Digital Reflections | Digital Twin Consortium

An Introduction to the Next Generation of Digital Twins

By Maximilian Mayr, Lead Web Developer, Tributech Solutions GmbH

A holistic view of products, processes, or even the whole value chain to enable closed-loop engineering, cross-company digital services, and more is a crucial success factor for any data-driven organization. The next generation of digital twin technologies will play a key role in standardizing and automating business processes across current boundaries of systems and companies. This blog post introduces the general concept behind digital twins and the latest standards in this area.

A digital twin is usually a digital representation or abstraction of a real-world entity like a machine or device. They are already a part of the daily business in many industries and applications. However, most of today's digital twin solutions are siloed applications with a minimal scope regarding the life cycle or value chain. We are just beginning a robust growing technology ecosystem that will transform businesses worldwide by rapidly increasing their process efficiency and automation. A recent market analysis from grand view research predicts an annual growth rate of 42.7% from 2021 to 2028, expanding the market size from $7.14 billion to $86.09 billion.

The next generation of digital twin technologies and standards will enable applications with a holistic view along the value chain and life cycle. These technologies will also simplify development and business operations across boundaries of a system or company by standardizing access, configuration, and representation of physical assets, processes, and more. As a member of the Digital Twin Consortium, Tributech is working at the forefront of a rapidly growing ecosystem around new digital twin technologies.

In this blog post, we are going to look at the general concept of digital twins. In a nutshell:

• Digital twins are a digital clone/representation of an entity, asset, process, or more.
• Organizations can use them to standardize operations by introducing a common and business-specific vocabulary.
• Organizations can use them to run simulations without affecting the actual devices.
• Organizations should reflect changes to an actual device in the digital twin and vice versa.

For example, an organization can model an (IoT) thermometer as a digital twin by describing its properties, interactions, and data. In technical terms, a digital twin should be the Single Source of Truth (SSOT) of a physical asset. The twin offers the possibility to communicate between the digital and the physical world. Due to that, organizations should apply any property change n the twin on the actual device. Additionally, real-time data of the device should also be accessible on-demand via its digital twin.

Going back to our thermometer example, instead of looking at the device from a high-level perspective, it is also possible to represent the thermometer as a combination of different components like sensors, processors, and antennas. If each of these components again has its capabilities and data output defined using a digital twin, new possibilities would arise. By basing all device interactions on these digital twins, we can, for example, take one of the components of the actual device and replace it with its digital twin. In this way, we simulate how a device will react to different states and inputs of a replaced component.  Modeling a complete production line on digital twins, an organization can see the production increases or decreases based on various parameters.

Available Digital Twin Standards

We define digital twin vocabulary based on ontology, which allows the description of categories, properties, and relationships between concepts, data, and entities. Ontologies are expressed in a machine-readable format like RDF, Turtle, JSON-LD, and others. The most commonly known ontology is the schema.org ontology created by Google, Microsoft, Yahoo, and Yandex that is used throughout the world wide web to add metadata to websites that can get picked up by crawlers. Google, for instance, uses this information to populate its knowledge graph. You can use the graph to display an infobox in your search, where the essential information is immediately displayed. Furthermore, it is used to improve the overall results by understanding how entities are connected.

The goal is to have entities that can describe themselves using a so-called self-description. This self-description, shortly called SD, should tell you everything you need to know about the digital twin.

There are different standards to describe digital twins, for example, the Asset Administration Shell (AAS), the Web of Things (WoT) standard, and the Digital Twins Definition Language (DTDL).

 

© https://xkcd.com/

Each of those standards has some heavy players behind them. We at Tributes evaluated all of the standards and decided to use the DTDL standard initiated by Microsoft. The main reason for choosing DTDL is that it allows us and our customers to easily describe their use-case as a custom vocabulary based on linked data. Combined with the limited set of building blocks (Interface, Telemetry, Property, Command, Component, and Relationship), organizations can use DTDL to describe physical or digital things, processes, products, or entities that offer enough flexibility keeping the complexity as low as possible. Additionally, the representation of a DTDL instance is always the same, eliminating the need for complex parsing logic on the edge side.

