The proposed project aims to collaboratively research on ice-templating of advanced porous materials through modern data system/sharing, experiments and numerical/physical modelling. The project will establish systematic data of ice-templated porous materials and predictive modelling supported by machining learning for establishing detailed structures-properties relationship. The project will investigate the cooling process through experimental, Phased field modelling and molecular simulation for ice/emulsion structure growth/evolution process. The data and advanced modelling approach established will be used to develop porous systems with tunable porosities for advanced applications. One key area is to control porosity and distribution to achieve uniform porosities or gradient/hierarchical structures in functional ceramics (carbon, pizeoceramics, BCZY-GDC ceramics for fuel cells, membrane for hydrogen separation, etc.); Another area is to develop tunable pore size/shape and porosity in monoliths, beads, and fibres of plastics (e.g. for PLA scaffolding, etc.). The resulted advanced data system, intelligent modelling program, new materials and improved scientific understanding will contribute to the research and innovation capacity in Europe and the world on ice-templating, porous materials and associated systems with a long term economic and social impact. The network will serve as a pivoting platform for future data sharing, research and innovation, speeding up material or product development cycle through interdisciplinary research. It will enhance the R&I capacity of academic and industrial partners, sustain the effort in collectively challenging issue in this most active research area and jointly developing major break-through technologies. It will also equip researchers with modern research facilities, innovation skills and critical thinking capacity through integrated data system, facilities, dynamic collaboration with everlasting impact.

Di-Hydro contributes towards harnessing the full potential of hydro-power (HP) plants and clusters in line with the objectives of the European Green Deal and the Paris Agreement, by development of tailored, optimized digital and smart decision-making tools for use in such plants, at scale. Indeed, Di-Hydro will develop smart devices and pro-active intelligent algorithms that utilize data acquired from static, dynamic, and alternative sources (e.g., satellites) so as to predict the operational and maintenance-related behavior of standalone HP plants and clusters, and we will then incorporate such algorithms in digital twins that can fully interrelate with such HP plants/clusters, yielding, eventually, an intelligent, fully-replicable decision-making tool for optimal coordination of environmentally mindful power generation from such plants/clusters (based on the foreseen market needs and the intended commercial strategy of their owners). Notably, a wide variety of previous plant digitization levels, weather and water flow conditions, biodiversity, environmental and societal issues pertinent to such HP clusters are incorporated in the integrated practical solution developed here, mainly by leveraging innovative sensor technologies, cutting-edge digital adaption for energy production, and optimized operation and maintenance practices based on next-gen information technology. The consortium is comprised of 13 partners, 3 of which are RTOs, 7 SMEs, and 3 large power enterprises (represented here by their hydro-power branches).