Digital Twin

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.

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.

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.

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.

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.

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.

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.