We learned that we could use digital twins to handle assets, entities, or processes standardized, but why stop there? Why shouldn't we take a step further and introduce the benefits like time and money savings to other areas?

One example we use internally is configuration twins for IoT devices and microservices. Instead of adapting service configurations, we only modify the corresponding twin manually, and the service automatically adjusts itself.

I hope this article provided some new insight into the next generation of digital twins and their possibilities.

The Strategy Behind the Digital Twin Glossary

by Sean Olcott, Technical Director, Gafcon, Digital Twin Consortium Technology, Terminology & Taxonomy Working Group (3T Tiger Team)

In this video blog, Digital Twin Consortium member Sean Olcott discusses the value of a digital twin glossary and the importance of digital twin terminology.

 

 

The Impact of Digital Twins on Smart Buildings

By Anto Budiardjo, CEO Padi, Inc.

The term smart building has always seemed a little odd to me. How can “a structure with a roof and walls,” the dictionary definition of a building made of steel and concrete, be smart? Surely, the word brainless, an antonym for smart, would be a better word to describe such structures!

In 1883, Warren Johnson, a Wisconsin teacher and later founder of Johnson Controls, patented an invention where a thermostat rang a bell when the temperature in a building was low, informing janitors to shovel coal into a furnace to increase the building temperature. A hundred years later, electronics took the reins with the introduction of Direct Digital Controls (DDCs). More recently, the mighty microprocessor and digital networking have taken charge in regard to the control of comfort in buildings.

The reality is that smart buildings have been focused on the comfort, safety, and after COVID-19, the health of the humans in buildings, not the building itself. While this may sound innocuous, the idea explains why the comfort of buildings has been the task to automate and control. Thus, the term smart is typically used to mean a more intelligent way to automate the control systems for the benefit of humans. This is akin to treating the symptoms of an illness rather than focus on its root cause.

What has been left to the side is the act of making the building itself truly smart across the entirety of its lifecycle, from design, construction, operation, maintenance, and occupation. For years, many in the smart building industry have been trying to address this challenge on the premise that if the building itself is smart, meaning that a well-organized and consistent set of information exists about the building which is cognizant of its design intent, has a rich history of why it was built, and a history of operational data, then with modern digital technology, buildings can truly be smart in order to perform more efficiently and adapt to unanticipated circumstances. This is far beyond just keeping the environment inside the building comfortable.

With the plethora of technology available, why has the field of smart buildings not broadened to encompass the full lifecycle mentioned above? Why has building automation and control remained mostly a comfort management system much more aligned with the boiler room than the board room’s business need to facilitate commerce?

To uncover the root cause of these questions, we need to know that, while a building is physically one of the most permanent entities in our daily lives, the people, industries, organizations, and economic drivers for a building are anything but harmonious. The responsibility of a specific building is handed from one entity to another throughout its life.

A building’s life typically begins as a vacant lot, owned by an individual or organization and governed by the relevant jurisdiction with its objectives and legal constraints. As the owner makes a decision to turn the lot into a building, they have financial objectives, either for their own use or in hopes that they can lease or sell the building once it is built. The design phase of a building involves architects and engineers that have a specific task to design the building. Once complete, the design is handed to contractors who start to build the building according to the design and economic specifications. Due to the fact that buildings are complex structures, there are dozens of different trades involved, each with their own concerns. Once built, the building may be sold, change hands, leased, or otherwise occupied by an entity who has their own requirements and objectives. Often this occupation becomes a cycle with refurbishments, during which time the building is operated and maintained by several organizations one after another, each with their individual concerns, and this process can go on for decades!

Because buildings are inherently large, each of the phases above involves significant amounts of money, expectations, risk, and returns. In the information era, each phase also involves significant amounts of data, including financial, engineering, and operational. Unfortunately, such information about the building during each phase is not retained, nor used, by anyone other than those directly involved in the phase of the building.

When an entity buys or leases a property, the essential transaction is when they “get the keys to the door.” There is typically no data being handed over regarding the information of the design, engineering drawings, construction, and history of past performance. Instead, the new owner is given the keys so that they can enter and use the building, which starts another isolated phase.

