Technology Related Outputs

DUET digital twins enable the prototyping of urban service changes and/or new ideas quickly and effectively. Improvement is achieved by simulating and experimenting with what-if scenarios involving policy-ready-data, cloud and advanced analytics. Predictive simulations create a better understanding of how a service can meet the needs of its target audience, enabling differing teams from across an organisation (e.g. sales, engineering, customer service etc.) to work together to make more informed choices.

Results from DUET that are relevant to technology can be found below.

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The DUET system contains multiple models to perform the calculations for the Digital Twin. The DUET T-cell architecture enables the integration of these models of the DUET system. Computational models (air, noise, traffic) are integrated in DUET by connecting to the DUET T-Cell by means of a suitable API.  They are described in this official deliverable.

Data Cloud

This report identifies the security and privacy implications and concerns stemming from the activities required to increase a city’s performance and growth regarding smart Digital Twin technologies.

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This deliverable covers the implementation of relevant models regarding traffic, air quality and noise. With this demonstrator deliverable and the closed beta version as a PoC, it is shown that an architecture built around the Message Broker in the DUET core is capable of creating an effective chain of models

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For maximum flexibility, DUET created a DUET-Cell architecture as a plug-in interface. The DUET data 3 broker corresponds to the DUET-Cell. It is shielded by APIs that allow the components to connect to the T-Cell’s internal message streaming system on which all data flows between the different components.

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This official project deliverable provided a first high-level blueprint of the overall technology development within the project. The role of this document is to be a common point of reference throughout the project

Digital Mind

Data interoperability is a key challenge in a digital twin context.  To facilitate the aggregation of data sources, the semantics of the data has to be unambiguous. This deliverable presents an approach to reach a common semantic understanding tackling the challenges preventing achieving this goal.

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