The EU’s 2030 goal is to establish a resilient energy plan that minimises dependence on fossil fuels. This drives demand for sustainable energy sources such as deep geothermal, which has significant potential for long-term heating and electricity generation. In this context, the EarthSafe doctoral network project, funded by the Marie Skłodowska-Curie Actions programme, will develop a data fusion method that remains robust even when data is missing.
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.
The project focuses on fluid-structure interaction (FSI) in the cardiovascular system, with the aim of developing innovative numerical methods, supported by machine learning techniques, to obtain fast and accurate solutions. Three main challenges are addressed: the stability of weakly coupled numerical schemes, the reduction of computational complexity by means of reduced models (ROMs), and the efficient solution of clinically relevant inverse problems (shape optimization and parameter calibration).
The FaReX project aims at developing new numerical methods for the efficient simulation of complex multi-scale and multiphysics problems, such as fluid-structure interaction or phase transition, using non-matching modeling techniques, immersed methods and model reduction techniques, also through machine learning. The involved units (SISSA, PoliTO, UniBS) work jointly on numerical analysis, complexity reduction and open source software development.
Il progetto SHip OPtimization MEsh Parameterization Assistant (SH.OP. ME.PA.) è un progetto svolto nell’ambito del Piano Nazionale di Ripresa e Resilienza (PNRR) in collaborazione con Fincantieri S.p.A., avente obiettivo l’integrazione di tecniche di ottimizzazione e di machine learning nella fase di progettazione strutturale delle navi da crociera.
