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.

The gas turbine industry is a vital driver of innovation, economic growth, jobs, trade and mobility in the EU in both the aviation and power generation sectors. It is a multi-billion Euro high-technology industry whose future competitiveness depends on a new generation of creative engineers with multi-disciplinary skills who can accelerate the development of new innovations needed for flexible, efficient power generation and sustainable aviation. Low-emission gas turbines are confronted with unsteady combustion problems often only discovered late into development or in service because our scientific and understanding is insufficient to predict them at the design stage. For reasons of cost and simplicity, we have been trying to solve unsteady combustion problems by studying them in single (or multiple) sectors. The lack of success of this approach is a striking gap in our community and is hindering innovation. ANNULIGhT will break this paradigm by bringing together academic and industry leaders to provide a new generation of engineers with an innovative, structured, multi-disciplinary training programme combining cutting-edge theory, computational and experimental methods that exclusively focuses on annular combustion chambers as found in real gas turbines. ANNULIGhT presents a new and innovative research and training methodologies that exploits three revolutions that have recently taken place in the field of unsteady combustion; i) new computational methods based on Large Eddy Simulations applied to full annular chambers that can reproduce phenomena observed in real systems, ii) new annular combustion facilities enabling these phenomena to be studied in detail in the laboratory, and (iii) new high-speed imaging diagnostics which gives time-resolved information. ANNULIGhT aims to deliver a new generation of engineers to develop the innovations needed for low-emission technologies and increase the competitiveness of the European gas turbine industry.

Compute- and data-driven research encompasses a broad spectrum of disciplines and is the key to Europe’s global success in various scientific and economic fields. The massive amount of data produced by such technologies demands novel methods to post-process, analyze, and to reveal valuable mechanisms. The development of artificial intelligence (AI) methods is rapidly proceeding and they are progressively applied to many stages of workflows to solve complex problems. Analyzing and processing big data require high computational power and scalable AI solutions. Therefore, it becomes mandatory to develop entirely new workflows from current applications that efficiently run on future high-performance computing architectures at Exascale. The Center of Excellence for Research on AI- and Simulation-based Engineering at Exascale (RAISE) will be the excellent enabler for the advancement of such technologies in Europe on industrial and academic levels, and a driver for novel intertwined AI and HPC methods. These technologies will be advanced along representative use-cases, covering a wide spectrum of academic and industrial applications, e.g., coming from wind energy harvesting, wetting hydrodynamics, manufacturing, physics, turbomachinery, and aerospace. It aims at closing the gap in full loops using forward simulation models and AI-based inverse inference models, in conjunction with statistical methods to learn from current and historical data. In this context, novel hardware technologies, i.e., Modular Supercomputing Architectures, Quantum Annealing, and prototypes from the DEEP project series will be used for exploring unseen performance in data processing. Best practices, support, and education for industry, SMEs, academia, and HPC centers on Tier-2 level and below will be developed and provided in RAISE's European network attracting new user communities. This goes along with the development of a business providing new services to various user communities.