PhD Defense by Stephanie Y. Zhu

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Stephanie Y. Zhu
(Advisor: Prof. Dimitri Mavris)

will defend a doctoral thesis entitled,

Development and Acquisition Modeling for Space Campaign Architecting


Thursday, March 7 at 10:00 a.m. EST
Collaborative Visualization Environment (CoVE)

Weber Space Science and Technology Building (SST III)


Microsoft Teams

Contemporary and future space exploration endeavors are highly complex. Multiple system deployments and concurrent missions—examined as part of a space campaign—are needed to fulfill long-term goals. Given the large time horizon and the development of new space systems to perform those missions, not all of the enabling technologies have been matured nor are the new systems completely designed, developed, and tested for implementation. Current conceptual phase methodology for Space Campaign Architecting (SCA) does not adequately account for the development and acquisition (D&A) of new systems and their dependent technologies. The D&A of new systems encompasses the time and resources for technology research and development (R&D), as well as subsystems and systems design, development, testing, and evaluation (DDT&E). This timing and resource allocation for D&A must be included when planning the main missions within a space campaign; otherwise, lack of accounting will result in programmatic gaps when architecting a campaign baseline, which affects the feasibility and viability of the constituent mission architectures.

This dissertation presents an approach to model and assess systems D&A for conceptual phase SCA. The SCA methodology is formalized to establish a System-of-Systems (SoS) taxonomy of campaign elements, and then an ontology is defined to map campaign elements to the D&A subproblem space to represent high-level programmatic decision-making. A Binary Integer Programming (BIP) optimization method is applied to the resulting D&A subproblem definition to formulate a programmatic assessment that represents multiple SoS levels of a campaign.

Two objective formulations are presented; each simulates a separate programmatic decision-making scenario with differing campaign-level goals and high-level interests. The first formulation is analogous to the programmatic goal of achieving a target end state or capability as quickly as possible—the choice of which systems’ D&A to invest in and when should be organized such that the total time required to reach the final mission architecture is minimized. The second formulation models decision-making scenarios where qualitative programmatic goals are quantified, and the choice of system D&A will achieve a mission architecture that best fulfills those goals through the assignation of interest points and maximizing the total points.

Both formulations are demonstrated on test campaigns of varying sizes and campaign scenario parameters to investigate the resilience of the presented approach in face of first-order campaign variations. The approach overall is able to represent campaigns within both scenarios of minimize time and maximize value, and both feasible and infeasible results from the optimization are shown to have utility in application. Experimental results provide further application insight into scoping a given campaign’s representation, signaling possible programmatic trade-offs, and the need to undergo proper quantification exercises. The Design Reference Architecture (DRA) 5.0 Nuclear Thermal Propulsion (NTP) mission architecture is the final demonstration of the programmatic assessment to represent a larger campaign and capturing the impact of system D&A.



  • Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
  • Prof. Koki Ho – School of Aerospace Engineering
  • Prof. Glenn Lightsey – School of Aerospace Engineering
  • Dr. Bradford Robertson – School of Aerospace Engineering
  • Dr. Stephen Edwards – Advanced Concepts Office, NASA Marshall Space Flight Center


  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:02/29/2024
  • Modified By:Tatianna Richardson
  • Modified:02/29/2024



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