By Naeema Bhyat, Technology & Engineering editor
Someday soon, your city, your house, your planet and even you could have a digital twin.
Digital twins are virtual models that exist alongside one or more physical objects, systems or processes. IBM describes them as “a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.”
Complex digital twin projects tap into machine learning (ML) and artificial intelligence (AI) to identify problems or project future scenarios in a way that is more powerful than traditional models. Less complex projects may be as straightforward as creating a virtual twin of an object or process and aggregating different sources of input data within it, giving users a new and more cohesive understanding of what’s happening.
While the definitions vary and models can be simpler than this, you can expect to hear much more about digital twins as the technology finds new uses across a wide range of sectors. Digital twins are expected to enable better decision-making capabilities by offering deeper, more accurate insights into physical objects and processes. These insights can be used to optimize the performance and efficiency of these physical objects and processes.
Using Digital Twins
Digital twins have been used in industries like manufacturing to monitor objects, assets, systems and processes, identify or predict problems, and optimize performance. New frontiers for the technology include everything from medicine and the life sciences to construction. A 2022 Cap Gemini report put the global market for digital twins at over US$5 billion in 2020 and predicts it will grow by 35% over the next 5 years.
Sustainability, Climate Change & Possibilities
One area where digital twins could have an impact is environmental sustainability. Carleton University’s Imagining Canada’s Digital Twin initiative focuses on the architecture, engineering, construction and owner/operator (AECOO) sector and is proposing to digitally prototype the Toronto-Ottawa-Montreal corridor. The group notes that buildings contribute 35% of Canada’s greenhouse gas emissions and use 53% of the country’s energy. The choice of this sector speaks to how digital twin projects that improve building construction, operation, and efficiencies could translate into real benefits by reducing emissions and energy use.
A digital twin of a single building, for example, combined with real-time sensors and other data sources, could show where excess heat is being lost or give insights into water use. It might tell operators how the building will respond to different temperatures due to climate change. Information could even flow in both directions, facilitating real-time changes to a building’s systems.
On a larger scale, digital twins of utility corridors, cities, and even countries are expected to offer a better understanding of current land use changes, water levels, and extreme weather events, as well as improved forecasting of environmental changes These virtual models might predict the impact of traffic patterns on air pollution and greenhouse gas emissions. Germany is planning to implement a digital twin of the entire country and the Massachusetts Institute of Technology (MIT) plans to digitally twin the Earth through its Grand Challenges program.
These latter projects represent ambitious visions for digital twins. The MIT project hopes to significantly reduce uncertainty in the predictions made by current climate models. Communities and decision-makers can use these more accurate models to better plan for events like floods, wildfires and other climate-related changes.
What’s Happening Now?
Digital twins are still early in their journey, although they are actively being implemented. In the commercial space, Calgary-based Veerum creates digital twins for large industrial operations like those in the oil and gas, mining, utilities and renewable energy sectors. The visual models allow their global clients to draw together different data sets, view sites remotely and act on the novel insights the simulations reveal. Jordan Mathieson, Director of Product Marketing, says many clients implement a digital twin as an early step toward digital transformation and new ways of operating. Mathieson says, “We’re creating a productivity improvement in organizations [and] allowing more people to see the emissions that are happening onsite at the time. A major benefit to the sustainability arc is to reduce travel to site.” Visual twin representations are particularly useful for reducing visits to hazardous or remote locations.
Further afield, companies like South Africa-based Zutari and UK-based Ore Catapault use digital twins to virtually model solar energy projects and wind turbines. Murray Walker, recently Zutari’s Lead for Interactive Visualization, notes that familiarizing and training employees with digital models saves time and allows maintenance and repairs to take place more efficiently. This makes adopting renewable technologies easier since “saving money makes these things more viable.” It’s a strategy that other companies are likewise pursuing globally through different manufacturing and industrial projects.
The nuts and bolts
As with most models, the value of digital twins depends on the quality of the input data that are used and how often the data are updated to keep the simulations relevant. The data that feed a digital twin model can be fairly simple or highly detailed, depending on the need and the resources available to create the model.
As digital twin applications evolve, they will need to be tested and data standards will be set in some sectors. In some cases, that testing will be done through multi-sector partnerships that involve researchers, governments and the private sector. Digital twins also have financial and other costs such as sensor failures or security risks that organizations need to consider before deciding to implement the technology.
Digital twin technology promises to provide a deeper look at what’s happening now, how things could change under different conditions, and how to plan for the future. With these insights, industry practitioners and policy-makers can make more informed decisions. Digital twins can reduce financial costs, enable greater efficiencies, optimize performance and improve the sustainability of our built world and manufacturing/diagnostic processes. To realize this bright future, digital twin users will need to consider how the technology can help tackle challenges and how to apply it to deliver truly valuable information.