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Digital Twin

By Moloy De posted Fri December 09, 2022 11:19 PM

  
Digital twins were anticipated by David Gelernter's 1991 book Mirror Worlds. The concept and model of the digital twin was first publicly introduced in 2002 by Michael Grieves, at a Society of Manufacturing Engineers conference in Troy, Michigan. Grieves proposed the digital twin as the conceptual model underlying product lifecycle management (PLM).

The digital twin concept, which has been known by different names e.g., virtual twin, digital replica, was subsequently called the "digital twin" by John Vickers of NASA in a 2010 Roadmap Report. The digital twin concept consists of three distinct parts: the physical object or process and its physical environment, the digital representation of the object or process, and the communication channel between the physical and virtual representations. The connections between the physical version and the digital version include information flows and data that includes physical sensor flows between the physical and virtual objects and environments. The communication connection is referred to as the digital thread.

An example of digital twins is the use of 3D modeling to create digital companions for the physical objects. It can be used to view the status of the actual physical object, which provides a way to project physical objects into the digital world. For example, when sensors collect data from a connected device, the sensor data can be used to update a "digital twin" copy of the device's state in real time. The term "device shadow" is also used for the concept of a digital twin. The digital twin is meant to be an up-to-date and accurate copy of the physical object's properties and states, including shape, position, gesture, status and motion.

Digital twins are commonly divided into subtypes that sometimes include: digital twin prototype (DTP), digital twin instance (DTI), and digital twin aggregate (DTA). The DTP consists of the designs, analyses, and processes that realize a physical product. The DTP exists before there is a physical product. The DTI is the digital twin of each individual instance of the product once it is manufactured. The DTI is linked with its physical counterpart from the remainder of the physical counterpart's life. The DTA is the aggregation of DTIs whose data and information can be used for interrogation about the physical product, prognostics, and learning. The specific information contained in the digital twins is driven by use cases. The digital twin is a logical construct, meaning that the actual data and information may be contained in other applications.

A digital twin also can be used for monitoring, diagnostics and prognostics to optimize asset performance and utilization. In this field, sensory data can be combined with historical data, human expertise and fleet and simulation learning to improve the outcome of prognostics. Therefore, complex prognostics and intelligent maintenance system platforms can use digital twins in finding the root cause of issues and improve productivity.

Digital twins of autonomous vehicles and their sensor suite embedded in a traffic and environment simulation have also been proposed as a means to overcome the significant development, testing and validation challenges for the automotive application, in particular when the related algorithms are based on artificial intelligence approaches that require extensive training data and validation data sets. It has applications in Manufacturing industry, Urban planning and construction industry, Healthcare industry, Automotive industry etc.

QUESTION I: What are the advantages of using Digital Twin?
QUESTION II: Does the concept of Digital Twin has application in space research?

REFERENCE: Digital Twin in Wikipedia 
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