What is Digital Twin Technology and How Does it Work
A digital twin is a digital representation of a physical object, process or service. A digital twin can be a digital replica of an object in the physical world, such as a jet engine or wind farms, or even larger items such as buildings or even whole cities.
As well as physical assets, the digital twin technology can be used to replicate processes in order to collect data to predict how they will perform.
A digital twin is, in essence, a computer program that uses real world data to create simulations that can predict how a product or process will perform. These programs can integrate the internet of things (Industry 4.0), artificial intelligence and software analytics to enhance the output.

With the advancement of machine learning and factors such as big data, these virtual models have become a staple in modern engineering to drive innovation and improve performance.

In short, creating one can allow the enhancement of strategic technology trends, prevent costly failures in physical objects and also, by using advanced analytical, monitoring and predictive capabilities, test processes and services.
How Does Digital Twin Technology Work?
The life of a digital twin begins with experts in applied mathematics or data science researching the physics and operational data of a physical object or system in order to develop a mathematical model that simulates the original.

The developers who create digital twins ensure that the virtual computer model can receive feedback from sensors that gather data from the real world version. This lets the digital version mimic and simulate what is happening with the original version in real time, creating opportunities to gather insights into performance and any potential problems.

A digital twin can be as complex or as simple as you require, with differing amounts of data determining how precisely the model simulates the real world physical version.

The twin can be used with a prototype to offer feedback on the product as it is developed or can even act as a prototype in its own right to model what could occur with a physical version when built.
What Challenges has it Solved?
Since it can be used across a wide range of industries, from automotive to healthcare and power generation, it has already been used to solve a large number of challenges. These challenges include fatigue testing and corrosion resistance for offshore wind turbines and efficiency improvements in racing cars. Other applications have included the modelling of hospitals to determine work flows and staffing to find procedure improvements.

A digital twin allows users to investigate solutions for product lifecycle extension, manufacturing and process improvements, and product development and prototype testing. In such cases, a digital twin can virtually represent a problem so that a solution can be devised and tested in the program rather than in the real worl
Who Invented It?
The concept of digital twins was first put forward by David Gelernter’s 1991 book ‘Mirror Worlds,’ with Michael Grieves of the Florida Institute of Technology going on to apply the concept to manufacturing.

By 2002, Grieves had moved to the University of Michigan when he formally introduced the digital twin concept at a Society of Manufacturing Engineers conference in Troy, Michigan.

However, it was NASA who first embraced the digital twin concept and, in a 2010 Roadmap Report, John Vickers of NASA gave the concept its name. The idea was used to create digital simulations of space capsules and craft for testing.

The digital twin concept spread further still in 2017, when Gartner named it as one of the top 10 strategic technology trends. Since then, the concept has been used in an ever-growing array of industrial applications and processes.
Why and How to Design Digital Twins?
As mentioned above, digital twins can be created for a wide range of applications, for example, to test a prototype or design, assess how a product or process will work under different conditions, and determine and monitor lifecycles.

A digital twin design is made by gathering data and creating computational models to test it. This can include an interface between the digital model and an actual physical object to send and receive feedback and data in real time.
Data
A digital twin requires data about an object or process in order for a virtual model to be created that can represent the behaviours or states of the real world item or procedure. This data may relate to the lifecycle of a product and include design specifications, production processes or engineering information. It can also include production information including equipment, materials, parts, methods and quality control. Data can also be related to operation, such as real-time feedback, historical analysis and maintenance records. Other data used in digital twin design can include business data or end-of-life procedures.

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