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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
H2020Докторантска мрежа2017–2021

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2017-09-01 → 2021-11-30
Финансиране от ЕС
3 873 159 €
Участници
17
Схема
MSCA-ITN-ETN

Линиите свързват координатора с партньорите.

Накратко на български

Алгоритми за машинно обучение се използват за предвиждане на опасни вибрации (термоакустика) в горивните камери на самолетните двигатели. Това помага за създаването на по-надеждни двигатели с по-ниски вредни емисии, които предпазват околната среда и здравето на хората.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

Clean combustion technology for aircraft engines can reduce the impact of air transportation on ecosystems and humans’ health. The vision for European aviation defines stringent regulations on pollutant emissions. To meet these goals, the major engine manufacturers develope lean premixed combustors. This development introduces a large risk for reduced reliability of engines due to pressure oscillations in the combustor (ThermoAcoustics). Industrial experience shows that pressure oscillations often only surface when the full engine has been built. Traditional engineering methods fail for the design of the engines due to a high sensitivity of the oscillations with respect to input parameters. Aviation industry encounters currently the 4th industrial revolution, cyber-physical systems analyse and monitor technical systems and take automated decisions with Machine Learning. The ITN [MAGISTER] has utilized ML to predict ThermoAcoustics in aircraft engine combustors. The participation of the aircraft engine OEMs GE, Rolls Royce, Safran ensured industrial relevance and outreach of the results. The project has shaped 15 Early Staged Researchers in a network of scientists and industry to work on these design issues in aviation technology using ML. Objectives Develop methods that can predict and control thermoacoustics from TRL 2 to 9. Apply machine learning algorithms to improve models to predict thermoacoustics in aircraft engines and derive combustor hardware design implications from the predictions. Devise and adapt machine learning algorithms to thermoacoustic experiments at the laboratory scale and to industrial scale for aircraft engines. Advance acoustic and combustion models to capture the interaction of acoustics with liquid fuel sprays with high accuracy. Generate a sophisticated experimental data base for thermoacoustics of liquid fuel combustion for validation.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз