Engineers leverage simulation tools to develop innovative and high-performing products in a virtual design space using design technology. Imagine what you could do as a designer or engineer if you could take your simulation a step further and study a digital working copy of a product under actual working conditions. It could help you rapidly optimize the design, life, and maintenance of a product.
Companies are collecting real-time operating data from product-mounted sensors. They use the data to create an exact replica of a working product, process, or service. This exact replica, called a digital twin, is a simulated model in a virtual space that performs under real-world conditions to help companies find performance issues, schedule predictive maintenance, reduce downtime, and minimise expenses.
An example is visibility into real-world bearing operating temperatures and the downstream effects on tolerances right within a digital model. By studying the digital twin under actual working conditions, companies can see the product in action. Engineers can make more informed choices during the design process and use digital twins to make their simulations more accurate.
For companies already using engineering simulation to design products, connecting the simulation to the physical product in the field is necessary to deploy the digital twin solution. For companies new to simulation, the engineering team must first build the 3D product model, optimise its performance, and replicate the real world in which the product system would operate. Re-using this data in similar product simulation scenarios for future product development saves time and money.
A digital twin is a complete 360° digital representation of a physical asset, i.e., a pump, motor, turbine, or an entire plant. By creating digital twins of physical assets, data generated by the asset during its design and operational life is collected, visualized and analyzed, enabling unified life-cycle simulation.
During the design phase, the digital twin allows for the analysis of processes, equipment and operations through multiple simulations for optimal safety, reliability and profitability. At the concept phase, fast evaluation of design alternatives are analyzed and continuously iterated through variable specifications allowing integrated asset modeling of interacting but separate systems. Each iteration provides a more complete data-set aiding in agile development.
The basic notion is that, for every physical product, there is a virtual counterpart that can perfectly mimic the physical attributes and dynamic performance of its physical twin. The virtual twin exists in a simulated environment that can be controlled in very exact ways that cannot be easily duplicated in the real world, such as speeding up time so that years of use can be simulated in a fraction of the time.
Hyper-accurate models and simulations offer engineers and product designers unmatched insights across the entire product development cycle. Still, digital twins are more than just an progression of digital models, although their goal is similar: Higher quality products and better product support at less cost and less effort.
For decades, engineers and designers have heavily relied on design application tools to digitally capture their ideas for physical objects as parametric models. Even today, more complex tools allow for the simulation of certain characteristics such as thermal properties or stresses and strains.
While use of simulations in product design is nothing new, they have historically relied on relatively small data sets or engineering assumptions when making predictions. Digital twins, however, have access to huge data sets thanks to sensors that monitor/measure literally every facet of a product's lifecycle and fed back into an iterative design-manufacture-observe-improve loop.
Once a product leaves the factory and is acquired by an end-user, the digital twin can begin to feed off real-world data collected by the onboard sensors. This is where the concept of the digital twin reaches its full potential. Sensors in the end item itself will track key performance characteristics of the device as it operates in real-world conditions. Comparing actual telemetry against the predictions of the various aspects of the digital twin model yield insights only dreamed of until now.
A useful loop results from this level of integration of the physical and virtual. Not only can the digital twin be improved based on real-world data, but future iterations can also be improved based on better understanding of actual data from end users. In some cases, where changes can be made through an update, products that have already been deployed can also benefit from lessons learned through using a digital twin.
Being able to get that feedback to a company is invaluable. Not having to rely on a customer to call a help desk and to have that data fed into a digital twin to influence future design iteration is even more incredible. In addition, it would be almost magical if a customer received an email from the company proactively describing steps they could perform to minimise the failures that they might be experiencing without having the customer even place a call or email in the first place.
Beyond the obvious use of rich datasets in maintenance prognostics, digital twins could have profound impacts on the design and engineering of subsequent product iterations. Understanding how a product is actually being used in an objective and data-driven manner will lead to faster development cycles and greatly reduce the time to detect product defects or identify useful tweaks, thus reducing waste by allowing manufacturers to make real-time improvements to the products still coming off the assembly line. This can translate to huge savings by avoiding costly rework.
Digital twins are not limited to assessing tweaks to physical properties of a design. Digital twins can also make it easier to study the impacts that tool revisions have on performance. Various configurations and settings can be rapidly tested and assessed to determine which ones will deliver optimum performance. Updates could then be pushed out seamlessly to all the devices, leveraging the same network connection that initially sent the data used to identify improvements.
Technology can continuously monitor, collect data, and conduct analysis. Meanwhile, humans can keep their attention on higher-level work such as exploring implications of various complex courses of actions and making informed decisions.
