Updated: Dec 31
The second article in our metaverse series focuses on defining the industrial metaverse, its building blocks, parallels with model-based systems engineering (MBSE), and examples of how modeling and simulation are being used to empower digital twin ecosystems.
Read part 1 of the Metaverse series here.
Most people define the metaverse as a “virtual reality” with avatars and 3D representations of real objects in spaces that simulate how we interact with the physical world. This part of the metaverse tends to be “consumer oriented,” allowing users to “suit up” in their VR systems and interact with people and things in a virtual world.
While the consumer (or social) metaverse focuses primarily on entertainment value, the industrial metaverse focuses on solving complex industry problems through advanced modeling and simulation. These modeling and simulation capabilities leverage the holistic knowledge and understanding made available by connected digital twins.
Gartner defines a digital twin as “a digital representation of a real-world entity or system.” Within the domain of a single product, this definition is not much different than object modelling or computer-aided design. But the true power of the industrial metaverse lies in the ability of these digital twins to interact with each other. Connecting digital twins can create entire virtualized “systems of systems” where inputs and resulting behaviors of one digital twin can affect other digital twins operating entirely within a virtual space.
The objective of the industrial metaverse is to model digital twins in such a way that they can be interconnected and interfaced to allow larger systems and industrial worlds to understand how to solve complex problems our global society faces today. For example, solving world hunger requires a host of digital twins – from farms and agricultural equipment to distribution centers and transportation systems – to form a virtual system of systems. Through modeling and simulation, the operations of all “constituent” digital twin systems are optimized for the food sustainability goals of the entire system of systems.
As another example, automakers can use the digital twin of a new vehicle design and virtually simulate its performance against various road conditions, gasoline grades, and maintenance timelines to predict gas mileage, reliability, and environmental impact. An industrial metaverse could enable this digital vehicle twin to be inserted into the digital twin of a larger system – a smart city – to assess road impact, emissions, and traffic patterns.
A variety of stimulus can be applied to a digital twin to analyze and predict product or system behavior. This allows for design optimization prior to building the physical product or implementing a physical system.
Digital Twins, Cyber-Physical Systems, and MBSE
Once a physical product is built, a digital twin has limited value unless it can accurately synchronize its state with that of its physical counterpart. Cyber-physical systems (CPS) provide a key interface between digital twins and physical systems. These systems are comprised of physical components that can be monitored, controlled, and optimized by smart sensors, software, and actuators.
In many respects, a CPS envelops both digital twin and IoT technologies to provide a foundation for autonomous systems such as driverless cars. However, digital twins require holistic information sets rather than rudimentary models, and a challenge in the development of a CPS is the large difference in design practices between the various engineering disciplines involved in system creation, such as software and mechanical engineering.
Enter the concept of model-based systems engineering (MBSE), which involves the methodology by which these complex systems can be designed, analyzed, and verified. MBSE provides a design “language” that is common to all the disciplines involved in the creation of the CPS.
When rapid innovation is essential, engineers from all disciplines must be able to explore system designs collaboratively, quickly allocate responsibilities to software and hardware elements, then analyze the tradeoffs between them. MBSE can be a driving force behind the development and adoption of digital twins that are indistinguishable from their physical counterparts across the entire system lifecycle.
Optimizing Smart Product Design
The combination of digital twins and CPS enables real-world systems to be analyzed and optimized, but this requires a transformation across the development lifecycle that will enable the adoption of MBSE and digital twin technologies and ensure all their benefits are realized.
For instance, smart products like cars are becoming intricate systems of systems, challenging engineers to harness layers of complexity and data and blurring boundaries between different engineering domains. The digital representation of all facets of a smart product in a comprehensive digital twin is no longer optional to manage the complexity of today’s product development – it has become a design requirement.
To fully leverage digital twin technology, we must first shift our focus towards developing digital twin models that can be utilized across the whole system lifecycle – including product design. A common misconception is that a digital twin is only utilized once its physical counterpart has been created, but “shift-left” philosophies (or the practice of improving quality by moving tasks like testing to as early in the development lifecycle as possible) can also be applied using digital twins to find and prevent defects early in the product or system design process.
“We add on top of that the ability to tie in these virtualized real-world environments … with the semiconductors operating well before going to silicon. That’s a true shift-left environment that enables the OEM to get a level of confidence about the decisions they’re making that is completely unprecedented” – Joseph Sawicki, Siemens
Ultimately, smart products will be tested, delivered, and provisioned with a digital twin that connects to other digital twins in an industrial metaverse, optimizing value and minimizing risks to end customers in complex system deployments.
Platforms for Building an Industrial Metaverse
NVIDIA Omniverse is an example of a scalable platform for federated, interoperable digital twin ecosystems that operate within the “industrial metaverse.” Omniverse is a multi-GPU enabled development platform for building and operating 3D simulations. It is based on Pixar’s Universal Scene Description (USD) – an open-sourced software framework for collaboratively constructing 3D scenes.
Omniverse also supports AI tools that enable the creation of mobile virtual elements that are capable of replicating what is happening in physical reality. Ecosystem software partners can leverage Omniverse alongside NVIDIA’s other various technologies. One example being a recent partnership with Siemens to create a photorealistic, physics-based industrial metaverse; combining Omniverse with Siemens’ Xcelerator and Simcenter tools will form a digital twin platform for industrial use cases.
A multi-level system of systems based on a distributed system architecture can enable holistic understanding, optimal decision-making, and effective action. Architectures like this can evolve into scalable mechanisms that support the flow of actionable information produced and consumed by connected, state-based systems.
Solving Social and Economic Challenges at Scale
The National Digital Twin programme (NDTp) run by the Centre for Digital Built Britain (CDBB) is an example of a government initiative to develop an ecosystem of connected digital twin systems that foster better outcomes for society and a national economy. Initial activity focused on aligning industry and government behind a common definition and approach to information management so that data could be shared openly and securely between future digital twins – at scale.
The initiative, which concluded this year, had several working groups spanning technology infrastructure to economic and environmental policy research, all with the aim of using the industrial metaverse to solve the world’s most complex problems.
Over the next several years, multiple digital twins developed separately in areas such as urban mobility, energy, environment, disaster prevention, and healthcare will be connected and their underlying data mutually linked. We will then have federated systems in a digital space that allow us to rehearse complex scenarios such as how urban transport and energy supplies could be affected by a large-scale disaster. This would allow us to prepare prompt and appropriate responses to systemic risks. It will also help us simulate the impact of climate change and future pandemics.
Whether you call this an industry paradigm shift or revolution, there is little doubt that the industrial metaverse and connected digital twins are critical components to drive solutions for our most complex industry, economic, and social problems.