The digital twin phenomenon is entering different industries with great success; to quote the International Council of Systems Engineers (INCOSE), on what is a digital twin:

• System Engineering Body of Knowledge (SEBoK) - A high-fidelity model of the system, which can be used to emulate the actual system.

Another view of what is a digital twin comes from the U.S. Department of Defense (DoD) Digital Engineering Strategy, stating in 2018 that a digital twin is:

• Defense Acquisition University (DAU) - An integrated multiphysics, multiscale, probabilistic simulation of an as-built system, enabled by a Digital Thread, that uses the best available models, sensor information, and input data to mirror and predict activities/performance over the life of its corresponding physical twin.

Today the use of this groundbreaking technology is being applied to healthcare, smart cities, automotive, manufacturing, and retail to name a few. The following text presents more details, history, types, use cases, benefits, challenges, and Internet of Everything Corporation's contribution to digital twin tech.

Digital twin brief history

The first steps into this groundbreaking technology were foreseen in 1991 in David Gelernter's book Mirror Worlds. It wasn't until 2002 when Michael Grieves introduced the digital twin concept and model at the Society of Manufacturing Engineers conference for a Product Lifecycle Management (PLM) center, referencing it as a “Conceptual Ideal for PLM.” Its final consolidation as a digital twin had to wait until 2010. John Vickers of NASA on a Roadmap Report used this terminology to use it in their technology roadmaps and proposals for sustainable space exploration.

In a later publication, Origins of the Digital Twin Concept, uploaded to ResearchGate in 2016, by Grieves, authored by Grieves and Vickers, Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (Excerpt), the definition of what is a digital twin is as follows:

• Digital Twin (DT) - the Digital Twin is a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level. At its optimum, any information that could be obtained from inspecting a physical manufactured product can be obtained from its Digital Twin. Digital Twins are of two types: Digital Twin Prototype (DTP) and Digital Twin Instance (DTI). DT's are operated on in a Digital Twin Environment (DTE).

By 2017 DT reached Gartners Top 10 Technology Trends, indicating that, Within three to five years, billions of things will be represented by digital twins, a dynamic software model of a physical thing or system.

Digital twin types

The development of DT technology is based on two types, as mentioned earlier: Digital Twin Prototype (DTP) and Digital Twin Instance (DTI). In addition, there are Digital Twin Environment (DTE) and Digital Twin Aggregate (DTA). Let's look at these in more detail.

Digital Twin Prototype (DTP)

A DTP describes the prototypical physical artifact, consisting of the designs, analyses, and processes that realize and exist before a physical product. In other words, it contains the informational sets necessary to describe and produce a physical version that duplicates or twins the virtual version. Some of the informational sets consists of the following components:

• Requirements
• Fully annotated 3D model
• Bill of:
  • Materials (with material specifications)
  • Processes
  • Services
  • Disposal

Digital Twin Instance (DTI)

The DTI describes a specific corresponding physical product that an individual digital twin remains linked to throughout the life of that physical product once it's manufactured. For it to function it requires some of the following information sets:

• A fully annotated 3D model with General Dimensioning and Tolerances (GD&T) describing a physical instance and its components' geometry.
• Bill of Materials listing current and past components.
• Bill of Process listing operations performed in creating the physical instance, along with the results of any measurements and tests on the instance.
• Service Record describing past services performed and components replaced.
• Operational States captured from actual sensor data, current, past actual, and future predicted.

These are just some of the information sets required, note that these are conditioned to the use cases.

Digital Twin Aggregate (DTA)

A DTA is simply the aggregation of all the DTIs and as such, it may not be an independent data structure. Its function works within a computing construct accessing all DTIs and querying them either ad-hoc or proactively:

• Ad hoc queries - A specific question is presented to a DTI, e.g., What is the Mean Time Between Failure (MTBF) of component X?
• Proactive queries - A continual sensor reading examines and correlates sensor readings with failures to enable prognostics.

Digital Twin Environment (DTE)

In this case, the DT is designed to be an integrated, multi-domain physics application space for operating on a variety of purposes. A couple of examples the DTE can focus on include:

• Predictive - As the name indicates, it's used for predicting future behavior and performance of the physical product at the Prototype and Instance stage:
  • Prototype - To ascertain that the as-designed product met the proposed requirements.
  • Instance - A specific instance of a physical product or multiple instances, incorporating present components and history, based on a current point and state in the product's lifecycle and moving forward.
• Interrogative - Applies to DTI's realization of the DTA. Interrogations could be for:
  • Current and past histories
  • Current system state:
    • Fuel amount
    • Throttle settings
    • Geographical location
    • Structure stress

Digital twin use cases

Digital twin use cases can be implemented in various industries. We already mentioned how NASA began to use it for sustainable space exploration. Currently, urban construction, i.e., smart cities use digital twin tech plus 3D and 4D models to learn about improvement possibilities and possible failure scenarios.

