The ARIAS research and innovation project directly targets “maintaining industrial leadership in aeronautics” of the European aircraft engine manufacturing sector in the important technology sub-area “development and validation of multi-disciplinary design tools that address key isolated or clustered industrial problems with low degree of confidence that need presently extensive experimental verification” by investigating aeromechanical phenomena (flutter and forced response) in three distinct sub-systems (compressor, low-pressure turbine and seals) of the aircraft engine. The interaction of these aeromechanical phenomena is not well characterised. This can lead to inefficient designs or unwanted blade vibration which can increase development time and costs and can ultimately lead to the failure of an engine during operation which can have fatal consequences (Kegworth 1989). Previous EU projects have treated forced response (ADTurB-1&2) and flutter (FUTURE) as isolated phenomena. They have delivered unique experimental data and testing of numerical models enabling industry to progress blade design and analysis. These projects also revealed the need for coupled flutter and forced response methods. ARIAS will provide deeper insight into aeromechanical technologies for more sophisticated and break-through coupled analyses, measurements and vibration mitigations. Beyond state-of-the-art methods that have never been used in blade or seal design, to assess aeromechanical vibrations will be used such as multimodal simulations and superimposition of flutter and forced response. Expected outcomes are a better understanding of flutter and forced response and the development of new higher fidelity analytical tools which will enable the design of more efficient and safer aircraft engines. The project will also contribute to a world-wide unique “MOOC-type” on-line learning material on aeromechanics, involving academic and industrial partners.

Engines ITD will work towards radical engine architectures and new engine technologies to power the aircraft of the future. The objective is to increase fuel and energy efficiency of the engine and reduce environmental impact, regardless of whether the engine is powering a large airliner or just a small utility aircraft, meaning more thrust while burning less fuel and emitting less CO2, NOx and noise.

InterQ project proposes a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure the quality in smart factories in a holistic manner, including process, product and data (PPD quality). The broad vision of InterQ project will allow controlling the quality of a smart manufacturing environment in an end-to-end approach by means of a PPD quality hallmark stored in a distributed ledger. The concepts of InterQ will be applied in three high-added value industrial applications. The main objective of InterQ project is to measure, predict and control the quality of the manufactured products, manufacturing processes and gathered data to assure Zero-Defect-Manufacturing by means of AI-driven tools powered with meaningful and reliable data. The project develops five modules: 1) Thanks to the InterQ PPD quality hallmark, the quality of the process, product and data are interlinked, integrated and time stamped. A hallmark will be created after each stage, and the quality will be traced across the supply chain. A trusted framework will be implemented using distributed ledger (InterQ-TrustedFramework module) to exchange quality information. 2) The InterQ-Process module of the project will obtain more meaningful process data for quality optimization. This data will be obtained using new sensors close to the tool and by AI-driven virtual sensors. 3) The project presents new solutions (InterQ-Product module) to predict the final quality of the processes using digital twins fed by experimental data and new digital sensors to measure the product quality. 4) The reliability of data will be checked in two layers: in real time and based on historical and statistical analysis of the data streams (InterQ-Data module). 5) Finally, InterQ-ZeroDefect module will use the reliable information about the process and product quality to improve the production for Zero-Defect-Manufacturing by means of AI-driven applications.