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

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

Robust Optimal Trajectory Planning under Uncertain Winds and Convective Risk

The existence of significant uncertainties in the models and systems required for trajectory prediction represent a major challenge for Trajectory-Based Operations concept. Weather can be considered as one of the most relevant sources of uncertainty. Understanding and managing the impact of these uncertainties is necessary in order to increase the predictability of the ATM system. We present preliminary results on robust trajectory planning in which weather is assumed to be the unique source of uncertainty. State-of-the-art forecasts from Ensemble Prediction Systems are used as input data for the wind field and to calculate convective risk. The term convective area is defined here as an area within which individual convective storms may develop, i.e., a necessary (though not sufficient) condition. An ad-hoc robustoptimal control methodology is presented. A set of Pareto-optimal trajectories is obtained for different preferences between predictability, convective risk and average efficiency.This work has been partially supported by project TBO-MET project (https://tbometh2020.com/), which has received funding from the SESAR JU under grant agreementNo 699294 under European Union’s Horizon 2020 research and innovation programme

Robust flight planning impact assessment considering convective phenomena

Thunderstorms are one of the leading causes of Air Traffic Management delays. In this paper, we assess how incorporating convective information into flight planning algorithms can lead to reductions in reroutings due to storm encounters during the execution of the flight. We use robust open-loop optimal control methodology at the flight planning level and incorporate meteorological uncertainties based on Ensemble Prediction System forecasts. Convective risk areas can be derived from the latter to be included in the objective function. At the execution level, the planned trajectories are included in an air traffic simulator (NAVSIM) under observed weather (wind and storms). In this simulation process, track modifications might be triggered in case of encountering an observed thunderstorm. A tool termed DIVMET based on pathfinding algorithms has been integrated into NAVSIM is considered to that end. Results show that planning robust trajectories (avoiding thus convective areas) reduces the number of storms encounters and increases predictability. This increase in predictability is at a cost in terms of fuel and time, also quantified. © 2021 Elsevier Lt

Robust Flight Planning Impact Assessment Considering Convective Phenomena

Thunderstorms are one of the leading causes of Air Traffic Management delays. In this paper, we assess how incorporating convective information into flight planning algorithms can lead to reductions in reroutings due to storm encounters during the execution of the flight. We use robust open-loop optimal control methodology at the flight planning level and incorporate meteorological uncertainties based on Ensemble Prediction System forecasts. Convective risk areas can be derived from the latter to be included in the objective function. At the execution level, the planned trajectories are included in an air traffic simulator (NAVSIM) under observed weather (wind and storms). In this simulation process, track modifications might be triggered in case of encountering an observed thunderstorm. A tool termed DIVMET based on pathfinding algorithms has been integrated into NAVSIM is considered to that end. Results show that planning robust trajectories (avoiding thus convective areas) reduces the number of storms encounters and increases predictability. This increase in predictability is at a cost in terms of fuel and time, also quantified.This work has been supported by TBO-MET project (https://tbomet-h2020.com/), which has received funding from the SESAR JU under grant agreement No 699294 under EU’s Horizon 2020 research and innovation programme. Consortium members are UNIVERSIDAD DE SEVILLA (Coordinator), AEMET (Agencia Española de Meteorología), METEOSOLUTIONS GmbH, PARIS-LODRON-UNIVERSITAT SALZBURG, and UNIVERSIDAD CARLOS III DE MADRID.Publicad