Digital Twins, along with the Internet Of Things and Edge computing, are expected to play a crucial role in the next decade’s industrial markets (Industry 4.0) enabling dramatic improvements in design and operation processes. A Digital Twin should be understood as a digital representation of a physical product, production process, or product’s utilization, in operation, with the required accuracy and fidelity to predict actual, physical performance. The twin replicates the real-world system throughout its lifecycle, being continuously updated, through sensors embedded in the physical system, to replicate its improvements, performances and issues. Digital Twins are usually run and maintained in the cloud while fed with data from field devices.
The added value of a Digital Twin is that it can provide a link between the physical and digital worlds. This link, or physical-digital loop, allows to understand past and present operations and make predictions based on real-time data by leveraging machine-learning approaches to condition monitoring, anomaly detection and failure forecasting.
Despite their potential, Digital Twins have not yet been widely used in engineering, other than some applications related very complex and demanding systems like in automotive and aerospace. The reason for this is that the development of digital twins is still very challenging, since it requires the collaboration of experts in multiple fields, as well as the use of robust and affordable computational tools, able to integrate multiple physics as well as diverse solving technologies. The kind of resources that are only available to OEMs or Tier-1 manufacturers.
The EdgeTwins project aims to develop a software toolchain to allow the generation of very compact Digital Twin apps able to run on the Edge; that is, twins that are installed and operate on the physical asset they represent, enabling a new breed of novel real-time applications from autonomous vehicles to small devices.
The new software tools will be open source, eventually deploying and freely sharing source code as needed, and will also allow leveraging HPC hardware for the training phase.
