This second in our series of webinars on smart and sustainable solutions for ATM will focus on greener arrivals and departures.
SESAR solutions aim to tackle fuel inefficiencies during the departure, approach and landing phases of the flight. This webinar will showcase some of these SESAR solutions as well as link to potential SESAR research, including:
- A completed SESAR 2020 research project on integrated arrival and departure runway sequencing, which has matured the technological developments and associated operational procedures that will make it possible to optimise engine start-up times so that aircraft spend less time with their engines on before take-off, in particular at the runway holding point, without negatively affecting runway throughput.
- Two ongoing SESAR Flight Management System (FMS) developments to support flight crews in the management of the vertical profile in the real ATM environment: the re-cruise FMS function, which saves fuel by levelling off after an early descent, and the permanent resume trajectory (PRT), which helps flight crews manage the rate of descent when flying on vectors.
- A recently launched SESAR 2020 exploratory research project that aims at investigating future concepts for providing pilots increased avionics automation support for the management of high-lift devices and landing gear actuation during the approach phase of flight in order to minimise the impact on the environment.
- Innovative applications of big data and machine learning for measuring the efficiency of the vertical profile based solely on surveillance data, which will enable a new generation of tools that will support controllers in factoring in the environmental impact of their clearances in real time.
Integrated arrival and departure runway sequence
Ake Wall, Senior Air Traffic Controller, Operational and Validation Expert, LFV
Avionics developments for improved vertical profile
Johan Boyer, System Engineer, Thales
Enabling environmentally friendly approach profiles
Fethi Abdelmoula, Research team leader, DLR
Can machine learning help improving environmental impact assessment ?
Gabriel Jarry, Researcher engineer, DSNA
Moderated by Olivia Nunez, ATM Expert SESAR Joint Undertaking