Embedded platforms, with their computational horsepower, efficient sensors, and reliable communications hardware, are critical to the collection and dissemination of telemetry data. This data is necessary to make digital twins smart enough so that their function is worthwhile. Then, all that data can be pumped into databases that are rapidly analyzed. Throw in the possibilities from Artificial Intelligence systems to analyze and make improvement recommendations, and it’s possible that products could improve over time without any human intervention.
The concepts of the digital thread and digital twin have been spearheaded by the military aircraft industry in their desire to improve the performance of future programs. They apply lessons learned through these digital technologies to current and upcoming programs.
The data in the digital twin of an aircraft includes things like specific geometry extracted from aircraft 3D models, aerodynamic models, engineering changes cut in during the production cycle, material properties, inspection, operation and maintenance data, aerodynamic models, and any deviations from the original design specifications approved due to issues and work-arounds on the specific product unit.
The benefits expected from having a digital twin for each product unit include ore effective assessment of a system’s current and future capabilities during its lifecycle and early discovery of system performance deficiencies by simulating results way before physical processes and product are developed Optimisation of operability, manufacturability, inspectability, and sustainability leveraging models and simulations applied during the entire lifecycle of each tail number. Continuous refinement of designs and models through data captured and easily crossed referenced to design details.
Product Engineers working together with Manufacturing Engineers create a 3D model linked to visuals for production process instructions. Product characteristics are linked to 3D models and extracted directly out of designs into conformance requirements.
Conformance requirements are linked to manufacturing process and inspection instructions. As-built data is delivered by Production along with product unit to customer and is made available for sustainment services to continue evolving the unit’s data during operation and maintenance services Product design changes follow the same data flow and automatically update downstream models, references and instructions.
The digital twin is a virtual copy of the real-world thing, or a complex system of connected things. It’s not just a 3D model—it’s a living model in 3D that sees the vehicle as part of a complex technology ecosystem of electronics, navigation, communication, collision avoidance, and so on. Engineers can analyze how a vehicle performs not just in its physical environment, under every condition imaginable, but over its entire lifecycle, from an early-stage digital prototype on screen to its last day on the road.
Aviation engineers today can use the digital twin to pinpoint when and under what conditions, or after how many hours of flying a critical part or sensor will fail. Similarly, rocket engineers are putting digital twins to work to predict and verify performance of the lightest possible materials and payloads, long before they ever perform costly tests on the launch pad. Digital twins are being used not just to model how such physical assets perform, but—longer-term—how ever-more complex systems of assets and humans will behave together as a whole.
Emerging technologies enabling the digital twin include simulation tools—the heart of the digital twin—which has come a long way since the early days of 3D design. Simulation tools today can codify, replicate and virtualise the performance of physical products and systems, all based on the hard-wired laws of physics. Digital twins are essentially complex simulations of any number of components in action like aircraft jet engines during takeoff but based on true operational data generated, over long periods of time, by sensors from every critical part.
How might digital twin value emerge in a few years with an autonomous vehicle? Engineers will construct a digital twin before they design or build the real thing—enabling dramatic cost savings and accelerating time to market. Designers will collaborate from the outset with operational teams and data analysts to begin gathering different data types to start modeling and verifying how future products will perform under every condition; how different types of drivers will interact with it; what its vulnerabilities are from a maintenance and breakdown standpoint.
Building the digital twin might start with the physical components. Engineers can pool data on type of motor, suspension, chassis and aerodynamic body they want to tap into, and the materials they’re built from. Then they would start adding new data layers—such as operational data logs of similar models, or traffic data to model performance in different operational scenarios. Engineers will pile up all that data and start tapping into machine intelligence tools to design and model out their ideal product—long before anything hits the assembly line.
Then comes the promise of the digital twin over the lifecycle of the vehicle. With digital twins, engineering and operations teams can see not just what is happening at any given time, but why. They can speed up simulations in operation and productivity, pinpoint when, why and how breakdowns will occur, and reduce the costs and risks of unplanned downtime.
Engineers are building digital twins of physical structures—dynamic, simulated models of the real thing, powered by the massive amounts of data that a single structure generates around the clock, such as physical specs, energy consumption and cost data, equipment parameters and live occupancy data pulled from elevators. The promise of all this is to use digital twins for everything from predictive maintenance and optimised facility management to streamlining workspace design based on the data flows showing how real people truly use the physical space.
Imagine a manufacturing system that, on first glance, might function like any other modern system. While seemingly ordinary, the automated machines in this system experience significantly less downtime than you’d expect. This is because each machine is being operated alongside a virtual, real-time model that corresponds to all of the dynamics and components inside the real machine. Whenever the machine might be close to malfunctioning, the virtual model is two steps ahead, either making slight adjustments to its controllers, or notifying a maintenance staff before the issue causes downtime.
