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We’ve all seen sci-fi representations of technology that predicts the future of the world, or perhaps crafts a universe all its own. This technology isn’t just limited to the fictitious holodeck of the Starship Enterprise, it's actually all around us, being used for practical applications everyday.
Digital twins, a powerful technology that can be applied to the research and design process, was conceptualized as early as 1991. Since then, the use of digital twins has appeared in every industry from healthcare to urban planning.
This technology uses machine learning to accurately represent the entire lifespan of a single unit or system of objects, drawing information from abundant databases to reflect real-life physical entities and predict outcomes.
A digital twin is defined by IBM as “a virtual model designed to accurately reflect a physical object.” In short, digital twins are computer programs that use data from the real world to create simulations.
The idea of using computer programs to create simulations is nothing new, this concept has been in practice since the creation of modern computing. What makes digital twins special is their ability to process information from incredibly rich, complex databases, and use that data to render extremely lengthy, detailed simulations. The goal of these simulations is to view a product or system’s lifespan in its entirety before it's ever manufactured.
Digital twins are born when developers seek to create a digital model of a physical space, object, or system that represents its real world counterpart with mathematical accuracy. To build a digital twin, data is collected about the real world aspects that the digital twin is intended to represent.
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The more informative the data, the better. For a digital twin to be effective, it needs to be informed about the physics, operational processes, special features, aging aspects, and structural measurements of its subject matter.
Digital twins are built with the ability to receive input from sensors that monitor real world content. This allows the digital twin to reflect the state of its subject matter in real time.
While the terms “digital twin” and “simulation” are sometimes used interchangeably, this does not accurately represent the capabilities of each of these program types. Simulations are freestanding programs that show the outcome of a single event. Digital twins are digital environments that are capable of running many simulations.
Simulations typically aren’t built with the ability to take in real time data, but digital twins benefit from information that flows two ways; receiving input and adjusting processes based on that input.
Individual simulations can be used for small-scale research and design, whereas digital twins are rich with useful information for detailed, large-scale study.
Due to their massive range of applications, many different types of digital twins exist. Digital twin technology is typically classified by the size of the project the twin is being used for, and the level of detail that developers hope to achieve with their virtual model.
Most digital twin models utilize each of these twin types, with every level of magnification building off the one below it. This allows the model to intuitively learn, reflecting a shifting, changing environment.
Complex digital modeling is useful in most fields, but digital twins are most commonly utilized in the following industries:
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The use of digital twins becomes increasingly necessary as the physical systems we build become more complex. More and more of our assets are becoming dependent on meticulously engineered software, complicated in its construction and delicate in its upkeep.
How do we operate these complex systems, and maintain them over time? How do we build models that can adapt intuitively, run efficiently, and scale productively?
Digital twin technology allows us to create and build better systems, as well as operate and monitor existing ones. From the tiniest functional unit of a model to a broad scope that encompasses millions of operational systems, this technology enables us to see every facet of the products we build, the neighborhoods we live in, the hospitals that care for us, the energy we consume, and so much more.
With digital twins giving us the ability to predict outcomes with greater accuracy than ever before, we can engineer more effective solutions to problems we face everyday.
Digital twins also allow us to operate existing systems with greater effectiveness than what was once thought possible, increasing the value of our assets and allowing our resources to spread further.
Studying digital twin models during the development process allows engineers to design better systems. Using digital twins during the production process allows manufacturers to produce better products. The applications for this technology are limitless, and aided by this powerful technology, the future of research and development looks bright.
In a world where technology often mirrors the far-reaching visions of science fiction, digital twins have emerged as a tangible and transformative force. From the realms of imagination to everyday practicality, these digital replicas of physical objects have found their place across diverse industries since their conception in 1991. Digital twins utilize the immense potential of machine learning, drawing from expansive databases to simulate the complete lifespans of objects and systems, predicting outcomes with remarkable accuracy.
As defined by IBM, digital twins are virtual models intricately mirroring their physical counterparts, powered by real-world data and intricate simulations. What sets them apart is their capacity to process intricate information, crafting exhaustive simulations that encompass the entire lifecycle of products and systems. This innovative technology grants us the unique ability to scrutinize and refine products long before they materialize.
The birth of a digital twin involves meticulously translating the nuances of a physical entity into a mathematical representation. Rich, informative data forms the foundation, capturing aspects ranging from physics and operational processes to aging characteristics and structural measurements. Real-time input from sensors adds a dynamic layer, enabling digital twins to mirror the ever-evolving states of their subjects.
Distinguishing digital twins from standalone simulations is crucial. While the terms are occasionally interchangeable, their capabilities differ significantly. Simulations spotlight individual events, while digital twins orchestrate multifaceted simulations, dynamically processing real-time inputs. These twins come in various forms, each catering to specific magnifications and scopes: process twins encapsulate expansive systems, system twins examine interactions between similar entities, asset twins delve into individual components, and component twins form the bedrock of the hierarchy.
This transformative technology holds sway in industries spanning utilities, manufacturing, transportation, urban planning, and healthcare. As we confront increasingly intricate systems, the relevance of digital twins surges. Navigating complex operations and ensuring seamless scalability becomes possible, thanks to their predictive precision and operational insights.
Digital twins unlock a future where solutions are designed with unparalleled accuracy. The development process embraces smarter systems, while production reaps the benefits of enhanced products. This technology's limitless applications shape a future where research and development flourish. With digital twins illuminating the path, our capacity to innovate and engineer resilient solutions grows, propelling us toward a future where technology truly fulfills its potential.
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