Building information modeling (BIM) has an exceptional extension in digital twins to digitize construction and urban planning processes, planning, design, construction, operation, and maintenance activities. An example of DT implementation is the partnership between the University of Cambridge and the UK's Department for Business, Energy, and Industry Strategy to solidify the concept of the digital twin in The Gemini Paper:

• The Gemini Papers set the Centre for Digital Built Britain's vision for the future, showcasing the vital role that connected digital twins play in improving social, economic, and environmental outcomes, to create a better quality of life for all.

Digital twin use cases in healthcare are applied to surgery training, people movement within hospitals, tracking where Healthcare-Associated Infections (HAIs) may exist, and potential infection areas. In a wider scope that touches many industries, equipment, and machinery digital twins provide insights to optimize maintenance.

Retail's DT use offers customers the possibility to engage with products before purchase. In the automotive industry, Daimler allows customers to test-drive the vehicle via their customer twin. Another use case for the auto industry is gathering and analyzing operational data from a vehicle in order to assess its status in real-time and inform product improvements.

As you can see, the potential of digital twin use cases is limitless and can be used throughout the value chain of practically all industries.

Benefits of DT implementation

Looking at specific digital twin benefits that can be implemented into, e.g., manufacturing, healthcare, automotive, construction, and oil and gas among others, are the following:

• Maintenance - Keeping machinery and equipment at optimal levels is vital to reach set revenue and budget targets. Investing in DTs allows industries to move away from reactive and preventive maintenance and enter precise predictive maintenance. For example, wind turbine and oil pipeline monitoring via DTE focused on predictive means ruptures and malfunctions are alerted in advance, resulting in millions of dollars saved by mitigating associated costs.
• Prototyping - Being able to prototype before entering the manufacturing line ignites product defects reduction and shortens time to market. Such an advantage produces customer loyalty, higher or reaching annual revenue targets, and unplanned budget rises are mitigated.
• Security - Learning about product and equipment malfunctions early on guarantees workforce and customer safety. It also secures workflows as downtime because of unforeseen failures are exposed beforehand. Well worth investing in, considering the potentially hazardous situations, for example, the oil and gas industry has to live with.
• Product quality - Performance testing in real-time is an exceptional advantage that DT technology provides. In addition, customization model analysis optimizes even further the quality of the end product before sending it to the manufacturing floor to be replicated in the thousands.

Digital twin challenges

Not surprisingly, the technology faces shared challenges in parallel with embedded tech, AI, and IoT. The most common are data standardization, data management, data security, and barriers to its implementation and legacy system transformation. Other challenges are updating old IT infrastructure, connectivity, privacy, sensitive data security, and the lack of a standardized modeling approach.

As for economic challenges likely to hamper DT's market growth include deployment's high cost and increased demand for power and storage. The technical obstacles are subject to existing systems or proprietary software integration and its architectural complexity.

Implement digital twin solutions is costly, and the investment can be significant considering the required:

• Technology platforms (sensors, software)
• Infrastructure development
• Maintenance
• Data quality control
• Security solutions

Therefore there are high fixed costs and complex infrastructure to successfully deploy digital twin tech into workflows. A situation predicting digital twin's market deployment slow down.

Internet of Everything Corporation's contribution to digital twin-tech development

It is impossible to predict where digital twin use will be in five years, but Internet of Everything Corporation (IoE Corp) strives to accelerate its deployment. Our technology is meticulously designed as a stepping stone into the fourth industrial revolution's data-driven industry. Digital twins require constant real-time data flows that are verifiable and transmitted through secure paths.

Adding to the obstacles mentioned, cost-effective data management is essential and its quality is paramount. Internet of Everything Corporation's contribution to digital twin tech purposes to accelerate DT to reach its full potential, deploying a decentralized software blockchain-secured platform. Using existing hardware computing power on-premises allows data generation at the source to be managed at the edge level.

Investing in this breakthrough approach relieves you from centralized data management costs, ensuring real-time data to information cost-effectively. Sustainability comes inherent via our sustainable computing approach to embedded systems and occurrence AI analytics instead of full-block AI analysis.

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• Mission - To accelerate the transition to a sustainable world through the Internet of Everything.
• Vision - Be the first generation of humanity to achieve global sustainability with technologies and put us on a path to a secure future here on earth opening up for future generations to explore the solar system and beyond.

Are you ready to step into Industry 4.0 ahead of the curve and achieve the promise of technology to provide better living standards for societies and reach the UN's SDGs? Digital twin tech is vital for this to happen and we know how to help. Apply to the Planet Partner Program here.