At a time of mass awareness of the impacts of climate change, mainly linked to the increase in carbon emissions, reducing and controlling energy consumption are major challenges for the future.
Meeting this challenge will help to contain the increase in climate change.
This project will make it possible to accelerate the identification of energy savings deposits in existing assets using clustered calculation and to test virtually before undertaking improvement work, the effectiveness of the technical solutions proposed by architects and engineers.
This project will make it possible to make the expected results more reliable with regard to the theoretical calculations corrected by the integration into the equations of the data from the physical sensors in place in buildings and will also make it possible to estimate, with almost absolute reliability, the impact of the reduction in the carbon footprint linked to the proposals for the resulting modification work.
- Develop a Building Information Model (BIM) for existing building
- Develop fast 3D thermal management model using IA and Physics simulation jointly
- Use sensors deployed in IBat platform
FEEL++, the Cemosis flagship software for numerical simulations, will be used throughout the project.
IA Neural Networks
- Christophe Prud’homme, Cemosis, U Strasbourg
- Romain Hild, Cemosis, U Strasbourg
- Vincent Chabannes, Cemosis, U Strasbourg
- Luc Kern, Synapse-Concept
- Matthieu Halter, Synapse-Concept
- Région Grand-Est