Modeling of Aircraft Takeoff Weight Using Gaussian Processes

Chati, Yashovardhan S.; Balakrishnan, Hamsa

doi:10.2514/1.d0099

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…The general problem of aircraft ground handling at airports has also been addressed (Su et al, 2018; Tabares & Mora‐Camino, 2017), as well as the aircrafts refuelling from the point of view of employees' optimization (Carotenuto, Giordani, Salvatore, & Biasini, 2019). But none of them consider on‐ground refuelling for the mass estimation before the take‐off when it is a crucial aspect of aircraft performance (Chati & Balakrishnan, 2018; Sun, Ellerbroek, & Hoekstra, 2018).…”

Section: Related Workmentioning

confidence: 99%

Knowledge based approach to ground refuelling optimization of commercial airplanes

Plaza

Santos

2020

Expert Systems

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This work aims to establish a general and optimized procedure for the initial refuelling of commercial airplanes, as this loading process is strongly related to safety and energy saving issues. The on‐ground refuelling is addressed as an optimization problem whose cost function involves expert knowledge about constraints and factors that influence the aircraft stability and performance. Several heterogeneous criteria (fuelling time, structural load, flow transfers, etc.) have been considered and weighted accordance to its importance in terms of stability. This allows us to adapt the strategy to any type and planned trip of the airplane. The priority is the positioning of the centre of mass of the civil aircraft within safety and manoeuvrability margins, and near the optimal position. Evolutive algorithms are applied, keeping feasible solutions by modifying genetic operators. As a case of study, the initial refuelling of a long range type commercial aircraft, the Airbus A330‐200, is analysed. Simulation results have proved this methodology to be efficient and optimal. Even more, this heuristic and general approach improves the traditional solution that follows a set of pre‐defined rules that are specific for each type of aircraft.

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Section: Related Workmentioning

confidence: 99%

Knowledge based approach to ground refuelling optimization of commercial airplanes

Plaza

Santos

2020

Expert Systems

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“…All these techniques do not take advantage of historical data as opposed to machine learning techniques. Using Flight Data Recorder (FDR) historical data and machine learning, [20] build a model that predicts the mass knowing the starting and ending speeds of the takeoff ground roll. Using Gaussian Process Regression (GPR), it predicts a Gaussian posterior distribution.…”

Section: B Literature Review On Trajectory Predictionmentioning

confidence: 99%

Predictive Joint Distribution of the Mass and Speed Profile to Improve Aircraft Climb Prediction

Alligier

2020

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

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Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. Focusing on the climb phase, we predict some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This speed intent is parameterized by three values (cas1, cas2, M). These missing parameters might be useful to predict the future trajectory of a climbing aircraft. In this work, an ensemble of neural networks uses the observed past trajectory of the considered aircraft as input and predicts a Gaussian Mixture Model (GMM) modeling the joint distribution of (mass, cas1, cas2, M). Ideally, this predicted distribution will be close to a conditional distribution: the distribution of possible (mass, cas1, cas2, M) values given the observed past trajectory of the considered aircraft. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The obtained data set contains millions of climbing segments from all over the world. Using this data, we show that using the proposed predictive model instead of a regression model brings almost as much information as using a regression model instead of a simple mean. The data set and the machine learning code are publicly available.

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“…Gaussian Process Regression (GPR) is one of these approaches. Chati and Balakrishnan 3) estimated the takeoff weight of an aircraft using GPR, and it shows the efficacy of GPR to model aircraft parameters using flight test data. Hemakumara and Sukkarieh 4) applied GPR to generate an aerodynamic model using flight data by applying local and global approximations obtained with GPs.…”

Section: Introductionmentioning

confidence: 99%

Estimating Aerodynamic Coefficients from Uncertain Data of D-SEND Aircraft with Gaussian Process Regression

Karino

Yairi

Ninomiya

et al. 2020

Trans. Japan Soc. Aero. S Sci.

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When simulating airflow we assume an ideal situation, however, flight test data includes measurement noise when actually conducted. Therefore, it is difficult to compare simulation data with flight test data without considering uncertainty. First, we applied the Noisy Input Gaussian Process (NIGP), which can utilize uncertain inputs to estimate aerodynamic coefficients with confidence intervals to an aircraft's simulation data. This enabled us to verify the effectiveness of NIGP. We then applied NIGP to the aircraft's real flight data and compared them with aerodynamic tables based on wind tunnel testing and CFD. We found that the lift coefficient estimated using the flight test data did not contradict that obtained using simulation data, while the drag coefficient estimated using the flight test data was smaller than that obtained using the simulation data.

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Modeling of Aircraft Takeoff Weight Using Gaussian Processes

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Cited by 19 publications

References 11 publications

Knowledge based approach to ground refuelling optimization of commercial airplanes

Knowledge based approach to ground refuelling optimization of commercial airplanes

Predictive Joint Distribution of the Mass and Speed Profile to Improve Aircraft Climb Prediction

Estimating Aerodynamic Coefficients from Uncertain Data of D-SEND Aircraft with Gaussian Process Regression

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