It’s hard not to notice how ubiquitous the term “digital twin” has become on the websites of today’s leading media outlets and technology companies alike. It seems like every other article is singing this “new” technology’s praises and cheering the innovations and wonders it can produce. But for those who don’t work closely with technology or engineering experts, digital twin conjures only a vague image, a hint of an idea without any real substance or context. And when you add all the different ways companies speak about digital twin – is it for healthcare? Aerospace? Manufacturing? Energy? All of the above? – and the layperson can easily get tangled and trapped by the maze of contradictory and complex definitions swirling around.
At Altair, we believe in the potential of digital twin technology – after all, we’ve been doing it for more than 37 years – but we also believe that companies and popular media have jumped the gun. The world has gotten so caught up in what the technology can do that many have failed to clearly and concisely lay out what the technology is, what it does, and why it’s used. After all, if this is a technology that has the potential to touch aspects of the things we do and use each day, people should be able to understand and conceptualize it without needing to weed through jargon and expert tech-speak. Altair has always believed technology is best when it’s democratized, and so in this article, we’ll lay out what digital twin is, what it does, and why it’s used.
Digital Twin: An Introduction
At its core, digital twin is a simple concept with a simple definition. In its broadest sense, digital twin technology is the process of using data streams to create a digital representation of a real-world asset to improve collaboration, information access, and decision-making. In other words, through a combination of simulation technology and data (gathered from sensors, historical records, and so on), engineers use software that builds a virtual twin of a physical object or process.
To use an example, it’s easiest to think of a piece of equipment, say, an assembly line robot. This robot’s digital twin would replicate its physical twin’s real-world performance, as gathered from data taken from the robot’s real-life operation. Using digital twins, companies and teams can see how physical objects will perform in certain conditions and under certain stresses. Doing this helps them reduce the reliance on physical prototypes, because where they’d used to build multiple real-life robots to run tests and gather accurate data, now their twin can do it all for them. This saves teams time, money, and reduces waste and material usage.
And in digital twin technology, the data the physical and virtual twins exchange creates a virtuous cycle – that is, the physical object’s data helps teams optimize the twin, and the twin’s data helps teams optimize the physical object. Expanding our example away from an assembly line robot, teams can use digital twin technology for a huge array of applications.
That’s digital twin at its most basic. But like any new technology underpinned by the latest advances in simulation, high-performance computing (HPC), artificial intelligence (AI), and data analytics, there’s more to learn.
Digital Twin's Different Forms
Of course, if digital twin was always used in such a straightforward way, there’d be little to stumble over regarding its definition and application. Obviously this isn’t the case – usually, far from it. Digital twin technology goes far beyond simple models of assembly line robots. In fact, teams and organizations can use the technology in a myriad of ways depending on what industry they’re in, what phase of the product lifecycle they’re in, what they’re trying to model and optimize, who the digital twin is for, and more.
For example, while digital twin technology can refer to a simple 3D model like the one used in our example, users can also use it to conceptualize and build 0D, 1D, 2D, and fully interactive physics-based models. Moreover, while we could consider a virtual representation of a robot a “digital twin,” we could also extend that definition to non-physical objects. Indeed, financial organizations use digital twin technology too, but instead of modeling physical products that will build cars or fly into space, they aim to model things like behaviors, tendencies, and histories. For example, if a bank wanted to ensure they’re not authorizing fraudulent purchases, they might build a “digital twin” of an individual’s spending tendencies and history so machine learning can identify when the real-life individual is making legitimate purchases and when there’s cases of suspected fraud. Social media also uses a form of digital twin by building virtual profiles that companies then use to tailor ads and features to.
These examples of “digital twins” might not be what comes to mind when we talk about the technology, but they’re widespread uses nonetheless. That said, sometimes it can be difficult to discern when companies are using digital twin principles and when they aren’t, often because it’s not always clear when they’re doing so due to what they advertise and what gets covered in the media.
It's the Little Things, Too
Even though digital twin’s definition is simple, implementing a successful digital twin strategy is anything but. This is because, in most digital twin offerings, companies need a vast repertoire of expert personnel, infrastructure (you have to gather and store all that data somewhere!), and software tools all working in harmony in order to create the circular data loop that defines digital twin. This requires time, effort, resources, and planning – as such, organizations usually utilize digital twin on their biggest, most lucrative projects to ensure they get them right.
These might be projects that involve highly complex, highly rewarding projects, such as building wind turbines, optimizing aircraft engines, streamlining maritime designs, and so on. These big projects tend to capture all the attention and headlines as well, making it hard to identify digital twin at work in smaller-scale projects. But if you look closely, you can see digital twin hard at work improving products that affect our daily life.
For example, even “mundane” products like smart thermostats utilize digital twin’s principles – after all, the thermostats, sensors, and apps work together to create a virtual representation of the climate in different rooms of a house or apartment. To do that, the thermostat has to understand the house’s layout, has to gather and organize data in real-time, and has to interact with its real-world counterpart to adjust and further monitor climate conditions. It does all this to optimize one thing – its user’s comfort and preferences. Keeping your bedroom cool for the night might not grab the headlines, but it’s something millions of people around the world care about and utilize every day. Such is digital twin technology at work.
Moving On Up
As any engineer or data scientist can attest, today’s digital twin strategies are far from simple to implement. But they’re worth the initial investment and then some. Today’s companies are rushing to implement digital twin because it’s proved it can reduce costs, project time, waste, and carbon footprint. Already today, different types of user personas are building and deploying cross-functional digital twins at different design phases and for different purposes, and these twins can help teams from around the world integrate and synchronize data and workflows. This is already a major step toward building digital maturity. Just as important, the technology has already proven it saves people time and headache and allows engineers and designers to develop better, longer-lasting products faster and more confidently. That said, the quality and ease of a digital twin strategy is only as good as the platforms teams can use to implement it. That’s where we come in.
Altair offers the most comprehensive, most streamlined digital twin offering on the market. Since we handle every aspect of the digital twin cycle – simulation, data, machine learning, and computing – there’s no need to go through other software vendors to handle data or simulation. Additionally, our open-architecture, vendor-agnostic philosophy means that organizations don’t have to migrate their data or models to a different data or model infrastructure. Lastly, and most importantly, our experts have been doing digital twin for decades and are available 24/7 for support and advice.
At Altair, we envision a future where digital twin technology is a democratized, household term that even laypeople understand. Moreover, we strive to create solutions and technology that brings the game-changing power of digital twin to more organizations and teams around the world, no matter the scope or scale of their project. Ultimately, we believe in a digital twin philosophy that prizes end-to-end, easy-to-understand functionalities and features – a platform that meets anyone’s digital twin needs under one roof.
As for the present, we hope to make digital twin more approachable and more flexible, and we hope this article has contributed to that effort. To learn more about Altair’s digital twin offerings and to see our latest digital twin customer stories, testimonials, videos, and case studies, visit https://altair.com/one-total-twin.