What’s a Digital Twin And How it Works
Imagine this: a patient has been found to have a rare genetic disease. Now, instead of making informed assumptions or relying solely on past case studies, doctors create a virtual replica of this patient, simulating their physiology down to the cellular level. This “replica” allows doctors to experiment with different treatment plans, analyze drug reactions, and predict outcomes—all without ever touching the actual patient. It might sound like science fiction, but it is a very real tech phenomenon. This replica is known as a digital twin.
Once a distant dream, digital twins are now at the forefront of digital transformation. With a global expenditure of $65 billion in 2023 and a projected compound growth rate of 35%–65% between 2023–2030, digital twins are emerging as a prominent future tech with significant impact (1). But what exactly is a digital twin? What marks its difference from good-old simulation? Finally, what are the real-life applications of this technology?
What is Meant by a Digital Twin?
A digital twin is a virtual replica of a physical object, system, or process. Put simply, it becomes a mirror of the real entity in real time by using data gathered from various sources such as sensors, devices, and other data streams. Moreover, what makes it unique is the fact that it can be updated in real time too, adapting to the ceaseless flow of information coming from its physical counterpart. Thus, a digital twin is highly accurate and efficient, reflecting changes, performance, and conditions in real time that occur in the physical counterpart. For instance, it enables businesses, engineers, and researchers to analyze the physical asset or process, predict future behaviors, and optimize performance without directly interfering with the actual physical entity.
A digital twin is built based on multiple advanced technologies:
- Internet of Things (IoT) sensors/sources to capture real-time data from the physical environment
- AI to process, learn from, and make predictions based on that data
- Machine Learning (ML) algorithms to make predictions and adjustments based on the insights gathered
- Cloud computing for vast data storage and real-time processing
- Virtual reality for building a virtual environment
This combination of technologies results in an ever-evolving, live model of the physical object, which can be manipulated, observed, and optimized to achieve specific outcomes.
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How are Digital Twins Different From Simulations?
To fully appreciate a digital twin, it is essential to distinguish it from a traditional simulation, of which NASA’s Apollo 13 mission stands as the textbook example. Here, NASA used simulators to replicate the spaceship’s systems and guide the astronauts safely back to Earth. However, these simulators were limited in scope. They relied on predefined conditions and did not interact with the actual spacecraft or reflect real-time data.
In contrast, today’s digital twins—like those used by SpaceX’s dragon capsule that recently reached the international space station to rescue astronauts Sunita Williams and Butch Wilmore who have been stranded there for the past few months—operate differently. They don’t rely on static conditions. Instead, they operate based on real-time data from sensors aboard the spacecraft, reflecting the live status of every system.
As a result, this allows engineers to simulate various scenarios while considering dynamic factors such as fuel consumption, equipment wear, and external environmental changes. In short, a simulation recreates a possible scenario in a controlled environment, whereas a digital twin creates a live, evolving mirror of the real object. It functions like a virtual environment, reflecting the dynamism that a live system—whether biological, technological, geographical—entails.
What is an Example of a Digital Twin?
To understand digital twins in an organic manner, it’s important to look at some real-life examples. Here’s three of them.
1. Sangam: India’s Digital Twin Initiative
The Sangam: Digital Twin initiative, launched by India’s Department of Telecommunications (DoT), aims to revolutionize infrastructure planning through digital twin technology (2). This groundbreaking venture invites contributions from industry leaders, startups, MSMEs, academia, and innovators. This process leverages cutting-edge technologies such as 5G, AI, IoT, and AR/VR, aiming for a nation-wide approach. The core idea is to create virtual replicas of various infrastructures enabling real-time monitoring, simulations, and analysis to optimize performance and adapt to changes, such as telecommunications. The project unfolds in two phases:
- The Exploratory Stage focuses on unleashing the creative potential and exploring possibilities
- Practical Demonstration involves implementing use cases and crafting a roadmap for future scaling
2. NeuroTwin: Advancing Brain Science
The NeuroTwin project offers a fascinating glimpse into how digital twins can be used in healthcare. It is focused on developing a digital twin of the human brain to help scientists understand and treat neurological disorders such as epilepsy and Alzheimer’s. The aim is to create a hybrid brain model bringing together electromagnetism and physiology (3). Here’s how it will work:
- A computational framework will simulate brain-electric field interactions using neuroimaging data
- The model will map individual brain activity and identify strategies to restore healthy dynamics
- Data from healthy individuals and Alzheimer’s patients will refine predictions for brain stimulation
- Multimodal data from humans and rodents will improve the accuracy of the brain models
- The insights will be used to create personalized brain stimulation protocols
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3. Digital Twin of Shanghai: Managing Megacities
The virtual clone of Shanghai city, one of the most populous cities in China, is another example of a digital twin. This was created by taking and processing information from numerous sources such as drones, satellites, and sensors. This digital twin mirrors the city in its entirety. Covering the total city area of 3,700+ kilometers, this digital twin incorporated every aspect of the city, including thousands of buildings, waterbodies, roads, and 20 iconic landmarks. This digital twin allows city officials to gather data on infrastructure, transportation, and public services using a vast network of sensors and IoT devices. Then, by analyzing this data, city officials can make informed decisions to improve things such as:
- Traffic management
- Pollution reduction
- Optimized energy usage
- Urban development planning
For instance, city planners/executives can use this digital twin to simulate the impact of building a new highway or introducing new public transport routes, all within the digital twin.
Other examples of deploying the digital twin technology include:
- Emirates Team New Zealand tests virtual boat designs to optimize performance
- Anheuser-Busch InBev optimizes brewing and supply chain in real time
- SoFi Stadium enhances stadium operations using real-time data
- Space Force creates virtual models of space objects and satellites.
- SpaceX monitors and adjusts spacecraft systems for safety and reliability.
What are the Three Types of Digital Twins?
Digital twins come in different forms, depending on their purpose and the complexity of the systems they replicate. There are mainly three types of digital twins.
1. Product Twins
A product twin means the digital clone of a physical thing or product. By creating a digital twin of a product, manufacturers can monitor its performance, predict failures, and identify ways to improve its design. In industries such as automobiles or aerospace, where safety and performance is of vital importance, using digital twins can be highly beneficial.
2. Process Twins
A process twin focuses on replicating a business or manufacturing process rather than a physical product. It allows companies to analyze and optimize workflows, improving efficiency and reducing costs. For example, a factory can use a process twin to simulate its production line, identifying bottlenecks and testing different ways to streamline operations.
3. System Twins
A system twin takes things one step further by replicating entire systems, such as a city’s transportation network or a factory’s entire production system. These twins are more complex and involve multiple interdependent components. A system twin provides a holistic view of how different parts of the system interact and allows for comprehensive analysis and optimization.
Is a Digital Twin an IoT?
In many ways, digital twins are intrinsically linked to the Internet of Things (IoT). To elaborate, the IoT provides the data that powers digital twins, with millions of connected devices gathering information from the physical world and feeding it into the virtual twin. However, a digital twin is not merely an IoT device. While IoT focuses on connecting devices and gathering data, a digital twin takes that data and creates a dynamic model that reflects the real-time status of the object or system it represents. In essence, IoT enables digital twins to exist by providing the necessary data. Still, the digital twin itself is a more complex entity that includes data collection, analysis, prediction, and optimization, which requires technologies such as artificial intelligence, machine learning, virtual reality, etc.
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In conclusion, the scope of digital twins is vast. From aerospace technology, personalized medicine and pathology, to urban planning and enhancing infrastructure, the digital twin is slowly emerging as a transformative force. In India, the Sangam initiative clearly marks a step in that direction.
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