Digital Twin Technology – How Does Digital Twin Technology
A digital twin is a digital representation of a physical object, process, service or environment that behaves and looks like its counterpart in the real world.
The term “digital twin” refers to a digital replica of a physical object, such as a jet engine, wind farm, or even a larger object, like a building or even an entire city. Alternatively, digital twin technology can replicate processes to gather data to predict how they will perform. A digital twin is a computer program that simulates how a process or product will work using data from the real world.
These programs can improve the output by incorporating artificial intelligence, software analytics, and the internet of things (Industry 4.0). These virtual models have become a mainstay in contemporary engineering to spur innovation and boost efficiency thanks to the development of machine learning and factors like big data.
To put it briefly, developing one can advance strategic technological trends, prevent expensive failures in physical objects, and test processes and services using advanced analytical, monitoring, and predictive capabilities.
How Does Digital Twin Technology Work?
To create a mathematical model that simulates the original, experts in applied mathematics or data science first study the physics and operational data of a physical object or system. The designers of digital twins ensure that sensors that collect data from the physical counterpart can provide feedback to the virtual computer model.
As a result, it is possible to mimic and simulate what is happening with the original version using the digital version in real-time, providing opportunities to learn more about performance and any potential issues. With varying amounts of data determining how precisely the model simulates the real-world physical version, a digital twin can be as complex or as simple as you need.
The twin can be used with a prototype to provide feedback on the design, or it can stand alone as a prototype to simulate what might happen when a built-in version is used.
What Challenges Has It Solved?
It has already been used to address many problems because it can be applied to various industries, including healthcare, automotive, and power generation. These difficulties include improving racing car efficiency, fatigue testing, and corrosion resistance for offshore wind turbines.
Hospital workflows and staffing have been modelled in other applications to identify process improvements. A digital twin enables users to look into options for lengthening a product’s lifecycle, streamlining production processes, improving product development, and prototyping.
In these situations, a digital twin can virtually depict a problem, allowing a solution to be developed and tested in the computer program rather than in the real world.
Who Invented It?
The idea of digital twins was first introduced in David Gelernter’s 1991 book “Mirror Worlds,” It was later applied to manufacturing by Michael Grieves of the Florida Institute of Technology.
Grieves officially introduced the idea of a digital twin in 2002 at a Society of Manufacturing Engineers conference in Troy, Michigan, after relocating to the University of Michigan. However, NASA was the organization that adopted the digital twin idea first, and John Vickers from NASA gave the idea its name in a Roadmap Report from 2010.
The concept was applied to developing digital spacecraft and capsule simulations for testing. When Gartner listed the “digital twin” as one of the top 10 strategic technology trends for 2017, the idea of the digital twin gained even more traction. Since then, various industrial applications and procedures have used the concept.
What Industries Use Digital Twin Technology?
Construction teams create digital twins to improve the planning of infrastructure, commercial, and residential projects and provide a real-time view of ongoing projects’ progress. By combining digital twin technology with 3D modelling of buildings, architects can use digital twins as a tool for project planning. Commercial building managers use digital twins to track temperature, occupancy, and air quality data in real-time and over time to enhance occupant comfort.
The entire manufacturing lifecycle uses digital twins, from planning and designing to maintaining existing facilities. A digital twin prototype enables continuous equipment monitoring and performance data analysis to determine how well a specific component or your entire plant operates.
In the energy sector, digital twins are frequently employed to support strategic project planning and improve the performance and lifespan of current assets, including offshore installations, refinery facilities, wind farms, and solar projects.
The automotive industry uses digital twins to create digital models of vehicles. With the help of digital twins, you can gain knowledge about the physical behaviour of the car in addition to its software, mechanical, and electrical models. Predictive maintenance is also useful in this area because a digital twin can notify a service center or user when it discovers a problem with component performance.
In the healthcare sector, digital twins are applied in a variety of situations. Some examples are building virtual twins of entire hospitals, medical facilities, labs, and human bodies to model organs and run simulations to demonstrate how patients react to various treatments.
Outside of manufacturing and industry, the digital twin is used in the retail sector to model and augment the customer experience, whether at the level of a shopping centre or for individual stores.
7. Disaster Management
The effects of global climate change have recently been felt all over the world. Still, digital twins can help to mitigate these effects by allowing for the intelligent design of smarter infrastructures, emergency response plans, and climate change monitoring.
8. Smart Cities
Cities can use digital twin technology to improve social, environmental, and economic sustainability. Virtual models can help plan and answer contemporary cities’ many difficult problems. For instance, real-time information from digital twins can inform problem-solving to enable assets like hospitals to respond to a crisis.
Benefits Of Digital Twin Technology
Digital twin technology is a virtual representation of a physical object or system that can be used to monitor and simulate its performance in real time. Here are some of the benefits of digital twin technology:
Utilizing digital twins produces a wealth of data about likely performance outcomes, facilitating more efficient product research and design. Before beginning production, businesses can use this data to gain insights that will help them make the necessary product improvements.
2. Greater efficiency
Even after a new product has gone into production, digital twins can help mirror and monitor production systems, aiming to achieve and maintain peak efficiency throughout the entire manufacturing process.
3. Product end-of-life
Digital twins can even assist manufacturers in determining how to handle products that have reached the end of their useful lives and require final processing, such as recycling or other actions. They can decide which product materials can be harvested by utilizing digital twins.
How Has It Impacted The Industry?
A digital twin is a simulation model that can be updated alongside or replace a physical counterpart by fusing data with technologies like artificial intelligence, machine learning, and software analytics. As a result, businesses can evaluate a fully computerized development cycle, from design to deployment and even decommissioning.
A digital twin helps businesses predict downtime, respond to shifting conditions, test design improvements, and do much more by simulating physical assets, frameworks, and operations to generate continuous data.
To provide automation, data exchange, and joined-up manufacturing processes and reduce risk in product launches, the digital twin is essential to developing Industry 4.0. Industry workers can keep an eye on things in real-time, giving them advance warning of potential failures and enabling real-time performance optimization and assessment with little loss of productivity.
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