Digital Twin Technology: The Potential to Shape our Future Cities

Digitizing Physical Assets from idea to implementation

Digitization has reached each sphere of the contemporary world, and newer technologies are significantly advancing. The rise in public digitization is fresh to the market. Real estate experts understand digital innovation’s positive impact on the industry. Digital twin technology offers real estate a significant opportunity to enhance how people invest in, construct, and develop structures and property.

You can considerably improve clients’ impressions in their commercial and private environments with the help of property technology. The technology enables more informed decisions in real estate transactions while simultaneously reducing the industry’s negative impact on the environment. Designers can gather information, convey it, 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?

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.

Component twins

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 twins

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

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.

Process twins

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.

The technology in digital twins

The subfield of computer science, 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.

Machine learning in practice

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 optimizing operations, executing upkeep, maximizing efficiencies, and reducing 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 creates a digital twin for every vehicle it sells
• Google Supply Chain Twin, which is a Google Cloud solution

Creating a digital twin

When creating digital twins, one must first remember that these digital twins are not only computer simulations of the real-world locations they represent. Digital twins constantly communicate with connected structures, transmitting real-time information. Additionally, their functionality enables the management of integrated building technologies, such as communication systems, data storage, and other enterprise solutions.

Creating a digital twin involves compiling vast data from configuration, production, inspection, maintenance, web sensor, and various 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 increase the reliability of the digital twin’s representation. 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. Instead, 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 comprises 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 essential to digital twinning because it transforms raw data into actionable information. Machine learning models are responsible for its operation in many instances. These software components are knowns 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.

• Accelerated Risk Analysis and Production Systems: With the help of a digital twin, companies can test and validate a product before its physical manifestation, streamlining the development process. Simulating the behavior of intricate systems using digital twin technology is used to pinpoint potential safety hazards and take corrective measures before launch. 
• Preventive analytics and predictive maintenance: 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. With digital twin technology, equipment failures can be anticipated before they occur, enabling operators to schedule maintenance activities before they lead to costly downtime.
• Real-time remote monitoring: A real-time and comprehensive perspective of an extensive physical system can be highly challenging and, in some cases, even impossible. Real-time performance monitoring of equipment and processes enables digital twin technology to highlight areas where engineers can ensure efficiency. Conversely, a digital twin can be accessed from any location, enabling users to oversee and control the system’s performance remotely.

The best examples of digital twin technologies in action

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 implementation of digital twin technology by numerous automotive manufacturers has wholly revolutionized the production of automobiles.
• In healthcare, medical professionals can create digital twins of patients or their organs, allowing them to simulate procedures and the exact conditions under which they will be performed.
• Energy Systems: By receiving real-time data from sensors installed on every rotor, digital twins enable more efficient designs and provide recommendations for enhancing the performance of each individual rotor in operation.

Smart cities: Put collaborative data management at the core of urban planning and transformation. The technology supports sustainable cities and future ESG transformation by integrating digital technology with urban operational systems.