You can compare this process to buying a business, for example, a retail establishment. In this case, the buyer is likely to be given a book of accounts, spreadsheet(s) of budgets, lists of customers and vendors, personnel performance, and much more. Without such data, it would be hard for the buyer to actualize the expected return from their acquisition.

There is very little, if any, motivation for all stakeholders involved with the phases of a building’s lifetime to volunteer information that would be useful for the follow-on phase of the building. This is because there is no expectation to provide information and no agreed-upon mechanism to do so. So, each phase starts and ends with a transaction of handing over the keys.

Digital Twin is the first, and only, mechanism that has been created that could change these transactions as well as stakeholder behavior.

As industries start to view the Digital Twin as a consistent category of information equivalent to what a book of accounts is to a business, it will become the natural way for professionals to manage the phase of a building they are involved in. The Digital Twin will also allow professionals to make building information available to others, such as the acquirer or lessee of the building.

Similar to a spreadsheet, a Digital Twin of physical assets allow users to see the information about the asset. Digital Twins also give the ability to model future behavior and anticipate how a building will react to changes; with such predictions being based on design intent and past history of gathered information. Furthermore, with AI, this modeling can happen continuously, thus making the modeling and actions autonomous in its behavior.

To achieve this automation, information in Digital Twins has to be present with the building from the initial intent of the building, through design, construction, commissioning, and multiple cycles of occupation. Digital Twin information must also be in a form that can be reused for different and unexpected purposes. Lastly, the information contained in the Digital Twin must be easily accessible and compatible between systems. The implementation of a Digital Twin will also need stringent controls in relation to access, privacy, and security that are critical to information flow of the building.

For buildings, the Digital Twin is a new way to look at a building by focusing on the information associated throughout its lifecycle. If the concept of a Digital Twin is understood and widely adopted, will we actually have buildings that can be defined as smart.

 

 

Transforming Businesses using Digital Twin

Authors

By Sean Olcott, Technical Director, Gafcon (left) and Casey Mullen, Distinguished Architect, Information Architecture for Federated Digital Twins, Bentley Systems (right)

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The Digital Twin Consortium released a definition in December of 2020 in a blog post focused on the foundational elements that constitute a digital twin. This post expands on the following statement in the definition: Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action.

Many of the requirements of the digital systems that manage real-world entities and processes originated in the constraints of the paper-based systems they replaced. Some of those features linger long after technology advances should have made them redundant. As a result, these systems inherit a limited perspective on the subject matter they manage, which can even narrow over time as industries ossify around these requirements.  This leads to decision-making based on incomplete (at best) or incorrect (at worst) data throughout the lifecycle of these entities and processes, which compounds negatively throughout the value chain.

Consider a typical visit to your doctor.  Your doctor examines you and your medical history and then identifies patterns to make a diagnosis, considers treatment scenarios, and prescribes action.  But technology exists (most likely already in your pocket or on your wrist) to capture richer and more accurate information about your health than what can be gleaned from the form you completed by hand in the waiting room, the medical diagnoses of your past transcribed by an intermediary in another office, or a five-minute examination.  Additionally, the technology exists to analyze your health data against an accumulation of medical knowledge at an unimaginable scale, with pattern recognition and simulation capabilities that are orders of magnitude beyond the powers of a single doctor.

A digital twin of you would provide you and your doctor with a more holistic understanding of your health.  It would initially integrate data from digital systems already used by your doctor, and eventually might replace them. It would enable optimal decision-making for diagnosis and prescription.  And it would ensure effective action by controlling, measuring, and learning from the results.  Your digital twin would accelerate and improve every step of the process.

It doesn’t mean you won’t need a doctor anymore; it just means you’ll have a better doctor.

This is a straightforward example of how a digital twin could transform your health and the business of healthcare. Similar transformations like this are underway across industries, including - most likely – your own.

What is a Digital Twin System?

Once you buy-in to the vision of a transformative digital twin, how do you get one?

A digital twin system is what enables the ue of a digital twin. Your existing digital systems may both enhance and constrain a digital twin system, and you will most likely need to buy and/or build some missing subsystems.  They will also need to be integrated – which may be a significant part of the effort.

