Summary
Significant adoption of renewable sources will be witnessed in future years to meet the long-term objective of CO2 neutrality and mitigate the effects of global warming. While electrification will play a key role in the transition to a sustainable energy system, combustion processes will remain part of the picture, requiring sustainable combustion technologies and renewable synthetic fuels. The design and development of novel combustion technologies in power and heat generation, transportation and manufacturing processes require developing a digital combustion infrastructure that promises to bring down the needed R&D investments for meeting the tightening environmental regulations. However, predicting combustion processes is a complex and challenging task, and the tools available today fall very short of what is needed for new design and optimisation. We made an innovation that formed a digital twin, combining theory, experiments, simulations and machine learning into one unique combination. With our approach, we can predict complex multi-physics systems that can be used for designing combustion-based energy generation applications for growing markets. Our approach is expected to impact significantly new combustion systems while reducing the resources and time for designing such fuel-flexible, nonpolluting and energy-efficient systems. This is expected to have vast commercialisation potential in the industries 1) designing environmentally friendly energy systems and 2) supplying digital tools for the design processes.
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| Web resources: | https://cordis.europa.eu/project/id/101123406 |
| Start date: | 01-09-2024 |
| End date: | 28-02-2026 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
Significant adoption of renewable sources will be witnessed in future years to meet the long-term objective of CO2 neutrality and mitigate the effects of global warming. While electrification will play a key role in the transition to a sustainable energy system, combustion processes will remain part of the picture, requiring sustainable combustion technologies and renewable synthetic fuels. The design and development of novel combustion technologies in power and heat generation, transportation and manufacturing processes require developing a digital combustion infrastructure that promises to bring down the needed R&D investments for meeting the tightening environmental regulations. However, predicting combustion processes is a complex and challenging task, and the tools available today fall very short of what is needed for new design and optimisation. We made an innovation that formed a digital twin, combining theory, experiments, simulations and machine learning into one unique combination. With our approach, we can predict complex multi-physics systems that can be used for designing combustion-based energy generation applications for growing markets. Our approach is expected to impact significantly new combustion systems while reducing the resources and time for designing such fuel-flexible, nonpolluting and energy-efficient systems. This is expected to have vast commercialisation potential in the industries 1) designing environmentally friendly energy systems and 2) supplying digital tools for the design processes.Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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