Digital Twin technology existed long before this term came into common use. Over time, existing technology has advanced, new applications and research initiatives have surfaced, and related technologies have been developed. This lack of centralized “ownership” of the term or technology has led to the proliferation of differing definitions of “digital twin.”
Some definitions focus on a specific application or technology – that developed by those offering the definition – presumably to coopt the term for their own purposes. Arguably, the most useful definition, however, is the broadest – one that encompasses the range of relevant technologies and applications, capturing their corresponding value to the field. To this end, I offer the following definition of digital twin:
An electronic representation of a physical entity – product, machine, process, system, or facility – that aids understanding of the entity’s design, operation, capabilities, or condition.
Life Cycle of Twins
The life cycle of a digital twin can be described by three phases of application, encompassing seven steps of progression. The three phases are:
In the design and analysis phase, only the digital twin exists. Tests and design modifications are conducted virtually. In the fabrication and testing phase, physical entities are created and subjected to real-world tests. Generalized tests are conducted to confirm strength of materials, corrosion resistance, aging (i.e. weathering), or other physical properties. Verification and validation tests are conducted to evaluate the entity’s performance in the range of operating parameters specified for its service environment. The operation and maintenance phase begins when the entity is placed in service. Operating conditions are monitored to ensure that all parameters remain in their specified ranges or alert users of conditions that have not been successfully controlled. Data collected from the entity in service is used to determine its maintenance needs. Maintenance activity can then be scheduled to minimize the impact on the entity’s performance and availability.
As a digital twin progresses through the phases, it becomes increasingly sophisticated. Typically, the basis of a digital twin is a 3D CAD solid model; for some process applications, a 2D schematic sufficiently represents the entity to be monitored. Sensor data is then layered on the model in text, charts, color-mapping, or other visual display format. A fully-developed physical-digital twin pair will use two-way communication to maintain a constant state of synchroneity. The digital twin becomes more than a mere reporter of physical twin status; it becomes a remote control. Proximity to the entity is no longer a requirement for its efficient operation. However, it is not practical for every physical-digital twin pair to reach this level of development; some applications simply do not warrant it.
Example Physical – Digital Twin Pairs
To better comprehend how digital twins are used to aid understanding of their physical counterparts, we will briefly discuss several examples. Though some may be unfamiliar, it is likely that you encountered others in your daily life; perhaps you hadn’t thought of them as twins before. If that is the case, the following examples may help you recognize more digital twins when you encounter them or identify potential applications of your own.
A CAD model or schematic, as mentioned previously, is often the foundation of a digital twin. The model can serve many purposes, including:
Heat treat and sintering furnaces can be monitored via digital twins to ensure the quality of processed material. Each zone of a belt-type furnace can be monitored in real time for temperature, humidity, and gas concentrations. In batch furnaces, these parameters are monitored across time to engender the required material properties. Without a digital twin, these processes require manual monitoring and adjustment in the heat treat environment, which is often inhospitable. It also limits the number of processes that an individual can effectively monitor and control.
Condition-based maintenance of equipment is facilitated by use of digital twins. Temperatures, vibrations, fluid levels, current draw, and various other parameters can be continuously monitored. This data allows technicians to evaluate a machine’s performance, track trends, and proactively service the machine to avert catastrophic failures.
Racecar telemetry allows a crew chief to monitor a digital twin of the car while it is on track. Tire pressures, various engine parameters, brake temperatures, kinetic energy recovery efficiency of a hybrid powertrain, fuel burn rate, and aerodynamic downforce, among other data, can be tracked throughout on-track sessions. The data collected helps the team diagnose issues, plan pit stops, and optimize performance. Endurance racing teams can also infer from the data when a driver has become fatigued and should be relieved.
Instead of transmitting data to a remote monitor, the Driver Information Center housed in the dashboard of most modern cars presents the digital twin directly to the driver. Tire pressures, fuel economy, condition of motor oil, and powertrain configuration details are available at the touch of a button. The system may also warn the driver of an open door, blown bulb, or other urgent situation.
A flight simulator is a digital twin of an aircraft used to predict performance in a wide range of conditions – normal and emergency. It is also used to train pilots how to safely operate the aircraft in these conditions. A simulator incorporates all of the aircraft’s flight dynamics information, including aerodynamic characteristics, engine performance curves, control surface parameters, and system interdependencies to create realistic flight scenarios. Though this digital twin does not include two-way communication and control of a physical twin, it is, nonetheless, very sophisticated, mirroring the incredible complexity of aerosystems.
