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Digital Twin Technology

 
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[Technologies Used in Digital Twins - Softengi]
 
 

- Overview

A digital twin is a digital replica of a living or non-living physical entity. Digital twin refers to a digital replica of potential and actual physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things (IoT) device operates and lives throughout its life cycle.  

The idea of the digital twin is a virtual representation of a product which plays an integral part in our technology-driven modern industrial world. In essence, a digital twin is a computer program that takes real-world data about a physical object or system as inputs and produces as outputs predications or simulations of how that physical object or system will be affected by those inputs.

Digital twins are virtual replicas of physical devices that data scientists and IT pros can use to run simulations before actual devices are built and deployed. They are also changing how technologies such as IoT, AI and analytics are optimized. Digital twin technology has moved beyond manufacturing and into the merging worlds of the Internet of Things, artificial intelligence and data analytics. As more complex “things” become connected with the ability to produce data, having a digital equivalent gives data scientists and other IT professionals the ability to optimize deployments for peak efficiency and create other what-if scenarios. 

 

- Digital Twin Adoption Grows

Digital Twin is most commonly defined as a software representation of a physical asset, system or process designed to detect, prevent, predict and optimize through real time analytics to deliver business value. The concept of digital twins has been around since the early 2000’s. Companies would program replicas on their computers or servers and input the necessary data to update the design or environment specifications. The information was rarely received in real-time. This methodology was a very clunky and slow way to render these virtual duplicates. With the advent of IoT technology, such as smart sensors and cloud systems, digital twins became easier and cheaper to create, maintain and use. Now, we’ve reached the stage of intelligent enterprise asset management that is fed by the influx of connected information from digital manufacturing devices, hyperautomation and other sources. 

Digital twins are becoming more prevalent in and out of the factory sector because these exact virtual replications of a system allow for greater experimentation with various scenarios to determine an outcome. It is a fail-safe way to test new ideas and different conditions. Positive results can then be communicated to the physical twin for implementation. The idea of digital twin as a live digital representation can be applied to more complex physical structures, often combining connected assets and products with specific business outcome in mind. The visualization allows companies to test for potential machine failures, optimize processes, plan for future capacity and more.  

 

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[Istanbul, Turkey - Civil Engineering Discoveries]

- Popular Use Cases for Digital Twin

Today, applications of Digital Twin are widely used across many industries. Providing accurate virtual representations of objects as well as simulations of operational processes, applications of Digital Twin enable various businesses to promptly identify areas for innovation and improve business processes and performance. Popular use cases for digital twins include: Manufacturing, Healthcare, Utilities, Disaster Management, Insurance, Smart Cities, etc.. Among digital twin examples are Product Digital Twin, Component Digital Twin, and Performance Digital Twin, all used for the visualization of objects and processes, be they single or more complex.

Industry 4.0 is sometimes referred to as the ‘fourth industrial revolution’. It is a current trend in which manufacturing is increasingly automated and reliant on the capture and exchange of data, often referred to as ‘Big Data’, via the IoT and cloud computing. Industry 4.0 creates what has been described as the ‘smart factory’. The digital twin fits into this model as a computer representation of a product.

 

- Technologies Used in Digital Twins

An application of Digital Twin embraces four technologies allowing to create a digital representation, gather and store real-time data, and based on obtained information provide valuable insights. Among Digital Twin technologies are Internet of Things (IoT), Extended Reality (XR), Cloud, and Artificial Intelligence. Depending on the application type, a particular technology can be used to a greater or lesser extent. 

  • Extended Reality (XR) is the visualization technology which creates digital representations of objects. XR capabilities enable Digital Twins to digitally model physical objects, allowing users to interact with digital content.
  • Cloud Computing  technology is used for efficient storing and accessing data over the Internet. As applications of Digital Twins operate with large volumes of data, cloud computing allows to store all data in the virtual cloud and easily access required information from any location.
  • Artificial Intelligence (AI) is an advanced analytical tool that is able to automatically analyze obtained data and provide valuable insights. It can also make  predications about possible outcomes and give suggestions as to how to avoid potential problems.
 
 

- An Example

A digital twin begins its life being built by specialists, often experts in data science or applied mathematics. These developers research the physics that underlie the physical object or system being mimicked and use that data to develop a mathematical model that simulates the real-world original in digital space. 

Sensors connected to the physical product collect data and send it back to the digital twin, and their interaction helps optimise the product's performance via a maintenance regime. For example, sensors within an aero engine might detect when components need changing or repairing. Jet engine maker Rolls-Royce already uses Engine Health Management (EHM) to track the health of thousands of engines, using onboard sensors and live satellite feeds. These data are used to enhance maintenance regimes. Eventually they could be used in the original aero engine design process.

 

 

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