What are Digital Twins?
A digital twin is a computerized model of a system or object that is intended to faithfully replicate a real object. It is updated based on real-time data, covers the entire object’s lifecycle, and makes judgments using machine learning, reasoning, and simulation.
How does a digital twin work?
The object under study, a wind turbine, is equipped with a number of sensors that are connected to essential functioning regions. These sensors generate data on a variety of performance characteristics of the physical thing, including energy output, temperature, weather, and more. After receiving this data, the processing system actively incorporates it into the digital copy.
Once the necessary data is supplied, the digital model can be used to perform different simulations, examine performance issues, and develop possible improvements. The ultimate goal is to gather insightful information that can be applied to enhance the original physical entity.
Types of digital twins
There are various types of digital twins depending on the level of product magnification. The biggest difference between these twins is the area of application. It is common to have different types of digital twins co-exist within a system or process. Let’s go through the types of digital twins to learn the differences and how they are applied.
System or Unit twins
System or unit twins are the next step up in magnification, allowing you to see how various components work together to create a fully functional system. System twins offer insight into how assets interact and can recommend ways to improve performance.
Process twins
The macro level of magnification, known as process twins, shows how systems interact to form a whole manufacturing plant. Do those systems work in unison to maximize efficiency, or will a delay in one system have an impact on the others? The specific timing schemes that eventually affect overall efficacy can be found with the use of process twins.
Asset twins
When two or more components work together, they form what is known as an asset. Asset twins let you study the interaction of those components, creating a wealth of performance data that can be processed and then turned into actionable insights.
Component twins or Parts twins
Component twins are the basic unit of a digital twin, the smallest example of a functioning component. Parts twins are roughly the same thing, but pertain to components of slightly less importance.
Digital twins versus simulations
While both simulations and digital twins use digital models to mimic the different operations of a system, the fact that a digital twin is a virtual environment renders it far more studyable. The main distinction between a simulation and a digital twin is size: A digital twin can run any number of helpful simulations to examine numerous processes, whereas a simulation usually analyzes just one.
There are yet more variances. For instance, real-time data is typically not beneficial for simulations. However, the architecture of digital twins revolves around a two-way information flow, which takes place when object sensors send pertinent data to the system processor and again when the processor shares insights it has generated with the original object.
Digital twins have the ability to study more issues from a wider range of perspectives than standard simulations can, thereby offering greater potential for improving products and processes. This is due to their access to better and continuously updated data related to a wide range of areas, as well as the additional computing power that comes with a virtual environment.
History of digital twin technology
With the release of David Gelernter’s Mirror Worlds in 1991, the concept of digital twin technology was first introduced. But it was Dr. Michael Grieves, who was teaching at the University of Michigan at the time, who is recognized for having introduced the idea of digital twin software and used the concept of digital twins in manufacturing for the first time in 2002. Finally, in 2010, NASA’s John Vickers coined the phrase “digital twin.”
However, it is actually possible to see the fundamental concept of utilizing a digital twin to analyze a physical thing far earlier. Indeed, it is accurate to say that NASA invented digital twin technology in the 1960s during its space exploration missions, when each traveling spacecraft was precisely duplicated in an earthbound version that NASA employees on flight crews used for research and simulation.
Advantages and benefits of digital twins
Better R&D
The use of digital twins enables more effective research and design of products, with an abundance of data created about likely performance outcomes. That information can lead to insights that help companies make needed product refinements before starting production.
Product end-of-life
Digital twins can even help manufacturers decide what to do with products that reach the end of their product lifecycle and need to receive final processing, through recycling or other measures. By using digital twins, they can determine which product materials can be harvested.
Greater efficiency
Even after a new product has gone into production, digital twins can help mirror and monitor production systems, with an eye to achieving and maintaining peak efficiency throughout the entire manufacturing process.
Digital twin market and industries
Although digital twins are highly valued for their capabilities, not all manufacturers or products are suitable candidates for their utilization. Not every object has the same level of complexity as a digital twin, which requires an intense and consistent flow of sensor data. Moreover, it is not financially advantageous to devote substantial resources to the development of a digital twin. (Remember that a digital twin is a precise duplicate of a real object, which could add to the cost of creation.)
Alternatively, numerous types of projects do specifically benefit from the use of digital models:
- Physically large projects: Buildings, bridges and other complex structures are bound by strict rules of engineering.
- Mechanically complex projects: Jet turbines, automobiles and aircraft. Digital twins can help improve efficiency within complicated machinery and mammoth engines.
- Power equipment: This includes both the mechanisms for generating power and transmitting it.
- Manufacturing projects: Digital twins excel at helping streamline process efficiency, as you would find in industrial environments with co-functioning machine systems.
Therefore, the industries that achieve the most tremendous success with digital twins are those involved with large-scale products or projects:
- Engineering (systems)
- Automobile manufacturing
- Aircraft production
- Railcar design
- Building construction
- Manufacturing
- Power utilities
Digital twin market: Poised for growth
The rapidly expanding digital twin market indicates that while digital twins are already in use across many industries, the demand for digital twins will continue to escalate for some time. In 2022, the global digital twins market was projected to reach USD 73.5 billion by 2027.1
The future of digital twin
Because more and more cognitive capacity is being allocated to the usage of digital twins, their potential is almost endless. Because digital twins are continuously picking up new abilities, they can continue to produce the insights required to improve processes and produce better goods.
There is a fundamental shift taking place in the current operating models. Asset-intensive businesses are undergoing a digital revolution that is radically altering operating paradigms and necessitating an integrated physical and digital perspective of assets, machinery, facilities, and procedures. An essential component of such readjustment are digital twins.