Physics guided deep learning for data-driven aircraft fuel consumption modeling

Mevlut Uzun*, Mustafa Umut Demirezen, Gokhan Inalhan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy.

Original languageEnglish
Article number44
Pages (from-to)1-22
Number of pages22
JournalAerospace
Volume8
Issue number2
DOIs
Publication statusPublished - Feb 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Funding

Funding: This work is supported in part by Boeing Grants GT-187-1 and GT-057-1.

FundersFunder number
BoeingGT-057-1, GT-187-1

    Keywords

    • Aircraft performance modeling
    • BADA
    • Fuel consumption modeling
    • Machine learnin
    • Neural networks
    • Physics guided deep learning

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