The most significant challenge in applied fluid dynamics (covering aerospace, energy and propulsion, automotive, maritime industries, chemical process industries) is posed by a lack of understanding of turbulence-dependent features and laminar-to-turbulent transition. As a consequence, the design and analysis of industrial equipment cannot be relied upon to be accurate in challenging flow conditions. Improving the capabilities of models for complex fluid flows, offers the potential of reducing energy consumption of aircraft, cars, and ships, with consequent reduction in emissions and noise of combustion-based engines The inevitable result is a major impact on economical and environmental factors as well as on economy, industrial leadership in the highly competitive global position. Hence, the ability to understand, model and predict turbulence and transition phenomena is the key requirement in the design of efficient and environmentally acceptable fluids-based energy transfer systems. Against this background, the present proposal sets out a highly ambitious and innovative program of work designed to address some influential deficiencies in advanced statistical models of turbulence. The program rests on the following pillars of excellence: • The exploitation of high-fidelity LES/DNS data for a range of -reference flows that contain key flow features of major interest • The application of novel artificial intelligence and machine-learning algorithms to identify significant correlations between representative turbulent quantities • The guidance of the research towards improved models by four world-renown industrial and academic experts in turbulence. The consortium is formed by major industrial aeronautical companies and software editor, an SME acting as coordinator, well-known research centra and academic groups, including ERCOFTAC, acting as a source of turbulence expertise and as a repository for the generated data, to be made openly available.

Sci-Fi-Turbo aims to revolutionise the aero engine design process by advancing and integrating high-order scale-resolving simulations (SRS) and optimization methodologies into standard industrial workflows. SRS are a key enabler for developing ultra-efficient propulsion systems that drastically reduce GHG emissions by 2035 and achieve the EU's target to be climate-neutral by 2050. The advancements will boost design process capabilities and reduce product development cycles. Future engine concepts require opening up the design space and solving complex design problems out of reach for today's standard industrial design processes within the required timeframe. To achieve the necessary step change in engine design, a similar step change is needed for the design approach. Sci-Fi-Turbo fills this urgent need by exploiting opportunities in three foundation technologies: High-performance computing, high-order numerical methods, and AI/ML. The combination is used to implement and demonstrate two key advancements. First, a highly integrated high-order SRS design process is established for modern CPU/GPU hardware, meeting robustness, accuracy, and turnaround time requirements. It will provide increased functionality and effectivity at an industrial level and pave the way for the uptake of SRS-based design by the industry. The high accuracy of the methodology will also reduce the need for low-TRL testing and enable new concepts and extended operating conditions. Second, an SRS-assisted multi-fidelity, data-driven optimisation framework is developed, which embeds and exploits the advantages of highly accurate high-order SRS while leveraging AI/ML methods to increase the predictive capability of lower-fidelity simulations and maximize overall process accuracy and speed. Dedicated experiments support the technology advancement and will enable the design of net-zero-emission engines in due time and contribute to the digital transformation of the aviation industry.

The overarching goal of PowerizeD is to develop breakthrough technologies of digitalized and intelligent power electronics, in order to enable sustainable and resilient energy generation, transmission and applications. PowerizeD enhances the level of mechanical and electrical integration of new driver circuits into power electronics and allows for the first time common optimization of all power switch functionalities. Regarding data sharing along the value chain, PowerizeD drives the novel approach of Federated Learning as a methodical approach to an intrinsically encrypted transfer of confidential and proprietary data. Also new is the usage of detailed electrical physical models in digital twins of real time digitally monitored and controlled power electronic devices. Unlike other projects focusing on competence and technology with limited effort on demonstration, this project will start from vital societal needs, by identifying and analyzing the key generic technology challenges from broad application scopes. Major effort will be spent on cross-domain research and innovation. The developed technologies will be demonstrated and evaluated via a large number of universally applicable results. To realize this ambition, a large project consortium will incorporate the needed competencies and resources along the whole value chain. 24 Large Entities, 19 Small Medium Enterprises and 22 research partners from 12 EU countries – representing the entire value chain from materials to “system of systems” – strive to demonstrate the applicability of these innovative approaches to multiple industrial domains. Among the concrete objectives are a 25% reduction or power losses, a device and system lifetime increase of 30%, a chip size reduction of at least 10% and a shortening of the design time by 50%. By this, PowerizeD addresses the three megatrends Independence, Sustainability and Digitalization, thereby opening pathways to massive economic and societal benefits for the EU.