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Uncertainty analysis of an aviation climate model and an aircraft price model for assessment of environmental effects

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dc.contributor.advisor Ian Waitz. en_US
dc.contributor.author Jun, Mina en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.date.accessioned 2008-09-03T14:51:10Z
dc.date.available 2008-09-03T14:51:10Z
dc.date.copyright 2007 en_US
dc.date.issued 2007 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/42191
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007. en_US
dc.description Includes bibliographical references (p. 91-95). en_US
dc.description.abstract Estimating, presenting, and assessing uncertainties are important parts in assessment of a complex system. This thesis focuses on the assessment of uncertainty in the price module and the climate module in the Aviation Environmental Portfolio Management Tool (APMT). The aircraft price module is a part of the Partial Equilibrium Block (PEB) and the climate module is a part of the Benefits Valuation Block (BVB) of the APMT. The PEB estimates a future fleet and flight schedule and evaluates manufacturer costs, operator costs, and consumer surplus. The BVB estimates changes in health and welfare for climate, local air quality, and noise from noise and emissions inventories output from the Aviation Environmental Design Tool (AEDT). The assessment was conducted with various uncertainty assessment and sensitivity analysis methods: the nominal range sensitivity analysis (NRSA), the hybrid Monte Carlo sensitivity analysis, the Monte Carlo regression analysis, the vary-all-but-one Monte Carlo analysis, and the global sensitivity analysis with Sobol' indices and total sensitivity indices. Except the NRSA, all other analysis methods are based on the Monte Carlo simulation with random sampling. All uncertainty assessment methods provided the same ranking of significant variables in both APMT modules. Two or three significant variables are clearly distinguished from other insignificant variables. In the price module, seat coefficients are the most significant parameters, and age is an insignificant factor between input variables of the regression model. In the climate module, statistical analyses showed that climate sensitivity and short-lived RF are most significant variables that contribute the variability of all three outputs. However, the HMC analysis suggested that discount rate is the most sensitive factor in the NPV estimation. en_US
dc.description.abstract (cont.) Comparing the Sobol's indices with the total sensitivity indices showed that there are no significant interactions to change the ranking of significant variables in both modules. en_US
dc.description.statementofresponsibility by Mina Jun. en_US
dc.format.extent 95 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Aeronautics and Astronautics. en_US
dc.title Uncertainty analysis of an aviation climate model and an aircraft price model for assessment of environmental effects en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.identifier.oclc 229893970 en_US


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