One of the greatest challenges humanity faces today is climate change. Although changes in climate occur naturally, during the last 200 years human activities have directly influenced the otherwise normal climate cycles in a very precise way: we are heating the world. Scientists can’t warn policy-makers enough of the disaster we’ll face in the coming decades if we don’t act effectively and with extreme urgency.
When Jensen Huang, NVIDIA's CEO, addressed this topic during his 2022 GTC Keynote, he mentioned that scientists believe we’ll need a billion-X scale in computing power to “effectively simulate regional climate change.” NVIDIA has developed three core technologies — GPU-accelerated computations, physics-informed AI models, and AI supercomputers — which together can grant a million-X speed-up, which basically can help address this topic. But concretely, how are they doing that? Well, with a digital twin of our own planet. They’re going to build the most powerful AI supercomputer designed for climate science: Earth-2. It’ll be a physically-accurate, high-fidelity, and ultra high-resolution replica of Earth continuously running to predict climate and weather events at the regional and global scales.
Now, I am sure that you're even more curious about what a Digital Twin is, and how it can help out in our businesses. This is why we are going to focus on Digital twins and their applications in manufacturing and beyond in this article.
Imagine that you have a complex business challenge at hand: you are the producer of telecommunications antennas, and you want to simulate the coverage and impact of your newest 5G cells on a city. Besides that, you also want to simulate the impact of natural disasters on your 5G cells. How do you do that, without spending millions of dollars?
Digital Twins can help us out: a great example is Ericsson working with Nvidia.
The question they addressed is: If a virtual tree falls and hits a 5G antenna in Stockholm, does it make a sound in Chicago?
Thanks to Ericsson, the answer is yes. Everything from the locations of trees to the height and composition of buildings is crucial because they impact 5G wireless signals in networks serving smartphones, tablets and millions of other internet-connected devices.
The Stockholm-based maker of telecommunications equipment is combining decades of radio network simulation expertise with NVIDIA Omniverse Enterprise, a real-time virtual world simulation and collaboration platform for 3D workflows. In NVIDIA Omniverse, Ericsson is building city-scale digital twins to help accurately simulate the interplay between 5G cells and the environment for maximum performance and coverage.
The rollout of 5G networks is creating new challenges, and with over 15 million microcells and towers to be deployed globally by network operators over the next five years, it’s no small undertaking.
But let's start by understanding what a Digital Twin is, and some stats around them: In essence, digital twins are software representations of assets and processes, which are enhanced with capabilities not present in the real-world entity. There are many proposed definitions which emphasize different aspects of digital twins, and the concept is still evolving, but there are typically a few common characteristics listed here:
First, you need data models and data structures to represent the observations, state and relations of the real-world objects of interest.
Second, you need to populate these models with knowledge, for example, actual data from the specific real-world objects to create the actual digital twin instance. In many cases this data collection is done continuously to enable an accurate and up-to-date view in the twin.
Third, you need tools that operate on the data to add value. These are the keys to unlocking the capabilities and benefits of the digital twin. Arguably the most integral group of tools are the analytics models to extract insights from the digital twin, spanning from simple data retrieval to complex algorithms used to predict future behavior, simulate different scenarios and other analytics tasks. But there are also tools for actuation to add improved steering capabilities to real-world objects.
Finally, you need ways to interact with the twin through different APIs & visualization The insights you gain will be used to make better decisions in the real-world, either via a human or by direct actuation from the digital twin itself.
Conceptually this looks simple, but often advanced software is needed to realize these ideas, as we will see later. Data management will be a challenge as data volume grows and execution of simulations needed for analysis can be very time consuming.
Some stats around Digital twins: the market is predicted to be worth US$48.2 billion by 2026, with a CAGR rate of 39%, and when it comes to impact on business, Digital twins play an important role in Ericsson's factories, reducing unplanned downtime by around 50% . Also, Lead times from ‘survey complete’ to ‘design complete’ for antenna/site installation are reduced by 50%.
It is clear then that Digital Twins are an important technology, part of the Metaverse in a sense, since it is a high-fidelity replica of the real world. The increasing availability of real-time operational data, thanks to IoT technologies, and the boost of AI capabilities in learning and reasoning represent drivers toward realizing a vision of physical products or processes having accompanying virtual representations that evolve through their entire life cycle
So among its applications in manufacturing, they can range from: Product design (During the designing phase, digital twins are created to reflect the expectations in the designer’s mind and the constraints in the physical world; Manufacturing Planning and Execution, where During the manufacturing process, the interaction and iteration between the virtual models and the physical systems allow for real-time monitoring and adjustment of manufacturing processes; Prognostics and Health Management, where even before a product has failed, based on the real-time data from a physical machine and historical data, the machine digital twin is able to accurately predict the machine remaining life, faults, and so on. Many important applications in manufacturing where the Metaverse and its digital twins take Industry 4.0 to another level!
And beyond manufacturing? Oftentimes we associate exclusively Digital Twins with manufacturing and Industry 4.0, but it goes much beyond: think about retail, think about healthcare, think about your own industry. What are the problems that you can solve with the help of Digital Twins?
I want you to think about this as an exercise.
