September 5, 2024

Rachel Truong  

12th Grade

Fountain Valley High School 

Digital twins are real-time virtual models of a physical entity that continuously reflect the state of the entity throughout the entity’s life cycle. Today, digital twin technology is used in many industries such as healthcare, urban planning, construction, and energy due to robust IoT systems and advancements in AI. 

In the 1960s, NASA utilized one of the earliest concepts of digital twin technology in the Apollo 13 spacecraft. NASA created a physical twin on the ground to simulate the conditions of the spacecraft. By doing this, engineers could identify potential problems and predict results without testing on the actual spacecraft. In 1993, the novel Mirror Worlds envisioned the connection between reality and the digital world. In his book, David Gelernter, Yale professor of computer science, speculated and explained how virtual models could mirror real-life systems. Gelernter highlighted the advantages of syncing software with reality that could allow humans to understand and explore complex and dangerous processes from the comfort of their homes. In the early 2000s, John Vickers, NASA's principal technologist, and his team applied the concept to spacecraft models and systems simulation. It wasn’t until 2002 when John Grieves, executive director of Digital Twin technology, formally proposed the idea of creating a digital model that replicated a physical product throughout the product life cycle. He aimed to incorporate digital twin technology into physical product systems so that the digital twin could monitor and simulate the state of the physical product with real-time data.

Digital twins model the behavior of real-life systems by using real-world data from sensors, actuators, IoT devices, etc. throughout the life cycle, making it easier for engineers to test design ideas before production. DTs can be used to simulate products, systems, or processes in different situations to discover solutions and changes to refine the original prototype. However, digital twins are not simulations. Rather, they are virtual environments that replicate all the processes of its physical twin with data gathered from its connected devices. 

Sensors on the physical entity send data to a central system which then continuously integrates data into the digital twin, updating the virtual model to reflect the current state of the physical entity throughout the lifecycle. Life Cycles involve the design, manufacturing, operation, and maintenance of the physical entity. In addition, DTs enable users to interact with the data to predict outcomes, test scenarios, and identify potential problems. Through machine learning, DTs can more accurately predict outcomes and provide feedback for optimization. These can include changes in the system, adjustments in operation, and improvements in design and maintenance. The ability of DTs to support informed decision-making allows users to simulate changes in the digital twin without impacting the physical entity. In these ways, DTs facilitate improved monitoring, control, and optimization for efficiency and productivity in lengthy complex processes.

Not only do digital twins exist in many things we see day to day but are also used in designing and creating large-scale projects. Amazon’s Alexa interfaces with digital twin technology integrated into smart devices to provide insights on performance and maintenance. Then it could alert users for potential issues with the predictions from the virtual model, improving user experience. On a larger scale, many smart cities have utilized digital twin technology to enhance urban planning, optimize traffic flow, and improve emergency responses. In 2022, Singapore constructed the world’s first country-scaled digital twin known as “Virtual Singapore” with software provided by Bentley Systems. This project involved a dynamic, 3D model of the entire nation, integrated with real-time data from various sources. Through its detailed infrastructure visualization formed by data sent from satellites, sensors, and IoT devices, its digital model provides an interactive representation of the urban environment to its users.

In the past twenty years, digital twin technology has driven groundbreaking advances across industries such as healthcare, business, automotive, manufacturing, and aerospace engineering. With the capability to augment the efficiency, accuracy, and reliability of products and systems through machine learning and configurable parameters, the appeal of adopting this technology in companies has become more compelling. By replicating real-world spaces with high precision, it could redefine traditional patterns of development and operation, making complex, dangerous, and costly processes more affordable, safe, and achievable. Despite challenges in integrating DTs, the market is projected to grow “from USD 2.16 billion in 2023 to USD 21.40 billion by 2030, exhibiting a CAGR of 38.8% during the forecast period” according to Fortune Business Insights. This could mean that more resources and manpower would be conserved, reducing the need for expending limited resources and human involvement in dangerous processes. With those advantages coupled with enhanced integration, more accurate simulation, and broader applications, the future of digital twin technology holds limitless potential to improve the quality of life.