DisabmDigTwin

Disambiguating the Digital Twin

There's lots of confusion around the Digital Twin. In this post and video, provide an overview of the three definitions used in the industry and highlight the one we use.

Let’s talk about the Digital Twin. If you’ve done any research on this, you know it’s a very hot topic amongst industry professionals today. The concept has been around for a while, but IoT technology has made it more cost-effective in recent years.

A Digital Twin is a virtual model of a product. By running simulations on a virtual model instead of the physical item, you can find potential problems before they even occur. If you choose to run a simulation in the cloud, it also helps avoid overloading the network with data. Think about getting data from a car, and how the car is being used. That’s a lot of data, and it’s impossible to transfer everything over a network.

However, there seem to be many different definitions of Digital Twin technology floating around out there. In general, you’re going to find it talked about in three ways:

Full Digital Representation

In the first way, people are saying that a digital twin is a full digital representation of a product. That includes not only definitions for the mechanical and electric components, but for embedded software (UML, code, etc) as well. That definition tends to be disconnected from the physical product. There’s no connection of sensor data or driving anything in that way.

Sensor Data in the Cloud

In another definition, people are collecting censored data from physical products and then storing it in the cloud or an on-premise data center. This is perfectly valid, and there’s value in doing that as well.

Feeding Sensor Data to Simulations

The third definition of the “digital twin” is when censored data is used to drive simulations. These could be 1D simulations that predict the behavior of systems, multibody dynamics, structural analyses, fluids, thermals, what have you. The idea is that you’re using censored data as a boundary condition. You’re actually driving the simulation, and the results of the simulation are more accurate, and frankly, richer than if you just had censored data alone. That’s the definition that I use.

But which one is right? Well, I think that’s kind of a mute point, or a mute question. There is value in all of these conversations about the digital twin, and I think the key lies in educating yourself so you can participate in all of these discussions and see how they can apply to your company. All three definitions are valid.  If you want to learn about the digital twin concept in more detail, we’ve created a post on that here.

Transitioning from traditional, mechanical products to smart, connected products is intimidating. Companies must gain new competencies to keep up with the shifting landscape. We published the ‘Mechanical Design is Changing in the Smart, Connected Products Era’ brief to provide ideas and insights for how to navigate this change.

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