Enhancing Aircraft Fuel Burn Modeling on the Airport Surface

Clemons, Emily; Reynolds, Tom; Badrinath, Sandeep; Chati, Yashovardhan S.; Balakrishnan, Hamsa

doi:10.2514/6.2018-3991

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Different works were led to compare physical models, such as the Eurocontrol aircraft performance model: BADA [3], to data-driven models [4]. Clemons et al identified first order enhancements to airport surface fuel burn modeling [5]. Turgut et al used the BADA model to estimate aircraft fuel flow for a three-degree flight-path-angle descent [6].…”

Section: State Of the Artmentioning

confidence: 99%

Approach and Landing Aircraft On-Board Parameters Estimation with LSTM Networks

Jarry

Delahaye

Féron

2020

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-At)

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This paper addresses the problem of estimating aircraft on-board parameters using ground surveillance available parameters. The proposed methodology consists in training supervised Neural Networks with Flight Data Records to estimate target parameters. This paper investigates the learning process upon three case study parameters: the fuel flow rate, the flap configuration, and the landing gear position. Particular attention is directed to the generalization to different aircraft types and airport approaches.From the Air Traffic Management point of view, these additional parameters enable a better understanding and awareness of aircraft behaviors. These estimations can be used to evaluate and enhance the air traffic management system performance in terms of safety and efficiency.

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Section: State Of the Artmentioning

confidence: 99%

Approach and Landing Aircraft On-Board Parameters Estimation with LSTM Networks

Jarry

Delahaye

Féron

2020

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-At)

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Spatial Modeling of Airport Surface Fuel Burn for Environmental Impact Analyses

Badrinath¹,

Abel²,

Balakrishnan³

et al. 2023

Journal of Air Transportation

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The assessment of the fuel burn and emissions impact of airport surface operations is a key part of understanding the environmental impacts of aviation. These assessments are needed at two levels: the analysis of inventories (the total amount of fuel burned and emissions discharged over some period of time), and the analysis of spatial distributions (the amount of emissions experienced at a particular location within or near the airport). Although the availability of taxi times for the operations of interest is sufficient for inventory analysis, the analysis of spatial distributions requires estimates of where on the airport surface an aircraft is located as it consumes fuel. In this paper, we show how a data-driven queuing network model can be developed in order to estimate the time that an aircraft spends at different congested locations on the airport surface. These models are useful both in spatial distribution analysis and in accurately predicting taxi times in the absence of measurements (for example, for projected demand sets). We use measurements of ultrafine particles at Los Angeles International Airport to demonstrate that the proposed model can help predict the measured emissions at different monitoring sites located in the vicinity of the airport. In the process, we show how one could develop a machine learning model of the spatial distribution of airport surface emissions given the pollutant measurements, air traffic demand, and prevailing weather conditions. Finally, we develop a clustering-based method to evaluate the generalizability of our surface operations modeling framework.

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Enhancing Aircraft Fuel Burn Modeling on the Airport Surface

Cited by 2 publications

References 2 publications

Approach and Landing Aircraft On-Board Parameters Estimation with LSTM Networks

Approach and Landing Aircraft On-Board Parameters Estimation with LSTM Networks

Spatial Modeling of Airport Surface Fuel Burn for Environmental Impact Analyses

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