Multi-level approach for the up-scaling of ultra high temperature energy storage...
Multi-level approach for the up-scaling of ultra high temperature energy storage and conversion
The growing interest for the integration of renewable energy sources, as solar energy, in the global energy mix, increases the need of developing of new methods that will assist on the up-scaling and demonstration of efficient ene...
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Información proyecto SHINE
Duración del proyecto: 26 meses
Fecha Inicio: 2024-03-13
Fecha Fin: 2026-05-31
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
The growing interest for the integration of renewable energy sources, as solar energy, in the global energy mix, increases the need of developing of new methods that will assist on the up-scaling and demonstration of efficient energy storage and conversion technologies. In this regard, advanced modelling methods can be an indispensable tool towards this effort. SHINE aims at developing a holistic numerical methodology – by using in-house codes coupled with commercial software– that will boost the cost-efficient and sustainable electricity production and storage at unprecedented ultra-high temperatures (> 1000 oC). The stepping stone for the modelling activities will be a compact latent heat thermophotovoltaic device recently patented in UPM targeted for energy storage and production at ultra-high temperatures. The core components in such a device are the latent heat thermal energy storage system and the thermophotovoltaic device. The modelling methodology will integrate rigorous multi-physics models (fluid dynamics, heat transfer and optoelectronics) targeted at a component level into a reduced order model (ROM) by using multi-variable polynomial functions. Key in the proposed methodology is the validation of the rigorous models through in-house measurements at ultra-high temperatures that will be undertaken at the host organisation. Key as well is the production of the multi-variable polynomials through artificial neural networks that will be undetaken during the Secondment phase. The whole project is highly interdisciplinary because it integrates highly interrelated diverse disciplines (physics, engineering, optoelectronics, thermo- and fluid-dynamics, photovoltaics and thermal storage, and artificial intelligence-AI) as well as know-how from experiments is a single holistic approach. Once developed the ROM will be used to predict the whole system's performance as being part of a solar-to-heat-to-power and a power-to-heat-to-power concepts.