Modeling Aircraft Fuel Consumption with a Neural Network (1997)
This research involves the development of an
aircraft fuel consumption model to simplify Bela Collins of the MITRE
Corporation aircraft fuelburn model in terms of level of computation and
level of capability. MATLAB and its accompanying Neural Network Toolbox,
has been applied to data from the base model to predict fuel consumption.
The approach to the base model and neural network is detailed in this paper.
It derives from the basic concepts of energy balance. Multivariate curve
fitting techniques used in conjunction with aircraft performance data derive
the aircraft specific constants. Aircraft performance limits are represented
by empirical relationships that also utilize aircraft specific constants. It
is based on generally known assumptions and approximations for commercial jet
operations. It will simulate fuel consumption by adaptation of a specific
aircraft using constants that represent the relationship of lift-to-drag and
thrust-to-fuel flow. The neural network model invokes the output from MITRE1s
algorithm and provides: (1) a comparison to the polynomial fuelburn function
in the fuelburn post- processor of the FAA Airport and Airspace Simulation
Model (SIMMOD), (2) an established sensitivity of system performance for a
range of variables that effect fuel consumption, (3) a comparison of post
fuel burn (fuel consumption algorithms) techniques to new techniques, and
(4) the development of a trained demo neural network. With the powerful
features of optimization, graphics, and hierarchical modeling, the MATLAB
toolboxes proved to be effective in this modeling process.
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