Future blog posts will describe the digital twin technology stack in more detail, but some typical subsystems of a digital twin system include:

• Digital models of various kinds (e.g., legacy business systems, engineering models, purpose-built knowledge graphs, other databases, IoT data feeds and data historians, satellite imagery, point clouds, video, etc.).
• One or more observational and interventional synchronization mechanisms (that keep the digital models in sync with the real-world).
• A digital thread implementation (which weaves digital models together along different dimensions to achieve a holistic view. See The Case for Digital Thread.
• Subsystems for visualization, analysis, automation, data integration, security, and other functionality.

Let’s consider a use case for the real estate and construction industry. How can a digital twin system transform the business of optimizing the health of a building? 

Building owners typically use multiple systems in the operational life of their facilities to manage space, maintenance, and building controls. If a building owner wanted to evaluate retrofit options to make their building more sustainable, none of these systems on their own would provide them with a holistic understanding of their building’s current state or with the capability to efficiently evaluate retrofit scenarios and prescribe an optimal scope of work.  For example, a building automation system would rely on historical energy consumption and occupancy patterns but would have a blind spot related to resource planning and work order history.  This could result in a diagnosis that prescribes an HVAC equipment upgrade (with more sensors and actuators) without considering that the organization also needs a reconfiguration of space to accommodate less staff in the office, or that the existing equipment is less expensive to maintain than the newer models.

Each system brings its own blind spots, but a digital twin system can integrate them to cover each others’ blind spots and yield a comprehensive view. The integrated digital twin system enables a more holistic understanding (e.g. the current HVAC system works fine, space allocation needs to be adjusted for a distributed workforce), resulting in optimal decision making (e.g. simulations validate program and design choices) and effective action (e.g. procure, manage and QA retrofit scope of work).

If this sounds like a lot of work, it can be. Digital twin platforms can give your digital twin system a boost by providing and integrating some of the necessary subsystems. They can supply the high-level user interface, visualization, and API that make your digital twin system usable by humans and machines. They can provide relevant digital models and synchronization mechanisms to keep them in sync with reality.

In summary, digital twin systems make digital twins functional, accessible, secure – and transformational.  As for defining the requirements and implementing a digital twin system for your organization’s use cases – that’s for another blog post.

The Case for the Digital Thread

By John Turner, VP of Innovative Solutions, Gafcon

You are a building or infrastructure owner.  You have made the decision.  You have decided that a Digital Twin would be beneficial in your business. How do you now implement successfully?  How do you balance the undoubted benefits against the cost of implementation? To start, lets consider the definition of a Digital Twin that has been adopted by Digital Twin Consortium:

A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. 

• Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action.
• Digital twins use real-time and historical data to represent the past and present and simulate predicted futures.
• Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems.

Unless the digital twin you have chosen to implement is totally self-contained, and that would be unusual in the world of buildings and infrastructure, it will have to:

• Be populated with data flowing from upstream or previous time phases in the digital lifecycle, for example a digital twin focusing on operational use cases would need to be populated with data from Planning, Design, Procurement and Construction phases
• Communicate with other systems within the same phase of the digital lifecycle:
• Pass data to downstream systems, which are systems that require the data in a later time phase of the digital lifecycle.

With due consideration of the above definition of a digital twin there are therefore key elements of the Digital Twin that are enabled externally.  It is likely that your organization will have multiple digital twins or an evolving digital twin that will eventually stabilize into an operational model.  These may be provided by different and competing software vendors. The question is how to link the data flows between the different digital twins into a holistic and beneficial system for an owner. Your environment may look something like the figure below.

Digital Twin Consortium defines the digital thread as a mechanism for correlating information across multiple dimensions of the virtual representation, where the dimensions include (but are not limited to) time or lifecycle stage (including design intent), kind-of-model, and configuration history; the mechanism generally relies on stable, consistent real-world identifiers. Why should you be so interested in it?

The business value will be derived from the use cases contained within and supported by the digital twin.  However, that business value will only be enabled in a cost-effective manner if the digital twins can communicate via the digital thread rather than having to be manually populated.  It has been estimated that the cost of manually populating an IoT digital twin can be as high as 1% of the cost of construction.  Anything that can reduce that cost would be beneficial.  So how can this be done?

