A Digital Twin. If you have stumbled upon the term, it might sound like a science fiction novel where people have their doppelganger living in a cloud. The reality is: digital twins are more intricate than that.
A digital twin is a complex model that is the counterpart of a physical asset or a process – it can be a workflow, a train, an oil rig, a space shuttle, or even a city. Through data gathering and processing, it can give people managing the asset crucial information about how it is doing in the real world. It is, therefore, a fertile solution for better decision-making.
How the first digital twin was born
Digital twins were most likely firstly used in NASA’s Apollo 13 mission. Although the term was not coined at the time, it used the fundamentals that make what we understand as a digital twin today. When the spacecraft was under construction, a faulty liquid oxygen tank was installed by the engineering team. This would almost cost the lives of the astronauts onboard and would terminate what would be supposed to become the third lunar landing.
As Jim Lovell, Apollo 13 Astronaut, explains, “it was a bomb waiting to go off”. The tank eventually exploded when the spacecraft was 320.000 kilometers away from home and just 64.000 km away from the Moon – where the three astronauts were ready to set foot a few days later.
“Houston, we’ve had a problem.” The mission control team was prompt to make sure the astronauts had enough oxygen to keep them alive in the upcoming minutes. To ensure the team drifting in space returned home safely, NASA’s team had to find a way to restart a command module that was not designed to be switched off while the spacecraft was navigating. Mission control had to use the 15 simulators to run over their options. Enter the digital twin. Using the simulators was not by itself what we now know as the first digital twin. Their capacity to adapt the simulator to the conditions of the malfunctioning spacecraft is what makes this the first digital twin in operation. With the simulation shift to the current scenario, NASA’s team was able to test, explore, and develop a strategy to bring the three rocket men home, making the first use of the digital twin pivotal in saving the lives of three space explorers.
Where it can lend a digital hand
Fifty-one years after the Apollo 13 mission, digital twins are still crucial in helping engineers, operators, and decision-makers understand how assets are performing in the present and how they will perform in the future. The sum of connected sensors and data gathered from other sources make these predictions possible.
This digital mirror can help troubleshoot equipment that is not nearby, making the Space Economic one of the best areas to apply this technology. However, since it can create real-world scenarios of most objects, numerous industries can benefit from having one.
Implementing a digital twin can be especially fruitful in three scenarios:
- Industries with million-dollar projects – developing a digital twin has the advantage to become substantially cheaper and to understand better how the product would behave in various real-world scenarios;
- Research and Development acceleration – through a virtual simulacrum, it is possible to do understand where the pain points are and to continuously improve the product without the need to test it in real-world scenarios;
- Behavioral Prediction.
In this last case, think of a traffic model of a city that supports real-time monitoring. The ability to move from point A to B in the shortest time possible is crucial in today’s modern cities. With endless possibilities – from micromobility platforms to subway, buses, private cars, and bicycles, as well as ride-hailing options – making sense of the best ways to improve mobility is challenging.
“Where should we add new bike lanes?” or “how can we improve public transportation?” are questions that can be easily answered if the city or region has a digital replica in place. Planners can test every possible scenario without disrupting real-world traffic.
Present analysis and future predictions are fundamental in the pandemic situation we are going through. If governments could understand how the health system and the population behave during the pandemic, they could make better-informed decisions.
This is the case with VirVi, Quidgest’s solution for the pandemic. Through monitorization and continuous improvements, the system is our answer to the present and future epidemics (that are posed to happen more often than ever).
The digital twin methodology can be applied to a variety of scenarios. Since Quidgest specialized its field of work within digital twins in socio-economic dynamics, we see greater value in implementing this technology in economics, demographics, employment, digital skills, income distribution, agriculture, industrial production, banking performance, healthcare, privacy, security, social issues, regional issues, smart cities, housing, environment, energy, culture, sports, external commerce, procurement, entrepreneurship, investment, R&D, innovation, infrastructures, transports, communication, education, migrations, or global sustainable development.
Since you are here, you may find our webinar about Digital Twins interesting.