SOURCE-LINKED INTELLIGENCE

Bridging Models at Different Scales To Design New Generation Fuel Cells for Electrified Mobility

CORDIS · observation · Publication date unknown

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

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

3501489.6

unit

EUR