It is suggested that the following five steps can increase the value of your digital twin by containing the cost of implementation.

1. Provide standards from an owner’s perspective - In order for systems to communicate effectively they need to have a common understanding of the data that is exchanged, just as people need a common language and a common interpretation of the idioms of that language, “ A cool room,” meaning a cold room or a trendy or acceptable location.  This is normally done by means of BIM standards and guidelines and data standards with an accompanying data dictionary together with all the technical constraints that systems integration provides.
2. Identify opportunities to do once and deploy many times - Computers are very good at replicating and automating tasks.  Identify all the tasks within the digital lifecycle and extract and build them in a form that enables reuse.  This could be something as simple as updating an equipment schedule or as complex as the reuse of a design of an office or a conference room, or even of a complete data center. This automation and reuse will provide the cost savings to cover the investment to implement other parts of the process, which will enable more cost savings, and so on.
3. Enable a digital thread through all processes and phases - Assess and understand all the components and processes that contribute to the value chain and package them in an integrated manner rather than allowing the current silos apparent in all organizations to propagate and strengthen. Collaboration cannot flourish in a siloed organization but a requirement to implement a digital thread will quickly confront the silos and enable efficiencies in the organization. This is not just a technical exercise.  It will also involve a great deal of organizational change management and is often something best addressed by leveraging the expertise of a consulting organization to avoid the impression that one of the silos is riding roughshod over the others.  A simple example is that a decision to reduce cost in the construction phase, for example, using a lower quality of paint, could increase the cost of operations as the paint would have to be maintained more frequently. Processes involving more collaboration and data flows that communicate the consequences of each decision to all parties in the digital thread will enable a better overall total cost of ownership and hence increase the value of the digital thread.
4. Configure systems to support the digital thread - The digital thread could be enabled manually, but this would introduce human error and rework all of which adds cost.  Far better to ensure that collaboration becomes institutional by implementing systems that support it and can provide the transparency and visibility on all processes to enable the cooperation and teamwork that makes any business successful.  A great example of this is that much data is lost to the Digital Thread because it resides in proprietary systems or systems used to support the siloed parties that contribute to the process, for example designers, general contractors and trade partners.  What better way to enable collaboration between all parties and phases than for the Owner to enable systems to support the digital thread.
5. Provide supervision to enable the digital thread - Every building or infrastructure project is brought to life under the watchful eye of a project or construction manager.  Every operational building is cared for by a Facilities Manager.  Why would a digital representation of that building or infrastructure project be any different?  Appoint knowledgeable resources to manage the digital thread that enables the digital lifecycle and ensures success and a predictable return of investment in your digital twin.

Digital twins have the power to transform an organization through the insights and use cases that they deliver. Do not underestimate the power of the digital thread to also deliver transformational and positive improvements.

Digital Twin Consortium Defines Digital Twin

 

Authors

By Sean Olcott, Technical Director, Gafcon (left) and Casey Mullen, Distinguished Architect, Information Architecture for Federated Digital Twins, Bentley Systems (right)

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Digital Twin Consortium is leading an effort to harmonize the understanding of a digital twin, capture best practices, and propel the innovation of digital twin technology. On December 3, 2020, the Consortium released our official definition of a digital twin. The process involved a cross-section of domain specialists, an extensive review of documentation on the subject, and a stress test of the definition based on digital twin use cases. The result is a definition that leverages established knowledge on the topic, is consensus-driven, and validated against practical considerations.

 

Digital Twin Consortium Definition

 

Below, please find the Consortium’s digital twin definition:

A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. 

• Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action.
• Digital twins use real-time and historical data to represent the past and present and simulate predicted futures.
• Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems.

The foundational elements of the definition are captured in the first sentence: the virtual representation, the real-world entities and processes it represents, and the mechanism by which the virtual and real-world entities are synchronized.

 

Virtual Representation

 

In the Consortium’s definition, virtual representation is a set of correlated digital models and supporting data (e.g. metadata, master data, reference data, etc.), which provide cohesive information about their subject matter.

