Innovation Highlight: Digital Twin Models

Digital twins are one of the latest innovations in asset management, project planning and maintenance.

They can provide a real-time feed of construction sites, rail networks or train fleets and even predict breakages or maintenance problems before they happen. Building up a true digital twin takes some time but it can save contractors, clients and operators significant time and money.

What Are Digital Twin Models?

A digital twin is a digital representation of a physical object. The technology was first used by NASA to mock up full-scale space capsules, but now it extends even to full Cities.

The twin model uses live data from sensors based on the real-world counterpart so it simulates the structure in real-time. They represent an ecosystem of data from a range of sources, rather than presenting a single source of truth.

Digital twins are a huge industry priority at this point in time. On 9th September 2019, CDBB and the Institute of Civil Engineers partnered to host ‘National Digital Twin Day’, a workshop focusing on digital transformation. Gartner predicts that by 2021, half of all large industrial companies will use digital twins, resulting in these organisations improving effectiveness by at least 10%.

Why Are Digital Twins Good For Construction?

• Real-time twinning offers early insight into potential faults.
• Manage assets more effectively.
• Plan and schedule maintenance.

How Are They Made?

To fully implement a digital twin takes six stages, along a scale of connectivity and complexity.

• Reality capture. A picture of the existing physical assets is built using surveying equipment such as drones.
• A 2D map or 3D model is built, reflecting the object only. This is shape without intelligence.
• The model is enriched with data sets. Static data (documents and drawings), as well as metadata such as BIM, are used.
• The twin is connected with real-time, dynamic data like weather and wind speed.
• Two-way integration from physical to digital is introduced. The physical object communicates with the digital twin.
• Autonomous operations and maintenance. The digital twin reflects the state and conditions of the physical object.

A Complete Network: Crossrail

London’s Crossrail uses a digital twin model of the entire network including facilities, systems and environments.  This detailed model helps engineers and data scientists assess the impact of changes, respond to problems quickly, optimise designs and minimise waste.

In the future, they can further use this learning to design smarter networks.

Railway fleet management, Source: AltexSoft

Fleet Maintenance: Alstom

Alstom uses a digital twin to manage the operations of the entire WCML fleet. The twin covers the fleet itself, depots and stations. This means that Alstom can:

• Understand fleet performance and find bottlenecks.
• Service trains more effectively with insight into scheduling strategies and depot capacity.
• Evaluate KPIs to make more informed decisions.
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In conclusion, digital twins offer a range of benefits to the rail industry, from improving operational efficiency to enhancing safety and reducing maintenance costs. As technology continues to evolve, digital twins are becoming increasingly sophisticated and capable of providing even more valuable insights into railway operations. This not only improves reliability and reduces costs, but also enhances safety by identifying potential hazards and enabling proactive risk management.

Furthermore, digital twins optimize the performance of rail networks by analyzing data. This can lead to better resource allocation, reduced congestion, and improved passenger experiences. In addition, digital twins can be used to model and test new infrastructure and operational changes before they are implemented, enabling operators to evaluate the potential impact and make informed decisions.

However, as with any new technology, there are also challenges associated with the implementation of digital twins in the rail industry. These include the need for accurate and up-to-date data, as well as the cost of developing and maintaining digital twin models. Additionally, there is a need for skilled personnel who can design, develop and operate digital twin systems, and for interoperability standards to enable data sharing between different systems and organizations.

Overall, the potential benefits of digital twins make it clear that they have an important role to play in the future of rail operations. With continued investment and development, digital twins can help the rail industry to overcome many of its current challenges and deliver a safer, more efficient and more reliable service to passengers.