A digital twin is a digital representation of a product, process, or system either in operation or in development. When in operation, it reflects the asset’s current condition and includes relevant historical data; digital twins are used to evaluate an asset’s current state and, more importantly, to predict future behavior, refine control systems, or optimize operations. During development, the digital twin acts as a model of a to-be-built product, process, or system that facilitates development, testing, and validation.
Why Are Digital Twins Important?
Digital twins mimic the behavior of their physical counterparts in order to help organizations improve and accelerate product development, testing, and validation. They support operation optimization, fault diagnostics, and predictive maintenance, leading to cost savings, improved reliability, and better customer experiences. Furthermore, if a digital twin is leveraged throughout the entire product life cycle, organizations can significantly enhance the value chain from concept to decommissioning by creating a virtuous cycle of feedback and improvement that drives innovation, reduces costs, improves quality, and ensures that products remain relevant and valuable over time.
Product Development
Facilitate Product Design: Digital twins enable real-time simulation of complex systems, letting designers observe system behavior under various operating conditions so they can optimize system responses, energy consumption, and operational efficiency. They can also be used to refine component or subsystem designs and develop control strategies to achieve desired performance levels, such as precision in movement, stability, and response time.
Krones Develops Package-Handling Robot Digital Twin
Using Simulink® and Simscape Multibody™, Krones created a digital twin that supports design optimization, fault testing, and predictive maintenance. Engineers were able to increase the performance of an automated beverage packaging system by incorporating a dynamic tripod robot into the design.
“Simulations of the digital twin in Simulink enabled us to obtain data and insights that would be either impossible to get via hardware tests or simply too costly and time-consuming. Visualizing forces and moments helped us to understand the effects of individual components on a highly dynamic robot.”
Benedikt Böttcher, Krones
Virtual Verification and Validation: You can use virtual replicas of physical products and systems created by digital twins to test and validate design concepts, evaluate performance, and identify potential issues early in the design process. This approach reduces the need for physical prototypes and shortens the design cycle.
Schindler Elevator Moves from Physical Testing to Simulation
Schindler Elevator recently introduced a model-based validation workflow into its development process. The EDEn (Elevator Dynamics Environment) is a set of tools developed in MATLAB®, Simulink, and Simscape™ to perform offline simulations using web-based applications as well as hardware-in-the-loop tests. With EDEn, software release testing is shortened from three or four weeks to one overnight run, greatly reducing cost and risk while enabling much broader test coverage.
“With the HIL [hardware-in-the-loop] approach, we can now cover a lot more test cases, just overnight. This also changes the design paradigm from securing worst cases to optimizing the software for typical use.”
Manuel Pijorr, Schindler Elevator Ltd.
Virtual Commissioning: Digital twins are instrumental in virtual commissioning, allowing for comprehensive testing, validation, and optimization of systems in a virtual environment. This approach minimizes risks and costs and ensures a smoother transition to physical implementation.
Virtual Sensing: By employing digital twins, companies can reduce dependence on physical sensors, enable predictive capabilities, optimize sensor placement, and improve system monitoring and performance, leading to cost savings and increased efficiency.
Operation and Maintenance
Operation Optimization: By mirroring the real-time status of physical assets, digital twins enable organizations to not only monitor but also optimize their operations dynamically. This optimization encompasses various aspects, from improving system performance to energy efficiency and resource allocation. With a digital twin, operators can run different operation scenarios to find the optimal operation condition or learn to react as part of their operation training.
Gas Turbine Digital Twins for Performance Diagnostics and Optimization
Siemens Energy built a physics-based digital twin using MATLAB and Simulink and validated it using a testbed prototype and fleet data. Engineers distributed digital twin functionalities across computational platforms: embedded, edge, cloud, and remote monitoring systems. By using Simulink Coder™ and Simulink Compiler™, there was little to no manual coding required for their deployments. As a result of creating a digital twin of its system, Siemens Energy improved gas turbine reliability, availability, and maintainability; optimized operation; reduced cost; and extended operational life.
Predictive Maintenance: By understanding the condition of each component or the overall system, the digital twin can detect subtle patterns and anomalies that may indicate a potential fault and forecast when maintenance or replacement is likely to be required. This foresight enables you to schedule maintenance at the most opportune times, avoid unplanned outages, and optimize the use of maintenance resources.
Digital Twin Extends Model-Based Design
Model-Based Design is the systematic use of models throughout the development process and improves how you deliver complex systems. Model-Based Design lays robust groundwork for digital twin applications. Model-Based Design and digital twin methodologies share a symbiotic relationship during the product development phase. Many scenarios that leverage digital twins are also use cases for Model-Based Design.
Combining the use of digital twins and Model-Based Design can be particularly beneficial for OEMs. While Model-Based Design is primarily focused on the product development phase, digital twins enable OEMs to extend their scope by offering digital products or services that support and enrich their customers’ operational and maintenance experiences. They can not only design and manufacture physical products but also provide a suite of digital tools that enhance the value of those products throughout their life cycles. The digital twin acts as a bridge that connects the physical product to its digital counterpart, enabling real-time monitoring, predictive maintenance, and operations optimization.
