Supercomputers have been extensively used to solve complex scientific and engineering problems, boosting the capability to design more efficient systems. The pace at which data are generated by scientific experiments and large simulations (e.g., multiphysics, climate, weather forecast, etc.) poses new challenges in terms of capability of efficiently and effectively analysing massive data sets. Artificial Intelligence, and more specifically Machine Learning (ML) and Deep Learning (DL) recently gained momentum for boosting simulations’ speed. ML/DL techniques are part of simulation processes, used to early detect patterns of interests from less accurate simulation results. To address these challenges, the ACROSS project will co-design and develop an HPC, BD, and Artificial Intelligence (AI) convergent platform, supporting applications in the Aeronautics, Climate and Weather, and Energy domains. To this end, ACROSS will leverage on next generation of pre-exascale infrastructures, still being ready for exascale systems, and on effective mechanisms to easily describe and manage complex workflows in these three domains. Energy efficiency will be achieved by massive use of specialized hardware accelerators, monitoring running systems and applying smart mechanisms of scheduling jobs. ACROSS will combine traditional HPC techniques with AI (specifically ML/DL) and BD analytic techniques to enhance the application test case outcomes (e.g., improve the existing operational system for global numerical weather prediction, climate simulations, develop an environment for user-defined in-situ data processing, improve and innovate the existing turbine aero design system, speed up the design process, etc.). The performance of ML/DL will be accelerated by using dedicated hardware devices. ACROSS will promote cooperation with other EU initiatives (e.g., BDVA, EPI) and future EuroHPC projects to foster the adoption of exascale-level computing among test case domain stakeholders.

To meet the goal of a carbon neutral growth of commercial aviation, the top level objective of IMOTHEP is to achieve a key step in assessing the potential offered by hybrid electric propulsion (HEP) and, ultimately, to build the corresponding aviation sector-wide roadmap for the maturation of the technology. The core of IMOTHEP is an integrated end-to-end investigation of hybrid-electric power trains for commercial aircraft, performed in close connexion with the propulsion system and aircraft architecture. Aircraft configurations will be selected based on their potential for fuel burn reduction and their representativeness of a variety of credible concepts, with a focus on regional and short-to-medium range missions. From the preliminary design of aircraft, target specifications will be defined for the architecture and components of the hybrid propulsion chain. Technological solutions and associated models will then be investigated with a twenty year timeframe perspective. In order to identify key technological enablers and technology gaps, the integrated performance of the electric components and power chain will be synthesized by assessing the fuel burn of the selected aircraft configurations, compared to conventional technologies extrapolated to 2035. The project will also address the infrastructures and tools required for HEP development, as well as the need for technology demonstrations or regulatory evolutions. Eventually, all these elements will feed the research and technology roadmap of HEP, which will constitute the final synthesis of the project. To achieve these ambitious goals, the 54-month project is supported by 7 R&D institutes, 11 industries (from aviation and electric systems), a service SME and 7 universities from 9 EU countries, plus 2 RTD organisations from Canada. The requested EU grant is 10 392 845 Euros.