Digital twins are powerful combinations of models and data that “age” throughout the lifecycle of an asset as they gather and integrate data from the field. This technology is a quantum leap from earlier efforts at modelling complex systems.
A digital twin is “a dynamic virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning, and reasoning.”1 For a complex system, a digital twin will collect all available system data, as well as the models that are used for decision support, throughout the lifecycle of the asset in question. The power of digital twins lies in the ability to perform ‘experiments’ in a computer rather than in the field, because it is much cheaper, safer and faster to do that on a digital platform than in the field.
Towards 2030, digital system representations in the concept phase (3D drawings, descriptions, requirement and use-case specifications, high-level risk analyses, generative design) will be detailed and optimized throughout the design phase, extended with simulation models representing all relevant system aspects and giving operators the ability to perform complex tasks in, for example, virtual system integration. In the production phase, this information can be used for vendor specification and follow-up, and detailed with both as-built vendor information and detailed interface and integration information. The project management information is then added, enabling simulation-based optimization of the installation and commissioning processes.
After handover to operation, process optimization, maintenance and modifications need follow-up and decision support. As a result, the system’s information repository is crucial during this stage. The increasing availability of live sensor data will open up a whole new world for optimization and decision support. Massive sensor technology and the possibility to store and handle huge amounts of data will lead to the possibility of keeping the digital twin “alive” throughout the lifecycle, and the possibility to integrate the data streams from the real asset in digital twin analyses. Further integration of data streams and integration of various digital sub-models (sub-twins) will, in addition, lead to the relatively easy construction (unthinkable until now) of models.
Digital twins are a ‘quantum leap’ from earlier efforts at modelling complex systems. Thus far, models have focused on particular aspects of an asset, and answered a particular set of questions. A single asset – a wind turbine, for example – might have more than 100 separate models to answer specific questions about its condition or performance. A digital twin brings all these models together and – paired with massive sensor data from the asset – ‘ages’ throughout the lifecycle of the asset, along with the asset itself.What lies ahead?
Early examples of digital twins exist today, but we are only beginning to develop true digital twins for complex assets and systems. There is still a great deal of work to put together the building blocks that are needed (many interconnected models, plus robust data streams from the operational phase). When it comes to large, complex assets, we can expect to begin seeing true digital twins that assemble and coordinate the needed elements between 2025 and 2030.Contributors
Main author: Siegfried Eisinger
Contributor: Øyvind Smogeli
Editor: Thomas Fries