system of systems | |
| dc.subject | cislunar | |
| dc.subject | architecture evaluation | |
| dc.title | Cislunar system of systems architecture evaluation and optimization | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Systems Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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