What is a Digital Twin? Definition, Use Cases, and Trends

CornerRight 8 min


Icons/Position/Pos. No. 1

Digital Products


Icons/Technology/Tech No.1

AI

Unless you’ve been living under the rock, you probably came across the term “digital twin” at some point. But what exactly is this technology all about? What potential use cases does it offer? And which industries stand to benefit from it most? This article is your ultimate resource of essential knowledge on the digital twin technology. Keep on reading to find out what a digital twin is, how it works, what benefits it offers, and where it’s going in the future. 

Table of contents:

What is a digital twin technology?

How do digital twins work?

How is digital twin technology different from simulation?

Types of digital twins

Key use cases of the digital twin model

1. Creating real-world scenarios with digital twin technology

2. Getting unparalleled performance overview

3. Excellent visualization for troubleshooting

Knowing how digital twin works is just the beginning

What is a digital twin technology? 

A digital twin is nothing else than a virtual representation of a system or object spanning its entire lifecycle. What’s interesting here is that a digital twin is always updated using real-time data. The technology uses machine learning, simulation, and reasoning to help companies in decision-making processes. 

In short, a digital twin is a highly complex virtual model which is an exact counterpart of the physical object or system – literally, its digital twin. 

That system or object can be anything: a bridge, car, building, or even jet engine. All it takes is adding sensors to the physical asset to collect data, which can then be mapped onto a virtual model. 

By analyzing the digital twin, we can get all the critical information about how that physical object or system is performing in the real world. 

How do digital twins work? 

As mentioned before, a digital twin is a virtual model designed to accurately reflect a physical object. 

It all starts with the object in question – for example, a wind turbine. You can outfit it with different sensors related to crucial areas of its functionality. These sensors will generate data about different aspects of the wind turbine’s performance – for example, temperature, weather conditions, and energy output. 

All of this data is then sent to a processing system and applied to the digital copy of that object. The virtual model informed with such data allows teams to run simulations, research performance problems, or generate improvements. 

The idea is to come up with valuable insights and new improvements that can be applied back to that original physical object. 

How is digital twin technology different from simulation? 

Simulations and digital twins both use digital models to represent a system and its different processes. But there’s one crucial difference between them. 

A digital twin is a fully virtual environment that offers a much richer material for study. It’s all a matter of scale. 

While simulation typically focuses on one particular process, a digital twin allows teams to run a number of helpful simulations in order to study multiple processes at the same time. 

Moreover, simulations don’t usually use real-time data. And digital twins are designed to enable a two-way flow of information. First, the object sensors generate data and send it to the system processor. Next, the insights generated by the digital twin return to influence the working of the original source object. 

By getting access to constantly updated data related to a wide range of different functionalities – always empowered by the computing power of the virtual environment – digital twins help teams to research an issue from more perspectives than any standard simulation could provide. Ultimately, a digital twin offers more avenues for improving the product. 

Types of digital twins

Digital twins come in different forms depending on the level of product magnification. The most significant difference between different twin types is their areas of application. On top of that, different types of digital twins can coexist within one process or system. 

Let’s go through the different types of digital twin technology to see how they work in the real world. 

Component or part twins

Component twins are the basic unit of every digital twin. They are the smallest example of a fully functioning product component. Part twins are more or less the same thing – but are generally less important than component twins. 

Asset twins

If we put two or more components to work together, we get an asset twin. An asset twin allows teams to research the interaction of those components and generate a lot of valuable performance data that later can be processed in the digital twin and transformed into actionable improvements. 

System or unit twins

The next level is system or unit twins. Such twin technologies allow you to check how different assets work when brought together to an entire functioning system. That way, system or unit twins offer excellent visibility into the interaction of assets – and often result in game-changing performance improvements. 

Process twins

This is the macro-level of the digital twin technology. Process twins are used to showing how systems work together to create a bigger system – for example, a production facility. The idea is for teams to learn whether the system is performant enough to operate well at peak efficiency or how a delay in one system might impact another system. Process twins are a great help in identifying the precise timing schemes that impact the overall effectiveness of all the systems involved in the process. 

Key use cases of the digital twin model

Digital twins offer great value to manufacturers and engineers, helping them accomplish a lot of different tasks – from visualizing products in use to troubleshooting equipment located far away geographically. 

Why are digital twin technologies important to engineers?

The job of every engineer is to design and test products. It doesn’t matter what they are – it can be cars, tunnels, household items, or jet engines. The idea is to get an overview of their entire lifecycle. 

Engineers need to make sure that the designed product is suitable for the purpose, can handle wear and tear, and respond to the environment where it will be used. 

1. Creating real-world scenarios with digital twin technology

An engineer who is testing a car acceleration system can run a complete computer simulation of that system to understand whether it performs well in various real-world scenarios by using a digital twin. This method is much faster than building multiple physical cars and then testing them in a testing facility. 

The digital twin technology is the only one that can help here. The regular simulation presents significant limitations to testing across different environments. It can’t predict how the car would react to future scenarios or changing circumstances. 

This is where the Internet of Things and digital twins come in. A digital twin takes advantage of data from connected sensors to show how the system is performing in real life throughout its lifecycle. By using Internet of Things data, engineers can measure different indicators of the product’s health and performance, from network bandwidth to temperature. 

Once they incorporate all of this data into their virtual model, engineers can get a full view of how that system is performing, using real-time feedback from the vehicle itself. 

2. Getting unparalleled performance overview 

By using digital twins, engineers get incredibly detailed insights into how the product performs in the real world. A digital twin helps them to easily identify potential problems, check how customers are using the product, and use this knowledge to improve customer satisfaction. 

This leads to greater product quality – whether it’s adding new services or investing in product differentiation. Knowing how customers are using your product after they bought it offers a wealth of insights. Thanks to the digital twin technology, all it takes is the implementation of sensors for manufacturers to get a wealth of data. 

3. Excellent visualization for troubleshooting

Another advantage of the digital twin is that they offer an in-depth review of the physical asset that might be located far away physically. By using a digital twin, the engineer doesn’t have to be in the same room (or even the same country) as the product to troubleshoot it. 

For example, a mechanical engineer in Los Angeles can use a digital twin to diagnose a car braking system of a vehicle located in New York City. 

Thanks to the insights coming from sensors that gather data about sound, vibration, altitude, and more, engineers can create a digital twin of the physical object practically anywhere in the world, gaining an outstanding level of control over its visualization. 

Knowing how digital twin works is just the beginning

If you’re looking for expert advice on how to take advantage of the Internet of Things and build a digital twin for your company, get in touch with us. We have an excellent track record in providing clients with this type of technology and offering consulting services focused on the development of truly innovative solutions. 

Get in touch with us and transform your company with digital twin technology.

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