Influence of atmospheric uncertainty, convective indicators, and cost-index on the leveled aircraft trajectory optimization problem

• Published in: Transportation Research Part C: Emerging Technologies
• Main Authors: Soler Arnedo, Manuel Fernando, González Arribas, Daniel, Sanjurjo Rivo, Manuel, García-Heras Carretero, Javier, Sacher, Daniel, Gelhardt, Ulrike, Lang, Juergen, Hauf, Thomas, Simarro, Juan
• Other Authors: European Commission
• Format: Article in Journal/Newspaper
• Language: English
• Published: ELSEVIER BV 2020
• Subjects: Optimal control; Robust planning; Convective weather; Aircraft trajectory planning; Ingeniería Mecánica; North Atlantic
• Online Access: http://hdl.handle.net/10016/35552; https://doi.org/10.1016/j.trc.2020.102784

The existence of significant uncertainties in the models and systems required for trajectory prediction represents a major challenge for the Air traffic Management (ATM) system. Weather can be considered as one of the most relevant sources of uncertainty. Understanding and managing the impact of these uncertainties is necessary to increase the predictability of the ATM system. State-of-the-art probabilistic forecasts from Ensemble Prediction Systems are employed to characterize uncertainty in the wind and potential convective areas. A robust optimal control methodology to produce efficient and predictable aircraft trajectories in the presence of these uncertainties is presented. Aircraft motion is assumed to be at a constant altitude and variable speed, considering BADA4 as the aircraft performance model. A set of Pareto-optimal trajectories is obtained for different preferences among predictability, convective risk, and average cost index running a thorough parametric study on a North Atlantic crossing use case. Results show that the cost of reducing the arrival time window by 10 s. is between 100 and 200 kg or 3 and 6 min., depending on the cost-index. They also show that reducing the exposure to convection by 50 km is on the order of 5 and 10 min. or 100 and 200 kg. of average fuel consumption. This work has been partially supported by project TBO-MET project (https://tbomet-h2020.com/), which has received funding from the SESAR JU under grant agreement No 699294 under European Union’s Horizon 2020 research and innovation programme.