To avoid confusion or any narrow connotations related to the term models, we looked to Miriam Webster for guidance. One of their definitions of the term model is “to produce a representation or simulation.”  They define simulation as “the imitative representation of the functioning of one system or process by means of the functioning of another.”  

We divided digital models into two categories:

• A representational model which consists of structured information which generally represents the states of entities or processes.
• A computational simulation model which is an executable model of a process and consists of data and algorithms that input and output representational models.

Digital models can be 3D meshes, 3D CAD Models, mathematical equations, databases, physics-based, AI-based, and even Excel spreadsheets. All these things can serve as digital representations or computational simulations related to the historical, present, and potential future states of real-world entities and processes – the subject matter of a digital twin.

 

The Real World

 

We chose entity over alternatives such as asset (not flexible enough), object (software connotations), thing (general aesthetics), and several other synonyms because our members needed a term that could work as effectively for an oil well as it could for a blood cell.

Miriam Webster’s definition includes the phrases independent, separate, self-contained existence, and “something that has separate and distinct existence and objective or conceptual reality.”  We intentionally – and perhaps ironically - do not mean entity in the digital sense of the word, (i.e. a data record with a unique identifier).

The inclusion of process was a matter of some debate, but in the spirit of flexibility we decided it was important to include it. Entities connect and interact to form systems that implement processes, which happen in time. These processes can be productive (the delivery of perishable goods to a grocery store) as well as non-productive (the spoiling of those goods in transit without proper handling). 

What is important to note is that the entities can refer not just to the assets of interest (e.g. perishable goods) but also the physical environment with which they interact (e.g. the temperature in the grocery store loading bay).  

This leads into the third foundational element: the synchronization of the virtual and real-world that makes digital twin use cases possible.

 

Synchronized   

 

This word choice was also the subject of intense debate, and we considered many alternatives from existing digital twin literature. In the end, candidates such as connected (too IoT-centric), mirrored (too abstract), and twinned (too circular), did not quite capture both the intent and the flexibility we needed from the formal definition. We will happily use them in supporting documentation in a narrower context.

In our definition to synchronize is to cause the virtual representation to match more-closely the real-world, and/or cause the real-world to match the virtual representation of a desired state more closely.  All digital twins must have synchronization mechanisms that can mirror the real world in the virtualization through observation (i.e. the water is rising), and/or mirror the virtualization in the real world through intervention (i.e. open the floodgates). 

Not only can these mechanisms be unidirectional or bi-directional, but they can also be multi-faceted.  Observation mechanisms could include sensors, laser scans, satellite imaging, radar, videos, and intervention mechanisms include actuators and increasingly robots. But people have not been entirely automated out of this equation. The most common mechanisms still feature a human-in-the-loop.  

Depending on how rapidly the real-world entities change and the means of real-to-virtual synchronization, the frequency of synchronization might vary (i.e. real time, daily, milestone, etc.).  For digital twins that rely on multiple synchronization mechanisms, the frequency might vary by mechanism (i.e. sensors and laser scans). There may also be an observational synchronization frequency and an interventional synchronization frequency, which may vary per-entity as well.  

Fidelity characterizes the degree of precision and accuracy applied to the virtual representation, as well as to its synchronization mechanisms.  The fidelity only needs to be sufficient for the desired use-cases. For example, a patient using a mobile device for wayfinding in a hospital would not need reality modeled at a molecular level (although simulating the impact of a virus on their blood cells would). Regardless of the level of fidelity specified, it can only be trusted if it can be validated.

 

Next Steps

 

While there are many existing digital twin definitions – some written by our members – this one addresses the Consortium’s unique requirements: it is flexible enough to accommodate the practical needs of our members and can be applied as effectively to a smart city as a satellite. Most importantly, the Consortium’s digital twin definition is extensible and will serve as semantic scaffolding for future documentation we release related to taxonomies, technology platform stacks, security, case studies and more.

Please register for this IoT Solutions World Congress webinar featuring the authors of the Digital Twin Consortium definition in a panel discussion. For more information about the digital twin definition, click here.