Scalable Digital Twin Simulations: A Powerful New Tool
When it comes to modeling complex behaviors in large systems, digital twin simulations are undeniably effective.
Digital twin simulations can model large, dynamic data systems and simulate the behaviors of thousands of interacting entities. They can provide valuable insights to decision-makers for many applications, including transportation safety, logistics networks, and city management systems.
A powerful tool for uncovering insights about how large dynamic systems behave is emerging. Digital twin simulations can give system planners and managers invaluable information about complex interactions that are otherwise difficult to study. Digital twins are virtual models of real-world entities that can represent physical attributes, state information, and behavior for each entity. They have been used for decades in the field of product lifecycle management (PLM), where they play a crucial role in the design and testing of various devices. Recent applications have extended digital twin technology to track the behavior of live systems with many components – such as vehicle fleets, IoT devices, and even people – both to monitor status in real-time and to predict future events.
Now, data analysts and operational managers can harness the power of digital twins to simulate the behaviors of complex systems with thousands of interacting entities. This enables them to explore scenarios they are likely to encounter in live systems, informing their decisions and helping identify potential issues in the planning phase. These capabilities also empower professionals to make accurate predictions that guide both current and future deployments, making digital twins an indispensable asset in the modern world of complex systems and ever-changing environments.
Using Digital Twins to Mitigate Disruptions in National Airline Systems
Consider the challenge of managing a nationwide airline system comprising hundreds of thousands of passengers, thousands of aircraft, pilots, gates, bags, and more. As we have seen in the news, issues such as weather delays, equipment outages, and problems with pilot scheduling can severely disrupt the intricate interplay of these entities. Take the widespread holiday Southwest Airlines cancelations or the January FAA system outage as prime examples. Operational managers need tools that can help them alert passengers to these problems and assess the impact of making changes to flight schedules. However, most current solutions rely on inefficient database queries and costly, time-consuming manual interventions, by which time disgruntled passengers are already on the phone with customer service, and flight delays have cascaded into intractable logjams.
What if airlines could respond to these issues immediately as they occur, or better yet, prevent them from happening altogether? Enter digital twins. This technology has the power to model entire airline systems quickly and accurately, representing various types of interacting components such as passengers, airplanes, and baggage handling. Digital twins can also maintain critical state information about each component, like aircraft types, mechanical issues, and passenger itineraries. Moreover, they can track live statistics that measure delays and bottlenecks and assess how well the airline is meeting its passengers’ needs.
Used in simulation, digital twins become even more powerful. They can interact by exchanging messages in a time-driven simulation that runs faster than real-time, simulating the system’s future behavior to predict the effects of management decisions. Digital twin simulations also can help airlines develop new real-time analytics that continuously tracks the progress of passengers during their itineraries and proactively respond to disruptions, both reducing stress and smoothing operations. For example, if a flight is delayed, digital twins combined with AI technology could notify each passenger and confirm potential re-routings before they need to call a customer service representative. Simulations can also deliver valuable insights to third-party decision-makers such as airports, air traffic controllers, travel agencies, and more.
See More: Forget the VR Vision of the Metaverse: Digital Twins Are Coming
Harnessing Digital Twin Simulations to Prevent Train Derailments
Transportation safety has also been in the news lately with the 50-car freight train derailment in Palestine, Ohio and the fatal passenger train derailment in the Netherlands. Digital twin simulations can help prevent similar emergencies as well as save valuable time and resources. Currently, mechanical issues that can cause derailments, such as severely overheated wheel bearings, are detected and radioed to train engineers from track-side sensors, often too late to prevent an accident. In the Ohio event, the NTSB preliminary report described increasing temperatures reported by three rail-side “hot box” detectors before the accident occurred. These types of detectors are positioned every few miles across the country; all the data needed to predict impending derailments is there and could help prevent these incidents if harnessed more effectively.
Real-time analytics using digital twins can track this sensor data and head off possible accidents by warning of impending failures much earlier. With this technology, managers can detect anomalies and take action faster before small problems escalate into derailments. Cloud-hosted analytics can simultaneously track the entire rail network’s rolling stock using scalable, in-memory computing techniques to host digital twins. They can analyze patterns of temperature changes for each car’s wheel bearings, combine this with known information about the rail car, such as its maintenance history, and then assess the likelihood of failure and alert personnel within milliseconds. Conversely, this contextual information can also be valuable in preventing unnecessary false-positive alerts that create costly delays.
To help railway engineers develop and test new predictive analytics software, large-scale digital twin simulations can model the flow of information from the hundreds of thousands of freight cars that cross the U.S. each day, as well as the detectors placed along the tracks. They can also statistically simulate emerging wheel bearing issues and generate the telemetry that would be emitted from thousands of existing track-side detectors across the country. This telemetry can be fed to real-time analytics software and used to evaluate how well the software predicts and avoids impending failures. This technology allows transportation agencies and public officials to develop next-generation safety systems to help eliminate dangerous and costly derailments.
Digital Twin Simulations: A Game-Changer for the Management of Complex Systems
Digital twin technology is revolutionizing the way complex systems are planned and managed. Beyond airline operations and rail networks, digital twins can be used to model a wide variety of systems with many components, including trucking fleets, physical and cybersecurity systems, logistics networks for disaster recovery, smart buildings, and city management systems.
By running simulations on scalable computing infrastructures, intricate behaviors can be observed, carefully analyzed, and their real-world impact evaluated, providing valuable insights into their complex dynamics. Digital twins can predict future behavior within live systems and assist decision-makers, creating a powerful new tool for both system developers and operational managers. These many benefits of digital twin simulations indicate that they will play an ever more crucial role in managing the complex systems we rely on every day.
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