What are Digital Twins?
Have you heard of the term digital twins? Or am I the only one who, since encountering it at a conference plenary, hasn't stopped hearing about it?
At its core, a digital twin is a virtual model of a physical entity – a machine, a building, or even a planet! This type of virtual replica is becoming a reality thanks to advancements in compute capacity in recent years.
But what exactly is a digital twin, and why is it considered the next frontier in technological innovation?
Let's explore it in this article!
What is a Digital Twins?
Let's imagine we had a twin, but instead of being an actual person, this twin is a digital version of ourselves. This digital twin would act just like us but exist on a computer. It could display real-time information about our body, such as stress levels or blood pressure, and even predict which symptoms we might develop in the future.
While the human body might be tricky to imagine, let's look at a more practical example. If we had a digital twin of our car, we could track how the engine is running, monitoring brake wear for example, or even get suggestions on when to take it in for maintenance. A car's digital twins could achieve this by using sensors that send real-time information from the actual car to the digital twin, allowing modern techniques to make predictions based on the data.
Formal Definition
In simple terms, a digital twin is a digital model or replica of physical entities or processes, with real-time data integration. In the definition there are three important core components:
- Physical entity: The machine, system, or even a living organism that we want to digitally replicate.
- Digital model: Virtual representation of the physical entity created using sensors, Internet-of-Things (IoT), and advanced algorithms.
- Data integration: Real-time data flow between the physical and virtual entities.
Digital Twins can incorporate AI and machine learning algorithms to predict future behaviors, self-optimize, and adapt to changing conditions autonomously. This allows them to suggest real-time interventions or predict failures before they occur.
Digital Twin vs Simulation
At this point, you might be wondering: isn't a digital twin a kind of simulation?
While some view digital twins as an evolution of traditional simulations or systems, there are three main areas in which they differ:
1. Real-Time Data Integration
Simulations typically rely on predefined input data and assumptions to model scenarios, while digital twins are continuously updated in real time by data streams from the physical entity via sensors.
Once a simulation is run, it provides static results based on the input data without ongoing updates from the physical environment. In contrast, due to this continuous information flow, a digital twin reflects the current state of the physical system, making it a live and dynamic model.
2. Usability
Simulations are used periodically and typically serve as one-time exercises to test scenarios. After a simulation run, it may no longer be applicable without reconfiguration with updated data.
In contrast, once a digital twin is deployed, it remains relevant throughout the operational life of the system, continuously providing insights, optimizations, and predictive feedback.
3. Bidirectional Communication
While simulations are typically unidirectional, meaning they do not interact with the physical entity after running, digital twins can interact with their physical counterparts. Not only do they receive real-time data, but they can also send feedback and provide suggestions to adjust the physical system in a closed loop.
In essence, while both digital twins and simulations serve to model and understand complex systems, digital twins offer a real-time, dynamic, and interactive connection to their physical counterparts, whereas simulations provide isolated, scenario-specific insights.
Why are Digital Twins a Novelty?
The emergence of digital twins is possible today, let's say unlike ten years ago, largely due to several technological advancements, including increased computing capacity and the development of IoT technology.
In terms of computing capacity, the development of supercomputers has enabled the support of computationally expensive systems such as digital twins. High-performance computing (HPC) systems have dramatically evolved, making possible to handle the larger volumes of data collected by sensors and perform the required computations in real time.
Furthermore, technical advances in the IoT sector, improvements in low-latency and high-bandwidth communications, reductions in storage costs, and the development of cloud computing have been crucial for the advancement of digital twins.
Real Examples of Digital Twins
Finally, I would like to share two examples of existing digital twins. The first has been around for a longer time due to the industry benefits it provides, while the second is an ongoing initiative and my favorite digital twin so far!
Cars Industry
The car industry makes extensive use of digital twin technology because it allows engineers to design, test, and validate vehicles from the conceptualization stage.
Digital twins are also used to optimize the vehicle production line by providing a dynamic, real-time view of the assembly process. This virtual counterpart enables the simulation of the entire production process, helping to identify inefficiencies before the physical production line is built.
Finally, digital twins have revolutionized vehicle maintenance by monitoring the performance of real cars and predicting faults using their digital counterparts. Each component of the car's engine has its digital twin, allowing engineers to perform preventive maintenance and alert drivers to potential issues, such as worn-out brake pads or a failing battery.
Car brands such as Renault Group and Porsche are sharing more information about their use of digital twins in the cars industry.
DestinE
Destination Earth (DestinE) is an initiative by the European Union and the European Space Agency (ESA) aimed at developing a digital twin of the Earth. This model will be used to track the impact of both natural phenomena and human activities on the planet, predict extreme events, and guide policy adjustments in response to climate-related challenges.
DestinE will be developed over the next five to six years, beginning with the creation of two initial digital twins: the Digital Twin for Weather-Induced and Geophysical Extremes and the Climate Change Adaptation Digital Twin. The first will focus on assessing and forecasting environmental extremes, enabling us to predict the occurrence and impacts of severe natural events, such as floods and wildfires, with greater accuracy and, most importantly, to act accordingly. The second will assist in predicting climate adaptation and developing mitigation policies.
Thanks to this project, a comprehensive digital replica of the Earth system is expected to be ready by 2030!
Final Thoughts
To wrap up, by bridging the physical and digital worlds, we can optimize and monitor physical entities while anticipating future challenges.
I believe digital twins are a fascinating technology, not only because of the significant benefits they offer but also as a technological challenge that drives continuous improvements in the systems supporting such applications.
The future of digital twins promises to reshape how we interact with and manage complex systems – potentially even digitalizing the Earth on a global scale! I believe digital twins will become an essential tool in the digital age.
And you? What are your thoughts on digital twins?
That is all! Many thanks for reading!
I hope this article helps in understanding digital twins a bit better!
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