A digital twin represents actual material that is stored in a computer’s memory and can be manipulated easily. Using a digital twin has many uses throughout many industries, providing valuable data on a product’s functionality, fortitude, and limitations without physically affecting a prototype.

Yes, the idea of a digital twin can be complex. But understanding this concept, even in its most basic form, can provide you with exciting opportunities that you can leverage without overextending your budget or your talent pool. It all relies on the digitization process.

So, continue reading to learn more about how you can use digital twin technology to start creating the future of our cities.

Digitizing Physical Assets from idea to implementation

The digitization process has reached each sphere of the contemporary world, and the area of newer technologies is making significant advancements. While this concept is not new, the rise in public digitization is fresh to the market. Nevertheless, it didn’t take long for real estate experts to comprehend the positive impact digital innovation will have on the industry. Digital twin technology offers real estate a major chance to enhance how people invest in, construct, oversee, and develop structures and property.

You can considerably improve clients’ impressions in their commercial and private environments with the help of property technology. Designers can gather information, convey that information, and take action on it with a level of accuracy and speed that was previously impossible. The technology enables more informed decisions in real estate transactions while simultaneously reducing the industry’s negative impact on the environment.

What is the definition of a digital twin?

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. Meaning 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 entity. 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. To do this, they built physically identical systems on the ground designed to mirror the ones in orbit. For example, a digital twin was created in order to make sure that Apollo 13 would be able to return safely to earth after the landing. Read the dramatic behind-the-scenes story here.

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 is extremely different from that of the others. Even though they all perform the same core function, digitally replicating a system or item that exists in the real world.

Component twins

Digital representations of specific products or system components are referred to as component twins. Moreover, they are more than merely used to represent all of the different pieces of a product. Rather, they are often utilized to simulate integral parts, including those subjected to a very high degree of 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 twins

Asset twins, also known as 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

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 one recognizes how various components connect, one will have the ability to enhance how those assets connect, which will, in turn, increase the company’s productivity and effectiveness.

Process twins

Process twins are a digital representation of two or more interdependent systems functioning simultaneously. A production line, for instance, may be modeled using a system twin, but a whole factory could be modeled using a process twin, right down to the personnel who operate the equipment on the production line.

What is the difference between simulation and digital twins?

A simulation and a digital twin are different concepts. As mentioned earlier a digital twin is a snapshot of a physical object, thus mimicking the behavior of the real object. Capabilities that can be added to a digital twin are simulation and predictive modeling. A simulation adds new variables to the environment of the digital twin in order to determine the outcome. mimics what might happen to a product under different conditions, while a digital twin is a digital replica of what occurs to a particular real-world product. The creativity of the developer who requires to integrate any modifications into a simulation is the only constraint limiting the alterations 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 any 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 based on real-world data.

What technology is used for digital twins?

The subfield of computer science known as artificial intelligence focuses on finding solutions to cognitive challenges that typically link to human cognition. These challenges include learning, solving problems, and analytical thinking. An application of artificial intelligence known as machine learning involves the creation of mathematical models and algorithms that allow computer systems to carry out tasks without the requirement for specific instructions, instead depending on trends and inferences.

The digital twin technology processes enormous amounts of sensor data using algorithms designed for machine learning and identifying patterns within the data. Artificial intelligence and machine learning deliver advanced analytics about how to optimize operations, execute upkeep, maximize efficiencies, and reduce emissions.

Some examples of companies that are working with digital twins are:

  • Microsoft with Azure digital twin which is an IoT- platform
  • Siemens digital twins – which lets you analyze how a product performs under various conditions
  • Unity digital twins for 3D projects
  • Amazon Web Services (AWS) digital twin is also an IoT service
  • IBM digital twin experience – for asset-intensive industries
  • Tesla digital twin – the company create a digital twin of every vehicle it sells
  • Google Supply Chain twin which is a Google cloud solution

How is a digital twin created?

When creating digital twins, the very first thing one must keep in mind is that these digital twins are not only computer simulations of the real-world locations they represent. They are in constant communication with the structures to which they are tied directly, which allows for the transmission of current and real-time information. Furthermore, integrated building technologies, such as communication systems, data storage solutions, and other enterprise solutions, can be managed by digital twin infrastructure thanks to its functionality.

