What is a digital twin and why is it important to IoT?
Today, digital twin technologies continue to gain traction because of their potential to bridge the gap between physical and virtual worlds, according to Grand View Research, which says the global digital-twin market is forecast to expand at a compound annual growth rate (CAGR) of 38% from 2023 to 2030. Incorporating technologies such as artificial intelligence (AI), cloud computing and IoT into digital twin systems is expected to boost market growth in the forecast period, Grand View says.
How does a digital twin work?
A digital twin begins its life being built by specialists, often experts in data science or applied mathematics. These developers research the physics that underlies the physical object or system being mimicked and use that data to develop a mathematical model that simulates the real-world original in digital space.
The twin is constructed so that it can receive input from sensors gathering data from a real-world counterpart. This allows the twin to simulate the physical object in real time, in the process offering insights into performance and potential problems. The twin could also be designed based on a prototype of its physical counterpart, in which case the twin can provide feedback as the product is refined; a twin could even serve as a prototype itself before any physical version is built.
Digital twin vs. simulation
The terms simulation and digital twin are often used interchangeably, but they are different things. A simulation is designed with a CAD system or similar platform, and can be put through its simulated paces, but may not have a one-to-one analog with a real physical object. A digital twin, by contrast, is built out of input from IoT sensors on real equipment, which means it replicates a real-world system and changes with that system over time. Simulations tend to be used during the design phase of a product’s lifecycle, trying to forecast how a future product will work, whereas a digital twin provides all parts of the business insight into how some product or system they’re already using is working now.
Digital twin use cases
Potential use cases for digital twins are expansive. Objects such as aircraft engines, trains, offshore oil platforms, and turbines can be designed and tested digitally before being physically produced. These digital twins could also be used to help with maintenance operations. For example, technicians could use a digital twin to test that a proposed fix for a piece of equipment works before applying the fix.
Manufacturing is the area where rollouts of digital twins are probably the furthest along, with factories already using digital twins to simulate their processes. Automotive digital twins are made possible because cars are already fitted with telemetry sensors, but refining the technology will become more important as more autonomous vehicles hit the road. Healthcare is the sector that could produce digital twins of people; tiny sensors could send health information back to a digital twin used to monitor and predict a patient’s well-being.
