How digital twins improve physical systems

There is a long line of technologies and tools used to model the physical world, including drawings, diagrams, and CAD models. There are also many ways to use technology to model real world systems and make predictions, including financial trading simulators, weather predictors, and traffic model models.

When you combine these two capabilities, combining a digital representation of a system in the physical world and a model that simulates the output conditions based on inputs from the physical environment, you get a digital twin. A digital twin allows you to validate the system against a wide range of real-world situations.

Engineers use digital twins in manufacturing, construction, energy, transportation, medicine, science, and more to develop products and validate real systems. It may sound like science fiction, but with advancements in machine learning, systems modeling, Internet of Things (IoT) sensors, data streaming platforms, simulation technologies and of cloud infrastructure, digital twins are becoming more and more prevalent every day.

To separate fact from fiction, I reached out to several experts to share information about digital twins and how sales and engineering teams are using them today.

What is a digital twin?

Prith Banerjee, chief technology officer at Ansys, defines digital twins as “a connected virtual replica of a physical entity in use, such as an asset, plant or process. The sensors mounted on the entity collect and transmit the data to a simulated model (the digital twin) to reflect the actual experience of that product.

Beyond a replica, digital twins receive the same real-time data streams as systems in the physical world. Simon Crosby, CTO of Swim, focuses on this aspect of digital twins in his definition. He says, “A digital twin is a live agent that continuously analyzes the real-world ‘thing’ events continuously as they are received in context and delivers the results in real time to d. ‘other agents, applications and user interfaces. These digital twins always accurately reflect the current state of the real world.

What Kinds of Problems Do Digital Twins Solve?

Crosby shares two ways of using the digital twins: augmented reality and real-time views of entire systems. Augmented reality apps have several practical use cases. He says: “Digital twins were designed as digital overlays at design time for use in augmented reality applications: for example, an engineer repairing a jet engine. “

Augmented reality can help train engineers or simulate procedures before a person implements them in the real world. Augmented reality and digital twins have applications in manufacturing, medicine, energy, and anytime complex training and procedures are performed on expensive equipment, or when human safety is a critical factor.

Crosby shares a broader way of thinking about digital twins. He adds, “Applications can link digital twins together to create powerful models that provide real-time, system-wide views, for example, of the current and forecasted traffic state in a city. “

In other words, a smart city’s digital twin is an aggregate formed by connecting the digital twins of buildings, transportation, government departments, and other systems.

Banerjee adds that engineers are using digital twins to model future behaviors and scenarios. He says, “Digital twins help track the asset’s past behavior, provide deeper insight into the present, and more importantly, they help predict and influence future behavior. “

Engineering teams also use digital twins to assess design tradeoffs and deliver production systems faster. Robin Yeman, Strategic Advisory Board Member and Director of Cyber-Physics Consulting Practices at Project and Team, said, “Creating digital twins for cyber-physical systems allows companies to validate several design compromises in the digital environment before implementation, reducing rework. and enable them to deliver faster.

How are digital twins created?

Andrew Clark, founding CTO of Monitaur, shares his take on the process of modeling and developing a digital twin. He says: “To create a digital twin, an environment representative of the object or ecosystem must be constructed, which involves in-depth knowledge of the domain of the behaviors and underlying mechanisms of the system in question. Once the input signals are incorporated into the digital twin and a model created through the identification of the systems, extrapolations or precise predictions of the future behavior of the system can occur.

Examples of building a digital twin may include build-in-build information models that have object-level details on all building components, such as doors, windows, or materials. In manufacturing, a digital twin can simulate the entire production process, including connections to manufacturing runtime systems to feed live data.

Digital twins cannot be one-off role models and they must reflect changes to the real world system. Clark adds, “Creating precise digital twins is a very complex endeavor that requires extensive domain expertise, otherwise you end up with unrepresentative and inaccurate models. To bridge this gap, digital twins are often configured to be e-learning systems, meaning they are constantly updating and retraining from new input data.

If you’re considering creating a digital twin, Brent Pookhay, Executive Vice President and CIO of Nutrien, suggests working directly with the operational people who have deep expertise in how systems work. He says: “Building a digital twin is as much about people as it is about technology. Who are the subject matter experts (operations, engineers, field teams) that manage these assets? Bringing in their in-depth understanding, experience and working knowledge of operating these assets can be as important as the data flows from your OT and SCADA systems.

What are the use cases of digital twins?

Banerjee shares several examples of digital twins. “Digital twins are used in various phases, including design, manufacturing and operations, and in industries such as aerospace, automotive, manufacturing, buildings, infrastructure and energy. They typically impact a variety of business goals, including overall equipment efficiency, predictive maintenance, performance, and budgets. “

Here is a sample of digital twin projects.

  • Thirteen experts share practical digital twin use cases that can be applied across multiple industries, including calculating product performance, simulating complex manufacturing scenarios, and facilitating hybrid education.
  • My colleague Thor Olavsrud recently shared success stories of digital twins from Rolls Royce, Mars, The Teachers Insurance and Annuity Association of America-College Retirement Equities Fund (TIAA) and Bayer Crop Science.
  • Las Vegas is creating a digital twin to help it go zero carbon.
  • A public-private partnership is developing a digital twin for the Brooklyn Navy Yard to reduce its energy footprint.
  • In urban planning, a professor at Texas A&M University is developing a digital twin of Texas coastal communities to study their resilience to natural hazards.
  • In manufacturing, digital twins are used for product design, supply chain management, predictive maintenance, and customer experience analysis.
  • CenterLine, a Canadian industrial automation technology and process company, used a digital factory twin to reduce tool issues by up to 90% and factory programming time by up to 75%.
  • Digital twins will transform healthcare as scientists develop virtual organs, improve the experience for caregivers, rank drug risks, and more.

I expect to see many more examples of digital twins, especially as companies plan to develop and support more complex products, processes, and other physical environments.

How can DevOps developers and engineers activate digital twins?

Digital twin platforms such as Ansys, Autodesk, Bosch, Dassault Systems, Siemens and other vendors provide modeling and simulation capabilities. Additionally, public clouds have extended their IoT platforms with digital twin capabilities, such as Azure Digital Twins and Google’s Supply Chain Twin. AWS digital twin architectures can include Amazon Kinesis Data Streams, Amazon SageMaker, AWS Lambda, and other services.

IT teams need to consider the infrastructure required to make digital twins work. Yeman says, “The need to deliver a product faster continues to grow; however, hardware and firmware delivery times can slow businesses down.

Developers also need to consider that IoT and other real-time data streams can power multiple systems, including digital twins. This means configuring data streaming technology to share real-time data between production systems and digital twin development and test environments.

Digital twins are an exciting emerging technology that demonstrates the convergence of many different technologies including machine learning, IoT, data streaming, and augmented reality. It will bring a new era of innovation, safety and efficiency to many industries.

Copyright © 2021 IDG Communications, Inc.

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