July 27, 2022

DIGITAL TWINS: An Industrial Win from the Metaverse

By Andres Fortino and Elizabeth Haas

Although the media has focused a large part of its Metaverse reporting on the customer experience, fluctuations in crypto currency, and the ownership of digital assets, the power of a digital twin is obvious, steeped in rich history, and not to be ignored.

If you scan recent news articles and social media posts on the Metaverse, you will notice a recurring theme that centers on: “the Metaverse being our future;” “the trend toward immersive experiences,” and “the lure of cryptocurrency.”  No one is denying that crypto currency has reason to take center stage in the Metaverse universe.  After all, the size of the prize is big and growing with more than $18 billion spent in cryptocurrency in 2021 and $80 billion projected to be spent by 2024.Yet with all the brouhaha surrounding crypto, another powerful and potentially more practical revolution is occurring in the digital twin space.  Although seemingly new to those of us who have been late to the Metaverse party, digital twins have been around for a while. 

The concept of using a digital twin as a means of studying a physical object was first introduced by NASA in the 1960s, when the space agency decided to replicate its spacecraft at ground level to match the systems being used in space for exploration missions. This technology was first (and notably) demonstrated in the Apollo 13 mission, which was to be the third lunar landing attempt but was aborted due to the rupture of a service module tank.  Considered a “successful failure,” the use of connected twins in the Apollo 13 mission enabled NASA Mission Control to adapt and modify simulations to match the conditions of the damaged spacecraft, and to troubleshoot strategies to bring the astronauts safely home.

In the early 1970s, mainframe computers were used as digital twin-like systems to monitor large facilities such as power plants. In the 1980s, 2D CAD systems like AutoCAD emerged to produce technical drawings, making it possible to design anything with a computer, and were quickly adopted by millions of designers and engineers. By the 2000s, 3D CAD with parametric modeling and simulation enabled the design of increasingly complex assemblies, creating a database of interconnected objects.  Fast-forward to the mid-2010s when all leading 3D CAD vendors launched cloud-connected solutions, primarily for collaboration and project management, while still using desk-top-based CAD tools.

2022 marks the beginning of the age of real-time 3D-powered digital twins, which are going well beyond dashboards and 3D models to unlock data from multiple sources for better collaboration, visualization, planning, engagement, and real-time decision-making.

Digital twins – a platform that uses AI and real time data to model a building, an area, a piece of equipment, or a process  – has leapfrogged infancy and is rapidly maturing across industry with a unique level of business impact.

Digital Twins’ rapid rise and accelerated adoption is being closely watched because of its basic premise: an ability to bring AI and simulation to the MetaverseLeaders in the tech world define a digital twin as a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation and machine learning to help decision-making.

More simply said – and for those of us less familiar with tech terms -- a digital twin is a dynamic, virtual copy of a physical asset, process, system, or environment that looks like and behaves like its real-world counterpart. It can be used to monitor its physical counterpart and predict outcomes of scenarios of operations, such as failures. It can also be used to model design changes to the item. Engineers can run experiments and proof of concept scenarios on the twin and get feedback. Saving time and resources from using physical prototyping, digital twins ingest data to predict possible performance outcomes and issues that the real-world product might face.

To understand how a digital twin works, imagine a virtual model designed to reflect a physical object. The object being studied — for example, a wind turbine — is outfitted with various sensors related to vital areas of functionality. These sensors produce data about different aspects of the physical object’s performance, such as energy output, temperature, weather conditions and more. This data is then relayed to a processing system and applied to the digital copy. Once informed with data, the virtual model can be used to run simulations, study performance issues, and generate possible improvements. These valuable insights can then be applied back to the original physical object.

If you were interested in creating a digital twin, you need to know that these twins are developed by importing conceptual models, or by scanning physical entities from the real world and then analyzing these entities in combination with enterprise and Internet of Things (IoT) data or other data sets being denigrated in the real world. A digital twin that is powered by real-time 3D, a computer graphics technology that generates interactive content faster than human perception, can also curate, organize and present multiple sources of data (both information and models) as life-like images, interactive visualizations.

Because each digital twin deployment is unique, deployments often occur in stages, with each phase increasing in complexity and business impact. Similar to how websites are developed today - several pages at a time. Few people build the complete website in one sitting.  A digital twin can range from a 3D model of a product component to a precise representation of a network or system as vast as a city, with each of its components dynamically linked to engineering, construction, and operational data.

The power of a digital twin lies in its ability to let us perform experiments in a computer rather than in the field, which results in a cheaper, safer, and faster result.

