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MAGISTER

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Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR...

Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance. 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 Euro... ver más

30/11/2021

UNIVERSITEIT TWENT...

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

UNIVERSITEIT TWENTE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 18 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-11-30

MSCA-ITN-2017: Innovative Training Networks

I+D

Cerrada hace 8 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

18 Participantes

UNIVERSITEIT TWENTE

766.12K€ | Lider

ELECTRIA WIND

BOARD OF TRUSTEES OF THE LEL...

ANSYS

262.88K€ | Participante

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Duración del proyecto: 51 meses Fecha Inicio: 2017-08-03
Fecha Fin: 2021-11-30

Líder del proyecto

UNIVERSITEIT TWENTE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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