Understanding the Digital Twin: From Concept to Reality

Asset owners in the marine and offshore industries are increasingly adopting digital tools to manage design, production, operations, maintenance, and recycling. This digitalization fuels interest in a process that starts with 3D plan drawings, advances through product lifecycle management, and creates a digital twin.

Digital twins can support informed decision-making throughout a vessel’s lifecycle. While widely discussed, many owners and shipbuilders still lack a clear understanding of their full potential, functionality, and value in improving safety, efficiency, and lifecycle management, from maintenance to end-of-life.

Models and Simulations as Digital Twin Precursors

Digital models and simulations have long supported phases of an asset’s lifecycle, from conceptual design to decommissioning. Traditionally, these models were created for specific stages and then set aside as the asset progressed.

With digitalization, these models can now be sustained and updated throughout the lifecycle, enhancing their value and paving the way for digital twins. Digital twins build on these models by providing asset-specific insights, particularly during the operational phase.

For instance, a simulation used in the design phase to predict performance under expected loads and conditions can evolve into a digital twin that monitors real-time performance under actual operating conditions.

Essentially, a digital twin is a specialized form of simulation that represents a unique physical asset rather than a generalized system. A single design model can support multiple digital twins, each customized for an individual asset derived from that original design.

What Are Digital Twins?

The distinction between a digital model, a simulation, and a digital twin is often unclear, leading to confusion and limiting the industry’s ability to fully leverage the concept. Rather than focusing on strict definitions, it’s more productive to ask whether the digital twin serves its intended purpose.

The idea of using a twin to support decision-making is not new. Historically, physical twins were used to represent real systems in a more accessible form—allowing users to model, simulate, and test outcomes.

One well-known example is military sandboards, which represented battlefields. These were regularly updated based on real-time reports and used to simulate strategies or mock wargames.

This early form of twinning provided decision-makers with a way to test different approaches without expending real-world resources. Today’s digital twins follow the same principle—offering a dynamic, efficient way to better understand, predict, and improve physical system performance.

Technology Advances

As technology has progressed, so has the development of digital twins. With increased computing power, data from physical systems can now be collected and shared digitally, allowing twins to exist as fully virtual, real-time representations of physical assets.

These digital twins help decision-makers simulate and better understand asset behavior. What defines a “sufficient” twin depends on the specific challenge—it must include the right elements and level of detail for the task.

“What makes digital twins unique is their ability to represent a specific physical system over time”

Instead of full replicas, designers and owners can develop function-specific twins that still provide valuable insights for maintenance, performance, and upgrade planning.

Digital Twin Credibility

What makes a digital twin a credible representation of the as-built asset depends on the specific use case. Like any engineering model, its assumptions, simplifications, and fidelity must be appropriate for the intended application, balancing accuracy with available time and resources.

Credibility is context-specific and cannot be precisely measured. The key is ensuring that the decision-maker has enough evidence to trust the digital twin’s accuracy for its purpose.

Trust builds through proper verification, validation, high-quality input, and operational data integrity. Usability and success in similar applications also influence confidence.

Ultimately, the decision-maker determines whether the digital twin is reliable enough to use. However, clear documentation from developers and users—covering development processes and validation methods— is essential to support trust and effectiveness during both acquisition and operational deployment of digital twin solutions.

Applying Use Cases

What makes digital twins unique is their ability to represent a specific physical system over time. The best use cases are those where capturing the unique operational and environmental context of an asset adds value.

Common applications include structural and machinery integrity management, operational performance optimization, asset efficiency, and decarbonization through datainformed operations.

A helpful way to view the digital twin is as a model administration unit—hosting a library of models that draw on real-time data from the physical asset. These models can be configured into specific digital twin setups tailored to individual use cases.

The challenge lies in ensuring seamless integration among these models.

Most digital twins today are singleuse, solving a specific problem. As industry standards evolve, greater model integration will let one twin support multiple functions and deliver broader operational insights.

Digital Maintenance

A core feature of a digital twin is its continuous data exchange with the physical system it represents. Like the physical asset, the twin requires regular updates to stay accurate and valuable throughout its lifecycle.

The method and timing of this data exchange depend on the use case. Two important terms are collection frequency—how often data is gathered (e.g., sensor rate in Hertz)—and synchronization frequency, which refers to how often that data updates the twin.

While these frequencies can match, they don’t always. In systems with limited connectivity or bandwidth, synchronization may occur less frequently.

Importantly, high collection rates aren’t always necessary. For instance, in cases like structural degradation due to corrosion, data might only be needed every few years. That’s acceptable if it aligns with the decision-making timeline—the period from receiving data to acting on it.

Maintaining a digital twin means ensuring timely updates that match the asset’s operational needs.

Conclusion

As the shipping industry moves closer to embracing digitalization, digital twins are well-positioned to play a significant role in enhancing safety, efficiency, and compliance.

Their ability to capture the unique aspects and circumstances of a system enables more proactive monitoring that supports early detection of anomalies or potential failures.

Digital twins support a more data-informed approach, reducing incident risk and helping ensure assets consistently meet regulatory standards. Digital twins also enable transparent documentation, remote inspections, and streamlined compliance for improved safety outcomes.