CReDo Phase 1
This case study uses content from the reports hosted by The Digital Twin Hub.
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The Climate Resilience Demonstrator (CReDo) is a innovative climate change adaptation project that provides a practical example of how digital twins and connected data can improve climate adaptation and resilience across infrastructure systems, and systems of systems.
CReDo looks specifically at the impact of flooding on energy, water, and telecommunications networks. It demonstrates how those who own and operate such networks can use secure, resilient, information sharing across sector boundaries to mitigate the risk of climate change and reduce the effect of flooding on network performance and service delivery.
Run by the National Digital Twin programme and enabled by funding from UKRI, The University of Cambridge, and Connected Places Catapult, the CReDo project took place in 2022. The vision for the first phase of CReDo was to enable asset owners, regulators and policymakers to collaborate using the CReDo digital twin to make decisions which maximise resilience across the infrastructure system rather than from a single sector point of view.
Data about infrastructure assets is brought together across three infrastructure asset owners — Anglian Water, BT, and UK Power Networks — into a connected digital twin of the infrastructure system network. Combining data sets from three separate organisations into one system model is not straightforward. Principled information management techniques, such as using the appropriate ontologies and striving for semantic precision, are essential to bringing the data together to present the clearest picture of the infrastructure system without inaccuracies.
Coastal and fluvial flood data has been sourced from the Environment Agency and the HiPIMS (High-Performance Integrated hydrodynamic Modelling System) model has been used to generate surface water flooding data that could be expected under a range of future climate change scenarios. Expert elicitation techniques have been employed to understand the impact of the flood scenarios on asset failure within the infrastructure networks. Operational research techniques have been employed to better understand the infrastructure interdependencies and to identify the propagation of asset failure, both across single networks and across the infrastructure system as a whole, resulting from the flood scenarios. This builds a picture of system impact from flooding scenarios that would not otherwise be available to the individual networks or regulators who would only see the impact of flooding on single networks.
This cross-sector figure below, demonstrates the impact of extreme weather events on the infrastructure system and can enable asset owners and regulators to better understand infrastructure interdependencies and identify the most effective, least cost and lowest carbon impact interventions to increase resilience. In addition, the incorporation of live data feeds would demonstrate the potential to inform shorter term operational response leading up to and during extreme weather events.
CMCL provided a knowledge graph as the underlying information integration mechanism. The knowledge graph used ontologies to represent the data in a way that enabled interoperability between the data and models within the connected digital twin. This brought many benefits, not least of which are the capacity to select specific models to run for specific assets and the ability to cascade changes efficiently through the network of connected assets, which will be true enablers of more diverse and sophisticated model catalogues in future iterations.
In the CReDo use case, the knowledge graph was regarded as ephemeral; a new one was created at the start of a modelling run, populated with asset data and a single flooding scenario, and used to calculate asset failures due to flooding, the failures were then propagated throughout the system connections (for as many time steps as required) and finally the results exported to the DAFNI datastore for later visualisation.
Upon ingestion, asset and flood data are mapped to ontologies developed specifically for this project, with an abstraction layer above that to enable extensibility. Ontologies help with scaling up and knowledge graphs naturally align with the use of ontologies so this approach is ripe for further development into a future “thin slice” of the National Digital Twin through further development and refinement of the ontology over time. Due to time and resource constraints, this was not an area we were able to explore in this initial phase of the project and thus is a recommended for future investigation, both as a key evolution of CReDo and as an exemplar of how existing frameworks in the digital twin community can be linked to the IMF.
The CReDo project found that it is possible to piece together different datasets across different organisations into one coherent digital twin that conveys a realistic picture of the infrastructure system and the impact of future flooding scenarios caused by climate change. The work undertaken in the first phase of the CReDo project shows what will happen in the future with current networks if we do nothing to adapt. This has been shown with the real data, with synthetic data and in artistic form through the CReDo film. Digital twin projects do not need to be entirely technical in nature, in order to communicate the purpose of the digital twin, such projects need to embrace the human and personal elements of impact.
A key finding of this first phase is that a technical approach alone does not lead to successful delivery. A technical approach needs to be supported through effective communications across a multi-disciplinary team. Adopting an agile approach is beneficial, but still requires a clear technical plan to ensure delivery within set timeframes. In this way, allowing for diversity of approach and interpretation as part of an overall project team is important for harnessing a wide range of capabilities needed to build a connected digital twin where new thinking is required in approaching data sharing, integrating models and developing visualisations. Whilst technology is crucial, people matter most when putting together a connected digital twin.