Digital twins: How virtual copies of real objects are changing engineering

Imagine being able to test how a jet engine will perform under extreme temperatures, identify when a factory machine might fail, or see how a building will respond to changing weather conditions without physically experimenting on any of them. Digital twin technology makes this possible by creating virtual representations of real-world objects, systems, and processes that can be updated with operational data.

A digital twin is more than a 3D model or computer simulation. It is a data-connected virtual counterpart that reflects the condition and behavior of a specific physical asset. By combining sensor data, engineering models, simulations, and artificial intelligence (AI), it can help engineers monitor performance, predict problems, and test potential changes before applying them in the real world.

How does a digital twin work?

A digital twin usually begins with a digital representation of a physical object. Engineers can build this using computer-aided design (CAD) files, technical specifications, engineering models, 3D scans, and other information describing the asset’s geometry and behavior.

The virtual model is then connected to its physical counterpart through sensors and data systems. These sensors collect information such as temperature, pressure, vibration, speed, location, and energy consumption, depending on the equipment that is being monitored.

The data is transmitted to the digital twin through communication networks and processed using computing platforms. This allows the virtual representation to reflect the physical asset’s operating conditions, sometimes in near real time.

Once synchronized, the twin can use physics-based models, simulations, and machine-learning algorithms to analyze the incoming information. Engineers can monitor performance, identify unusual behavior, and run scenarios to understand how the physical system might respond to different operating conditions.

For example, a digital twin of an industrial pump could use vibration and temperature readings to detect changes that indicate developing mechanical problems. Engineers could then simulate different operating conditions to estimate how the equipment might behave and determine when maintenance is needed.

In some applications, the process can also work in the opposite direction. Insights from the digital twin can inform adjustments to the physical system, with automated controls or human operators applying the recommended changes.

How is a digital twin different from a simulation?

Although digital twins rely heavily on simulation, the two technologies are not interchangeable.

A simulation uses a computer model to predict how an object or process might behave under specified conditions. It can help engineers test different designs or operating scenarios, but it does not necessarily receive information from a particular physical asset once the simulation begins.

A digital twin maintains a connection to its real-world counterpart through operational data. This allows it to reflect changes in the physical system over time and improve its predictions using actual performance information.

A static 3D model, meanwhile, primarily represents an object’s appearance or geometry. It may contain detailed engineering information, but without a connection to operational data and analytical capabilities, it is not a digital twin.

Where are digital twins being used?

Manufacturing is among the main applications. Factories can create digital twins of individual machines, production lines, or entire facilities to monitor equipment health, identify bottlenecks, and optimize production schedules. The US National Institute of Standards and Technology (NIST) is also researching how digital twins can improve manufacturing through predictive maintenance, virtual commissioning, and process optimization.

In aerospace, engineers can use digital twins to monitor aircraft engines and other complex systems throughout their operational lifetimes. NASA has explored the concept for spacecraft and aerospace systems, where virtual models can help assess performance and evaluate potential failures.

The technology is also being applied to energy infrastructure. Digital twins of wind turbines can combine operational data with engineering models to assess performance under changing wind conditions and identify maintenance requirements. Buildings and infrastructure can similarly be modeled to monitor energy consumption, structural conditions, and operating efficiency.

Healthcare is another emerging area, with researchers exploring digital representations of organs and biological systems to study diseases and evaluate treatments. However, these applications vary considerably in maturity and should not be confused with fully validated, patient-specific medical replicas.

What are the limitations?

Despite their potential, digital twins are not automatically accurate or predictive. Their usefulness depends on the quality of the underlying data, the accuracy of their models, and how well those models represent real-world conditions.

Developing and maintaining them can also require substantial investment in sensors, computing infrastructure, software integration, and specialized expertise. Connecting multiple systems introduces additional challenges, including data interoperability and cybersecurity risks.

A digital twin must also be continuously validated against its physical counterpart. If its data becomes outdated or its model fails to account for changing conditions, its predictions can become unreliable.

Digital twins provide a way to connect engineering models with the real-world operation of physical systems. As sensor networks, computing platforms, and AI tools continue to advance, they could become more and more valuable for designing, monitoring and maintaining the machines and infrastructure that modern industries depend on.