Ph.D. Defense
Efe Yamac Yarbasi
(Advisor: Prof. Mavris)
"A Methodology for Identifying Experiments for Uncertainty Mitigation in Complex Multi-Disciplinary Design"
Tuesday, July 27th
8:30 a.m.
Collaborative Visualization Environment (CoVE)
Weber Space Science and Technology Building (SST II)
And
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Meeting ID: 214 323 108 816
Passcode: kqh5Zi
Abstract
Design of a flight vehicle is a long, costly process that takes many years. Thanks to the increase in computational capabilities, designers have been relying on computer models to make predictions about the real-life performance of an aircraft. However, the push for more efficient aircraft necessitates incorporation novel technologies and configurations, for which the historical data does not exist. Due to the lack of understanding of the physics phenomena, modeling and product abstractions; the results obtained from computational tools are uncertain, creating risk in the product and program. Any decision made on quantities involving significant uncertainty may result in budget overruns, schedule delays and performance shortcomings including safety concerns. Therefore, the goal of this thesis is to develop a systematic methodology to identify and mitigate the sources uncertainty in aircraft design.
An aircraft is a complex, multi-disciplinary system that is built as integration of other highly-complex subsystems. In order to make sure that all subsystems and the integrated system meet pre-defined requirements, Systems Engineering (SE) practices are widely adopted throughout the aerospace industry. However, SE methods fall short of accounting for the implications of used modeling and simulation environments, and simulation-borne uncertainties in the overall process design. The first goal of this thesis is to develop a systematic framework based on established SE principles, so that a fit-for-purpose modeling and simulation (M&S) environment can be developed. A M&S environment developed for complex, multi-disciplinary aerospace systems will still include many parameters representing physical components and the physics phenomena. Because of the sheer number of such parameters in aircraft design, there will be uncertainty parameters. Due to practical limitations, modeling every component under every possible scenario with high-fidelity tools or physical experiments is simply infeasible. The second research area of this thesis addresses some prominent issues faced in identifying critical uncertainties, so that resources can be allocated to uncertainties that would make the biggest impact on the design. After the critical uncertainties are identified, computational and/or physical experiments can be designed to create new information, so that any uncertainty due to a lack of knowledge (i.e., epistemic) can be reduced. However, real-life operational conditions cannot be exactly duplicated in tests due to many reasons such as testing facility constraints. The third and final research area addresses the identification of optimal experiment conditions for computational and physical experiments such that real-life operational conditions can be best approximated. The overall objective of this thesis is to develop and demonstrate a methodology to identify and mitigate uncertainties by tying these research areas.
Committee
- Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
- Prof. Daniel Schrage– School of Aerospace Engineering
- Prof. Graeme J. Kennedy – School of Aerospace Engineering
- Dr. Burak Bagdatli – Research Engineer II, School of Aerospace Engineering
- Dr. Eric Walker – Chief Engineer for Test Operations Excellence, NASA Langley Research Center