A digital twin is created and briefed by an enormous quantity of configuration, production, inspection, maintenance, web sensor, and data sources. It uses a collection of highly accurate algorithmic physics-based frameworks and advanced analytics to foresee the health and functionality of investment throughout the product’s life.

Over time, the amount of data used to enhance the model and the number of comparable resources deployed alongside its digital twins contribute to an increase in the reliability of the representation that the digital twin provides. Data collection takes place continuously to keep the model as current as possible.

In a nutshell, digital twins are not exact copies of their environments. Rather, they are extensions of those environments. Developers can establish connections more efficiently if they maintain this guiding idea front and center in their processes.

What are the components of a digital twin?

A digital twin technology consists of software and hardware components, with middleware for data administration.

Hardware components

The sensors linked to the Internet of Things or IoT, which are the mechanism that drives digital twins and begins the communication of data between physical assets and their digital representations, are the essential component of this technology. Actuators, which translate digital impulses into mechanical motion, network equipment like routers, peripheral processors, and IoT portals, amongst others, are also included in the hardware component.

Data management middleware

Its primary function is to act as a central location for the storage and accumulation of data derived from a variety of sources. In an ideal situation, the middleware system will take care of additional responsibilities, including networking, configuration management, information processing, data quality management, visualization, data modeling and administration, and many more.

Software Components

The analytics engine is the most important component of digital twinning because it is responsible for transforming raw data into actionable information. Machine learning models are responsible for its operation in many instances. These software components are often referred to as Building Management Systems or Building Automation Systems.

Benefits of digital twin software

The notion of digital twin technology is among the advanced concepts that are developing at the quickest rate.

  • Enhanced speed in risk analysis and production systems: Firms can test and validate a product before it emerges in the physical world with the assistance of a digital twin.
  • Preventive analytics: Firms can evaluate their data to anticipate potential issues inside a system because the IoT sensors that comprise a digital twin system create large amounts of data in real time.
  • Real-time remote monitoring: A real-time and comprehensive perspective of a large physical system can be extremely challenging to obtain and, in some cases, even impossible. On the other hand, a digital twin may be accessed from anywhere, allowing users to monitor and regulate the system’s operation remotely.

The best examples of digital twins

Digital twin technology has almost infinite applications in the real world. But, here are three of the most common uses for digital twin technology:

  • Manufacturing: The use of digital twins significantly impacts operations in several different industries, including manufacturing. The production of autos has been completely rethought because of the implementation of digital twin technology by many automotive manufacturers.
  • Healthcare: In healthcare, creating digital twins of patients or their organs enables medical professionals to mimic procedures and the precise circumstances in which they will be carried out.
  • Energy systems: The real-time information that is supplied to the digital copies from the sensors mounted on all of the rotors allows for more productive designs and even offers suggestions for improvements that you can make to make each operational rotor more efficient.
  • Smart cities: Put collaborative data management at the core of urban planning and transformation. The technology helps in building sustainable cities and the future ESG transformation (Environmental, social and corporate governance) combining digital technology with urban operational mechanisms.

Why do companies and the public sector need this technology?

Digital twins are mostly used for resource maintenance, administration, and commercial requirements but there are many other usages. Digital twins are effective schemers for boosting innovation and productivity. Think of it as the greatest skilled product experts equipped with the most cutting-edge surveillance, analytical, and predictive tools.

Where is it used?

Digital twin technologies are now mainly used in manufacturing, healthcare, environmental systems, energy systems, and many more, as it has a broad range of capabilities.

What challenges has it solved?

Digital twin technology applications are perfect for developers, industrial designers, or anybody trying to cut costs, streamline processes, and enhance their clients’ experiences.

  • Maximise operational efficiencies in various functions like in engineering and product design solutions
  • Cost-cutting and process improvement
  • Improve decision-making and collaboration
  • Customer experience improvement
  • Energy savings
  • Reduce risk

On top of these challenges, digital twins can also be used to help acquire certifications such as LEED certification and BREEAM certification.

What is the future of digital twins?

Digital twin is an emerging technology that will shape the future of organizations. The potential uses for digital twins are nearly endless. They can help formerly compartmentalized divisions forecast upkeep, enhance the design, monitor usage, and alter costs.

With sustainability in mind, digital twins are helping organizations create more environmentally friendly buildings by gaining a true understanding of their building systems.