A Multi-Level, Hierarchical Approach to Technology Selection and Optimization

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AIAA-2002-5423.pdf (734.9 KB)
Author(s)
Nixon, Janel Nicole
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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http://hdl.handle.net/1853/6299
Abstract
Advances in performance and increases in revenue are most often facilitated by the development and application of new technologies. Recent efforts in multidisciplinary design have yielded methods for the evaluation and selection of technologies in the presence of uncertainty. Many of these methods aim to forecast the impacts of new technologies amidst the uncertainties associated with technology performance and operating conditions. These forecasting abilities aid in the selection of the technology that gives the highest probability of success. Many methods offer efficient probabilistic assessments that allow the designer to extract the optimal solution. However, a single optimal solution may not be sufficient for systems that are heavily influenced by operating conditions. All aerospace and industrial power systems are influenced by at least a few parameters such as air density, pressure, temperature, humidity, etc. For instance, power plant output fluctuates significantly with changes in ambient conditions. In order to evaluate proposed technologies for such a system, a new approach is needed in order to define a framework where operational uncertainties may be quantified and modeled. A robust design methodology has been developed, whereby operating conditions and their impacts can be modeled easily and accurately. An industrial gas turbine power plant is used as an example, and the proposed methodology is integrated with existing methods developed by Mavris and Kirby in order to predict the overall impact of a technology over a yearlong period of operation in a specified region. This paper demonstrates how to use this model to refine the design of the technology. Hence, the technology development is treated as a suboptimization problem in which the optimum design settings of the technologies are found. This ambient model is then used to forecast the impact of each technology. Finally, these results are then used to select the most promising technology for implementation into the final design.
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2002-09
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1905 bytes
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