Digital Twin

by Imogen Hartmann, Journalist, Infrastructure magazine

The last few years have seen digital twins explode in popularity due to the significant benefits around cost-effectiveness and accuracy, and the ability to identify problems before they occur. They are allowing for more informed decision-making, which keeps project costs from soaring and avoids unnecessary delays – issues infrastructure professionals are constantly facing, especially now with the industry’s key role in the post-COVID economic recovery. Here, Infrastructure explores the technology and its impact on construction operations and maintenance.

The concept of a digital twin refers to a digital model of a physical object, process or system. In order to make the model identical, or a “twin” to the real-life subject, the digital model utilises historical and real-time sensor data.

The originators of the term, Dr Michael Grieves and John Vickers, said that a digital twin is “based on the idea that a digital informational construct about a virtual or a physical system could be created as an entity on its own.

This digital information would be a ‘twin’ of the information that was embedded within the virtual or physical system itself and be linked with that system through the entire lifecycle of the system”.

The ability for a digital twin to be able to mirror the developments and changes in an asset is an important function for the infrastructure industry, as it allows for the use of the technology throughout all phases of an asset’s development, including the planning, construction, operation and maintenance stages.

By replicating the physical world in the virtual world, data can be analysed and systems can be monitored to prevent problems before they occur.

This allows for infrastructure companies to potentially save costs by using the twin to troubleshoot any issues before the asset is built, and avoid wasting resources, labour and time that would have otherwise been spent fixing a problem.

Opportunities and risks

Digital twins offer several benefits, such as the ability to provide a safe virtual environment where users can simulate and test the impact of changes to assets, products, systems and processes based on real-time data.

Benefits to the infrastructure industry include:

  • Monitoring of an asset in real time
  • Virtual scenario modelling and testing
  • Supporting informed planning decisions, detecting and troubleshooting issues
  • Providing more accurate predictions of potential problems
  • Measuring performance and efficiencies of assets and equipment
  • Information sharing with citizens and businesses

Although the technology is developing quickly, there are still some challenges to overcome. Potential risks of using digital twins include:

  • The type and quality of data that is shared between the asset and the digital twin may mean that the latter is less accurate
  • Potential challenges in the digital twin’s functionality with existing assets, products, systems and processes
  • Digital twins with long lifecycles (such as buildings and infrastructure) may outlast the short lifecycles of data formats. Design software formats have a high risk of becoming unreadable by modern systems at some point in their service life
  • Potential disputes over ownership of the data being input into, and contained in, the twin
  • Due to the high level of complexity, digital twins may be expensive to design and create

Implementation in infrastructure

The creation of a digital twin often requires a specialist, such as an expert in data science or mathematics that can identify the physics underpinning the physical object, so that the data can then be used to develop a virtual simulation of the original.

Sensors are then integrated into the original asset to gather data about its real-time status, working condition, or position, all of which is then input into the twin.

This is achieved through a cloud-based system that receives and processes all the data the sensors monitor. Throughout this process, the twin is able to simulate the physical object in real time, whilst offering insights into performance and potential problems.

To use an example, a factory might employ digital twin technology to measure the effectiveness of its systems and machinery. This would first require the use of historical data to replicate the factory in a digital simulation.

Then, the sensors would be able to feed in real-time data to the digital twin user, allowing them to remotely assess the factory activities as they’re occuring.

In other cases, a prototype of an asset (such as a building, railway, or water pipe) can be used before the end product is built, allowing the technology to provide feedback as the end product is refined. In the absence of a physical asset, the twin itself can also act as a prototype.

For example, a digital twin of a building before it is built can allow the designers, architects and engineers to identify any potential problems or opportunities for improvement before construction commences.

Digital twin technology has the capability to process complex data and systems, but it can also be simplified depending on the level of data used to construct and update it.

For example, a digital twin of a road junction or traffic light might require more complex data in order for the user to monitor peak times, types of vehicles passing through, weather conditions and safety hazards.

However, a simpler digital twin might be beneficial for an energy company that operates a wind farm if it wants to optimise operations but is struggling because of the unpredictability of the weather.

A digital twin could use historical data from a certain period of time to see how much energy was generated and in what conditions, then use this to predict the ideal angles for the wind turbines, as well as monitor wear and tear to forecast the lifespan of the assets.

Achieving a new scope of maintenance

Digital twin technology not only helps to inform decision-making in planning and construction phases, but it can also support companies in maintenance and condition monitoring.

If an issue arises in an asset that the asset manager isn’t sure how to fix, technicians can use digital twins to test solutions before applying them to the physical twin, allowing them to ensure safety and effectiveness.

The live data feed provided by the sensors also allows the digital twin to monitor the condition of an asset for issues in real time. This live condition monitoring means digital twins can be used for predictive maintenance, where faulty components or assets at the end of their useful life can be replaced prior to an issue arising.

This process also eliminates the need for some manual inspection, and increases safety by allowing the operator or owner to proactively identify and remedy potential problems before they occur.

One example of digital twins being utilised for maintenance and condition monitoring is a project by Victorian Power Networks (VPN) and United Energy (UE) which saw the development of a digital twin using LiDAR data to help with vegetation management of their electricity infrastructure.

VPN and UE own and operate large electricity distribution networks, totalling over 850,000 poles and more than 800,000km of powerline.

The energy networks are also responsible for complying with strict regulations, requiring innovative and cost-effective safety solutions for maintaining network infrastructure.

To do this, the companies launched a LiDAR Lab to use digital twin technology as a surveillance method, measuring the distance to a target by illuminating the target with a pulsed laser light, and measuring the reflected pulses with a sensor, to create a digital model of the terrain.

The digital mapping allowed the networks to measure the distance between vegetation and powerlines to within 10cm. As vegetation-to-powerline proximity poses significant bushfire risks, the ability to closely monitor these distances affords the networks an important level of infrastructure safety.

It then used LiDAR to develop a digital twin of the entire network for asset management-related activities, such as determining the clearance between the conductors and the ground – a process that is currently done manually.

Future-bound asset management

Despite some potential challenges, digital twins are being dubbed by experts as one of the most imperative strategic technologies for businesses and governments, particularly within construction and asset management.

Dr Samad Sepasgozar from UNSW Built Environment said the digital twin is “not a pipedream, but the next frontier of construction management”.

“At the strategic level, the digital twin is a new game-changing approach to construction automation … that will transform the industry quicker than ever before,” Dr Sepasgozar said.

Digital twins have already been adopted by factories and manufacturing industries, automotive companies, aerospace engineers, local governments and councils, as well as the utility, energy and healthcare industries.

Digital twins are expected to unlock new collaboration opportunities among physical world product experts and data scientists in the future, as well as enhancing customer experiences and improving products, operations and services.

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