Detailed Analysis of Aircraft Fuel Flow Using Data from Flight Data Recorder

Abstract

This paper aims to perform accurate prediction of fuel flow (FF) by employing various models: deep learning (DL), random forest (RF), generalized linear model (GLM), and the Eurocontrol Base of Aircraft Data (BADA) model, and to examine the link between FF and different aircraft performance parameters. The flight data set used in this study is obtained from real turbofan engine narrow-body aircraft performing short-distance domestic subsonic flights, containing a total of 2,674 cruise flights between 31 city pairs. Several statistical error analyses are conducted to compare the performance of the models. Root mean square error, mean absolute error, and mean squared error values for DL are calculated to be 0.01, 0.008, and 0.0001, respectively. On the coefficient of determination ( [Formula: see text]) test for validation, the DL model has the highest value of 0.94. Results of the analysis in this paper show that the DL model offers the best ability to predict FF in all statistical tests, which makes it the best-suited model to estimate FF. These findings can provide the means to make more efficient trajectory planning and forecasts in air traffic management, and more accurately predict fuel consumption of aircraft, thereby decreasing emission levels of carbon gases and of various pollutants and airline operating expenses from fuel costs.

Anomaly detection of four-engine aircraft based on Parameter differences

“…However, neither the actual value nor the difference of the EGT can be directly used to classify anomalies reasonably. According to the practical experience of engineering, it can be shown that: (1) In the cruising stage, the operating environmental conditions, thrust settings, and working conditions of all engines on the same aircraft are very similar, so the difference in engine gas path parameters generally obeys the Gaussian distribution of the constant mean value; (2) The performance degradation will affect the engine gas path parameters and cause the bias of the parameters, but the changes of the parameters are almost the same; (3) For multi-engine aircraft, the probability of two or more engines failing at the same time is very low, and intermittent failures are generally manifested as multiple gas path parameters abnormalities at the same time.…”

“…The Flight Data Recorder (FDR) records a wide range of engine gas path parameters, covering all flight phases [1]. At the same time, FDR has a high recording frequency and can meet the requirements of continuous condition monitoring.…”

A critical review of energy consumption and optimization strategies in the Nigerian aviation sector: challenges and prospects

“…Furthermore, beyond the aircraft and their operations, several extrinsic factors play into the energy scenario. For instance, air traffic conditions, especially in bustling hubs, can lead to extended waiting times on runways or holding patterns in the skies-both scenarios that can inflate fuel consumption (Atasoy 2023;Hamdan et al 2022). Similarly, weather patterns, from storm systems that necessitate flight reroutes to temperature fluctuations that might require different flight profiles, play a significant role (Singh and Sharma 2015).…”