Study on Prediction Method of Flight Fuel Consumption with Machine Learning

Zixuan, Wu; Zhang, Ning; Hong, Weijun; Yu, Shengquan

doi:10.1109/iciba50161.2020.9277445

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…In [5] aircraft fuel consumption is calculated based on the energy balance method. In [6], [7], [8], [9], and [10] aircraft fuel consumption is calculated based on statistical learning algorithms. Other studies (see, e.g., [11], [12], [13], [14]), apply statistical techniques to data obtained from the aircraft's flight data recorder (FDR) in order to address the problem of estimating aircraft fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model for the Analysis of Jet Engine Fuel Consumption During Aircraft Climb and Descent

Velásquez-Sanmartín,

Insausti,

Zárraga-Rodríguez

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a mathematical model that studies the fuel consumption of a jet engine aircraft during the climbing and descent flight phases. Such problem is addressed by providing a closed-form formula of the aircraft's rate-of-climb and rate-of-descent, which then enables obtaining a closed-form formula of the aircraft's fuel consumption that provides the closed-form relationship between the aircraft's fuel consumption and aerodynamic, engine and design parameters. In order to validate our mathematical model, a comparison is made between our results and results provided by Piano-X software and accuracy between both is proven. Furthermore, our mathematical model is applied to the calculation of pollutant gas emissions, specifically, we present a closed-form expression that provides the dependency between the mass of pollutant gas emitted and the aircraft's aerodynamic, engine and design parameters.

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Section: Introductionmentioning

confidence: 99%

A Mathematical Model for the Analysis of Jet Engine Fuel Consumption During Aircraft Climb and Descent

Velásquez-Sanmartín,

Insausti,

Zárraga-Rodríguez

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Flight trajectory prediction enabled by time-frequency wavelet transform

Zhang,

Guo,

Zhou

et al. 2023

Nat Commun

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Accurate flight trajectory prediction is a crucial and challenging task in air traffic control, especially for maneuver operations. Modern data-driven methods are typically formulated as a time series forecasting task and fail to retain high accuracy. Meantime, as the primary modeling method for time series forecasting, frequency-domain analysis is underutilized in the flight trajectory prediction task. In this work, an innovative wavelet transform-based framework is proposed to perform time-frequency analysis of flight patterns to support trajectory forecasting. An encoder-decoder neural architecture is developed to estimate wavelet components, focusing on the effective modeling of global flight trends and local motion details. A real-world dataset is constructed to validate the proposed approach, and the experimental results demonstrate that the proposed framework exhibits higher accuracy than other comparative baselines, obtaining improved prediction performance in terms of four measurements, especially in the climb and descent phase with maneuver control. Most importantly, the time-frequency analysis is confirmed to be effective to achieve the flight trajectory prediction task.

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