A digital twin’s power comes from connecting real-world assets with real-world data from any platform, so users can better visualize a scenario, actively simulate real-world conditions, and visualize the outcomes instantaneously. This real-time connection enables cross-functional teams to collaboratively analyze, design, build, simulate, test, deploy, and operate complex systems in interactive and immersive ways – which in turn helps companies understand the past, view present conditions, and prepare for  future eventualities. This holistic and sophisticated view delivers a well-informed and highly reliable level of decision-making, and greatly enhances forecasting and prediction capability. It takes a lot of work to set up but the benefits can be incalculable.

Digital twins are already making their way onto the Metaverse stage and into our daily lives.

It hasn’t been long since digital twins made an appearance across industries. For example, we are seeing digital twins used in:

  • Highly Complex Learning Scenarios via flight simulations, heart transplant surgery, and other situations where timing, and life and death decisions, make the difference between success and failure.
  • Emergency Planning and Event Simulations used to model emergency events, such as a hurricane or flood, when urgent decisions are required.
  • Innovative Customer Service and Entertainment Options as seen during a professional baseball game when stadium managers simulate crowd movement (like Waze simulates traffic), engage game attendees to participate in an online game, or guide customers to the shortest line for beer given where they are seated.

Future roles might include asking a community to help build a stadium by visiting, commenting on, and collectively offering design suggestions in the Metaverse.  As teams across disciplines and locations design, engineer, build, sell and eventually operate and maintain complex buildings and operations, digital twins will inform their decision-making at every stage of the life cycle.

The benefits of, value from, and difference between digital twins and ordinary simulations are just beginning to emerge – but a bright future appears to be on the horizon.

Although simulations and digital twins both utilize digital models to replicate a system’s various processes, a digital twin is actually a virtual environment, which makes it considerably richer for study. The difference between a digital twin and simulation is largely a matter of scale: While a simulation typically studies one particular process, a digital twin can itself run any number of useful simulations in order to study multiple processes. The first versions of a digital twin can be a simple representation of only some of the attributes of its life counterpart.  Over time, and as needed, the twin becomes more sophisticated. Or it can continue to be operated at the current level of sophistication without change.

But the differences don’t end there because simulations typically don’t benefit from having access to real-time, in-the-moment data. Digital twins, on the other hand, are designed to provide a two-way flow of information that first occurs when object sensors provide relevant data to the system processor, and then happens again when insights created by the processor are shared back with the original source object.

With digital twin deployments, users immediately realize improved access to data. And, as a digital twin matures, other benefits arise, including reduced maintenance costs, more informed process change decisions with large potential savings, and improvements in maintenance and operational efficiency. Having better designs from the start pays dividends over a project’s lifetime, as 80–90% of costs incurred during the production, use, and maintenance of a facility are determined at the design stage.

In the design industry, using digital twins has improved multi-user collaboration and communication, with pre-construction users experiencing seamless aggregation of data and trade coordination.

And across industries, but most notably in the construction industry, digital twins have significantly reduced accidents and mistakes via improved safety training, quality assurance and quality control. When digital twin initiatives are used for maintenance and operations, the benefits include optimized operations, reduced downtime, and decreased maintenance and personnel costs.

A digital twin of the human body may be used for doctors to teach and to learn. Some day a digital twin of our physical bodies may even be used by our doctor to try out drugs and procedures before applying it to the physical us.

The value of and benefit from being able to interact with data in real-time is changing the way people make design, construction, operations, and maintenance decisions. The power to visualize and simulate complex operations in real-time 3D elevates how people interact with their assets, transforming the way every physical space is created, built, and operated.

As for the challenges, the most notable are:

  • An ability to consume data in an intelligent way. The best decisions are made using data, but your data is only as good as your ability to bring it to life to simulate and predict business scenarios.
  • A well-known risk is the tendency to drown in raw data before finding a way to process and leverage it. Today, capturing raw data is less of a challenge than processing it, filtering the useless parts, combining it, and transforming it into information that makes sense to the user in the context of their application.

As it happens, the main challenge seems to be in unlocking the power of available (and often hidden) information. Accessing enterprise and IoT data, long buried in databases, spreadsheets, and models may now be possible via real-time, 3D digital twins that can bring this exponentially increasing data to life.

Digital twins -- once a back room and undervalued resource – are increasingly used to simulate products, equipment, factories, buildings, and cities. These things, all considered a part of the physical world, now have accurate virtual counterparts. With the Metaverse unlocking a new economy, digital twins are about to have their long-awaited moment. Innovation in this space is just starting.

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