Integrating Model-Based Systems and Digital Engineering for Crewed Mars Mission Planning
AffiliationUniversity of Arizona
Model Based System Engineering
Space Systems Engineering
In Situ Resource Utilization
Heavy Lift Launch Vehicle
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CitationKirshner, M., & Valerdi, R. (2022). Integrating Model-Based Systems and Digital Engineering for Crewed Mars Mission Planning. Journal of Aerospace Information Systems, 19(10), 668–676.
RightsCopyright © 2021 by Mitchell Kirshner. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractWith technological advances in the 21st century and the rise of the commercial space industry, crewed Mars missions to land humans on our red neighbor have become increasingly feasible. One of the key challenges in accomplishing this goal is determining whether model-based systems engineering training is necessary for space systems engineers to adopt digital engineering. This paper reviews model-based systems engineering benefits and limitations, as well as historically significant crewed Mars missions that planned to land on the surface. Models are presented for a Mars transit habitat developed in the modeling language SysML using Cameo Systems Modeler. These models leverage Cameo’s simulation capabilities to execute MATLAB scripts integrating digital engineering software Systems Tool Kit into the modeling process, demonstrating a reusable interoperability methodology for Mars mission planning not yet existing in this domain’s literature. It can be concluded that integrating model-based systems engineering with digital engineering for crewed Mars landing mission planning can lead to multiple benefits for space systems digital engineering, including 1) greater system simulation capabilities, 2) enhanced model fidelity, and 3) improved system understanding. Future research to further enhance these benefits includes adding cybersecurity considerations and greater system detail to the models.
VersionFinal accepted manuscript
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