This approach to modern, efficient machine design is quickly becoming a key technology for reducing costs and ensuring smarter products. The technology behind this process uses the digital twin, and by using modern system-level modeling tools, they are becoming an important part of the virtual commissioning process.
For those who are just getting started, a digital twin is a dynamic, virtual representation of a corresponding physical product. These models can range widely in their purpose and fidelity, but they serve as a powerful connection to the product for diagnostics, design changes, and an important new process called virtual commissioning. Companies are using digital twins across industries, allowing them to optimise their products in ways that were previously impossible.
Initially, digital twins were typically created once the physical product was in operation. Using vast amounts of data from sensors on the product, predictive models could be created to assist with diagnostics and improvements. By using a system-level modeling tools , the creation of a model-driven digital twin can begin alongside the design process. Meaningful digital twins can be created before a physical product is finalised, allowing for a powerful test platform to validate product performance earlier than ever.
During virtual commissioning, engineers can import their designs directly into a simulation tool to create a high-fidelity model of their system dynamics. This model gives the engineer new abilities to get quick insight into system integrations issues, long before the expensive commissioning stage. They can import their motor parameters, define their product’s motion profiles, and have easy access to a full set of results after simulation.
Systems are tested against a high-fidelity, functional model of the physical product, giving engineers a much clearer expectation of how their automated designs will perform after physical deployment. Every issue caught at the virtual commissioning stage then becomes one less unexpected issue that might crop up during physical commissioning. Catching even a handful of issues early can often save companies huge amounts of time and money, since fixing issues on a virtual platform is far easier than revising physical products.
The power of model-driven digital twins doesn’t end with virtual commissioning. By having a high-fidelity model paired with the physical product, there are benefits along the entire design process – even after the commissioning process itself. Companies who have adopted model-driven practices with digital twins are using these models throughout the operation of their machines, using them for real-time diagnostics, as the inline digital twins can provide information about torques, inertias, and more. This helps ensure less downtime, as issues can be spotted early and fixed faster. Many companies also experience the cost-savings of using a digital twin, as the high-fidelity model can give them much of the information that they had previously needed to obtain from expensive physical sensors.
As the cycle of innovation quickens, digital twins are becoming increasingly popular across industries to reduce risks and get new products to market faster. With advanced modeling tools, model-driven digital twins are securing their place as an essential component for the modern design process.
Part of the reason for creating a digital twin is to keep the list of features straight for each individual product. In high-volume production, that includes tracking the multiple features that change from product instance to product instance. The digital twin keeps a specific record for each product. Large auto manufacturers produce so much they want to keep the information on each vehicle as precise as possible. You don’t have all of the engineering information—that would be too overwhelming—but you have digital threads that lead to that information in case there are problems with the vehicle. Then you can follow the digital thread to the product lifecycle data.
In addition to the value of a digital twin on the manufacturing line, there is also value in capturing data when the product is in the field. The digital twin is also designed to send info back to the automaker while the vehicle is being used. You continue to communicate with the vehicle through some connectivity. You can look at the fleet or individual vehicles, so you can determine maintenance or upgrading needs and alert the customers.
So, who is actually utilising digital twin technology? Is it being used now? “We see examples of digital twin use in aerospace and defense. We have a customer who provides training and they actually use a pretty good digital twin in their training.”
1. Formalise planning development, integration, and use of models to inform enterprise, programme decision making, support engineering activities to digitally represent the system of interest
2. Ensure models are accurate, complete, usable across disciplines to support communication, collaboration and performance and decision making across lifecycle activities
3. Provide enduring, authoritative source of secure authentication with access/controls to establish technical baseline, product digital artifacts, and support reviews for accurate decision making
4. Incorporate technological innovation to establish end-to-end digital engineering enterprise and foster conditions for productive step advances towards goals
5. Enable end-to-end decision making using advance human-machine interactions
6. Establish mature supporting digital engineering activity infrastructure to perform activities with connected information networks
7. Develop, mature, and implement technology tools to realise digital engineering goals and share best practices using models to collaborate with stakeholders
8. Improve digital engineering knowledge base, policy, guidance, specifications, and standards and streamline contracting, procurement and business operations
9. Lead and support digital engineering transformation efforts, vision, strategy, and implementation to establish accountability to measure and demonstrate results across programmes
10. Build and prepare workforce to develop knowledge, competence, and skills with active participation and engagement in planning and implementing transformation efforts