Martin RG, Head of Digital Innovation and Technology, bp
Martin RG, Head of Digital Innovation and Technology, bp
At its core, a digital twin is a tool designed to provide decision-makers with context so they can take action. Practitioners of the technology sometimes use the functional definition proposed by the Digital Twin Alliance.
Digital twins are virtual representations of real-world entities and processes that are synchronized with a specified frequency and fidelity.
Beyond the promise that we can visit mini versions of our factories or buildings at any time, I believe there is a more fundamental reason why the digital twin concept has proliferated so dramatically since NASA popularized the term in 2010 (Piascik), R ., et al., Technology Area 12: Materials, structures, mechanical systems and manufacturing roadmaps. year 2010, NASA’s Office of the Chief Technologist. ) Simply put, this is because people—all people—sometimes get bogged down in trying to use data to understand what will or might happen next.
The real benefit to be gained from deploying digital twins into the workplace is helping humans think.
It’s not just the heavy lifting of retrieving, visualizing, and creating models from data. The cognitive load of interpreting data in context places a burden on workers across all industries and can slow down human decision-making. In fact, this is the real reward of deploying digital twins in the workplace: helping humans think.
how? Digital twins bring our data together in an intuitive way, helping us understand (sometimes literally) what is really happening with the assets we own and operate. We can break down twinning into three main components that must come together to produce the insights we need.
(1) data. This includes data that describes the asset in some way, such as design information, maintenance history, or usage plans. It also includes data from physical asset sensors, such as temperature and pressure.
(2) visualize. The twin’s visual environment must evoke the asset being modeled, almost by definition. That’s why most people think of 3DV first when they think of twins. Arguably, the real requirement for twin visualization is that the material must be displayed in a way that promotes understanding of the context.
For a digital twin to be compelling, the visual aspects must appeal to our intuition about how different messages relate to each other. While 3DV helps create a comprehensive look and feel, this can also be done in 2D if good UX design principles are applied.
(3) simulation. Broadly speaking, this is adding mechanisms that represent the behavior of an asset or simulate the dynamics of or movement through an asset. The simulation brings the twins to life and can provide users with an immersive experience. Within this category, it is fair to include physics-based simulations, machine learning models, and even workflow modeling, which introduce a sense of time or change to the digital twin.
Technologies for accessing and integrating data continue to advance. One innovation is the use of knowledge graphs to facilitate the integration of data from disparate sources.
Visualization and simulation technologies are also advancing. Combined, they give us a physics-based 3D environment for training drones, for example.
In the future, we can expect not only more diagnostic and predictive capabilities from digital twins, but also more automation and integration with robotics. The data used will come from further sources such as acoustic and spectroscopic measurements. Generative artificial intelligence promises to add another layer of complexity, allowing twins to converse and potentially generate scenes and 3D environments spontaneously or on demand.
The environment can be viewed on different types of devices, including headsets. Additionally, with the multi-faceted interest in multiverse technology, it’s not hard to imagine that next-generation digital twins will enable even more immersive and collaborative experiences than today.
It’s important to point out that while the discussion here focuses primarily on physical assets, the same description applies to the digital twins we might create based on concepts and ideas. As a result, we may soon find new ways to interact with things we can’t see in the real world, including sound waves and molecules. We’ll quickly say that a twin is not a single representation of an asset, but brings together all possible representations into a single interface.
However, what every digital twin has in common is that it provides knowledge to the user in the form of contextualized data to accelerate human thinking. Faster thinking means faster decision-making, and the quicker you make a decision, the quicker you take action.
