What is the definition of a digital twin?
A digital twin is a computerized representation of an actual product, operation, or function. To add context, a digital twin is a snapshot of a physical asset/product, which means that a digital twin is an as-is representation of a physical asset. You can use digital twins interchangeably with the term digital copy. It acts as a digital counterpart of an entity in the physical world, replicating gathered data about the physical commodity. This object could be one entity, like a jet motor or wind turbine, or an aggregated entity, such as a skyscraper, power plant, or even an entire city and its related processes.
You can employ digital twin technology to recreate processes and physical resources to accumulate information that you can use to predict how well those processes will function.
A digital twin technology is a software application that uses data collected from the real world to produce simulations that may accurately forecast how a process or a product will carry out in the future.
What is the origin and history of digital twins?
2002 was the year that brought widespread attention to the concept of digital twins. The construction of a product lifecycle management center was the subject of the discussion. It included the actual space, the virtual space, and the dispersion of information and data that flows between the real and virtual worlds. These are all components common to the concept of the digital twin. Since its inception, the idea of establishing a physical and digital twin as a single entity has stayed the same, even though some associated terms may have evolved through time.
It is a popular misconception that digital twin technology hit the market in 2002. However, this technology has existed for much longer. Tech experts created the notion of digital twin and digital twin technology in the 1960s. IT professionals put the idea into effect shortly after its conception. Specifically, during this time, NASA’s space exploration coding would use certain fundamental digital twin concepts. For example, NASA created a digital twin to ensure that Apollo 13 could return safely to Earth after the landing. To do this, they built physically identical systems on the ground designed to mirror the ones in orbit. Read the dramatic behind-the-scenes story here.
What are digital twins in Real Estate?
A digital twin in real estate refers to a virtual replica of a physical asset, such as a building, property, or an entire city, created using sophisticated technologies like sensors, 3D modeling, and artificial intelligence. Digital twins manage, monitor, and optimize buildings and properties. They can emulate various scenarios, such as changes in occupancy, energy usage, and maintenance schedules, assisting real estate professionals in making informed decisions.
For instance, a digital twin of a building can simulate changes in temperature, humidity, and occupancy levels to establish the optimum temperature settings for different parts of the building. It can also anticipate maintenance needs, such as when a particular component is likely to fail and needs replacement.
What types of digital twins exist?
The simulation capabilities of digital twins extend down to the level of individual elements and up to the level of large systems. The aim of each type of digital twin and their unique function differs significantly from the others. Even though they all perform the same core function, they digitally replicate a system or item in the real world.
Digital representations of specific products or system components are known as component twins. They simulate integral parts, including those subjected to high heat or stress. Developers and engineers can determine how you can enhance the components to maintain their integrity in plausible options by electronically designing the parts and submitting them to simulation studies.
Asset or product twins are digital representations of a real object instead of the product’s component pieces individually. The objective of asset twins is to understand how the various components of a particular product interact with one another, even though, theoretically, you can form one asset twin of multiple component twins.
System twins, sometimes known as unit twins, are digital models of production systems created to function as a unit. The difference between asset twins and system twins is that asset twins represent real-world products of multiple parts. In contrast, system twins represent these specific products as pieces of a broader system. If you recognize how various components connect, you can enhance the company’s productivity and effectiveness.
Two or more interdependent systems functioning simultaneously can be digitally represented as process twins. For example, a production line can be modeled using a system twin. Still, a process twin can model an entire factory, including the personnel operating the equipment on the production line. This approach offers a powerful and flexible tool for analyzing and optimizing complex systems, allowing developers and engineers to identify potential bottlenecks and inefficiencies and devise practical solutions.
What is the difference between simulation and digital twins?
It’s essential to differentiate between a simulation and a digital twin. A digital twin is a virtual replica of a physical object that imitates its behavior. At the same time, a simulation adds new variables to the digital twin’s environment to determine potential outcomes under different conditions. In contrast, a digital twin replicates what occurs to a real-world product. Both simulation and predictive modeling can be added as capabilities to a digital twin, enhancing its functionality and versatility. The developer’s creativity is the only constraint on the modifications that can be made to the simulation.
Nevertheless, because a digital twin provides genuine input, the developer can assess whether or not it is functioning as expected. Then, they can make necessary adjustments to reflect how it is utilized. This feature transcends from the capital to other purposes, such as for a production process, which you may evaluate with actual data to respond to shifting expectations, needs, or economic situations.
The primary distinction between a digital twin and a simulation is that the latter is purely hypothetical, whereas the former is rooted in real-world data.