As the amount of data we have increases, so does the amount of useful information we can use to make important decisions in the real world. Summaries, models, and simulations of data points inform these decisions. In the age of Big Data, the ultimate next step to this is the concept of digital twins.
A digital twin is a virtual representation of some physical object or process. These are simulations that can predict how a particular object or service will perform in the real world.
Various industries have started looking into implementing digital twins of their own products and services to make all sorts of improvements.
Let’s take a look at the history of digital twins, and how they’re different from the simulations most commonly used today. We’ll also look at how digital twins can revolutionize various industries, from healthcare, to manufacturing or even entire cities.
What is Digital Twin?
The term “digital twin” first appeared in NASA documents in 2010. The digital twin was described as “an integrated multi-physic, multi-scale probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin.”
This technology later inspired the US Air Force, which employed digital twins of their aircraft’s airframes to predict fatigue and damage. They called this technology the Airframe Digital Twin, and it aimed to serve as a virtual health sensor throughout the lifecycle of the individual aircraft.
Digital Twin vs Models
One of the key aspects of a digital twin is that the twin must have a corresponding object in the real world. The digital twin is more than just a blueprint or schematic.
The modern definition of the digital twin considers it best that the digital model and the physical object are conceived at the same time. These twins “grow” together as time passes by.
A digital twin approach to manufacturing would involve the digital twin even past the prototype stage. Data retrieved from the prototype could be used to improve the digital twin. The improved model can then predict the performance of future prototypes.
Characteristics of Digital Twins
A digital twin requires connectivity. The relationship between a digital twin and its real-life counterpart requires a reliable flow of data. Digital twin technology can use Internet of Things (IoT) and Machine Learning (ML) to analyze data continuously coming from sensors from multiple sources.
Due to the rise in computing power available, we are now able to realize near homogenization of data coming from different sources. And because all the needed data is captured in a single entity, it is much more easily shareable.
Digital twin technology allows us to reprogram services and products based on feedback in real-time. Using ML, we can have digital twins that even become more intelligent in decision making as more data is collected.
Large, complex systems captured using digital twin technology benefit from the modularity of the design. DTs can enable manufacturers to find out which particular components in a device are underperforming.
Digital Twin Applications
Digital twins can be applied to almost any industry. Such a powerful model could improve the design, manufacturing, and operation stages of a particular product or service. The following are some examples of how digital twin technologies can be applied to certain sectors.
Using digital twins, companies can now have a digital footprint of a product’s entire lifestyle, from design to operations.
For example, the aerospace company Boeing uses digital twins to design their aircraft. They can run simulations of all the plane’s parts to predict how and when they might fail in the future.
This type of model-based engineering accelerates research and development and allows for an integrated system. The design, manufacturing and operation stages now run in parallel and share data with each other.
2. Supply Chains
Digital twins can be used in actual supply chains to create a detailed model of the supply chain’s behavior. Digital twins enable on-the-fly adjustments and a very detailed view of the entire supply chain.
Supply chain digital twins use real-time information feeds. Data such as upcoming shipments, vehicle locations, and inventory can help assess the current state of the supply chain. These digital twins can be programed to take a specific action once certain events occur, such as when a product is out of stock.
In light of the COVID-19 pandemic, digital models of the supply chain can help mitigate risks. Digital twins allow accurate tracking and delivery of important assets such as vaccines, lab samples, and other medical equipment.
Assets such as vaccines require certain temperatures during transportation, which can be monitored using digital twins.
Using digital twin technology, doctors can soon create virtual organs that can be customized to a specific patient. Scientists at the Clinic for Cardiology at Heidelberg University Hospital, Germany have already begun simulating a digital twin of a heart. The virtual heart can be used to predict the progression of a patient’s heart disease and responses to drug treatments.
Using these digital twins, doctors can see the success rate of heart surgery before any decisions are made. More complex risk models like digital twins can find solutions that fit a specific patient and not just a solution for a specific risk group.
4. Digital Twin Cities
With the rising demand for smart cities, there will soon be a vast amount of data collected in cities. Smart cities aim to track all kinds of city activity, from traffic data, contact tracing, and environmental indicators.
As a result, the availability of this data will allow us to soon create digital twins out of entire cities.
According to Arup, “the promise of the city digital twin is to help provide a simulation environment, to test policy options, bring out dependencies and allow for collaboration across policy areas, whilst improving engagement with citizens and communities.”
All this data can be used for scenario planning and preventing future catastrophes.
A successful digital twin city will help inform policy-making decisions as well. Data on weather, transportation patterns, and census data can allow for more data-driven initiatives from local government officials.
If cities can provide useful portals for its citizens, then a city’s digital twin can also capture the needs and requirements of its real-life counterpart.
Digital twin technology is empowering various industries into making better decisions.
When stakes are high, such as in healthcare or the aviation industry, companies are willing to invest in digital twins to ensure that any risk is mitigated.
Complex sectors such as supply chain management will benefit from being able to see virtually every level of detail in a system.
Furthermore, these sectors may then use AI and Machine Learning to improve the system as more data is collected from the real world.
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