Project description
Green planning for the sake of flying
Emissions from aviation contribute to climate change. The big question concerns the size of its carbon footprint. The EU-funded REFMAP project will find the answer by quantifying the environmental footprints of air mobility for airliners and unmanned aircraft systems at a multi-scale level, where single trajectories and the flow traffic of multiple vehicles are optimised to minimise their environmental impact in a wide range of communities. REFMAP will investigate how the aviation business models will be affected by the availability of environmental data for each type and route of air vehicle. To predict outcomes, it will develop an analytics platform that can process environmental and weather data like wind, noise and emissions (both CO2 and non-CO2).
Objective
"The mission of RefMap is to develop a digital service aimed at quantifying the environmental footprints of air mobility for airliners and unmanned aircraft systems (UAS) at a ""multi-scale"" level, where single-trajectories (micro) and the flow traffic of multiple vehicles (macro) are optimised to minimise their environmental impact in a wide range of communities. RefMap investigates how the aviation business models will be affected by the availability of environmental data for each type and route of air vehicle, as this will enable stricter evidence-based Green policy making in the sector. This will be achieved via the development of the RefMap analytics platform processing environmental and weather data such as wind, noise, CO2 and non-CO2 emissions for both U-space and ATM. This platform will rely on a number of technical solutions, including numerical simulation, predictive models, and deep-learning methods. The latter will be used to construct accurate non-intrusive prediction frameworks and to optimize the trajectories of the various vehicles given the predicted flow conditions via deep reinforcement learning (DRL). These will enable the development of a new aviation business models aligned with EU’s Green Agenda."
Fields of science
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- social scienceseconomics and businessbusiness and managementbusiness models
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
100 44 Stockholm
Sweden