Atlas Copco Minimizes Cost of Ownership Using Simulation and Digital Twins
Atlas Copco integrates simulation and data analytics from engineering through production to sales and service using digital twins as a single source of truth, relying on MATLAB and Simulink to build its Model-Based Engineering Platform. This platform provides sales engineers with access to reliable performance simulations, giving customers tailor-made products. Current models of Atlas Copco compressors are equipped with up to 50 sensors, preparing them for predictive maintenance, and the service division can set up customer-specific maintenance strategies based on real-time data collection from more than 100,000 machines in the field, creating a wealth of insights the company has only just begun to explore.
Digital Twin Workflow
Despite the variability in digital twin use cases, there are shared strategies you can adopt to ensure success in digital twin projects. These strategies revolve around a consistent framework of defining clear objectives, designing and validating models, deploying them effectively, and maintaining them with continuous monitoring and updates.
Step 1: Determine Goal and Scope
The journey to a successful digital twin application begins with a clear vision of what you aim to achieve. Ask yourself, what is the purpose of your digital twin? Is it to aid in product development, help diagnose equipment problems, optimize operation, or provide simulations for training purposes?
Next, define the digital twin scope. Will your digital twin represent an individual component, a collection of components functioning as a subsystem, or the entire system itself? And will it serve a singular function or multiple purposes? These initial decisions will guide the complexity and direction of your project.
Step 2: Design and Build
The creation of a digital twin requires a thoughtful approach that hinges on your expertise, preferred methodology, and, most often, what is available to you. For a complete new product design, due to lack of testing or operation data, many may have to start with physics-based modeling, which relies on the laws of physics to construct a framework for the twin. When there is sufficient data available, a data-driven or AI-based approach can be used, which can forecast outcomes and behaviors by incorporating machine learning or deep learning. Also, consider any pre-existing models or data that could be repurposed to expedite the digital twin’s development, ensuring that you’re not reinventing the wheel.
Step 3: Test and Validate
Once your digital twin is built, put it through rigorous testing and validation. This stage is about building trust in the digital twin by assessing how accurately it reflects the physical counterpart. You also need to measure the accuracy of its predictions and simulations. Equally important is understanding the risks associated with making decisions based on the twin’s insights. It’s a matter of ensuring that the digital twin is not just a sophisticated model but a reliable tool for real-world applications.
Step 4: Deploy and Operate
With a validated digital twin, you’re ready to deploy it. The deployment strategy should align with the twin’s intended use, whether it’s connected directly to the physical counterpart onsite, using edge computing to benefit from proximity and reduced latency, or leveraging the cloud for its vast computational resources and scalability.
Step 5: Monitor and Update
A digital twin is not a set-it-and-forget-it solution. Continuous monitoring is necessary to ensure that the twin remains a faithful and accurate representation of its physical counterpart. By establishing performance metrics and regularly validating the twin against real-world data, you can maintain its integrity. Additionally, the digital twin will likely need to evolve over time, with mechanisms in place for parameter tuning or complete model rebuilding when certain thresholds are exceeded. This adaptability is key to the longevity and usefulness of the digital twin.
Keep Exploring This Topic
Digital Twins with MATLAB and Simulink
MATLAB and Simulink offer a comprehensive platform for the creation, simulation, verification, and implementation of digital twins. The combined strengths in physics-based modeling, advanced data analytics and AI, as well as easy deployment options (PLCs, embedded, web, cloud, etc.), enable you to design digital twins that enhance the understanding, operation, and maintenance of complex physical systems.
Simscape enables engineers to rapidly create models of physical systems within the Simulink environment. They can model systems such as electric motors, bridge rectifiers, hydraulic actuators, and refrigeration systems by assembling fundamental components into a schematic. With Simscape add-on products, you can model and analyze more complex components and systems.
On the data-driven side, MATLAB offers a rich set of tools for statistics, machine learning, deep learning, and system identification. Engineers can use these tools to build data-driven digital twins to identify patterns, optimize performance, predict maintenance needs, and more. The seamless integration of these data-driven digital twins with physics-based digital twins offers a holistic view of the system’s performance and potential issues.
Validation and verification are critical steps in ensuring the digital twin accurately reflects its physical counterpart and performs as expected. By following the high-integrity verification workflow, engineers can use simulation-based testing and static analysis to find defects and shorten time to market while maintaining high standards of quality.
Deployment options with MATLAB and Simulink are versatile, supporting implementations in PLCs, industrial controllers, embedded systems, web platforms, and the cloud. This flexibility ensures that the digital twin can be integrated into existing workflows and infrastructure, making real-time monitoring, predictive maintenance, and operational optimization actionable across different environments. Ultimately, the value of a digital twin is realized through its implementation, enabling stakeholders to leverage insights and predictions to make informed decisions and achieve operational excellence.
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