Innovating faster with digital twin technology

What a digital twin actually is

A digital twin is more than a static CAD model or one off simulation.

At Xi, we define a digital twin as:

A multiphysics model that is tethered to a real product, asset or process using data transfer at an appropriate update rate.

Three elements matter:

1. A robust virtual model
   A simulation that captures the key physics of your system – structural, acoustic, thermal, electromagnetic, fluid or a combination.
2. A data connection
   Sensor or system data that is streamed or transferred to update the model periodically or in real time.
3. A clear purpose
   A defined set of questions the twin is there to answer – for example performance under different conditions, remaining life, or the impact of design changes.

Digital twins sit on a spectrum. Some are updated every few seconds, others once a day or once per operating cycle. The right approach depends on what you need to monitor and decide.

From one off tests to continuous insight

Historically, many complex products were only examined in detail twice in their life:

• At development and type testing
• At the end of life teardown

Everything in between was largely inferred from occasional inspections and maintenance records.

With connected sensors and modern data infrastructure, that has changed. Today it is practical to:

• Monitor critical parameters in service, from vibration and temperature to load and flow
• Combine that data with a calibrated model to estimate stresses, damage and remaining life
• Run what if scenarios – for example how output, settings or environment will affect performance and risk

In sectors such as aviation and power generation, digital twins are already used to optimise maintenance, reduce downtime and improve safety for assets like jet engines, turbines and locomotives.

The same principles can apply to many other systems, from factory equipment and consumer products to infrastructure and cities.

Getting to market faster with virtual prototypes

For new products and systems, digital twins start life as virtual prototypes.

By building and validating a high quality model early, you can:

• Replace some rounds of physical prototyping with virtual experiments
• Test performance across a wider range of scenarios than would be practical in the lab
• Explore sensitivities to different design variables before you lock in geometry and materials
• Optimise control strategies and operating envelopes ahead of deployment

Once the physical product exists, the same model can be:

• Updated with measurement data to improve accuracy
• Simplified into a reduced order model suitable for real time or near real time use
• Connected to live data streams to become a full digital twin

The result is a smoother transition from R and D into operations, with fewer surprises along the way.

When a digital twin is worth the investment

Not every system needs a digital twin. In our experience, it becomes compelling when:

• Your product or asset has many interacting variables that are costly to explore physically
• You face high consequences for failure – safety, regulatory, financial or reputational
• You plan multiple generations of a product and want to build structured learning into each cycle
• You operate fleets of similar assets and need to understand both typical and outlier behaviour

Examples include:

• Wind turbines and other renewable energy assets
• High value rotating equipment and drives
• Vehicles and transport infrastructure
• Complex consumer products where over the air updates are possible

If you recognise your context in this list, the question is less whether to use a digital twin and more how to design one that is proportionate and useful.