This dissertation investigates the development of a methodology suitable for the evaluation of advanced propulsion concepts. At early stages of development, both the future performance of these concepts and their requirements are highly uncertain, making it difficult to forecast their future value. A systematic methodology to identify potential advanced propulsion concepts and assess their robustness is necessary to reduce the risk of developing advanced propulsion concepts.
Existing advanced design methodologies have evaluated the robustness of technologies or concepts to variations in requirements, but they are not suitable to evaluate a large number of dissimilar concepts. Variations in requirements have been shown to impact the development of advanced propulsion concepts, and any method designed to evaluate these concepts must incorporate the possible variations of the requirements into the assessment. In order to do so, a methodology had to do two things. First, it had to systemically identify a probabilistic distribution for the future requirements. Such a distribution would allow decision-makers to quantify the uncertainty introduced by variations in requirements. Second, the methodology must assess the robustness of the propulsion concepts as a function of that distribution.
These enabling elements have been synthesized into new methodology, the Evolving Requirements Technology Assessment (ERTA) method. The ERTA method was used to evaluate and compare advanced propulsion systems as possible power systems for a hurricane tracking, High Altitude, Long Endurance (HALE) unmanned aerial vehicle (UAV). The problem served as a good demonstration of the ERTA methodology because conventional propulsion systems will not be sufficient to power the UAV, but the requirements for such a vehicle are still uncertain.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/11555 |
| Date | 07 July 2006 |
| Creators | McClure, Erin Kathleen |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 2948761 bytes, application/pdf |
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