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

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

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.
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Coordinator

UNIVERSITEIT TWENTE

Address

Drienerlolaan 5
7522 Nb Enschede

Netherlands

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 766 122,84

Participants (9)

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GENERAL ELECTRIC DEUTSCHLAND HOLDING GMBH

Germany

EU Contribution

€ 498 432,96

TECHNISCHE UNIVERSITAET MUENCHEN

Germany

EU Contribution

€ 498 432,96

KARLSRUHER INSTITUT FUER TECHNOLOGIE

Germany

EU Contribution

€ 249 216,48

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

United Kingdom

EU Contribution

€ 546 575,76

CENTRE EUROPEEN DE RECHERCHE ET DE FORMATION AVANCEE EN CALCUL SCIENTIFIQUE

France

EU Contribution

€ 262 875,60

ASSOCIATION POUR LA RECHERCHE ET LE DEVELOPPEMENT DES METHODES ET PROCESSUS INDUSTRIELS

France

EU Contribution

€ 262 875,60

SAFRAN SA

France

EU Contribution

€ 262 875,60

SAFRAN HELICOPTER ENGINES

France

EU Contribution

€ 262 875,60

ANSYS FRANCE SAS

France

EU Contribution

€ 262 875,60

Partners (7)

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ROLLS-ROYCE POWER ENGINEERING PLC

GENERAL ELECTRIC (SWITZERLAND) GMBH

SHELL RESEARCH LIMITED

KONINKLIJKE LUCHTVAART MAATSCHAPPIJNV

FDX Fluid Dynamix GmbH

BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY

GEORGIA INSTITUTE OF TECHNOLOGY

Project information

Grant agreement ID: 766264

Status

Ongoing project

  • Start date

    1 September 2017

  • End date

    31 August 2021

Funded under:

H2020-EU.1.3.1.

  • Overall budget:

    € 3 873 159

  • EU contribution

    € 3 873 159

Coordinated by:

UNIVERSITEIT TWENTE

Netherlands