Digital twins present an opportunity to improve the way users envision, design, build, operate, and manage almost any asset in the built and natural environment. With applicability to a wide variety of industries, digital twins can help to create more sustainable, healthy places, realise greater efficiencies to reduce waste and emissions, and optimise the planning and performance of transport and infrastructure networks to improve people’s lives.
With this wide variety of capabilities and applications, it can be challenging to understand what the term ‘digital twin’ really means, and even harder for organisations to understand whether they are ready to implement digital twin technology solutions.
WSP’s Digital team have defined and simplified digital twins through the Digital Twin Guide, which allows users to enter information to gain an understanding of how a digital twin could be applied to benefit a given project or asset, or a group of projects or assets. This helps to create a clearer picture of the opportunities that are available given the needs of the project, and the data and systems already in place, ensuring that organisations are Future ReadyTM.
Try the Digital Twin Guide here
What is a Digital Twin?
A digital twin is a dynamic digital representation of the built and natural environment that can be used to plan, visualise, report on and control assets and operations.
“In very simple terms, a digital twin helps us to understand what has happened, what is happening, and what will happen,” explains Damien Cutcliffe, Director of Business Development and Growth - Digital. “The data rich model is what has happened, real-time remote sensing shows us what is happening, and predictive modelling and simulation assesses what will happen.”
Every twin is different
While digital twins reflect their real-world counterparts, they can vary greatly between organisations and use cases.
Based on an organisation’s level of digital maturity, and the kind of outcomes the platform ultimately needs to deliver, digital twins differ in complexity and capability. On the simpler, smaller end of the scale, a digital twin can mean an easy way to visualise data and make decisions, relating to a particular building, or piece of equipment.
In other instances, a digital twin could be a statewide, visual platform which can help decision makers predict future scenarios based on existing data. At the same time, it can inform policy, illustrate opportunities, and enhance a user’s ability to make informed decisions based on multiple interdependent variables. A good example of this is LandiQ, a digital twin delivered by WSP in partnership with Giraffe, that spans all of New South Wales and is designed to facilitate land use and planning.
Why invest in digital twin technology
A digital twin allows users to understand and explore scenarios virtually, without making them a reality first. The insights from the twin allow users to make more informed decisions, reduce and quantify risks, and optimise business processes. It can enable users to understand the data and what is happening, without the user being physically present, or requiring software or modelling skills. A digital twin can be used for training or for modelling simulations to discover the most optimal designs and decisions before they become concrete.
Understanding the dimensions of digital twins
There are three key dimensions of a digital twin that describe its size, intent, and data maturity. They can apply to any industry and have been used to categorise the variety of use-cases that can be applied to digital twin systems.
- The size of the asset(s) to be twinned (Size)
- The assets life cycle stage (Intent)
- The maturity of systems and available data (Data Maturity)
Looking closely at each dimension reveals the different options available.
When it comes to a digital twin, size does matter. The questions, insights, views, and technologies change depending on the size of your twin. The question is; what is small and what is big?
A small digital twin is anything from a jet engine to a building. This kind of twin has minimal geographic information or context.
In contrast, a big digital twin covers a large geographic area. This could be a precinct, rail line, city, or country. Often, a large twin is a network, such as a road network, or a distribution network, or a large twin might represent an estate of many sites.
There are four critical stages of an asset’s lifecycle in which a twin can be used:
The lifecycle is important because it determines what data already exists and affects the way you will use a twin.
The use cases that an asset owner will have for a digital twin will vary depending on where in the asset lifecycle it sits. Throughout the life of an asset, the digital twin that supports it will need to evolve and adapt to changing needs and data.
Fundamentally a digital twin is a collection of data related to an asset and several ways to view, interrogate, predict, and simulate from that data. When looking at the data maturity feature of a digital twin, there is a continuum where each step builds on the last in the following order:
Data that does not change on a regular basis, typically updated through manual import/ export processes.
Data that is updated on a regular basis in an automated fashion. Examples could include data from Internet of Things (IoT) devices, or web-services.
Using predictive analytics, machine learning, and artificial intelligence methods to capture and understand historical data, predict future events, or detect anomalies.
The ability to answer, ‘What if?’, allowing organisations to measure asset condition and performance in different hypothetical scenarios using artificial intelligence and machine learning.
For example, when twinning an active rail line, you will:
- start with the static information about the rail line to view the asset;
- then you can ingest live details on where trains are for monitoring;
- with that, you can predict where trains will be, and identify potential delays;
- and finally, you can simulate what would happen if a train line was changed.
Predictive models and simulations can be driven by historical data of an asset or from theoretical models in the planning phase. Hypothetical scenarios can be developed using a combination of known or historical quantities, and new variables.
Given the variability of the different types of digital twins, and the latency of understanding around this technology, this new tool will help planners, engineers and decision makers more easily understand the options available, and the advantages to be gained from utilising digital twin technology throughout the lifecycle of their assets.
To learn more, contact Damien Cutcliffe or visit our Digital Solutions page.
Try the Digital Twin Guide here
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