Radical changes in aircraft configurations and operations are required to meet the target of climate-neutral aviation. To foster this transformation, innovative digital methodologies are of utmost importance to enable the optimisation of aircraft performances. NEXTAIR will develop and demonstrate innovative design methodologies, data-fusion techniques and smart health-assessment tools enabling the digital transformation of aircraft design, manufacturing and maintenance. NEXTAIR proposes digital enablers covering the whole aircraft life-cycle devoted to ease breakthrough technology maturation, their flawless entry into service and smart health assessment. They will be demonstrated in 8 industrial test cases, representative of multi-physics industrial design, maintenance problems and environmental challenges and interest for aircraft and engines manufacturers. NEXTAIR will increase high-fidelity modelling and simulation capabilities to accelerate and derisk new disruptive configurations and breakthrough technologies design. NEXTAIR will also improve the efficiency of uncertainty quantification and robust optimisation techniques to effectively account for manufacturing uncertainty and operational variability in the industrial multi-disciplinary design of aircraft and engine components. Finally, NEXTAIR will extend the usability of machine learning-driven methodologies to contribute to aircraft and engine components' digital twinning for smart prototyping and maintenance. NEXTAIR brings together 16 partners from 6 countries specialised in various disciplines: digital tools, advanced modelling and simulation, artificial intelligence, machine learning, aerospace design, and innovative manufacturing. The consortium includes 9 research organisations, 4 leading aeronautical industries providing digital-physical scaled demonstrator aircraft and engines and 2 high-Tech SME providing expertise in industrial scientific computing and data intelligence.

To achieve the goals of the European Green Deal on climate neutrality, a 90% reduction in transport emissions is needed by 2050. The automotive industry urgently needs to accelerate the introduction of alternative powertrains for electrified vehicles. Hydrogen-powered Proton Exchange Membrane Fuel Cells (PEMFCs) are carbon-free power devices that meet these goals in both mobile and stationary applications. BLESSED aims at revolutionising the design process of next generation PEMFCs, to improve efficiency, durability and affordability for widespread use, with direct implications in clean energy and sustainable industry/mobility. BLESSED will train 15 Doctoral Candidates (DCs) to solve Multi-Scale (MS) engineering challenges, from the electrons up to the device level, through a unique combination of multi-disciplinary computational methods with Machine Learning (ML) to bridge each length scale’s highly accurate model to adjacent scales. Then, a top-down length scale approach will be followed to optimise PEMFC and its components. To this end, the 15 DCs will synergistically develop a unique MS computational framework for the all-scale PEMFC analysis/design, assisted by ML tools. This will allow the simultaneous consideration of complex physico-chemical phenomena occurring at all length scales, such as catalytically-assisted chemical reactions, contact of rough surfaces, mechanical/chemical degradation of membranes, fluid flows in porous media etc., at affordable computational cost. The proposed ID-network brings together world-class academic expertise on numerical modelling and simulation in electrochemistry, reacting flows, fluid mechanics, materials, optimisation methods and ML, with industrial developers. With a strong focus on industrial applications, BLESSED will develop methodologies and tools to exceed state-of-the-art in PEMFCs by minimising the Platinum group metal content and corrosion while maximising mass transport and electrical conductivity.