Forward-thinking urban planners are also beginning to use digital twin technology. Modelling an entire city – a monumental task – allows proposed developments to be more thoroughly analyzed than has ever been possible before. Infrastructure capacity – roads, bridges, power grids, water and sewer systems – can be more accurately assessed prior to project approval, minimizing unexpected service interruptions or system overloads. The relationship between construction projects and climatic factors can also be studied in advance. Contractors can better prepare for the impacts that weather patterns may have on construction. Conversely, the effects of completed projects on sun exposure, runoff, and wind can be predicted prior to construction.
Other examples include “smart home” technology (remote control of residential lighting, HVAC, security system, etc.), power-generation (nuclear, solar, wind) facility control rooms, chemical process industries (oil refinery, brewery), and testing of electrical load capacity and system redundancy in aircraft or other safety-critical systems. Even healthcare is being impacted by the digital phenomenon. As medical technology advances, sensors and scans are capable of creating ever-more accurate models of patients, allowing doctors to analyze the impacts of treatment choices on various systems in the human body.
Advantages of “Twinning”
To summarize the discussion, we will focus on reasons that organization pursue “twinning” – creating digital twins of products, processes, and systems. Preparing accurate and functionalized digital twins can yield a number of benefits, including:
If you, or others in your organization, are not yet convinced of the value of digital twins, I recommend choosing a pilot project or “proof of concept” application. Simply put, “start small.” As confidence builds, the twins can be developed further, adding capability and sophistication. Additional twinning projects can also be launched as new applications are identified.
As is the case with most initiatives, a rapid transformation is unlikely; the resources are simply not available to achieve it. Therefore, a hard-sell all-or-nothing approach is usually counterproductive. A soft launch is much better than a stone wall; the pilot project approach is consistent with the philosophy of continuous improvement.
If your organization is ready to launch or accelerate its twinning efforts, feel free to contact JayWink Solutions for guidance. We have the cure for double vision!
[Link] “Promoting Digital Twin Applications for Sustainable Manufacturing.” Navigant Research, August 15, 2019.
[Link] “Navigant Research Report Shows Digital Twins Can Aid Manufacturer Sustainability Efforts.” Business Wire, November 19, 2019.
[Link] “Leveraging Digital Twin Approach for Sustainable Manufacturing.” Navigant Research, 3Q 2019.
[Link] “Exploiting digital twin technology to meet sustainability goals.” Smart Energy International, November 19, 2019.
[Link] “Supply Chain News: The Opportunities for Using Digital Twins in Manufacturing.” Supply Chain Digest, August 30, 2017.
[Link] “How To Use Digital Twins To Disrupt Manufacturing.” Digitalist Magazine , April 4, 2018.
[Link] “Digital Twins: Enabling Next-Gen Manufacturing.” Digitalist Magazine, November 4, 2018.
[Link] “Network Of Digital Twins Series.” Digitalist Magazine, June 21 – August 30, 2018.
[Link] “Using Digital Twins to Reduce Costs in Machine Commissioning.” Design News, January 2, 2018.
[Link] “Cheat sheet: What is Digital Twin?” IBM Internet of Things blog, January 4, 2018.
[Link] “What Is Digital Twin Technology - And Why Is It So Important?” Forbes, March 6, 2017.
[Link] “Digital Twin.” GE Digital.
[Link] “Digital Twins.” Happiest Minds.
[Link] “What Is a Digital Twin?” IoT for All, January 3, 2019.
[Link] “The Digital Twin: Powerful Use Cases for Industry 4.0.” Medium, October 14, 2018.
[Link] “A Better Way: Finding Efficiencies in the Product Design and Manufacturing Process.” Daily CADCAM, August 2, 2016.
[Link] “A Model City.” PM Network, August 2019.
[Link] “Connecting the Digital Twin: From Idea Through Production, to Customers and Back.” Tech Briefs, June 2018.
[Link] “Employing the Electrical Digital Twin to Mitigate Compliance Risk in Aerospace.” Tech Briefs, December 2019.
[Link] “Leveraging Digital Twin Technology in Model-Based Systems Engineering.” Systems, January 2019.
[Link] “Industry 4.0 and the digital twin: Manufacturing meets its match.” Deloitte Insights, May 12, 2017.
Jody W. Phelps, MSc, PMP®, MBA
JayWink Solutions, LLC
If you'd like to contribute to this blog, please email email@example.com with your suggestions.
© JayWink Solutions, LLC