The Digital Twin

by Richard Gate - Technical Lead for ObjectSpectrum

Jan 01 2023

All Posts The Digital Twin

Welcome to the New Year of 2023. Could this be the year where a number of strands come together? IoT, Networks, Virtual Worlds, Virtual Reality, and Artificial Intelligence to shape the existence and growth of the Digital Twin? Not so much the “The ghost in the machine” as “The ghost outside the machine.”

What is a “Digital Twin”? Is this just another gathering of existing technologies and names into a new name, like the Internet, Data Centers, APIs, and Distributed Processing became “The Cloud”? After all, this is not new. Remote Surgery, as an example, where the surgeon and the patient are at physically separate locations, dates back to 1985 when the first recorded surgical robot was used in a brain biopsy procedure, with the surgeon remotely controlling and monitoring a surgical robot, performing the surgery in a virtual world affecting the real world.

The term Digital Twin was originally used by NASA engineer John Vickers in 2002 to be used in NASA product development. Good old Wikipedia says, “A Digital Twin is a digital representation of an intended or actual real-world physical product, system, or process that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, integration, testing, monitoring, and maintenance”. Gartner has sited Digital Twins as a top-10 trend. Their Strategic Technology Trends 2017 report noted that “… billions of things will be represented by Digital Twins, a dynamic software model of a physical thing or system, within three to five years”. One year later they stated that “with an estimated 21 billion connected sensors and endpoints by 2020, Digital Twins will exist for billions of things in the near future”.

This is a connection between the real world and a virtual world. Much more than just remote control, which is where it started, but now with the increased sophistication of virtual modeling, the Digital Twin of a system can be used to gain insights into corresponding real systems that simply cannot easily be gained by directly dealing with the real system itself.

There are several applications for Digital Twins across industries, such as manufacturing, construction, urban planning, healthcare, automotive, retail, housing, and agriculture. In fact, anything that would benefit from creating a virtualized model (Digital Twin) of a physical system can be interacted with.

Most of the applications seem to focus on the representation of the physical world via the virtualized model and how it is interacted with, without much attention being paid to how the virtualized model is constructed and updated. This is understandable as the greatest impact is made at the visualization end of the process. However, feeding the virtualized model from real-world data creates a much more vibrant virtualized model. Changes in the real world can then interact with the virtualized model to see the effects of changes made in that virtualized model. This is where sensors and IoT technologies come into play. The use of Digital Twins in combination with IoT can greatly simplify the visualization of data collected by IoT sensors by creating representations that are more in tune with human perception. With many IoT sensors and the vast amount of data they collect, this simplifies how the data can be visualized and interpreted.

An excellent example of a data-driven Digital Twin was constructed by the Australian government agency, The Commonwealth Scientific and Industrial Research Organisation (CSIRO). It released a Digital Twin of Western Sydney, named the NSW Spatial Digital Twin. It includes live transport data, above and below-ground infrastructure, building information models, property boundaries, and more, and is designed to enable integrated city planning. The Digital Twin is rendered as a 4D visual representation of the area data is collected from.

Examples of Digital Twins being constructed from initial data feeds and modeling which are then used almost in isolation from data feeds can be found in manufacturing. Here the goal is to build models of real manufactured systems and their components. They are generally not used to visualize the real world but to perform actions on the virtual model that would be dangerous, difficult, or cost-prohibitive with the real system, such as safety testing, destruction testing, product development, and exploring various what-if scenarios.

The Digital Twin is a major tool set and, fed with IoT data, is an excellent visualization system. It will only become more important over time.

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