Aircraft Fuel Flow Modelling with Ageing Effects: From Parametric Corrections to Neural Networks
This work addresses the need for more reliable fuel-flow predictions for operational and environmental planning in aviation, though it is incremental due to limitations in dataset diversity and generalization.
The paper tackled the problem of accurately modeling aircraft fuel flow by integrating engine ageing effects, finding that age-dependent correction factors and neural networks reduced bias and improved prediction accuracy compared to baseline models that underestimated consumption for older aircraft.
Accurate modelling of aircraft fuel-flow is crucial for both operational planning and environmental impact assessment, yet standard parametric models often neglect performance deterioration that occurs as aircraft age. This paper investigates multiple approaches to integrate engine ageing effects into fuel-flow prediction for the Airbus A320-214, using a comprehensive dataset of approximately nineteen thousand Quick Access Recorder flights from nine distinct airframes with varying years in service. We systematically evaluate classical physics-based models, empirical correction coefficients, and data-driven neural network architectures that incorporate age either as an input feature or as an explicit multiplicative bias. Results demonstrate that while baseline models consistently underestimate fuel consumption for older aircraft, the use of age-dependent correction factors and neural models substantially reduces bias and improves prediction accuracy. Nevertheless, limitations arise from the small number of airframes and the lack of detailed maintenance event records, which constrain the representativeness and generalization of age-based corrections. This study emphasizes the importance of accounting for the effects of ageing in parametric and machine learning frameworks to improve the reliability of operational and environmental assessments. The study also highlights the need for more diverse datasets that can capture the complexity of real-world engine deterioration.