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In order to gain a deeper understanding of digital twins and their integration within the metaverse, the 80 Level Research Team has executed comprehensive research.
In a practical sense, a digital twin is a virtual model that uses data from multiple sources, including sensors to simulate scenarios that are identical to the physical object or system that’s being emulated. The technology works by using artificial intelligence (AI), the Internet of Things (IoT), and software analytics to collect data. Then, a cloud-based system is used to send that data to the digital systems powering a digital twin model.
We can capture data from the real world through IoT sensors and devices and feed it into the digital model, keeping the “twins” in sync. Applying an accurate physical simulation to the digital twin creates incredible superpowers for those who use these digital twins. Users can teleport to any part of the digital twin and inspect any aspect of it reflected from the real world. The next step in the evolution of this connection is to enable the creation of an “operational digital twin” that synchronizes input from real-world sensors to the digital twin and provides feedback to, or allows control of, the physical devices in real-time.
The main benefit of using digital twin technology within the company is eradicating planning mistakes. Not fully, of course, but many mistakes are found because complex factory halls have huge amounts of CAD data — and there’s bound to be mistakes. But, if the mistakes are found when they build the factory, even if one steel beam is just one centimeter shorter, it can lead to a huge holdup in time and a waste of money.
Collaborating, especially in the company’s AR applications, is another advantage, as anyone can basically repair a machine (if they don't need special equipment) when the instructors sit somewhere in the company headquarters and see it through AR. It's also beneficial for worker training and onboarding — for example, when a company doesn’t have the machine yet, but has the workers already.
Companies use metaverse solutions for training and to generate end-to-end employee experiences. Digital twins provide a digital copy of the real world that the metaverse needs for its immersive 3D experiences, without the risk and with the possibility of introducing different variables and analyzing different results.
Beyond entertainment and training, the metaverse has the potential to help solve complex problems through technology.
The industrial metaverse refers to a virtual environment where businesses can collaborate and digital twins can interact with each other to optimize outcomes. Organizations, for example, can use digital twins and the metaverse to play out responses to natural disasters.
BMW uses Omniverse to build digital twins of its factories to optimize its production line ahead of new vehicle launches, Siemens Gamesa simulates wind farms to maximize energy yield, and Ericsson uses the platform to create digital twins of cities to decide optimal 5G tower placement and virtually fix problems before deploying crews on the ground.
The virtual representations and real-time insights from digital twins, coupled with the immersive and collaborative capabilities of the metaverse, empower stakeholders to make informed decisions, respond swiftly to challenges, and create resilient and efficient supply chains. This integration holds the potential to reshape the future of supply chain management, unlocking new opportunities for sustainable growth, customer satisfaction, and competitive advantage.
We worked alongside Lockheed Martin and U.S. Fire Management Agencies to simulate and predict the path of forest fires to rapidly respond and suppress them. NVIDIA is also building a digital twin of the Earth to enable researchers and scientists to predict the climate decades into the future.
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.