Kainos: Digital Twins may be pivotal to transforming Public Services #techUKDigitalPS

Digital Twins have long been heralded as the emerging technology which will save billions of pounds of capital investment by understanding how systems perform which in turn informs a more realistic investment strategy for maintaining those systems. Now could be the time for the true value of this technology to be realised by helping government solve some of the key policy challenges. 

Digital Twins are virtual replicas of physical systems, processes, or assets. It can be a representation of a residential building, a wind farm, a road, a train, or an electric vehicle. The term was first coined by NASA in 2010 to describe the digital replicas of spacecraft they used for testing and analysis. However, it wasn't until the advent of the Internet of Things (IoT) and the development of sophisticated data analytics and machine learning algorithms that the use of digital twins became widespread.

Digital twins enable real-time monitoring, analysis, and performance optimisation. They provide early warnings about problems and highlight opportunities to optimise performance. The major use cases aligned to key Government Policy areas being scoped are:

Levelling Up Communities

To ‘level up’ communities digital replicas of neighbourhoods and communities can simulate various scenarios and test the impact of different policy interventions, enabling policymakers to make more informed decisions. 

Energy Security and Net Zero

Digital twins will play a vital role in supporting the government target of achieving Net Zero carbon emissions by 2050. A new Energy Digital Spine would create a network of digital twins to monitor and optimise the UK's energy infrastructure. It will track energy consumption in real-time, allowing policy makers to identify areas of inefficiency and take corrective action to reduce carbon emissions whilst maintaining energy security. 

For example, a digital twin of a wind farm can monitor wind speeds and turbine performance in real-time. This will help identify opportunities to optimise energy production and reduce the cost of energy generation.

Road Transport

On our roads digital twins improve the safety and efficiency of the country's road network. By creating digital replicas of road infrastructure, the agency can monitor traffic flows, identify congestion hotspots, and optimise routes for maximum efficiency. 

For example, a digital twin of a busy motorway can simulate different traffic scenarios and test the impact of different road layouts. This will help reduce congestion, poor air quality and improve the safety of the road network.

Rail Transport

National rail services would benefit from using digital twins to report on the performance of its trains, track and signalling systems. They could also monitor the integration of the rail network with other modes of transport.

By creating digital replicas of trains and the network, potential issues can be identified before they occur and performance of trains on the integrated network can be optimised. This will reduce the energy required to deliver the same capacity on the network and improve the mobility of passengers based on their door-to-door journey requirements. 

Building Safety

Buildings Safety Regulators are responsible for ensuring that high-rise residential buildings are safe for people to live in. Digital twins would allow efficient real-time monitoring of building safety performance e.g., fire safety systems, identify areas of weakness, and provide early warning of potential hazards, enabling building owners and management companies to; help prevent disasters, ensure that residents are safe and secure and efficiently meet the new regulatory requirements.

Climate change

Climate models are computer simulations that aim to predict how the climate will change over time. They are based on factors such as greenhouse gas emissions, ocean currents, and temperature patterns. These models are critical for understanding the impacts of climate change, and for developing strategies to mitigate its effects.

Closer to home, by incorporating real-time data from sensors a more accurate picture of local weather patterns and how they are changing over time could be achieved. This could help better predict extreme weather events, such as floods or heatwaves, and to develop more effective prevention strategies.

Similarly, by incorporating data on local air quality, we could create more accurate models of how pollution is affecting our cities enabling the development of effective strategies for reducing emissions and improving public health. 

For more information or to discuss the Cloud Platform or Artificial Intelligence support of Digital Twins please contact [email protected]

This article was written by Ben Wilkins, Digital Advisory Consultant at Kainos. To learn more about this author, you can get in touch via his email at [email protected]. To learn more about Kainos, please visit their LinkedIn and Twitter.

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