Digital twin use cases date back to the 1960s. The first ones were developed at that time for the Apollo program. NASA’s scientists created duplicates of devices and systems that operated in their spacecraft. When the oxygen tanks exploded during the Apollo 13 mission, they used them for testing out ideas to rescue the astronauts and assess different mission outcomes.
Nowadays, digital twins are helping businesses worldwide by providing better insights into their products, processes, and customers. This, in turn, improves safety, reduces costs, and optimizes operations. The global digital twin market was valued at almost 7 million USD in 2021 and is expected to grow to more than 96 million within the next eight years (source: Fortune Business Insights).
But what exactly are digital twins? They are, in principle, digital representations of physical objects or systems such as machines and buildings. These computerized replicas are created using data from sensors that are placed on the physical object being monitored. The technology is used in various industries, such as automotive, manufacturing, energy, healthcare, and transportation.
Digital twins vs. virtual twins – is there a difference?
If you search for the phrase digital twin on the internet, you might find another similar term – virtual twin. Are those different names for the same thing? Generally speaking, yes. Many people use the two terms interchangeably. Other sources give a similar but different definition for the two.
According to some people, we should label 3D virtual representations of objects as digital twins and use the term virtual twins for experiences that let us visualize, model and simulate more complex systems. As you can see, this differentiation is somewhat vague. Although we could agree with the idea of naming virtual counterparts of real objects in another way than whole simulations of real-world systems.
How do digital twins work?
But let’s use the two terms interchangeably in this article. In that case, digital twins (or virtual twins) mean any virtual object, contraption, process or system that’s a replica of something that we created or will create in the real world. It can be just a 3D visualization, a simplified simulator or a full working model with all the details.
Creating a digital twin usually starts with collecting initial data from the physical object we want to replicate. This data can include measurements, material properties, dimensions, and other relevant information. Usually, you want to connect the object to the cloud to access the data from any location.
Next, we create a 3D model of the object in order to simulate its behaviors and reactions in different scenarios. We continue to collect and analyze data from the physical object, using this data to update the model as needed. This helps us to optimize the performance of the physical object.
Once the model is complete, we can test it in a virtual environment. This helps us to identify potential problems and make the necessary adjustments before the object is released into the real world. We can also use the model to simulate different scenarios and predict the performance of the object in different contexts. This enables optimization of the design before it’s released.
Immersive technologies like virtual and augmented reality can also enhance virtual twins. Finally, they can create a basis for developing an industrial metaverse.
Digital twin use cases
Digital twin use cases in the manufacturing industry include virtual prototyping, optimizing processes and predictive maintenance. Global companies are already using this to their advantage.
For example, Siemens developed a world-record-setting electric aircraft motor thanks to digital twin technology. The engine is not only lightweight but also five times more powerful than comparable designs. Watch the video below to learn more.
By creating a virtual replica of a product, you can speed up the process of designing and testing it. After all, creating a digital object is much faster than gathering the materials, machinery & workforce and waiting for the labor to be finished. It’s also cheaper. And a well-designed virtual twin is a very accurate base for testing, optimizing, predicting and making adjustments to the product.
Next, many companies are creating digital twins of workflow systems and processes, mainly for the optimization of procedures. The great advantage of using virtual counterparts is that you don’t have to disrupt the process to try things out or risk damaging efficiency.
Digital twins can simulate every imaginable condition or simply monitor and analyze real-time data from sensors placed on the equipment. This data can be used to identify areas of inefficiency and allow manufacturers to make adjustments.
Finally, digital twins are extensively used for predictive maintenance. They can essentially simulate when a machine or piece of equipment is likely to fail, allowing the manufacturer to plan maintenance schedules before it does.
The healthcare industry has started using digital twins relatively recently. It’s a pity they weren’t used more before the pandemic, as they are an excellent tool for predicting bed occupancy, for example. Besides that, we can use digital twins of patients and medical devices to accurately simulate the outcomes of procedures, surgeries and different types of treatment.
Virtual patient models can also monitor patient health by recording and analyzing real-time data from wearable devices such as fitness trackers. This data can be used to identify early signs of illness, as well as to provide a better picture of a patient’s overall health.
Another digital twin use case is simulating drug reactions, helping to reduce the risk of adverse events and improve the efficacy of treatments. Finally, digital twins can help diagnose diseases by comparing patient data with a library of known conditions.
Representations of physical energy grids, power plants and their elements are the most common digital twin use cases in the energy sector. As with healthcare and manufacturing, virtual twins play a significant role in optimizing and testing energy infrastructure and devices. They can predict demand and supply fluctuations, helping energy companies to manage their resources better.
Furthermore, digital twins are a great way to monitor and optimize energy systems by collecting real-time data from sensors placed on infrastructure such as power plants and transmission lines. This data can be used to identify inefficiencies in energy consumption and suggest ways to reduce energy waste.
GE digital twin use case
How do companies in the energy sector use virtual twins in practice? General Electrics has recently introduced its Digital Wind Farm. It’s a wind turbine paired with a cloud-based model of a wind power plant. Once the physical generator is up and running, the digital twin software can start collecting data, analyzing it and running simulations.
GE predicts that this will allow them to increase energy production by up to 20%. That means around an additional 100 million USD in revenue over the lifetime of a turbine.
Digital twins can monitor traffic flows and predict delays by collecting and analyzing real-time data from sensors placed on roads and traffic lights. This is an excellent tool for traffic engineers. With virtual models of urban infrastructure, it’s easier to plan and organize traffic in the most efficient and safest way.
A shipping company could use the same infrastructure and urban models to plan and optimize its routes, avoiding traffic jams and, therefore, delays. This would mean savings in both time and money, as couriers could get to their destination faster and use less fuel. And fewer emissions, which is important for the environment.
Digital twins in your company
Digital twin use cases aren’t limited to the four industries we discussed. Virtually any company can benefit from these AI-powered tools. At 4Experience, we offer a suite of services to help you develop digital twins and immersive business solutions for your company. Our team of experts can guide you through the process of gathering data, creating a 3D model, connecting your object to the cloud, and simulating different scenarios.
If you’re considering using digital twins in your organization, leave us your e-mail address. We’ll get in touch with you in just a few hours. The next step is a completely free 60-minute consultation with our experts, no hidden conditions!
The author generated this text about digital twin use cases in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.