Identification of the abnormal fuel consumption in a commercial flight by an artificial neural network surrogate model

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2022-9-02

Author

Demirhan, Oğulcan

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Since the 1980s, numerous fuel consumption modeling studies have been conducted in the literature. Lately, fuel efficiency studies have become more of an issue due to carbon emission and its consequence; global warming. In this study, a feed-forward neural network is modeled, and trained with high-fidelity simulation data (operational flight plans). Later, the actual flight data from Quick Access Recorder (QAR) is used for tuning the hyperparameters of the model. The ten models which have the least loss have been selected and subjected to test with a portion of QAR data. The best model has been selected at the end of the statistical comparison between these ten models. Finally, an identification process has been explained for the flights whose fuel consumption estimation errors exceed the three-sigma boundary. Although this model is built only by five fundamental parameters (takeoff weight, air distance, mean cruise Mach number, altitude parameter, and fuel mileage deviation), the accuracy level of the model is promising when compared to the studies in the literature. Moreover, the study proposes an additional method for the identification of abnormal fuel consumption.

Subject Keywords

Aircraft Fuel Consumption Modeling, Neural Networks, Surrogate Models, Medium-Fidelity Modeling

URI

https://hdl.handle.net/11511/99592

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

O. Demirhan, “Identification of the abnormal fuel consumption in a commercial flight by an artificial neural network surrogate model,” M.S. - Master of Science, Middle East Technical University, 2022.