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Karen E. Willcox. Opgenoord, Max Maria Jacques Massachusetts Institute of Technology. Department of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2016-07-01T18:22:37Z 2016-07-01T18:22:37Z 2016 2016 http://hdl.handle.net/1721.1/103423 952113632 Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 107-112). Quantification and management of uncertainty are critical in the design of engineering systems, especially in the early stages of conceptual design. This thesis presents an approach to defining budgets on the acceptable levels of uncertainty in design quantities of interest, such as the allowable risk in not meeting a critical design constraint and the allowable deviation in a system performance metric. A sensitivity-based method analyzes the effects of design decisions on satisfying those budgets, and a multiobjective optimization formulation permits the designer to explore the tradespace of uncertainty reduction activities while also accounting for a cost budget. For models that are computationally costly to evaluate, a surrogate modeling approach based on high dimensional model representation achieves efficient computation of the sensitivities. Example problems in aircraft conceptual design illustrate the approach. The first example investigates the influence of uncertainty in the propulsion technology on the overall aircraft design, whereas the second problem looks at the influence of six different uncertain design parameters from three different disciplines within the aircraft design. Secondly, the distributional sensitivity analysis (DSA) method is extended for better computational efficiency and wider applicability. Instead of assuming that all uncertainty in an input parameter can be reduced, DSA apportions output uncertainty as a function of the uncertainty reduction of a particular input parameter. This leads to more information on influences of uncertainty reduction, and to a more informative ranking of input parameters. In this thesis the ANOVA-HDMR framework is used for DSA to increase computational efficiency. Additionally, this approach allows for using DSA for more general distributions. by Max Maria Jacques Opgenoord. S.M. 112 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Aeronautics and Astronautics. Uncertainty budgeting methods for conceptual aircraft design Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4d27a9f1-d2a2-40eb-b711-36a69d4fa58d"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Uncertainty budgeting methods for conceptual aircraft design</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <Authors> <Author> <DisplayName>Opgenoord, Max Maria Jacques</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Aeronautics and Astronautics.</Keyword> <Abstract>Quantification and management of uncertainty are critical in the design of engineering systems, especially in the early stages of conceptual design. This thesis presents an approach to defining budgets on the acceptable levels of uncertainty in design quantities of interest, such as the allowable risk in not meeting a critical design constraint and the allowable deviation in a system performance metric. A sensitivity-based method analyzes the effects of design decisions on satisfying those budgets, and a multiobjective optimization formulation permits the designer to explore the tradespace of uncertainty reduction activities while also accounting for a cost budget. For models that are computationally costly to evaluate, a surrogate modeling approach based on high dimensional model representation achieves efficient computation of the sensitivities. Example problems in aircraft conceptual design illustrate the approach. The first example investigates the influence of uncertainty in the propulsion technology on the overall aircraft design, whereas the second problem looks at the influence of six different uncertain design parameters from three different disciplines within the aircraft design. Secondly, the distributional sensitivity analysis (DSA) method is extended for better computational efficiency and wider applicability. Instead of assuming that all uncertainty in an input parameter can be reduced, DSA apportions output uncertainty as a function of the uncertainty reduction of a particular input parameter. This leads to more information on influences of uncertainty reduction, and to a more informative ranking of input parameters. In this thesis the ANOVA-HDMR framework is used for DSA to increase computational efficiency. Additionally, this approach allows for using DSA for more general distributions.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>