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dc.contributor.advisorOlivier L. de Weck, Rebecca A. Masterson, Brian C. Williams and Michel D. Ingham.en_US
dc.contributor.authorChodas, Mark A.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Aeronautics and Astronautics.en_US
dc.date.accessioned2019-10-04T21:30:24Z
dc.date.available2019-10-04T21:30:24Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122369
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 193-202).en_US
dc.description.abstractWhen developing a space system, many properties of the design space are initially unknown and are discovered during the development process. Therefore, the problem exhibits deep uncertainty. Deep uncertainty refers to the condition where the full range of outcomes of a decision is not knowable. A key strategy to mitigate deep uncertainty is to update decisions when new information is learned. NASA's current uncertainty management processes do not emphasize revisiting decisions and therefore are vulnerable to deep uncertainty. Examples from the development of the James Webb Space Telescope are provided to illustrate these vulnerabilities. In this research, the spacecraft development problem is modeled as a dynamic, chance-constrained, stochastic optimization problem. The Model-based Adaptive Design under Uncertainty (MADU) framework is introduced, in which conflict-directed search is combined with reuse of conflicts to solve the problem efficiently.en_US
dc.description.abstractThe framework is built within a Model-based Systems Engineering (MBSE) paradigm in which a SysML model contains the design and conflicts identified during search. Changes between problems can involve the addition or removal a design variable, expansion or contraction of the domain of a design variable, addition or removal of constraints, or changes to the objective function. These changes are processed to determine their effect on the set of known conflicts. Using Python, an optimization problem is composed from information in the SysML model, including conflicts from past problems, and is solved using IBM ILOG CP Optimizer. The framework is tested on a case study drawn from the thermal design of the REgolith X-ray Imaging Spectrometer (REXIS) instrument and a case study based on the Starshade exoplanet direct imaging mission concept which is sizeable at 35 design variables, 40 constraints, and 10¹⁰ possible solutions.en_US
dc.description.abstractIn these case studies, the MADU framework performs 58% faster on average than an algorithm that doesn't reuse information. Adding a requirement or changing the objective function are particularly efficient types of changes. With this framework, designers can more efficiently explore the design space and perform updates to a design when new information is learned.en_US
dc.description.sponsorship"This research was supported by a NASA Space Technology Research Fellowship (NNX14AL57H) and by the REXIS project (NN12FD70C, PO363458)"--Page 5.en_US
dc.description.statementofresponsibilityby Mark A. Chodas.en_US
dc.format.extent202 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectAeronautics and Astronautics.en_US
dc.titleAddressing deep uncertainty in space system development through model-based adaptive designen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.identifier.oclc1119667510en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Department of Aeronautics and Astronauticsen_US
dspace.imported2019-10-04T21:30:24Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentAeroen_US


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