THESIS
2018
xii, 66 pages : illustrations (some color) ; 30 cm
Abstract
The rapid growth in commercial air transportation and the volatility of fuel price push for fuel
reduction policy to be implemented. Some changes in technology (e.g., improved aircraft design
or engine), operations (e.g., improved flight routes), or both have showed promising results on fuel
reduction in air transportation. While there are some existing fuel burn evaluation models, some of
them are computationally expensive or built based on data that might be outdated; and some others
suffer from the lack of accuracy due to simplification assumptions and computations. This might
impose limitations in the aforementioned policy analysis, in particular when we need to predict
future projections for different scenarios. As such, I develop a fast, efficient, and yet accurate fuel
bu...[
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The rapid growth in commercial air transportation and the volatility of fuel price push for fuel
reduction policy to be implemented. Some changes in technology (e.g., improved aircraft design
or engine), operations (e.g., improved flight routes), or both have showed promising results on fuel
reduction in air transportation. While there are some existing fuel burn evaluation models, some of
them are computationally expensive or built based on data that might be outdated; and some others
suffer from the lack of accuracy due to simplification assumptions and computations. This might
impose limitations in the aforementioned policy analysis, in particular when we need to predict
future projections for different scenarios. As such, I develop a fast, efficient, and yet accurate fuel
burn evaluation model by combining low-fidelity physics-based model with BADA trajectory simulation
results. In particular, I derive correction factors based on the simulation data and incorporate
them in the modeling, to achieve a higher accuracy that would not have been possible with the low-fidelity
physics-based models alone. In this thesis, a fuel burn database corresponding to 40 aircraft
types is generated based on the Bureau of Transportation Statistic (BTS) flight missions database
from the year of 2015. A sample-based surrogate model is then derived for each aircraft type. The
verification and validation results show that the model can estimate the total aggregate fuel burn
for each aircraft type with less than 1% prediction errors using flight missions data from 2016,
and less than 6% prediction errors when compared with the actual fuel burn data corresponding to
three commercial airliners in 2015 and 2016. The developed models are then used to investigate
the two common simplifying assumptions in fuel burn evaluation, namely the cruise-only approximation
and the similar aircraft type mapping. The results provide insight into the inaccuracies
caused by these simplifications in fuel burn computation. The developed models would open doors
to performing more computationally intensive analyses, such as sensitivity analyses, uncertainty
analyses, and optimization.
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