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How Does a Digital Twin Work?

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[Princeton University]

- Overview

A digital twin (DT) is a virtual model of a physical object. It spans the lifecycle of an object and uses real-time data sent by sensors on the object to simulate behavior and monitor operations. DTs can replicate many real-world objects, from individual pieces of equipment in factories to complete installations such as wind turbines or even entire cities. DT technology enables you to monitor asset performance, identify potential failures, and make more informed decisions about maintenance and lifecycle.

Wind turbines, engines and cranes are now able to understand each other and speak to engineers in natural language. This is not science fiction, they are digital twins, a disruptive technology that is critical to the rise and development of the Fourth Industrial Revolution (Industry 4.0).

A DT is a virtual replica made from an image of a product - an aircraft turbine, a wind turbine blade, a building facade, etc. It contains real-time data that can be captured by sensors or technologies related to big data. Once this information is collected, it will be processed through artificial intelligence, cloud computing, and machine learning to produce living manifestations that feel, think, and behave.


- The Creation of Digital Twins

A digital twin works by digitally replicating a physical asset, including its functions, characteristics and behavior, in a virtual environment. Create real-time digital representations of assets using smart sensors that collect data from products. You can use this representation throughout the asset's lifecycle, from initial product testing to actual operation and decommissioning.  

Digital twins can be created during the design phase of an object's lifecycle, enhancing the creative phase of inventing new products and refining them into detailed product models. At this stage, the digital twin can effectively assess the impact of design decisions on product quality and functionality early on, reducing the need to develop expensive physical prototypes. After the design phase, there is a physical phase, where the digital twin begins to exist. 

The digital twin of the design generates physical objects and updates when there are any deviations. Use sensors and AutoID devices to monitor the current and historical state and condition of physical products during operational use. 

Additionally, digital twins can be used to remotely control objects via actuators. Finally, the processing phase occurs, where physical objects are processed, but conceptual objects may be retained for a period of time, such as for traceability, compliance, and learning.


- The Key Technology Enabling Digital Twins

A digital twin uses a variety of techniques to provide a digital model of an asset. Although derived from the product lifecycle literature, the key technology enabling digital twins is the Internet of Things (IoTs). Interaction between real/physical and digital/virtual objects is the fundamental concept behind IoT. 

In IoT, physical entities have digital counterparts; things themselves become context-aware, they can sense, communicate, act, interact with digital counterparts and others, exchange data, information, and knowledge. These counterparts are twins of the physical object and can be linked to and synchronized with the physical object throughout its lifetime. 

The Internet acts as a storage and communication infrastructure that holds virtual representations of things that link related information to objects. As such, the digital twin acts as a central hub for object information, continuously combining and updating data from a wide range of sources.


- Internet of Things (IoTs)

The Internet of Things refers to the collective network of connected devices and the technologies that facilitate communication between devices and the cloud, as well as between the devices themselves. Thanks to cheap computer chips and high-bandwidth telecommunications, we now have billions of devices connected to the internet. 

Digital twins rely on IoT sensor data to transfer information from real-world objects to digital-world objects. Data is entered into a software platform or dashboard where you can see data updates in real time.


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- Artificial Intelligence

Artificial intelligence (AI) is the field of computer science that addresses cognitive problems related to human intelligence, such as learning, problem solving, and pattern recognition. 

Machine learning (ML) is an artificial intelligence technique that develops statistical models and algorithms so that computer systems can perform tasks without explicit instructions, relying instead on patterns and reasoning. 

Digital twins use machine learning algorithms to process large amounts of sensor data and identify patterns in the data. Artificial intelligence and machine learning (AI/ML) provide data insights on performance optimization, maintenance, emissions and efficiency.


- Digital Twins Compared To Simulations

Both digital twins and simulations are simulations based on virtual models, but there are some key differences. Simulation is often used for design and, in some cases, offline optimization. Designers input changes into simulations to observe what-if scenarios. 

Digital twins, on the other hand, are complex virtual environments that you can interact with and update in real time. They are larger in scale and in scope of application.  

For example, consider a car simulation. New drivers can get an immersive training experience, learn the operation of various parts of the car, and face different real-world scenarios while driving virtually. However, these scenarios have nothing to do with actual physical cars. 

A digital twin of a car is connected to the physical vehicle and knows everything about the actual car, such as vital performance statistics, parts replaced in the past, potential problems observed by sensors, previous service records, and more.


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