Objective
Air transportation is expected to grow persistently over the next decades. Clean combustion technology for aircraft engines is a key enabler to reduce the impact of this growth on ecosystems and humans’ health. The vision for European aviation is shaped by the Advisory Council for Aviation Research and Innovation in Europe in the Flight Path 2050 goals, which define stringent regulations on pollutant emissions. To meet these goals, the major engine manufacturers develop lean premixed combustors operated at very high pressure. This development introduces a large risk for reduced reliability and lifetime of engines: pressure oscillations in the combustor called thermoacoustics. Much research has been dedicated to study this phenomenon over the last decades with mixed success. Industrial experience shows that the pressure oscillations often surface as late as the full engine has been built and tested. Traditional engineering methods fall short of predictability during the design of the engines due to a high sensitivity of thermoacoustics with respect to barely known input parameters. Aviation industry encounters currently the fourth industrial revolution: cyber-physical systems analyze and monitor technical systems and take automated decisions. This industrial revolution is known as “Industry 4.0” in Germany and “Industrial Internet” in the USA. An essential enabler of the fourth industrial revolution is Machine Learning. The ITN MAGISTER will utilize Machine Learning to predict and understand thermoacoustics in aircraft engine combustors, and lead combustion research a revolutionary new approach in this area. The participation of the major aircraft engine OEMs GE, Rolls Royce, Safran ensures industrial relevance and outreach of the results. The project will shape early career talents in a network of world leading scientists and industrial partners to work on one of the most severe design issues in aviation technology in the spirit of the fourth industrial revolution.
Fields of science
- engineering and technologyenvironmental engineeringenergy and fuelsliquid fuels
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- social sciencespolitical sciencespolitical transitionsrevolutions
- natural sciencesphysical sciencesacoustics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
MSCA-ITN-ETN - European Training Networks
Coordinator
7522 NB Enschede
Netherlands
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Participants (9)
60313 Frankfurt Am Main
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80333 Muenchen
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76131 Karlsruhe
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CB2 1TN Cambridge
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31057 Toulouse Cedex
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75272 Paris
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75015 Paris 15
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64510 Bordes
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78180 Montigny Le Bretonneux
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Partners (7)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
DE24 8BJ Derby
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
5401 Baden
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
SE1 7NA London
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
1182 GP Amstelveen
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
10623 Berlin
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
94305 2004 Stanford
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
30332 0325 Atlanta
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