Neural Network Aided Aviation Fuel Consumption Modeling (1997)
This thesis deals with the potential application of
neural network technology to aviation fuel consumption estimation. This is
achieved by developing neural networks representative jet aircraft. Fuel
consumption information obtained directly from the pilot’s flight manual was
trained by the neural network. The trained network was able to accurately and
efficiently estimate fuel consumption of an aircraft for a given mission.
Statistical analysis was conducted to test the reliability of this model for
all segments of flight. Since the neural network model does not require any
wind tunnel testing nor extensive aircraft analysis, compared to existing
models used in aviation simulation programs, this model shows good potential.
The design of the model is described in depth, and the MATLAB source code are
included in appendices.
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