Url https://www.cimne.com/sgp/rtd/Project.aspx?id=874
LogoEntFinanc LogoPrograma
Acronym EdgeTwins HPC
Project title Bringing Digital Twins to the Edge for mass Industry 4.0 applications
Official Website https://edgetwins.eu/
Reference 946009
Principal investigator Riccardo ROSSI - rrossi@cimne.upc.edu
Start date 01/06/2020 End date 30/11/2021
Coordinator CIMNE
Consortium members
  • EFFECTIA INNOVATION SOLUTIONS S.L.
  • BSC
Program H2020 (2014-2020) Call H2020-FETOPEN-2018-2020
Subprogram SC7-Secure societies Category Europeo
Funding body(ies) EC Grant 57.500,00 €
Abstract Digital twins, along with the Internet of Things and Edge computing , are expected to play a decisive role in the next decade’s industrial markets (Industry 4.0) enabling dramatic improvements in complex systems design and operation. However, this technology has not been yet widely implemented, since it requires the collaboration of experts in multiple fields and costly computational tools. The EdgeTwins HPC project brings a different approach that will extend digital twins to new market segments and users (i.e. SMEs). It aims to develop an open-source software tool (builder) to produce digital twins that run on the Edge. That is, they are installed on the physical asset they represent, and operate in very constrained compute environments. This approach will enable a new breed of novel real-time applications, from autonomous vehicles to small devices. The main objective of this FET Innovation Launchpad project is to evaluate the business feasibility of an open-source Digital Twin builder software for Edge applications and develop and test a demonstrator (alpha) for one or more use cases identified in the market analysis. To do so, the participants will leverage on the high-performance computing software stemming of the ExaQute FET Proactive project. These assets will allow, using Reduced Order Modelling techniques, to model complex 4D simulations to later extract the essential features of the solution so that similar results can be obtained at a vastly reduced computational cost.