Data-Driven Modeling of Aircraft Engine Fuel Burn in Climb Out and Approach

Abstract

Fuel burn is a key driver of aircraft performance, and contributes to airline costs and emissions. Low-altitude fuel burn and emissions, such as those that occur during climb out and approach, have a significant impact on the environment in the vicinity of airports. This paper proposes a new methodology to statistically model fuel burn in the climb out and approach phases using the trajectory of an aircraft. The model features are chosen by leveraging a physical understanding of aircraft and engine dynamics. Model development is conducted through the use of Gaussian Process Regression on a limited Flight Data Recorder archive, which also provides ground truth estimates of the fuel flow rate and total fuel burn. The result is a class of models that provide predictive distributions of the fuel burn corresponding to a given aircraft trajectory, thereby also quantifying the uncertainty in the predictions. The performance of the proposed models is compared with other frequently used Aircraft Performance Models. The statistical models are found to reduce the error in the estimated total fuel burn by more than 73% in climb out and by 59% in approach.